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Deterioration Alerts on Medical Wards
Timely interventions are essential in the management of complex medical conditions such as new‐onset sepsis in order to prevent rapid progression to severe sepsis and septic shock.[1, 2, 3, 4, 5] Similarly, rapid identification and appropriate treatment of other medical and surgical conditions have been associated with improved outcomes.[6, 7, 8] We previously developed a real‐time, computerized prediction tool (PT) using recursive partitioning regression tree analysis for the identification of impending sepsis for use on general hospital wards.[9] We also showed that implementation of a real‐time computerized sepsis alert on hospital wards based on the PT resulted in increased use of early interventions, including antibiotic escalation, intravenous fluids, oxygen therapy, and diagnostics in patients identified as at risk.[10]
The first goal of this study was to develop an updated PT for use on hospital wards that could be used to predict subsequent global clinical deterioration and the need for a higher level of care. The second goal was to determine whether simply providing a real‐time alert to nursing staff based on the updated PT resulted in any demonstrable changes in patient outcomes.
METHODS
Study Location
The study was conducted at Barnes‐Jewish Hospital, a 1250‐bed academic medical center in St. Louis, Missouri. Eight adult medicine wards were assessed from July 2007 through December 2011. The medicine wards are closed areas with patient care delivered by dedicated house staff physicians under the supervision of a board‐certified attending physician. The study was approved by the Washington University School of Medicine Human Studies Committee.
Study Period
The period from July 2007 through January 2010 was used to train and retrospectively test the prediction model. The period from January 2011 through December 2011 was used to prospectively validate the model during a randomized trial using alerts generated from the prediction model.
Patients
Electronically captured clinical data were housed in a centralized clinical data repository. This repository cataloged 28,927 hospital visits from 19,116 distinct patients between July 2007 and January 2010. It contained a rich set of demographic and medical data for each of the visits, such as patient age, manually collected vital‐sign data, pharmacy data, laboratory data, and intensive care unit (ICU) transfer. This study served as a proof of concept for our vision of using machine learning to identify at‐risk patients and ultimately to perform real‐time event detection and interventions.
Algorithm Overview
Details regarding the predictive model development have been previously described.[11] To predict ICU transfer for patients housed on general medical wards, we used logistic regression, employing a novel framework to analyze the data stream from each patient, assigning scores to reflect the probability of ICU transfer to each patient.
Before building the model, several preprocessing steps were applied to eliminate outliers and find an appropriate representation of patients' states. For each of 36 input variables we specified acceptable ranges based on the domain knowledge of the medical experts on our team. For any value that was outside of the medically conceivable range, we replaced it by the mean value for that patient, if available. Values for every continuous parameter were scaled so that all measurements lay in the interval [0, 1] and were normalized by the minimum and maximum of the parameter. To capture the temporal effects in our data, we retained a sliding window of all the collected data points within the last 24 hours. We then subdivided these data into a series of 6 sequential buckets of 4 hours each.
To capture variations within a bucket, we computed 3 values for each feature in the bucket: the minimum, maximum, and mean data points. Each of the resulting 3n values was input to the logistic regression equation as separate variables. To deal with missing data points within the buckets, we used the patients' most recent reading from any time earlier in the hospital stay, if available. If no prior values existed, we used mean values calculated over the entire historical dataset. Bucket 6 max/min/mean represents the most recent 4‐hour window from the preceding 24‐hour time period for the maximum, minimum, and mean values, respectively. By itself, logistic regression does not operate on time‐series data. That is, each variable input to the logistic equation corresponds to exactly 1 data point (eg, a blood‐pressure variable would consist of a single blood‐pressure reading). In a clinical application, however, it is important to capture unusual changes in vital‐sign data over time. Such changes may precede clinical deterioration by hours, providing a chance to intervene if detected early enough. In addition, not all readings in time‐series data should be treated equally; the value of some kinds of data may change depending on their age. For example, a patient's condition may be better reflected by a blood‐oxygenation reading collected 1 hour ago than a reading collected 12 hours ago. This is the rationale for our use of a sliding window of all collected data points within the last 24 hours performed in a real‐time basis.
The algorithm was first implemented in MATLAB (Natick, MA). For the purposes of training, we used a single 24‐hour window of data from each patient. For patients admitted to ICU, this window was 26 hours to 2 hours prior to ICU admission; for all other patients, this window consisted of the first 24 hours of their hospital stay. The dataset's 36 input variables were divided into buckets and min/mean/max features wherever applicable, resulting in 398 variables. The first half of the dataset was used to train the model. We then used the second half of the dataset as the validation dataset. We generated a predicted outcome for each case in the validation data, using the model parameter coefficients derived from the training data. We also employed bootstrap aggregation to improve classification accuracy and to address overfitting. We then applied various threshold cut‐points to convert these predictions into binary values and compared the results against the ICU transfer outcome. A threshold of 0.9760 for specificity was chosen to achieve a sensitivity of approximately 40%. These operating characteristics were chosen in turn to generate a manageable number of alerts per hospital nursing unit per day (estimated at 12 per nursing unit per day). At this cut‐point the C‐statistic was 0.8834, with an overall accuracy of 0.9292.
In order to train the logistic model, we used a single 24‐hour window of data for each patient. However, in a system that predicts patients' outcomes in real time, scores are recomputed each time new data are entered into the database. Hence, patients have a series of scores over the length of their hospital stay, and an alert is triggered when any one of these scores is above the chosen threshold.
Once the model was developed, we implemented it in an internally developed, Java‐based clinical decision support rules engine, which identified when new data relevant to the model were available in a real‐time central data repository. The rules engine queried the data repository to acquire all data needed to evaluate the model. The score was calculated with each relevant new data point, and an alert was generated when the score exceeded the cut‐point threshold. We then prospectively validated these alerts on patients on 8 general medical wards at Barnes Jewish Hospital. Details regarding the architecture of our clinical decision support system have been previously published.[12] The sensitivity and positive predictive values for ICU transfer for these alerts were tracked during an intervention trial that ran from January 24, 2011, through December 31, 2011. Four general medical wards were randomized to the intervention group and 4 wards were randomized to the control group. The 8 general medical wards were ordered according to their alert rates based upon the historical data from July 2007 through January 2010, creating 4 pairs of wards in ascending order of alert rate. Within each of the 4 pairs, 1 member of the pair was randomized to the intervention group and the other to the control group using a random number generator.
Real‐time automated alerts generated 24 hours per day, 7 days per week from the predictive algorithm were sent to the charge‐nurse pager on the intervention units. Alerts were also generated and stored in the database on the control units, but these alerts were not sent to the charge nurse on those units. The alerts were sent to the charge nurses on the respective wards, as these individuals were thought to be in the best position to perform the initial assessment of the alerted patients, especially during evening hours when physician staffing was reduced. The charge nurses assessed the intervention‐group patients and were instructed to contact the responsible physician (hospitalist or internal medicine house officer) to inform them of the alert, or to call the rapid response team (RRT) if the patient's condition already appeared to be significantly deteriorating.
Descriptive statistics for algorithm sensitivity and positive predictive value and for patient outcomes were performed. Associations between alerts and the primary outcome, ICU transfer, were determined, as well as the impact of alerts in the intervention group compared with the control group, using [2] tests. The same analyses were performed for patient death. Differences in length of stay (LOS) were assessed using the Wilcoxon rank sum test.
RESULTS
Predictive Model
The variables with the greatest coefficients contributing to the PT model included respiratory rate, oxygen saturation, shock index, systolic blood pressure, anticoagulation use, heart rate, and diastolic blood pressure. A complete list of variables is provided in the Appendix (see Supporting Information in the online version of this article). All but 1 are routinely collected vital‐sign measures, and all but 1 occur in the 4‐hour period immediately prior to the alert (bucket 6).
Prospective Trial
Patient characteristics are presented in Table 1. Patients were well matched for race, sex, age, and underlying diagnoses. All alerts reported to the charge nurses were to be associated with a call from the charge nurse to the responsible physician caring for the alerted patient. The mean number of alerts per alerted patient was 1.8 (standard deviation=1.7). Patients meeting the alert threshold were at nearly 5.3‐fold greater risk of ICU transfer (95% confidence interval [CI]: 4.6‐6.0) than those not satisfying the alert threshold (358 of 2353 [15.2%; 95% CI: 13.8%‐16.7%] vs 512 of 17678 [2.9%; 95% CI: 2.7%‐3.2%], respectively; P<0.0001). Patients with alerts were at 8.9‐fold greater risk of death (95% CI: 7.4‐10.7) than those without alerts (244 of 2353 [10.4%; 95% CI: 9.2%‐11.7%] vs 206 of 17678 [1.2%; 95% CI: 1.0%‐1.3%], respectively; P<0.0001). Operating characteristics of the PT from the prospective trial are shown in Table 2. Alerts occurred a median of 25.5 hours prior to ICU transfer (interquartile range, 7.00‐81.75) and 8 hours prior to death (interquartile range, 4.09‐15.66).
Study Group | ||||||
---|---|---|---|---|---|---|
Control (N=10,120) | Intervention (N=9911) | |||||
| ||||||
Race | N | % | N | % | ||
White | 5,062 | 50 | 4,934 | 50 | ||
Black | 4,864 | 48 | 4,790 | 48 | ||
Other | 194 | 2 | 187 | 2 | ||
Sex | ||||||
F | 5,355 | 53 | 5,308 | 54 | ||
M | 4,765 | 47 | 4,603 | 46 | ||
Age at discharge, median (IQR), y | 57 (4469) | 57 (4470) | ||||
Top 10 ICD‐9 descriptions and counts, n (%) | ||||||
1 | Diseases of the digestive system | 1,774 (17.5) | Diseases of the digestive system | 1,664 (16.7) | ||
2 | Diseases of the circulatory system | 1,252 (12.4) | Diseases of the circulatory system | 1,253 (12.6) | ||
3 | Diseases of the respiratory system | 1,236 (12.2) | Diseases of the respiratory system | 1,210 (12.2) | ||
4 | Injury and poisoning | 864 (8.5) | Injury and poisoning | 849 (8.6) | ||
5 | Endocrine, nutritional, and metabolic diseases, and immunity disorders | 797 (7.9) | Diseases of the genitourinary system | 795 (8.0) | ||
6 | Diseases of the genitourinary system | 762 (7.5) | Endocrine, nutritional, and metabolic diseases, and immunity disorders | 780 (7.9) | ||
7 | Infectious and parasitic diseases | 555 (5.5) | Infectious and parasitic diseases | 549 (5.5) | ||
8 | Neoplasms | 547 (5.4) | Neoplasms | 465 (4.7) | ||
9 | Diseases of the blood and blood‐forming organs | 426 (4.2) | Diseases of the blood and blood‐forming organs | 429 (4.3) | ||
10 | Symptoms, signs, and ill‐defined conditions and factors influencing health status | 410 (4.1) | Diseases of the musculoskeletal system and connective tissue | 399 (4.0) |
Sensitivity, % | Specificity, % | PPV, % | NPV, % | Positive Likelihood Ratio | Negative Likelihood Ratio | |||
---|---|---|---|---|---|---|---|---|
| ||||||||
ICU Transfer | Yes (N=870) | No (N=19,161) | ||||||
Alert | 358 | 1,995 | 41.1 (95% CI: 37.944.5) | 89.6 (95% CI: 89.290.0) | 15.2 (95% CI: 13.816.7) | 97.1 (95% CI: 96.897.3) | 3.95 (95% CI: 3.614.30) | 0.66 (95% CI: 0.620.70) |
No Alert | 512 | 17,166 | ||||||
Death | Yes (N=450) | No (N=19,581) | ||||||
Alert | 244 | 2109 | 54.2 (95% CI: 49.658.8) | 89.2 (95% CI: 88.889.7) | 10.4 (95% CI: 9.211.7) | 98.8 (95% CI: 98.799.0) | 5.03 (95% CI: 4.585.53) | 0.51 (95% CI: 0.460.57) |
No Alert | 206 | 17,472 |
Among patients identified by the PT, there were no differences in the proportion of patients who were transferred to the ICU or who died in the intervention group as compared with the control group (Table 3). In addition, although there was no difference in LOS in the intervention group compared with the control group, identification by the PT was associated with a significantly longer median LOS (7.01 days vs 2.94 days, P<0.001). The largest numbers of patients who were transferred to the ICU or died did so in the first hospital day, and 60% of patients who were transferred to the ICU did so in the first 4 days, whereas deaths were more evenly distributed across the hospital stay.
