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The Use of Valeriana officinalis (Valerian) in Improving Sleep in Patients Who Are Undergoing Treatment for Cancer: A Phase III Randomized, Placebo-Controlled, Double-Blind Study (NCCTG Trial, N01C5)

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The Use of Valeriana officinalis (Valerian) in Improving Sleep in Patients Who Are Undergoing Treatment for Cancer: A Phase III Randomized, Placebo-Controlled, Double-Blind Study (NCCTG Trial, N01C5)

Original research

The Use of Valeriana officinalis (Valerian) in Improving Sleep in Patients Who Are Undergoing Treatment for Cancer: A Phase III Randomized, Placebo-Controlled, Double-Blind Study (NCCTG Trial, N01C5)

Debra L. Barton RN, PhD, AOCN, FAAN

, a,
, Pamela J. Atherton MSa, Brent A. Bauer MDa, Dennis F. Moore Jr MDa, Bassam I. Mattar MDa, Beth I. LaVasseur RNa, Kendrith M. Rowland Jr MDa, Robin T. Zon MDa, Nguyet A. LeLindqwister MDa, Gauri G. Nagargoje MDa, Timothy I. Morgenthaler MDa, Jeff A. Sloan PhDa and Charles L. Loprinzi MDa

a Mayo Clinic Rochester, Rochester, and Metro-MN CCOP, St. Louis Park, Minnesota; Wichita Community Clinical Oncology, Wichita, Kansas; Michigan Cancer Research Consortium, Ann Arbor, Michigan; Carle Cancer Center CCOP, Urbana, and the Illinois Oncology Research Association CCOP, Peoria, Illinois; Northern Indiana Cancer Research Consortium, South Bend, Indiana

Received 19 April 2010; 

accepted 13 December 2010. 

Available online 13 February 2011.

Abstract

Sleep disorders are a substantial problem for cancer survivors, with prevalence estimates ranging from 23% to 61%. Although numerous prescription hypnotics are available, few are approved for long-term use or have demonstrated benefit in this circumstance. Hypnotics may have unwanted side effects and are costly, and cancer survivors often wish to avoid prescription drugs. New options with limited side effects are needed. The purpose of this trial was to evaluate the efficacy of a Valerian officinalis supplement for sleep in people with cancer who were undergoing cancer treatment. Participants were randomized to receive 450 mg of valerian or placebo orally 1 hour before bedtime for 8 weeks. The primary end point was area under the curve (AUC) of the overall Pittsburgh Sleep Quality Index (PSQI). Secondary outcomes included the Functional Outcomes of Sleep Questionnaire, the Brief Fatigue Inventory (BFI), and the Profile of Mood States (POMS). Toxicity was evaluated with both self-reported numeric analogue scale questions and the Common Terminology Criteria for Adverse Events (CTCAE), version 3.0. Questionnaires were completed at baseline and at 4 and 8 weeks. A total of 227 patients were randomized into this study between March 19, 2004, and March 9, 2007, with 119 being evaluable for the primary end point. The AUC over the 8 weeks for valerian was 51.4 (SD = 16), while that for placebo was 49.7 (SD = 15), with a P value of 0.6957. A supplemental, exploratory analysis revealed that several fatigue end points, as measured by the BFI and POMS, were significantly better for those taking valerian over placebo. Participants also reported less trouble with sleep and less drowsiness on valerian than placebo. There were no significant differences in toxicities as measured by self-report or the CTCAE except for mild alkaline phosphatase increases, which were slightly more common in the placebo group. This study failed to provide data to support the hypothesis that valerian, 450 mg, at bedtime could improve sleep as measured by the PSQI. However, exploratory analyses revealed improvement in some secondary outcomes, such as fatigue. Further research with valerian exploring physiologic effects in oncology symptom management may be warranted.

Article Outline

Pharmacological Treatments for Insomnia

The Use of Valeriana officinalis for Sleep

Methods

Results

Discussion

Acknowledgements

References

Disordered sleep has been found to be common in cancer survivors and to contribute to fatigue and impaired overall functioning. The true prevalence and incidence of sleep disorders in the oncology population is not well documented, though reports range from 23% to 61%.[1], [2], [3], [4], [5], [6], [7] and [8] Some research suggests that sleep–wake disturbances are more prevalent in patients with cancer than in other populations.9 In addition, Miller and colleagues6 suggest that a disturbed sleep–wake cycle may well be a main predictor of, and contributor to, other symptoms such as fatigue, depressed mood, and cognitive dysfunction. Evaluating and improving sleep may have broad ramifications for cancer survivors.

Insomnia is present when there is repeated difficulty initiating or maintaining sleep or impairment in sleep quality that occurs despite adequate time and opportunity for sleep, and there is some form of daytime impairment as a result.10 Secondary insomnia is denoted when insomnia is prominent and develops in the setting of another primary medical or psychiatric illness or in the setting of a separate sleep disorder such as sleep apnea.[10], [11] and [12] Sleep disturbance can be associated with poor work performance, increased anxiety and depression, poor cognitive functioning, and impairment of overall quality of life (QOL).[13], [14], [15] and [16] A recent Institute of Medicine report highlighted the severe costs to individuals and society of untreated insomnia.17

Davidson and colleagues2 conducted a cross-sectional descriptive study in six malignant disease clinics from a regional cancer center in Canada. Those surveyed included patients with breast, gastrointestinal, gynecological, genitourinary, lung, and nonmelanoma skin cancers. Insomnia was defined as a report of trouble sleeping on at least 7 of the previous 28 nights, interfering with daytime functioning. More patients who had treatment within the past 6 months reported insomnia, use of sleeping pills, sleeping more than usual, or fatigue. There were no differences based on type of cancer or treatment. Baker and colleagues18 surveyed 752 adult patients who had been diagnosed with 1 of the 10 most commonly occurring cancers to identify which problems cancer survivors experience in dealing with their cancer and its treatment 1 year after diagnosis. Sleep difficulties ranked fifth on the list and were reported by 48% of the sample.

Fatigue is related to sleep disturbance. Although cancer-related fatigue is not necessarily relieved by sleep or rest, insomnia and sleep disturbances clearly contribute to fatigue issues. Fatigue and sleep disturbances are undoubtedly interwoven symptoms and may be difficult to separate. It is not known how much variance in fatigue is explained by sleep problems or in what situations sleep is a major contributor.

Pharmacological Treatments for Insomnia

Because sleep complaints are common, hypnotics are among the most commonly prescribed medications for cancer patients, being prescribed for insomnia in up to 44% of patients.19 Agents most commonly used are benzodiazepine receptor agonists, including true benzodiazepines, such as flurazepam, triazolam, quazepam, estazolam, and temazepam, and the nonbenzodiazepine agents zolpidem (Ambien®), zaleplon (Sonata®), and eszopiclone (Lunesta®), which decrease subjective time to sleep onset, improve sleep efficiency, decrease the number of awakenings, and increase total sleep duration.[20], [21], [22] and [23] Eszopiclone, extended-release formulations of zolpidem (Ambien), and ramelteon (a melatonin receptor agonist) are approved for prolonged use in patients with chronic insomnia;24 but other hypnotics lack well-established effectiveness and safety data for use beyond brief intervals in situational insomnia or as part of a combined approach using cognitive-behavioral therapy (CBT) and brief pharmacological therapy.

In general, improvements in various sleep end points with pharmacologic therapy have been modest, with mean differences in sleep latency being about 15 minutes, wake after sleep onset improving by about 26 minutes, and total sleep time improving by about 40 minutes.[22], [24] and [25] Although subjective improvements are often noted, hypnotic medications are associated with a number of risks, including residual next-day hypersomnia, dizziness, lightheadedness, impaired mental status, and increased risk of falls and hip fractures, especially in elderly patients when taking longer-acting hypnotics.[26], [27], [28], [29], [30] and [31] Clearly, better options to improve sleep are still needed.

The Use of Valeriana officinalis for Sleep

Valeriana officinalis is a perennial herb found in North America, Europe, and Asia. In the United States, it is primarily sold as a sleeping aid, while in Europe it is used for restlessness, tremors, and anxiety. There are three main chemicals that are thought to be the active components of the plant. These are the essential oils valerenic acid and valenol, valepotriates, and a few alkaloids. Herbal extracts of V. officinalis can be ground root, aqueous or aqueous-alcoholic extracts using 70% ethanol and herb-to-extract ratios of 4–7:1. Single recommended doses range from 400 to 900 mg at bedtime.32 Most sleep studies have used 400 or 450 mg for their trials, with a couple of dose-finding trials showing that 900 mg was not significantly better than 450 mg.[33] and [34] The main impact of valerian from those studies has been on sleep latency (time to fall asleep), and this has improved more in patients who had reported a longer time to fall asleep and who considered themselves poor sleepers.[33], [34], [35], [36] and [37]

Most reviews proclaim V. officinalis to be a safe herb with no drug interactions, the only adverse event being daytime sedation at higher doses.[38] and [39] Anecdotal reports of side effects include headaches, nausea, heart palpitations, and benzodiazepine-like withdrawal symptoms when stopping the agent.40 Some concern has been raised as to whether valerian might interfere with cytochrome P-450 metabolism. An article by Budzinski and colleagues reviews numerous herbs and quantitates their interaction with cytochrome P-450.41 Out of 21 herbs tested, V. officinalis ranked at the bottom of interaction potential, rating a 15 out of a possible 16 (1 being the highest, 16 being the lowest).

The cost of V. officinalis, compared to other prescription sleep aids, is less, with a 1-month supply costing around $10 per month. By contrast, zolpidem, for example, costs over $80 per month.

Therefore, based on the favorable toxicity profile, low cost, and promising but limited pilot data, this current trial was designed to evaluate 450 mg of valerian at bedtime for sleep disturbance.

Methods

The primary purpose of this trial was to assess the effect of a standardized preparation of valerian in improving sleep in patients undergoing therapy for cancer. Secondary goals were to assess its safety as well as effect on anxiety, fatigue, and activities of daily living.

Patients eligible for this trial included adults diagnosed with cancer and receiving therapy (radiation, chemotherapy, oral antitumor agents, or endocrine therapy). Patients had to report difficulty sleeping of 4 or more on a scale of 0–10, had to have a life expectancy ≥6 months, and had to have an Eastern Cooperative Oncology Group (ECOG) performance score (PS) of 0 or 1. They could not have an abnormally elevated serum glutamic-oxaloacetic transaminase (SGOT) and/or alkaline phosphatase. Patients were excluded for prior use of valerian for sleep, use of other prescription sleep aids in the past 30 days, or a diagnosis of obstructive sleep apnea or primary insomnia per Diagnostic and Statistical Manual, 4th edition (DSM-IV), criteria. Pregnant and nursing women were also excluded, as were patients with known sleep disturbance etiologies such as nighttime hot flashes, uncontrolled pain, and/or diarrhea.

Participants were randomized to receive 450 mg of oral valerian or placebo, to be taken 1 hour before bedtime for 8 weeks. The valerian used was pure ground, raw root, from one lot and standardized to contain 0.8% valerenic acid. Valerian capsules and matching placebo, a gelatin capsule, were supplied by Hi-Health (Scottsdale, AZ). Both valerian and placebo were stored in the same containers so that the placebo would acquire some of the valerian smell. Self-report booklets were completed at baseline and at weeks 4 and 8 and contained the Pittsburgh Sleep Quality Index (PSQI),42 the Profile of Moods States (POMS),43 the Functional Outcomes of Sleep Questionnaire (FOSQ),44 and the Brief Fatigue Inventory (BFI).45 Assessments were scored according to the appropriate algorithms, and total and subscale scores were transformed to a 0–100 scale, with 100 being best. Self-reported symptoms were recorded weekly using a self-report numeric analogue scale, called the Symptom Experience Diary (SED). Toxicity was also assessed every 2 weeks during a clinical research associate/nurse phone call using the Common Terminology Criteria for Adverse Events (CTCAE, v 3.0).

The primary end point was the normalized (averaged) area under the curve (AUC) of the PSQI between the two arms, compared using the Kruskal-Wallis test. Secondary analyses compared AUC scores of other assessments and toxicity incidence. Toxicity comparisons were performed using the chi-squared test or the Kruskal-Wallis test, as appropriate. As an intent-to-treat (ITT) analysis, using chi-squares tests, patients were categorized as a success if there was a 10-point improvement in the assessment score at week 4 or 8 and a failure if there was no improvement or data were missing.

All hypothesis testing was carried out using a two-sided alternative hypothesis and a 5% Type I error rate. A two-sample t-test with 100 patients per group provided 94% power to detect 50% times the standard deviation (SD) of the end point under study.46 This effect size is considered moderate and has been declared the minimally clinically significant difference for QOL end points.[47] and [48]

Results

A total of 227 patients were randomized into this study between March 19, 2004, and March 9, 2007. The consort diagram depicts the flow of data (Figure 1). Twenty-three patients withdrew before starting the study treatment. Primary end-point data were available on 119 patients (62 receiving valerian and 57 receiving placebo). Baseline characteristics and baseline patient reported outcomes were well balanced between arms with no statistically significant differences ([Table 1] and [Table 2]).

 

 



Figure 1. 

Consort Diagram

Table 1. Demographic Characteristics

VALERIAN (N = 102)PLACEBO (N = 100)P
Gender0.387
 Female82 (80%)85 (85%)
Age (years)0.546
 Mean (SD)59.5 (11.95)58.3 (12.71)
Sleep scale group0.963
 Mildly impaired67 (66%)66 (66%)
 Moderately or severely impaired35 (34%)34 (34%)
Sleep scale score0.841
 Mean (SD)6.6 (1.43)6.6 (1.69)
Primary tumor site0.526
 Breast64 (63%)66 (67%)
 Colon9 (9%)5 (5%)
 Prostate3 (3%)1 (1%)
 Other25 (25%)27 (27%)
Tumor status0.322
 Resected with no residual64 (64%)71 (71%)
 Resected with known residual17 (17%)12 (13%)
 Unresected19 (19%)13 (14%)
Treatment type0.966
 Radiation therapy6 (5.9%)6 (6%)
 Parenteral chemotherapy38 (37%)39 (39%)
 Oral therapy40 (39%)40 (40%)
 Combined modality18 (18%)15 (15%)
Concurrent radiation0.926
 Yes23 (23%)22 (22%)
Concurrent cancer therapy0.679
 Yes56 (55%)52 (53%)
Planned or concurrent hormone0.667
 Yes51 (51%)53 (54%)

Table 2. Distribution of Baseline Assessment Scores

VALERIAN (N = 101)PLACEBO (N = 96)P
PSQI total10.695
 Mean (SD)41.3 (13.92)42.4 (14.97)
POMS-SF total0.883
 Mean (SD)65.0 (14.28)63.9 (16.46)
FOSQ total0.927
 Mean (SD)73.7 (16.07)72.8 (18.37)
Fatigue Now0.285
 Mean (SD)45.7 (24.41)49.4 (25.00)
Usual Fatigue0.216
 Mean (SD)46.8 (23.27)51.1 (24.73)
Worst Fatigue0.522
 Mean (SD)35.2 (24.67)37.9 (26.37)
Total Interference0.268
 Mean (SD)61.4 (25.05)57.1 (27.37)

The primary end point of treatment effectiveness was measured using the normalized AUC calculated using baseline, week 4, and week 8 PSQI total scores. The Wilcoxon rank-sum test P value for the total PSQI score was nonsignificant (valerian AUC = 51.4, SD = 16; placebo AUC = 49.7, SD = 15; P = 0.696) (Figure 2). Similarly the FOSQ was not significantly different between groups either overall or on any subscale score.



Figure 2. 

Pittsburgh Sleep Quality Index Overall Score, AUC

Supplemental and exploratory analyses using changes from baseline, however, showed a significant difference in the change from baseline in the amount of sleep at night at week 4 (P = 0.008), favoring the valerian group. Change from baseline in the categorical value for sleep latency was also significantly different at week 4, where 10% of valerian patients indicated longer time to fall asleep compared to 28% on placebo and 43% of valerian patients reported less time to fall asleep compared to 32% on placebo (P = 0.03) (Table 3). The ITT analysis indicated that about 9% more patients experienced a success on valerian relative to placebo, but this was not statistically significant. When scores on the PSQI were divided into ≤5 and >5 (this latter group representing sleep problems), there were fewer patients in the valerian group having sleep problems by week 8 (64% vs 80%, P = 0.56).

Table 3. Percent of patients reporting categorical changes from baseline on the Pittsburgh Sleep Quality Index Subscales

VALERIANPLACEBOP
Sleep quality0.199
 Week 4
  Worse2 (3%)5 (8%)
  Same33 (49%)37 (57%)
  Better33 (49%)23 (35%)
 Week 80.927
  Worse3 (5%)2 (3%)
  Same26 (41%)25 (42%)
  Better35 (55%)32 (54%)
Sleep latency0.030
 Week 4
  Worse6 (10%)18 (28%)
  Same30 (48%)26 (40%)
  Better27 (43%)21 (32%)
 Week 80.072
  Worse3 (5%)11 (18%)
  Same28 (47%)29 (48%)
  Better27 (47%)21 (34%)
Sleep duration0.244
 Week 4
  Worse6 (9%)10 (16%)
  Same26 (39%)29 (46%)
  Better34 (52%)24 (38%)
 Week 80.148
  Worse8 (13%)4 (7%)
  Same19 (31%)28 (48%)
  Better34 (56%)27 (46%)
Sleep efficiency0.295
 Week 4
  Worse7 (12%)13 (22%)
  Same26 (43%)23 (39%)
  Better28 (46%)23 (39%)
 Week 80.758
  Worse11 (19%)9 (16%)
  Same19 (33%)22 (39%)
  Better28 (48%)25 (45%)
Sleep disturbance0.738
 Week 4
  Worse9 (15%)11 (18%)
  Same41 (66%)40 (67%)
  Better12 (19%)9 (15%)
 Week 80.177
  Worse10 (16%)7 (13%)
  Same35 (57%)41 (73%)
  Better16 (26%)8 (14%)
Daytime dysfunction0.114
 Week 4
  Worse6 (9%)13 (19%)
  Same42 (60%)40 (60%)
  Better22 (31%)14 (21%)
 Week 80.478
  Worse6 (10%)8 (13%)
  Same27 (43%)31 (50%)
  Better30 (48%)23 (37%)

While the POMS AUC scores indicated no difference between treatment arms, the mean change from baseline at weeks 4 and 8 was significantly different for the Fatigue-Inertia subscale at weeks 4 (P = 0.004) and 8 (P = 0.02), with the valerian arm reporting better scores (Table 4). On the BFI, the valerian arm scored significantly better than the placebo arm in the mean change from baseline at weeks 4 and 8 on the Fatigue Now (P = 0.003 and P = 0.01, respectively) and Usual Fatigue (P = 0.02 and P = 0.046, respectively) items (Table 4).

Table 4. Brief Fatigue Inventory (BFI) and Profile of Mood States (POMS): Change from Baseline—Higher Numbers Are Better

SIDE EFFECTWEEKVALERIANPLACEBOP
BFI
 Fatigue NowWeek 413.21.5<0.01
Week 822.110.5<0.01
 Usual FatigueWeek 412.84.20.02
Week 819.410.00.05
 Worst FatigueWeek 411.23.20.03
Week 814.812.40.65
 Activity InterferenceWeek 46.24.10.75
Week 812.310.80.75
POMS
 Anger-HostilityWeek 43.52.00.53
Week 83.94.20.89
 Vigor-ActivityWeek 42.0-0.40.43
Week 82.04.70.34
 Depression-DejectionWeek 43.75.50.21
Week 83.75.40.25
 Confusion-BewildermentWeek 44.82.60.26
Week 85.33.40.79
 Fatigue-InertiaWeek 413.92.8<0.01
Week 817.59.20.02
 TensionAnxietyWeek 46.35.60.85
Week 89.28.90.54
 Total scoreWeek 45.73.00.19
Week 86.96.00.90

In terms of toxicity, there were no significant differences between arms for the self-reported side effect items (headache, trouble waking, nausea) at baseline, week 4, or week 8 (Table 5). The valerian arm change from baseline at both weeks 4 and 8 showed significant improvement in drowsiness (P = 0.04 and P = 0.03, respectively) and sleep problems (P = 0.005 and P = 0.03, respectively) compared to placebo (Table 5). The maximum severity over time for each self-reported toxicity resulted in no significant differences between arms. There was a significant difference in the CTCAE reporting of alkaline phosphatase, with the placebo arm having a higher incidence of grade 1 toxicity (P = 0.049).

 

 

Table 5. Self-Reported Side Effects: Change from Baseline—Higher Numbers Are Better

SIDE EFFECTWEEKVALERIANPLACEBOP
NauseaWeek 43.0–2.10.07
Week 83.40.00.06
HeadacheWeek 44.81.50.09
Week 86.74.60.27
Trouble wakingWeek 48.84.30.42
Week 89.55.70.36
DrowsinessWeek 421.09.70.04
Week 824.014.00.03
Sleep problemsWeek 418.74.3<0.01
Week 824.013.00.03


Discussion

This study failed to identify any significant improvements in sleep as measured by the overall PSQI or the FOSQ in this population. This corroborates data from a recent study by Taibi and colleagues,49 who evaluated 300 mg of valerian, taken half an hour before bed. They reported that valerian did not improve any self-reported or polysomnographic sleep outcomes significantly more than placebo. The Taibi et al. study has several possible limitations, including a small sample size (n = 16), a dose lower than that used in the majority of pilot trials with promising results, and a duration of only 15 days on the study agent.

The current study is one of the few randomized placebo-controlled trials evaluating pharmacological treatment of insomnia complaints among cancer patients. Most randomized trials of treatments directed at insomnia in cancer patients compare CBT with usual care or wait-list care and find it of substantial benefit.[50], [51], [52], [53], [54], [55], [56], [57], [58] and [59] One prior trial in terminal cancer patients evaluated intravenous agents for effectiveness, and another controlled trial found mirtazapine to be effective at improving sleep complaints in cancer patients with depression.[51] and [60] Otherwise, there are no other controlled trials assessing pharmacologic agents to primarily address sleep-related complaints in cancer patients.

While there was no significant improvement in sleep quality as assessed by the PSQI, there were consistent improvements in the secondary fatigue outcomes as measured by both the BFI and the POMS Fatigue-Inertia subscale. Although caution is required in interpreting these secondary results, the raw differences in change scores between the two arms are fairly large, often over 10 points (on a 100-point scale). In addition, several other secondary end points—change from baseline related to sleep latency, amount of sleep per night, improvement in sleep problems, and less drowsiness—all support the valerian arm outperforming placebo.

There are several hypotheses related to the inconsistencies in the results. The PSQI may measure different dimensions of well-being from the BFI or POMS, the former concentrating on sleep-quality measures, while the latter two concentrate on daytime symptoms. The correlation between sleep-quality and daytime symptoms may not be very strong in this study's population. Another possibility is that there was a beta-error. Some of the data were incomplete due to the patients' inability to complete the questionnaires appropriately. The power analysis suggested 100 patients per arm were required, and only about 60 per group provided data for analysis. Another hypothesis is that the effects of valerian were too modest and limited to one aspect, perhaps sleep latency, that were not detectable with multidimensional scales such as the PSQI or the FOSQ that look at impact on activity.

There were more patients who withdrew from the placebo arm early compared to the valerian arm. The reasons for this are not known. However, patients on this trial were getting active treatment for cancer, so numerous and varied reasons could explain early withdrawals including complications from treatment, increased fatigue, and worsening sleep problems.

In summary, this trial did not provide data to support that valerian is helpful in improving sleep during cancer treatment in this population. It is not clear whether valerian may have helpful physiologic activity supporting research in oncology symptom management related to fatigue. Perhaps further exploration is warranted.

Acknowledgments

This study was conducted as a collaborative trial of the North Central Cancer Treatment Group and Mayo Clinic and was supported in part by Public Health Service grants CA-25224, CA-37404, CA-124477 (Mentorship Grant), CA-35431, CA-63848, CA-35195, CA-35133, CA-35267, CA-35269, CA-35103, CA-35101, CA-63849, CA-35119, CA-52352, CA-35448, CA-35103, CA-03011, CA-107586, CA-35261, CA-67575, CA-95968, CA-67753, and CA-35415. The content is solely the responsibility of the authors and does not necessarily represent the views of the National Cancer Institute or the National Institutes of Health.

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Additional participating institutions include Duluth Clinic CCOP, Duluth, MN (Daniel A. Nikcevich, MD, PhD); CCOP Sioux Community Cancer Consortium, Sioux Falls, SD (Loren K. Tschetter, MD); Iowa Oncology Research Association CCOP, Des Moines, IA (Robert J. Behrens, MD); Mayo Clinic Arizona, Scottsdale AZ (Tom R. Fitch, MD); Missouri Valley Cancer Consortium CCOP, Omaha, NE (Gamini S. Soori, MD); Medical College of Georgia Minority-Based CCOP, Augusta, GA (Anand P. Jillella, MD); Columbus CCOP, Columbus, OH (J. Philip Kuebler, MD, PhD); Upstate Carolina CCOP, Spartanburg, SC (James D. Bearden, MD); Cedar Rapids Oncology Project CCOP, Cedar Rapids, IA (Martin Wiesenfeld, MD); Altru Cancer Center, Grand Forks, ND (Todor Dentchev, MD); Montana Cancer Consortium CCOP, Billings, MT (Benjamin T. Marchello, MD); Saint Vincent Hospital CCOP, Green Bay, WI (Anthony J. Jaslowski, MD); Colorado Cancer Research Program CCOP, Denver, CO (Eduardo R. Pajon, Jr, MD); Geisinger Medical Center CCOP, Danville, PA (Albert M. Bernath, Jr, MD); Rapid City Regional Hospital, Rapid City, SD (Richard C. Tenglin, MD); Siouxland Hematology Oncology Associates, Sioux City, IA (Donald B. Wender, MD, PhD); Toledo Community Hospital Oncology Program CCOP, Toledo, OH (Paul L. Schaefer, MD).

Correspondence to: Debra L. Barton, RN, PhD, AOCN, FAAN, Mayo Clinic, 200 First Street, SW, Rochester, MN 55905; telephone: 507-255-3812; fax: 507-538-8300


The Journal of Supportive Oncology
Volume 9, Issue 1, January-February 2011, Pages 24-31
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Original research

The Use of Valeriana officinalis (Valerian) in Improving Sleep in Patients Who Are Undergoing Treatment for Cancer: A Phase III Randomized, Placebo-Controlled, Double-Blind Study (NCCTG Trial, N01C5)

Debra L. Barton RN, PhD, AOCN, FAAN

, a,
, Pamela J. Atherton MSa, Brent A. Bauer MDa, Dennis F. Moore Jr MDa, Bassam I. Mattar MDa, Beth I. LaVasseur RNa, Kendrith M. Rowland Jr MDa, Robin T. Zon MDa, Nguyet A. LeLindqwister MDa, Gauri G. Nagargoje MDa, Timothy I. Morgenthaler MDa, Jeff A. Sloan PhDa and Charles L. Loprinzi MDa

a Mayo Clinic Rochester, Rochester, and Metro-MN CCOP, St. Louis Park, Minnesota; Wichita Community Clinical Oncology, Wichita, Kansas; Michigan Cancer Research Consortium, Ann Arbor, Michigan; Carle Cancer Center CCOP, Urbana, and the Illinois Oncology Research Association CCOP, Peoria, Illinois; Northern Indiana Cancer Research Consortium, South Bend, Indiana

Received 19 April 2010; 

accepted 13 December 2010. 

Available online 13 February 2011.

Abstract

Sleep disorders are a substantial problem for cancer survivors, with prevalence estimates ranging from 23% to 61%. Although numerous prescription hypnotics are available, few are approved for long-term use or have demonstrated benefit in this circumstance. Hypnotics may have unwanted side effects and are costly, and cancer survivors often wish to avoid prescription drugs. New options with limited side effects are needed. The purpose of this trial was to evaluate the efficacy of a Valerian officinalis supplement for sleep in people with cancer who were undergoing cancer treatment. Participants were randomized to receive 450 mg of valerian or placebo orally 1 hour before bedtime for 8 weeks. The primary end point was area under the curve (AUC) of the overall Pittsburgh Sleep Quality Index (PSQI). Secondary outcomes included the Functional Outcomes of Sleep Questionnaire, the Brief Fatigue Inventory (BFI), and the Profile of Mood States (POMS). Toxicity was evaluated with both self-reported numeric analogue scale questions and the Common Terminology Criteria for Adverse Events (CTCAE), version 3.0. Questionnaires were completed at baseline and at 4 and 8 weeks. A total of 227 patients were randomized into this study between March 19, 2004, and March 9, 2007, with 119 being evaluable for the primary end point. The AUC over the 8 weeks for valerian was 51.4 (SD = 16), while that for placebo was 49.7 (SD = 15), with a P value of 0.6957. A supplemental, exploratory analysis revealed that several fatigue end points, as measured by the BFI and POMS, were significantly better for those taking valerian over placebo. Participants also reported less trouble with sleep and less drowsiness on valerian than placebo. There were no significant differences in toxicities as measured by self-report or the CTCAE except for mild alkaline phosphatase increases, which were slightly more common in the placebo group. This study failed to provide data to support the hypothesis that valerian, 450 mg, at bedtime could improve sleep as measured by the PSQI. However, exploratory analyses revealed improvement in some secondary outcomes, such as fatigue. Further research with valerian exploring physiologic effects in oncology symptom management may be warranted.

Article Outline

Pharmacological Treatments for Insomnia

The Use of Valeriana officinalis for Sleep

Methods

Results

Discussion

Acknowledgements

References

Disordered sleep has been found to be common in cancer survivors and to contribute to fatigue and impaired overall functioning. The true prevalence and incidence of sleep disorders in the oncology population is not well documented, though reports range from 23% to 61%.[1], [2], [3], [4], [5], [6], [7] and [8] Some research suggests that sleep–wake disturbances are more prevalent in patients with cancer than in other populations.9 In addition, Miller and colleagues6 suggest that a disturbed sleep–wake cycle may well be a main predictor of, and contributor to, other symptoms such as fatigue, depressed mood, and cognitive dysfunction. Evaluating and improving sleep may have broad ramifications for cancer survivors.

Insomnia is present when there is repeated difficulty initiating or maintaining sleep or impairment in sleep quality that occurs despite adequate time and opportunity for sleep, and there is some form of daytime impairment as a result.10 Secondary insomnia is denoted when insomnia is prominent and develops in the setting of another primary medical or psychiatric illness or in the setting of a separate sleep disorder such as sleep apnea.[10], [11] and [12] Sleep disturbance can be associated with poor work performance, increased anxiety and depression, poor cognitive functioning, and impairment of overall quality of life (QOL).[13], [14], [15] and [16] A recent Institute of Medicine report highlighted the severe costs to individuals and society of untreated insomnia.17

Davidson and colleagues2 conducted a cross-sectional descriptive study in six malignant disease clinics from a regional cancer center in Canada. Those surveyed included patients with breast, gastrointestinal, gynecological, genitourinary, lung, and nonmelanoma skin cancers. Insomnia was defined as a report of trouble sleeping on at least 7 of the previous 28 nights, interfering with daytime functioning. More patients who had treatment within the past 6 months reported insomnia, use of sleeping pills, sleeping more than usual, or fatigue. There were no differences based on type of cancer or treatment. Baker and colleagues18 surveyed 752 adult patients who had been diagnosed with 1 of the 10 most commonly occurring cancers to identify which problems cancer survivors experience in dealing with their cancer and its treatment 1 year after diagnosis. Sleep difficulties ranked fifth on the list and were reported by 48% of the sample.

Fatigue is related to sleep disturbance. Although cancer-related fatigue is not necessarily relieved by sleep or rest, insomnia and sleep disturbances clearly contribute to fatigue issues. Fatigue and sleep disturbances are undoubtedly interwoven symptoms and may be difficult to separate. It is not known how much variance in fatigue is explained by sleep problems or in what situations sleep is a major contributor.

Pharmacological Treatments for Insomnia

Because sleep complaints are common, hypnotics are among the most commonly prescribed medications for cancer patients, being prescribed for insomnia in up to 44% of patients.19 Agents most commonly used are benzodiazepine receptor agonists, including true benzodiazepines, such as flurazepam, triazolam, quazepam, estazolam, and temazepam, and the nonbenzodiazepine agents zolpidem (Ambien®), zaleplon (Sonata®), and eszopiclone (Lunesta®), which decrease subjective time to sleep onset, improve sleep efficiency, decrease the number of awakenings, and increase total sleep duration.[20], [21], [22] and [23] Eszopiclone, extended-release formulations of zolpidem (Ambien), and ramelteon (a melatonin receptor agonist) are approved for prolonged use in patients with chronic insomnia;24 but other hypnotics lack well-established effectiveness and safety data for use beyond brief intervals in situational insomnia or as part of a combined approach using cognitive-behavioral therapy (CBT) and brief pharmacological therapy.

In general, improvements in various sleep end points with pharmacologic therapy have been modest, with mean differences in sleep latency being about 15 minutes, wake after sleep onset improving by about 26 minutes, and total sleep time improving by about 40 minutes.[22], [24] and [25] Although subjective improvements are often noted, hypnotic medications are associated with a number of risks, including residual next-day hypersomnia, dizziness, lightheadedness, impaired mental status, and increased risk of falls and hip fractures, especially in elderly patients when taking longer-acting hypnotics.[26], [27], [28], [29], [30] and [31] Clearly, better options to improve sleep are still needed.

The Use of Valeriana officinalis for Sleep

Valeriana officinalis is a perennial herb found in North America, Europe, and Asia. In the United States, it is primarily sold as a sleeping aid, while in Europe it is used for restlessness, tremors, and anxiety. There are three main chemicals that are thought to be the active components of the plant. These are the essential oils valerenic acid and valenol, valepotriates, and a few alkaloids. Herbal extracts of V. officinalis can be ground root, aqueous or aqueous-alcoholic extracts using 70% ethanol and herb-to-extract ratios of 4–7:1. Single recommended doses range from 400 to 900 mg at bedtime.32 Most sleep studies have used 400 or 450 mg for their trials, with a couple of dose-finding trials showing that 900 mg was not significantly better than 450 mg.[33] and [34] The main impact of valerian from those studies has been on sleep latency (time to fall asleep), and this has improved more in patients who had reported a longer time to fall asleep and who considered themselves poor sleepers.[33], [34], [35], [36] and [37]

Most reviews proclaim V. officinalis to be a safe herb with no drug interactions, the only adverse event being daytime sedation at higher doses.[38] and [39] Anecdotal reports of side effects include headaches, nausea, heart palpitations, and benzodiazepine-like withdrawal symptoms when stopping the agent.40 Some concern has been raised as to whether valerian might interfere with cytochrome P-450 metabolism. An article by Budzinski and colleagues reviews numerous herbs and quantitates their interaction with cytochrome P-450.41 Out of 21 herbs tested, V. officinalis ranked at the bottom of interaction potential, rating a 15 out of a possible 16 (1 being the highest, 16 being the lowest).

The cost of V. officinalis, compared to other prescription sleep aids, is less, with a 1-month supply costing around $10 per month. By contrast, zolpidem, for example, costs over $80 per month.

Therefore, based on the favorable toxicity profile, low cost, and promising but limited pilot data, this current trial was designed to evaluate 450 mg of valerian at bedtime for sleep disturbance.

Methods

The primary purpose of this trial was to assess the effect of a standardized preparation of valerian in improving sleep in patients undergoing therapy for cancer. Secondary goals were to assess its safety as well as effect on anxiety, fatigue, and activities of daily living.

Patients eligible for this trial included adults diagnosed with cancer and receiving therapy (radiation, chemotherapy, oral antitumor agents, or endocrine therapy). Patients had to report difficulty sleeping of 4 or more on a scale of 0–10, had to have a life expectancy ≥6 months, and had to have an Eastern Cooperative Oncology Group (ECOG) performance score (PS) of 0 or 1. They could not have an abnormally elevated serum glutamic-oxaloacetic transaminase (SGOT) and/or alkaline phosphatase. Patients were excluded for prior use of valerian for sleep, use of other prescription sleep aids in the past 30 days, or a diagnosis of obstructive sleep apnea or primary insomnia per Diagnostic and Statistical Manual, 4th edition (DSM-IV), criteria. Pregnant and nursing women were also excluded, as were patients with known sleep disturbance etiologies such as nighttime hot flashes, uncontrolled pain, and/or diarrhea.

Participants were randomized to receive 450 mg of oral valerian or placebo, to be taken 1 hour before bedtime for 8 weeks. The valerian used was pure ground, raw root, from one lot and standardized to contain 0.8% valerenic acid. Valerian capsules and matching placebo, a gelatin capsule, were supplied by Hi-Health (Scottsdale, AZ). Both valerian and placebo were stored in the same containers so that the placebo would acquire some of the valerian smell. Self-report booklets were completed at baseline and at weeks 4 and 8 and contained the Pittsburgh Sleep Quality Index (PSQI),42 the Profile of Moods States (POMS),43 the Functional Outcomes of Sleep Questionnaire (FOSQ),44 and the Brief Fatigue Inventory (BFI).45 Assessments were scored according to the appropriate algorithms, and total and subscale scores were transformed to a 0–100 scale, with 100 being best. Self-reported symptoms were recorded weekly using a self-report numeric analogue scale, called the Symptom Experience Diary (SED). Toxicity was also assessed every 2 weeks during a clinical research associate/nurse phone call using the Common Terminology Criteria for Adverse Events (CTCAE, v 3.0).

The primary end point was the normalized (averaged) area under the curve (AUC) of the PSQI between the two arms, compared using the Kruskal-Wallis test. Secondary analyses compared AUC scores of other assessments and toxicity incidence. Toxicity comparisons were performed using the chi-squared test or the Kruskal-Wallis test, as appropriate. As an intent-to-treat (ITT) analysis, using chi-squares tests, patients were categorized as a success if there was a 10-point improvement in the assessment score at week 4 or 8 and a failure if there was no improvement or data were missing.

All hypothesis testing was carried out using a two-sided alternative hypothesis and a 5% Type I error rate. A two-sample t-test with 100 patients per group provided 94% power to detect 50% times the standard deviation (SD) of the end point under study.46 This effect size is considered moderate and has been declared the minimally clinically significant difference for QOL end points.[47] and [48]

Results

A total of 227 patients were randomized into this study between March 19, 2004, and March 9, 2007. The consort diagram depicts the flow of data (Figure 1). Twenty-three patients withdrew before starting the study treatment. Primary end-point data were available on 119 patients (62 receiving valerian and 57 receiving placebo). Baseline characteristics and baseline patient reported outcomes were well balanced between arms with no statistically significant differences ([Table 1] and [Table 2]).

 

 



Figure 1. 

Consort Diagram

Table 1. Demographic Characteristics

VALERIAN (N = 102)PLACEBO (N = 100)P
Gender0.387
 Female82 (80%)85 (85%)
Age (years)0.546
 Mean (SD)59.5 (11.95)58.3 (12.71)
Sleep scale group0.963
 Mildly impaired67 (66%)66 (66%)
 Moderately or severely impaired35 (34%)34 (34%)
Sleep scale score0.841
 Mean (SD)6.6 (1.43)6.6 (1.69)
Primary tumor site0.526
 Breast64 (63%)66 (67%)
 Colon9 (9%)5 (5%)
 Prostate3 (3%)1 (1%)
 Other25 (25%)27 (27%)
Tumor status0.322
 Resected with no residual64 (64%)71 (71%)
 Resected with known residual17 (17%)12 (13%)
 Unresected19 (19%)13 (14%)
Treatment type0.966
 Radiation therapy6 (5.9%)6 (6%)
 Parenteral chemotherapy38 (37%)39 (39%)
 Oral therapy40 (39%)40 (40%)
 Combined modality18 (18%)15 (15%)
Concurrent radiation0.926
 Yes23 (23%)22 (22%)
Concurrent cancer therapy0.679
 Yes56 (55%)52 (53%)
Planned or concurrent hormone0.667
 Yes51 (51%)53 (54%)

Table 2. Distribution of Baseline Assessment Scores

VALERIAN (N = 101)PLACEBO (N = 96)P
PSQI total10.695
 Mean (SD)41.3 (13.92)42.4 (14.97)
POMS-SF total0.883
 Mean (SD)65.0 (14.28)63.9 (16.46)
FOSQ total0.927
 Mean (SD)73.7 (16.07)72.8 (18.37)
Fatigue Now0.285
 Mean (SD)45.7 (24.41)49.4 (25.00)
Usual Fatigue0.216
 Mean (SD)46.8 (23.27)51.1 (24.73)
Worst Fatigue0.522
 Mean (SD)35.2 (24.67)37.9 (26.37)
Total Interference0.268
 Mean (SD)61.4 (25.05)57.1 (27.37)

The primary end point of treatment effectiveness was measured using the normalized AUC calculated using baseline, week 4, and week 8 PSQI total scores. The Wilcoxon rank-sum test P value for the total PSQI score was nonsignificant (valerian AUC = 51.4, SD = 16; placebo AUC = 49.7, SD = 15; P = 0.696) (Figure 2). Similarly the FOSQ was not significantly different between groups either overall or on any subscale score.



Figure 2. 

Pittsburgh Sleep Quality Index Overall Score, AUC

Supplemental and exploratory analyses using changes from baseline, however, showed a significant difference in the change from baseline in the amount of sleep at night at week 4 (P = 0.008), favoring the valerian group. Change from baseline in the categorical value for sleep latency was also significantly different at week 4, where 10% of valerian patients indicated longer time to fall asleep compared to 28% on placebo and 43% of valerian patients reported less time to fall asleep compared to 32% on placebo (P = 0.03) (Table 3). The ITT analysis indicated that about 9% more patients experienced a success on valerian relative to placebo, but this was not statistically significant. When scores on the PSQI were divided into ≤5 and >5 (this latter group representing sleep problems), there were fewer patients in the valerian group having sleep problems by week 8 (64% vs 80%, P = 0.56).

Table 3. Percent of patients reporting categorical changes from baseline on the Pittsburgh Sleep Quality Index Subscales

VALERIANPLACEBOP
Sleep quality0.199
 Week 4
  Worse2 (3%)5 (8%)
  Same33 (49%)37 (57%)
  Better33 (49%)23 (35%)
 Week 80.927
  Worse3 (5%)2 (3%)
  Same26 (41%)25 (42%)
  Better35 (55%)32 (54%)
Sleep latency0.030
 Week 4
  Worse6 (10%)18 (28%)
  Same30 (48%)26 (40%)
  Better27 (43%)21 (32%)
 Week 80.072
  Worse3 (5%)11 (18%)
  Same28 (47%)29 (48%)
  Better27 (47%)21 (34%)
Sleep duration0.244
 Week 4
  Worse6 (9%)10 (16%)
  Same26 (39%)29 (46%)
  Better34 (52%)24 (38%)
 Week 80.148
  Worse8 (13%)4 (7%)
  Same19 (31%)28 (48%)
  Better34 (56%)27 (46%)
Sleep efficiency0.295
 Week 4
  Worse7 (12%)13 (22%)
  Same26 (43%)23 (39%)
  Better28 (46%)23 (39%)
 Week 80.758
  Worse11 (19%)9 (16%)
  Same19 (33%)22 (39%)
  Better28 (48%)25 (45%)
Sleep disturbance0.738
 Week 4
  Worse9 (15%)11 (18%)
  Same41 (66%)40 (67%)
  Better12 (19%)9 (15%)
 Week 80.177
  Worse10 (16%)7 (13%)
  Same35 (57%)41 (73%)
  Better16 (26%)8 (14%)
Daytime dysfunction0.114
 Week 4
  Worse6 (9%)13 (19%)
  Same42 (60%)40 (60%)
  Better22 (31%)14 (21%)
 Week 80.478
  Worse6 (10%)8 (13%)
  Same27 (43%)31 (50%)
  Better30 (48%)23 (37%)

While the POMS AUC scores indicated no difference between treatment arms, the mean change from baseline at weeks 4 and 8 was significantly different for the Fatigue-Inertia subscale at weeks 4 (P = 0.004) and 8 (P = 0.02), with the valerian arm reporting better scores (Table 4). On the BFI, the valerian arm scored significantly better than the placebo arm in the mean change from baseline at weeks 4 and 8 on the Fatigue Now (P = 0.003 and P = 0.01, respectively) and Usual Fatigue (P = 0.02 and P = 0.046, respectively) items (Table 4).

Table 4. Brief Fatigue Inventory (BFI) and Profile of Mood States (POMS): Change from Baseline—Higher Numbers Are Better

SIDE EFFECTWEEKVALERIANPLACEBOP
BFI
 Fatigue NowWeek 413.21.5<0.01
Week 822.110.5<0.01
 Usual FatigueWeek 412.84.20.02
Week 819.410.00.05
 Worst FatigueWeek 411.23.20.03
Week 814.812.40.65
 Activity InterferenceWeek 46.24.10.75
Week 812.310.80.75
POMS
 Anger-HostilityWeek 43.52.00.53
Week 83.94.20.89
 Vigor-ActivityWeek 42.0-0.40.43
Week 82.04.70.34
 Depression-DejectionWeek 43.75.50.21
Week 83.75.40.25
 Confusion-BewildermentWeek 44.82.60.26
Week 85.33.40.79
 Fatigue-InertiaWeek 413.92.8<0.01
Week 817.59.20.02
 TensionAnxietyWeek 46.35.60.85
Week 89.28.90.54
 Total scoreWeek 45.73.00.19
Week 86.96.00.90

In terms of toxicity, there were no significant differences between arms for the self-reported side effect items (headache, trouble waking, nausea) at baseline, week 4, or week 8 (Table 5). The valerian arm change from baseline at both weeks 4 and 8 showed significant improvement in drowsiness (P = 0.04 and P = 0.03, respectively) and sleep problems (P = 0.005 and P = 0.03, respectively) compared to placebo (Table 5). The maximum severity over time for each self-reported toxicity resulted in no significant differences between arms. There was a significant difference in the CTCAE reporting of alkaline phosphatase, with the placebo arm having a higher incidence of grade 1 toxicity (P = 0.049).

 

 

Table 5. Self-Reported Side Effects: Change from Baseline—Higher Numbers Are Better

SIDE EFFECTWEEKVALERIANPLACEBOP
NauseaWeek 43.0–2.10.07
Week 83.40.00.06
HeadacheWeek 44.81.50.09
Week 86.74.60.27
Trouble wakingWeek 48.84.30.42
Week 89.55.70.36
DrowsinessWeek 421.09.70.04
Week 824.014.00.03
Sleep problemsWeek 418.74.3<0.01
Week 824.013.00.03


Discussion

This study failed to identify any significant improvements in sleep as measured by the overall PSQI or the FOSQ in this population. This corroborates data from a recent study by Taibi and colleagues,49 who evaluated 300 mg of valerian, taken half an hour before bed. They reported that valerian did not improve any self-reported or polysomnographic sleep outcomes significantly more than placebo. The Taibi et al. study has several possible limitations, including a small sample size (n = 16), a dose lower than that used in the majority of pilot trials with promising results, and a duration of only 15 days on the study agent.

The current study is one of the few randomized placebo-controlled trials evaluating pharmacological treatment of insomnia complaints among cancer patients. Most randomized trials of treatments directed at insomnia in cancer patients compare CBT with usual care or wait-list care and find it of substantial benefit.[50], [51], [52], [53], [54], [55], [56], [57], [58] and [59] One prior trial in terminal cancer patients evaluated intravenous agents for effectiveness, and another controlled trial found mirtazapine to be effective at improving sleep complaints in cancer patients with depression.[51] and [60] Otherwise, there are no other controlled trials assessing pharmacologic agents to primarily address sleep-related complaints in cancer patients.

While there was no significant improvement in sleep quality as assessed by the PSQI, there were consistent improvements in the secondary fatigue outcomes as measured by both the BFI and the POMS Fatigue-Inertia subscale. Although caution is required in interpreting these secondary results, the raw differences in change scores between the two arms are fairly large, often over 10 points (on a 100-point scale). In addition, several other secondary end points—change from baseline related to sleep latency, amount of sleep per night, improvement in sleep problems, and less drowsiness—all support the valerian arm outperforming placebo.

There are several hypotheses related to the inconsistencies in the results. The PSQI may measure different dimensions of well-being from the BFI or POMS, the former concentrating on sleep-quality measures, while the latter two concentrate on daytime symptoms. The correlation between sleep-quality and daytime symptoms may not be very strong in this study's population. Another possibility is that there was a beta-error. Some of the data were incomplete due to the patients' inability to complete the questionnaires appropriately. The power analysis suggested 100 patients per arm were required, and only about 60 per group provided data for analysis. Another hypothesis is that the effects of valerian were too modest and limited to one aspect, perhaps sleep latency, that were not detectable with multidimensional scales such as the PSQI or the FOSQ that look at impact on activity.

There were more patients who withdrew from the placebo arm early compared to the valerian arm. The reasons for this are not known. However, patients on this trial were getting active treatment for cancer, so numerous and varied reasons could explain early withdrawals including complications from treatment, increased fatigue, and worsening sleep problems.

In summary, this trial did not provide data to support that valerian is helpful in improving sleep during cancer treatment in this population. It is not clear whether valerian may have helpful physiologic activity supporting research in oncology symptom management related to fatigue. Perhaps further exploration is warranted.

Acknowledgments

This study was conducted as a collaborative trial of the North Central Cancer Treatment Group and Mayo Clinic and was supported in part by Public Health Service grants CA-25224, CA-37404, CA-124477 (Mentorship Grant), CA-35431, CA-63848, CA-35195, CA-35133, CA-35267, CA-35269, CA-35103, CA-35101, CA-63849, CA-35119, CA-52352, CA-35448, CA-35103, CA-03011, CA-107586, CA-35261, CA-67575, CA-95968, CA-67753, and CA-35415. The content is solely the responsibility of the authors and does not necessarily represent the views of the National Cancer Institute or the National Institutes of Health.

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Additional participating institutions include Duluth Clinic CCOP, Duluth, MN (Daniel A. Nikcevich, MD, PhD); CCOP Sioux Community Cancer Consortium, Sioux Falls, SD (Loren K. Tschetter, MD); Iowa Oncology Research Association CCOP, Des Moines, IA (Robert J. Behrens, MD); Mayo Clinic Arizona, Scottsdale AZ (Tom R. Fitch, MD); Missouri Valley Cancer Consortium CCOP, Omaha, NE (Gamini S. Soori, MD); Medical College of Georgia Minority-Based CCOP, Augusta, GA (Anand P. Jillella, MD); Columbus CCOP, Columbus, OH (J. Philip Kuebler, MD, PhD); Upstate Carolina CCOP, Spartanburg, SC (James D. Bearden, MD); Cedar Rapids Oncology Project CCOP, Cedar Rapids, IA (Martin Wiesenfeld, MD); Altru Cancer Center, Grand Forks, ND (Todor Dentchev, MD); Montana Cancer Consortium CCOP, Billings, MT (Benjamin T. Marchello, MD); Saint Vincent Hospital CCOP, Green Bay, WI (Anthony J. Jaslowski, MD); Colorado Cancer Research Program CCOP, Denver, CO (Eduardo R. Pajon, Jr, MD); Geisinger Medical Center CCOP, Danville, PA (Albert M. Bernath, Jr, MD); Rapid City Regional Hospital, Rapid City, SD (Richard C. Tenglin, MD); Siouxland Hematology Oncology Associates, Sioux City, IA (Donald B. Wender, MD, PhD); Toledo Community Hospital Oncology Program CCOP, Toledo, OH (Paul L. Schaefer, MD).

Correspondence to: Debra L. Barton, RN, PhD, AOCN, FAAN, Mayo Clinic, 200 First Street, SW, Rochester, MN 55905; telephone: 507-255-3812; fax: 507-538-8300


The Journal of Supportive Oncology
Volume 9, Issue 1, January-February 2011, Pages 24-31

Original research

The Use of Valeriana officinalis (Valerian) in Improving Sleep in Patients Who Are Undergoing Treatment for Cancer: A Phase III Randomized, Placebo-Controlled, Double-Blind Study (NCCTG Trial, N01C5)

Debra L. Barton RN, PhD, AOCN, FAAN

, a,
, Pamela J. Atherton MSa, Brent A. Bauer MDa, Dennis F. Moore Jr MDa, Bassam I. Mattar MDa, Beth I. LaVasseur RNa, Kendrith M. Rowland Jr MDa, Robin T. Zon MDa, Nguyet A. LeLindqwister MDa, Gauri G. Nagargoje MDa, Timothy I. Morgenthaler MDa, Jeff A. Sloan PhDa and Charles L. Loprinzi MDa

a Mayo Clinic Rochester, Rochester, and Metro-MN CCOP, St. Louis Park, Minnesota; Wichita Community Clinical Oncology, Wichita, Kansas; Michigan Cancer Research Consortium, Ann Arbor, Michigan; Carle Cancer Center CCOP, Urbana, and the Illinois Oncology Research Association CCOP, Peoria, Illinois; Northern Indiana Cancer Research Consortium, South Bend, Indiana

Received 19 April 2010; 

accepted 13 December 2010. 

Available online 13 February 2011.

Abstract

Sleep disorders are a substantial problem for cancer survivors, with prevalence estimates ranging from 23% to 61%. Although numerous prescription hypnotics are available, few are approved for long-term use or have demonstrated benefit in this circumstance. Hypnotics may have unwanted side effects and are costly, and cancer survivors often wish to avoid prescription drugs. New options with limited side effects are needed. The purpose of this trial was to evaluate the efficacy of a Valerian officinalis supplement for sleep in people with cancer who were undergoing cancer treatment. Participants were randomized to receive 450 mg of valerian or placebo orally 1 hour before bedtime for 8 weeks. The primary end point was area under the curve (AUC) of the overall Pittsburgh Sleep Quality Index (PSQI). Secondary outcomes included the Functional Outcomes of Sleep Questionnaire, the Brief Fatigue Inventory (BFI), and the Profile of Mood States (POMS). Toxicity was evaluated with both self-reported numeric analogue scale questions and the Common Terminology Criteria for Adverse Events (CTCAE), version 3.0. Questionnaires were completed at baseline and at 4 and 8 weeks. A total of 227 patients were randomized into this study between March 19, 2004, and March 9, 2007, with 119 being evaluable for the primary end point. The AUC over the 8 weeks for valerian was 51.4 (SD = 16), while that for placebo was 49.7 (SD = 15), with a P value of 0.6957. A supplemental, exploratory analysis revealed that several fatigue end points, as measured by the BFI and POMS, were significantly better for those taking valerian over placebo. Participants also reported less trouble with sleep and less drowsiness on valerian than placebo. There were no significant differences in toxicities as measured by self-report or the CTCAE except for mild alkaline phosphatase increases, which were slightly more common in the placebo group. This study failed to provide data to support the hypothesis that valerian, 450 mg, at bedtime could improve sleep as measured by the PSQI. However, exploratory analyses revealed improvement in some secondary outcomes, such as fatigue. Further research with valerian exploring physiologic effects in oncology symptom management may be warranted.

Article Outline

Pharmacological Treatments for Insomnia

The Use of Valeriana officinalis for Sleep

Methods

Results

Discussion

Acknowledgements

References

Disordered sleep has been found to be common in cancer survivors and to contribute to fatigue and impaired overall functioning. The true prevalence and incidence of sleep disorders in the oncology population is not well documented, though reports range from 23% to 61%.[1], [2], [3], [4], [5], [6], [7] and [8] Some research suggests that sleep–wake disturbances are more prevalent in patients with cancer than in other populations.9 In addition, Miller and colleagues6 suggest that a disturbed sleep–wake cycle may well be a main predictor of, and contributor to, other symptoms such as fatigue, depressed mood, and cognitive dysfunction. Evaluating and improving sleep may have broad ramifications for cancer survivors.

Insomnia is present when there is repeated difficulty initiating or maintaining sleep or impairment in sleep quality that occurs despite adequate time and opportunity for sleep, and there is some form of daytime impairment as a result.10 Secondary insomnia is denoted when insomnia is prominent and develops in the setting of another primary medical or psychiatric illness or in the setting of a separate sleep disorder such as sleep apnea.[10], [11] and [12] Sleep disturbance can be associated with poor work performance, increased anxiety and depression, poor cognitive functioning, and impairment of overall quality of life (QOL).[13], [14], [15] and [16] A recent Institute of Medicine report highlighted the severe costs to individuals and society of untreated insomnia.17

Davidson and colleagues2 conducted a cross-sectional descriptive study in six malignant disease clinics from a regional cancer center in Canada. Those surveyed included patients with breast, gastrointestinal, gynecological, genitourinary, lung, and nonmelanoma skin cancers. Insomnia was defined as a report of trouble sleeping on at least 7 of the previous 28 nights, interfering with daytime functioning. More patients who had treatment within the past 6 months reported insomnia, use of sleeping pills, sleeping more than usual, or fatigue. There were no differences based on type of cancer or treatment. Baker and colleagues18 surveyed 752 adult patients who had been diagnosed with 1 of the 10 most commonly occurring cancers to identify which problems cancer survivors experience in dealing with their cancer and its treatment 1 year after diagnosis. Sleep difficulties ranked fifth on the list and were reported by 48% of the sample.

Fatigue is related to sleep disturbance. Although cancer-related fatigue is not necessarily relieved by sleep or rest, insomnia and sleep disturbances clearly contribute to fatigue issues. Fatigue and sleep disturbances are undoubtedly interwoven symptoms and may be difficult to separate. It is not known how much variance in fatigue is explained by sleep problems or in what situations sleep is a major contributor.

Pharmacological Treatments for Insomnia

Because sleep complaints are common, hypnotics are among the most commonly prescribed medications for cancer patients, being prescribed for insomnia in up to 44% of patients.19 Agents most commonly used are benzodiazepine receptor agonists, including true benzodiazepines, such as flurazepam, triazolam, quazepam, estazolam, and temazepam, and the nonbenzodiazepine agents zolpidem (Ambien®), zaleplon (Sonata®), and eszopiclone (Lunesta®), which decrease subjective time to sleep onset, improve sleep efficiency, decrease the number of awakenings, and increase total sleep duration.[20], [21], [22] and [23] Eszopiclone, extended-release formulations of zolpidem (Ambien), and ramelteon (a melatonin receptor agonist) are approved for prolonged use in patients with chronic insomnia;24 but other hypnotics lack well-established effectiveness and safety data for use beyond brief intervals in situational insomnia or as part of a combined approach using cognitive-behavioral therapy (CBT) and brief pharmacological therapy.

In general, improvements in various sleep end points with pharmacologic therapy have been modest, with mean differences in sleep latency being about 15 minutes, wake after sleep onset improving by about 26 minutes, and total sleep time improving by about 40 minutes.[22], [24] and [25] Although subjective improvements are often noted, hypnotic medications are associated with a number of risks, including residual next-day hypersomnia, dizziness, lightheadedness, impaired mental status, and increased risk of falls and hip fractures, especially in elderly patients when taking longer-acting hypnotics.[26], [27], [28], [29], [30] and [31] Clearly, better options to improve sleep are still needed.

The Use of Valeriana officinalis for Sleep

Valeriana officinalis is a perennial herb found in North America, Europe, and Asia. In the United States, it is primarily sold as a sleeping aid, while in Europe it is used for restlessness, tremors, and anxiety. There are three main chemicals that are thought to be the active components of the plant. These are the essential oils valerenic acid and valenol, valepotriates, and a few alkaloids. Herbal extracts of V. officinalis can be ground root, aqueous or aqueous-alcoholic extracts using 70% ethanol and herb-to-extract ratios of 4–7:1. Single recommended doses range from 400 to 900 mg at bedtime.32 Most sleep studies have used 400 or 450 mg for their trials, with a couple of dose-finding trials showing that 900 mg was not significantly better than 450 mg.[33] and [34] The main impact of valerian from those studies has been on sleep latency (time to fall asleep), and this has improved more in patients who had reported a longer time to fall asleep and who considered themselves poor sleepers.[33], [34], [35], [36] and [37]

Most reviews proclaim V. officinalis to be a safe herb with no drug interactions, the only adverse event being daytime sedation at higher doses.[38] and [39] Anecdotal reports of side effects include headaches, nausea, heart palpitations, and benzodiazepine-like withdrawal symptoms when stopping the agent.40 Some concern has been raised as to whether valerian might interfere with cytochrome P-450 metabolism. An article by Budzinski and colleagues reviews numerous herbs and quantitates their interaction with cytochrome P-450.41 Out of 21 herbs tested, V. officinalis ranked at the bottom of interaction potential, rating a 15 out of a possible 16 (1 being the highest, 16 being the lowest).

The cost of V. officinalis, compared to other prescription sleep aids, is less, with a 1-month supply costing around $10 per month. By contrast, zolpidem, for example, costs over $80 per month.

Therefore, based on the favorable toxicity profile, low cost, and promising but limited pilot data, this current trial was designed to evaluate 450 mg of valerian at bedtime for sleep disturbance.

Methods

The primary purpose of this trial was to assess the effect of a standardized preparation of valerian in improving sleep in patients undergoing therapy for cancer. Secondary goals were to assess its safety as well as effect on anxiety, fatigue, and activities of daily living.

Patients eligible for this trial included adults diagnosed with cancer and receiving therapy (radiation, chemotherapy, oral antitumor agents, or endocrine therapy). Patients had to report difficulty sleeping of 4 or more on a scale of 0–10, had to have a life expectancy ≥6 months, and had to have an Eastern Cooperative Oncology Group (ECOG) performance score (PS) of 0 or 1. They could not have an abnormally elevated serum glutamic-oxaloacetic transaminase (SGOT) and/or alkaline phosphatase. Patients were excluded for prior use of valerian for sleep, use of other prescription sleep aids in the past 30 days, or a diagnosis of obstructive sleep apnea or primary insomnia per Diagnostic and Statistical Manual, 4th edition (DSM-IV), criteria. Pregnant and nursing women were also excluded, as were patients with known sleep disturbance etiologies such as nighttime hot flashes, uncontrolled pain, and/or diarrhea.

Participants were randomized to receive 450 mg of oral valerian or placebo, to be taken 1 hour before bedtime for 8 weeks. The valerian used was pure ground, raw root, from one lot and standardized to contain 0.8% valerenic acid. Valerian capsules and matching placebo, a gelatin capsule, were supplied by Hi-Health (Scottsdale, AZ). Both valerian and placebo were stored in the same containers so that the placebo would acquire some of the valerian smell. Self-report booklets were completed at baseline and at weeks 4 and 8 and contained the Pittsburgh Sleep Quality Index (PSQI),42 the Profile of Moods States (POMS),43 the Functional Outcomes of Sleep Questionnaire (FOSQ),44 and the Brief Fatigue Inventory (BFI).45 Assessments were scored according to the appropriate algorithms, and total and subscale scores were transformed to a 0–100 scale, with 100 being best. Self-reported symptoms were recorded weekly using a self-report numeric analogue scale, called the Symptom Experience Diary (SED). Toxicity was also assessed every 2 weeks during a clinical research associate/nurse phone call using the Common Terminology Criteria for Adverse Events (CTCAE, v 3.0).

The primary end point was the normalized (averaged) area under the curve (AUC) of the PSQI between the two arms, compared using the Kruskal-Wallis test. Secondary analyses compared AUC scores of other assessments and toxicity incidence. Toxicity comparisons were performed using the chi-squared test or the Kruskal-Wallis test, as appropriate. As an intent-to-treat (ITT) analysis, using chi-squares tests, patients were categorized as a success if there was a 10-point improvement in the assessment score at week 4 or 8 and a failure if there was no improvement or data were missing.

All hypothesis testing was carried out using a two-sided alternative hypothesis and a 5% Type I error rate. A two-sample t-test with 100 patients per group provided 94% power to detect 50% times the standard deviation (SD) of the end point under study.46 This effect size is considered moderate and has been declared the minimally clinically significant difference for QOL end points.[47] and [48]

Results

A total of 227 patients were randomized into this study between March 19, 2004, and March 9, 2007. The consort diagram depicts the flow of data (Figure 1). Twenty-three patients withdrew before starting the study treatment. Primary end-point data were available on 119 patients (62 receiving valerian and 57 receiving placebo). Baseline characteristics and baseline patient reported outcomes were well balanced between arms with no statistically significant differences ([Table 1] and [Table 2]).

 

 



Figure 1. 

Consort Diagram

Table 1. Demographic Characteristics

VALERIAN (N = 102)PLACEBO (N = 100)P
Gender0.387
 Female82 (80%)85 (85%)
Age (years)0.546
 Mean (SD)59.5 (11.95)58.3 (12.71)
Sleep scale group0.963
 Mildly impaired67 (66%)66 (66%)
 Moderately or severely impaired35 (34%)34 (34%)
Sleep scale score0.841
 Mean (SD)6.6 (1.43)6.6 (1.69)
Primary tumor site0.526
 Breast64 (63%)66 (67%)
 Colon9 (9%)5 (5%)
 Prostate3 (3%)1 (1%)
 Other25 (25%)27 (27%)
Tumor status0.322
 Resected with no residual64 (64%)71 (71%)
 Resected with known residual17 (17%)12 (13%)
 Unresected19 (19%)13 (14%)
Treatment type0.966
 Radiation therapy6 (5.9%)6 (6%)
 Parenteral chemotherapy38 (37%)39 (39%)
 Oral therapy40 (39%)40 (40%)
 Combined modality18 (18%)15 (15%)
Concurrent radiation0.926
 Yes23 (23%)22 (22%)
Concurrent cancer therapy0.679
 Yes56 (55%)52 (53%)
Planned or concurrent hormone0.667
 Yes51 (51%)53 (54%)

Table 2. Distribution of Baseline Assessment Scores

VALERIAN (N = 101)PLACEBO (N = 96)P
PSQI total10.695
 Mean (SD)41.3 (13.92)42.4 (14.97)
POMS-SF total0.883
 Mean (SD)65.0 (14.28)63.9 (16.46)
FOSQ total0.927
 Mean (SD)73.7 (16.07)72.8 (18.37)
Fatigue Now0.285
 Mean (SD)45.7 (24.41)49.4 (25.00)
Usual Fatigue0.216
 Mean (SD)46.8 (23.27)51.1 (24.73)
Worst Fatigue0.522
 Mean (SD)35.2 (24.67)37.9 (26.37)
Total Interference0.268
 Mean (SD)61.4 (25.05)57.1 (27.37)

The primary end point of treatment effectiveness was measured using the normalized AUC calculated using baseline, week 4, and week 8 PSQI total scores. The Wilcoxon rank-sum test P value for the total PSQI score was nonsignificant (valerian AUC = 51.4, SD = 16; placebo AUC = 49.7, SD = 15; P = 0.696) (Figure 2). Similarly the FOSQ was not significantly different between groups either overall or on any subscale score.



Figure 2. 

Pittsburgh Sleep Quality Index Overall Score, AUC

Supplemental and exploratory analyses using changes from baseline, however, showed a significant difference in the change from baseline in the amount of sleep at night at week 4 (P = 0.008), favoring the valerian group. Change from baseline in the categorical value for sleep latency was also significantly different at week 4, where 10% of valerian patients indicated longer time to fall asleep compared to 28% on placebo and 43% of valerian patients reported less time to fall asleep compared to 32% on placebo (P = 0.03) (Table 3). The ITT analysis indicated that about 9% more patients experienced a success on valerian relative to placebo, but this was not statistically significant. When scores on the PSQI were divided into ≤5 and >5 (this latter group representing sleep problems), there were fewer patients in the valerian group having sleep problems by week 8 (64% vs 80%, P = 0.56).

Table 3. Percent of patients reporting categorical changes from baseline on the Pittsburgh Sleep Quality Index Subscales

VALERIANPLACEBOP
Sleep quality0.199
 Week 4
  Worse2 (3%)5 (8%)
  Same33 (49%)37 (57%)
  Better33 (49%)23 (35%)
 Week 80.927
  Worse3 (5%)2 (3%)
  Same26 (41%)25 (42%)
  Better35 (55%)32 (54%)
Sleep latency0.030
 Week 4
  Worse6 (10%)18 (28%)
  Same30 (48%)26 (40%)
  Better27 (43%)21 (32%)
 Week 80.072
  Worse3 (5%)11 (18%)
  Same28 (47%)29 (48%)
  Better27 (47%)21 (34%)
Sleep duration0.244
 Week 4
  Worse6 (9%)10 (16%)
  Same26 (39%)29 (46%)
  Better34 (52%)24 (38%)
 Week 80.148
  Worse8 (13%)4 (7%)
  Same19 (31%)28 (48%)
  Better34 (56%)27 (46%)
Sleep efficiency0.295
 Week 4
  Worse7 (12%)13 (22%)
  Same26 (43%)23 (39%)
  Better28 (46%)23 (39%)
 Week 80.758
  Worse11 (19%)9 (16%)
  Same19 (33%)22 (39%)
  Better28 (48%)25 (45%)
Sleep disturbance0.738
 Week 4
  Worse9 (15%)11 (18%)
  Same41 (66%)40 (67%)
  Better12 (19%)9 (15%)
 Week 80.177
  Worse10 (16%)7 (13%)
  Same35 (57%)41 (73%)
  Better16 (26%)8 (14%)
Daytime dysfunction0.114
 Week 4
  Worse6 (9%)13 (19%)
  Same42 (60%)40 (60%)
  Better22 (31%)14 (21%)
 Week 80.478
  Worse6 (10%)8 (13%)
  Same27 (43%)31 (50%)
  Better30 (48%)23 (37%)

While the POMS AUC scores indicated no difference between treatment arms, the mean change from baseline at weeks 4 and 8 was significantly different for the Fatigue-Inertia subscale at weeks 4 (P = 0.004) and 8 (P = 0.02), with the valerian arm reporting better scores (Table 4). On the BFI, the valerian arm scored significantly better than the placebo arm in the mean change from baseline at weeks 4 and 8 on the Fatigue Now (P = 0.003 and P = 0.01, respectively) and Usual Fatigue (P = 0.02 and P = 0.046, respectively) items (Table 4).

Table 4. Brief Fatigue Inventory (BFI) and Profile of Mood States (POMS): Change from Baseline—Higher Numbers Are Better

SIDE EFFECTWEEKVALERIANPLACEBOP
BFI
 Fatigue NowWeek 413.21.5<0.01
Week 822.110.5<0.01
 Usual FatigueWeek 412.84.20.02
Week 819.410.00.05
 Worst FatigueWeek 411.23.20.03
Week 814.812.40.65
 Activity InterferenceWeek 46.24.10.75
Week 812.310.80.75
POMS
 Anger-HostilityWeek 43.52.00.53
Week 83.94.20.89
 Vigor-ActivityWeek 42.0-0.40.43
Week 82.04.70.34
 Depression-DejectionWeek 43.75.50.21
Week 83.75.40.25
 Confusion-BewildermentWeek 44.82.60.26
Week 85.33.40.79
 Fatigue-InertiaWeek 413.92.8<0.01
Week 817.59.20.02
 TensionAnxietyWeek 46.35.60.85
Week 89.28.90.54
 Total scoreWeek 45.73.00.19
Week 86.96.00.90

In terms of toxicity, there were no significant differences between arms for the self-reported side effect items (headache, trouble waking, nausea) at baseline, week 4, or week 8 (Table 5). The valerian arm change from baseline at both weeks 4 and 8 showed significant improvement in drowsiness (P = 0.04 and P = 0.03, respectively) and sleep problems (P = 0.005 and P = 0.03, respectively) compared to placebo (Table 5). The maximum severity over time for each self-reported toxicity resulted in no significant differences between arms. There was a significant difference in the CTCAE reporting of alkaline phosphatase, with the placebo arm having a higher incidence of grade 1 toxicity (P = 0.049).

 

 

Table 5. Self-Reported Side Effects: Change from Baseline—Higher Numbers Are Better

SIDE EFFECTWEEKVALERIANPLACEBOP
NauseaWeek 43.0–2.10.07
Week 83.40.00.06
HeadacheWeek 44.81.50.09
Week 86.74.60.27
Trouble wakingWeek 48.84.30.42
Week 89.55.70.36
DrowsinessWeek 421.09.70.04
Week 824.014.00.03
Sleep problemsWeek 418.74.3<0.01
Week 824.013.00.03


Discussion

This study failed to identify any significant improvements in sleep as measured by the overall PSQI or the FOSQ in this population. This corroborates data from a recent study by Taibi and colleagues,49 who evaluated 300 mg of valerian, taken half an hour before bed. They reported that valerian did not improve any self-reported or polysomnographic sleep outcomes significantly more than placebo. The Taibi et al. study has several possible limitations, including a small sample size (n = 16), a dose lower than that used in the majority of pilot trials with promising results, and a duration of only 15 days on the study agent.

The current study is one of the few randomized placebo-controlled trials evaluating pharmacological treatment of insomnia complaints among cancer patients. Most randomized trials of treatments directed at insomnia in cancer patients compare CBT with usual care or wait-list care and find it of substantial benefit.[50], [51], [52], [53], [54], [55], [56], [57], [58] and [59] One prior trial in terminal cancer patients evaluated intravenous agents for effectiveness, and another controlled trial found mirtazapine to be effective at improving sleep complaints in cancer patients with depression.[51] and [60] Otherwise, there are no other controlled trials assessing pharmacologic agents to primarily address sleep-related complaints in cancer patients.

While there was no significant improvement in sleep quality as assessed by the PSQI, there were consistent improvements in the secondary fatigue outcomes as measured by both the BFI and the POMS Fatigue-Inertia subscale. Although caution is required in interpreting these secondary results, the raw differences in change scores between the two arms are fairly large, often over 10 points (on a 100-point scale). In addition, several other secondary end points—change from baseline related to sleep latency, amount of sleep per night, improvement in sleep problems, and less drowsiness—all support the valerian arm outperforming placebo.

There are several hypotheses related to the inconsistencies in the results. The PSQI may measure different dimensions of well-being from the BFI or POMS, the former concentrating on sleep-quality measures, while the latter two concentrate on daytime symptoms. The correlation between sleep-quality and daytime symptoms may not be very strong in this study's population. Another possibility is that there was a beta-error. Some of the data were incomplete due to the patients' inability to complete the questionnaires appropriately. The power analysis suggested 100 patients per arm were required, and only about 60 per group provided data for analysis. Another hypothesis is that the effects of valerian were too modest and limited to one aspect, perhaps sleep latency, that were not detectable with multidimensional scales such as the PSQI or the FOSQ that look at impact on activity.

There were more patients who withdrew from the placebo arm early compared to the valerian arm. The reasons for this are not known. However, patients on this trial were getting active treatment for cancer, so numerous and varied reasons could explain early withdrawals including complications from treatment, increased fatigue, and worsening sleep problems.

In summary, this trial did not provide data to support that valerian is helpful in improving sleep during cancer treatment in this population. It is not clear whether valerian may have helpful physiologic activity supporting research in oncology symptom management related to fatigue. Perhaps further exploration is warranted.

Acknowledgments

This study was conducted as a collaborative trial of the North Central Cancer Treatment Group and Mayo Clinic and was supported in part by Public Health Service grants CA-25224, CA-37404, CA-124477 (Mentorship Grant), CA-35431, CA-63848, CA-35195, CA-35133, CA-35267, CA-35269, CA-35103, CA-35101, CA-63849, CA-35119, CA-52352, CA-35448, CA-35103, CA-03011, CA-107586, CA-35261, CA-67575, CA-95968, CA-67753, and CA-35415. The content is solely the responsibility of the authors and does not necessarily represent the views of the National Cancer Institute or the National Institutes of Health.

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50 A. Berger, B. Kuhn and J. Farr et al., Behavioral therapy intervention trial to improve sleep quality and cancer-related fatigue, Psychooncology 18 (2009), pp. 634–646. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (14)

51 E. Cankurtaran, E. Ozalp and H. Soygur et al., Mirtazapine improves sleep and lowers anxiety and depression in cancer patients: superiority over imipramine, Support Care Cancer 16 (2008), pp. 1291–1298. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (5)

52 C. Espie, L. Fleming and J. Cassidy et al., Randomized controlled clinical effectiveness trial of cognitive behavior therapy compared with treatment as usual for persistent insomnia in patients with cancer, J Clin Oncol 26 (28) (2008), pp. 4651–4658. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (37)

53 D. Epstein and S. Dirksen, Randomized trial of a cognitive-behavioral intervention for insomnia in breast cancer survivors, Oncol Nurs Forum 34 (5) (2007), pp. E51–E59. Full Text via CrossRef

54 J. Savard, S. Simard and I. Giguère et al., Randomized clinical trial on cognitive therapy for depression in women with metastatic breast cancer: psychological and immunological effects, Palliat Support Care 4 (3) (2006), pp. 219–237. View Record in Scopus | Cited By in Scopus (30)

55 J. Savard, S. Simard and H. Ivers et al., Randomized study on the efficacy of cognitive-behavioral therapy for insomnia secondary to breast cancer, part I: Sleep and psychological effects, J Clin Oncol 23 (25) (2005), pp. 6083–6096. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (89)

56 J. Savard, S. Simard and H. Ivers, Randomized study on the efficacy of cognitive behavioral therapy for insomnia secondary to breast cancer, part II: Immunologic effects, J Clin Oncol 23 (25) (2005), pp. 6097–6106. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (32)

57 P. Sherwood, B. Given and C. Given et al., A cognitive behavioral intervention for symptom management in patients with advanced cancer, Oncol Nurs Forum 32 (6) (2005), pp. 1190–1198. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (22)

58 C. Quesnel, J. Savard and S. Simard et al., Efficacy of cognitive-behavioral therapy for insomnia in women treated for nonmetastatic breast cancer, J Consult Clin Psychol 71 (1) (2003), pp. 189–200. Abstract |

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Additional participating institutions include Duluth Clinic CCOP, Duluth, MN (Daniel A. Nikcevich, MD, PhD); CCOP Sioux Community Cancer Consortium, Sioux Falls, SD (Loren K. Tschetter, MD); Iowa Oncology Research Association CCOP, Des Moines, IA (Robert J. Behrens, MD); Mayo Clinic Arizona, Scottsdale AZ (Tom R. Fitch, MD); Missouri Valley Cancer Consortium CCOP, Omaha, NE (Gamini S. Soori, MD); Medical College of Georgia Minority-Based CCOP, Augusta, GA (Anand P. Jillella, MD); Columbus CCOP, Columbus, OH (J. Philip Kuebler, MD, PhD); Upstate Carolina CCOP, Spartanburg, SC (James D. Bearden, MD); Cedar Rapids Oncology Project CCOP, Cedar Rapids, IA (Martin Wiesenfeld, MD); Altru Cancer Center, Grand Forks, ND (Todor Dentchev, MD); Montana Cancer Consortium CCOP, Billings, MT (Benjamin T. Marchello, MD); Saint Vincent Hospital CCOP, Green Bay, WI (Anthony J. Jaslowski, MD); Colorado Cancer Research Program CCOP, Denver, CO (Eduardo R. Pajon, Jr, MD); Geisinger Medical Center CCOP, Danville, PA (Albert M. Bernath, Jr, MD); Rapid City Regional Hospital, Rapid City, SD (Richard C. Tenglin, MD); Siouxland Hematology Oncology Associates, Sioux City, IA (Donald B. Wender, MD, PhD); Toledo Community Hospital Oncology Program CCOP, Toledo, OH (Paul L. Schaefer, MD).