Outcomes by Alert Statusa | ||||||||
---|---|---|---|---|---|---|---|---|
Alert Study Group | No‐Alert Study Group | |||||||
Intervention, N=1194 | Control, N=1159 | Intervention, N=8717 | Control, N=8961 | |||||
N | % | N | % | N | % | N | % | |
| ||||||||
ICU Transfer | ||||||||
Yes | 192 | 16 | 166 | 14 | 252 | 3 | 260 | 3 |
No | 1002 | 84 | 993 | 86 | 8465 | 97 | 8701 | 97 |
Death | ||||||||
Yes | 127 | 11 | 117 | 10 | 96 | 1 | 110 | 1 |
No | 1067 | 89 | 1042 | 90 | 8621 | 99 | 8851 | 99 |
LOS from admit to discharge, median (IQR), da | 7.07 (3.9912.15) | 6.92 (3.8212.67) | 2.97 (1.775.33) | 2.91 (1.745.19) |
DISCUSSION
We have demonstrated that a relatively simple hospital‐specific method for generating a PT derived from routine laboratory and hemodynamic values is capable of predicting clinical deterioration and the need for ICU transfer, as well as hospital mortality, in non‐ICU patients admitted to general hospital wards. We also found that the PT identified a sicker patient population as manifest by longer hospital LOS. The methods used in generating this real‐time PT are relatively simple and easily executed with the use of an electronic medical record (EMR) system. However, our data also showed that simply providing an alert to nursing units based on the PT did not result in any demonstrable improvement in patient outcomes. Moreover, our PT and intervention in their current form have substantial limitations, including low sensitivity and positive predictive value, high possibility of alert fatigue, and no clear clinical impact. These limitations suggest that this approach has limited applicability in its current form.
Unplanned ICU transfers occurring as early as within 8 hours of hospitalization are relatively common and associated with increased mortality.[13] Bapoje et al evaluated a total of 152 patients over 1 year who had unplanned ICU transfers.[14] The most common reason was worsening of the problem for which the patient was admitted (48%). Other investigators have also attempted to identify predictors for clinical deterioration resulting in unplanned ICU transfer that could be employed in a PT or early warning system (EWS). Keller et al evaluated 50 consecutive general medical patients with unplanned ICU transfers between 2003 and 2004.[15] Using a case‐control methodology, these investigators found shock index values>0.85 to be the best predictor for subsequent unplanned ICU transfer (P<0.02; odds ratio: 3.0).
Organizations such as the Institute for Healthcare Improvement have called for the development and implementation of EWSs in order to direct the activities of RRTs and improve outcomes.[16] Escobar et al carried out a retrospective case‐control study using as the unit of analysis 12‐hour patient shifts on hospital wards.[17] Using logistic regression and split validation, they developed a PT for ICU transfer from clinical variables available in their EMR. The EMR derived PT had a C‐statistic of 0.845 in the derivation dataset and 0.775 in the validation dataset, concluding that EMR‐based detection of impending deterioration outside the ICU is feasible in integrated healthcare delivery systems.
We found that simply providing an alert to nursing units did not result in any demonstrable improvements in the outcomes of high‐risk patients identified by our PT. This may have been due to simply relying on the alerted nursing staff to make phone calls to physicians and not linking a specific and effective patient‐directed intervention to the PT. Other investigators have similarly observed that the use of an EWS or PT may not result in outcome improvements.[18] Gao et al performed an analysis of 31 studies describing hospital track and trigger EWSs.[19] They found little evidence of reliability, validity, and utility of these systems. Peebles et al showed that even when high‐risk non‐ICU patients are identified, delays in providing appropriate therapies occur, which may explain the lack of efficacy of EWSs and RRTs.[20] These observations suggest that there is currently a paucity of validated interventions available to improve outcome in deteriorating patients, despite our ability to identify patients who are at risk for such deterioration.
As a result of mandates from quality‐improvement organizations, most US hospitals currently employ RRTs for emergent mobilization of resources when a clinically deteriorating patient is identified on a hospital ward.[21] However, as noted above, there is limited evidence to suggest that RRTs contribute to improved patient outcomes.[22, 23, 24, 25, 26, 27] The potential importance of this is reflected in a recent report suggesting that 2900 US hospitals now have rapid‐response systems in place without clear demonstration of their overall efficacy.[28] Linking rapid‐response interventions with a validated real‐time alert may represent a way of improving the effectiveness of such interventions.[29, 30, 31, 32, 33, 34] Our data showed that hospital LOS was statistically longer among alerted patients compared with nonalerted patients. This supports the conclusion that the alerts helped identify a sicker group of patients, but the nursing alerts did not appear to change outcomes. This finding also seems to refute the hypothesis that simply linking an intervention to a PT will improve outcomes, albeit the intervention we employed may not have been robust enough to influence patient outcomes.
The development of accurate real‐time EWSs holds the potential to identify patients at risk for clinical deterioration at an earlier point in time when rescue interventions can be implemented in a potentially more effective manner in both adults and children.[35] Unfortunately, the ideal intervention to be applied in this situation is unknown. Our experience suggests that successful interventions will require a more integrated approach than simply providing an alert with general management principles. As a result of our experience, we are undertaking a randomized clinical trial in 2013 to determine whether linking a patient‐specific intervention to a PT will result in improved outcomes. The intervention we will be testing is to have the RRT immediately notified about alerted patients so as to formally evaluate them and to determine the need for therapeutic interventions, and to administer such interventions as needed and/or transfer the alerted patients to a higher level of care as deemed necessary. Additionally, we are updating our PT with more temporal data to determine if this will improve its accuracy. One of these updates will include linking the PT to wirelessly obtained continuous oximetry and heart‐rate data, using minimally intrusive sensors, to establish a 2‐tiered EWS.[11]
Our study has several important limitations. First, the PT was developed using local data, and thus the results may not be applicable to other settings. However, our model shares many of the characteristics identified in other clinical‐deterioration PTs.[15, 17] Second, the positive prediction value of 15.2% for ICU transfer may not be clinically useful due to the large number of false‐positive results. Moreover, the large number of false positives could result in alert fatigue, causing alerts to be ignored. Third, although the charge nurses were supposed to call the responsible physicians for the alerted patients, we did not determine whether all these calls occurred or whether they resulted in any meaningful changes in monitoring or patient treatment. This is important because lack of an effective intervention or treatment would make the intervention group much more like our control group. Future studies are needed to assess the impact of an integrated intervention (eg, notification of experienced RRT members with adequate resource access) to determine if patient outcomes can be impacted by the use of an EWS. Finally, we did not compare the performance of our PT to other models such as the modified early warning score (MEWS).
An additional limitation to consider is that our PT offered no new information to the nurse manager, or the PT did not change the opinions of the charge nurses. This is supported by a recent study of 63 serious adverse outcomes in a Belgian teaching hospital where death was the final outcome.[36] Survey results revealed that nurses were often unaware that their patients were deteriorating before the crisis. Nurses also reported threshold levels for concern for abnormal vital signs that suggested they would call for assistance relatively late in clinical crises. The limited ability of nursing staff to identify deteriorating patients is also supported by a recent simulation study demonstrating that nurses did identify that patients were deteriorating, but as each patient deteriorated staff performance declined, with a reduction in all observational records and actions.[37]
In summary, we have demonstrated that a relatively simple hospital‐specific PT could accurately identify patients on general medicine wards who subsequently developed clinical deterioration and the need for ICU transfer, as well as hospital mortality. However, no improvements in patient outcomes were found from reporting this information to nursing wards on a real‐time basis. The low positive predictive value of the alerts, local development of the EWS, and absence of improved outcomes substantially limits the broader application of this system in its current form. Continued efforts are needed to identify and implement systems that will not only accurately identify high‐risk patients on general wards but also intervene to improve their outcomes.
Acknowledgments
Disclosures: This study was funded in part by the Barnes‐Jewish Hospital Foundation and by Grant No. UL1 RR024992 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH), and the NIH Roadmap for Medical Research. Its contents are solely the responsibility of the authors and do not necessarily represent the official view of NCRR or NIH.
- Improvement in process of care and outcome after a multicenter severe sepsis educational program in Spain. JAMA. 2008;299:2294–2303. , , , et al.
- Early goal‐directed therapy in the treatment of severe sepsis and septic shock. N Engl J Med. 2001;345:1368–1377. , , , et al.
- Before‐after study of a standardized hospital order set for the management of septic shock. Crit Care Med. 2007;34:2707–2713. , , , et al.
- Surviving Sepsis Campaign: international guidelines for management of severe sepsis and septic shock: 2008. Crit Care Med. 2008;36:296–327. , , .
- Septic shock: an analysis of outcomes for patients with onset on hospital wards versus intensive care units. Crit Care Med. 1998;26:1020–1024. , , , et al.
- Inpatient transfers to the intensive care unit: delays are associated with increased mortality and morbidity. J Gen Intern Med. 2003;18:77–83. , , , , .
- Causes and effects of surgical delay in patients with hip fracture: a cohort study. Ann Intern Med. 2011;155:226–233. , , , , , .
- Comprehensive stroke centers overcome the weekend versus weekday gap in stroke treatment and mortality. Stroke. 2011;42:2403–2409. , , , .
- Hospital‐wide impact of a standardized order set for the management of bacteremic severe sepsis. Crit Care Med. 2009;37:819–824. , , , , , .
- Implementation of a real‐time computerized sepsis alert in non–intensive care unit patients. Crit Care Med. 2011;39:469–473. , , , et al.
- Toward a two‐tier clinical warning system for hospitalized patients. AMIA Annu Symp Proc. 2011;2011:511–519. , , , et al.
- Migrating toward a next‐generation clinical decision support application: the BJC HealthCare experience. AMIA Annu Symp Proc. 2007;344–348. , , , , , .
- Adverse outcomes associated with delayed intensive care unit transfers in an integrated healthcare system. J Hosp Med. 2011;7:224–230. , , , .
- Unplanned transfers to a medical intensive care unit: causes and relationship to preventable errors in care. J Hosp Med. 2011;6:68–72. , , , .
- Unplanned transfers to the intensive care unit: the role of the shock index. J Hosp Med. 2010;5:460–465. , , , , , .
- Institute for Healthcare Improvement. Early warning systems: the next level of rapid response. Available at: http://www.ihi.org/IHI/Programs/AudioAndWebPrograms/ExpeditionEarlyWarningSystemsTheNextLevelofRapidResponse.htmplayerwmp. Accessed April 6, 2011.
- Early detection of impending physiologic deterioration among patients who are not in intensive care: development of predictive models using data from an automated electronic medical record. J Hosp Med. 2012;7:388–395. , , , , , .
- Early warning systems: the next level of rapid response. Nursing. 2012;42:38–44. , , .
- Systematic review and evaluation of physiological track and trigger warning systems for identifying at‐risk patients on the ward. Intensive Care Med. 2007;33:667–679. , , , et al.
- Timing and teamwork—an observational pilot study of patients referred to a Rapid Response Team with the aim of identifying factors amenable to re‐design of a Rapid Response System. Resuscitation. 2012;83:782–787. , , , .
- Rapid response: a quality improvement conundrum. J Hosp Med. 2009;4:255–257. , , , .
- Introducing critical care outreach: a ward‐randomised trial of phased introduction in a general hospital. Intensive Care Med. 2004;30:1398–1404. , , , et al.
- Out of our reach? Assessing the impact of introducing critical care outreach service. Anaesthesiology. 2003;58:882–885. .
- Effect of the critical care outreach team on patient survival to discharge from hospital and readmission to critical care: non‐randomised population based study. BMJ. 2003;327:1014–1016. , , .
- Reducing mortality and avoiding preventable ICU utilization: analysis of a successful rapid response program using APR DRGs [published online ahead of print March 10, 2010]. J Healthc Qual. doi: 10.1111/j.1945‐1474.2010.00084.x. , , .