Correspondence to: Debra L. Barton, RN, PhD, AOCN, FAAN, Mayo Clinic, 200 First Street, SW, Rochester, MN 55905; telephone: 507-255-3812; fax: 507-538-8300


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Pilot Study of the Prospective Identification of Changes in Cognitive Function During Chemotherapy Treatment for Advanced Ovarian Cancer

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Pilot Study of the Prospective Identification of Changes in Cognitive Function During Chemotherapy Treatment for Advanced Ovarian Cancer

Original research

Pilot Study of the Prospective Identification of Changes in Cognitive Function During Chemotherapy Treatment for Advanced Ovarian Cancer

Lisa M. Hess PhD

, a,
, Setsuko K. Chambers MDa, Kenneth Hatch MDa, Alton Hallum MDa, Mike F. Janicek MDa, Joseph Buscema MDa, Matthew Borst MDa, Cynthia Johnson MAa, Lisa Slayton LPNa, Yuda Chongpison MS, MBAa and David S. Alberts MDa

a University of Arizona, Arizona Cancer Center, Tucson, AZ; Departments of Public Health and Obstetrics and Gynecology, Indiana University School of Medicine, Indianapolis, IN; Arizona Oncology Associates, Tucson, AZ; Scottsdale Healthcare and Oncogyn, P.C., Scottsdale, AZ; and Arizona Gynecologic Oncology, Phoenix, AZ

Received 8 June 2010; 

accepted 9 September 2010. 

Available online 25 January 2011.

Abstract

Change in cognitive function is increasingly being recognized as an adverse outcome related to chemotherapy treatment. These changes need not be severe to impact patient functional ability and quality of life. The primary goal of this study was to determine if there is evidence of changes in the cognitive function domains of attention, processing speed, and response time among women with newly diagnosed advanced ovarian cancer who receive chemotherapy. Eligible patients were women diagnosed with stage III–IV epithelial ovarian or primary peritoneal cancer who had not yet received chemotherapy but who were prescribed a minimum of six cycles (courses) of chemotherapy treatment. Cognitive function was assessed by a computerized, Web-based assessment (attention, processing speed, and reaction time) and by patient self-report. Cognitive function was assessed at three time points: prior to the first course (baseline), course three, and course six. Medical records were reviewed to abstract information on chemotherapy treatment, concomitant medications, and blood test results (eg, hemoglobin, CA-125). Of the 27 eligible participants, 92% and 86% demonstrated cognitive impairments from baseline to course three and from baseline to course six of chemotherapy, respectively. Impairment was detected in two or more cognitive domains among 48% (12 of 25) and 41% (9 of 22) of participants at course three and course six of chemotherapy, respectively. This study shows evidence of decline in cognitive function among women being treated for ovarian cancer. There is a need for additional, prospective research to better understand the impact of chemotherapy on cognitive function among ovarian cancer patients so that effective preventive and treatment strategies can be developed.

Article Outline

Materials and Methods
Assessment Tools
Statistical Plan

Results
Web-Assessed Cognitive Function
Patient-Reported Cognitive Function
Blood Chemistries and Toxicity

Discussion

Acknowledgements

References

Many cancer patients receiving chemotherapy develop persistent changes in cognitive function, characterized by declines in memory, attention, and executive function.[1] and [2] Neurocognitive symptoms may be associated with discrete or multiple etiologies, including direct or indirect effects of cancer on the central nervous system, comorbid neurologic or psychiatric diagnoses, and diffuse and specific effects of cancer treatment, including radiation therapy, immunotherapy, and chemotherapy,[2] and [3] as well as fatigue, pain, and anemia, all of which have been associated with poorer performance on neurocognitive testing among cancer patients.4 Even mild cognitive changes can be expected to influence quality of life and the ability to function effectively in performing day-to-day activities.

Although the perception of cognitive decline is a common complaint among individuals treated with chemotherapy, it is poorly understood and limited efforts have been made to identify the extent of this problem among women with ovarian cancer. To date, the few studies documenting the neuropsychological consequences of ovarian cancer and its treatment have shown that patients report cognitive problems but that these problems were not quantifiable using objective measures due to the lack of sensitivity of standard instruments to the subtle changes that occur during cancer treatment.[5], [6] and [7]

Although studies of cognitive function among oncology patients have used instruments that have been validated in their own disciplines and with a variety of diseases, the evidence is emerging that they are not comprehensive or appropriate tools for the detection and evaluation of chemotherapy-related change in cognitive function.8 Furthermore, the likelihood of having these tests conducted in a similar manner across multiple institutions, sites, and interviewers with any degree of consistency is very low. This study was designed as a pilot study of the identification of chemotherapy-related changes in cognitive function among women with advanced ovarian cancer using a Web-based assessment tool (Headminder, Inc., New York, NY).7 The primary goal of the current study was to determine if there is evidence of changes in the cognitive function domains of attention, processing speed, and reaction time as well as self-reported changes in the memory, sensory-perception, and cognitive-intellectual domains of cognitive function during chemotherapy among women with newly diagnosed advanced ovarian cancer.

Materials and Methods

All study methods and procedures were reviewed and approved by the University of Arizona Institutional Review Board. Eligible patients included women with a histologically or pathologically confirmed diagnosis of stage III–IV epithelial ovarian or primary peritoneal cancer who were prescribed at least six courses of platinum-based therapy. Patients were excluded if they had a prior history of any cancer (other than nonmelanoma skin cancer), chemotherapy, radiation therapy, erythropoietin treatment (within the last 6 months), or severe head injury. Initially, patients were excluded if they received intraperitoneal therapy, but the protocol was later amended to permit the use of any platinum-based therapy, regardless of route of administration.

Assessment Tools

After providing informed consent, patients completed a neurocognitive battery of tests and the Functional Assessment of Cancer Therapy—Neurotoxicity (FACT-Ntx, to assess patient-reported neuropathy).[9] and [10] The neurocognitive evaluation included both a computerized, Web-based and a patient-reported assessment. The Web-based assessment was provided by HeadMinders, Inc.[7] and [11] and was a modified version of the Cognitive Stability Index. The modified battery was comprised of two warm-up tasks and three empirically-derived cognitive factors: Processing Speed (Animal Decoding and Symbol Scanning subtests), Attention (Number Recall and Number Sequencing subtests), and Reaction Time (Response Direction 1 and Response Direction 2 subtests). The subtests have been validated against traditional neuropsychological tests in healthy and clinical populations, including cancer patients.12 Cognitive domain correlations in the battery's healthy normative sample range from 0.52 to 0.74, and correlations are similar or higher in clinical populations. Test–retest reliability of the factor scores between first and second administrations ranges from 0.74 to 0.82.12 This Web-based neurocognitive assessment tool is 21 CFR Part 11– and Health on the Net (HON)–compliant to ensure patient confidentiality. Prior to undergoing the Web-based cognitive tests, all study participants completed a keyboard proficiency test as a “warm-up task” to the computerized assessment.

The patient-reported cognitive function tool used was the Patient Assessment of Own Functioning Scale (PAF).[13], [14] and [15] The PAF includes eight scales that are grouped into the nature of the ability being considered. The Memory, Sensory-Perceptual, and Cognitive-Intellectual subscales of the PAF are included in this self-assessment questionnaire. Respondents are asked to rate on a six-point scale, from almost always to almost never, how often they experience a particular kind of difficulty in their everyday lives. For this study, the Memory and Cognitive-Intellectual subscales of the PAF were used, similar to other clinical research protocols investigating cognitive changes during chemotherapy treatment.15 The PAF has been shown to be directly related to the Minnesota Multiphasic Personality Inventory (MMPI)13 and to be highly correlated with other cognitive impairment indices, such as the American College of Rheumatology neuropsychology research battery of tests.16 Of note, self-reported cognitive change has not been shown to correlate formal assessments of cognitive function among individuals who have experienced cancer.[17], [18], [19], [20] and [21]

The FACT-Ntx is a validated instrument[9] and [10] that was used to evaluate neurotoxicity. This scale includes 11 items: nine to assess neurotoxicity, one to assess bodily weakness, and one to assess anemia. Neurotoxicity may affect a patient's ability to use the keyboard in the computerized neurocognitive evaluation. This complete assessment battery of tests was completed at baseline (within 5 days of initiation of chemotherapy) and again during follow-up assessments at cycle three and cycle six of chemotherapy. The medical record was reviewed and data were abstracted related to chemotherapy medications, all concomitant medications, and blood test results (eg, hemoglobin, CA-125).

Statistical Plan

This prospective study was exploratory in nature and designed to collect pilot data to determine if there is evidence of neurocognitive change in attention, processing speed, response time, or self-reported cognitive function during the course of chemotherapy among women being treated for advanced ovarian cancer. The purpose of this study was to obtain preliminary estimates of the incidence and degree of cognitive decline to aid in the planning of future studies. While prior estimates of cognitive function were not available for this population, power analyses demonstrate that with a target recruitment goal of 30 patients, a McNemar's test has 78% power at the 0.05 level of significance to detect a significant decline in impairment in a cognitive domain if 12 patients are found to have impairment prior to course six of treatment (but not at course three) and if as few as two patients demonstrate impairment prior to course three but not at course six. This study was therefore powered to detect declines in one or more of the domains that may have occurred at less than both of the study time points following the baseline assessment.

To be considered fully evaluable, patients had to have completed at least one follow-up neurocognitive evaluation and may not have received antipsychotic neuropsychological medications during the study (eg, chlorpromazine, haloperidol, clozapine). Antidepressants and antianxiety medications (eg, serotonin/norepinephrine reuptake inhibitors or benzodiazepines) were permitted and use was recorded throughout study participation. A summary score for each cognitive domain (processing speed, reaction time, and attention) was recorded at each assessment time point using the HeadMinder Web-based assessment. This summary score was assessed by time (processing speed and reaction time), measured to the hundredth of a second, and by number of errors (attention). If a cognitive domain summary score at a follow-up assessment time declined at least one standard error of measurement (SEM) from baseline, the patient was considered to have experienced a decline at that time point. For the purposes of this article, such declines are referred to as “impairments” within the cognitive domain under investigation. A cognitive index score (CIS) was calculated as the number of cognitive domains impaired for the time point. The range of a CIS is 0–3, with zero equal to no impairment on any cognitive domain and three equal to impairment on all cognitive domains. Patients with only one cognitive domain decline (CIS = 1) at any one of the follow-up assessment time points were considered as having possible cognitive function decline. Patients with more than one cognitive domain impairment (CIS >1) at any follow-up assessment time points were considered as having evidence of cognitive function decline. The incidence of cognitive function impairment was determined by the percentage of patients who experienced any cognitive domain impairment (including possible and evidence of decline) at any follow-up assessment.

A repeated-measures analyses of variance (ANOVA) was used to further explore the neurocognitive values at the various time points during the study. Many of the neurocognitive values were not normally distributed but skewed either positively or negatively, so the square roots of the values were used in the analyses. Since this is an exploratory analysis, no corrections for multiple comparisons were performed.

The patient-reported cognitive function instrument (PAF) contains items scored on a Likert-type scale from almost never to almost always (range 0–5). Patient-reported outcomes as measured with the PAF are measured as mean scale values, ranging from 0, indicating no impairment, to 5.0, indicating complete impairment. PAF score ranges indicate low (≤1.25), medium (1.26–1.92), and high (≥1.93) levels of cognitive impairment.13 A total FACT-Ntx score was obtained; lower scores represent greater neurotoxicity, ranging from 0 (extreme neurotoxicity) to 44 (no neurotoxicity). The total score was reported, with adjustments made for missing values as described elsewhere.22

Results

Thirty patients were enrolled in this study; however, two were later deemed ineligible, and one was unable to complete the baseline neurocognitive assessment prior to chemotherapy and was withdrawn from the study, resulting in 27 patients available for assessment. Five of these patients did not complete all neurocognitive assessments. The primary reason for nonadherence to the study schedule was clinical scheduling (eg, chemotherapy was administered prior to the neurocognitive assessment). The characteristics of eligible patients are provided in Table 1. The majority of patients were receiving intravenous chemotherapy (intraperitoneal therapy was at first not permitted but later was allowable following an amendment to the protocol) and taking concomitant sleep, antianxiety, and/or antidepressant medications outside of every 3- to 4-week chemotherapy regimen (primarily zolpidem, lorazepam, sertraline, and/or trazodone).

 

 

Table 1. Characteristics of Eligible Participants

n = 27
Mean age, years (range)59.3 (40.3–81.5)
Education, n (%)
 High school or less3 (11.1%)
 Some college12 (44.4%)
 College graduate12 (44.4%)
Race/ethnicity, n (%)
 White, non-Hispanic25 (92.6%)
 Hispanic1 (3.7%)
 Native American1 (3.7%)
Marital status, n (%)
 Married/cohabitating19 (70.4%)
 Divorced/separated1 (3.7%)
 Widowed5 (18.5%)
 Never married2 (7.4%)
Mean courses of chemotherapy, n (range)5.9 (4–6)
Chemotherapy route, n (%)
 Intraperitoneal5 (18.5%)
 Intravenous22 (81.5%)
Concurrent medication use, n (%)
 Antidepressant7 (25.9%)
 Antianxiety16 (59.3%)
 Sleep aids5 (18.5%)


Web-Assessed Cognitive Function

Keyboard proficiency remained unchanged over time (P = 0.39). As shown in Table 2, most participants demonstrated cognitive impairments in at least one of the three cognitive domains assessed during this study (92% and 86% at course 3 and course 6, respectively). Nearly half of the study participants demonstrated impairment from baseline in two or more of the three cognitive domains assessed (Table 3). Table 4 shows a detailed summary of the subscales within the Web-based cognitive tests that comprised the CIS.This table demonstrates the statistically significant increase in test subscale errors, despite the test-taking improvements over time, as shown by reduction in testing time.

Table 2. Number and Percent of Patients Demonstrating Cognitive Impairments by Cognitive Domain as Measured by Web-Based Assessment

COGNITIVE DOMAINCOURSE 3COURSE 6
Attention10/25 (40%)8/22 (36%)
Processing speed10/25 (40%)11/22 (50%)
Reaction time16/25 (64%)11/22 (50%)

Table 3. Number and Percent of Patients Showing Impairment in Multiple Cognitive Domains as Measured by the Web-Assessed Cognitive Impairment Scale (CIS)

CISCOURSE 3COURSE 6
No decline (CIS = 0)2 (8%)3 (14%)
One impairment (CIS = 1)11 (44%)10 (45%)
Two impairments (CIS = 2)11 (44%)7 (32%)
Three impairments (CIS = 3)1 (4%)2 (9%)

Table 4. Subscales Within the Cognitive Domains Assessed by the Web-Based Assessment

COGNITIVE IMPAIRMENT SCALE (CIS) FACTORS
BASELINE
COURSE 3
COURSE 6
NMEANSDNMEANSDNMEANSDP
Attention
 Number recall (number correct)257.081.75257.162.03227.451.920.887
 Number sequencing (number correct)266.230.98255.962.65235.612.290.476
Processing speed
 Animal decoding (number of errors)250.40.5250.720.84233.260.86<0.0001
 Animal decoding (number correct)2532.486.482532.968.902332.228.700.678
 Symbol scanning (number correct)2718.591.152518.761.22118.671.350.883
 Symbol scanning (response time)274.381.37254.261.66213.610.840.002
Reaction time
 Response direction 1 (number of omissions)270.040.19260.622.3523000.028
 Response direction 1 (response time, seconds)270.520.06260.550.22230.520.070.567
 Response direction 2 (number of omissions)270.631.33260.52.18230.430.950.135
 Response direction 2 (response time, seconds)270.750.13260.720.20230.710.170.467
 Response direction, shift failures (number)274.333.13262.772.29233.042.580.007


Patient-Reported Cognitive Function

The mean values and 95% confidence intervals of the patient-reported cognitive function outcomes are presented in Figure 1. Mean values remained within the low impairment range (less than 1.25) during chemotherapy.



Figure 1. 

Mean Patient-Reported Cognitive Function Scores with 95% Confidence Intervals


Blood Chemistries and Toxicity

The mean values and 95% confidence intervals of significant differences in blood chemistries and toxicities are presented in [Figure 2] and [Figure 3]. Total patient-reported neurotoxicity increased significantly during chemotherapy (ANOVA; F = 6.851, P = 0.002), while several mean blood chemistry values decreased during chemotherapy treatment (hemoglobin F = 2.465, P = 0.09; white blood cell count F = 16.95, P < 0.001; platelets F = 13.72, P < 0.001; and CA-125 F = 4.91, P = 0.01). One study participant received a blood transfusion at the final course of chemotherapy, and two and three participants received cytokines (erythropoietin or darbepoietin) at course 3 and course 6, respectively.



Figure 2. 

Total Patient-Reported Neurotoxicity with 95% Confidence Intervals (Lower Scores Represent Greater Neurotoxicity)

Figure 3. 

Mean Hemoglobin, White Blood Cell, Platelet, and CA-125 Levels with 95% Confidence Intervals


Discussion

This study shows preliminary evidence that cognitive decline is a significant factor experienced by women who are treated for advanced ovarian cancer. Most participants self-reported mild declines, and these were detectable by a sensitive Web-based assessment tool. There are many potential mechanisms of cognitive decline during chemotherapy, ranging from oxidative damage to reduced blood oxygenation due to anemia to stress and anxiety. While it is outside of the scope of this small pilot study to examine the causative factors of decline, it does suggest the need for further investigation of the effect and potential mechanisms of cognitive decline in this population. While most of the prior work in cognitive function has been conducted among breast cancer patients, ovarian cancer patients appear to experience cognitive decline as well. There is a need to further understand this issue so that effective preventive or treatment strategies can be developed.

The significant increase in patient-reported neurotoxicity across each study visit may be a concern for computerized assessments that require dexterity. However, the keyboard proficiency tests did not decline over time, suggesting that the neurotoxicity reported by patients in this study was not great enough to affect their ability to use the computer keyboard. Patients appear to report higher levels of difficulty with memory (eg, forgetfulness) following diagnosis than following the initiation of chemotherapy; however, higher-level cognitive processes (eg, logic, organizational abilities, calculations) reported by patients appear to decline following the initiation of chemotherapy. Although larger, adequately powered trials are needed to determine the extent of this decline, this suggests that patients experience increasing challenges that may interfere with their ability to perform necessary tasks at work and in the household. Further work is needed to examine the duration of these effects following chemotherapy. Since the cognitive impact of chemotherapy reported by patients is mild, investigators must ensure the use of appropriate patient-reported tools that are able to detect these differences. While reported decline may occur, this is likely to remain within the mild category of traditional assessment tools. It is of benefit to use patient-reported tools such as the PAF that also permit the analysis of continuous data.

This study is limited by its design as a pilot study and was challenged by several logistical issues. Four patients were unable to complete all the neurocognitive evaluations. This was due to remote study staff, who would visit various clinics in the Tucson and Phoenix metropolitan regions in Arizona (range of travel more than 120 miles). The lack of completion was entirely due to communication and travel complications. When a patient was rescheduled to a different chemotherapy date, it was not always possible for this to be communicated to the Arizona Cancer Center researchers in a timely manner, resulting in missed visits. It is recommended for future studies that require strict timelines for study assessments (such as this cognitive function study) that the assessments be conducted by staff in those practices who can identify changes in infusion dates when they occur. This will reduce the communication barriers and rate of missed visits. This study was also not designed to be a comprehensive assessment of neurocognitive function but was focused on assessing three domains: attention, processing speed, and response time. It is possible that many other domains of cognitive function could be impacted by chemotherapy that were not evaluated in this study. Many patients were also taking antidepressant medications during the study; however, these were generally not new prescriptions and were also being taken at the baseline assessment. Nevertheless, future studies should incorporate assessments of mood, depression, and anxiety to account for the potential effect of these factors on cognitive assessment scores.

Despite these limitations, the study provides preliminary data demonstrating cognitive decline during chemotherapy among ovarian cancer patients treated in the front-line setting of advanced disease. More than 90% of all patients experienced measurable impairments in cognitive function during primary chemotherapy. More than half of all patients demonstrated impairment on two or more cognitive domains. Prior work has shown that even mild cognitive impairments can influence quality of life and the ability to perform routine daily activities (eg, taking medications, returning to work, managing household finances).23 The data emphasize the critical need to further understand the impact of chemotherapy on cognitive function among ovarian cancer patients so that effective preventive and treatment strategies can be developed. Additional research is needed to understand how long these declines may persist following chemotherapy treatment.

 

 

Acknowledgments

This study was funded by an investigator-initiated grant from Ortho Biotech, Inc., to the University of Arizona Cancer Center. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of Ortho Biotech.

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15 K. Paraska and C.M. Bender, Cognitive dysfunction following adjuvant chemotherapy for breast cancer: two case studies, Oncol Nurs Forum 30 (3) (2003), pp. 473–478. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (15)

16 E. Kozora, M.C. Ellison and S. West, Depression, fatigue, and pain in systemic lupus erythematosus (SLE): relationship to the American College of Rheumatology SLE neuropsychological battery, Arthritis Rheum 55 (4) (2006), pp. 628–635. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (36)

17 T.A. Ahles, A.J. Saykin and C.T. Furstenberg et al., Neuropsychologic impact of standard-dose systemic chemotherapy in long-term survivors of breast cancer and lymphoma, J Clin Oncol 20 (2) (2002), pp. 485–493. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (251)

18 P. Klepstad, P. Hilton, J. Moen, B. Fougner, P.C. Borchgrevink and S. Kaasa, Self-reports are not related to objective assessments of cognitive function and sedation in patients with cancer pain admitted to a palliative care unit, Palliat Med 16 (6) (2002), pp. 513–519. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (25)

19 S.B. Schagen, F.S. van Dam, M.J. Muller, W. Boogerd, J. Lindeboom and P.F. Bruning, Cognitive deficits after postoperative adjuvant chemotherapy for breast carcinoma, Cancer 85 (3) (1999), pp. 640–650. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (263)

20 S.B. Schagen, M.J. Muller and W. Boogerd et al., Late effects of adjuvant chemotherapy on cognitive function: a follow-up study in breast cancer patients, Ann Oncol 13 (9) (2002), pp. 1387–1397. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (99)

21 F.S. van Dam, S.B. Schagen and M.J. Muller et al., Impairment of cognitive function in women receiving adjuvant treatment for high-risk breast cancer: high-dose versus standard-dose chemotherapy, J Natl Cancer Inst 90 (3) (1998), pp. 210–218. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (322)

22 D.L. Fairclough and D.F. Cella, Functional Assessment of Cancer Therapy (FACT-G): non-response to individual questions, Qual Life Res 5 (1996), pp. 321–329. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (75)

23 C.L. Burton, E. Strauss, D.F. Hultsch and M.A. Hunter, Cognitive functioning and everyday problem solving in older adults, Clin Neuropsychol 20 (3) (2006), pp. 432–452. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (20)

 

 

Conflicts of interest: None to disclose.

Correspondence to: Lisa M. Hess, PhD, Indiana University School of Medicine, Department of Public Health, 714 N Senate Avenue, Indianapolis, IN 46202; telephone: (317) 274-3148; Fax (317) 274-3443


1 PubMed ID in brackets


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Original research

Pilot Study of the Prospective Identification of Changes in Cognitive Function During Chemotherapy Treatment for Advanced Ovarian Cancer

Lisa M. Hess PhD

, a,
, Setsuko K. Chambers MDa, Kenneth Hatch MDa, Alton Hallum MDa, Mike F. Janicek MDa, Joseph Buscema MDa, Matthew Borst MDa, Cynthia Johnson MAa, Lisa Slayton LPNa, Yuda Chongpison MS, MBAa and David S. Alberts MDa

a University of Arizona, Arizona Cancer Center, Tucson, AZ; Departments of Public Health and Obstetrics and Gynecology, Indiana University School of Medicine, Indianapolis, IN; Arizona Oncology Associates, Tucson, AZ; Scottsdale Healthcare and Oncogyn, P.C., Scottsdale, AZ; and Arizona Gynecologic Oncology, Phoenix, AZ

Received 8 June 2010; 

accepted 9 September 2010. 

Available online 25 January 2011.

Abstract

Change in cognitive function is increasingly being recognized as an adverse outcome related to chemotherapy treatment. These changes need not be severe to impact patient functional ability and quality of life. The primary goal of this study was to determine if there is evidence of changes in the cognitive function domains of attention, processing speed, and response time among women with newly diagnosed advanced ovarian cancer who receive chemotherapy. Eligible patients were women diagnosed with stage III–IV epithelial ovarian or primary peritoneal cancer who had not yet received chemotherapy but who were prescribed a minimum of six cycles (courses) of chemotherapy treatment. Cognitive function was assessed by a computerized, Web-based assessment (attention, processing speed, and reaction time) and by patient self-report. Cognitive function was assessed at three time points: prior to the first course (baseline), course three, and course six. Medical records were reviewed to abstract information on chemotherapy treatment, concomitant medications, and blood test results (eg, hemoglobin, CA-125). Of the 27 eligible participants, 92% and 86% demonstrated cognitive impairments from baseline to course three and from baseline to course six of chemotherapy, respectively. Impairment was detected in two or more cognitive domains among 48% (12 of 25) and 41% (9 of 22) of participants at course three and course six of chemotherapy, respectively. This study shows evidence of decline in cognitive function among women being treated for ovarian cancer. There is a need for additional, prospective research to better understand the impact of chemotherapy on cognitive function among ovarian cancer patients so that effective preventive and treatment strategies can be developed.

Article Outline

Materials and Methods
Assessment Tools
Statistical Plan

Results
Web-Assessed Cognitive Function
Patient-Reported Cognitive Function
Blood Chemistries and Toxicity

Discussion

Acknowledgements

References

Many cancer patients receiving chemotherapy develop persistent changes in cognitive function, characterized by declines in memory, attention, and executive function.[1] and [2] Neurocognitive symptoms may be associated with discrete or multiple etiologies, including direct or indirect effects of cancer on the central nervous system, comorbid neurologic or psychiatric diagnoses, and diffuse and specific effects of cancer treatment, including radiation therapy, immunotherapy, and chemotherapy,[2] and [3] as well as fatigue, pain, and anemia, all of which have been associated with poorer performance on neurocognitive testing among cancer patients.4 Even mild cognitive changes can be expected to influence quality of life and the ability to function effectively in performing day-to-day activities.

Although the perception of cognitive decline is a common complaint among individuals treated with chemotherapy, it is poorly understood and limited efforts have been made to identify the extent of this problem among women with ovarian cancer. To date, the few studies documenting the neuropsychological consequences of ovarian cancer and its treatment have shown that patients report cognitive problems but that these problems were not quantifiable using objective measures due to the lack of sensitivity of standard instruments to the subtle changes that occur during cancer treatment.[5], [6] and [7]

Although studies of cognitive function among oncology patients have used instruments that have been validated in their own disciplines and with a variety of diseases, the evidence is emerging that they are not comprehensive or appropriate tools for the detection and evaluation of chemotherapy-related change in cognitive function.8 Furthermore, the likelihood of having these tests conducted in a similar manner across multiple institutions, sites, and interviewers with any degree of consistency is very low. This study was designed as a pilot study of the identification of chemotherapy-related changes in cognitive function among women with advanced ovarian cancer using a Web-based assessment tool (Headminder, Inc., New York, NY).7 The primary goal of the current study was to determine if there is evidence of changes in the cognitive function domains of attention, processing speed, and reaction time as well as self-reported changes in the memory, sensory-perception, and cognitive-intellectual domains of cognitive function during chemotherapy among women with newly diagnosed advanced ovarian cancer.

Materials and Methods

All study methods and procedures were reviewed and approved by the University of Arizona Institutional Review Board. Eligible patients included women with a histologically or pathologically confirmed diagnosis of stage III–IV epithelial ovarian or primary peritoneal cancer who were prescribed at least six courses of platinum-based therapy. Patients were excluded if they had a prior history of any cancer (other than nonmelanoma skin cancer), chemotherapy, radiation therapy, erythropoietin treatment (within the last 6 months), or severe head injury. Initially, patients were excluded if they received intraperitoneal therapy, but the protocol was later amended to permit the use of any platinum-based therapy, regardless of route of administration.

Assessment Tools

After providing informed consent, patients completed a neurocognitive battery of tests and the Functional Assessment of Cancer Therapy—Neurotoxicity (FACT-Ntx, to assess patient-reported neuropathy).[9] and [10] The neurocognitive evaluation included both a computerized, Web-based and a patient-reported assessment. The Web-based assessment was provided by HeadMinders, Inc.[7] and [11] and was a modified version of the Cognitive Stability Index. The modified battery was comprised of two warm-up tasks and three empirically-derived cognitive factors: Processing Speed (Animal Decoding and Symbol Scanning subtests), Attention (Number Recall and Number Sequencing subtests), and Reaction Time (Response Direction 1 and Response Direction 2 subtests). The subtests have been validated against traditional neuropsychological tests in healthy and clinical populations, including cancer patients.12 Cognitive domain correlations in the battery's healthy normative sample range from 0.52 to 0.74, and correlations are similar or higher in clinical populations. Test–retest reliability of the factor scores between first and second administrations ranges from 0.74 to 0.82.12 This Web-based neurocognitive assessment tool is 21 CFR Part 11– and Health on the Net (HON)–compliant to ensure patient confidentiality. Prior to undergoing the Web-based cognitive tests, all study participants completed a keyboard proficiency test as a “warm-up task” to the computerized assessment.

The patient-reported cognitive function tool used was the Patient Assessment of Own Functioning Scale (PAF).[13], [14] and [15] The PAF includes eight scales that are grouped into the nature of the ability being considered. The Memory, Sensory-Perceptual, and Cognitive-Intellectual subscales of the PAF are included in this self-assessment questionnaire. Respondents are asked to rate on a six-point scale, from almost always to almost never, how often they experience a particular kind of difficulty in their everyday lives. For this study, the Memory and Cognitive-Intellectual subscales of the PAF were used, similar to other clinical research protocols investigating cognitive changes during chemotherapy treatment.15 The PAF has been shown to be directly related to the Minnesota Multiphasic Personality Inventory (MMPI)13 and to be highly correlated with other cognitive impairment indices, such as the American College of Rheumatology neuropsychology research battery of tests.16 Of note, self-reported cognitive change has not been shown to correlate formal assessments of cognitive function among individuals who have experienced cancer.[17], [18], [19], [20] and [21]

The FACT-Ntx is a validated instrument[9] and [10] that was used to evaluate neurotoxicity. This scale includes 11 items: nine to assess neurotoxicity, one to assess bodily weakness, and one to assess anemia. Neurotoxicity may affect a patient's ability to use the keyboard in the computerized neurocognitive evaluation. This complete assessment battery of tests was completed at baseline (within 5 days of initiation of chemotherapy) and again during follow-up assessments at cycle three and cycle six of chemotherapy. The medical record was reviewed and data were abstracted related to chemotherapy medications, all concomitant medications, and blood test results (eg, hemoglobin, CA-125).

Statistical Plan

This prospective study was exploratory in nature and designed to collect pilot data to determine if there is evidence of neurocognitive change in attention, processing speed, response time, or self-reported cognitive function during the course of chemotherapy among women being treated for advanced ovarian cancer. The purpose of this study was to obtain preliminary estimates of the incidence and degree of cognitive decline to aid in the planning of future studies. While prior estimates of cognitive function were not available for this population, power analyses demonstrate that with a target recruitment goal of 30 patients, a McNemar's test has 78% power at the 0.05 level of significance to detect a significant decline in impairment in a cognitive domain if 12 patients are found to have impairment prior to course six of treatment (but not at course three) and if as few as two patients demonstrate impairment prior to course three but not at course six. This study was therefore powered to detect declines in one or more of the domains that may have occurred at less than both of the study time points following the baseline assessment.

To be considered fully evaluable, patients had to have completed at least one follow-up neurocognitive evaluation and may not have received antipsychotic neuropsychological medications during the study (eg, chlorpromazine, haloperidol, clozapine). Antidepressants and antianxiety medications (eg, serotonin/norepinephrine reuptake inhibitors or benzodiazepines) were permitted and use was recorded throughout study participation. A summary score for each cognitive domain (processing speed, reaction time, and attention) was recorded at each assessment time point using the HeadMinder Web-based assessment. This summary score was assessed by time (processing speed and reaction time), measured to the hundredth of a second, and by number of errors (attention). If a cognitive domain summary score at a follow-up assessment time declined at least one standard error of measurement (SEM) from baseline, the patient was considered to have experienced a decline at that time point. For the purposes of this article, such declines are referred to as “impairments” within the cognitive domain under investigation. A cognitive index score (CIS) was calculated as the number of cognitive domains impaired for the time point. The range of a CIS is 0–3, with zero equal to no impairment on any cognitive domain and three equal to impairment on all cognitive domains. Patients with only one cognitive domain decline (CIS = 1) at any one of the follow-up assessment time points were considered as having possible cognitive function decline. Patients with more than one cognitive domain impairment (CIS >1) at any follow-up assessment time points were considered as having evidence of cognitive function decline. The incidence of cognitive function impairment was determined by the percentage of patients who experienced any cognitive domain impairment (including possible and evidence of decline) at any follow-up assessment.

A repeated-measures analyses of variance (ANOVA) was used to further explore the neurocognitive values at the various time points during the study. Many of the neurocognitive values were not normally distributed but skewed either positively or negatively, so the square roots of the values were used in the analyses. Since this is an exploratory analysis, no corrections for multiple comparisons were performed.

The patient-reported cognitive function instrument (PAF) contains items scored on a Likert-type scale from almost never to almost always (range 0–5). Patient-reported outcomes as measured with the PAF are measured as mean scale values, ranging from 0, indicating no impairment, to 5.0, indicating complete impairment. PAF score ranges indicate low (≤1.25), medium (1.26–1.92), and high (≥1.93) levels of cognitive impairment.13 A total FACT-Ntx score was obtained; lower scores represent greater neurotoxicity, ranging from 0 (extreme neurotoxicity) to 44 (no neurotoxicity). The total score was reported, with adjustments made for missing values as described elsewhere.22

Results

Thirty patients were enrolled in this study; however, two were later deemed ineligible, and one was unable to complete the baseline neurocognitive assessment prior to chemotherapy and was withdrawn from the study, resulting in 27 patients available for assessment. Five of these patients did not complete all neurocognitive assessments. The primary reason for nonadherence to the study schedule was clinical scheduling (eg, chemotherapy was administered prior to the neurocognitive assessment). The characteristics of eligible patients are provided in Table 1. The majority of patients were receiving intravenous chemotherapy (intraperitoneal therapy was at first not permitted but later was allowable following an amendment to the protocol) and taking concomitant sleep, antianxiety, and/or antidepressant medications outside of every 3- to 4-week chemotherapy regimen (primarily zolpidem, lorazepam, sertraline, and/or trazodone).

 

 

Table 1. Characteristics of Eligible Participants

n = 27
Mean age, years (range)59.3 (40.3–81.5)
Education, n (%)
 High school or less3 (11.1%)
 Some college12 (44.4%)
 College graduate12 (44.4%)
Race/ethnicity, n (%)
 White, non-Hispanic25 (92.6%)
 Hispanic1 (3.7%)
 Native American1 (3.7%)
Marital status, n (%)
 Married/cohabitating19 (70.4%)
 Divorced/separated1 (3.7%)
 Widowed5 (18.5%)
 Never married2 (7.4%)
Mean courses of chemotherapy, n (range)5.9 (4–6)
Chemotherapy route, n (%)
 Intraperitoneal5 (18.5%)
 Intravenous22 (81.5%)
Concurrent medication use, n (%)
 Antidepressant7 (25.9%)
 Antianxiety16 (59.3%)
 Sleep aids5 (18.5%)


Web-Assessed Cognitive Function

Keyboard proficiency remained unchanged over time (P = 0.39). As shown in Table 2, most participants demonstrated cognitive impairments in at least one of the three cognitive domains assessed during this study (92% and 86% at course 3 and course 6, respectively). Nearly half of the study participants demonstrated impairment from baseline in two or more of the three cognitive domains assessed (Table 3). Table 4 shows a detailed summary of the subscales within the Web-based cognitive tests that comprised the CIS.This table demonstrates the statistically significant increase in test subscale errors, despite the test-taking improvements over time, as shown by reduction in testing time.

Table 2. Number and Percent of Patients Demonstrating Cognitive Impairments by Cognitive Domain as Measured by Web-Based Assessment

COGNITIVE DOMAINCOURSE 3COURSE 6
Attention10/25 (40%)8/22 (36%)
Processing speed10/25 (40%)11/22 (50%)
Reaction time16/25 (64%)11/22 (50%)

Table 3. Number and Percent of Patients Showing Impairment in Multiple Cognitive Domains as Measured by the Web-Assessed Cognitive Impairment Scale (CIS)

CISCOURSE 3COURSE 6
No decline (CIS = 0)2 (8%)3 (14%)
One impairment (CIS = 1)11 (44%)10 (45%)
Two impairments (CIS = 2)11 (44%)7 (32%)
Three impairments (CIS = 3)1 (4%)2 (9%)

Table 4. Subscales Within the Cognitive Domains Assessed by the Web-Based Assessment

COGNITIVE IMPAIRMENT SCALE (CIS) FACTORS
BASELINE
COURSE 3
COURSE 6
NMEANSDNMEANSDNMEANSDP
Attention
 Number recall (number correct)257.081.75257.162.03227.451.920.887
 Number sequencing (number correct)266.230.98255.962.65235.612.290.476
Processing speed
 Animal decoding (number of errors)250.40.5250.720.84233.260.86<0.0001
 Animal decoding (number correct)2532.486.482532.968.902332.228.700.678
 Symbol scanning (number correct)2718.591.152518.761.22118.671.350.883
 Symbol scanning (response time)274.381.37254.261.66213.610.840.002
Reaction time
 Response direction 1 (number of omissions)270.040.19260.622.3523000.028
 Response direction 1 (response time, seconds)270.520.06260.550.22230.520.070.567
 Response direction 2 (number of omissions)270.631.33260.52.18230.430.950.135
 Response direction 2 (response time, seconds)270.750.13260.720.20230.710.170.467
 Response direction, shift failures (number)274.333.13262.772.29233.042.580.007


Patient-Reported Cognitive Function

The mean values and 95% confidence intervals of the patient-reported cognitive function outcomes are presented in Figure 1. Mean values remained within the low impairment range (less than 1.25) during chemotherapy.



Figure 1. 

Mean Patient-Reported Cognitive Function Scores with 95% Confidence Intervals


Blood Chemistries and Toxicity

The mean values and 95% confidence intervals of significant differences in blood chemistries and toxicities are presented in [Figure 2] and [Figure 3]. Total patient-reported neurotoxicity increased significantly during chemotherapy (ANOVA; F = 6.851, P = 0.002), while several mean blood chemistry values decreased during chemotherapy treatment (hemoglobin F = 2.465, P = 0.09; white blood cell count F = 16.95, P < 0.001; platelets F = 13.72, P < 0.001; and CA-125 F = 4.91, P = 0.01). One study participant received a blood transfusion at the final course of chemotherapy, and two and three participants received cytokines (erythropoietin or darbepoietin) at course 3 and course 6, respectively.



Figure 2. 

Total Patient-Reported Neurotoxicity with 95% Confidence Intervals (Lower Scores Represent Greater Neurotoxicity)

Figure 3. 

Mean Hemoglobin, White Blood Cell, Platelet, and CA-125 Levels with 95% Confidence Intervals


Discussion

This study shows preliminary evidence that cognitive decline is a significant factor experienced by women who are treated for advanced ovarian cancer. Most participants self-reported mild declines, and these were detectable by a sensitive Web-based assessment tool. There are many potential mechanisms of cognitive decline during chemotherapy, ranging from oxidative damage to reduced blood oxygenation due to anemia to stress and anxiety. While it is outside of the scope of this small pilot study to examine the causative factors of decline, it does suggest the need for further investigation of the effect and potential mechanisms of cognitive decline in this population. While most of the prior work in cognitive function has been conducted among breast cancer patients, ovarian cancer patients appear to experience cognitive decline as well. There is a need to further understand this issue so that effective preventive or treatment strategies can be developed.

The significant increase in patient-reported neurotoxicity across each study visit may be a concern for computerized assessments that require dexterity. However, the keyboard proficiency tests did not decline over time, suggesting that the neurotoxicity reported by patients in this study was not great enough to affect their ability to use the computer keyboard. Patients appear to report higher levels of difficulty with memory (eg, forgetfulness) following diagnosis than following the initiation of chemotherapy; however, higher-level cognitive processes (eg, logic, organizational abilities, calculations) reported by patients appear to decline following the initiation of chemotherapy. Although larger, adequately powered trials are needed to determine the extent of this decline, this suggests that patients experience increasing challenges that may interfere with their ability to perform necessary tasks at work and in the household. Further work is needed to examine the duration of these effects following chemotherapy. Since the cognitive impact of chemotherapy reported by patients is mild, investigators must ensure the use of appropriate patient-reported tools that are able to detect these differences. While reported decline may occur, this is likely to remain within the mild category of traditional assessment tools. It is of benefit to use patient-reported tools such as the PAF that also permit the analysis of continuous data.

This study is limited by its design as a pilot study and was challenged by several logistical issues. Four patients were unable to complete all the neurocognitive evaluations. This was due to remote study staff, who would visit various clinics in the Tucson and Phoenix metropolitan regions in Arizona (range of travel more than 120 miles). The lack of completion was entirely due to communication and travel complications. When a patient was rescheduled to a different chemotherapy date, it was not always possible for this to be communicated to the Arizona Cancer Center researchers in a timely manner, resulting in missed visits. It is recommended for future studies that require strict timelines for study assessments (such as this cognitive function study) that the assessments be conducted by staff in those practices who can identify changes in infusion dates when they occur. This will reduce the communication barriers and rate of missed visits. This study was also not designed to be a comprehensive assessment of neurocognitive function but was focused on assessing three domains: attention, processing speed, and response time. It is possible that many other domains of cognitive function could be impacted by chemotherapy that were not evaluated in this study. Many patients were also taking antidepressant medications during the study; however, these were generally not new prescriptions and were also being taken at the baseline assessment. Nevertheless, future studies should incorporate assessments of mood, depression, and anxiety to account for the potential effect of these factors on cognitive assessment scores.

Despite these limitations, the study provides preliminary data demonstrating cognitive decline during chemotherapy among ovarian cancer patients treated in the front-line setting of advanced disease. More than 90% of all patients experienced measurable impairments in cognitive function during primary chemotherapy. More than half of all patients demonstrated impairment on two or more cognitive domains. Prior work has shown that even mild cognitive impairments can influence quality of life and the ability to perform routine daily activities (eg, taking medications, returning to work, managing household finances).23 The data emphasize the critical need to further understand the impact of chemotherapy on cognitive function among ovarian cancer patients so that effective preventive and treatment strategies can be developed. Additional research is needed to understand how long these declines may persist following chemotherapy treatment.

 

 

Acknowledgments

This study was funded by an investigator-initiated grant from Ortho Biotech, Inc., to the University of Arizona Cancer Center. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of Ortho Biotech.

References1

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2 A.J. Saykin, T.A. Ahles and B.C. McDonald, Mechanisms of chemotherapy-induced cognitive disorders: neuropsychological, pathophysiological, and neuroimaging perspectives, Semin Clin Neuropsychiatry 8 (4) (2003), pp. 201–216. View Record in Scopus | Cited By in Scopus (63)

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4 C.A. Meyers, Neurocognitive dysfunction in cancer patients, Oncology (Williston Park) 14 (1) (2000), pp. 75–79 discussion 9, 81–82, 85. View Record in Scopus | Cited By in Scopus (68)

5 M.L. Hensley, D.D. Correa and H. Thaler et al., Phase I/II study of weekly paclitaxel plus carboplatin and gemcitabine as first-line treatment of advanced-stage ovarian cancer: pathologic complete response and longitudinal assessment of impact on cognitive functioning, Gynecol Oncol 102 (2) (2006), pp. 270–277. Article |

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7 J. Vardy, K. Wong and Q.L. Yi et al., Assessing cognitive function in cancer patients, Support Care Cancer 14 (11) (2006), pp. 1111–1118. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (27)

8 J. Vardy, S. Rourke and I.F. Tannock, Evaluation of cognitive function associated with chemotherapy: a review of published studies and recommendations for future research, J Clin Oncol 25 (17) (2007), pp. 2455–2463. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (58)

9 H.Q. Huang, M.F. Brady, D. Cella and G. Fleming, Validation and reduction of FACT/GOG-Ntx subscale for platinum/paclitaxel-induced neurologic symptoms: a Gynecologic Oncology Group study, Int J Gynecol Cancer 17 (2) (2007), pp. 387–393. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (16)

10 E.A. Calhoun, E.E. Welshman and C.H. Chang et al., Psychometric evaluation of the Functional Assessment of Cancer Therapy/Gynecologic Oncology Group-Neurotoxicity (Fact/GOG-Ntx) questionnaire for patients receiving systemic chemotherapy, Int J Gynecol Cancer 13 (6) (2003), pp. 741–748. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (56)

11 D.M. Erlanger, D.J. Feldman, D. Kaplan and A. Theodoracopulos, Development and validation of the cognitive stability index, a Web-based protocol for monitoring change in cognitive function, Arch Clin Neuropsychol 15 (2000), pp. 693–694. Abstract |

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12 D.M. Erlanger, T. Kaushik, D. Broshek, J. Freeman, D. Feldman and J. Festa, Development and validation of a Web-based screening tool for monitoring cognitive status, J Head Trauma Rehabil 17 (5) (2002), pp. 458–476. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (22)

13 G.J. Chelune, R.K. Heaton and R.A.W. Lehman, Neuropsychological and personality correlates of patients' complaints of disability. In: G. Goldstein and R.E. Tarter, Editors, Advances in Clinical Neuropsychology, Plenum Press, New York (1986).

14 C.E. Schwartz, E. Kozora and Q. Zeng, Towards patient collaboration in cognitive assessment: specificity, sensitivity and incremental validity of self-report, Ann Behav Med 18 (3) (1996), pp. 177–184. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (16)

15 K. Paraska and C.M. Bender, Cognitive dysfunction following adjuvant chemotherapy for breast cancer: two case studies, Oncol Nurs Forum 30 (3) (2003), pp. 473–478. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (15)

16 E. Kozora, M.C. Ellison and S. West, Depression, fatigue, and pain in systemic lupus erythematosus (SLE): relationship to the American College of Rheumatology SLE neuropsychological battery, Arthritis Rheum 55 (4) (2006), pp. 628–635. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (36)

17 T.A. Ahles, A.J. Saykin and C.T. Furstenberg et al., Neuropsychologic impact of standard-dose systemic chemotherapy in long-term survivors of breast cancer and lymphoma, J Clin Oncol 20 (2) (2002), pp. 485–493. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (251)

18 P. Klepstad, P. Hilton, J. Moen, B. Fougner, P.C. Borchgrevink and S. Kaasa, Self-reports are not related to objective assessments of cognitive function and sedation in patients with cancer pain admitted to a palliative care unit, Palliat Med 16 (6) (2002), pp. 513–519. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (25)

19 S.B. Schagen, F.S. van Dam, M.J. Muller, W. Boogerd, J. Lindeboom and P.F. Bruning, Cognitive deficits after postoperative adjuvant chemotherapy for breast carcinoma, Cancer 85 (3) (1999), pp. 640–650. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (263)

20 S.B. Schagen, M.J. Muller and W. Boogerd et al., Late effects of adjuvant chemotherapy on cognitive function: a follow-up study in breast cancer patients, Ann Oncol 13 (9) (2002), pp. 1387–1397. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (99)

21 F.S. van Dam, S.B. Schagen and M.J. Muller et al., Impairment of cognitive function in women receiving adjuvant treatment for high-risk breast cancer: high-dose versus standard-dose chemotherapy, J Natl Cancer Inst 90 (3) (1998), pp. 210–218. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (322)

22 D.L. Fairclough and D.F. Cella, Functional Assessment of Cancer Therapy (FACT-G): non-response to individual questions, Qual Life Res 5 (1996), pp. 321–329. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (75)

23 C.L. Burton, E. Strauss, D.F. Hultsch and M.A. Hunter, Cognitive functioning and everyday problem solving in older adults, Clin Neuropsychol 20 (3) (2006), pp. 432–452. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (20)

 

 

Conflicts of interest: None to disclose.

Correspondence to: Lisa M. Hess, PhD, Indiana University School of Medicine, Department of Public Health, 714 N Senate Avenue, Indianapolis, IN 46202; telephone: (317) 274-3148; Fax (317) 274-3443


1 PubMed ID in brackets


Original research

Pilot Study of the Prospective Identification of Changes in Cognitive Function During Chemotherapy Treatment for Advanced Ovarian Cancer

Lisa M. Hess PhD

, a,
, Setsuko K. Chambers MDa, Kenneth Hatch MDa, Alton Hallum MDa, Mike F. Janicek MDa, Joseph Buscema MDa, Matthew Borst MDa, Cynthia Johnson MAa, Lisa Slayton LPNa, Yuda Chongpison MS, MBAa and David S. Alberts MDa

a University of Arizona, Arizona Cancer Center, Tucson, AZ; Departments of Public Health and Obstetrics and Gynecology, Indiana University School of Medicine, Indianapolis, IN; Arizona Oncology Associates, Tucson, AZ; Scottsdale Healthcare and Oncogyn, P.C., Scottsdale, AZ; and Arizona Gynecologic Oncology, Phoenix, AZ

Received 8 June 2010; 

accepted 9 September 2010. 

Available online 25 January 2011.

Abstract

Change in cognitive function is increasingly being recognized as an adverse outcome related to chemotherapy treatment. These changes need not be severe to impact patient functional ability and quality of life. The primary goal of this study was to determine if there is evidence of changes in the cognitive function domains of attention, processing speed, and response time among women with newly diagnosed advanced ovarian cancer who receive chemotherapy. Eligible patients were women diagnosed with stage III–IV epithelial ovarian or primary peritoneal cancer who had not yet received chemotherapy but who were prescribed a minimum of six cycles (courses) of chemotherapy treatment. Cognitive function was assessed by a computerized, Web-based assessment (attention, processing speed, and reaction time) and by patient self-report. Cognitive function was assessed at three time points: prior to the first course (baseline), course three, and course six. Medical records were reviewed to abstract information on chemotherapy treatment, concomitant medications, and blood test results (eg, hemoglobin, CA-125). Of the 27 eligible participants, 92% and 86% demonstrated cognitive impairments from baseline to course three and from baseline to course six of chemotherapy, respectively. Impairment was detected in two or more cognitive domains among 48% (12 of 25) and 41% (9 of 22) of participants at course three and course six of chemotherapy, respectively. This study shows evidence of decline in cognitive function among women being treated for ovarian cancer. There is a need for additional, prospective research to better understand the impact of chemotherapy on cognitive function among ovarian cancer patients so that effective preventive and treatment strategies can be developed.

Article Outline

Materials and Methods
Assessment Tools
Statistical Plan

Results
Web-Assessed Cognitive Function
Patient-Reported Cognitive Function
Blood Chemistries and Toxicity

Discussion

Acknowledgements

References

Many cancer patients receiving chemotherapy develop persistent changes in cognitive function, characterized by declines in memory, attention, and executive function.[1] and [2] Neurocognitive symptoms may be associated with discrete or multiple etiologies, including direct or indirect effects of cancer on the central nervous system, comorbid neurologic or psychiatric diagnoses, and diffuse and specific effects of cancer treatment, including radiation therapy, immunotherapy, and chemotherapy,[2] and [3] as well as fatigue, pain, and anemia, all of which have been associated with poorer performance on neurocognitive testing among cancer patients.4 Even mild cognitive changes can be expected to influence quality of life and the ability to function effectively in performing day-to-day activities.

Although the perception of cognitive decline is a common complaint among individuals treated with chemotherapy, it is poorly understood and limited efforts have been made to identify the extent of this problem among women with ovarian cancer. To date, the few studies documenting the neuropsychological consequences of ovarian cancer and its treatment have shown that patients report cognitive problems but that these problems were not quantifiable using objective measures due to the lack of sensitivity of standard instruments to the subtle changes that occur during cancer treatment.[5], [6] and [7]

Although studies of cognitive function among oncology patients have used instruments that have been validated in their own disciplines and with a variety of diseases, the evidence is emerging that they are not comprehensive or appropriate tools for the detection and evaluation of chemotherapy-related change in cognitive function.8 Furthermore, the likelihood of having these tests conducted in a similar manner across multiple institutions, sites, and interviewers with any degree of consistency is very low. This study was designed as a pilot study of the identification of chemotherapy-related changes in cognitive function among women with advanced ovarian cancer using a Web-based assessment tool (Headminder, Inc., New York, NY).7 The primary goal of the current study was to determine if there is evidence of changes in the cognitive function domains of attention, processing speed, and reaction time as well as self-reported changes in the memory, sensory-perception, and cognitive-intellectual domains of cognitive function during chemotherapy among women with newly diagnosed advanced ovarian cancer.

Materials and Methods

All study methods and procedures were reviewed and approved by the University of Arizona Institutional Review Board. Eligible patients included women with a histologically or pathologically confirmed diagnosis of stage III–IV epithelial ovarian or primary peritoneal cancer who were prescribed at least six courses of platinum-based therapy. Patients were excluded if they had a prior history of any cancer (other than nonmelanoma skin cancer), chemotherapy, radiation therapy, erythropoietin treatment (within the last 6 months), or severe head injury. Initially, patients were excluded if they received intraperitoneal therapy, but the protocol was later amended to permit the use of any platinum-based therapy, regardless of route of administration.

Assessment Tools

After providing informed consent, patients completed a neurocognitive battery of tests and the Functional Assessment of Cancer Therapy—Neurotoxicity (FACT-Ntx, to assess patient-reported neuropathy).[9] and [10] The neurocognitive evaluation included both a computerized, Web-based and a patient-reported assessment. The Web-based assessment was provided by HeadMinders, Inc.[7] and [11] and was a modified version of the Cognitive Stability Index. The modified battery was comprised of two warm-up tasks and three empirically-derived cognitive factors: Processing Speed (Animal Decoding and Symbol Scanning subtests), Attention (Number Recall and Number Sequencing subtests), and Reaction Time (Response Direction 1 and Response Direction 2 subtests). The subtests have been validated against traditional neuropsychological tests in healthy and clinical populations, including cancer patients.12 Cognitive domain correlations in the battery's healthy normative sample range from 0.52 to 0.74, and correlations are similar or higher in clinical populations. Test–retest reliability of the factor scores between first and second administrations ranges from 0.74 to 0.82.12 This Web-based neurocognitive assessment tool is 21 CFR Part 11– and Health on the Net (HON)–compliant to ensure patient confidentiality. Prior to undergoing the Web-based cognitive tests, all study participants completed a keyboard proficiency test as a “warm-up task” to the computerized assessment.

The patient-reported cognitive function tool used was the Patient Assessment of Own Functioning Scale (PAF).[13], [14] and [15] The PAF includes eight scales that are grouped into the nature of the ability being considered. The Memory, Sensory-Perceptual, and Cognitive-Intellectual subscales of the PAF are included in this self-assessment questionnaire. Respondents are asked to rate on a six-point scale, from almost always to almost never, how often they experience a particular kind of difficulty in their everyday lives. For this study, the Memory and Cognitive-Intellectual subscales of the PAF were used, similar to other clinical research protocols investigating cognitive changes during chemotherapy treatment.15 The PAF has been shown to be directly related to the Minnesota Multiphasic Personality Inventory (MMPI)13 and to be highly correlated with other cognitive impairment indices, such as the American College of Rheumatology neuropsychology research battery of tests.16 Of note, self-reported cognitive change has not been shown to correlate formal assessments of cognitive function among individuals who have experienced cancer.[17], [18], [19], [20] and [21]

The FACT-Ntx is a validated instrument[9] and [10] that was used to evaluate neurotoxicity. This scale includes 11 items: nine to assess neurotoxicity, one to assess bodily weakness, and one to assess anemia. Neurotoxicity may affect a patient's ability to use the keyboard in the computerized neurocognitive evaluation. This complete assessment battery of tests was completed at baseline (within 5 days of initiation of chemotherapy) and again during follow-up assessments at cycle three and cycle six of chemotherapy. The medical record was reviewed and data were abstracted related to chemotherapy medications, all concomitant medications, and blood test results (eg, hemoglobin, CA-125).

Statistical Plan

This prospective study was exploratory in nature and designed to collect pilot data to determine if there is evidence of neurocognitive change in attention, processing speed, response time, or self-reported cognitive function during the course of chemotherapy among women being treated for advanced ovarian cancer. The purpose of this study was to obtain preliminary estimates of the incidence and degree of cognitive decline to aid in the planning of future studies. While prior estimates of cognitive function were not available for this population, power analyses demonstrate that with a target recruitment goal of 30 patients, a McNemar's test has 78% power at the 0.05 level of significance to detect a significant decline in impairment in a cognitive domain if 12 patients are found to have impairment prior to course six of treatment (but not at course three) and if as few as two patients demonstrate impairment prior to course three but not at course six. This study was therefore powered to detect declines in one or more of the domains that may have occurred at less than both of the study time points following the baseline assessment.

To be considered fully evaluable, patients had to have completed at least one follow-up neurocognitive evaluation and may not have received antipsychotic neuropsychological medications during the study (eg, chlorpromazine, haloperidol, clozapine). Antidepressants and antianxiety medications (eg, serotonin/norepinephrine reuptake inhibitors or benzodiazepines) were permitted and use was recorded throughout study participation. A summary score for each cognitive domain (processing speed, reaction time, and attention) was recorded at each assessment time point using the HeadMinder Web-based assessment. This summary score was assessed by time (processing speed and reaction time), measured to the hundredth of a second, and by number of errors (attention). If a cognitive domain summary score at a follow-up assessment time declined at least one standard error of measurement (SEM) from baseline, the patient was considered to have experienced a decline at that time point. For the purposes of this article, such declines are referred to as “impairments” within the cognitive domain under investigation. A cognitive index score (CIS) was calculated as the number of cognitive domains impaired for the time point. The range of a CIS is 0–3, with zero equal to no impairment on any cognitive domain and three equal to impairment on all cognitive domains. Patients with only one cognitive domain decline (CIS = 1) at any one of the follow-up assessment time points were considered as having possible cognitive function decline. Patients with more than one cognitive domain impairment (CIS >1) at any follow-up assessment time points were considered as having evidence of cognitive function decline. The incidence of cognitive function impairment was determined by the percentage of patients who experienced any cognitive domain impairment (including possible and evidence of decline) at any follow-up assessment.

A repeated-measures analyses of variance (ANOVA) was used to further explore the neurocognitive values at the various time points during the study. Many of the neurocognitive values were not normally distributed but skewed either positively or negatively, so the square roots of the values were used in the analyses. Since this is an exploratory analysis, no corrections for multiple comparisons were performed.

The patient-reported cognitive function instrument (PAF) contains items scored on a Likert-type scale from almost never to almost always (range 0–5). Patient-reported outcomes as measured with the PAF are measured as mean scale values, ranging from 0, indicating no impairment, to 5.0, indicating complete impairment. PAF score ranges indicate low (≤1.25), medium (1.26–1.92), and high (≥1.93) levels of cognitive impairment.13 A total FACT-Ntx score was obtained; lower scores represent greater neurotoxicity, ranging from 0 (extreme neurotoxicity) to 44 (no neurotoxicity). The total score was reported, with adjustments made for missing values as described elsewhere.22

Results

Thirty patients were enrolled in this study; however, two were later deemed ineligible, and one was unable to complete the baseline neurocognitive assessment prior to chemotherapy and was withdrawn from the study, resulting in 27 patients available for assessment. Five of these patients did not complete all neurocognitive assessments. The primary reason for nonadherence to the study schedule was clinical scheduling (eg, chemotherapy was administered prior to the neurocognitive assessment). The characteristics of eligible patients are provided in Table 1. The majority of patients were receiving intravenous chemotherapy (intraperitoneal therapy was at first not permitted but later was allowable following an amendment to the protocol) and taking concomitant sleep, antianxiety, and/or antidepressant medications outside of every 3- to 4-week chemotherapy regimen (primarily zolpidem, lorazepam, sertraline, and/or trazodone).

 

 

Table 1. Characteristics of Eligible Participants

n = 27
Mean age, years (range)59.3 (40.3–81.5)
Education, n (%)
 High school or less3 (11.1%)
 Some college12 (44.4%)
 College graduate12 (44.4%)
Race/ethnicity, n (%)
 White, non-Hispanic25 (92.6%)
 Hispanic1 (3.7%)
 Native American1 (3.7%)
Marital status, n (%)
 Married/cohabitating19 (70.4%)
 Divorced/separated1 (3.7%)
 Widowed5 (18.5%)
 Never married2 (7.4%)
Mean courses of chemotherapy, n (range)5.9 (4–6)
Chemotherapy route, n (%)
 Intraperitoneal5 (18.5%)
 Intravenous22 (81.5%)
Concurrent medication use, n (%)
 Antidepressant7 (25.9%)
 Antianxiety16 (59.3%)
 Sleep aids5 (18.5%)


Web-Assessed Cognitive Function

Keyboard proficiency remained unchanged over time (P = 0.39). As shown in Table 2, most participants demonstrated cognitive impairments in at least one of the three cognitive domains assessed during this study (92% and 86% at course 3 and course 6, respectively). Nearly half of the study participants demonstrated impairment from baseline in two or more of the three cognitive domains assessed (Table 3). Table 4 shows a detailed summary of the subscales within the Web-based cognitive tests that comprised the CIS.This table demonstrates the statistically significant increase in test subscale errors, despite the test-taking improvements over time, as shown by reduction in testing time.

Table 2. Number and Percent of Patients Demonstrating Cognitive Impairments by Cognitive Domain as Measured by Web-Based Assessment

COGNITIVE DOMAINCOURSE 3COURSE 6
Attention10/25 (40%)8/22 (36%)
Processing speed10/25 (40%)11/22 (50%)
Reaction time16/25 (64%)11/22 (50%)

Table 3. Number and Percent of Patients Showing Impairment in Multiple Cognitive Domains as Measured by the Web-Assessed Cognitive Impairment Scale (CIS)

CISCOURSE 3COURSE 6
No decline (CIS = 0)2 (8%)3 (14%)
One impairment (CIS = 1)11 (44%)10 (45%)
Two impairments (CIS = 2)11 (44%)7 (32%)
Three impairments (CIS = 3)1 (4%)2 (9%)

Table 4. Subscales Within the Cognitive Domains Assessed by the Web-Based Assessment

COGNITIVE IMPAIRMENT SCALE (CIS) FACTORS
BASELINE
COURSE 3
COURSE 6
NMEANSDNMEANSDNMEANSDP
Attention
 Number recall (number correct)257.081.75257.162.03227.451.920.887
 Number sequencing (number correct)266.230.98255.962.65235.612.290.476
Processing speed
 Animal decoding (number of errors)250.40.5250.720.84233.260.86<0.0001
 Animal decoding (number correct)2532.486.482532.968.902332.228.700.678
 Symbol scanning (number correct)2718.591.152518.761.22118.671.350.883
 Symbol scanning (response time)274.381.37254.261.66213.610.840.002
Reaction time
 Response direction 1 (number of omissions)270.040.19260.622.3523000.028
 Response direction 1 (response time, seconds)270.520.06260.550.22230.520.070.567
 Response direction 2 (number of omissions)270.631.33260.52.18230.430.950.135
 Response direction 2 (response time, seconds)270.750.13260.720.20230.710.170.467
 Response direction, shift failures (number)274.333.13262.772.29233.042.580.007


Patient-Reported Cognitive Function

The mean values and 95% confidence intervals of the patient-reported cognitive function outcomes are presented in Figure 1. Mean values remained within the low impairment range (less than 1.25) during chemotherapy.



Figure 1. 

Mean Patient-Reported Cognitive Function Scores with 95% Confidence Intervals


Blood Chemistries and Toxicity

The mean values and 95% confidence intervals of significant differences in blood chemistries and toxicities are presented in [Figure 2] and [Figure 3]. Total patient-reported neurotoxicity increased significantly during chemotherapy (ANOVA; F = 6.851, P = 0.002), while several mean blood chemistry values decreased during chemotherapy treatment (hemoglobin F = 2.465, P = 0.09; white blood cell count F = 16.95, P < 0.001; platelets F = 13.72, P < 0.001; and CA-125 F = 4.91, P = 0.01). One study participant received a blood transfusion at the final course of chemotherapy, and two and three participants received cytokines (erythropoietin or darbepoietin) at course 3 and course 6, respectively.



Figure 2. 

Total Patient-Reported Neurotoxicity with 95% Confidence Intervals (Lower Scores Represent Greater Neurotoxicity)

Figure 3. 

Mean Hemoglobin, White Blood Cell, Platelet, and CA-125 Levels with 95% Confidence Intervals


Discussion

This study shows preliminary evidence that cognitive decline is a significant factor experienced by women who are treated for advanced ovarian cancer. Most participants self-reported mild declines, and these were detectable by a sensitive Web-based assessment tool. There are many potential mechanisms of cognitive decline during chemotherapy, ranging from oxidative damage to reduced blood oxygenation due to anemia to stress and anxiety. While it is outside of the scope of this small pilot study to examine the causative factors of decline, it does suggest the need for further investigation of the effect and potential mechanisms of cognitive decline in this population. While most of the prior work in cognitive function has been conducted among breast cancer patients, ovarian cancer patients appear to experience cognitive decline as well. There is a need to further understand this issue so that effective preventive or treatment strategies can be developed.

The significant increase in patient-reported neurotoxicity across each study visit may be a concern for computerized assessments that require dexterity. However, the keyboard proficiency tests did not decline over time, suggesting that the neurotoxicity reported by patients in this study was not great enough to affect their ability to use the computer keyboard. Patients appear to report higher levels of difficulty with memory (eg, forgetfulness) following diagnosis than following the initiation of chemotherapy; however, higher-level cognitive processes (eg, logic, organizational abilities, calculations) reported by patients appear to decline following the initiation of chemotherapy. Although larger, adequately powered trials are needed to determine the extent of this decline, this suggests that patients experience increasing challenges that may interfere with their ability to perform necessary tasks at work and in the household. Further work is needed to examine the duration of these effects following chemotherapy. Since the cognitive impact of chemotherapy reported by patients is mild, investigators must ensure the use of appropriate patient-reported tools that are able to detect these differences. While reported decline may occur, this is likely to remain within the mild category of traditional assessment tools. It is of benefit to use patient-reported tools such as the PAF that also permit the analysis of continuous data.

This study is limited by its design as a pilot study and was challenged by several logistical issues. Four patients were unable to complete all the neurocognitive evaluations. This was due to remote study staff, who would visit various clinics in the Tucson and Phoenix metropolitan regions in Arizona (range of travel more than 120 miles). The lack of completion was entirely due to communication and travel complications. When a patient was rescheduled to a different chemotherapy date, it was not always possible for this to be communicated to the Arizona Cancer Center researchers in a timely manner, resulting in missed visits. It is recommended for future studies that require strict timelines for study assessments (such as this cognitive function study) that the assessments be conducted by staff in those practices who can identify changes in infusion dates when they occur. This will reduce the communication barriers and rate of missed visits. This study was also not designed to be a comprehensive assessment of neurocognitive function but was focused on assessing three domains: attention, processing speed, and response time. It is possible that many other domains of cognitive function could be impacted by chemotherapy that were not evaluated in this study. Many patients were also taking antidepressant medications during the study; however, these were generally not new prescriptions and were also being taken at the baseline assessment. Nevertheless, future studies should incorporate assessments of mood, depression, and anxiety to account for the potential effect of these factors on cognitive assessment scores.

Despite these limitations, the study provides preliminary data demonstrating cognitive decline during chemotherapy among ovarian cancer patients treated in the front-line setting of advanced disease. More than 90% of all patients experienced measurable impairments in cognitive function during primary chemotherapy. More than half of all patients demonstrated impairment on two or more cognitive domains. Prior work has shown that even mild cognitive impairments can influence quality of life and the ability to perform routine daily activities (eg, taking medications, returning to work, managing household finances).23 The data emphasize the critical need to further understand the impact of chemotherapy on cognitive function among ovarian cancer patients so that effective preventive and treatment strategies can be developed. Additional research is needed to understand how long these declines may persist following chemotherapy treatment.

 

 

Acknowledgments

This study was funded by an investigator-initiated grant from Ortho Biotech, Inc., to the University of Arizona Cancer Center. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of Ortho Biotech.

References1

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2 A.J. Saykin, T.A. Ahles and B.C. McDonald, Mechanisms of chemotherapy-induced cognitive disorders: neuropsychological, pathophysiological, and neuroimaging perspectives, Semin Clin Neuropsychiatry 8 (4) (2003), pp. 201–216. View Record in Scopus | Cited By in Scopus (63)

3 L.M. Hess and K.C. Insel, Chemotherapy-related change in cognitive function: a conceptual model, Oncol Nurs Forum 34 (5) (2007), pp. 981–994. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (11)

4 C.A. Meyers, Neurocognitive dysfunction in cancer patients, Oncology (Williston Park) 14 (1) (2000), pp. 75–79 discussion 9, 81–82, 85. View Record in Scopus | Cited By in Scopus (68)

5 M.L. Hensley, D.D. Correa and H. Thaler et al., Phase I/II study of weekly paclitaxel plus carboplatin and gemcitabine as first-line treatment of advanced-stage ovarian cancer: pathologic complete response and longitudinal assessment of impact on cognitive functioning, Gynecol Oncol 102 (2) (2006), pp. 270–277. Article |

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6 C.A. Meyers and J.S. Wefel, The use of the mini-mental state examination to assess cognitive functioning in cancer trials: no ifs, ands, buts, or sensitivity, J Clin Oncol 21 (19) (2003), pp. 3557–3558. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (55)

7 J. Vardy, K. Wong and Q.L. Yi et al., Assessing cognitive function in cancer patients, Support Care Cancer 14 (11) (2006), pp. 1111–1118. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (27)

8 J. Vardy, S. Rourke and I.F. Tannock, Evaluation of cognitive function associated with chemotherapy: a review of published studies and recommendations for future research, J Clin Oncol 25 (17) (2007), pp. 2455–2463. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (58)

9 H.Q. Huang, M.F. Brady, D. Cella and G. Fleming, Validation and reduction of FACT/GOG-Ntx subscale for platinum/paclitaxel-induced neurologic symptoms: a Gynecologic Oncology Group study, Int J Gynecol Cancer 17 (2) (2007), pp. 387–393. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (16)

10 E.A. Calhoun, E.E. Welshman and C.H. Chang et al., Psychometric evaluation of the Functional Assessment of Cancer Therapy/Gynecologic Oncology Group-Neurotoxicity (Fact/GOG-Ntx) questionnaire for patients receiving systemic chemotherapy, Int J Gynecol Cancer 13 (6) (2003), pp. 741–748. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (56)

11 D.M. Erlanger, D.J. Feldman, D. Kaplan and A. Theodoracopulos, Development and validation of the cognitive stability index, a Web-based protocol for monitoring change in cognitive function, Arch Clin Neuropsychol 15 (2000), pp. 693–694. Abstract |

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12 D.M. Erlanger, T. Kaushik, D. Broshek, J. Freeman, D. Feldman and J. Festa, Development and validation of a Web-based screening tool for monitoring cognitive status, J Head Trauma Rehabil 17 (5) (2002), pp. 458–476. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (22)

13 G.J. Chelune, R.K. Heaton and R.A.W. Lehman, Neuropsychological and personality correlates of patients' complaints of disability. In: G. Goldstein and R.E. Tarter, Editors, Advances in Clinical Neuropsychology, Plenum Press, New York (1986).

14 C.E. Schwartz, E. Kozora and Q. Zeng, Towards patient collaboration in cognitive assessment: specificity, sensitivity and incremental validity of self-report, Ann Behav Med 18 (3) (1996), pp. 177–184. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (16)

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16 E. Kozora, M.C. Ellison and S. West, Depression, fatigue, and pain in systemic lupus erythematosus (SLE): relationship to the American College of Rheumatology SLE neuropsychological battery, Arthritis Rheum 55 (4) (2006), pp. 628–635. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (36)

17 T.A. Ahles, A.J. Saykin and C.T. Furstenberg et al., Neuropsychologic impact of standard-dose systemic chemotherapy in long-term survivors of breast cancer and lymphoma, J Clin Oncol 20 (2) (2002), pp. 485–493. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (251)

18 P. Klepstad, P. Hilton, J. Moen, B. Fougner, P.C. Borchgrevink and S. Kaasa, Self-reports are not related to objective assessments of cognitive function and sedation in patients with cancer pain admitted to a palliative care unit, Palliat Med 16 (6) (2002), pp. 513–519. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (25)

19 S.B. Schagen, F.S. van Dam, M.J. Muller, W. Boogerd, J. Lindeboom and P.F. Bruning, Cognitive deficits after postoperative adjuvant chemotherapy for breast carcinoma, Cancer 85 (3) (1999), pp. 640–650. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (263)

20 S.B. Schagen, M.J. Muller and W. Boogerd et al., Late effects of adjuvant chemotherapy on cognitive function: a follow-up study in breast cancer patients, Ann Oncol 13 (9) (2002), pp. 1387–1397. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (99)

21 F.S. van Dam, S.B. Schagen and M.J. Muller et al., Impairment of cognitive function in women receiving adjuvant treatment for high-risk breast cancer: high-dose versus standard-dose chemotherapy, J Natl Cancer Inst 90 (3) (1998), pp. 210–218. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (322)

22 D.L. Fairclough and D.F. Cella, Functional Assessment of Cancer Therapy (FACT-G): non-response to individual questions, Qual Life Res 5 (1996), pp. 321–329. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (75)

23 C.L. Burton, E. Strauss, D.F. Hultsch and M.A. Hunter, Cognitive functioning and everyday problem solving in older adults, Clin Neuropsychol 20 (3) (2006), pp. 432–452. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (20)

 

 

Conflicts of interest: None to disclose.

Correspondence to: Lisa M. Hess, PhD, Indiana University School of Medicine, Department of Public Health, 714 N Senate Avenue, Indianapolis, IN 46202; telephone: (317) 274-3148; Fax (317) 274-3443


1 PubMed ID in brackets


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Cost–Utility Analysis of Palonosetron-Based Therapy in Preventing Emesis Among Breast Cancer Patients

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Cost–Utility Analysis of Palonosetron-Based Therapy in Preventing Emesis Among Breast Cancer Patients

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Cost–Utility Analysis of Palonosetron-Based Therapy in Preventing Emesis Among Breast Cancer Patients

Elenir B.C. Avritscher MD, PhD, MBA/MHA

, a,
, Ya-Chen T. Shih PhDa, Charlotte C. Sun DrPHa, Richard J. Gralla MDa, Steven M. Grunberg MDa, Ying Xu MD, MSa and Linda S. Elting DrPHa

a Division of Quantitative Sciences, Department of Gynecologic Oncology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas; The Monter Cancer Center, North Shore-Long Island Jewish Health System, Lake Success, New York; Division of Hematology/Oncology, University of Vermont, Burlington, Vermont

Received 8 February 2010; 

accepted 28 September 2010. 

Available online 25 January 2011.

Abstract

We estimated the cost-utility of palonosetron-based therapy compared with generic ondansetron-based therapy throughout four cycles of anthracycline and cyclophosphamide for treating women with breast cancer. We developed a Markov model comparing six strategies in which ondansetron and palonosetron are combined with either dexamethasone alone, dexamethasone plus aprepitant following emesis, or dexamethasone plus aprepitant up front. Data on the effectiveness of antiemetics and emesis-related utility were obtained from published sources. Relative to the ondansetron-based two-drug therapy, the incremental cost–effectiveness ratios for the palonosetron-based regimens were $115,490/quality-adjusted life years (QALY) for the two-drug strategy, $199,375/QALY for the two-drug regimen plus aprepitant after emesis, and $200,526/QALY for the three-drug strategy. In sensitivity analysis, using the $100,000/QALY benchmark, the palonosetron-based two-drug strategy and the two-drug regimen plus aprepitant following emesis were shown to be cost-effective in 39% and 26% of the Monte Carlo simulations, respectively, and with changes in values for the effectiveness of antiemetics and the rate of hospitalization. The cost-utility of palonosetron-based therapy exceeds the $100,000/QALY threshold. Future research incorporating the price structure of all antiemetics following ondansetron's recent patent expiration is needed.

Article Outline

Patients and Methods
Probability Data
Two-drug prophylactic regimens
Three-drug prophylactic regimens
Effectiveness of antiemetics over multiple cycles of chemotherapy
Resource Utilization and Cost Data
Utility Data
Analysis

Results

Discussion

Conclusion

Acknowledgements

References

Over the past decade, regimens containing anthracycline and cyclophosphamide (AC) have become the mainstay of adjuvant chemotherapy for treatment of breast cancer. Although each of these agents is individually considered moderately emetogenic, the combination of the two can lead to substantial nausea and vomiting.1 Despite remarkable recent progress in antiemetic prophylaxis, chemotherapy-induced emesis continues to be a major burden for patients with breast cancer and one of the most feared side effects of cancer treatment in general.[2] and [3]

Recent advances in emesis control have been possible due to the availability of increasingly more effective antiemetic agents. During the 1990s, the development of first-generation 5-hydroxytryptamine-3 (5-HT3) antagonists (ondansetron, granisetron, tropisetron, and dolasetron) marked a significant improvement in the control of emesis induced by chemotherapy, particularly acute emesis (ie, occurring within 24 hours following chemotherapy).

More recently, two new drugs—palonosetron, a second-generation 5-HT3 antagonist, and aprepitant, a centrally acting neurokinin-1 antagonist—were added to the armamentarium of antiemetic therapy. Compared with other single-dose 5-HT3 antagonists, palonosetron has a higher 5-HT3 binding affinity and longer plasma half-life and has shown superiority in the prevention of delayed emesis (ie, occurring more than 24 hours after chemotherapy administration) following moderately emetogenic chemotherapy with methotrexate, epirubicin, or cisplatin (MEC), including AC-based regimens.[4] and [5] In a recently published clinical trial conducted by Saito et al,6 palonosetron was also shown to be superior to granisetron in preventing delayed and overall emesis when both drugs were combined with dexamethasone following chemotherapy with either AC or cisplatin. As for aprepitant, when added to the standard of a 5-HT3 antagonist and dexamethasone therapy, it has been shown to improve emesis prevention among patients receiving AC-based chemotherapy during the acute, delayed, and overall periods.7

Such benefits have led to a recent revision in the antiemetics guidelines of both the American Society of Clinical Oncology (ASCO) and the National Comprehensive Cancer Network (NCCN), incorporating both palonosetron as one of the recommended 5-HT3 antagonists and aprepitant in combination with a 5-HT3 antagonist and dexamethasone for patients receiving AC-based chemotherapy.[8] and [9] Of note is that the revised 2010 NCCN antiemetic guidelines suggest that palonosetron may be used prior to the start of multiday chemotherapy, which is more likely to cause significant delayed emesis, instead of repeated daily doses of other first-generation 5-HT3 antagonists.9

Given the multiplicity of antiemetic strategies available for prophylaxis of nausea and vomiting associated with AC-based chemotherapy with inherent variability in effectiveness and price, it is critical for existing therapies to be analyzed in terms of both their outcomes and costs. Thus, the purpose of this study is to determine, from a third-party payer perspective, the cost-utility of palonosetron-based therapy in preventing emesis among breast cancer patients receiving four cycles of AC-based chemotherapy relative to generic ondansetron-based antiemetic therapy. Due to variations in the definition of complete emetic response found across antiemetic studies, the analysis will focus on chemotherapy-induced emesis only, rather than nausea and vomiting, as vomiting can be more objectively measured than nausea and, as such, has been more consistently reported.

Patients and Methods

We developed a Markov model to estimate the costs (in 2008 U.S. dollars) and health outcomes associated with emesis among breast cancer patients receiving multiple cycles of AC-based chemotherapy under six prophylactic strategies containing either generic ondansetron (onda) or palonosetron (palo) when each is combined with either dexamethasone (dex) alone, dex plus aprepitant in the subsequent cycles following the occurrence of emesis, or dex plus aprepitant up front (Figure 1). The time horizon for the risk of chemotherapy-induced emesis during each cycle of chemotherapy was 21 days, which is the standard duration of a cycle of AC-based chemotherapy.