- Introduction of the medical emergency team (MET) system: a cluster‐randomised control trial. Lancet. 2005;365:2091–2097. , , , et al.
- The impact of the introduction of critical care outreach services in England: a multicentre interrupted time‐series analysis. Crit Care. 2007;11:R113. , , , , , .
- Rapid response systems now established at 2,900 hospitals. Hospitalist News. March 2010;3:1. .
- Rapid response teams: a systematic review and meta‐analysis. Arch Intern Med. 2010;170:18–26. , , , , .
- Outreach and early warning systems (EWS) for the prevention of intensive care admission and death of critically ill adult patients on general hospital wards. Cochrane Database Syst Rev. 2007;3:CD005529. , , , et al.
- Rapid‐response teams. N Engl J Med. 2011;365:139–146. , , .
- Early warning systems. Hosp Chron. 2012;7(suppl 1):37–43. , , .
- Grand challenges in clinical decision support. J Biomed Inform. 2008;41(2):387–392. , , , et al.
- Utility of commonly captured data from an EHR to identify hospitalized patients at risk for clinical deterioration. AMIA Annu Symp Proc. 2007;404–408. , , , et al.
- Sensitivity of the pediatric early warning score to identify patient deterioration. Pediatrics. 2010;125(4)e763–e769. , , , , , .
- In‐hospital mortality after serious adverse events on medical and surgical nursing units: a mixed methods study [published online ahead of print July 24, 2012]. J Clin Nurs. doi: 10.1111/j.1365‐2702.2012.04154.x. , , , .
- Managing deteriorating patients: registered nurses' performance in a simulated setting. Open Nurs J. 2011;5:120–126. , , , et al.
Timely interventions are essential in the management of complex medical conditions such as new‐onset sepsis in order to prevent rapid progression to severe sepsis and septic shock.[1, 2, 3, 4, 5] Similarly, rapid identification and appropriate treatment of other medical and surgical conditions have been associated with improved outcomes.[6, 7, 8] We previously developed a real‐time, computerized prediction tool (PT) using recursive partitioning regression tree analysis for the identification of impending sepsis for use on general hospital wards.[9] We also showed that implementation of a real‐time computerized sepsis alert on hospital wards based on the PT resulted in increased use of early interventions, including antibiotic escalation, intravenous fluids, oxygen therapy, and diagnostics in patients identified as at risk.[10]
The first goal of this study was to develop an updated PT for use on hospital wards that could be used to predict subsequent global clinical deterioration and the need for a higher level of care. The second goal was to determine whether simply providing a real‐time alert to nursing staff based on the updated PT resulted in any demonstrable changes in patient outcomes.
METHODS
Study Location
The study was conducted at Barnes‐Jewish Hospital, a 1250‐bed academic medical center in St. Louis, Missouri. Eight adult medicine wards were assessed from July 2007 through December 2011. The medicine wards are closed areas with patient care delivered by dedicated house staff physicians under the supervision of a board‐certified attending physician. The study was approved by the Washington University School of Medicine Human Studies Committee.
Study Period
The period from July 2007 through January 2010 was used to train and retrospectively test the prediction model. The period from January 2011 through December 2011 was used to prospectively validate the model during a randomized trial using alerts generated from the prediction model.
Patients
Electronically captured clinical data were housed in a centralized clinical data repository. This repository cataloged 28,927 hospital visits from 19,116 distinct patients between July 2007 and January 2010. It contained a rich set of demographic and medical data for each of the visits, such as patient age, manually collected vital‐sign data, pharmacy data, laboratory data, and intensive care unit (ICU) transfer. This study served as a proof of concept for our vision of using machine learning to identify at‐risk patients and ultimately to perform real‐time event detection and interventions.
Algorithm Overview
Details regarding the predictive model development have been previously described.[11] To predict ICU transfer for patients housed on general medical wards, we used logistic regression, employing a novel framework to analyze the data stream from each patient, assigning scores to reflect the probability of ICU transfer to each patient.
Before building the model, several preprocessing steps were applied to eliminate outliers and find an appropriate representation of patients' states. For each of 36 input variables we specified acceptable ranges based on the domain knowledge of the medical experts on our team. For any value that was outside of the medically conceivable range, we replaced it by the mean value for that patient, if available. Values for every continuous parameter were scaled so that all measurements lay in the interval [0, 1] and were normalized by the minimum and maximum of the parameter. To capture the temporal effects in our data, we retained a sliding window of all the collected data points within the last 24 hours. We then subdivided these data into a series of 6 sequential buckets of 4 hours each.
To capture variations within a bucket, we computed 3 values for each feature in the bucket: the minimum, maximum, and mean data points. Each of the resulting 3n values was input to the logistic regression equation as separate variables. To deal with missing data points within the buckets, we used the patients' most recent reading from any time earlier in the hospital stay, if available. If no prior values existed, we used mean values calculated over the entire historical dataset. Bucket 6 max/min/mean represents the most recent 4‐hour window from the preceding 24‐hour time period for the maximum, minimum, and mean values, respectively. By itself, logistic regression does not operate on time‐series data. That is, each variable input to the logistic equation corresponds to exactly 1 data point (eg, a blood‐pressure variable would consist of a single blood‐pressure reading). In a clinical application, however, it is important to capture unusual changes in vital‐sign data over time. Such changes may precede clinical deterioration by hours, providing a chance to intervene if detected early enough. In addition, not all readings in time‐series data should be treated equally; the value of some kinds of data may change depending on their age. For example, a patient's condition may be better reflected by a blood‐oxygenation reading collected 1 hour ago than a reading collected 12 hours ago. This is the rationale for our use of a sliding window of all collected data points within the last 24 hours performed in a real‐time basis.
The algorithm was first implemented in MATLAB (Natick, MA). For the purposes of training, we used a single 24‐hour window of data from each patient. For patients admitted to ICU, this window was 26 hours to 2 hours prior to ICU admission; for all other patients, this window consisted of the first 24 hours of their hospital stay. The dataset's 36 input variables were divided into buckets and min/mean/max features wherever applicable, resulting in 398 variables. The first half of the dataset was used to train the model. We then used the second half of the dataset as the validation dataset. We generated a predicted outcome for each case in the validation data, using the model parameter coefficients derived from the training data. We also employed bootstrap aggregation to improve classification accuracy and to address overfitting. We then applied various threshold cut‐points to convert these predictions into binary values and compared the results against the ICU transfer outcome. A threshold of 0.9760 for specificity was chosen to achieve a sensitivity of approximately 40%. These operating characteristics were chosen in turn to generate a manageable number of alerts per hospital nursing unit per day (estimated at 12 per nursing unit per day). At this cut‐point the C‐statistic was 0.8834, with an overall accuracy of 0.9292.
In order to train the logistic model, we used a single 24‐hour window of data for each patient. However, in a system that predicts patients' outcomes in real time, scores are recomputed each time new data are entered into the database. Hence, patients have a series of scores over the length of their hospital stay, and an alert is triggered when any one of these scores is above the chosen threshold.
Once the model was developed, we implemented it in an internally developed, Java‐based clinical decision support rules engine, which identified when new data relevant to the model were available in a real‐time central data repository. The rules engine queried the data repository to acquire all data needed to evaluate the model. The score was calculated with each relevant new data point, and an alert was generated when the score exceeded the cut‐point threshold. We then prospectively validated these alerts on patients on 8 general medical wards at Barnes Jewish Hospital. Details regarding the architecture of our clinical decision support system have been previously published.[12] The sensitivity and positive predictive values for ICU transfer for these alerts were tracked during an intervention trial that ran from January 24, 2011, through December 31, 2011. Four general medical wards were randomized to the intervention group and 4 wards were randomized to the control group. The 8 general medical wards were ordered according to their alert rates based upon the historical data from July 2007 through January 2010, creating 4 pairs of wards in ascending order of alert rate. Within each of the 4 pairs, 1 member of the pair was randomized to the intervention group and the other to the control group using a random number generator.
Real‐time automated alerts generated 24 hours per day, 7 days per week from the predictive algorithm were sent to the charge‐nurse pager on the intervention units. Alerts were also generated and stored in the database on the control units, but these alerts were not sent to the charge nurse on those units. The alerts were sent to the charge nurses on the respective wards, as these individuals were thought to be in the best position to perform the initial assessment of the alerted patients, especially during evening hours when physician staffing was reduced. The charge nurses assessed the intervention‐group patients and were instructed to contact the responsible physician (hospitalist or internal medicine house officer) to inform them of the alert, or to call the rapid response team (RRT) if the patient's condition already appeared to be significantly deteriorating.
Descriptive statistics for algorithm sensitivity and positive predictive value and for patient outcomes were performed. Associations between alerts and the primary outcome, ICU transfer, were determined, as well as the impact of alerts in the intervention group compared with the control group, using [2] tests. The same analyses were performed for patient death. Differences in length of stay (LOS) were assessed using the Wilcoxon rank sum test.
RESULTS
Predictive Model
The variables with the greatest coefficients contributing to the PT model included respiratory rate, oxygen saturation, shock index, systolic blood pressure, anticoagulation use, heart rate, and diastolic blood pressure. A complete list of variables is provided in the Appendix (see Supporting Information in the online version of this article). All but 1 are routinely collected vital‐sign measures, and all but 1 occur in the 4‐hour period immediately prior to the alert (bucket 6).
Prospective Trial
Patient characteristics are presented in Table 1. Patients were well matched for race, sex, age, and underlying diagnoses. All alerts reported to the charge nurses were to be associated with a call from the charge nurse to the responsible physician caring for the alerted patient. The mean number of alerts per alerted patient was 1.8 (standard deviation=1.7). Patients meeting the alert threshold were at nearly 5.3‐fold greater risk of ICU transfer (95% confidence interval [CI]: 4.6‐6.0) than those not satisfying the alert threshold (358 of 2353 [15.2%; 95% CI: 13.8%‐16.7%] vs 512 of 17678 [2.9%; 95% CI: 2.7%‐3.2%], respectively; P<0.0001). Patients with alerts were at 8.9‐fold greater risk of death (95% CI: 7.4‐10.7) than those without alerts (244 of 2353 [10.4%; 95% CI: 9.2%‐11.7%] vs 206 of 17678 [1.2%; 95% CI: 1.0%‐1.3%], respectively; P<0.0001). Operating characteristics of the PT from the prospective trial are shown in Table 2. Alerts occurred a median of 25.5 hours prior to ICU transfer (interquartile range, 7.00‐81.75) and 8 hours prior to death (interquartile range, 4.09‐15.66).