 

 



Figure 1. 

Markov Model Comparing Palo-Based Therapy vs Onda-Based Therapy for Prophylaxis of Chemotherapy-Induced Emesis in Breast Cancer Patients Receiving Four Cycles of AC-Based Chemotherapy (1) Onda (32 mg intravenously) + dex (8 mg intravenously) on day 1, followed by dex (4 mg orally twice a day) on days 2−5. (2) Onda (32 mg intravenously) + dex (8 mg intravenously) on day 1, followed by dex (4 mg orally twice a day) on days 2−5 and aprepitant in the subsequent cycles following the occurrence of emesis (ie, onda 16 mg orally + aprepitant 125 mg orally + dex 12 mg orally on day 1 followed by aprepitant 80 mg orally on days 2−3). (3) Palo (0.25 mg intravenously) + dex (8 mg intravenously) on day 1, followed by dex (4 mg orally twice a day) on days 2−5. (4) Palo (0.25 mg intravenously) + dex (8 mg intravenously) on day 1, followed by dex (4 mg orally twice a day) on days 2−5 and aprepitant in the subsequent cycles following the occurrence of emesis (ie, palo 0.25 mg intravenously + aprepitant 125 mg orally + dex 12 mg orally on day 1 followed by aprepitant 80 mg orally on days 2−3). (5) Onda (16 mg orally) + aprepitant (125 mg orally) + dex (12 mg orally) on day 1 followed by aprepitant (80 mg orally) on days 2−3. (6) Palo (0.25 mg intravenously) + aprepitant (125 mg orally) + dex (12 mg orally) on day 1 followed by aprepitant (80 mg orally) on days 2−3. Palo = palonosetron; onda = ondansetron; AC = anthracycline and cyclophosphamide; dex, dexamethasone

We modeled emesis-related outcomes and direct medical costs (from a third-party payer perspective within the context of the U.S. health-care system) over a total of four cycles of chemotherapy as patients receiving AC-based regimens usually undergo at least four cycles of AC.10 We performed all analyses using TreeAge Pro 2009 Suite (Decision Analysis; TreeAge Software, Williamstown, MA). The study was submitted to our institutional review board and was determined to be exempt from review.

Probability Data

Two-drug prophylactic regimens

We estimated the effectiveness of the 5-HT3 antagonists based on secondary analysis of the raw data from the randomized clinical trial (RCT) directly comparing onda and palo when used alone for prevention of emesis associated with MEC, including 90 breast cancer patients from the palo 0.25-mg arm and 82 from the onda 32-mg arm who received AC-based chemotherapy (Table 1).5 Effectiveness estimates for palo 0.25 mg were augmented by data on 117 breast cancer patients on AC-based chemotherapy participating in a multicenter RCT comparing palo with dolasetron (Table 1).4 We assumed that dex adds the same relative benefit to either first- or second-generation 5-HT3 antagonists and obtained the expected additional benefit of dex in preventing acute emesis based on the results of an RCT comparing a single-dose of granisetron in combination with dex vs granisetron given alone to patients undergoing MEC (Table 2).11 Since in the aforementioned study dex was only given on day 1 of chemotherapy, the estimated additional benefit of adding dex to a 5-HT3 inhibitor on the delayed period was obtained from another RCT; this study, conducted by the Italian Group for Antiemetic Research, compared dex alone, dex plus onda, or placebo on days 2−5 of MEC.12

Table 1. Emesis Control During the Initial 5-Day Period Following Administration of AC-Based Chemotherapy to Patients with Breast Cancer

From Eisenberg et al4 and Gralla et al5

EMESIS CONTROLPALO (0.25 MG)[4] and [5] (n = 207), % PATIENTS (95% CI)ONDA (32 MG)5 (n = 82), % PATIENTS (95% CI)P
Acute (day 1)0.70 (0.63−0.76)0.61 (0.50−0.71)0.14
Delayed (days 2−5)0.65 (0.58−0.71)0.50 (0.39−0.61)0.02
Overall (days 1−5)0.55 (0.48−0.62)0.40 (0.30−0.52)0.02

AC = anthracycline and cyclophosphamide; palo = palonosetron; onda = ondansetron; CI = confidence interval

Table 2. Base-Case Probabilities, Utility Weights, and Data Sourcese

MODEL PARAMETERSBASE-CASE VALUES (RANGES)DATA SOURCES
Probability of acute emesis control on cycle 1 of AC:
 Onda-based two-drug strategyc0.84 (0.74−0.93)Gralla et al,a The Italian Group[5] and [11]
 Palo-based two-drug strategyc0.87 (0.81−0.94)Eisenberg et al,a Gralla et al,a The Italian Group[4], [5] and [11]
 Onda-based three-drug strategyd0.88 (0.85−0.91)Warr et al7
 Palo-based three-drug strategyd0.96 (0.89−0.99)Grote et al, Grunberg et al[40] and [41]
Probability of delayed emesis control following control of acute emesis on cycle 1 of ACc:
 Onda-based two-drug strategyd0.75 (0.62–0.85)The Italian Group12
 Palo-based two-drug strategyc0.85 (0.78–0.91)Eisenberg et al,a Gralla et al,a The Italian Group[4], [5] and [12]
 Onda-based three-drug strategyd0.86 (0.82–0.90)Warr et al7
 Palo-based three-drug strategyc0.96 (0.91–0.97)Eisenberg et al,a Gralla et al,a Warr et al[4], [5] and [7]
Probability of delayed emesis control following acute emesis on cycle 1 of ACc:
 Onda-based two-drug strategyc0.46 (0.31–0.62)Gralla et al,a The Italian Group[5] and [12]
 Palo-based two-drug strategyc0.44 (0.27–0.59)Eisenberg et al,a Gralla et al,a The Italian Group[4], [5] and [12]
 Onda-based three-drug strategyd0.44 (0.29–0.57)Warr et al7
 Palo-based three-drug strategyc0.51 (0.41–0.67)Eisenberg et al,a Gralla et al,a Warr et al[4], [5] and [7]
Relative probability of emesis control in subsequent cycles of ACc:
 Two-drug therapy0.987 (0.970–1.0)Herrstedt et al14e
 Three-drug therapy1.013 (1.0–1.030)Herrstedt et al14e
Probability of hospitalization (among patients who develop emesis) per cycle of ACd:
 Onda-based regimens0.0035 (0.0001−0.019)Data from Medstat MarketScan16
 Palo-based regimens0.0017 (0.00004−0.0089)Data from Medstat MarketScan, Haislip et al[16] and [19]b
Probability of office visit use (among patients who develop emesis) per cycle of ACd:
 Onda-based regimens0.10 (0.07−0.14)Data from Medstat MarketScan16
 Palo-based regimens0.05 (0.03−0.07)Data from Medstat MarketScan, Haislip et al[16] and [19]b
Probability of rescue medicine utilization use (among patients who develop emesis) per cycle of ACd:
 Onda-based regimens0.61 (0.46−0.75)Gralla et al5a
 Palo-based regimens0.56 (0.45−0.66)Eisenberg et al, Gralla et al[4] and [5]a
Utility weights for emesis per cycle of ACf:
 Acute and delayed emesis0.15 (0.10−0.20)Sun et al20
 Acute emesis and no delayed emesis0.76 (0.70−0.83)Sun et al20
 No acute emesis and delayed emesis0.20 (0.14−0.26)Sun et al20
 No acute and no delayed emesis0.92 (0.86−0.99)Sun et al20

AC = anthracycline and cyclophosphamide; onda = ondansetron; palo = palonosetron.

Included in the analysis was the subset of women with breast cancer receiving AC-based chemotherapy.We obtained an estimate of emesis-related hospitalization and office visit utilization based on data from Medstat MarketScan, HPM subset (Medstat Group, Inc., Ann Arbor, MI) on 707 breast cancer patients who received the first cycle of AC-based chemotherapy from 1996 to 2002 and either were admitted to the hospital or had an office visit for treatment of vomiting or dehydration. Since palo was only introduced into the U.S. market in 2003, we assumed that all these breast cancer patients received onda-based antiemetic prophylaxis. As a result, we estimated the differential rate of health-care resource utilization based on Haislip et al's19 reported differential incidence of extreme events associated with chemotherapy-induced nausea and vomiting experienced by community-based breast cancer patients who received either onda or palo for emesis prophylaxis following the first cycle of chemotherapy.Of note is that there are two different methods for applying the benefit of adding dex and/or aprepitant to a 5-HT3 antagonist: (1) rate of emesis with 5-HT3* relative risk of emesis by adding dex and/or aprepitant and (2) rate of emesis control with 5-HT3 * relative risk of emesis control by adding dex and/or aprepitant. These produce substantially different results, with the former method skewing the results toward the least effective 5-HT3 and the latter skewing it toward the most effective one. As a result, we estimated the probability of emesis by averaging the results obtained using the two different methods. Of note is that the ranges for these effectiveness estimates were obtained by applying the two different methods to the lower and upper bounds of the 95% confidence intervals derived from the clinical trials comparing the 5-HT3 antagonists when used alone.Ranges were obtained by constructing 95% confidence intervals for observed proportions using the normal approximation to the binomial distribution.Ranges are based on the minimum and maximum values observed in Herrstedt et al's14 clinical trial of multicycle chemotherapy.Ranges are based on the estimate's actual 95% confidence intervals obtained from Sun et al's20 data.

 

 


Three-drug prophylactic regimens

We estimated the rate of acute emesis for the three-drug regimens based on data from published studies in which either onda or palo was given in combination with dex and aprepitant on day 1 of MEC (Table 2).[5], [7] and [13] Because aprepitant was either used in combination with dexamethasone or not used on days 2−3 in the trials of palo-based three-drug therapy, we estimated the benefit of adding aprepitant alone to palo on days 2−3 by assuming that the added benefit in the delayed period would be the same as the benefit added to onda. Specifically, we obtained information on the relative risk of delayed emesis control when aprepitant is added on days 2−3 from a large clinical trial of aprepitant combined with onda and dex in breast cancer patients receiving either A or AC chemotherapy (Table 2).7

Effectiveness of antiemetics over multiple cycles of chemotherapy

The estimates of changes in the probability of emesis control over multiple cycles of chemotherapy were obtained from a RCT conducted by Herrstedt et al14 of ondansetron-based two- and three-drug regimens for prevention of chemotherapy-induced nausea and vomiting among breast cancer patients undergoing multiple cycles of AC-based chemotherapy. We assumed that changes in emesis control over four cycles of AC for the palo-based two- and three-drug regimens were similar to the observed changes for the onda-based two- and three-drug strategies, respectively.14

Resource Utilization and Cost Data

The cost of antiemetic prophylaxis was based on the 2008 Medicare Part B reimbursement rates for pharmaceuticals, which reflects the price of ondansetron following its recent patent expiration (Table 3).15 The costs of prophylaxis failures were estimated as follows. In the majority of prophylaxis failures, the only cost is the cost of rescue medication. In such cases, we obtained costs by multiplying the individual doses used for rescue treatment of breast cancer patients on AC participating in the clinical trials comparing palo 0.25 mg with single doses of onda or dolasetron by their unit costs based on the 2008 Medicare Part B reimbursement rates.[5] and [15] For the few patients who are seen in the office for uncontrolled emesis, we obtained estimates of the risk of such emesis-related office visits based on the MarketScan Health Productivity Management (HPM) database from Thomson Reuters on 707 breast cancer patients who received their first cycle of AC-based chemotherapy between 1997 and 2002 (Table 2) and its costs from the 2008 Medicare Physician Fee Schedule Reimbursement for a level III office visit (CPT 99213).[16] and [17]

Table 3. Emesis-Related Base-Case Costs per Cycle of AC-Based Chemotherapy Among Breast Cancer Patients

COST COMPONENT2008 U.S.$ (RANGES)DATA SOURCE
Hospitalization$5,237.00 ($3,921−$6,112)aHCUP charge data18
Consumer Price Index42
Medicare cost-to-charge ratio43
Level III office visit (CPT 99213)$60.30 ($19.96–$122.46)d2008 Medicare Physician Fee Schedule Reimbursement17
Prophylactic antiemetics2008 Medicare Part B reimbursement rates for pharmaceuticals15
 Onda-based two-drug regimen$49.74
 Palo-based two-drug regimen$207.20
 Onda-based three-drug regimen$324.51
 Palo-based three-drug regimen$482.46
Rescue medicinesb$35.25 ($21.66–$48.80)cEisenberg et al,4 Gralla et al,5 2008 Medicare Part B reimbursement rates for pharmaceuticals15

AC = anthracycline and cyclophosphamide; onda = ondansetron; palo = palonosetron; HCUP = Healthcare Cost and Utilization Project

Charges were inflated to 2008 U.S. dollars using the Consumer Price Index (CPI) for medical care and adjusted to costs using Medicare cost-to-charge ratio. The ranges were based on estimates of the 95% confidence interval.In the randomized clinical trial directly comparing ondansetron and palonosetron, propulsives accounted for 71% of the rescue medicines used, 5-hydroxytryptamine antagonists for 20%, glucocorticoids for 7%, and aminoalkyl ethers for 2%.5Costs for rescue medication were obtained by multiplying all drug unit costs by the individual doses used for rescue treatment of breast cancer patients on AC participating in the clinical trials comparing palo 0.25 mg with single doses of onda or dolasetron.[5] and [15] The ranges were based on estimates of the 95% confidence interval.Ranges were based on the Medicare physician fee schedule for levels I and VI office visits.

Finally, although hospitalization for emesis is extremely rare in this population, when it occurs, it is quite expensive. For completeness, we obtained estimates of the risk of emesis-related hospitalization from the same population of breast cancer patients from whom we obtained the estimate for the risk of emesis-related office visit, whereas hospital costs were obtained from Healthcare Cost and Utilization Project (HCUP) data on 2,342 breast cancer patients who were hospitalized with a primary or admitting diagnosis of vomiting or dehydration from 1997 to 2003 ([Table 2] and [Table 3]).[16] and [18]

Of note is that since palo was only introduced into the U.S. market in 2003, we anticipated the observed risk of emesis-related office visit and hospital admission obtained from MarketScan data during the period 1997−2002 reflected the risk associated with prophylaxis with onda. As a result, given that, when compared with onda, palo has also shown superiority in reducing the severity of emetic episodes when they occur, we estimated the differential rate of health-care resource utilization for palo and onda based on Haislip et al's reported differential incidence of extreme events associated with chemotherapy-induced nausea and vomiting (CINV) experienced by community-based breast cancer patients who received either palo or onda for emesis prophylaxis following the first cycle of chemotherapy (Table 2).[5] and [19]

Utility Data

We obtained the utility weights for acute and delayed emesis from a published study of preferences elicited from ovarian cancer patients undergoing chemotherapy using a modified visual analog scale (VAS) (Table 2).20 We equally applied these emesis-related utility weights to the initial 5-day period of chemotherapy (the standard duration of follow-up in clinical trials of prophylactic antiemetics) in all six prophylactic strategies of the decision tree. Furthermore, because the risk of CINV after 5 days of chemotherapy is usually so negligible as to be unmeasured in clinical trials of antiemetics, we assumed the utility weights for the remaining 16 days of each of the chemotherapy cycles to be the same as the weight associated with complete emesis control (ie, 0.92). We subsequently converted the resulting estimates of quality-adjusted life days into quality-adjusted life years (QALY).

Analysis

We used a stepwise method to calculate the incremental cost–effectiveness ratios of the different prophylactic therapy strategies, with the generic onda-based two-drug therapy (ie, the lowest cost strategy) as the base comparator (also known as the “anchor”).21 We adopted the benchmark range of U.S. $50,000−$100,000 per QALY, which has been commonly cited for oncology-related interventions as the threshold for acceptable cost–effectiveness, and examined the robustness of the results by performing one-way sensitivity analyses of plausible ranges for the model's key parameters based on the data sources used as well as probabilistic sensitivity analysis using Monte Carlo simulation.[21] and [22]

Results

The overall rate of emesis control (on days 1−5) among breast cancer patients following a cycle of AC-based chemotherapy was estimated to be 63% (range 46%−79%) for the onda-based two-drug therapy, 74% (range 66%−85%) for the palo-based two-drug therapy, 76% (range 75%−82%) for the onda-based three-drug therapy, and 92% (range 81%−96%) for the palo-based three-drug therapy. Based on these estimates, relative to the onda-based two-drug therapy, the incremental cost–effectiveness ratios (ICERs) for the palo-based regimens were $115,490/QALY for the two-drug strategy, $199,375/QALY for the two-drug regimen plus aprepitant after emesis, and $200,526/QALY for the three-drug strategy (Table 4). The onda-based two-drug combination plus aprepitant after the onset of emesis was eliminated through extended dominance as it has a greater ICER than the next more effective therapy, the palo-based two-drug treatment strategy (Table 4). The onda-based three-drug strategy was dominated by the palo-based two-drug combination plus aprepitant after the onset of emesis as the former strategy is both less effective and more expensive than the latter (Table 4).

 

 

Table 4. Stepwise Comparison of Costs (2008 U.S. dollars) per QALY of the Different Prophylactic Therapy Strategies over Four Cycles of AC-Based Chemotherapy, with the Onda-Based Two-Drug Therapy as the Base Comparator

STRATEGYTOTAL COST (U.S.$)INCREMENTAL COST (U.S.$)EFFECTIVENESS (QALY)INCREMENTAL EFFECTIVENESS (QALY)INCREMENTAL COST–EFFECTIVENESS (U.S.$/QALY)
Onda-based two-drug therapy$2690.1989
Onda-based two-drug therapy with aprepitant after emesis$635$3660.20100.0021$174, 286 Eliminated through extended dominancea
Palo-based two-drug therapy$858$5890.20400.0051$115,490c
Palo-based two-drug therapy plus aprepitant after emesis$1,177$3190.20560.0016199,375
Onda-based three-drug therapy$1,336$1590.205(0.0006)Dominatedb
Palo-based three-drug therapy$1,939$6030.20940.0044$200,526d

QALY = quality-adjusted life year; AC = anthracycline and cyclophosphamide; ICER = incremental cost–effectiveness ratio; onda = ondansetron; palo = palonosetron

Extended dominance occurs when one of the treatment alternatives has a greater ICER than the next more effective alternative.One intervention is said to be dominated by another when it is both less effective and more expensive than the previous less costly alternative.Because the onda-based two-drug combination plus aprepitant after the onset of emesis was eliminated through extended dominance, the palo-based two-drug therapy was compared with the onda-based two-drug therapy.Because the onda-based three-drug combination was dominated by the palo-based two-drug combination plus aprepitant after the onset of emesis, the palo-based three-drug therapy was compared with the latter regimen.

In sensitivity analyses using the commonly accepted cost–effectiveness benchmark range of $50,000−$100,000/QALY, the results were sensitive to changes in the overall emesis control rates for the onda-based two-drug strategy. If the probability of overall emesis control for the onda-based two-drug strategy was as low as its estimated lower bound (46%), the ICER for the palo-based two-drug treatment alternative would drop to $53,892/QALY. The results were also sensitive to changes in the effectiveness for the palo-based two-drug regimen: When its overall control rate was as high as its estimated upper bound (86%), its ICER would be $71,472. In contrast, the results were not sensitive to variations in the probability of overall emesis control for the three-drug strategies, nor were they sensitive to changes in the relative probability of emesis control in subsequent cycles of AC for either the two- or three-drug strategies.

If the probability of emesis-related hospitalization was as high as the upper limit of its 95% confidence interval (CI), the ICER for the palo-based two-drug regimen would be $97,301/QALY. However, changes in the cost of an emesis-related admission (95% CI $3,921−$6,112) did not significantly alter the results, nor did variations in office visit and rescue medicine utilization and their associated costs. The results were also not sensitive to variations in the values for the utility weights throughout their 95% CIs. We performed a threshold analysis to explore the price per dose of palo that would result in an acceptable cost–effectiveness ratio under the $100,000/QALY benchmark and found that the ICER for the palo-based two-drug treatment alternative would only fall to a $100,000/QALY threshold when the cost of palo is decreased by 11%.

Figure 2 shows the cost–effectiveness acceptability curves for each strategy, with the onda-based two-drug therapy as the base comparator. These curves show the proportion of the 100,000 simulations in which the comparing antiemetic regimen was considered more cost-effective than the base comparator at different thresholds. Using the benchmark of U.S. $100,000/QALY, the palo-based two-drug strategy and the two-drug regimen plus aprepitant following the onset of emesis were shown to be cost-effective in 39% and 26% of the simulations with the onda-based standard therapy as the baseline, respectively, whereas the palo-based and onda-based three-drug strategies and the onda-based two-drug regimen with aprepitant after emesis were cost-effective in fewer than 10% of the simulations. Of note is that the slope of the acceptability curves for the palo-based two-drug strategies are steep when willingness to pay exceeds $50,000/QALY, indicating that the greater the threshold, the greater the increase in the level of confidence that these strategies could be cost-effective. For example, the probability that the palo-based two-drug strategy is more cost-effective than the onda-based two-drug strategy rises to 51% at a threshold value of $125,000/QALY and exceeds 60% at $150,000/QALY.



Figure 2. 

Acceptability Curves in Terms of Likelihood of a Strategy Being Cost-Effective in Monte Carlo Simulations Relative to Willingness to Pay (2008 U.S.$) per Quality-Adjusted Life Year (QALY) with the Onda-Based Two-Drug Therapy as the Base Comparator

 

 

Figure 3 presents the scatterplot of the results of the probabilistic sensitivity analysis for the palo-based two-drug strategy. Nearly 96% of the simulations fell within the first quadrant of the chart (ie, on the upper right quadrant), which represents the scenario where the palo-based two-drug therapy is more costly but also more effective than the onda-based standard therapy. However, only 39% of the simulations fell below the $100,000/QALY dashed threshold line, which represents the scenario where the palo-based two-drug strategy is more cost-effective than the onda-based standard therapy at the $100,000/QALY benchmark.



Figure 3. 

Scatterplot of the Incremental Cost–Effectiveness Ratios Comparing the Palo-Based Two-Drug Strategy with the Onda-Based Two-Drug Therapy. The dashed line indicates the $100,000/QALY threshold, whereas the ellipse represents the 95% confidence ellipse, which includes 95% of the observations


Discussion

Our estimates of emesis-related costs and outcomes following four cycles of AC-based chemotherapy in women with breast cancer indicate that at current antiemetic prices and utilities placed on emesis, the additional costs of palo and aprepitant are not warranted at the $100,000/QALY threshold. In probabilistic sensitivity analysis, the palo-based two-drug strategy and the two-drug regimen plus aprepitant following the onset of emesis were shown to be cost-effective at the $100,000/QALY threshold in only 39% and 26% of the simulations, respectively. The model was sensitive to changes in the values of antiemetic effectiveness for the two-drug regimens and the risk of emesis-related hospitalization.

In threshold analysis, the two-drug palo-based regimen was cost-effective at the $100,000/QALY benchmark when the cost of palo is decreased by 11%. Because the use of the $100,000/QALY threshold is uncommon in clinical practice, the cost-effectiveness of the palo-based two-drug strategy (estimated at $115,490/QALY in our study) compares favorably with other commonly used supportive care measures for women with breast cancer. Such measures include primary prophylaxis with granulocyte colony-stimulating factor in women undergoing chemotherapy with moderate to high myelosuppressive risk (ICER of $116,000/QALY, or $125,948/QALY in 2008 U.S. dollars) and the use of bisphosphonates for the prevention of skeletal complications in breast cancer patients with lytic bone metastases (ICER ranging from $108,200/QALY with chemotherapy as systemic therapy to $305,300 in conjunction with hormonal systemic therapy, or $166,381/QALY to $469,466/QALY in 2008 U.S. dollars, respectively).[23] and [24] Both interventions are considered recommended standards of supportive care for patients with breast cancer and are widely used in breast oncology practices.[25] and [26]

Decision-analytic models, such as the Markov model presented in our study, aim to reflect the reality of clinical practice in a simplified way. Therefore, modelers often need to make decisions regarding the study time frame and model parameters based on the best use of available data. In our study, we obtained estimates for the probability of chemotherapy-induced emesis from studies in which the standard duration of follow-up is 5 days. By so doing, we may have underestimated the cost-effectiveness for the palo-based and aprepitant-based regimens. Although the risk of CINV after 5 days of chemotherapy is usually negligible, anticipation of vomiting may affect a patient's quality of life throughout the cycle of chemotherapy.

In addition, our estimates of costs, which were mostly obtained from Medicare, may differ from those of other third-party payers. However, Medicare is among the largest payers for breast cancer care as 42% of the women diagnosed with cancer in the United States are older than 64 years, and many private organizations set their own reimbursement rates based on the Medicare schedule. Therefore, we believe that Medicare reimbursement data provide a suitable estimate for emesis-related medical costs for all breast cancer patients in the United States.[27] and [28]

The present results should solely be interpreted in light of the cost–effectiveness benchmark of $50,000−$100,000/QALY, which has been frequently used in the context of the U.S. health-care system.[22] and [29] Such a benchmark, however, is a historic, precedent-based threshold set by the cost of caring for patients on dialysis, which was estimated at $50,000/QALY in 1982 ($74,000−$95,000 in 1997 U.S. dollars).[30] and [31] Given the arbitrariness of such a threshold, it has been suggested that the current willingness to pay for medical interventions in the United States probably exceeds $100,000/QALY, with values as high as $300,000/QALY being cited in some oncology publications.[22], [29], [31], [32], [33] and [34] In support of that argument is the public and policy makers' strong negative reaction to the National Institutes of Health Consensus Panel not recommending mammography screening for women aged 40−49 years, a procedure reported to provide an ICER of $105,000 per life-year gained.[35] and [36] As a result, if willingness to pay goes beyond $100,000/QALY, the alternative of adding aprepitant to palo plus dex may also be deemed attractive as the slope of its acceptability curve becomes substantially steep when the willingness to pay for a QALY exceeds $125,000 (Figure 2), suggesting that its marginal gain may exceed its marginal costs at higher thresholds.

In addition, it is worth noting that the present analysis has been conducted from the perspective of a third-party payer within the context of the U.S. health-care system. The large difference in the acquisition cost of palo-based and onda-based therapy observed in the United States is mostly driven by the differential stage of product life cycles for palo and onda. Although at the time of this study palo was still under patent protection, generic onda had entered the U.S. market prior to our study. The large price discrepancy between brand and generic drugs explains the difference in drug costs in this U.S.-based analysis. As such, our results may not reflect the situation in countries with a widely different cost structure, in which the acquisition cost of palo may be substantially lower. When that is the case, the cost–effectiveness profile of the palo-based prophylactic therapy may be deemed substantially more favorable than the profile presented here. Similarly, we anticipate finding a more attractive cost–effectiveness profile for the palo-based therapies as palo reaches the end of its product life cycle in the U.S. market.37 Also of note is that the cost–effectiveness of the palo-based therapy may greatly differ when different perspectives (other than the third-party payer's perspective) are adopted.

Our study, however, has several limitations. First, the utility scores used in our model were derived with a VAS instrument, which does not incorporate patients' preferences under uncertainty. Nevertheless, the VAS approach has been shown to provide utility scores for nausea and vomiting with more variability than scores derived using other methods such as the Standard Gamble (personal communication, Grunberg SM et al, CALGB study 309801). Notwithstanding that, it remains unclear which method gives utility scores for transient health states, such as CINV, with the greatest validity.

Also of note is that due to a lack of information on emesis-related utilities among breast cancer patients in the literature, we used utilities elicited from patients with ovarian cancer. To the best of our knowledge, the utilities in Sun et al20 were the only ones available in the literature that were elicited from a homogeneous population of cancer patients (ie, solely patients with ovarian cancer) and were based on a wide range of health states combining the presence and absence of emesis during either the acute or the delayed period. In addition, the participants in the Sun et al study were treated with carboplatin, which, like the regimen used in our model, is classified as moderately emetogenic in established antiemetic guidelines.[8], [9] and [38] It is also important to emphasize that the population in that study, like our study's population, was composed exclusively of women, who are known to be at increased risk for developing CINV.39

Second, in the absence of clinical trial data, we assumed conservatively that dex and aprepitant add the same relative benefit to both onda and palo. This assumption results in an imperfect estimate of cost–effectiveness. As such, we may have overestimated or underestimated the cost–effectiveness of palo as dex and aprepitant may potentially add less value to the intrinsically more active 5-HT3 antagonist or uniquely complementary mechanisms of action could contribute to even greater activity with the palo-based therapy. However, our study's estimate of the relative effectiveness of the palo-based two-drug prophylactic therapy versus the onda-based two-drug therapy for preventing delayed emesis is consistent with that reported in a recently published clinical trial comparing palo and granisetron when both drugs are combined with dex following chemotherapy with either AC or cisplatin (1.18 vs 1.17, respectively).6

Third, our study did not include the outcomes associated with the adverse effects of antiemetics, and by so doing, we may have underestimated the costs associated with antiemetic prophylaxis. However, the incidence and duration of treatment-related adverse events occurring in the two RCTs comparing palo with either onda or dolasetron were mild and similar across treatment cohorts.[4] and [5]

Fourth, we assumed that changes in emesis control in subsequent cycles of AC for the palo-based regimens were the same as for the onda-based therapy. By so doing, we may have underestimated the cost–effectiveness of palo as the superiority of the more active 5-HT3 antagonist could be maintained in the subsequent cycles of chemotherapy (or even increased, as seen in the aprepitant-based arm of Herrstedt et al's14 study). As a result, if future prospective trials of palo-based antiemetic prophylaxis confirm its superiority in maintaining antiemetic efficacy over multiple cycles of AC, the cost–effectiveness profiles for the palo-based strategies may be more favorable than the profiles presented herein.

Last, the incremental gains in QALY observed in cost–utility analysis of interventions associated with transitory and non-life-threatening health states, such as the antiemetic regimens analyzed in our study, tend to render small denominators to be used in the incremental cost–effectiveness ratios. The issue of small denominators has led some researchers to question whether the current methodology of cost–effectiveness analysis is appropriate to determine the cost–effectiveness of treatments for terminal or supportive care.32 However, despite this shortcoming, these types of analysis benefit from having a wider scope as they allow comparisons over different types of health interventions across various diseases. In addition, by incorporating patients' utility levels over different health states (instead of merely looking into cost per additional patient controlled), cost–utility analysis makes explicit the impact of the target population's preferences for the different outcomes. Of importance is that both the Panel on Cost–Effectiveness in Health and Medicine and the Institute of Medicine (IOM) Committee on Regulatory Cost–Effectiveness Analysis recommend the use of QALY as the preferred outcome measure for economic evaluation of health-care interventions.

Conclusion

Although our base-case analysis suggests that, from a third-party payer perspective within the context of the U.S. health-care system, the cost–utility of the palo-based two-drug prophylactic therapy for breast cancer patients receiving four cycles of AC-based chemotherapy exceeds the $50,000–$100,000/QALY threshold, it is comparable to other commonly used supportive care interventions for women with breast cancer. In sensitivity analyses, such a strategy was associated with a 39% chance of being cost-effective at the $100,000/QALY threshold, and the model was sensitive to changes in the values of antiemetic effectiveness and of the probability of emesis-related hospitalization. In threshold analysis, the combination of palo and dex was shown to become cost-effective (at the $100,000/QALY benchmark) when the cost of palo is decreased by 11%. As a result, future research incorporating the price structure of all antiemetics following the recent expiration of onda's patent is needed.

 

 

Acknowledgments

The authors thank Dr. Sharon H. Giordano for providing valuable clinical input and Dr. Catherine D. Cooksley for providing suggestions for improving this article.

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This study was supported in part by a grant from MGI Pharma, Inc. MGI Pharma, Inc., had no role in the study design, data analysis, interpretation of results, content of the final article, or the decision to submit it for publication.

Conflicts of interest: Dr. Sun discloses that her husband was an employee of MGI Pharma, Inc., at the time this article was being written. Dr. Gralla discloses that he is a consultant for MGI Pharma, Inc., GlaxoSmithKline, Sanofi-aventis, and Merck; he also receives honoraria from MGI Pharma, Inc., and Merck and research support from Sanofi-aventis. Dr. Grunberg discloses that he is a consultant for MGI Pharma, Inc.

Correspondence to: Elenir B. C. Avritscher, MD, PhD, MBA/MHA, Section of Health Services Research, Department of Biostatistics and Applied Mathematics, The University of Texas M. D. Anderson Cancer Center, 1400 Pressler Street, Unit 1411, Houston, TX 77230; telephone: (713) 563-8920; fax: (713) 563-4243


1 PubMed ID in brackets


The Journal of Supportive Oncology
Volume 8, Issue 6, November-December 2010, Pages 242-25
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Original research

Cost–Utility Analysis of Palonosetron-Based Therapy in Preventing Emesis Among Breast Cancer Patients

Elenir B.C. Avritscher MD, PhD, MBA/MHA

, a,
, Ya-Chen T. Shih PhDa, Charlotte C. Sun DrPHa, Richard J. Gralla MDa, Steven M. Grunberg MDa, Ying Xu MD, MSa and Linda S. Elting DrPHa

a Division of Quantitative Sciences, Department of Gynecologic Oncology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas; The Monter Cancer Center, North Shore-Long Island Jewish Health System, Lake Success, New York; Division of Hematology/Oncology, University of Vermont, Burlington, Vermont

Received 8 February 2010; 

accepted 28 September 2010. 

Available online 25 January 2011.

Abstract

We estimated the cost-utility of palonosetron-based therapy compared with generic ondansetron-based therapy throughout four cycles of anthracycline and cyclophosphamide for treating women with breast cancer. We developed a Markov model comparing six strategies in which ondansetron and palonosetron are combined with either dexamethasone alone, dexamethasone plus aprepitant following emesis, or dexamethasone plus aprepitant up front. Data on the effectiveness of antiemetics and emesis-related utility were obtained from published sources. Relative to the ondansetron-based two-drug therapy, the incremental cost–effectiveness ratios for the palonosetron-based regimens were $115,490/quality-adjusted life years (QALY) for the two-drug strategy, $199,375/QALY for the two-drug regimen plus aprepitant after emesis, and $200,526/QALY for the three-drug strategy. In sensitivity analysis, using the $100,000/QALY benchmark, the palonosetron-based two-drug strategy and the two-drug regimen plus aprepitant following emesis were shown to be cost-effective in 39% and 26% of the Monte Carlo simulations, respectively, and with changes in values for the effectiveness of antiemetics and the rate of hospitalization. The cost-utility of palonosetron-based therapy exceeds the $100,000/QALY threshold. Future research incorporating the price structure of all antiemetics following ondansetron's recent patent expiration is needed.

Article Outline

Patients and Methods
Probability Data
Two-drug prophylactic regimens
Three-drug prophylactic regimens
Effectiveness of antiemetics over multiple cycles of chemotherapy
Resource Utilization and Cost Data
Utility Data
Analysis

Results

Discussion

Conclusion

Acknowledgements

References

Over the past decade, regimens containing anthracycline and cyclophosphamide (AC) have become the mainstay of adjuvant chemotherapy for treatment of breast cancer. Although each of these agents is individually considered moderately emetogenic, the combination of the two can lead to substantial nausea and vomiting.1 Despite remarkable recent progress in antiemetic prophylaxis, chemotherapy-induced emesis continues to be a major burden for patients with breast cancer and one of the most feared side effects of cancer treatment in general.[2] and [3]

Recent advances in emesis control have been possible due to the availability of increasingly more effective antiemetic agents. During the 1990s, the development of first-generation 5-hydroxytryptamine-3 (5-HT3) antagonists (ondansetron, granisetron, tropisetron, and dolasetron) marked a significant improvement in the control of emesis induced by chemotherapy, particularly acute emesis (ie, occurring within 24 hours following chemotherapy).

More recently, two new drugs—palonosetron, a second-generation 5-HT3 antagonist, and aprepitant, a centrally acting neurokinin-1 antagonist—were added to the armamentarium of antiemetic therapy. Compared with other single-dose 5-HT3 antagonists, palonosetron has a higher 5-HT3 binding affinity and longer plasma half-life and has shown superiority in the prevention of delayed emesis (ie, occurring more than 24 hours after chemotherapy administration) following moderately emetogenic chemotherapy with methotrexate, epirubicin, or cisplatin (MEC), including AC-based regimens.[4] and [5] In a recently published clinical trial conducted by Saito et al,6 palonosetron was also shown to be superior to granisetron in preventing delayed and overall emesis when both drugs were combined with dexamethasone following chemotherapy with either AC or cisplatin. As for aprepitant, when added to the standard of a 5-HT3 antagonist and dexamethasone therapy, it has been shown to improve emesis prevention among patients receiving AC-based chemotherapy during the acute, delayed, and overall periods.7

Such benefits have led to a recent revision in the antiemetics guidelines of both the American Society of Clinical Oncology (ASCO) and the National Comprehensive Cancer Network (NCCN), incorporating both palonosetron as one of the recommended 5-HT3 antagonists and aprepitant in combination with a 5-HT3 antagonist and dexamethasone for patients receiving AC-based chemotherapy.[8] and [9] Of note is that the revised 2010 NCCN antiemetic guidelines suggest that palonosetron may be used prior to the start of multiday chemotherapy, which is more likely to cause significant delayed emesis, instead of repeated daily doses of other first-generation 5-HT3 antagonists.9

Given the multiplicity of antiemetic strategies available for prophylaxis of nausea and vomiting associated with AC-based chemotherapy with inherent variability in effectiveness and price, it is critical for existing therapies to be analyzed in terms of both their outcomes and costs. Thus, the purpose of this study is to determine, from a third-party payer perspective, the cost-utility of palonosetron-based therapy in preventing emesis among breast cancer patients receiving four cycles of AC-based chemotherapy relative to generic ondansetron-based antiemetic therapy. Due to variations in the definition of complete emetic response found across antiemetic studies, the analysis will focus on chemotherapy-induced emesis only, rather than nausea and vomiting, as vomiting can be more objectively measured than nausea and, as such, has been more consistently reported.

Patients and Methods

We developed a Markov model to estimate the costs (in 2008 U.S. dollars) and health outcomes associated with emesis among breast cancer patients receiving multiple cycles of AC-based chemotherapy under six prophylactic strategies containing either generic ondansetron (onda) or palonosetron (palo) when each is combined with either dexamethasone (dex) alone, dex plus aprepitant in the subsequent cycles following the occurrence of emesis, or dex plus aprepitant up front (Figure 1). The time horizon for the risk of chemotherapy-induced emesis during each cycle of chemotherapy was 21 days, which is the standard duration of a cycle of AC-based chemotherapy.

 

 



Figure 1. 

Markov Model Comparing Palo-Based Therapy vs Onda-Based Therapy for Prophylaxis of Chemotherapy-Induced Emesis in Breast Cancer Patients Receiving Four Cycles of AC-Based Chemotherapy (1) Onda (32 mg intravenously) + dex (8 mg intravenously) on day 1, followed by dex (4 mg orally twice a day) on days 2−5. (2) Onda (32 mg intravenously) + dex (8 mg intravenously) on day 1, followed by dex (4 mg orally twice a day) on days 2−5 and aprepitant in the subsequent cycles following the occurrence of emesis (ie, onda 16 mg orally + aprepitant 125 mg orally + dex 12 mg orally on day 1 followed by aprepitant 80 mg orally on days 2−3). (3) Palo (0.25 mg intravenously) + dex (8 mg intravenously) on day 1, followed by dex (4 mg orally twice a day) on days 2−5. (4) Palo (0.25 mg intravenously) + dex (8 mg intravenously) on day 1, followed by dex (4 mg orally twice a day) on days 2−5 and aprepitant in the subsequent cycles following the occurrence of emesis (ie, palo 0.25 mg intravenously + aprepitant 125 mg orally + dex 12 mg orally on day 1 followed by aprepitant 80 mg orally on days 2−3). (5) Onda (16 mg orally) + aprepitant (125 mg orally) + dex (12 mg orally) on day 1 followed by aprepitant (80 mg orally) on days 2−3. (6) Palo (0.25 mg intravenously) + aprepitant (125 mg orally) + dex (12 mg orally) on day 1 followed by aprepitant (80 mg orally) on days 2−3. Palo = palonosetron; onda = ondansetron; AC = anthracycline and cyclophosphamide; dex, dexamethasone

We modeled emesis-related outcomes and direct medical costs (from a third-party payer perspective within the context of the U.S. health-care system) over a total of four cycles of chemotherapy as patients receiving AC-based regimens usually undergo at least four cycles of AC.10 We performed all analyses using TreeAge Pro 2009 Suite (Decision Analysis; TreeAge Software, Williamstown, MA). The study was submitted to our institutional review board and was determined to be exempt from review.