Study Group | ||||||
---|---|---|---|---|---|---|
Control (N=10,120) | Intervention (N=9911) | |||||
| ||||||
Race | N | % | N | % | ||
White | 5,062 | 50 | 4,934 | 50 | ||
Black | 4,864 | 48 | 4,790 | 48 | ||
Other | 194 | 2 | 187 | 2 | ||
Sex | ||||||
F | 5,355 | 53 | 5,308 | 54 | ||
M | 4,765 | 47 | 4,603 | 46 | ||
Age at discharge, median (IQR), y | 57 (4469) | 57 (4470) | ||||
Top 10 ICD‐9 descriptions and counts, n (%) | ||||||
1 | Diseases of the digestive system | 1,774 (17.5) | Diseases of the digestive system | 1,664 (16.7) | ||
2 | Diseases of the circulatory system | 1,252 (12.4) | Diseases of the circulatory system | 1,253 (12.6) | ||
3 | Diseases of the respiratory system | 1,236 (12.2) | Diseases of the respiratory system | 1,210 (12.2) | ||
4 | Injury and poisoning | 864 (8.5) | Injury and poisoning | 849 (8.6) | ||
5 | Endocrine, nutritional, and metabolic diseases, and immunity disorders | 797 (7.9) | Diseases of the genitourinary system | 795 (8.0) | ||
6 | Diseases of the genitourinary system | 762 (7.5) | Endocrine, nutritional, and metabolic diseases, and immunity disorders | 780 (7.9) | ||
7 | Infectious and parasitic diseases | 555 (5.5) | Infectious and parasitic diseases | 549 (5.5) | ||
8 | Neoplasms | 547 (5.4) | Neoplasms | 465 (4.7) | ||
9 | Diseases of the blood and blood‐forming organs | 426 (4.2) | Diseases of the blood and blood‐forming organs | 429 (4.3) | ||
10 | Symptoms, signs, and ill‐defined conditions and factors influencing health status | 410 (4.1) | Diseases of the musculoskeletal system and connective tissue | 399 (4.0) |
Sensitivity, % | Specificity, % | PPV, % | NPV, % | Positive Likelihood Ratio | Negative Likelihood Ratio | |||
---|---|---|---|---|---|---|---|---|
| ||||||||
ICU Transfer | Yes (N=870) | No (N=19,161) | ||||||
Alert | 358 | 1,995 | 41.1 (95% CI: 37.944.5) | 89.6 (95% CI: 89.290.0) | 15.2 (95% CI: 13.816.7) | 97.1 (95% CI: 96.897.3) | 3.95 (95% CI: 3.614.30) | 0.66 (95% CI: 0.620.70) |
No Alert | 512 | 17,166 | ||||||
Death | Yes (N=450) | No (N=19,581) | ||||||
Alert | 244 | 2109 | 54.2 (95% CI: 49.658.8) | 89.2 (95% CI: 88.889.7) | 10.4 (95% CI: 9.211.7) | 98.8 (95% CI: 98.799.0) | 5.03 (95% CI: 4.585.53) | 0.51 (95% CI: 0.460.57) |
No Alert | 206 | 17,472 |
Among patients identified by the PT, there were no differences in the proportion of patients who were transferred to the ICU or who died in the intervention group as compared with the control group (Table 3). In addition, although there was no difference in LOS in the intervention group compared with the control group, identification by the PT was associated with a significantly longer median LOS (7.01 days vs 2.94 days, P<0.001). The largest numbers of patients who were transferred to the ICU or died did so in the first hospital day, and 60% of patients who were transferred to the ICU did so in the first 4 days, whereas deaths were more evenly distributed across the hospital stay.
Outcomes by Alert Statusa | ||||||||
---|---|---|---|---|---|---|---|---|
Alert Study Group | No‐Alert Study Group | |||||||
Intervention, N=1194 | Control, N=1159 | Intervention, N=8717 | Control, N=8961 | |||||
N | % | N | % | N | % | N | % | |
| ||||||||
ICU Transfer | ||||||||
Yes | 192 | 16 | 166 | 14 | 252 | 3 | 260 | 3 |
No | 1002 | 84 | 993 | 86 | 8465 | 97 | 8701 | 97 |
Death | ||||||||
Yes | 127 | 11 | 117 | 10 | 96 | 1 | 110 | 1 |
No | 1067 | 89 | 1042 | 90 | 8621 | 99 | 8851 | 99 |
LOS from admit to discharge, median (IQR), da | 7.07 (3.9912.15) | 6.92 (3.8212.67) | 2.97 (1.775.33) | 2.91 (1.745.19) |
DISCUSSION
We have demonstrated that a relatively simple hospital‐specific method for generating a PT derived from routine laboratory and hemodynamic values is capable of predicting clinical deterioration and the need for ICU transfer, as well as hospital mortality, in non‐ICU patients admitted to general hospital wards. We also found that the PT identified a sicker patient population as manifest by longer hospital LOS. The methods used in generating this real‐time PT are relatively simple and easily executed with the use of an electronic medical record (EMR) system. However, our data also showed that simply providing an alert to nursing units based on the PT did not result in any demonstrable improvement in patient outcomes. Moreover, our PT and intervention in their current form have substantial limitations, including low sensitivity and positive predictive value, high possibility of alert fatigue, and no clear clinical impact. These limitations suggest that this approach has limited applicability in its current form.
Unplanned ICU transfers occurring as early as within 8 hours of hospitalization are relatively common and associated with increased mortality.[13] Bapoje et al evaluated a total of 152 patients over 1 year who had unplanned ICU transfers.[14] The most common reason was worsening of the problem for which the patient was admitted (48%). Other investigators have also attempted to identify predictors for clinical deterioration resulting in unplanned ICU transfer that could be employed in a PT or early warning system (EWS). Keller et al evaluated 50 consecutive general medical patients with unplanned ICU transfers between 2003 and 2004.[15] Using a case‐control methodology, these investigators found shock index values>0.85 to be the best predictor for subsequent unplanned ICU transfer (P<0.02; odds ratio: 3.0).
Organizations such as the Institute for Healthcare Improvement have called for the development and implementation of EWSs in order to direct the activities of RRTs and improve outcomes.[16] Escobar et al carried out a retrospective case‐control study using as the unit of analysis 12‐hour patient shifts on hospital wards.[17] Using logistic regression and split validation, they developed a PT for ICU transfer from clinical variables available in their EMR. The EMR derived PT had a C‐statistic of 0.845 in the derivation dataset and 0.775 in the validation dataset, concluding that EMR‐based detection of impending deterioration outside the ICU is feasible in integrated healthcare delivery systems.
We found that simply providing an alert to nursing units did not result in any demonstrable improvements in the outcomes of high‐risk patients identified by our PT. This may have been due to simply relying on the alerted nursing staff to make phone calls to physicians and not linking a specific and effective patient‐directed intervention to the PT. Other investigators have similarly observed that the use of an EWS or PT may not result in outcome improvements.[18] Gao et al performed an analysis of 31 studies describing hospital track and trigger EWSs.[19] They found little evidence of reliability, validity, and utility of these systems. Peebles et al showed that even when high‐risk non‐ICU patients are identified, delays in providing appropriate therapies occur, which may explain the lack of efficacy of EWSs and RRTs.[20] These observations suggest that there is currently a paucity of validated interventions available to improve outcome in deteriorating patients, despite our ability to identify patients who are at risk for such deterioration.
As a result of mandates from quality‐improvement organizations, most US hospitals currently employ RRTs for emergent mobilization of resources when a clinically deteriorating patient is identified on a hospital ward.[21] However, as noted above, there is limited evidence to suggest that RRTs contribute to improved patient outcomes.[22, 23, 24, 25, 26, 27] The potential importance of this is reflected in a recent report suggesting that 2900 US hospitals now have rapid‐response systems in place without clear demonstration of their overall efficacy.[28] Linking rapid‐response interventions with a validated real‐time alert may represent a way of improving the effectiveness of such interventions.[29, 30, 31, 32, 33, 34] Our data showed that hospital LOS was statistically longer among alerted patients compared with nonalerted patients. This supports the conclusion that the alerts helped identify a sicker group of patients, but the nursing alerts did not appear to change outcomes. This finding also seems to refute the hypothesis that simply linking an intervention to a PT will improve outcomes, albeit the intervention we employed may not have been robust enough to influence patient outcomes.
The development of accurate real‐time EWSs holds the potential to identify patients at risk for clinical deterioration at an earlier point in time when rescue interventions can be implemented in a potentially more effective manner in both adults and children.[35] Unfortunately, the ideal intervention to be applied in this situation is unknown. Our experience suggests that successful interventions will require a more integrated approach than simply providing an alert with general management principles. As a result of our experience, we are undertaking a randomized clinical trial in 2013 to determine whether linking a patient‐specific intervention to a PT will result in improved outcomes. The intervention we will be testing is to have the RRT immediately notified about alerted patients so as to formally evaluate them and to determine the need for therapeutic interventions, and to administer such interventions as needed and/or transfer the alerted patients to a higher level of care as deemed necessary. Additionally, we are updating our PT with more temporal data to determine if this will improve its accuracy. One of these updates will include linking the PT to wirelessly obtained continuous oximetry and heart‐rate data, using minimally intrusive sensors, to establish a 2‐tiered EWS.[11]
Our study has several important limitations. First, the PT was developed using local data, and thus the results may not be applicable to other settings. However, our model shares many of the characteristics identified in other clinical‐deterioration PTs.[15, 17] Second, the positive prediction value of 15.2% for ICU transfer may not be clinically useful due to the large number of false‐positive results. Moreover, the large number of false positives could result in alert fatigue, causing alerts to be ignored. Third, although the charge nurses were supposed to call the responsible physicians for the alerted patients, we did not determine whether all these calls occurred or whether they resulted in any meaningful changes in monitoring or patient treatment. This is important because lack of an effective intervention or treatment would make the intervention group much more like our control group. Future studies are needed to assess the impact of an integrated intervention (eg, notification of experienced RRT members with adequate resource access) to determine if patient outcomes can be impacted by the use of an EWS. Finally, we did not compare the performance of our PT to other models such as the modified early warning score (MEWS).
An additional limitation to consider is that our PT offered no new information to the nurse manager, or the PT did not change the opinions of the charge nurses. This is supported by a recent study of 63 serious adverse outcomes in a Belgian teaching hospital where death was the final outcome.[36] Survey results revealed that nurses were often unaware that their patients were deteriorating before the crisis. Nurses also reported threshold levels for concern for abnormal vital signs that suggested they would call for assistance relatively late in clinical crises. The limited ability of nursing staff to identify deteriorating patients is also supported by a recent simulation study demonstrating that nurses did identify that patients were deteriorating, but as each patient deteriorated staff performance declined, with a reduction in all observational records and actions.[37]
In summary, we have demonstrated that a relatively simple hospital‐specific PT could accurately identify patients on general medicine wards who subsequently developed clinical deterioration and the need for ICU transfer, as well as hospital mortality. However, no improvements in patient outcomes were found from reporting this information to nursing wards on a real‐time basis. The low positive predictive value of the alerts, local development of the EWS, and absence of improved outcomes substantially limits the broader application of this system in its current form. Continued efforts are needed to identify and implement systems that will not only accurately identify high‐risk patients on general wards but also intervene to improve their outcomes.
Acknowledgments
Disclosures: This study was funded in part by the Barnes‐Jewish Hospital Foundation and by Grant No. UL1 RR024992 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH), and the NIH Roadmap for Medical Research. Its contents are solely the responsibility of the authors and do not necessarily represent the official view of NCRR or NIH.
Timely interventions are essential in the management of complex medical conditions such as new‐onset sepsis in order to prevent rapid progression to severe sepsis and septic shock.[1, 2, 3, 4, 5] Similarly, rapid identification and appropriate treatment of other medical and surgical conditions have been associated with improved outcomes.[6, 7, 8] We previously developed a real‐time, computerized prediction tool (PT) using recursive partitioning regression tree analysis for the identification of impending sepsis for use on general hospital wards.[9] We also showed that implementation of a real‐time computerized sepsis alert on hospital wards based on the PT resulted in increased use of early interventions, including antibiotic escalation, intravenous fluids, oxygen therapy, and diagnostics in patients identified as at risk.[10]
The first goal of this study was to develop an updated PT for use on hospital wards that could be used to predict subsequent global clinical deterioration and the need for a higher level of care. The second goal was to determine whether simply providing a real‐time alert to nursing staff based on the updated PT resulted in any demonstrable changes in patient outcomes.
METHODS
Study Location
The study was conducted at Barnes‐Jewish Hospital, a 1250‐bed academic medical center in St. Louis, Missouri. Eight adult medicine wards were assessed from July 2007 through December 2011. The medicine wards are closed areas with patient care delivered by dedicated house staff physicians under the supervision of a board‐certified attending physician. The study was approved by the Washington University School of Medicine Human Studies Committee.
Study Period
The period from July 2007 through January 2010 was used to train and retrospectively test the prediction model. The period from January 2011 through December 2011 was used to prospectively validate the model during a randomized trial using alerts generated from the prediction model.
Patients
Electronically captured clinical data were housed in a centralized clinical data repository. This repository cataloged 28,927 hospital visits from 19,116 distinct patients between July 2007 and January 2010. It contained a rich set of demographic and medical data for each of the visits, such as patient age, manually collected vital‐sign data, pharmacy data, laboratory data, and intensive care unit (ICU) transfer. This study served as a proof of concept for our vision of using machine learning to identify at‐risk patients and ultimately to perform real‐time event detection and interventions.
Algorithm Overview
Details regarding the predictive model development have been previously described.[11] To predict ICU transfer for patients housed on general medical wards, we used logistic regression, employing a novel framework to analyze the data stream from each patient, assigning scores to reflect the probability of ICU transfer to each patient.
Before building the model, several preprocessing steps were applied to eliminate outliers and find an appropriate representation of patients' states. For each of 36 input variables we specified acceptable ranges based on the domain knowledge of the medical experts on our team. For any value that was outside of the medically conceivable range, we replaced it by the mean value for that patient, if available. Values for every continuous parameter were scaled so that all measurements lay in the interval [0, 1] and were normalized by the minimum and maximum of the parameter. To capture the temporal effects in our data, we retained a sliding window of all the collected data points within the last 24 hours. We then subdivided these data into a series of 6 sequential buckets of 4 hours each.
To capture variations within a bucket, we computed 3 values for each feature in the bucket: the minimum, maximum, and mean data points. Each of the resulting 3n values was input to the logistic regression equation as separate variables. To deal with missing data points within the buckets, we used the patients' most recent reading from any time earlier in the hospital stay, if available. If no prior values existed, we used mean values calculated over the entire historical dataset. Bucket 6 max/min/mean represents the most recent 4‐hour window from the preceding 24‐hour time period for the maximum, minimum, and mean values, respectively. By itself, logistic regression does not operate on time‐series data. That is, each variable input to the logistic equation corresponds to exactly 1 data point (eg, a blood‐pressure variable would consist of a single blood‐pressure reading). In a clinical application, however, it is important to capture unusual changes in vital‐sign data over time. Such changes may precede clinical deterioration by hours, providing a chance to intervene if detected early enough. In addition, not all readings in time‐series data should be treated equally; the value of some kinds of data may change depending on their age. For example, a patient's condition may be better reflected by a blood‐oxygenation reading collected 1 hour ago than a reading collected 12 hours ago. This is the rationale for our use of a sliding window of all collected data points within the last 24 hours performed in a real‐time basis.
The algorithm was first implemented in MATLAB (Natick, MA). For the purposes of training, we used a single 24‐hour window of data from each patient. For patients admitted to ICU, this window was 26 hours to 2 hours prior to ICU admission; for all other patients, this window consisted of the first 24 hours of their hospital stay. The dataset's 36 input variables were divided into buckets and min/mean/max features wherever applicable, resulting in 398 variables. The first half of the dataset was used to train the model. We then used the second half of the dataset as the validation dataset. We generated a predicted outcome for each case in the validation data, using the model parameter coefficients derived from the training data. We also employed bootstrap aggregation to improve classification accuracy and to address overfitting. We then applied various threshold cut‐points to convert these predictions into binary values and compared the results against the ICU transfer outcome. A threshold of 0.9760 for specificity was chosen to achieve a sensitivity of approximately 40%. These operating characteristics were chosen in turn to generate a manageable number of alerts per hospital nursing unit per day (estimated at 12 per nursing unit per day). At this cut‐point the C‐statistic was 0.8834, with an overall accuracy of 0.9292.
In order to train the logistic model, we used a single 24‐hour window of data for each patient. However, in a system that predicts patients' outcomes in real time, scores are recomputed each time new data are entered into the database. Hence, patients have a series of scores over the length of their hospital stay, and an alert is triggered when any one of these scores is above the chosen threshold.
Once the model was developed, we implemented it in an internally developed, Java‐based clinical decision support rules engine, which identified when new data relevant to the model were available in a real‐time central data repository. The rules engine queried the data repository to acquire all data needed to evaluate the model. The score was calculated with each relevant new data point, and an alert was generated when the score exceeded the cut‐point threshold. We then prospectively validated these alerts on patients on 8 general medical wards at Barnes Jewish Hospital. Details regarding the architecture of our clinical decision support system have been previously published.[12] The sensitivity and positive predictive values for ICU transfer for these alerts were tracked during an intervention trial that ran from January 24, 2011, through December 31, 2011. Four general medical wards were randomized to the intervention group and 4 wards were randomized to the control group. The 8 general medical wards were ordered according to their alert rates based upon the historical data from July 2007 through January 2010, creating 4 pairs of wards in ascending order of alert rate. Within each of the 4 pairs, 1 member of the pair was randomized to the intervention group and the other to the control group using a random number generator.
Real‐time automated alerts generated 24 hours per day, 7 days per week from the predictive algorithm were sent to the charge‐nurse pager on the intervention units. Alerts were also generated and stored in the database on the control units, but these alerts were not sent to the charge nurse on those units. The alerts were sent to the charge nurses on the respective wards, as these individuals were thought to be in the best position to perform the initial assessment of the alerted patients, especially during evening hours when physician staffing was reduced. The charge nurses assessed the intervention‐group patients and were instructed to contact the responsible physician (hospitalist or internal medicine house officer) to inform them of the alert, or to call the rapid response team (RRT) if the patient's condition already appeared to be significantly deteriorating.
Descriptive statistics for algorithm sensitivity and positive predictive value and for patient outcomes were performed. Associations between alerts and the primary outcome, ICU transfer, were determined, as well as the impact of alerts in the intervention group compared with the control group, using [2] tests. The same analyses were performed for patient death. Differences in length of stay (LOS) were assessed using the Wilcoxon rank sum test.
RESULTS
Predictive Model
The variables with the greatest coefficients contributing to the PT model included respiratory rate, oxygen saturation, shock index, systolic blood pressure, anticoagulation use, heart rate, and diastolic blood pressure. A complete list of variables is provided in the Appendix (see Supporting Information in the online version of this article). All but 1 are routinely collected vital‐sign measures, and all but 1 occur in the 4‐hour period immediately prior to the alert (bucket 6).
Prospective Trial
Patient characteristics are presented in Table 1. Patients were well matched for race, sex, age, and underlying diagnoses. All alerts reported to the charge nurses were to be associated with a call from the charge nurse to the responsible physician caring for the alerted patient. The mean number of alerts per alerted patient was 1.8 (standard deviation=1.7). Patients meeting the alert threshold were at nearly 5.3‐fold greater risk of ICU transfer (95% confidence interval [CI]: 4.6‐6.0) than those not satisfying the alert threshold (358 of 2353 [15.2%; 95% CI: 13.8%‐16.7%] vs 512 of 17678 [2.9%; 95% CI: 2.7%‐3.2%], respectively; P<0.0001). Patients with alerts were at 8.9‐fold greater risk of death (95% CI: 7.4‐10.7) than those without alerts (244 of 2353 [10.4%; 95% CI: 9.2%‐11.7%] vs 206 of 17678 [1.2%; 95% CI: 1.0%‐1.3%], respectively; P<0.0001). Operating characteristics of the PT from the prospective trial are shown in Table 2. Alerts occurred a median of 25.5 hours prior to ICU transfer (interquartile range, 7.00‐81.75) and 8 hours prior to death (interquartile range, 4.09‐15.66).
Study Group | ||||||
---|---|---|---|---|---|---|
Control (N=10,120) | Intervention (N=9911) | |||||
| ||||||
Race | N | % | N | % | ||
White | 5,062 | 50 | 4,934 | 50 | ||
Black | 4,864 | 48 | 4,790 | 48 | ||
Other | 194 | 2 | 187 | 2 | ||
Sex | ||||||
F | 5,355 | 53 | 5,308 | 54 | ||
M | 4,765 | 47 | 4,603 | 46 | ||
Age at discharge, median (IQR), y | 57 (4469) | 57 (4470) | ||||
Top 10 ICD‐9 descriptions and counts, n (%) | ||||||
1 | Diseases of the digestive system | 1,774 (17.5) | Diseases of the digestive system | 1,664 (16.7) | ||
2 | Diseases of the circulatory system | 1,252 (12.4) | Diseases of the circulatory system | 1,253 (12.6) | ||
3 | Diseases of the respiratory system | 1,236 (12.2) | Diseases of the respiratory system | 1,210 (12.2) | ||
4 | Injury and poisoning | 864 (8.5) | Injury and poisoning | 849 (8.6) | ||
5 | Endocrine, nutritional, and metabolic diseases, and immunity disorders | 797 (7.9) | Diseases of the genitourinary system | 795 (8.0) | ||
6 | Diseases of the genitourinary system | 762 (7.5) | Endocrine, nutritional, and metabolic diseases, and immunity disorders | 780 (7.9) | ||
7 | Infectious and parasitic diseases | 555 (5.5) | Infectious and parasitic diseases | 549 (5.5) | ||
8 | Neoplasms | 547 (5.4) | Neoplasms | 465 (4.7) | ||
9 | Diseases of the blood and blood‐forming organs | 426 (4.2) | Diseases of the blood and blood‐forming organs | 429 (4.3) | ||
10 | Symptoms, signs, and ill‐defined conditions and factors influencing health status | 410 (4.1) | Diseases of the musculoskeletal system and connective tissue | 399 (4.0) |
Sensitivity, % | Specificity, % | PPV, % | NPV, % | Positive Likelihood Ratio | Negative Likelihood Ratio | |||
---|---|---|---|---|---|---|---|---|
| ||||||||
ICU Transfer | Yes (N=870) | No (N=19,161) | ||||||
Alert | 358 | 1,995 | 41.1 (95% CI: 37.944.5) | 89.6 (95% CI: 89.290.0) | 15.2 (95% CI: 13.816.7) | 97.1 (95% CI: 96.897.3) | 3.95 (95% CI: 3.614.30) | 0.66 (95% CI: 0.620.70) |
No Alert | 512 | 17,166 | ||||||
Death | Yes (N=450) | No (N=19,581) | ||||||
Alert | 244 | 2109 | 54.2 (95% CI: 49.658.8) | 89.2 (95% CI: 88.889.7) | 10.4 (95% CI: 9.211.7) | 98.8 (95% CI: 98.799.0) | 5.03 (95% CI: 4.585.53) | 0.51 (95% CI: 0.460.57) |
No Alert | 206 | 17,472 |
Among patients identified by the PT, there were no differences in the proportion of patients who were transferred to the ICU or who died in the intervention group as compared with the control group (Table 3). In addition, although there was no difference in LOS in the intervention group compared with the control group, identification by the PT was associated with a significantly longer median LOS (7.01 days vs 2.94 days, P<0.001). The largest numbers of patients who were transferred to the ICU or died did so in the first hospital day, and 60% of patients who were transferred to the ICU did so in the first 4 days, whereas deaths were more evenly distributed across the hospital stay.
Outcomes by Alert Statusa | ||||||||
---|---|---|---|---|---|---|---|---|
Alert Study Group | No‐Alert Study Group | |||||||
Intervention, N=1194 | Control, N=1159 | Intervention, N=8717 | Control, N=8961 | |||||
N | % | N | % | N | % | N | % | |
| ||||||||
ICU Transfer | ||||||||
Yes | 192 | 16 | 166 | 14 | 252 | 3 | 260 | 3 |
No | 1002 | 84 | 993 | 86 | 8465 | 97 | 8701 | 97 |
Death | ||||||||
Yes | 127 | 11 | 117 | 10 | 96 | 1 | 110 | 1 |
No | 1067 | 89 | 1042 | 90 | 8621 | 99 | 8851 | 99 |
LOS from admit to discharge, median (IQR), da | 7.07 (3.9912.15) | 6.92 (3.8212.67) | 2.97 (1.775.33) | 2.91 (1.745.19) |
DISCUSSION
We have demonstrated that a relatively simple hospital‐specific method for generating a PT derived from routine laboratory and hemodynamic values is capable of predicting clinical deterioration and the need for ICU transfer, as well as hospital mortality, in non‐ICU patients admitted to general hospital wards. We also found that the PT identified a sicker patient population as manifest by longer hospital LOS. The methods used in generating this real‐time PT are relatively simple and easily executed with the use of an electronic medical record (EMR) system. However, our data also showed that simply providing an alert to nursing units based on the PT did not result in any demonstrable improvement in patient outcomes. Moreover, our PT and intervention in their current form have substantial limitations, including low sensitivity and positive predictive value, high possibility of alert fatigue, and no clear clinical impact. These limitations suggest that this approach has limited applicability in its current form.