Probability Data

Two-drug prophylactic regimens

We estimated the effectiveness of the 5-HT3 antagonists based on secondary analysis of the raw data from the randomized clinical trial (RCT) directly comparing onda and palo when used alone for prevention of emesis associated with MEC, including 90 breast cancer patients from the palo 0.25-mg arm and 82 from the onda 32-mg arm who received AC-based chemotherapy (Table 1).5 Effectiveness estimates for palo 0.25 mg were augmented by data on 117 breast cancer patients on AC-based chemotherapy participating in a multicenter RCT comparing palo with dolasetron (Table 1).4 We assumed that dex adds the same relative benefit to either first- or second-generation 5-HT3 antagonists and obtained the expected additional benefit of dex in preventing acute emesis based on the results of an RCT comparing a single-dose of granisetron in combination with dex vs granisetron given alone to patients undergoing MEC (Table 2).11 Since in the aforementioned study dex was only given on day 1 of chemotherapy, the estimated additional benefit of adding dex to a 5-HT3 inhibitor on the delayed period was obtained from another RCT; this study, conducted by the Italian Group for Antiemetic Research, compared dex alone, dex plus onda, or placebo on days 2−5 of MEC.12

Table 1. Emesis Control During the Initial 5-Day Period Following Administration of AC-Based Chemotherapy to Patients with Breast Cancer

From Eisenberg et al4 and Gralla et al5

EMESIS CONTROLPALO (0.25 MG)[4] and [5] (n = 207), % PATIENTS (95% CI)ONDA (32 MG)5 (n = 82), % PATIENTS (95% CI)P
Acute (day 1)0.70 (0.63−0.76)0.61 (0.50−0.71)0.14
Delayed (days 2−5)0.65 (0.58−0.71)0.50 (0.39−0.61)0.02
Overall (days 1−5)0.55 (0.48−0.62)0.40 (0.30−0.52)0.02

AC = anthracycline and cyclophosphamide; palo = palonosetron; onda = ondansetron; CI = confidence interval

Table 2. Base-Case Probabilities, Utility Weights, and Data Sourcese

MODEL PARAMETERSBASE-CASE VALUES (RANGES)DATA SOURCES
Probability of acute emesis control on cycle 1 of AC:
 Onda-based two-drug strategyc0.84 (0.74−0.93)Gralla et al,a The Italian Group[5] and [11]
 Palo-based two-drug strategyc0.87 (0.81−0.94)Eisenberg et al,a Gralla et al,a The Italian Group[4], [5] and [11]
 Onda-based three-drug strategyd0.88 (0.85−0.91)Warr et al7
 Palo-based three-drug strategyd0.96 (0.89−0.99)Grote et al, Grunberg et al[40] and [41]
Probability of delayed emesis control following control of acute emesis on cycle 1 of ACc:
 Onda-based two-drug strategyd0.75 (0.62–0.85)The Italian Group12
 Palo-based two-drug strategyc0.85 (0.78–0.91)Eisenberg et al,a Gralla et al,a The Italian Group[4], [5] and [12]
 Onda-based three-drug strategyd0.86 (0.82–0.90)Warr et al7
 Palo-based three-drug strategyc0.96 (0.91–0.97)Eisenberg et al,a Gralla et al,a Warr et al[4], [5] and [7]
Probability of delayed emesis control following acute emesis on cycle 1 of ACc:
 Onda-based two-drug strategyc0.46 (0.31–0.62)Gralla et al,a The Italian Group[5] and [12]
 Palo-based two-drug strategyc0.44 (0.27–0.59)Eisenberg et al,a Gralla et al,a The Italian Group[4], [5] and [12]
 Onda-based three-drug strategyd0.44 (0.29–0.57)Warr et al7
 Palo-based three-drug strategyc0.51 (0.41–0.67)Eisenberg et al,a Gralla et al,a Warr et al[4], [5] and [7]
Relative probability of emesis control in subsequent cycles of ACc:
 Two-drug therapy0.987 (0.970–1.0)Herrstedt et al14e
 Three-drug therapy1.013 (1.0–1.030)Herrstedt et al14e
Probability of hospitalization (among patients who develop emesis) per cycle of ACd:
 Onda-based regimens0.0035 (0.0001−0.019)Data from Medstat MarketScan16
 Palo-based regimens0.0017 (0.00004−0.0089)Data from Medstat MarketScan, Haislip et al[16] and [19]b
Probability of office visit use (among patients who develop emesis) per cycle of ACd:
 Onda-based regimens0.10 (0.07−0.14)Data from Medstat MarketScan16
 Palo-based regimens0.05 (0.03−0.07)Data from Medstat MarketScan, Haislip et al[16] and [19]b
Probability of rescue medicine utilization use (among patients who develop emesis) per cycle of ACd:
 Onda-based regimens0.61 (0.46−0.75)Gralla et al5a
 Palo-based regimens0.56 (0.45−0.66)Eisenberg et al, Gralla et al[4] and [5]a
Utility weights for emesis per cycle of ACf:
 Acute and delayed emesis0.15 (0.10−0.20)Sun et al20
 Acute emesis and no delayed emesis0.76 (0.70−0.83)Sun et al20
 No acute emesis and delayed emesis0.20 (0.14−0.26)Sun et al20
 No acute and no delayed emesis0.92 (0.86−0.99)Sun et al20

AC = anthracycline and cyclophosphamide; onda = ondansetron; palo = palonosetron.

Included in the analysis was the subset of women with breast cancer receiving AC-based chemotherapy.We obtained an estimate of emesis-related hospitalization and office visit utilization based on data from Medstat MarketScan, HPM subset (Medstat Group, Inc., Ann Arbor, MI) on 707 breast cancer patients who received the first cycle of AC-based chemotherapy from 1996 to 2002 and either were admitted to the hospital or had an office visit for treatment of vomiting or dehydration. Since palo was only introduced into the U.S. market in 2003, we assumed that all these breast cancer patients received onda-based antiemetic prophylaxis. As a result, we estimated the differential rate of health-care resource utilization based on Haislip et al's19 reported differential incidence of extreme events associated with chemotherapy-induced nausea and vomiting experienced by community-based breast cancer patients who received either onda or palo for emesis prophylaxis following the first cycle of chemotherapy.Of note is that there are two different methods for applying the benefit of adding dex and/or aprepitant to a 5-HT3 antagonist: (1) rate of emesis with 5-HT3* relative risk of emesis by adding dex and/or aprepitant and (2) rate of emesis control with 5-HT3 * relative risk of emesis control by adding dex and/or aprepitant. These produce substantially different results, with the former method skewing the results toward the least effective 5-HT3 and the latter skewing it toward the most effective one. As a result, we estimated the probability of emesis by averaging the results obtained using the two different methods. Of note is that the ranges for these effectiveness estimates were obtained by applying the two different methods to the lower and upper bounds of the 95% confidence intervals derived from the clinical trials comparing the 5-HT3 antagonists when used alone.Ranges were obtained by constructing 95% confidence intervals for observed proportions using the normal approximation to the binomial distribution.Ranges are based on the minimum and maximum values observed in Herrstedt et al's14 clinical trial of multicycle chemotherapy.Ranges are based on the estimate's actual 95% confidence intervals obtained from Sun et al's20 data.

 

 


Three-drug prophylactic regimens

We estimated the rate of acute emesis for the three-drug regimens based on data from published studies in which either onda or palo was given in combination with dex and aprepitant on day 1 of MEC (Table 2).[5], [7] and [13] Because aprepitant was either used in combination with dexamethasone or not used on days 2−3 in the trials of palo-based three-drug therapy, we estimated the benefit of adding aprepitant alone to palo on days 2−3 by assuming that the added benefit in the delayed period would be the same as the benefit added to onda. Specifically, we obtained information on the relative risk of delayed emesis control when aprepitant is added on days 2−3 from a large clinical trial of aprepitant combined with onda and dex in breast cancer patients receiving either A or AC chemotherapy (Table 2).7

Effectiveness of antiemetics over multiple cycles of chemotherapy

The estimates of changes in the probability of emesis control over multiple cycles of chemotherapy were obtained from a RCT conducted by Herrstedt et al14 of ondansetron-based two- and three-drug regimens for prevention of chemotherapy-induced nausea and vomiting among breast cancer patients undergoing multiple cycles of AC-based chemotherapy. We assumed that changes in emesis control over four cycles of AC for the palo-based two- and three-drug regimens were similar to the observed changes for the onda-based two- and three-drug strategies, respectively.14

Resource Utilization and Cost Data

The cost of antiemetic prophylaxis was based on the 2008 Medicare Part B reimbursement rates for pharmaceuticals, which reflects the price of ondansetron following its recent patent expiration (Table 3).15 The costs of prophylaxis failures were estimated as follows. In the majority of prophylaxis failures, the only cost is the cost of rescue medication. In such cases, we obtained costs by multiplying the individual doses used for rescue treatment of breast cancer patients on AC participating in the clinical trials comparing palo 0.25 mg with single doses of onda or dolasetron by their unit costs based on the 2008 Medicare Part B reimbursement rates.[5] and [15] For the few patients who are seen in the office for uncontrolled emesis, we obtained estimates of the risk of such emesis-related office visits based on the MarketScan Health Productivity Management (HPM) database from Thomson Reuters on 707 breast cancer patients who received their first cycle of AC-based chemotherapy between 1997 and 2002 (Table 2) and its costs from the 2008 Medicare Physician Fee Schedule Reimbursement for a level III office visit (CPT 99213).[16] and [17]

Table 3. Emesis-Related Base-Case Costs per Cycle of AC-Based Chemotherapy Among Breast Cancer Patients

COST COMPONENT2008 U.S.$ (RANGES)DATA SOURCE
Hospitalization$5,237.00 ($3,921−$6,112)aHCUP charge data18
Consumer Price Index42
Medicare cost-to-charge ratio43
Level III office visit (CPT 99213)$60.30 ($19.96–$122.46)d2008 Medicare Physician Fee Schedule Reimbursement17
Prophylactic antiemetics2008 Medicare Part B reimbursement rates for pharmaceuticals15
 Onda-based two-drug regimen$49.74
 Palo-based two-drug regimen$207.20
 Onda-based three-drug regimen$324.51
 Palo-based three-drug regimen$482.46
Rescue medicinesb$35.25 ($21.66–$48.80)cEisenberg et al,4 Gralla et al,5 2008 Medicare Part B reimbursement rates for pharmaceuticals15

AC = anthracycline and cyclophosphamide; onda = ondansetron; palo = palonosetron; HCUP = Healthcare Cost and Utilization Project

Charges were inflated to 2008 U.S. dollars using the Consumer Price Index (CPI) for medical care and adjusted to costs using Medicare cost-to-charge ratio. The ranges were based on estimates of the 95% confidence interval.In the randomized clinical trial directly comparing ondansetron and palonosetron, propulsives accounted for 71% of the rescue medicines used, 5-hydroxytryptamine antagonists for 20%, glucocorticoids for 7%, and aminoalkyl ethers for 2%.5Costs for rescue medication were obtained by multiplying all drug unit costs by the individual doses used for rescue treatment of breast cancer patients on AC participating in the clinical trials comparing palo 0.25 mg with single doses of onda or dolasetron.[5] and [15] The ranges were based on estimates of the 95% confidence interval.Ranges were based on the Medicare physician fee schedule for levels I and VI office visits.

Finally, although hospitalization for emesis is extremely rare in this population, when it occurs, it is quite expensive. For completeness, we obtained estimates of the risk of emesis-related hospitalization from the same population of breast cancer patients from whom we obtained the estimate for the risk of emesis-related office visit, whereas hospital costs were obtained from Healthcare Cost and Utilization Project (HCUP) data on 2,342 breast cancer patients who were hospitalized with a primary or admitting diagnosis of vomiting or dehydration from 1997 to 2003 ([Table 2] and [Table 3]).[16] and [18]

Of note is that since palo was only introduced into the U.S. market in 2003, we anticipated the observed risk of emesis-related office visit and hospital admission obtained from MarketScan data during the period 1997−2002 reflected the risk associated with prophylaxis with onda. As a result, given that, when compared with onda, palo has also shown superiority in reducing the severity of emetic episodes when they occur, we estimated the differential rate of health-care resource utilization for palo and onda based on Haislip et al's reported differential incidence of extreme events associated with chemotherapy-induced nausea and vomiting (CINV) experienced by community-based breast cancer patients who received either palo or onda for emesis prophylaxis following the first cycle of chemotherapy (Table 2).[5] and [19]

Utility Data

We obtained the utility weights for acute and delayed emesis from a published study of preferences elicited from ovarian cancer patients undergoing chemotherapy using a modified visual analog scale (VAS) (Table 2).20 We equally applied these emesis-related utility weights to the initial 5-day period of chemotherapy (the standard duration of follow-up in clinical trials of prophylactic antiemetics) in all six prophylactic strategies of the decision tree. Furthermore, because the risk of CINV after 5 days of chemotherapy is usually so negligible as to be unmeasured in clinical trials of antiemetics, we assumed the utility weights for the remaining 16 days of each of the chemotherapy cycles to be the same as the weight associated with complete emesis control (ie, 0.92). We subsequently converted the resulting estimates of quality-adjusted life days into quality-adjusted life years (QALY).

Analysis

We used a stepwise method to calculate the incremental cost–effectiveness ratios of the different prophylactic therapy strategies, with the generic onda-based two-drug therapy (ie, the lowest cost strategy) as the base comparator (also known as the “anchor”).21 We adopted the benchmark range of U.S. $50,000−$100,000 per QALY, which has been commonly cited for oncology-related interventions as the threshold for acceptable cost–effectiveness, and examined the robustness of the results by performing one-way sensitivity analyses of plausible ranges for the model's key parameters based on the data sources used as well as probabilistic sensitivity analysis using Monte Carlo simulation.[21] and [22]

Results

The overall rate of emesis control (on days 1−5) among breast cancer patients following a cycle of AC-based chemotherapy was estimated to be 63% (range 46%−79%) for the onda-based two-drug therapy, 74% (range 66%−85%) for the palo-based two-drug therapy, 76% (range 75%−82%) for the onda-based three-drug therapy, and 92% (range 81%−96%) for the palo-based three-drug therapy. Based on these estimates, relative to the onda-based two-drug therapy, the incremental cost–effectiveness ratios (ICERs) for the palo-based regimens were $115,490/QALY for the two-drug strategy, $199,375/QALY for the two-drug regimen plus aprepitant after emesis, and $200,526/QALY for the three-drug strategy (Table 4). The onda-based two-drug combination plus aprepitant after the onset of emesis was eliminated through extended dominance as it has a greater ICER than the next more effective therapy, the palo-based two-drug treatment strategy (Table 4). The onda-based three-drug strategy was dominated by the palo-based two-drug combination plus aprepitant after the onset of emesis as the former strategy is both less effective and more expensive than the latter (Table 4).

 

 

Table 4. Stepwise Comparison of Costs (2008 U.S. dollars) per QALY of the Different Prophylactic Therapy Strategies over Four Cycles of AC-Based Chemotherapy, with the Onda-Based Two-Drug Therapy as the Base Comparator

STRATEGYTOTAL COST (U.S.$)INCREMENTAL COST (U.S.$)EFFECTIVENESS (QALY)INCREMENTAL EFFECTIVENESS (QALY)INCREMENTAL COST–EFFECTIVENESS (U.S.$/QALY)
Onda-based two-drug therapy$2690.1989
Onda-based two-drug therapy with aprepitant after emesis$635$3660.20100.0021$174, 286 Eliminated through extended dominancea
Palo-based two-drug therapy$858$5890.20400.0051$115,490c
Palo-based two-drug therapy plus aprepitant after emesis$1,177$3190.20560.0016199,375
Onda-based three-drug therapy$1,336$1590.205(0.0006)Dominatedb
Palo-based three-drug therapy$1,939$6030.20940.0044$200,526d

QALY = quality-adjusted life year; AC = anthracycline and cyclophosphamide; ICER = incremental cost–effectiveness ratio; onda = ondansetron; palo = palonosetron

Extended dominance occurs when one of the treatment alternatives has a greater ICER than the next more effective alternative.One intervention is said to be dominated by another when it is both less effective and more expensive than the previous less costly alternative.Because the onda-based two-drug combination plus aprepitant after the onset of emesis was eliminated through extended dominance, the palo-based two-drug therapy was compared with the onda-based two-drug therapy.Because the onda-based three-drug combination was dominated by the palo-based two-drug combination plus aprepitant after the onset of emesis, the palo-based three-drug therapy was compared with the latter regimen.

In sensitivity analyses using the commonly accepted cost–effectiveness benchmark range of $50,000−$100,000/QALY, the results were sensitive to changes in the overall emesis control rates for the onda-based two-drug strategy. If the probability of overall emesis control for the onda-based two-drug strategy was as low as its estimated lower bound (46%), the ICER for the palo-based two-drug treatment alternative would drop to $53,892/QALY. The results were also sensitive to changes in the effectiveness for the palo-based two-drug regimen: When its overall control rate was as high as its estimated upper bound (86%), its ICER would be $71,472. In contrast, the results were not sensitive to variations in the probability of overall emesis control for the three-drug strategies, nor were they sensitive to changes in the relative probability of emesis control in subsequent cycles of AC for either the two- or three-drug strategies.

If the probability of emesis-related hospitalization was as high as the upper limit of its 95% confidence interval (CI), the ICER for the palo-based two-drug regimen would be $97,301/QALY. However, changes in the cost of an emesis-related admission (95% CI $3,921−$6,112) did not significantly alter the results, nor did variations in office visit and rescue medicine utilization and their associated costs. The results were also not sensitive to variations in the values for the utility weights throughout their 95% CIs. We performed a threshold analysis to explore the price per dose of palo that would result in an acceptable cost–effectiveness ratio under the $100,000/QALY benchmark and found that the ICER for the palo-based two-drug treatment alternative would only fall to a $100,000/QALY threshold when the cost of palo is decreased by 11%.

Figure 2 shows the cost–effectiveness acceptability curves for each strategy, with the onda-based two-drug therapy as the base comparator. These curves show the proportion of the 100,000 simulations in which the comparing antiemetic regimen was considered more cost-effective than the base comparator at different thresholds. Using the benchmark of U.S. $100,000/QALY, the palo-based two-drug strategy and the two-drug regimen plus aprepitant following the onset of emesis were shown to be cost-effective in 39% and 26% of the simulations with the onda-based standard therapy as the baseline, respectively, whereas the palo-based and onda-based three-drug strategies and the onda-based two-drug regimen with aprepitant after emesis were cost-effective in fewer than 10% of the simulations. Of note is that the slope of the acceptability curves for the palo-based two-drug strategies are steep when willingness to pay exceeds $50,000/QALY, indicating that the greater the threshold, the greater the increase in the level of confidence that these strategies could be cost-effective. For example, the probability that the palo-based two-drug strategy is more cost-effective than the onda-based two-drug strategy rises to 51% at a threshold value of $125,000/QALY and exceeds 60% at $150,000/QALY.



Figure 2. 

Acceptability Curves in Terms of Likelihood of a Strategy Being Cost-Effective in Monte Carlo Simulations Relative to Willingness to Pay (2008 U.S.$) per Quality-Adjusted Life Year (QALY) with the Onda-Based Two-Drug Therapy as the Base Comparator

 

 

Figure 3 presents the scatterplot of the results of the probabilistic sensitivity analysis for the palo-based two-drug strategy. Nearly 96% of the simulations fell within the first quadrant of the chart (ie, on the upper right quadrant), which represents the scenario where the palo-based two-drug therapy is more costly but also more effective than the onda-based standard therapy. However, only 39% of the simulations fell below the $100,000/QALY dashed threshold line, which represents the scenario where the palo-based two-drug strategy is more cost-effective than the onda-based standard therapy at the $100,000/QALY benchmark.



Figure 3. 

Scatterplot of the Incremental Cost–Effectiveness Ratios Comparing the Palo-Based Two-Drug Strategy with the Onda-Based Two-Drug Therapy. The dashed line indicates the $100,000/QALY threshold, whereas the ellipse represents the 95% confidence ellipse, which includes 95% of the observations


Discussion

Our estimates of emesis-related costs and outcomes following four cycles of AC-based chemotherapy in women with breast cancer indicate that at current antiemetic prices and utilities placed on emesis, the additional costs of palo and aprepitant are not warranted at the $100,000/QALY threshold. In probabilistic sensitivity analysis, the palo-based two-drug strategy and the two-drug regimen plus aprepitant following the onset of emesis were shown to be cost-effective at the $100,000/QALY threshold in only 39% and 26% of the simulations, respectively. The model was sensitive to changes in the values of antiemetic effectiveness for the two-drug regimens and the risk of emesis-related hospitalization.

In threshold analysis, the two-drug palo-based regimen was cost-effective at the $100,000/QALY benchmark when the cost of palo is decreased by 11%. Because the use of the $100,000/QALY threshold is uncommon in clinical practice, the cost-effectiveness of the palo-based two-drug strategy (estimated at $115,490/QALY in our study) compares favorably with other commonly used supportive care measures for women with breast cancer. Such measures include primary prophylaxis with granulocyte colony-stimulating factor in women undergoing chemotherapy with moderate to high myelosuppressive risk (ICER of $116,000/QALY, or $125,948/QALY in 2008 U.S. dollars) and the use of bisphosphonates for the prevention of skeletal complications in breast cancer patients with lytic bone metastases (ICER ranging from $108,200/QALY with chemotherapy as systemic therapy to $305,300 in conjunction with hormonal systemic therapy, or $166,381/QALY to $469,466/QALY in 2008 U.S. dollars, respectively).[23] and [24] Both interventions are considered recommended standards of supportive care for patients with breast cancer and are widely used in breast oncology practices.[25] and [26]

Decision-analytic models, such as the Markov model presented in our study, aim to reflect the reality of clinical practice in a simplified way. Therefore, modelers often need to make decisions regarding the study time frame and model parameters based on the best use of available data. In our study, we obtained estimates for the probability of chemotherapy-induced emesis from studies in which the standard duration of follow-up is 5 days. By so doing, we may have underestimated the cost-effectiveness for the palo-based and aprepitant-based regimens. Although the risk of CINV after 5 days of chemotherapy is usually negligible, anticipation of vomiting may affect a patient's quality of life throughout the cycle of chemotherapy.

In addition, our estimates of costs, which were mostly obtained from Medicare, may differ from those of other third-party payers. However, Medicare is among the largest payers for breast cancer care as 42% of the women diagnosed with cancer in the United States are older than 64 years, and many private organizations set their own reimbursement rates based on the Medicare schedule. Therefore, we believe that Medicare reimbursement data provide a suitable estimate for emesis-related medical costs for all breast cancer patients in the United States.[27] and [28]

The present results should solely be interpreted in light of the cost–effectiveness benchmark of $50,000−$100,000/QALY, which has been frequently used in the context of the U.S. health-care system.[22] and [29] Such a benchmark, however, is a historic, precedent-based threshold set by the cost of caring for patients on dialysis, which was estimated at $50,000/QALY in 1982 ($74,000−$95,000 in 1997 U.S. dollars).[30] and [31] Given the arbitrariness of such a threshold, it has been suggested that the current willingness to pay for medical interventions in the United States probably exceeds $100,000/QALY, with values as high as $300,000/QALY being cited in some oncology publications.[22], [29], [31], [32], [33] and [34] In support of that argument is the public and policy makers' strong negative reaction to the National Institutes of Health Consensus Panel not recommending mammography screening for women aged 40−49 years, a procedure reported to provide an ICER of $105,000 per life-year gained.[35] and [36] As a result, if willingness to pay goes beyond $100,000/QALY, the alternative of adding aprepitant to palo plus dex may also be deemed attractive as the slope of its acceptability curve becomes substantially steep when the willingness to pay for a QALY exceeds $125,000 (Figure 2), suggesting that its marginal gain may exceed its marginal costs at higher thresholds.

In addition, it is worth noting that the present analysis has been conducted from the perspective of a third-party payer within the context of the U.S. health-care system. The large difference in the acquisition cost of palo-based and onda-based therapy observed in the United States is mostly driven by the differential stage of product life cycles for palo and onda. Although at the time of this study palo was still under patent protection, generic onda had entered the U.S. market prior to our study. The large price discrepancy between brand and generic drugs explains the difference in drug costs in this U.S.-based analysis. As such, our results may not reflect the situation in countries with a widely different cost structure, in which the acquisition cost of palo may be substantially lower. When that is the case, the cost–effectiveness profile of the palo-based prophylactic therapy may be deemed substantially more favorable than the profile presented here. Similarly, we anticipate finding a more attractive cost–effectiveness profile for the palo-based therapies as palo reaches the end of its product life cycle in the U.S. market.37 Also of note is that the cost–effectiveness of the palo-based therapy may greatly differ when different perspectives (other than the third-party payer's perspective) are adopted.

Our study, however, has several limitations. First, the utility scores used in our model were derived with a VAS instrument, which does not incorporate patients' preferences under uncertainty. Nevertheless, the VAS approach has been shown to provide utility scores for nausea and vomiting with more variability than scores derived using other methods such as the Standard Gamble (personal communication, Grunberg SM et al, CALGB study 309801). Notwithstanding that, it remains unclear which method gives utility scores for transient health states, such as CINV, with the greatest validity.

Also of note is that due to a lack of information on emesis-related utilities among breast cancer patients in the literature, we used utilities elicited from patients with ovarian cancer. To the best of our knowledge, the utilities in Sun et al20 were the only ones available in the literature that were elicited from a homogeneous population of cancer patients (ie, solely patients with ovarian cancer) and were based on a wide range of health states combining the presence and absence of emesis during either the acute or the delayed period. In addition, the participants in the Sun et al study were treated with carboplatin, which, like the regimen used in our model, is classified as moderately emetogenic in established antiemetic guidelines.[8], [9] and [38] It is also important to emphasize that the population in that study, like our study's population, was composed exclusively of women, who are known to be at increased risk for developing CINV.39

Second, in the absence of clinical trial data, we assumed conservatively that dex and aprepitant add the same relative benefit to both onda and palo. This assumption results in an imperfect estimate of cost–effectiveness. As such, we may have overestimated or underestimated the cost–effectiveness of palo as dex and aprepitant may potentially add less value to the intrinsically more active 5-HT3 antagonist or uniquely complementary mechanisms of action could contribute to even greater activity with the palo-based therapy. However, our study's estimate of the relative effectiveness of the palo-based two-drug prophylactic therapy versus the onda-based two-drug therapy for preventing delayed emesis is consistent with that reported in a recently published clinical trial comparing palo and granisetron when both drugs are combined with dex following chemotherapy with either AC or cisplatin (1.18 vs 1.17, respectively).6

Third, our study did not include the outcomes associated with the adverse effects of antiemetics, and by so doing, we may have underestimated the costs associated with antiemetic prophylaxis. However, the incidence and duration of treatment-related adverse events occurring in the two RCTs comparing palo with either onda or dolasetron were mild and similar across treatment cohorts.[4] and [5]

Fourth, we assumed that changes in emesis control in subsequent cycles of AC for the palo-based regimens were the same as for the onda-based therapy. By so doing, we may have underestimated the cost–effectiveness of palo as the superiority of the more active 5-HT3 antagonist could be maintained in the subsequent cycles of chemotherapy (or even increased, as seen in the aprepitant-based arm of Herrstedt et al's14 study). As a result, if future prospective trials of palo-based antiemetic prophylaxis confirm its superiority in maintaining antiemetic efficacy over multiple cycles of AC, the cost–effectiveness profiles for the palo-based strategies may be more favorable than the profiles presented herein.

Last, the incremental gains in QALY observed in cost–utility analysis of interventions associated with transitory and non-life-threatening health states, such as the antiemetic regimens analyzed in our study, tend to render small denominators to be used in the incremental cost–effectiveness ratios. The issue of small denominators has led some researchers to question whether the current methodology of cost–effectiveness analysis is appropriate to determine the cost–effectiveness of treatments for terminal or supportive care.32 However, despite this shortcoming, these types of analysis benefit from having a wider scope as they allow comparisons over different types of health interventions across various diseases. In addition, by incorporating patients' utility levels over different health states (instead of merely looking into cost per additional patient controlled), cost–utility analysis makes explicit the impact of the target population's preferences for the different outcomes. Of importance is that both the Panel on Cost–Effectiveness in Health and Medicine and the Institute of Medicine (IOM) Committee on Regulatory Cost–Effectiveness Analysis recommend the use of QALY as the preferred outcome measure for economic evaluation of health-care interventions.

Conclusion

Although our base-case analysis suggests that, from a third-party payer perspective within the context of the U.S. health-care system, the cost–utility of the palo-based two-drug prophylactic therapy for breast cancer patients receiving four cycles of AC-based chemotherapy exceeds the $50,000–$100,000/QALY threshold, it is comparable to other commonly used supportive care interventions for women with breast cancer. In sensitivity analyses, such a strategy was associated with a 39% chance of being cost-effective at the $100,000/QALY threshold, and the model was sensitive to changes in the values of antiemetic effectiveness and of the probability of emesis-related hospitalization. In threshold analysis, the combination of palo and dex was shown to become cost-effective (at the $100,000/QALY benchmark) when the cost of palo is decreased by 11%. As a result, future research incorporating the price structure of all antiemetics following the recent expiration of onda's patent is needed.

 

 

Acknowledgments

The authors thank Dr. Sharon H. Giordano for providing valuable clinical input and Dr. Catherine D. Cooksley for providing suggestions for improving this article.

References1

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38 F. Roila, P.J. Hesketh, J. Herrstedt and Antiemetic Subcommittee of the Multinational Association of Supportive Care in Cancer, Prevention of chemotherapy- and radiotherapy-induced emesis: results of the 2004 Perugia International Antiemetic Consensus Conference, Ann Oncol 17 (2006), pp. 20–28 [16314401]. View Record in Scopus | Cited By in Scopus (90)

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40 T. Grote, J. Hajdenberg, A. Cartmell, S. Ferguson, A. Ginkel and V. Charu, Combination therapy for chemotherapy-induced nausea and vomiting in patients receiving moderately emetogenic chemotherapy: palonosetron, dexamethasone, and aprepitant, J Support Oncol 4 (2006), pp. 403–408 [17004515]. View Record in Scopus | Cited By in Scopus (38)

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43 Department of Health and Human Services. Centers for Medicare & Medicaid Services, Medicare Program; Proposed Changes to the Hospital Inpatient Prospective Payment Systems and Fiscal Year 2008 Rates CMS-1533-P, pp 1070–1073 http://www.cms.hhs.gov/AcuteInpatientPPS/downloads/CMS-1533-P.pdf Accessed May 16, 2010.

 

 

This study was supported in part by a grant from MGI Pharma, Inc. MGI Pharma, Inc., had no role in the study design, data analysis, interpretation of results, content of the final article, or the decision to submit it for publication.

Conflicts of interest: Dr. Sun discloses that her husband was an employee of MGI Pharma, Inc., at the time this article was being written. Dr. Gralla discloses that he is a consultant for MGI Pharma, Inc., GlaxoSmithKline, Sanofi-aventis, and Merck; he also receives honoraria from MGI Pharma, Inc., and Merck and research support from Sanofi-aventis. Dr. Grunberg discloses that he is a consultant for MGI Pharma, Inc.

Correspondence to: Elenir B. C. Avritscher, MD, PhD, MBA/MHA, Section of Health Services Research, Department of Biostatistics and Applied Mathematics, The University of Texas M. D. Anderson Cancer Center, 1400 Pressler Street, Unit 1411, Houston, TX 77230; telephone: (713) 563-8920; fax: (713) 563-4243


1 PubMed ID in brackets


The Journal of Supportive Oncology
Volume 8, Issue 6, November-December 2010, Pages 242-25

Original research

Cost–Utility Analysis of Palonosetron-Based Therapy in Preventing Emesis Among Breast Cancer Patients

Elenir B.C. Avritscher MD, PhD, MBA/MHA

, a,
, Ya-Chen T. Shih PhDa, Charlotte C. Sun DrPHa, Richard J. Gralla MDa, Steven M. Grunberg MDa, Ying Xu MD, MSa and Linda S. Elting DrPHa

a Division of Quantitative Sciences, Department of Gynecologic Oncology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas; The Monter Cancer Center, North Shore-Long Island Jewish Health System, Lake Success, New York; Division of Hematology/Oncology, University of Vermont, Burlington, Vermont

Received 8 February 2010; 

accepted 28 September 2010. 

Available online 25 January 2011.

Abstract

We estimated the cost-utility of palonosetron-based therapy compared with generic ondansetron-based therapy throughout four cycles of anthracycline and cyclophosphamide for treating women with breast cancer. We developed a Markov model comparing six strategies in which ondansetron and palonosetron are combined with either dexamethasone alone, dexamethasone plus aprepitant following emesis, or dexamethasone plus aprepitant up front. Data on the effectiveness of antiemetics and emesis-related utility were obtained from published sources. Relative to the ondansetron-based two-drug therapy, the incremental cost–effectiveness ratios for the palonosetron-based regimens were $115,490/quality-adjusted life years (QALY) for the two-drug strategy, $199,375/QALY for the two-drug regimen plus aprepitant after emesis, and $200,526/QALY for the three-drug strategy. In sensitivity analysis, using the $100,000/QALY benchmark, the palonosetron-based two-drug strategy and the two-drug regimen plus aprepitant following emesis were shown to be cost-effective in 39% and 26% of the Monte Carlo simulations, respectively, and with changes in values for the effectiveness of antiemetics and the rate of hospitalization. The cost-utility of palonosetron-based therapy exceeds the $100,000/QALY threshold. Future research incorporating the price structure of all antiemetics following ondansetron's recent patent expiration is needed.

Article Outline

Patients and Methods
Probability Data
Two-drug prophylactic regimens
Three-drug prophylactic regimens
Effectiveness of antiemetics over multiple cycles of chemotherapy
Resource Utilization and Cost Data
Utility Data
Analysis

Results

Discussion

Conclusion

Acknowledgements

References

Over the past decade, regimens containing anthracycline and cyclophosphamide (AC) have become the mainstay of adjuvant chemotherapy for treatment of breast cancer. Although each of these agents is individually considered moderately emetogenic, the combination of the two can lead to substantial nausea and vomiting.1 Despite remarkable recent progress in antiemetic prophylaxis, chemotherapy-induced emesis continues to be a major burden for patients with breast cancer and one of the most feared side effects of cancer treatment in general.[2] and [3]

Recent advances in emesis control have been possible due to the availability of increasingly more effective antiemetic agents. During the 1990s, the development of first-generation 5-hydroxytryptamine-3 (5-HT3) antagonists (ondansetron, granisetron, tropisetron, and dolasetron) marked a significant improvement in the control of emesis induced by chemotherapy, particularly acute emesis (ie, occurring within 24 hours following chemotherapy).

More recently, two new drugs—palonosetron, a second-generation 5-HT3 antagonist, and aprepitant, a centrally acting neurokinin-1 antagonist—were added to the armamentarium of antiemetic therapy. Compared with other single-dose 5-HT3 antagonists, palonosetron has a higher 5-HT3 binding affinity and longer plasma half-life and has shown superiority in the prevention of delayed emesis (ie, occurring more than 24 hours after chemotherapy administration) following moderately emetogenic chemotherapy with methotrexate, epirubicin, or cisplatin (MEC), including AC-based regimens.[4] and [5] In a recently published clinical trial conducted by Saito et al,6 palonosetron was also shown to be superior to granisetron in preventing delayed and overall emesis when both drugs were combined with dexamethasone following chemotherapy with either AC or cisplatin. As for aprepitant, when added to the standard of a 5-HT3 antagonist and dexamethasone therapy, it has been shown to improve emesis prevention among patients receiving AC-based chemotherapy during the acute, delayed, and overall periods.7

Such benefits have led to a recent revision in the antiemetics guidelines of both the American Society of Clinical Oncology (ASCO) and the National Comprehensive Cancer Network (NCCN), incorporating both palonosetron as one of the recommended 5-HT3 antagonists and aprepitant in combination with a 5-HT3 antagonist and dexamethasone for patients receiving AC-based chemotherapy.[8] and [9] Of note is that the revised 2010 NCCN antiemetic guidelines suggest that palonosetron may be used prior to the start of multiday chemotherapy, which is more likely to cause significant delayed emesis, instead of repeated daily doses of other first-generation 5-HT3 antagonists.9

Given the multiplicity of antiemetic strategies available for prophylaxis of nausea and vomiting associated with AC-based chemotherapy with inherent variability in effectiveness and price, it is critical for existing therapies to be analyzed in terms of both their outcomes and costs. Thus, the purpose of this study is to determine, from a third-party payer perspective, the cost-utility of palonosetron-based therapy in preventing emesis among breast cancer patients receiving four cycles of AC-based chemotherapy relative to generic ondansetron-based antiemetic therapy. Due to variations in the definition of complete emetic response found across antiemetic studies, the analysis will focus on chemotherapy-induced emesis only, rather than nausea and vomiting, as vomiting can be more objectively measured than nausea and, as such, has been more consistently reported.

Patients and Methods

We developed a Markov model to estimate the costs (in 2008 U.S. dollars) and health outcomes associated with emesis among breast cancer patients receiving multiple cycles of AC-based chemotherapy under six prophylactic strategies containing either generic ondansetron (onda) or palonosetron (palo) when each is combined with either dexamethasone (dex) alone, dex plus aprepitant in the subsequent cycles following the occurrence of emesis, or dex plus aprepitant up front (Figure 1). The time horizon for the risk of chemotherapy-induced emesis during each cycle of chemotherapy was 21 days, which is the standard duration of a cycle of AC-based chemotherapy.

 

 



Figure 1. 

Markov Model Comparing Palo-Based Therapy vs Onda-Based Therapy for Prophylaxis of Chemotherapy-Induced Emesis in Breast Cancer Patients Receiving Four Cycles of AC-Based Chemotherapy (1) Onda (32 mg intravenously) + dex (8 mg intravenously) on day 1, followed by dex (4 mg orally twice a day) on days 2−5. (2) Onda (32 mg intravenously) + dex (8 mg intravenously) on day 1, followed by dex (4 mg orally twice a day) on days 2−5 and aprepitant in the subsequent cycles following the occurrence of emesis (ie, onda 16 mg orally + aprepitant 125 mg orally + dex 12 mg orally on day 1 followed by aprepitant 80 mg orally on days 2−3). (3) Palo (0.25 mg intravenously) + dex (8 mg intravenously) on day 1, followed by dex (4 mg orally twice a day) on days 2−5. (4) Palo (0.25 mg intravenously) + dex (8 mg intravenously) on day 1, followed by dex (4 mg orally twice a day) on days 2−5 and aprepitant in the subsequent cycles following the occurrence of emesis (ie, palo 0.25 mg intravenously + aprepitant 125 mg orally + dex 12 mg orally on day 1 followed by aprepitant 80 mg orally on days 2−3). (5) Onda (16 mg orally) + aprepitant (125 mg orally) + dex (12 mg orally) on day 1 followed by aprepitant (80 mg orally) on days 2−3. (6) Palo (0.25 mg intravenously) + aprepitant (125 mg orally) + dex (12 mg orally) on day 1 followed by aprepitant (80 mg orally) on days 2−3. Palo = palonosetron; onda = ondansetron; AC = anthracycline and cyclophosphamide; dex, dexamethasone

We modeled emesis-related outcomes and direct medical costs (from a third-party payer perspective within the context of the U.S. health-care system) over a total of four cycles of chemotherapy as patients receiving AC-based regimens usually undergo at least four cycles of AC.10 We performed all analyses using TreeAge Pro 2009 Suite (Decision Analysis; TreeAge Software, Williamstown, MA). The study was submitted to our institutional review board and was determined to be exempt from review.