Unplanned ICU transfers occurring as early as within 8 hours of hospitalization are relatively common and associated with increased mortality.[13] Bapoje et al evaluated a total of 152 patients over 1 year who had unplanned ICU transfers.[14] The most common reason was worsening of the problem for which the patient was admitted (48%). Other investigators have also attempted to identify predictors for clinical deterioration resulting in unplanned ICU transfer that could be employed in a PT or early warning system (EWS). Keller et al evaluated 50 consecutive general medical patients with unplanned ICU transfers between 2003 and 2004.[15] Using a case‐control methodology, these investigators found shock index values>0.85 to be the best predictor for subsequent unplanned ICU transfer (P<0.02; odds ratio: 3.0).
Organizations such as the Institute for Healthcare Improvement have called for the development and implementation of EWSs in order to direct the activities of RRTs and improve outcomes.[16] Escobar et al carried out a retrospective case‐control study using as the unit of analysis 12‐hour patient shifts on hospital wards.[17] Using logistic regression and split validation, they developed a PT for ICU transfer from clinical variables available in their EMR. The EMR derived PT had a C‐statistic of 0.845 in the derivation dataset and 0.775 in the validation dataset, concluding that EMR‐based detection of impending deterioration outside the ICU is feasible in integrated healthcare delivery systems.
We found that simply providing an alert to nursing units did not result in any demonstrable improvements in the outcomes of high‐risk patients identified by our PT. This may have been due to simply relying on the alerted nursing staff to make phone calls to physicians and not linking a specific and effective patient‐directed intervention to the PT. Other investigators have similarly observed that the use of an EWS or PT may not result in outcome improvements.[18] Gao et al performed an analysis of 31 studies describing hospital track and trigger EWSs.[19] They found little evidence of reliability, validity, and utility of these systems. Peebles et al showed that even when high‐risk non‐ICU patients are identified, delays in providing appropriate therapies occur, which may explain the lack of efficacy of EWSs and RRTs.[20] These observations suggest that there is currently a paucity of validated interventions available to improve outcome in deteriorating patients, despite our ability to identify patients who are at risk for such deterioration.
As a result of mandates from quality‐improvement organizations, most US hospitals currently employ RRTs for emergent mobilization of resources when a clinically deteriorating patient is identified on a hospital ward.[21] However, as noted above, there is limited evidence to suggest that RRTs contribute to improved patient outcomes.[22, 23, 24, 25, 26, 27] The potential importance of this is reflected in a recent report suggesting that 2900 US hospitals now have rapid‐response systems in place without clear demonstration of their overall efficacy.[28] Linking rapid‐response interventions with a validated real‐time alert may represent a way of improving the effectiveness of such interventions.[29, 30, 31, 32, 33, 34] Our data showed that hospital LOS was statistically longer among alerted patients compared with nonalerted patients. This supports the conclusion that the alerts helped identify a sicker group of patients, but the nursing alerts did not appear to change outcomes. This finding also seems to refute the hypothesis that simply linking an intervention to a PT will improve outcomes, albeit the intervention we employed may not have been robust enough to influence patient outcomes.
The development of accurate real‐time EWSs holds the potential to identify patients at risk for clinical deterioration at an earlier point in time when rescue interventions can be implemented in a potentially more effective manner in both adults and children.[35] Unfortunately, the ideal intervention to be applied in this situation is unknown. Our experience suggests that successful interventions will require a more integrated approach than simply providing an alert with general management principles. As a result of our experience, we are undertaking a randomized clinical trial in 2013 to determine whether linking a patient‐specific intervention to a PT will result in improved outcomes. The intervention we will be testing is to have the RRT immediately notified about alerted patients so as to formally evaluate them and to determine the need for therapeutic interventions, and to administer such interventions as needed and/or transfer the alerted patients to a higher level of care as deemed necessary. Additionally, we are updating our PT with more temporal data to determine if this will improve its accuracy. One of these updates will include linking the PT to wirelessly obtained continuous oximetry and heart‐rate data, using minimally intrusive sensors, to establish a 2‐tiered EWS.[11]
Our study has several important limitations. First, the PT was developed using local data, and thus the results may not be applicable to other settings. However, our model shares many of the characteristics identified in other clinical‐deterioration PTs.[15, 17] Second, the positive prediction value of 15.2% for ICU transfer may not be clinically useful due to the large number of false‐positive results. Moreover, the large number of false positives could result in alert fatigue, causing alerts to be ignored. Third, although the charge nurses were supposed to call the responsible physicians for the alerted patients, we did not determine whether all these calls occurred or whether they resulted in any meaningful changes in monitoring or patient treatment. This is important because lack of an effective intervention or treatment would make the intervention group much more like our control group. Future studies are needed to assess the impact of an integrated intervention (eg, notification of experienced RRT members with adequate resource access) to determine if patient outcomes can be impacted by the use of an EWS. Finally, we did not compare the performance of our PT to other models such as the modified early warning score (MEWS).
An additional limitation to consider is that our PT offered no new information to the nurse manager, or the PT did not change the opinions of the charge nurses. This is supported by a recent study of 63 serious adverse outcomes in a Belgian teaching hospital where death was the final outcome.[36] Survey results revealed that nurses were often unaware that their patients were deteriorating before the crisis. Nurses also reported threshold levels for concern for abnormal vital signs that suggested they would call for assistance relatively late in clinical crises. The limited ability of nursing staff to identify deteriorating patients is also supported by a recent simulation study demonstrating that nurses did identify that patients were deteriorating, but as each patient deteriorated staff performance declined, with a reduction in all observational records and actions.[37]
In summary, we have demonstrated that a relatively simple hospital‐specific PT could accurately identify patients on general medicine wards who subsequently developed clinical deterioration and the need for ICU transfer, as well as hospital mortality. However, no improvements in patient outcomes were found from reporting this information to nursing wards on a real‐time basis. The low positive predictive value of the alerts, local development of the EWS, and absence of improved outcomes substantially limits the broader application of this system in its current form. Continued efforts are needed to identify and implement systems that will not only accurately identify high‐risk patients on general wards but also intervene to improve their outcomes.
Acknowledgments
Disclosures: This study was funded in part by the Barnes‐Jewish Hospital Foundation and by Grant No. UL1 RR024992 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH), and the NIH Roadmap for Medical Research. Its contents are solely the responsibility of the authors and do not necessarily represent the official view of NCRR or NIH.
- Improvement in process of care and outcome after a multicenter severe sepsis educational program in Spain. JAMA. 2008;299:2294–2303. , , , et al.
- Early goal‐directed therapy in the treatment of severe sepsis and septic shock. N Engl J Med. 2001;345:1368–1377. , , , et al.
- Before‐after study of a standardized hospital order set for the management of septic shock. Crit Care Med. 2007;34:2707–2713. , , , et al.
- Surviving Sepsis Campaign: international guidelines for management of severe sepsis and septic shock: 2008. Crit Care Med. 2008;36:296–327. , , .
- Septic shock: an analysis of outcomes for patients with onset on hospital wards versus intensive care units. Crit Care Med. 1998;26:1020–1024. , , , et al.
- Inpatient transfers to the intensive care unit: delays are associated with increased mortality and morbidity. J Gen Intern Med. 2003;18:77–83. , , , , .
- Causes and effects of surgical delay in patients with hip fracture: a cohort study. Ann Intern Med. 2011;155:226–233. , , , , , .
- Comprehensive stroke centers overcome the weekend versus weekday gap in stroke treatment and mortality. Stroke. 2011;42:2403–2409. , , , .
- Hospital‐wide impact of a standardized order set for the management of bacteremic severe sepsis. Crit Care Med. 2009;37:819–824. , , , , , .
- Implementation of a real‐time computerized sepsis alert in non–intensive care unit patients. Crit Care Med. 2011;39:469–473. , , , et al.
- Toward a two‐tier clinical warning system for hospitalized patients. AMIA Annu Symp Proc. 2011;2011:511–519. , , , et al.
- Migrating toward a next‐generation clinical decision support application: the BJC HealthCare experience. AMIA Annu Symp Proc. 2007;344–348. , , , , , .
- Adverse outcomes associated with delayed intensive care unit transfers in an integrated healthcare system. J Hosp Med. 2011;7:224–230. , , , .
- Unplanned transfers to a medical intensive care unit: causes and relationship to preventable errors in care. J Hosp Med. 2011;6:68–72. , , , .
- Unplanned transfers to the intensive care unit: the role of the shock index. J Hosp Med. 2010;5:460–465. , , , , , .
- Institute for Healthcare Improvement. Early warning systems: the next level of rapid response. Available at: http://www.ihi.org/IHI/Programs/AudioAndWebPrograms/ExpeditionEarlyWarningSystemsTheNextLevelofRapidResponse.htmplayerwmp. Accessed April 6, 2011.
- Early detection of impending physiologic deterioration among patients who are not in intensive care: development of predictive models using data from an automated electronic medical record. J Hosp Med. 2012;7:388–395. , , , , , .
- Early warning systems: the next level of rapid response. Nursing. 2012;42:38–44. , , .
- Systematic review and evaluation of physiological track and trigger warning systems for identifying at‐risk patients on the ward. Intensive Care Med. 2007;33:667–679. , , , et al.
- Timing and teamwork—an observational pilot study of patients referred to a Rapid Response Team with the aim of identifying factors amenable to re‐design of a Rapid Response System. Resuscitation. 2012;83:782–787. , , , .
- Rapid response: a quality improvement conundrum. J Hosp Med. 2009;4:255–257. , , , .
- Introducing critical care outreach: a ward‐randomised trial of phased introduction in a general hospital. Intensive Care Med. 2004;30:1398–1404. , , , et al.
- Out of our reach? Assessing the impact of introducing critical care outreach service. Anaesthesiology. 2003;58:882–885. .
- Effect of the critical care outreach team on patient survival to discharge from hospital and readmission to critical care: non‐randomised population based study. BMJ. 2003;327:1014–1016. , , .
- Reducing mortality and avoiding preventable ICU utilization: analysis of a successful rapid response program using APR DRGs [published online ahead of print March 10, 2010]. J Healthc Qual. doi: 10.1111/j.1945‐1474.2010.00084.x. , , .
- Introduction of the medical emergency team (MET) system: a cluster‐randomised control trial. Lancet. 2005;365:2091–2097. , , , et al.
- The impact of the introduction of critical care outreach services in England: a multicentre interrupted time‐series analysis. Crit Care. 2007;11:R113. , , , , , .
- Rapid response systems now established at 2,900 hospitals. Hospitalist News. March 2010;3:1. .
- Rapid response teams: a systematic review and meta‐analysis. Arch Intern Med. 2010;170:18–26. , , , , .
- Outreach and early warning systems (EWS) for the prevention of intensive care admission and death of critically ill adult patients on general hospital wards. Cochrane Database Syst Rev. 2007;3:CD005529. , , , et al.
- Rapid‐response teams. N Engl J Med. 2011;365:139–146. , , .
- Early warning systems. Hosp Chron. 2012;7(suppl 1):37–43. , , .
- Grand challenges in clinical decision support. J Biomed Inform. 2008;41(2):387–392. , , , et al.
- Utility of commonly captured data from an EHR to identify hospitalized patients at risk for clinical deterioration. AMIA Annu Symp Proc. 2007;404–408. , , , et al.
- Sensitivity of the pediatric early warning score to identify patient deterioration. Pediatrics. 2010;125(4)e763–e769. , , , , , .
- In‐hospital mortality after serious adverse events on medical and surgical nursing units: a mixed methods study [published online ahead of print July 24, 2012]. J Clin Nurs. doi: 10.1111/j.1365‐2702.2012.04154.x. , , , .
- Managing deteriorating patients: registered nurses' performance in a simulated setting. Open Nurs J. 2011;5:120–126. , , , et al.
- Improvement in process of care and outcome after a multicenter severe sepsis educational program in Spain. JAMA. 2008;299:2294–2303. , , , et al.
- Early goal‐directed therapy in the treatment of severe sepsis and septic shock. N Engl J Med. 2001;345:1368–1377. , , , et al.
- Before‐after study of a standardized hospital order set for the management of septic shock. Crit Care Med. 2007;34:2707–2713. , , , et al.
- Surviving Sepsis Campaign: international guidelines for management of severe sepsis and septic shock: 2008. Crit Care Med. 2008;36:296–327. , , .
- Septic shock: an analysis of outcomes for patients with onset on hospital wards versus intensive care units. Crit Care Med. 1998;26:1020–1024. , , , et al.
- Inpatient transfers to the intensive care unit: delays are associated with increased mortality and morbidity. J Gen Intern Med. 2003;18:77–83. , , , , .
- Causes and effects of surgical delay in patients with hip fracture: a cohort study. Ann Intern Med. 2011;155:226–233. , , , , , .
- Comprehensive stroke centers overcome the weekend versus weekday gap in stroke treatment and mortality. Stroke. 2011;42:2403–2409. , , , .
- Hospital‐wide impact of a standardized order set for the management of bacteremic severe sepsis. Crit Care Med. 2009;37:819–824. , , , , , .
- Implementation of a real‐time computerized sepsis alert in non–intensive care unit patients. Crit Care Med. 2011;39:469–473. , , , et al.
- Toward a two‐tier clinical warning system for hospitalized patients. AMIA Annu Symp Proc. 2011;2011:511–519. , , , et al.
- Migrating toward a next‐generation clinical decision support application: the BJC HealthCare experience. AMIA Annu Symp Proc. 2007;344–348. , , , , , .
- Adverse outcomes associated with delayed intensive care unit transfers in an integrated healthcare system. J Hosp Med. 2011;7:224–230. , , , .
- Unplanned transfers to a medical intensive care unit: causes and relationship to preventable errors in care. J Hosp Med. 2011;6:68–72. , , , .
- Unplanned transfers to the intensive care unit: the role of the shock index. J Hosp Med. 2010;5:460–465. , , , , , .
- Institute for Healthcare Improvement. Early warning systems: the next level of rapid response. Available at: http://www.ihi.org/IHI/Programs/AudioAndWebPrograms/ExpeditionEarlyWarningSystemsTheNextLevelofRapidResponse.htmplayerwmp. Accessed April 6, 2011.
- Early detection of impending physiologic deterioration among patients who are not in intensive care: development of predictive models using data from an automated electronic medical record. J Hosp Med. 2012;7:388–395. , , , , , .
- Early warning systems: the next level of rapid response. Nursing. 2012;42:38–44. , , .
- Systematic review and evaluation of physiological track and trigger warning systems for identifying at‐risk patients on the ward. Intensive Care Med. 2007;33:667–679. , , , et al.
- Timing and teamwork—an observational pilot study of patients referred to a Rapid Response Team with the aim of identifying factors amenable to re‐design of a Rapid Response System. Resuscitation. 2012;83:782–787. , , , .
- Rapid response: a quality improvement conundrum. J Hosp Med. 2009;4:255–257. , , , .
- Introducing critical care outreach: a ward‐randomised trial of phased introduction in a general hospital. Intensive Care Med. 2004;30:1398–1404. , , , et al.
- Out of our reach? Assessing the impact of introducing critical care outreach service. Anaesthesiology. 2003;58:882–885. .
- Effect of the critical care outreach team on patient survival to discharge from hospital and readmission to critical care: non‐randomised population based study. BMJ. 2003;327:1014–1016. , , .
- Reducing mortality and avoiding preventable ICU utilization: analysis of a successful rapid response program using APR DRGs [published online ahead of print March 10, 2010]. J Healthc Qual. doi: 10.1111/j.1945‐1474.2010.00084.x. , , .
- Introduction of the medical emergency team (MET) system: a cluster‐randomised control trial. Lancet. 2005;365:2091–2097. , , , et al.
- The impact of the introduction of critical care outreach services in England: a multicentre interrupted time‐series analysis. Crit Care. 2007;11:R113. , , , , , .
- Rapid response systems now established at 2,900 hospitals. Hospitalist News. March 2010;3:1. .
- Rapid response teams: a systematic review and meta‐analysis. Arch Intern Med. 2010;170:18–26. , , , , .
- Outreach and early warning systems (EWS) for the prevention of intensive care admission and death of critically ill adult patients on general hospital wards. Cochrane Database Syst Rev. 2007;3:CD005529. , , , et al.
- Rapid‐response teams. N Engl J Med. 2011;365:139–146. , , .
- Early warning systems. Hosp Chron. 2012;7(suppl 1):37–43. , , .
- Grand challenges in clinical decision support. J Biomed Inform. 2008;41(2):387–392. , , , et al.
- Utility of commonly captured data from an EHR to identify hospitalized patients at risk for clinical deterioration. AMIA Annu Symp Proc. 2007;404–408. , , , et al.
- Sensitivity of the pediatric early warning score to identify patient deterioration. Pediatrics. 2010;125(4)e763–e769. , , , , , .
- In‐hospital mortality after serious adverse events on medical and surgical nursing units: a mixed methods study [published online ahead of print July 24, 2012]. J Clin Nurs. doi: 10.1111/j.1365‐2702.2012.04154.x. , , , .
- Managing deteriorating patients: registered nurses' performance in a simulated setting. Open Nurs J. 2011;5:120–126. , , , et al.
Copyright © 2013 Society of Hospital Medicine
Subspecialties Working Together in Multidisciplinary Cosmetic Centers
Dr. Feldman discusses a multidisciplinary approach to cosmetic medicine and reviews the findings from his survey of physicians from different specialties. For more information, read Dr. Feldman's article in the July 2012 issue, "Academic Physicians' Attitudes Toward Implementation of Multidisciplinary Cosmetic Centers and the Challenges of Subspecialties Working Together."
Dr. Feldman discusses a multidisciplinary approach to cosmetic medicine and reviews the findings from his survey of physicians from different specialties. For more information, read Dr. Feldman's article in the July 2012 issue, "Academic Physicians' Attitudes Toward Implementation of Multidisciplinary Cosmetic Centers and the Challenges of Subspecialties Working Together."
Dr. Feldman discusses a multidisciplinary approach to cosmetic medicine and reviews the findings from his survey of physicians from different specialties. For more information, read Dr. Feldman's article in the July 2012 issue, "Academic Physicians' Attitudes Toward Implementation of Multidisciplinary Cosmetic Centers and the Challenges of Subspecialties Working Together."
'I never know when to call palliative care'
This morbidity and mortality conference was like any other: First, we reviewed the case of a patient with a complication from anticoagulation and then another with an anastomotic leak. Finally, we discussed an elderly patient who had a major emergent procedure to treat complications from an underlying life-limiting condition, only to die in hospital weeks later after developing insurmountable medical, surgical, and infectious complications.
This elderly patient was a man in his mid-70s recently diagnosed with recurrent melanoma. He came to our emergency department with peritonitis and hypotension. His wife of 52 years sat beside him. She was tearful and afraid. "We want everything done," she said. Eight weeks ago, he was working full time and playing golf. But, 6 weeks ago he became confused. A CT scan revealed brain metastasis. He spent 5 of the last 6 weeks in the ICU and in a step-down unit, or a nursing home after a series of complications from his brain biopsy. Now he was back in the hospital with a bowel perforation.
Before our surgical team even saw him, he was told he needed surgery, or he would die. We discussed the surgical risks including the likelihood of a protracted ICU stay, and the high risk he would never go home. Still, he and his wife were unprepared for death and so we went to the operating room.
The following weeks were fraught with complications. His symptoms – including delirium, tumor headaches, and pain – were all difficult to manage on his cocktail of steroids, opiates, and antipsychotics. Occasionally, he would mumble something about dying but we couldn’t determine if he was lucid. His symptoms and "talk about death" were distressing for his family. All in all, our team spent almost an hour each day answering their questions and tending to their anxiety and suffering. It took a high emotional toll on our entire team, as we each worried to ourselves that we were doing more harm than good.
One organ system failed after the other. And finally, after two operations, 10 different consultants, and 3 weeks in the hospital, we stopped talking about organ systems and told the family that this man was dying. That day we consulted palliative care to help us with "goals of care." The next day, he became oliguric, and we shifted our focus to comfort. He died within hours surrounded by his loving extended family.
When we discussed this case at M&M, there were no objections to the decision to operate or how we managed his laundry list of complications. His death was deemed "nonpreventable." But, at then end of the discussion, a colleague asked, with some exasperation, "I never know when to call palliative care. How do you decide when the patient is dying?"
In retrospect, it is clear that this patient was dying when we met him in the emergency department. He was malnourished and disabled from his cancer and treatment. His bowel perforation was caused by the steroids prescribed to treat his underlying terminal disease. The best outcome we could hope for was a good quality of life in his last days, a peaceful and dignified death, and an uncomplicated bereavement for his survivors. Our emergency, life-saving surgery was, in fact, palliative. Death was near, but we just didn’t want him to die this way.
According to the American College of Surgeons code of professional conduct, surgeons play a pivotal role in facilitating the transition from curative to palliative treatment for the patients and the entire health care team. Furthermore, "effective palliation obligates sensitive discussion with patients and their families." These conversations can be particularly onerous for surgeons because we take on tremendous sense of personal responsibility for postoperative outcomes. Once we commit to operating on a patient, their death, especially if it follows complications, can be equated with personal defeat. We may benefit from consulting specialists who can help us set the stage, and smooth the transition for our patients and their families. Surgeons may also personally benefit from the support of other providers to help us cope with these emotionally difficult cases.
Palliative care is a multidisciplinary model of care to address the physical, intellectual, emotional, social, and spiritual needs of patients and families facing serious illness. The goal of palliative care is to support the best possible quality of life for patients at all stages of serious illness, through providing aggressive symptom management, psychosocial and spiritual care, and grief and bereavement counseling before and after death. Palliative care seeks to be life affirming and is based on the understanding of death as a normal life process. It can and should be delivered along with life-prolonging treatment.
In this case, palliative care should have been offered in the emergency department as soon as this patient was admitted to our service. The patient, and his family, would have benefited from a team of physicians, nurses, pharmacists, social workers, and chaplains with the time and expertise to manage distressing symptoms from his cancer, and attend to the grief and suffering that characterized his final weeks. Earlier palliative care may have also steered us away from the slog of high-burden treatments that ultimately offered him little benefit. For the surgeons, palliative care would have provided additional resources to take the best possible care of our patient who, whether or not he made it home, was near the end of his life from an advanced illness.
Dr. Zara Cooper is an ACS Fellow, and assistant professor of surgery, Harvard Medical School, and department of surgery, division of trauma, burns and critical care at Brigham and Women’s Hospital, Boston. Dr. Cooper has no disclosures relevant to this editorial.
This morbidity and mortality conference was like any other: First, we reviewed the case of a patient with a complication from anticoagulation and then another with an anastomotic leak. Finally, we discussed an elderly patient who had a major emergent procedure to treat complications from an underlying life-limiting condition, only to die in hospital weeks later after developing insurmountable medical, surgical, and infectious complications.
This elderly patient was a man in his mid-70s recently diagnosed with recurrent melanoma. He came to our emergency department with peritonitis and hypotension. His wife of 52 years sat beside him. She was tearful and afraid. "We want everything done," she said. Eight weeks ago, he was working full time and playing golf. But, 6 weeks ago he became confused. A CT scan revealed brain metastasis. He spent 5 of the last 6 weeks in the ICU and in a step-down unit, or a nursing home after a series of complications from his brain biopsy. Now he was back in the hospital with a bowel perforation.
Before our surgical team even saw him, he was told he needed surgery, or he would die. We discussed the surgical risks including the likelihood of a protracted ICU stay, and the high risk he would never go home. Still, he and his wife were unprepared for death and so we went to the operating room.
The following weeks were fraught with complications. His symptoms – including delirium, tumor headaches, and pain – were all difficult to manage on his cocktail of steroids, opiates, and antipsychotics. Occasionally, he would mumble something about dying but we couldn’t determine if he was lucid. His symptoms and "talk about death" were distressing for his family. All in all, our team spent almost an hour each day answering their questions and tending to their anxiety and suffering. It took a high emotional toll on our entire team, as we each worried to ourselves that we were doing more harm than good.