Probability Data

Two-drug prophylactic regimens

We estimated the effectiveness of the 5-HT3 antagonists based on secondary analysis of the raw data from the randomized clinical trial (RCT) directly comparing onda and palo when used alone for prevention of emesis associated with MEC, including 90 breast cancer patients from the palo 0.25-mg arm and 82 from the onda 32-mg arm who received AC-based chemotherapy (Table 1).5 Effectiveness estimates for palo 0.25 mg were augmented by data on 117 breast cancer patients on AC-based chemotherapy participating in a multicenter RCT comparing palo with dolasetron (Table 1).4 We assumed that dex adds the same relative benefit to either first- or second-generation 5-HT3 antagonists and obtained the expected additional benefit of dex in preventing acute emesis based on the results of an RCT comparing a single-dose of granisetron in combination with dex vs granisetron given alone to patients undergoing MEC (Table 2).11 Since in the aforementioned study dex was only given on day 1 of chemotherapy, the estimated additional benefit of adding dex to a 5-HT3 inhibitor on the delayed period was obtained from another RCT; this study, conducted by the Italian Group for Antiemetic Research, compared dex alone, dex plus onda, or placebo on days 2−5 of MEC.12

Table 1. Emesis Control During the Initial 5-Day Period Following Administration of AC-Based Chemotherapy to Patients with Breast Cancer

From Eisenberg et al4 and Gralla et al5

EMESIS CONTROLPALO (0.25 MG)[4] and [5] (n = 207), % PATIENTS (95% CI)ONDA (32 MG)5 (n = 82), % PATIENTS (95% CI)P
Acute (day 1)0.70 (0.63−0.76)0.61 (0.50−0.71)0.14
Delayed (days 2−5)0.65 (0.58−0.71)0.50 (0.39−0.61)0.02
Overall (days 1−5)0.55 (0.48−0.62)0.40 (0.30−0.52)0.02

AC = anthracycline and cyclophosphamide; palo = palonosetron; onda = ondansetron; CI = confidence interval

Table 2. Base-Case Probabilities, Utility Weights, and Data Sourcese

MODEL PARAMETERSBASE-CASE VALUES (RANGES)DATA SOURCES
Probability of acute emesis control on cycle 1 of AC:
 Onda-based two-drug strategyc0.84 (0.74−0.93)Gralla et al,a The Italian Group[5] and [11]
 Palo-based two-drug strategyc0.87 (0.81−0.94)Eisenberg et al,a Gralla et al,a The Italian Group[4], [5] and [11]
 Onda-based three-drug strategyd0.88 (0.85−0.91)Warr et al7
 Palo-based three-drug strategyd0.96 (0.89−0.99)Grote et al, Grunberg et al[40] and [41]
Probability of delayed emesis control following control of acute emesis on cycle 1 of ACc:
 Onda-based two-drug strategyd0.75 (0.62–0.85)The Italian Group12
 Palo-based two-drug strategyc0.85 (0.78–0.91)Eisenberg et al,a Gralla et al,a The Italian Group[4], [5] and [12]
 Onda-based three-drug strategyd0.86 (0.82–0.90)Warr et al7
 Palo-based three-drug strategyc0.96 (0.91–0.97)Eisenberg et al,a Gralla et al,a Warr et al[4], [5] and [7]
Probability of delayed emesis control following acute emesis on cycle 1 of ACc:
 Onda-based two-drug strategyc0.46 (0.31–0.62)Gralla et al,a The Italian Group[5] and [12]
 Palo-based two-drug strategyc0.44 (0.27–0.59)Eisenberg et al,a Gralla et al,a The Italian Group[4], [5] and [12]
 Onda-based three-drug strategyd0.44 (0.29–0.57)Warr et al7
 Palo-based three-drug strategyc0.51 (0.41–0.67)Eisenberg et al,a Gralla et al,a Warr et al[4], [5] and [7]
Relative probability of emesis control in subsequent cycles of ACc:
 Two-drug therapy0.987 (0.970–1.0)Herrstedt et al14e
 Three-drug therapy1.013 (1.0–1.030)Herrstedt et al14e
Probability of hospitalization (among patients who develop emesis) per cycle of ACd:
 Onda-based regimens0.0035 (0.0001−0.019)Data from Medstat MarketScan16
 Palo-based regimens0.0017 (0.00004−0.0089)Data from Medstat MarketScan, Haislip et al[16] and [19]b
Probability of office visit use (among patients who develop emesis) per cycle of ACd:
 Onda-based regimens0.10 (0.07−0.14)Data from Medstat MarketScan16
 Palo-based regimens0.05 (0.03−0.07)Data from Medstat MarketScan, Haislip et al[16] and [19]b
Probability of rescue medicine utilization use (among patients who develop emesis) per cycle of ACd:
 Onda-based regimens0.61 (0.46−0.75)Gralla et al5a
 Palo-based regimens0.56 (0.45−0.66)Eisenberg et al, Gralla et al[4] and [5]a
Utility weights for emesis per cycle of ACf:
 Acute and delayed emesis0.15 (0.10−0.20)Sun et al20
 Acute emesis and no delayed emesis0.76 (0.70−0.83)Sun et al20
 No acute emesis and delayed emesis0.20 (0.14−0.26)Sun et al20
 No acute and no delayed emesis0.92 (0.86−0.99)Sun et al20

AC = anthracycline and cyclophosphamide; onda = ondansetron; palo = palonosetron.

Included in the analysis was the subset of women with breast cancer receiving AC-based chemotherapy.We obtained an estimate of emesis-related hospitalization and office visit utilization based on data from Medstat MarketScan, HPM subset (Medstat Group, Inc., Ann Arbor, MI) on 707 breast cancer patients who received the first cycle of AC-based chemotherapy from 1996 to 2002 and either were admitted to the hospital or had an office visit for treatment of vomiting or dehydration. Since palo was only introduced into the U.S. market in 2003, we assumed that all these breast cancer patients received onda-based antiemetic prophylaxis. As a result, we estimated the differential rate of health-care resource utilization based on Haislip et al's19 reported differential incidence of extreme events associated with chemotherapy-induced nausea and vomiting experienced by community-based breast cancer patients who received either onda or palo for emesis prophylaxis following the first cycle of chemotherapy.Of note is that there are two different methods for applying the benefit of adding dex and/or aprepitant to a 5-HT3 antagonist: (1) rate of emesis with 5-HT3* relative risk of emesis by adding dex and/or aprepitant and (2) rate of emesis control with 5-HT3 * relative risk of emesis control by adding dex and/or aprepitant. These produce substantially different results, with the former method skewing the results toward the least effective 5-HT3 and the latter skewing it toward the most effective one. As a result, we estimated the probability of emesis by averaging the results obtained using the two different methods. Of note is that the ranges for these effectiveness estimates were obtained by applying the two different methods to the lower and upper bounds of the 95% confidence intervals derived from the clinical trials comparing the 5-HT3 antagonists when used alone.Ranges were obtained by constructing 95% confidence intervals for observed proportions using the normal approximation to the binomial distribution.Ranges are based on the minimum and maximum values observed in Herrstedt et al's14 clinical trial of multicycle chemotherapy.Ranges are based on the estimate's actual 95% confidence intervals obtained from Sun et al's20 data.

 

 


Three-drug prophylactic regimens

We estimated the rate of acute emesis for the three-drug regimens based on data from published studies in which either onda or palo was given in combination with dex and aprepitant on day 1 of MEC (Table 2).[5], [7] and [13] Because aprepitant was either used in combination with dexamethasone or not used on days 2−3 in the trials of palo-based three-drug therapy, we estimated the benefit of adding aprepitant alone to palo on days 2−3 by assuming that the added benefit in the delayed period would be the same as the benefit added to onda. Specifically, we obtained information on the relative risk of delayed emesis control when aprepitant is added on days 2−3 from a large clinical trial of aprepitant combined with onda and dex in breast cancer patients receiving either A or AC chemotherapy (Table 2).7

Effectiveness of antiemetics over multiple cycles of chemotherapy

The estimates of changes in the probability of emesis control over multiple cycles of chemotherapy were obtained from a RCT conducted by Herrstedt et al14 of ondansetron-based two- and three-drug regimens for prevention of chemotherapy-induced nausea and vomiting among breast cancer patients undergoing multiple cycles of AC-based chemotherapy. We assumed that changes in emesis control over four cycles of AC for the palo-based two- and three-drug regimens were similar to the observed changes for the onda-based two- and three-drug strategies, respectively.14

Resource Utilization and Cost Data

The cost of antiemetic prophylaxis was based on the 2008 Medicare Part B reimbursement rates for pharmaceuticals, which reflects the price of ondansetron following its recent patent expiration (Table 3).15 The costs of prophylaxis failures were estimated as follows. In the majority of prophylaxis failures, the only cost is the cost of rescue medication. In such cases, we obtained costs by multiplying the individual doses used for rescue treatment of breast cancer patients on AC participating in the clinical trials comparing palo 0.25 mg with single doses of onda or dolasetron by their unit costs based on the 2008 Medicare Part B reimbursement rates.[5] and [15] For the few patients who are seen in the office for uncontrolled emesis, we obtained estimates of the risk of such emesis-related office visits based on the MarketScan Health Productivity Management (HPM) database from Thomson Reuters on 707 breast cancer patients who received their first cycle of AC-based chemotherapy between 1997 and 2002 (Table 2) and its costs from the 2008 Medicare Physician Fee Schedule Reimbursement for a level III office visit (CPT 99213).[16] and [17]

Table 3. Emesis-Related Base-Case Costs per Cycle of AC-Based Chemotherapy Among Breast Cancer Patients

COST COMPONENT2008 U.S.$ (RANGES)DATA SOURCE
Hospitalization$5,237.00 ($3,921−$6,112)aHCUP charge data18
Consumer Price Index42
Medicare cost-to-charge ratio43
Level III office visit (CPT 99213)$60.30 ($19.96–$122.46)d2008 Medicare Physician Fee Schedule Reimbursement17
Prophylactic antiemetics2008 Medicare Part B reimbursement rates for pharmaceuticals15
 Onda-based two-drug regimen$49.74
 Palo-based two-drug regimen$207.20
 Onda-based three-drug regimen$324.51
 Palo-based three-drug regimen$482.46
Rescue medicinesb$35.25 ($21.66–$48.80)cEisenberg et al,4 Gralla et al,5 2008 Medicare Part B reimbursement rates for pharmaceuticals15

AC = anthracycline and cyclophosphamide; onda = ondansetron; palo = palonosetron; HCUP = Healthcare Cost and Utilization Project

Charges were inflated to 2008 U.S. dollars using the Consumer Price Index (CPI) for medical care and adjusted to costs using Medicare cost-to-charge ratio. The ranges were based on estimates of the 95% confidence interval.In the randomized clinical trial directly comparing ondansetron and palonosetron, propulsives accounted for 71% of the rescue medicines used, 5-hydroxytryptamine antagonists for 20%, glucocorticoids for 7%, and aminoalkyl ethers for 2%.5Costs for rescue medication were obtained by multiplying all drug unit costs by the individual doses used for rescue treatment of breast cancer patients on AC participating in the clinical trials comparing palo 0.25 mg with single doses of onda or dolasetron.[5] and [15] The ranges were based on estimates of the 95% confidence interval.Ranges were based on the Medicare physician fee schedule for levels I and VI office visits.

Finally, although hospitalization for emesis is extremely rare in this population, when it occurs, it is quite expensive. For completeness, we obtained estimates of the risk of emesis-related hospitalization from the same population of breast cancer patients from whom we obtained the estimate for the risk of emesis-related office visit, whereas hospital costs were obtained from Healthcare Cost and Utilization Project (HCUP) data on 2,342 breast cancer patients who were hospitalized with a primary or admitting diagnosis of vomiting or dehydration from 1997 to 2003 ([Table 2] and [Table 3]).[16] and [18]

Of note is that since palo was only introduced into the U.S. market in 2003, we anticipated the observed risk of emesis-related office visit and hospital admission obtained from MarketScan data during the period 1997−2002 reflected the risk associated with prophylaxis with onda. As a result, given that, when compared with onda, palo has also shown superiority in reducing the severity of emetic episodes when they occur, we estimated the differential rate of health-care resource utilization for palo and onda based on Haislip et al's reported differential incidence of extreme events associated with chemotherapy-induced nausea and vomiting (CINV) experienced by community-based breast cancer patients who received either palo or onda for emesis prophylaxis following the first cycle of chemotherapy (Table 2).[5] and [19]

Utility Data

We obtained the utility weights for acute and delayed emesis from a published study of preferences elicited from ovarian cancer patients undergoing chemotherapy using a modified visual analog scale (VAS) (Table 2).20 We equally applied these emesis-related utility weights to the initial 5-day period of chemotherapy (the standard duration of follow-up in clinical trials of prophylactic antiemetics) in all six prophylactic strategies of the decision tree. Furthermore, because the risk of CINV after 5 days of chemotherapy is usually so negligible as to be unmeasured in clinical trials of antiemetics, we assumed the utility weights for the remaining 16 days of each of the chemotherapy cycles to be the same as the weight associated with complete emesis control (ie, 0.92). We subsequently converted the resulting estimates of quality-adjusted life days into quality-adjusted life years (QALY).

Analysis

We used a stepwise method to calculate the incremental cost–effectiveness ratios of the different prophylactic therapy strategies, with the generic onda-based two-drug therapy (ie, the lowest cost strategy) as the base comparator (also known as the “anchor”).21 We adopted the benchmark range of U.S. $50,000−$100,000 per QALY, which has been commonly cited for oncology-related interventions as the threshold for acceptable cost–effectiveness, and examined the robustness of the results by performing one-way sensitivity analyses of plausible ranges for the model's key parameters based on the data sources used as well as probabilistic sensitivity analysis using Monte Carlo simulation.[21] and [22]

Results

The overall rate of emesis control (on days 1−5) among breast cancer patients following a cycle of AC-based chemotherapy was estimated to be 63% (range 46%−79%) for the onda-based two-drug therapy, 74% (range 66%−85%) for the palo-based two-drug therapy, 76% (range 75%−82%) for the onda-based three-drug therapy, and 92% (range 81%−96%) for the palo-based three-drug therapy. Based on these estimates, relative to the onda-based two-drug therapy, the incremental cost–effectiveness ratios (ICERs) for the palo-based regimens were $115,490/QALY for the two-drug strategy, $199,375/QALY for the two-drug regimen plus aprepitant after emesis, and $200,526/QALY for the three-drug strategy (Table 4). The onda-based two-drug combination plus aprepitant after the onset of emesis was eliminated through extended dominance as it has a greater ICER than the next more effective therapy, the palo-based two-drug treatment strategy (Table 4). The onda-based three-drug strategy was dominated by the palo-based two-drug combination plus aprepitant after the onset of emesis as the former strategy is both less effective and more expensive than the latter (Table 4).

 

 

Table 4. Stepwise Comparison of Costs (2008 U.S. dollars) per QALY of the Different Prophylactic Therapy Strategies over Four Cycles of AC-Based Chemotherapy, with the Onda-Based Two-Drug Therapy as the Base Comparator

STRATEGYTOTAL COST (U.S.$)INCREMENTAL COST (U.S.$)EFFECTIVENESS (QALY)INCREMENTAL EFFECTIVENESS (QALY)INCREMENTAL COST–EFFECTIVENESS (U.S.$/QALY)
Onda-based two-drug therapy$2690.1989
Onda-based two-drug therapy with aprepitant after emesis$635$3660.20100.0021$174, 286 Eliminated through extended dominancea
Palo-based two-drug therapy$858$5890.20400.0051$115,490c
Palo-based two-drug therapy plus aprepitant after emesis$1,177$3190.20560.0016199,375
Onda-based three-drug therapy$1,336$1590.205(0.0006)Dominatedb
Palo-based three-drug therapy$1,939$6030.20940.0044$200,526d

QALY = quality-adjusted life year; AC = anthracycline and cyclophosphamide; ICER = incremental cost–effectiveness ratio; onda = ondansetron; palo = palonosetron

Extended dominance occurs when one of the treatment alternatives has a greater ICER than the next more effective alternative.One intervention is said to be dominated by another when it is both less effective and more expensive than the previous less costly alternative.Because the onda-based two-drug combination plus aprepitant after the onset of emesis was eliminated through extended dominance, the palo-based two-drug therapy was compared with the onda-based two-drug therapy.Because the onda-based three-drug combination was dominated by the palo-based two-drug combination plus aprepitant after the onset of emesis, the palo-based three-drug therapy was compared with the latter regimen.

In sensitivity analyses using the commonly accepted cost–effectiveness benchmark range of $50,000−$100,000/QALY, the results were sensitive to changes in the overall emesis control rates for the onda-based two-drug strategy. If the probability of overall emesis control for the onda-based two-drug strategy was as low as its estimated lower bound (46%), the ICER for the palo-based two-drug treatment alternative would drop to $53,892/QALY. The results were also sensitive to changes in the effectiveness for the palo-based two-drug regimen: When its overall control rate was as high as its estimated upper bound (86%), its ICER would be $71,472. In contrast, the results were not sensitive to variations in the probability of overall emesis control for the three-drug strategies, nor were they sensitive to changes in the relative probability of emesis control in subsequent cycles of AC for either the two- or three-drug strategies.

If the probability of emesis-related hospitalization was as high as the upper limit of its 95% confidence interval (CI), the ICER for the palo-based two-drug regimen would be $97,301/QALY. However, changes in the cost of an emesis-related admission (95% CI $3,921−$6,112) did not significantly alter the results, nor did variations in office visit and rescue medicine utilization and their associated costs. The results were also not sensitive to variations in the values for the utility weights throughout their 95% CIs. We performed a threshold analysis to explore the price per dose of palo that would result in an acceptable cost–effectiveness ratio under the $100,000/QALY benchmark and found that the ICER for the palo-based two-drug treatment alternative would only fall to a $100,000/QALY threshold when the cost of palo is decreased by 11%.

Figure 2 shows the cost–effectiveness acceptability curves for each strategy, with the onda-based two-drug therapy as the base comparator. These curves show the proportion of the 100,000 simulations in which the comparing antiemetic regimen was considered more cost-effective than the base comparator at different thresholds. Using the benchmark of U.S. $100,000/QALY, the palo-based two-drug strategy and the two-drug regimen plus aprepitant following the onset of emesis were shown to be cost-effective in 39% and 26% of the simulations with the onda-based standard therapy as the baseline, respectively, whereas the palo-based and onda-based three-drug strategies and the onda-based two-drug regimen with aprepitant after emesis were cost-effective in fewer than 10% of the simulations. Of note is that the slope of the acceptability curves for the palo-based two-drug strategies are steep when willingness to pay exceeds $50,000/QALY, indicating that the greater the threshold, the greater the increase in the level of confidence that these strategies could be cost-effective. For example, the probability that the palo-based two-drug strategy is more cost-effective than the onda-based two-drug strategy rises to 51% at a threshold value of $125,000/QALY and exceeds 60% at $150,000/QALY.



Figure 2. 

Acceptability Curves in Terms of Likelihood of a Strategy Being Cost-Effective in Monte Carlo Simulations Relative to Willingness to Pay (2008 U.S.$) per Quality-Adjusted Life Year (QALY) with the Onda-Based Two-Drug Therapy as the Base Comparator

 

 

Figure 3 presents the scatterplot of the results of the probabilistic sensitivity analysis for the palo-based two-drug strategy. Nearly 96% of the simulations fell within the first quadrant of the chart (ie, on the upper right quadrant), which represents the scenario where the palo-based two-drug therapy is more costly but also more effective than the onda-based standard therapy. However, only 39% of the simulations fell below the $100,000/QALY dashed threshold line, which represents the scenario where the palo-based two-drug strategy is more cost-effective than the onda-based standard therapy at the $100,000/QALY benchmark.



Figure 3. 

Scatterplot of the Incremental Cost–Effectiveness Ratios Comparing the Palo-Based Two-Drug Strategy with the Onda-Based Two-Drug Therapy. The dashed line indicates the $100,000/QALY threshold, whereas the ellipse represents the 95% confidence ellipse, which includes 95% of the observations


Discussion

Our estimates of emesis-related costs and outcomes following four cycles of AC-based chemotherapy in women with breast cancer indicate that at current antiemetic prices and utilities placed on emesis, the additional costs of palo and aprepitant are not warranted at the $100,000/QALY threshold. In probabilistic sensitivity analysis, the palo-based two-drug strategy and the two-drug regimen plus aprepitant following the onset of emesis were shown to be cost-effective at the $100,000/QALY threshold in only 39% and 26% of the simulations, respectively. The model was sensitive to changes in the values of antiemetic effectiveness for the two-drug regimens and the risk of emesis-related hospitalization.

In threshold analysis, the two-drug palo-based regimen was cost-effective at the $100,000/QALY benchmark when the cost of palo is decreased by 11%. Because the use of the $100,000/QALY threshold is uncommon in clinical practice, the cost-effectiveness of the palo-based two-drug strategy (estimated at $115,490/QALY in our study) compares favorably with other commonly used supportive care measures for women with breast cancer. Such measures include primary prophylaxis with granulocyte colony-stimulating factor in women undergoing chemotherapy with moderate to high myelosuppressive risk (ICER of $116,000/QALY, or $125,948/QALY in 2008 U.S. dollars) and the use of bisphosphonates for the prevention of skeletal complications in breast cancer patients with lytic bone metastases (ICER ranging from $108,200/QALY with chemotherapy as systemic therapy to $305,300 in conjunction with hormonal systemic therapy, or $166,381/QALY to $469,466/QALY in 2008 U.S. dollars, respectively).[23] and [24] Both interventions are considered recommended standards of supportive care for patients with breast cancer and are widely used in breast oncology practices.[25] and [26]

Decision-analytic models, such as the Markov model presented in our study, aim to reflect the reality of clinical practice in a simplified way. Therefore, modelers often need to make decisions regarding the study time frame and model parameters based on the best use of available data. In our study, we obtained estimates for the probability of chemotherapy-induced emesis from studies in which the standard duration of follow-up is 5 days. By so doing, we may have underestimated the cost-effectiveness for the palo-based and aprepitant-based regimens. Although the risk of CINV after 5 days of chemotherapy is usually negligible, anticipation of vomiting may affect a patient's quality of life throughout the cycle of chemotherapy.

In addition, our estimates of costs, which were mostly obtained from Medicare, may differ from those of other third-party payers. However, Medicare is among the largest payers for breast cancer care as 42% of the women diagnosed with cancer in the United States are older than 64 years, and many private organizations set their own reimbursement rates based on the Medicare schedule. Therefore, we believe that Medicare reimbursement data provide a suitable estimate for emesis-related medical costs for all breast cancer patients in the United States.[27] and [28]

The present results should solely be interpreted in light of the cost–effectiveness benchmark of $50,000−$100,000/QALY, which has been frequently used in the context of the U.S. health-care system.[22] and [29] Such a benchmark, however, is a historic, precedent-based threshold set by the cost of caring for patients on dialysis, which was estimated at $50,000/QALY in 1982 ($74,000−$95,000 in 1997 U.S. dollars).[30] and [31] Given the arbitrariness of such a threshold, it has been suggested that the current willingness to pay for medical interventions in the United States probably exceeds $100,000/QALY, with values as high as $300,000/QALY being cited in some oncology publications.[22], [29], [31], [32], [33] and [34] In support of that argument is the public and policy makers' strong negative reaction to the National Institutes of Health Consensus Panel not recommending mammography screening for women aged 40−49 years, a procedure reported to provide an ICER of $105,000 per life-year gained.[35] and [36] As a result, if willingness to pay goes beyond $100,000/QALY, the alternative of adding aprepitant to palo plus dex may also be deemed attractive as the slope of its acceptability curve becomes substantially steep when the willingness to pay for a QALY exceeds $125,000 (Figure 2), suggesting that its marginal gain may exceed its marginal costs at higher thresholds.

In addition, it is worth noting that the present analysis has been conducted from the perspective of a third-party payer within the context of the U.S. health-care system. The large difference in the acquisition cost of palo-based and onda-based therapy observed in the United States is mostly driven by the differential stage of product life cycles for palo and onda. Although at the time of this study palo was still under patent protection, generic onda had entered the U.S. market prior to our study. The large price discrepancy between brand and generic drugs explains the difference in drug costs in this U.S.-based analysis. As such, our results may not reflect the situation in countries with a widely different cost structure, in which the acquisition cost of palo may be substantially lower. When that is the case, the cost–effectiveness profile of the palo-based prophylactic therapy may be deemed substantially more favorable than the profile presented here. Similarly, we anticipate finding a more attractive cost–effectiveness profile for the palo-based therapies as palo reaches the end of its product life cycle in the U.S. market.37 Also of note is that the cost–effectiveness of the palo-based therapy may greatly differ when different perspectives (other than the third-party payer's perspective) are adopted.

Our study, however, has several limitations. First, the utility scores used in our model were derived with a VAS instrument, which does not incorporate patients' preferences under uncertainty. Nevertheless, the VAS approach has been shown to provide utility scores for nausea and vomiting with more variability than scores derived using other methods such as the Standard Gamble (personal communication, Grunberg SM et al, CALGB study 309801). Notwithstanding that, it remains unclear which method gives utility scores for transient health states, such as CINV, with the greatest validity.

Also of note is that due to a lack of information on emesis-related utilities among breast cancer patients in the literature, we used utilities elicited from patients with ovarian cancer. To the best of our knowledge, the utilities in Sun et al20 were the only ones available in the literature that were elicited from a homogeneous population of cancer patients (ie, solely patients with ovarian cancer) and were based on a wide range of health states combining the presence and absence of emesis during either the acute or the delayed period. In addition, the participants in the Sun et al study were treated with carboplatin, which, like the regimen used in our model, is classified as moderately emetogenic in established antiemetic guidelines.[8], [9] and [38] It is also important to emphasize that the population in that study, like our study's population, was composed exclusively of women, who are known to be at increased risk for developing CINV.39

Second, in the absence of clinical trial data, we assumed conservatively that dex and aprepitant add the same relative benefit to both onda and palo. This assumption results in an imperfect estimate of cost–effectiveness. As such, we may have overestimated or underestimated the cost–effectiveness of palo as dex and aprepitant may potentially add less value to the intrinsically more active 5-HT3 antagonist or uniquely complementary mechanisms of action could contribute to even greater activity with the palo-based therapy. However, our study's estimate of the relative effectiveness of the palo-based two-drug prophylactic therapy versus the onda-based two-drug therapy for preventing delayed emesis is consistent with that reported in a recently published clinical trial comparing palo and granisetron when both drugs are combined with dex following chemotherapy with either AC or cisplatin (1.18 vs 1.17, respectively).6

Third, our study did not include the outcomes associated with the adverse effects of antiemetics, and by so doing, we may have underestimated the costs associated with antiemetic prophylaxis. However, the incidence and duration of treatment-related adverse events occurring in the two RCTs comparing palo with either onda or dolasetron were mild and similar across treatment cohorts.[4] and [5]

Fourth, we assumed that changes in emesis control in subsequent cycles of AC for the palo-based regimens were the same as for the onda-based therapy. By so doing, we may have underestimated the cost–effectiveness of palo as the superiority of the more active 5-HT3 antagonist could be maintained in the subsequent cycles of chemotherapy (or even increased, as seen in the aprepitant-based arm of Herrstedt et al's14 study). As a result, if future prospective trials of palo-based antiemetic prophylaxis confirm its superiority in maintaining antiemetic efficacy over multiple cycles of AC, the cost–effectiveness profiles for the palo-based strategies may be more favorable than the profiles presented herein.

Last, the incremental gains in QALY observed in cost–utility analysis of interventions associated with transitory and non-life-threatening health states, such as the antiemetic regimens analyzed in our study, tend to render small denominators to be used in the incremental cost–effectiveness ratios. The issue of small denominators has led some researchers to question whether the current methodology of cost–effectiveness analysis is appropriate to determine the cost–effectiveness of treatments for terminal or supportive care.32 However, despite this shortcoming, these types of analysis benefit from having a wider scope as they allow comparisons over different types of health interventions across various diseases. In addition, by incorporating patients' utility levels over different health states (instead of merely looking into cost per additional patient controlled), cost–utility analysis makes explicit the impact of the target population's preferences for the different outcomes. Of importance is that both the Panel on Cost–Effectiveness in Health and Medicine and the Institute of Medicine (IOM) Committee on Regulatory Cost–Effectiveness Analysis recommend the use of QALY as the preferred outcome measure for economic evaluation of health-care interventions.

Conclusion

Although our base-case analysis suggests that, from a third-party payer perspective within the context of the U.S. health-care system, the cost–utility of the palo-based two-drug prophylactic therapy for breast cancer patients receiving four cycles of AC-based chemotherapy exceeds the $50,000–$100,000/QALY threshold, it is comparable to other commonly used supportive care interventions for women with breast cancer. In sensitivity analyses, such a strategy was associated with a 39% chance of being cost-effective at the $100,000/QALY threshold, and the model was sensitive to changes in the values of antiemetic effectiveness and of the probability of emesis-related hospitalization. In threshold analysis, the combination of palo and dex was shown to become cost-effective (at the $100,000/QALY benchmark) when the cost of palo is decreased by 11%. As a result, future research incorporating the price structure of all antiemetics following the recent expiration of onda's patent is needed.

 

 

Acknowledgments

The authors thank Dr. Sharon H. Giordano for providing valuable clinical input and Dr. Catherine D. Cooksley for providing suggestions for improving this article.

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19 S. Haislip, J. Gilmore, W.H. Lenz, T. Gondesen and B. Feinberg, Theory in practice: improving patient outcomes and practice efficiency with a simple change in 5-HT3 receptor antagonist for preventing chemotherapy-induced nausea and vomiting (CINV) In: Third Annual Meeting of the Hematology/Oncology Pharmacy Association; Abstract #PR6. June 14–16, 2007; Denver, Colorado http://www.hoparx.org/documents/2007programbook.pdf Accessed November 2, 2010.

20 C.C. Sun, D.C. Bodurka and C.B. Weaver et al., Rankings and symptom assessments of side effects from chemotherapy: insights from experienced patients with ovarian cancer, Support Care Cancer 13 (2005), pp. 219–227 [15538640]. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (53)

21 M.F. Drummond, M.J. Sculpher, G.W. Torrance, B.J. O'Brien and G.L. Stoddart, Methods for the Economic Evaluation of Health Care Programmes (3rd ed.), Oxford University Press, New York (2005).

22 J. Hayman, J. Weeks and P. Mauch, Economic analyses in health care: an introduction to the methodology with an emphasis on radiation therapy, Int J Radiat Oncol Biol Phys 35 (1996), pp. 827–841 [8690653]. Article |

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23 B.E. Hillner, J.C. Weeks, C.E. Desch and T.J. Smith, Pamidronate in prevention of bone complications in metastatic breast cancer: a cost–effectiveness analysis, J Clin Oncol 18 (2000), pp. 72–79 [10623695]. View Record in Scopus | Cited By in Scopus (90)

24 S.D. Ramsey, Z. Liu and R. Boer et al., Cost–effectiveness of primary versus secondary prophylaxis with pegfilgrastim in women with early-stage breast cancer receiving chemotherapy, Value Health 11 (2008), pp. 172–179 [18673353].

25 B.E. Hillner, J.N. Ingle, R.T. Chlebowski et al. and American Society of Clinical Oncology, American Society of Clinical Oncology 2003 update on the role of bisphosphonates and bone health issues in women with breast cancer, J Clin Oncol 21 (2003), pp. 4042–4057 [12963702]. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (533)

26 T.J. Smith, J. Khatcheressian and G.H. Lyman et al., 2006 update of recommendations for the use of white blood cell growth factors: an evidence-based clinical practice guideline, J Clin Oncol 24 (2006), pp. 3187–3205 [16682719]. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (485)

27 National Cancer Institute, Surveillance Epidemiology and End Results: SEER Stat Fact Sheets: Breast http://seer.cancer.gov/statfacts/html/breast.html Accessed May 16, 2010.

28 J.W. Tumeh, S.G. Moore, R. Shapiro and C.R. Flowers, Practical approach for using Medicare data to estimate costs for cost–effectiveness analysis, Expert Rev Pharmacoecon Outcomes Res 5 (2005), pp. 153–162 [19807571]. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (5)

29 P.A. Ubel, R.A. Hirth, M.E. Chernew and A.M. Fendrick, What is the price of life and why doesn't it increase at the rate of inflation?, Arch Intern Med 163 (2003), pp. 1637–1641 [12885677]. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (225)

30 J.C. Hornberger, D.A. Redelmeier and J. Petersen, Variability among methods to assess patients' well-being and consequent effect on a cost–effectiveness analysis, J Clin Epidemiol 45 (1992), pp. 505–512 [1588356]. Article |

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31 R.A. Hirth, M.E. Chernew, E. Miller, A.M. Fendrick and W.G. Weissert, Willingness to pay for a quality-adjusted life year: in search of a standard, Med Decis Making 20 (2000), pp. 332–342 [10929856]. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (291)

32 Y.C. Shih and M.T. Halpern, Economic evaluations of medical care interventions for cancer patients: how, why, and what does it mean?, CA Cancer J Clin 58 (2008), pp. 231–244 [18596196]. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (18)

33 E. Nadler, B. Eckert and P.J. Neumann, Do oncologists believe new cancer drugs offer good value?, Oncologist 11 (2006), pp. 90–95 [16476830]. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (50)

34 R.S. Braithwaite, D.O. Meltzer, J.T. King Jr, D. Leslie and M.S. Roberts, What does the value of modern medicine say about the $50,000 per quality-adjusted life-year decision rule?, Med Care 46 (2008), pp. 349–356 [18362813]. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (64)

35 National Institutes of Health Consensus Development Panel, 1997 Consensus Statement: Breast Cancer Screening for Women Ages 40–49 http://consensus.nih.gov/1997/1997BreastCancerScreening103html.htm Accessed October 13, 2007.

36 P. Salzmann, K. Kerlikowske and K. Phillips, Cost–effectiveness of extending screening mammography guidelines to include women 40 to 49 years of age, Ann Intern Med 127 (1997), pp. 955–965 [9412300]. View Record in Scopus | Cited By in Scopus (169)

37 Y.C. Shih, S. Han and S.B. Cantor, Impact of generic drug entry on cost–effectiveness analysis, Med Decis Making 25 (2005), pp. 71–80 [15673583]. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (6)

38 F. Roila, P.J. Hesketh, J. Herrstedt and Antiemetic Subcommittee of the Multinational Association of Supportive Care in Cancer, Prevention of chemotherapy- and radiotherapy-induced emesis: results of the 2004 Perugia International Antiemetic Consensus Conference, Ann Oncol 17 (2006), pp. 20–28 [16314401]. View Record in Scopus | Cited By in Scopus (90)

39 S.M. Grunberg and A. Ireland, Epidemiology of chemotherapy-induced nausea and vomiting, Adv Studies Nurs 3 (1) (2005), pp. 9–15 http://www.jhasin.com/files/articlefiles/pdf/XASIN_3_1_p9_15.pdf Accessed September 16, 2010.

40 T. Grote, J. Hajdenberg, A. Cartmell, S. Ferguson, A. Ginkel and V. Charu, Combination therapy for chemotherapy-induced nausea and vomiting in patients receiving moderately emetogenic chemotherapy: palonosetron, dexamethasone, and aprepitant, J Support Oncol 4 (2006), pp. 403–408 [17004515]. View Record in Scopus | Cited By in Scopus (38)

41 S.M. Grunberg, M. Dugan, H.B. Muss, M. Wood, S. Burdette-Radoux and T. Weisberg, Efficacy of a 1-day 3-drug antiemetic regimen for prevention of acute and delayed nausea and vomiting induced by moderately emetogenic chemotherapy, J Clin Oncol 25 (18S) (2007), p. 9111.

42 U. S. Department of Labor. Bureau of Labor Statistics. Consumer Price Index http://www.bls.gov/cpi/home.htm Accessed May 16, 2010.

43 Department of Health and Human Services. Centers for Medicare & Medicaid Services, Medicare Program; Proposed Changes to the Hospital Inpatient Prospective Payment Systems and Fiscal Year 2008 Rates CMS-1533-P, pp 1070–1073 http://www.cms.hhs.gov/AcuteInpatientPPS/downloads/CMS-1533-P.pdf Accessed May 16, 2010.

 

 

This study was supported in part by a grant from MGI Pharma, Inc. MGI Pharma, Inc., had no role in the study design, data analysis, interpretation of results, content of the final article, or the decision to submit it for publication.

Conflicts of interest: Dr. Sun discloses that her husband was an employee of MGI Pharma, Inc., at the time this article was being written. Dr. Gralla discloses that he is a consultant for MGI Pharma, Inc., GlaxoSmithKline, Sanofi-aventis, and Merck; he also receives honoraria from MGI Pharma, Inc., and Merck and research support from Sanofi-aventis. Dr. Grunberg discloses that he is a consultant for MGI Pharma, Inc.

Correspondence to: Elenir B. C. Avritscher, MD, PhD, MBA/MHA, Section of Health Services Research, Department of Biostatistics and Applied Mathematics, The University of Texas M. D. Anderson Cancer Center, 1400 Pressler Street, Unit 1411, Houston, TX 77230; telephone: (713) 563-8920; fax: (713) 563-4243


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Retrospective analysis of communication with patients undergoing radiological breast biopsy

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Retrospective analysis of communication with patients undergoing radiological breast biopsy

Following a diagnostic or screening mammogram, patients with breast lesions are often referred for a biopsy.1 Time between the procedure and the notification of results is anxiety-provoking as women wait to find out if the lesion is malignant or benign.2

The preferences of women in this scenario regarding the method of communication and the provider who gives results are unknown. Providers try to balance different priorities: getting the information to the woman quickly,[3], [4] and [5] providing as much information as possible,[3], [4], [5], [6] and [7] having the person who talks to the woman be someone they know and trust,8 and giving the information in person rather than on the phone.3 One may not be able to maximize all of the competing variables. If the sole priority is speed, then one would develop a system where the radiologist or nurse calls as soon as the biopsy result comes back; if complete information is most important, having someone from an oncologist's office call about positive results may be best. The problem is that there is a lack of published data on women's preferences, leading different programs to be based on assumptions rather than evidence.

Complicating this problem is that women likely have different preferences when the results are benign versus when they are malignant. To our knowledge, the communication preferences of these two groups of patients have never been compared. With evidence-based data about what women prefer, programs can develop more patient-centered programs to communicate biopsy results.

The goal of this study was to ascertain how women who have had a breast biopsy prefer to receive their test results. We also wanted to determine their satisfaction with the way they did receive their biopsy results at our cancer center and whether satisfaction differed based on patient age, race, or biopsy results. It is hoped that these data will help other programs develop communication policies.

 

Materials and Methods

 

Study Setting and Patient Recruitment

This study was based on a telephone survey of all radiologic breast biopsy patients seen at a large urban academic breast center between June 1, 2008, and July 31, 2008. The study was approved by the University of Pittsburgh Institutional Review Board. Study participants were adult females receiving a minimally invasive radiologic breast biopsy who spoke English and had a working phone. All patients fitting the inclusion criteria were approached before their procedure and given the option to participate in the study.

The center performed over 3,500 breast biopsies in 2009. At the center, two nurses gave results to an average of 30 patients a day. In addition to making calls, the nurses are responsible for admitting and discharging patients and work on rotation in order to call patients they have personally met. They make notes in patients' charts about their demeanor and concerns to help them tailor the phone calls to the patients' personalities. When patients leave after the biopsy, the nurses discuss the results call and ask the patients if they would prefer the nurse to call them or if they would like to call the nurse on their own time. No option is available for an in-person results visit except by the physician ordering the biopsy.

Study participants received biopsy results within 4 business days. Information about positive and negative results is communicated in different ways. While all results are faxed to the referring physician, positive results have a cover sheet indicating the urgency of the information. If results are positive or require further surgical biopsy, the nurses call in a room with a closed door and a “do not disturb” sign, to minimize interruptions. Nurses provide information using a standardized script that describes the result and their implications. Patients with a malignant biopsy are given a phone number to make a breast magnetic resonance imaging appointment. They are also told that they need to make a surgical appointment, but the choice of surgeon is left up to the patient and the referring physician. Patients are given time to ask questions and the direct phone number if they wish to speak to the nurse again.

 

Phone Survey Procedure

Two weeks were allotted between receipt of results and the study interview to allow time for patients to understand their diagnosis and seek follow-up care as necessary. After the 2-week waiting period, a study staff member contacted patients by telephone. Calls were conducted in the order in which patients received their results, and four attempts were made to call each participant, with a message left each time.

 

Study Survey

The phone survey consisted of four sections: an informational section, which collected data about how the patient received the results; a communication skills section, which assessed patient impressions of the person giving results; an improvement section, which assessed patient views about how to improve the communication of results; and finally, a communication priorities section, which assessed the relative importance of four distinct aspects of communication (Table 1). In addition, patients were asked “What did you like best about how you were told your results?” and “What can we do to make the process of giving results better?” The communication skills and improvement sections were scored on a five-point Likert scale, and the communication priorities section was scored on a rank scale from most to least important. (The survey is available on request.) Demographic information as well as the number of previous biopsies the woman had were also collected.

 

 

 

Table 1. Options Presented to Patients in the Communication Priorities Section of the Survey: To Be Ranked in Order of Least to Greatest Priority

 

Receiving the results of the biopsy as soon as possible
Being told by a person who knows the most about what the results mean
Being told the results in person
Being told by your primary care provider

 


 

Statistical Analysis

Survey statistics were analyzed using IBM SPSS Statistics software (SPSS, Inc., Chicago, IL). A one-sample Kolmogorov-Smirnov test was used to test variable normality. As all Likert-scaled survey variables were not normally distributed, a Mann-Whitney U-test was used to compare Likert scores between cancer and benign groups as well as first-time biopsy and repeat biopsy groups. Ordinal regression was used to evaluate the effects of age on Likert-scaled variables.

 

Results

We screened 133 patients, and of these, 131 patients consented to participate in the study. Of these, 64 could not be reached during follow-up and one patient withdrew from the study, for a total of 66 patients completing the study. The overall response rate was 50.4%. Of the patients who completed the telephone interview, 39 had benign biopsies and 27 had cancer. Of the patients who did not complete the survey, 10 had cancer and 55 were benign (P = 0.004). Other demographic data from the survey cohort are illustrated in Table 2. As the vast majority of patients were white, a comparison between different races could not be performed. Age did not have any significant effect on any of study variables.

 

Table 2. Patient Demographic Data

 

RACENUMBER OF PATIENTSMEAN AGE (YEARS)SURVEY RESPONSE RATECANCER RATE
White11751.8 ± 13.90.500.29
African American1250.4 ± 9.60.500.17
Asian1411.001.00
Unreported1Unreported0.001.00

 


 

Communication Interactions

Of all patients in the study, 53 (80.3%) were contacted by a nurse from the breast center. The other patients were contacted by their primary care provider first (n = 5) or a radiologist (n = 3) or did not know who they were contacted by (n = 5). Forty-one patients (62.1%) recalled meeting the provider they spoke with, while 15 patients reported they did not meet the person who contacted them and 10 were not sure. Sixty-three patients (95.5%) were told their results over the phone, two were told in person, and one person did not respond.

 

Communication Skills

Mean Likert scales are reported in Table 3. There were no significant differences in the patients' assessments based on demographic or clinical variables. Overall, patients rated the communication skills of the person who gave their results very positively.

 

Table 3. Assessment of Communication Skills Used by Breast Center

 

SURVEY ITEMMEAN LIKERT SCORESTANDARD DEVIATION% OF PATIENTS REPORTING “AGREE” OR “STRONGLY AGREE”
You were given the diagnosis in a timely fashion4.350.7693.2%
The person who gave you the diagnosis was considerate and tactful4.600.6393.2%
The person who told you the diagnosis was honest4.460.50100%
The person told you the results in a way you could understand4.310.7794.9%
The person who told you the results did not rush4.080.8586.4%
The person who told you the results gave you the opportunity to ask questions4.130.7787.9%
The person who told you the results was sensitive to your emotional reaction4.260.8187.5%
You were satisfied with hearing your results by the method you did (eg, over the phone)3.980.9879.7%
You were satisfied with hearing the results from the person you heard from4.110.9586.4%

 


 

Areas for Improvement

The proportions of patients responding “agree” or “strongly agree” to each item are reported in Figure 1 and are compared between several patient groups in Table 4. Patients were more likely to want additional materials to help them understand their diagnosis. This was significantly more common among patients having a first biopsy and patients who had cancer (P < 0.05). For example, 65.4% of cancer patients wanted more information versus 43.5% of benign patients. Also, 60.5% of patients having a first biopsy wanted additional information versus 37.0% of patients having a repeat biopsy. For all other items, less than 50% of patients answered “agree” or “strongly agree,” and there were no significant trends based on clinical or sociodemographic variables.

Table 4. Comparison of Potential Methods of Improvement of Communication Between Cancer and Benign Patients and Patients Experiencing a First or Repeat Biopsy

 

SURVEY ITEM
MEAN LIKERTa ± SD
P
MEAN LIKERTa ± SD
P
 CANCERBENIGN FIRST BIOPSYNOT FIRST BIOPSY 
You would have preferred additional materials to help you understand the diagnosis3.50 ± 0.992.82 ± 1.140.018
3.34 ± 1.052.74 ± 1.160.036
You would have preferred to talk to someone beforehand to discuss how much you wanted to know about your results2.78 ± 1.092.77 ± 1.160.9772.97 ± 1.082.50 ± 1.130.068
You would have preferred more assistance making follow-up appointments2.19 ± 0.622.58 ± 1.220.3702.57 ± 1.022.21 ± 1.030.088
You would have preferred to choose who gave you the results2.54 ± 1.072.54 ± 1.070.8962.55 ± 1.002.46 ± 1.070.618
You would have preferred to receive the results faster2.85 ± 1.202.85 ± 1.200.4283.00 ± 1.272.86 ± 1.170.638
 Statistically significant difference
a 1, strongly disagree; 2, disagree; 3, neutral; 4, agree; 5, strongly agree
 

 

 


 

Patient Priorities for Receiving Biopsy Results

For patients with benign and those with malignant disease, receiving results quickly was the most important factor, followed by being told by a person who knows the most about what the results mean (Figure 2). Hearing from a primary care provider and hearing in person were of much lower priority. Cancer patients ranked “Being told by a person who knows the most about what the results mean” significantly higher in priority than benign patients (P < 0.05).

 

 

 

Discussion

In our study population of women who had a breast biopsy, the number one priority was receiving the results as soon as possible. We found that women were generally satisfied with learning about their results from a nurse over the phone. However, a majority of patients said they would prefer additional materials to help them understand their diagnosis. This trend was more pronounced in women with cancer or those for whom this was the first biopsy. Previous literature has shown that both doctors and patients agree that potentially bad news should be given in a quiet, uninterrupted, face-to-face conversation3 and that it should be given by a provider they know well.8 However, it has also been suggested that many women want to hear test results as quickly as possible, even if that means they will receive them from a provider they do not know well.[3], [4] and [5] This study is unique in that it forced women to choose which of these aspects of communication were most important as we believe that rapid results and hearing in person are often mutually exclusive. Women in this study clearly preferred to hear quickly more than in person, whether they were given a diagnosis of cancer or not.

Some studies suggest that providing written information along with verbal communication would be beneficial. For example, in a 2004 study by Lobb et al,9 genetic counselors who added a summary letter after communicating breast cancer risks to a patient significantly increased realistic risk assessments in patients, as well as lowering anxiety. Our study found that women would prefer additional materials. Since most communication in this study was over the phone, women may not have had time to fully process all the information given and therefore wanted information supplements as well. In women with cancer or those receiving a first biopsy, information and follow-up instructions are even more complex or overwhelming; and it follows that they are more likely to want informational materials than other patients.

As expected, patients who are diagnosed with cancer want to talk to someone with more knowledge of their specific condition. While patients in this study were not asked specifically about the apparent knowledge base of the nurse who gave results, their overall satisfaction (86.4%) seems to suggest that our system with specific scripts for each diagnosis was adequate.

This study is limited in several ways. First, as a single-center satisfaction study, we are only measuring the experience of patients with a particular system of communication. While these data reflect the general system in place at the breast center, they are also contingent on the specific providers communicating with patients. However, we do believe our results are reproducible at other centers as scripts are often used for sharing patient results. This also means that these data are a reflection of a population that was contacted by telephone. We cannot make any assumptions about how these preferences may differ from those of patients contacted in person. Second, only general data were gathered about the interaction of the nurse with each woman. It is not known what was specifically said to each patient, so we can only report the patient's view of the discussion. However, since responses are scripted at the breast center, we assume that the communication was relatively standardized for all conversations. Third, patients in the study were more likely to have a cancer diagnosis than the total population of biopsy patients. This may represent a greater commitment by cancer patients to aid in the improvement of communication. It also may indicate that women with a benign biopsy were less opinionated about their communication in general and less likely to want to express their opinions in the study.

In conclusion, patients generally prefer to hear breast biopsy results quickly over other factors, including hearing in person or from a more experienced practitioner. Therefore, a program similar to the one at our center meets most patient needs; it minimizes wait time by calling patients as soon as biopsy results are in, utilizes nurses to facilitate the large amount of calls that must be made every day, and ensures patients are contacted by a provider they personally met during their biopsy. A majority of patients desired additional materials to supplement phone communication. We highly recommend providing a variety of materials, including both written and Web-based, to address this need. Further research is necessary to determine the effects of these interventions on patient understanding and long-term emotional outcomes.

 

 

References

1 A.J. Doyle, K.A. Murray, E.W. Nelson and D.G. Bragg, Selective use of image-guided large-core needle biopsy of the breast: accuracy and cost-effectiveness, Am J Roentgenol 165 (1995), pp. 281–284. View Record in Scopus | Cited By in Scopus (59)

2 J.R. Maxwell, M.E. Bugbee and D. Wellisch et al., Imaging-guided core needle biopsy of the breast: study of psychological outcomes, Breast J 1 (2000), pp. 53–61. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (24)

3 A. Girgis, R.W. Sanson-Fisher and M.J. Schofield, Is there consensus between breast cancer patients and providers on guidelines for breaking bad news?, Behav Med 25 (1999), pp. 69–77. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (44)

4 S. Liu, L.W. Bassett and J. Sayre, Women's attitudes about receiving mammographic results directly from radiologists, Radiology 193 (1994), pp. 783–786. View Record in Scopus | Cited By in Scopus (26)

5 S.R. Vallely and J.O. Manton Mills, Should radiologists talk to patients?, Br Med J 300 (1990), pp. 305–306. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (16)

6 J. Graydon, S. Galloway and S. Palmer-Wickham et al., Information needs of women during early treatment for breast cancer, J Adv Nurs Sci 26 (1997), pp. 59–64. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (90)

7 M. Cawley, J. Kostic and C. Cappello, Informational and psychological needs of women choosing conservative surgery/primary radiation for early stage breast cancer, Cancer Nurs 13 (1990), pp. 90–94. View Record in Scopus | Cited By in Scopus (41)

8 G.L. Krahn, A. Hallum and C. Kime, Are there good ways to give “bad news”?, Pediatrics 91 (1993), pp. 578–582. View Record in Scopus | Cited By in Scopus (57)

9 E.A. Lobb, P.N. Butow and A. Barratt et al., Communication and information-giving in high-risk breast cancer consultations: influence on patient outcomes, Br J Cancer 90 (2004), pp. 321–327. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (54)

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Following a diagnostic or screening mammogram, patients with breast lesions are often referred for a biopsy.1 Time between the procedure and the notification of results is anxiety-provoking as women wait to find out if the lesion is malignant or benign.2

The preferences of women in this scenario regarding the method of communication and the provider who gives results are unknown. Providers try to balance different priorities: getting the information to the woman quickly,[3], [4] and [5] providing as much information as possible,[3], [4], [5], [6] and [7] having the person who talks to the woman be someone they know and trust,8 and giving the information in person rather than on the phone.3 One may not be able to maximize all of the competing variables. If the sole priority is speed, then one would develop a system where the radiologist or nurse calls as soon as the biopsy result comes back; if complete information is most important, having someone from an oncologist's office call about positive results may be best. The problem is that there is a lack of published data on women's preferences, leading different programs to be based on assumptions rather than evidence.

Complicating this problem is that women likely have different preferences when the results are benign versus when they are malignant. To our knowledge, the communication preferences of these two groups of patients have never been compared. With evidence-based data about what women prefer, programs can develop more patient-centered programs to communicate biopsy results.

The goal of this study was to ascertain how women who have had a breast biopsy prefer to receive their test results. We also wanted to determine their satisfaction with the way they did receive their biopsy results at our cancer center and whether satisfaction differed based on patient age, race, or biopsy results. It is hoped that these data will help other programs develop communication policies.

 

Materials and Methods

 

Study Setting and Patient Recruitment

This study was based on a telephone survey of all radiologic breast biopsy patients seen at a large urban academic breast center between June 1, 2008, and July 31, 2008. The study was approved by the University of Pittsburgh Institutional Review Board. Study participants were adult females receiving a minimally invasive radiologic breast biopsy who spoke English and had a working phone. All patients fitting the inclusion criteria were approached before their procedure and given the option to participate in the study.

The center performed over 3,500 breast biopsies in 2009. At the center, two nurses gave results to an average of 30 patients a day. In addition to making calls, the nurses are responsible for admitting and discharging patients and work on rotation in order to call patients they have personally met. They make notes in patients' charts about their demeanor and concerns to help them tailor the phone calls to the patients' personalities. When patients leave after the biopsy, the nurses discuss the results call and ask the patients if they would prefer the nurse to call them or if they would like to call the nurse on their own time. No option is available for an in-person results visit except by the physician ordering the biopsy.

Study participants received biopsy results within 4 business days. Information about positive and negative results is communicated in different ways. While all results are faxed to the referring physician, positive results have a cover sheet indicating the urgency of the information. If results are positive or require further surgical biopsy, the nurses call in a room with a closed door and a “do not disturb” sign, to minimize interruptions. Nurses provide information using a standardized script that describes the result and their implications. Patients with a malignant biopsy are given a phone number to make a breast magnetic resonance imaging appointment. They are also told that they need to make a surgical appointment, but the choice of surgeon is left up to the patient and the referring physician. Patients are given time to ask questions and the direct phone number if they wish to speak to the nurse again.

 

Phone Survey Procedure

Two weeks were allotted between receipt of results and the study interview to allow time for patients to understand their diagnosis and seek follow-up care as necessary. After the 2-week waiting period, a study staff member contacted patients by telephone. Calls were conducted in the order in which patients received their results, and four attempts were made to call each participant, with a message left each time.

 

Study Survey

The phone survey consisted of four sections: an informational section, which collected data about how the patient received the results; a communication skills section, which assessed patient impressions of the person giving results; an improvement section, which assessed patient views about how to improve the communication of results; and finally, a communication priorities section, which assessed the relative importance of four distinct aspects of communication (Table 1). In addition, patients were asked “What did you like best about how you were told your results?” and “What can we do to make the process of giving results better?” The communication skills and improvement sections were scored on a five-point Likert scale, and the communication priorities section was scored on a rank scale from most to least important. (The survey is available on request.) Demographic information as well as the number of previous biopsies the woman had were also collected.

 

 

 

Table 1. Options Presented to Patients in the Communication Priorities Section of the Survey: To Be Ranked in Order of Least to Greatest Priority

 

Receiving the results of the biopsy as soon as possible
Being told by a person who knows the most about what the results mean
Being told the results in person
Being told by your primary care provider

 


 

Statistical Analysis

Survey statistics were analyzed using IBM SPSS Statistics software (SPSS, Inc., Chicago, IL). A one-sample Kolmogorov-Smirnov test was used to test variable normality. As all Likert-scaled survey variables were not normally distributed, a Mann-Whitney U-test was used to compare Likert scores between cancer and benign groups as well as first-time biopsy and repeat biopsy groups. Ordinal regression was used to evaluate the effects of age on Likert-scaled variables.

 

Results

We screened 133 patients, and of these, 131 patients consented to participate in the study. Of these, 64 could not be reached during follow-up and one patient withdrew from the study, for a total of 66 patients completing the study. The overall response rate was 50.4%. Of the patients who completed the telephone interview, 39 had benign biopsies and 27 had cancer. Of the patients who did not complete the survey, 10 had cancer and 55 were benign (P = 0.004). Other demographic data from the survey cohort are illustrated in Table 2. As the vast majority of patients were white, a comparison between different races could not be performed. Age did not have any significant effect on any of study variables.

 

Table 2. Patient Demographic Data

 

RACENUMBER OF PATIENTSMEAN AGE (YEARS)SURVEY RESPONSE RATECANCER RATE
White11751.8 ± 13.90.500.29
African American1250.4 ± 9.60.500.17
Asian1411.001.00
Unreported1Unreported0.001.00

 


 

Communication Interactions

Of all patients in the study, 53 (80.3%) were contacted by a nurse from the breast center. The other patients were contacted by their primary care provider first (n = 5) or a radiologist (n = 3) or did not know who they were contacted by (n = 5). Forty-one patients (62.1%) recalled meeting the provider they spoke with, while 15 patients reported they did not meet the person who contacted them and 10 were not sure. Sixty-three patients (95.5%) were told their results over the phone, two were told in person, and one person did not respond.

 

Communication Skills

Mean Likert scales are reported in Table 3. There were no significant differences in the patients' assessments based on demographic or clinical variables. Overall, patients rated the communication skills of the person who gave their results very positively.

 

Table 3. Assessment of Communication Skills Used by Breast Center

 

SURVEY ITEMMEAN LIKERT SCORESTANDARD DEVIATION% OF PATIENTS REPORTING “AGREE” OR “STRONGLY AGREE”
You were given the diagnosis in a timely fashion4.350.7693.2%
The person who gave you the diagnosis was considerate and tactful4.600.6393.2%
The person who told you the diagnosis was honest4.460.50100%
The person told you the results in a way you could understand4.310.7794.9%
The person who told you the results did not rush4.080.8586.4%
The person who told you the results gave you the opportunity to ask questions4.130.7787.9%
The person who told you the results was sensitive to your emotional reaction4.260.8187.5%
You were satisfied with hearing your results by the method you did (eg, over the phone)3.980.9879.7%
You were satisfied with hearing the results from the person you heard from4.110.9586.4%

 


 

Areas for Improvement

The proportions of patients responding “agree” or “strongly agree” to each item are reported in Figure 1 and are compared between several patient groups in Table 4. Patients were more likely to want additional materials to help them understand their diagnosis. This was significantly more common among patients having a first biopsy and patients who had cancer (P < 0.05). For example, 65.4% of cancer patients wanted more information versus 43.5% of benign patients. Also, 60.5% of patients having a first biopsy wanted additional information versus 37.0% of patients having a repeat biopsy. For all other items, less than 50% of patients answered “agree” or “strongly agree,” and there were no significant trends based on clinical or sociodemographic variables.

Table 4. Comparison of Potential Methods of Improvement of Communication Between Cancer and Benign Patients and Patients Experiencing a First or Repeat Biopsy

 

SURVEY ITEM
MEAN LIKERTa ± SD
P
MEAN LIKERTa ± SD
P
 CANCERBENIGN FIRST BIOPSYNOT FIRST BIOPSY 
You would have preferred additional materials to help you understand the diagnosis3.50 ± 0.992.82 ± 1.140.018
3.34 ± 1.052.74 ± 1.160.036
You would have preferred to talk to someone beforehand to discuss how much you wanted to know about your results2.78 ± 1.092.77 ± 1.160.9772.97 ± 1.082.50 ± 1.130.068
You would have preferred more assistance making follow-up appointments2.19 ± 0.622.58 ± 1.220.3702.57 ± 1.022.21 ± 1.030.088
You would have preferred to choose who gave you the results2.54 ± 1.072.54 ± 1.070.8962.55 ± 1.002.46 ± 1.070.618
You would have preferred to receive the results faster2.85 ± 1.202.85 ± 1.200.4283.00 ± 1.272.86 ± 1.170.638
 Statistically significant difference
a 1, strongly disagree; 2, disagree; 3, neutral; 4, agree; 5, strongly agree
 

 

 


 

Patient Priorities for Receiving Biopsy Results

For patients with benign and those with malignant disease, receiving results quickly was the most important factor, followed by being told by a person who knows the most about what the results mean (Figure 2). Hearing from a primary care provider and hearing in person were of much lower priority. Cancer patients ranked “Being told by a person who knows the most about what the results mean” significantly higher in priority than benign patients (P < 0.05).

 

 

 

Discussion

In our study population of women who had a breast biopsy, the number one priority was receiving the results as soon as possible. We found that women were generally satisfied with learning about their results from a nurse over the phone. However, a majority of patients said they would prefer additional materials to help them understand their diagnosis. This trend was more pronounced in women with cancer or those for whom this was the first biopsy. Previous literature has shown that both doctors and patients agree that potentially bad news should be given in a quiet, uninterrupted, face-to-face conversation3 and that it should be given by a provider they know well.8 However, it has also been suggested that many women want to hear test results as quickly as possible, even if that means they will receive them from a provider they do not know well.[3], [4] and [5] This study is unique in that it forced women to choose which of these aspects of communication were most important as we believe that rapid results and hearing in person are often mutually exclusive. Women in this study clearly preferred to hear quickly more than in person, whether they were given a diagnosis of cancer or not.

Some studies suggest that providing written information along with verbal communication would be beneficial. For example, in a 2004 study by Lobb et al,9 genetic counselors who added a summary letter after communicating breast cancer risks to a patient significantly increased realistic risk assessments in patients, as well as lowering anxiety. Our study found that women would prefer additional materials. Since most communication in this study was over the phone, women may not have had time to fully process all the information given and therefore wanted information supplements as well. In women with cancer or those receiving a first biopsy, information and follow-up instructions are even more complex or overwhelming; and it follows that they are more likely to want informational materials than other patients.

As expected, patients who are diagnosed with cancer want to talk to someone with more knowledge of their specific condition. While patients in this study were not asked specifically about the apparent knowledge base of the nurse who gave results, their overall satisfaction (86.4%) seems to suggest that our system with specific scripts for each diagnosis was adequate.

This study is limited in several ways. First, as a single-center satisfaction study, we are only measuring the experience of patients with a particular system of communication. While these data reflect the general system in place at the breast center, they are also contingent on the specific providers communicating with patients. However, we do believe our results are reproducible at other centers as scripts are often used for sharing patient results. This also means that these data are a reflection of a population that was contacted by telephone. We cannot make any assumptions about how these preferences may differ from those of patients contacted in person. Second, only general data were gathered about the interaction of the nurse with each woman. It is not known what was specifically said to each patient, so we can only report the patient's view of the discussion. However, since responses are scripted at the breast center, we assume that the communication was relatively standardized for all conversations. Third, patients in the study were more likely to have a cancer diagnosis than the total population of biopsy patients. This may represent a greater commitment by cancer patients to aid in the improvement of communication. It also may indicate that women with a benign biopsy were less opinionated about their communication in general and less likely to want to express their opinions in the study.

In conclusion, patients generally prefer to hear breast biopsy results quickly over other factors, including hearing in person or from a more experienced practitioner. Therefore, a program similar to the one at our center meets most patient needs; it minimizes wait time by calling patients as soon as biopsy results are in, utilizes nurses to facilitate the large amount of calls that must be made every day, and ensures patients are contacted by a provider they personally met during their biopsy. A majority of patients desired additional materials to supplement phone communication. We highly recommend providing a variety of materials, including both written and Web-based, to address this need. Further research is necessary to determine the effects of these interventions on patient understanding and long-term emotional outcomes.

 

 

Following a diagnostic or screening mammogram, patients with breast lesions are often referred for a biopsy.1 Time between the procedure and the notification of results is anxiety-provoking as women wait to find out if the lesion is malignant or benign.2

The preferences of women in this scenario regarding the method of communication and the provider who gives results are unknown. Providers try to balance different priorities: getting the information to the woman quickly,[3], [4] and [5] providing as much information as possible,[3], [4], [5], [6] and [7] having the person who talks to the woman be someone they know and trust,8 and giving the information in person rather than on the phone.3 One may not be able to maximize all of the competing variables. If the sole priority is speed, then one would develop a system where the radiologist or nurse calls as soon as the biopsy result comes back; if complete information is most important, having someone from an oncologist's office call about positive results may be best. The problem is that there is a lack of published data on women's preferences, leading different programs to be based on assumptions rather than evidence.

Complicating this problem is that women likely have different preferences when the results are benign versus when they are malignant. To our knowledge, the communication preferences of these two groups of patients have never been compared. With evidence-based data about what women prefer, programs can develop more patient-centered programs to communicate biopsy results.

The goal of this study was to ascertain how women who have had a breast biopsy prefer to receive their test results. We also wanted to determine their satisfaction with the way they did receive their biopsy results at our cancer center and whether satisfaction differed based on patient age, race, or biopsy results. It is hoped that these data will help other programs develop communication policies.

 

Materials and Methods

 

Study Setting and Patient Recruitment

This study was based on a telephone survey of all radiologic breast biopsy patients seen at a large urban academic breast center between June 1, 2008, and July 31, 2008. The study was approved by the University of Pittsburgh Institutional Review Board. Study participants were adult females receiving a minimally invasive radiologic breast biopsy who spoke English and had a working phone. All patients fitting the inclusion criteria were approached before their procedure and given the option to participate in the study.

The center performed over 3,500 breast biopsies in 2009. At the center, two nurses gave results to an average of 30 patients a day. In addition to making calls, the nurses are responsible for admitting and discharging patients and work on rotation in order to call patients they have personally met. They make notes in patients' charts about their demeanor and concerns to help them tailor the phone calls to the patients' personalities. When patients leave after the biopsy, the nurses discuss the results call and ask the patients if they would prefer the nurse to call them or if they would like to call the nurse on their own time. No option is available for an in-person results visit except by the physician ordering the biopsy.

Study participants received biopsy results within 4 business days. Information about positive and negative results is communicated in different ways. While all results are faxed to the referring physician, positive results have a cover sheet indicating the urgency of the information. If results are positive or require further surgical biopsy, the nurses call in a room with a closed door and a “do not disturb” sign, to minimize interruptions. Nurses provide information using a standardized script that describes the result and their implications. Patients with a malignant biopsy are given a phone number to make a breast magnetic resonance imaging appointment. They are also told that they need to make a surgical appointment, but the choice of surgeon is left up to the patient and the referring physician. Patients are given time to ask questions and the direct phone number if they wish to speak to the nurse again.

 

Phone Survey Procedure

Two weeks were allotted between receipt of results and the study interview to allow time for patients to understand their diagnosis and seek follow-up care as necessary. After the 2-week waiting period, a study staff member contacted patients by telephone. Calls were conducted in the order in which patients received their results, and four attempts were made to call each participant, with a message left each time.

 

Study Survey

The phone survey consisted of four sections: an informational section, which collected data about how the patient received the results; a communication skills section, which assessed patient impressions of the person giving results; an improvement section, which assessed patient views about how to improve the communication of results; and finally, a communication priorities section, which assessed the relative importance of four distinct aspects of communication (Table 1). In addition, patients were asked “What did you like best about how you were told your results?” and “What can we do to make the process of giving results better?” The communication skills and improvement sections were scored on a five-point Likert scale, and the communication priorities section was scored on a rank scale from most to least important. (The survey is available on request.) Demographic information as well as the number of previous biopsies the woman had were also collected.

 

 

 

Table 1. Options Presented to Patients in the Communication Priorities Section of the Survey: To Be Ranked in Order of Least to Greatest Priority

 

Receiving the results of the biopsy as soon as possible
Being told by a person who knows the most about what the results mean
Being told the results in person
Being told by your primary care provider

 


 

Statistical Analysis

Survey statistics were analyzed using IBM SPSS Statistics software (SPSS, Inc., Chicago, IL). A one-sample Kolmogorov-Smirnov test was used to test variable normality. As all Likert-scaled survey variables were not normally distributed, a Mann-Whitney U-test was used to compare Likert scores between cancer and benign groups as well as first-time biopsy and repeat biopsy groups. Ordinal regression was used to evaluate the effects of age on Likert-scaled variables.

 

Results

We screened 133 patients, and of these, 131 patients consented to participate in the study. Of these, 64 could not be reached during follow-up and one patient withdrew from the study, for a total of 66 patients completing the study. The overall response rate was 50.4%. Of the patients who completed the telephone interview, 39 had benign biopsies and 27 had cancer. Of the patients who did not complete the survey, 10 had cancer and 55 were benign (P = 0.004). Other demographic data from the survey cohort are illustrated in Table 2. As the vast majority of patients were white, a comparison between different races could not be performed. Age did not have any significant effect on any of study variables.

 

Table 2. Patient Demographic Data

 

RACENUMBER OF PATIENTSMEAN AGE (YEARS)SURVEY RESPONSE RATECANCER RATE
White11751.8 ± 13.90.500.29
African American1250.4 ± 9.60.500.17
Asian1411.001.00
Unreported1Unreported0.001.00

 


 

Communication Interactions

Of all patients in the study, 53 (80.3%) were contacted by a nurse from the breast center. The other patients were contacted by their primary care provider first (n = 5) or a radiologist (n = 3) or did not know who they were contacted by (n = 5). Forty-one patients (62.1%) recalled meeting the provider they spoke with, while 15 patients reported they did not meet the person who contacted them and 10 were not sure. Sixty-three patients (95.5%) were told their results over the phone, two were told in person, and one person did not respond.

 

Communication Skills

Mean Likert scales are reported in Table 3. There were no significant differences in the patients' assessments based on demographic or clinical variables. Overall, patients rated the communication skills of the person who gave their results very positively.

 

Table 3. Assessment of Communication Skills Used by Breast Center

 

SURVEY ITEMMEAN LIKERT SCORESTANDARD DEVIATION% OF PATIENTS REPORTING “AGREE” OR “STRONGLY AGREE”
You were given the diagnosis in a timely fashion4.350.7693.2%
The person who gave you the diagnosis was considerate and tactful4.600.6393.2%
The person who told you the diagnosis was honest4.460.50100%
The person told you the results in a way you could understand4.310.7794.9%
The person who told you the results did not rush4.080.8586.4%
The person who told you the results gave you the opportunity to ask questions4.130.7787.9%
The person who told you the results was sensitive to your emotional reaction4.260.8187.5%
You were satisfied with hearing your results by the method you did (eg, over the phone)3.980.9879.7%
You were satisfied with hearing the results from the person you heard from4.110.9586.4%

 


 

Areas for Improvement

The proportions of patients responding “agree” or “strongly agree” to each item are reported in Figure 1 and are compared between several patient groups in Table 4. Patients were more likely to want additional materials to help them understand their diagnosis. This was significantly more common among patients having a first biopsy and patients who had cancer (P < 0.05). For example, 65.4% of cancer patients wanted more information versus 43.5% of benign patients. Also, 60.5% of patients having a first biopsy wanted additional information versus 37.0% of patients having a repeat biopsy. For all other items, less than 50% of patients answered “agree” or “strongly agree,” and there were no significant trends based on clinical or sociodemographic variables.

Table 4. Comparison of Potential Methods of Improvement of Communication Between Cancer and Benign Patients and Patients Experiencing a First or Repeat Biopsy

 

SURVEY ITEM
MEAN LIKERTa ± SD
P
MEAN LIKERTa ± SD
P
 CANCERBENIGN FIRST BIOPSYNOT FIRST BIOPSY 
You would have preferred additional materials to help you understand the diagnosis3.50 ± 0.992.82 ± 1.140.018
3.34 ± 1.052.74 ± 1.160.036
You would have preferred to talk to someone beforehand to discuss how much you wanted to know about your results2.78 ± 1.092.77 ± 1.160.9772.97 ± 1.082.50 ± 1.130.068
You would have preferred more assistance making follow-up appointments2.19 ± 0.622.58 ± 1.220.3702.57 ± 1.022.21 ± 1.030.088
You would have preferred to choose who gave you the results2.54 ± 1.072.54 ± 1.070.8962.55 ± 1.002.46 ± 1.070.618
You would have preferred to receive the results faster2.85 ± 1.202.85 ± 1.200.4283.00 ± 1.272.86 ± 1.170.638
 Statistically significant difference
a 1, strongly disagree; 2, disagree; 3, neutral; 4, agree; 5, strongly agree
 

 

 


 

Patient Priorities for Receiving Biopsy Results

For patients with benign and those with malignant disease, receiving results quickly was the most important factor, followed by being told by a person who knows the most about what the results mean (Figure 2). Hearing from a primary care provider and hearing in person were of much lower priority. Cancer patients ranked “Being told by a person who knows the most about what the results mean” significantly higher in priority than benign patients (P < 0.05).

 

 

 

Discussion

In our study population of women who had a breast biopsy, the number one priority was receiving the results as soon as possible. We found that women were generally satisfied with learning about their results from a nurse over the phone. However, a majority of patients said they would prefer additional materials to help them understand their diagnosis. This trend was more pronounced in women with cancer or those for whom this was the first biopsy. Previous literature has shown that both doctors and patients agree that potentially bad news should be given in a quiet, uninterrupted, face-to-face conversation3 and that it should be given by a provider they know well.8 However, it has also been suggested that many women want to hear test results as quickly as possible, even if that means they will receive them from a provider they do not know well.[3], [4] and [5] This study is unique in that it forced women to choose which of these aspects of communication were most important as we believe that rapid results and hearing in person are often mutually exclusive. Women in this study clearly preferred to hear quickly more than in person, whether they were given a diagnosis of cancer or not.

Some studies suggest that providing written information along with verbal communication would be beneficial. For example, in a 2004 study by Lobb et al,9 genetic counselors who added a summary letter after communicating breast cancer risks to a patient significantly increased realistic risk assessments in patients, as well as lowering anxiety. Our study found that women would prefer additional materials. Since most communication in this study was over the phone, women may not have had time to fully process all the information given and therefore wanted information supplements as well. In women with cancer or those receiving a first biopsy, information and follow-up instructions are even more complex or overwhelming; and it follows that they are more likely to want informational materials than other patients.

As expected, patients who are diagnosed with cancer want to talk to someone with more knowledge of their specific condition. While patients in this study were not asked specifically about the apparent knowledge base of the nurse who gave results, their overall satisfaction (86.4%) seems to suggest that our system with specific scripts for each diagnosis was adequate.

This study is limited in several ways. First, as a single-center satisfaction study, we are only measuring the experience of patients with a particular system of communication. While these data reflect the general system in place at the breast center, they are also contingent on the specific providers communicating with patients. However, we do believe our results are reproducible at other centers as scripts are often used for sharing patient results. This also means that these data are a reflection of a population that was contacted by telephone. We cannot make any assumptions about how these preferences may differ from those of patients contacted in person. Second, only general data were gathered about the interaction of the nurse with each woman. It is not known what was specifically said to each patient, so we can only report the patient's view of the discussion. However, since responses are scripted at the breast center, we assume that the communication was relatively standardized for all conversations. Third, patients in the study were more likely to have a cancer diagnosis than the total population of biopsy patients. This may represent a greater commitment by cancer patients to aid in the improvement of communication. It also may indicate that women with a benign biopsy were less opinionated about their communication in general and less likely to want to express their opinions in the study.

In conclusion, patients generally prefer to hear breast biopsy results quickly over other factors, including hearing in person or from a more experienced practitioner. Therefore, a program similar to the one at our center meets most patient needs; it minimizes wait time by calling patients as soon as biopsy results are in, utilizes nurses to facilitate the large amount of calls that must be made every day, and ensures patients are contacted by a provider they personally met during their biopsy. A majority of patients desired additional materials to supplement phone communication. We highly recommend providing a variety of materials, including both written and Web-based, to address this need. Further research is necessary to determine the effects of these interventions on patient understanding and long-term emotional outcomes.

 

 

References

1 A.J. Doyle, K.A. Murray, E.W. Nelson and D.G. Bragg, Selective use of image-guided large-core needle biopsy of the breast: accuracy and cost-effectiveness, Am J Roentgenol 165 (1995), pp. 281–284. View Record in Scopus | Cited By in Scopus (59)

2 J.R. Maxwell, M.E. Bugbee and D. Wellisch et al., Imaging-guided core needle biopsy of the breast: study of psychological outcomes, Breast J 1 (2000), pp. 53–61. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (24)

3 A. Girgis, R.W. Sanson-Fisher and M.J. Schofield, Is there consensus between breast cancer patients and providers on guidelines for breaking bad news?, Behav Med 25 (1999), pp. 69–77. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (44)

4 S. Liu, L.W. Bassett and J. Sayre, Women's attitudes about receiving mammographic results directly from radiologists, Radiology 193 (1994), pp. 783–786. View Record in Scopus | Cited By in Scopus (26)

5 S.R. Vallely and J.O. Manton Mills, Should radiologists talk to patients?, Br Med J 300 (1990), pp. 305–306. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (16)

6 J. Graydon, S. Galloway and S. Palmer-Wickham et al., Information needs of women during early treatment for breast cancer, J Adv Nurs Sci 26 (1997), pp. 59–64. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (90)

7 M. Cawley, J. Kostic and C. Cappello, Informational and psychological needs of women choosing conservative surgery/primary radiation for early stage breast cancer, Cancer Nurs 13 (1990), pp. 90–94. View Record in Scopus | Cited By in Scopus (41)

8 G.L. Krahn, A. Hallum and C. Kime, Are there good ways to give “bad news”?, Pediatrics 91 (1993), pp. 578–582. View Record in Scopus | Cited By in Scopus (57)

9 E.A. Lobb, P.N. Butow and A. Barratt et al., Communication and information-giving in high-risk breast cancer consultations: influence on patient outcomes, Br J Cancer 90 (2004), pp. 321–327. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (54)

References

1 A.J. Doyle, K.A. Murray, E.W. Nelson and D.G. Bragg, Selective use of image-guided large-core needle biopsy of the breast: accuracy and cost-effectiveness, Am J Roentgenol 165 (1995), pp. 281–284. View Record in Scopus | Cited By in Scopus (59)

2 J.R. Maxwell, M.E. Bugbee and D. Wellisch et al., Imaging-guided core needle biopsy of the breast: study of psychological outcomes, Breast J 1 (2000), pp. 53–61. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (24)

3 A. Girgis, R.W. Sanson-Fisher and M.J. Schofield, Is there consensus between breast cancer patients and providers on guidelines for breaking bad news?, Behav Med 25 (1999), pp. 69–77. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (44)

4 S. Liu, L.W. Bassett and J. Sayre, Women's attitudes about receiving mammographic results directly from radiologists, Radiology 193 (1994), pp. 783–786. View Record in Scopus | Cited By in Scopus (26)

5 S.R. Vallely and J.O. Manton Mills, Should radiologists talk to patients?, Br Med J 300 (1990), pp. 305–306. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (16)

6 J. Graydon, S. Galloway and S. Palmer-Wickham et al., Information needs of women during early treatment for breast cancer, J Adv Nurs Sci 26 (1997), pp. 59–64. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (90)

7 M. Cawley, J. Kostic and C. Cappello, Informational and psychological needs of women choosing conservative surgery/primary radiation for early stage breast cancer, Cancer Nurs 13 (1990), pp. 90–94. View Record in Scopus | Cited By in Scopus (41)

8 G.L. Krahn, A. Hallum and C. Kime, Are there good ways to give “bad news”?, Pediatrics 91 (1993), pp. 578–582. View Record in Scopus | Cited By in Scopus (57)

9 E.A. Lobb, P.N. Butow and A. Barratt et al., Communication and information-giving in high-risk breast cancer consultations: influence on patient outcomes, Br J Cancer 90 (2004), pp. 321–327. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (54)

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