One organ system failed after the other. And finally, after two operations, 10 different consultants, and 3 weeks in the hospital, we stopped talking about organ systems and told the family that this man was dying. That day we consulted palliative care to help us with "goals of care." The next day, he became oliguric, and we shifted our focus to comfort. He died within hours surrounded by his loving extended family.
When we discussed this case at M&M, there were no objections to the decision to operate or how we managed his laundry list of complications. His death was deemed "nonpreventable." But, at then end of the discussion, a colleague asked, with some exasperation, "I never know when to call palliative care. How do you decide when the patient is dying?"
In retrospect, it is clear that this patient was dying when we met him in the emergency department. He was malnourished and disabled from his cancer and treatment. His bowel perforation was caused by the steroids prescribed to treat his underlying terminal disease. The best outcome we could hope for was a good quality of life in his last days, a peaceful and dignified death, and an uncomplicated bereavement for his survivors. Our emergency, life-saving surgery was, in fact, palliative. Death was near, but we just didn’t want him to die this way.
According to the American College of Surgeons code of professional conduct, surgeons play a pivotal role in facilitating the transition from curative to palliative treatment for the patients and the entire health care team. Furthermore, "effective palliation obligates sensitive discussion with patients and their families." These conversations can be particularly onerous for surgeons because we take on tremendous sense of personal responsibility for postoperative outcomes. Once we commit to operating on a patient, their death, especially if it follows complications, can be equated with personal defeat. We may benefit from consulting specialists who can help us set the stage, and smooth the transition for our patients and their families. Surgeons may also personally benefit from the support of other providers to help us cope with these emotionally difficult cases.
Palliative care is a multidisciplinary model of care to address the physical, intellectual, emotional, social, and spiritual needs of patients and families facing serious illness. The goal of palliative care is to support the best possible quality of life for patients at all stages of serious illness, through providing aggressive symptom management, psychosocial and spiritual care, and grief and bereavement counseling before and after death. Palliative care seeks to be life affirming and is based on the understanding of death as a normal life process. It can and should be delivered along with life-prolonging treatment.
In this case, palliative care should have been offered in the emergency department as soon as this patient was admitted to our service. The patient, and his family, would have benefited from a team of physicians, nurses, pharmacists, social workers, and chaplains with the time and expertise to manage distressing symptoms from his cancer, and attend to the grief and suffering that characterized his final weeks. Earlier palliative care may have also steered us away from the slog of high-burden treatments that ultimately offered him little benefit. For the surgeons, palliative care would have provided additional resources to take the best possible care of our patient who, whether or not he made it home, was near the end of his life from an advanced illness.
Dr. Zara Cooper is an ACS Fellow, and assistant professor of surgery, Harvard Medical School, and department of surgery, division of trauma, burns and critical care at Brigham and Women’s Hospital, Boston. Dr. Cooper has no disclosures relevant to this editorial.
This morbidity and mortality conference was like any other: First, we reviewed the case of a patient with a complication from anticoagulation and then another with an anastomotic leak. Finally, we discussed an elderly patient who had a major emergent procedure to treat complications from an underlying life-limiting condition, only to die in hospital weeks later after developing insurmountable medical, surgical, and infectious complications.
This elderly patient was a man in his mid-70s recently diagnosed with recurrent melanoma. He came to our emergency department with peritonitis and hypotension. His wife of 52 years sat beside him. She was tearful and afraid. "We want everything done," she said. Eight weeks ago, he was working full time and playing golf. But, 6 weeks ago he became confused. A CT scan revealed brain metastasis. He spent 5 of the last 6 weeks in the ICU and in a step-down unit, or a nursing home after a series of complications from his brain biopsy. Now he was back in the hospital with a bowel perforation.
Before our surgical team even saw him, he was told he needed surgery, or he would die. We discussed the surgical risks including the likelihood of a protracted ICU stay, and the high risk he would never go home. Still, he and his wife were unprepared for death and so we went to the operating room.
The following weeks were fraught with complications. His symptoms – including delirium, tumor headaches, and pain – were all difficult to manage on his cocktail of steroids, opiates, and antipsychotics. Occasionally, he would mumble something about dying but we couldn’t determine if he was lucid. His symptoms and "talk about death" were distressing for his family. All in all, our team spent almost an hour each day answering their questions and tending to their anxiety and suffering. It took a high emotional toll on our entire team, as we each worried to ourselves that we were doing more harm than good.
One organ system failed after the other. And finally, after two operations, 10 different consultants, and 3 weeks in the hospital, we stopped talking about organ systems and told the family that this man was dying. That day we consulted palliative care to help us with "goals of care." The next day, he became oliguric, and we shifted our focus to comfort. He died within hours surrounded by his loving extended family.
When we discussed this case at M&M, there were no objections to the decision to operate or how we managed his laundry list of complications. His death was deemed "nonpreventable." But, at then end of the discussion, a colleague asked, with some exasperation, "I never know when to call palliative care. How do you decide when the patient is dying?"
In retrospect, it is clear that this patient was dying when we met him in the emergency department. He was malnourished and disabled from his cancer and treatment. His bowel perforation was caused by the steroids prescribed to treat his underlying terminal disease. The best outcome we could hope for was a good quality of life in his last days, a peaceful and dignified death, and an uncomplicated bereavement for his survivors. Our emergency, life-saving surgery was, in fact, palliative. Death was near, but we just didn’t want him to die this way.
According to the American College of Surgeons code of professional conduct, surgeons play a pivotal role in facilitating the transition from curative to palliative treatment for the patients and the entire health care team. Furthermore, "effective palliation obligates sensitive discussion with patients and their families." These conversations can be particularly onerous for surgeons because we take on tremendous sense of personal responsibility for postoperative outcomes. Once we commit to operating on a patient, their death, especially if it follows complications, can be equated with personal defeat. We may benefit from consulting specialists who can help us set the stage, and smooth the transition for our patients and their families. Surgeons may also personally benefit from the support of other providers to help us cope with these emotionally difficult cases.
Palliative care is a multidisciplinary model of care to address the physical, intellectual, emotional, social, and spiritual needs of patients and families facing serious illness. The goal of palliative care is to support the best possible quality of life for patients at all stages of serious illness, through providing aggressive symptom management, psychosocial and spiritual care, and grief and bereavement counseling before and after death. Palliative care seeks to be life affirming and is based on the understanding of death as a normal life process. It can and should be delivered along with life-prolonging treatment.
In this case, palliative care should have been offered in the emergency department as soon as this patient was admitted to our service. The patient, and his family, would have benefited from a team of physicians, nurses, pharmacists, social workers, and chaplains with the time and expertise to manage distressing symptoms from his cancer, and attend to the grief and suffering that characterized his final weeks. Earlier palliative care may have also steered us away from the slog of high-burden treatments that ultimately offered him little benefit. For the surgeons, palliative care would have provided additional resources to take the best possible care of our patient who, whether or not he made it home, was near the end of his life from an advanced illness.
Dr. Zara Cooper is an ACS Fellow, and assistant professor of surgery, Harvard Medical School, and department of surgery, division of trauma, burns and critical care at Brigham and Women’s Hospital, Boston. Dr. Cooper has no disclosures relevant to this editorial.
Dabigatran noninferior to warfarin for preventing recurrent VTE

Credit: Andre E.X. Brown
New research suggests dabigatran is noninferior to warfarin as extended prophylaxis for recurrent venous thromboembolism (VTE), and warfarin presents a significantly higher risk of bleeding.
These results are from the RE-MEDY study, which compared the 2 drugs as long-term prophylaxis in patients who had received at least 3 months of VTE treatment.
The data appear in an NEJM article alongside results of the RE-SONATE study, which compared dabigatran and placebo in a similar patient population.
Both of these randomized, double-blind studies were sponsored by the makers of dabigatran, Boehringer Ingelheim.
In the RE-MEDY trial, 2856 patients were randomized in a 1:1 ratio to receive dabigatran or warfarin for up to 36 months. Patients either received active dabigatran at 150 mg twice daily and a warfarin-like placebo or active warfarin and a dabigatran-like placebo. The warfarin dose was adjusted to maintain an INR of 2.0 to 3.0.
In the RE-SONATE trial, 1343 patients were randomized to receive treatment for 6 months. They were assigned in a 1:1 ratio to receive dabigatran at 150 mg twice daily or a matching placebo.
Extended follow-up to evaluate the long-term risk of VTE recurrence took place 12 months after the completion of study treatment.
In RE-MEDY, recurrent VTE occurred in 1.8% of patients in the dabigatran arm and 1.3% of patients in the warfarin arm (P=0.01 for noninferiority).
In RE-SONATE, recurrent VTE occurred in 0.4% of patients in the dabigatran arm and 5.6% of patients in the placebo arm (P<0.001 for superiority).
The rate of clinically relevant or major bleeding was lower with dabigatran than with warfarin—at 5.6% and 10.2%, respectively (P<0.001).
But the rate of clinically relevant or major bleeding was higher with dabigatran than with placebo, at 5.3% and 1.8%, respectively (P=0.001).
“[These results] suggest dabigatran is a good option to prevent deep vein thrombosis and pulmonary embolism from happening again after an initial event,” said lead study author Sam Schulman, MD, PhD, of McMaster University in Hamilton, Ontario, Canada.
“They reinforce the efficacy and favorable safety profile of dabigatran seen in the RE-COVER trials, where dabigatran showed similar efficacy and a significant reduction in clinically relevant bleeding versus warfarin in the treatment of acute venous thromboembolism.”

Credit: Andre E.X. Brown
New research suggests dabigatran is noninferior to warfarin as extended prophylaxis for recurrent venous thromboembolism (VTE), and warfarin presents a significantly higher risk of bleeding.
These results are from the RE-MEDY study, which compared the 2 drugs as long-term prophylaxis in patients who had received at least 3 months of VTE treatment.
The data appear in an NEJM article alongside results of the RE-SONATE study, which compared dabigatran and placebo in a similar patient population.
Both of these randomized, double-blind studies were sponsored by the makers of dabigatran, Boehringer Ingelheim.
In the RE-MEDY trial, 2856 patients were randomized in a 1:1 ratio to receive dabigatran or warfarin for up to 36 months. Patients either received active dabigatran at 150 mg twice daily and a warfarin-like placebo or active warfarin and a dabigatran-like placebo. The warfarin dose was adjusted to maintain an INR of 2.0 to 3.0.
In the RE-SONATE trial, 1343 patients were randomized to receive treatment for 6 months. They were assigned in a 1:1 ratio to receive dabigatran at 150 mg twice daily or a matching placebo.
Extended follow-up to evaluate the long-term risk of VTE recurrence took place 12 months after the completion of study treatment.
In RE-MEDY, recurrent VTE occurred in 1.8% of patients in the dabigatran arm and 1.3% of patients in the warfarin arm (P=0.01 for noninferiority).
In RE-SONATE, recurrent VTE occurred in 0.4% of patients in the dabigatran arm and 5.6% of patients in the placebo arm (P<0.001 for superiority).
The rate of clinically relevant or major bleeding was lower with dabigatran than with warfarin—at 5.6% and 10.2%, respectively (P<0.001).
But the rate of clinically relevant or major bleeding was higher with dabigatran than with placebo, at 5.3% and 1.8%, respectively (P=0.001).
“[These results] suggest dabigatran is a good option to prevent deep vein thrombosis and pulmonary embolism from happening again after an initial event,” said lead study author Sam Schulman, MD, PhD, of McMaster University in Hamilton, Ontario, Canada.
“They reinforce the efficacy and favorable safety profile of dabigatran seen in the RE-COVER trials, where dabigatran showed similar efficacy and a significant reduction in clinically relevant bleeding versus warfarin in the treatment of acute venous thromboembolism.”

Credit: Andre E.X. Brown
New research suggests dabigatran is noninferior to warfarin as extended prophylaxis for recurrent venous thromboembolism (VTE), and warfarin presents a significantly higher risk of bleeding.
These results are from the RE-MEDY study, which compared the 2 drugs as long-term prophylaxis in patients who had received at least 3 months of VTE treatment.
The data appear in an NEJM article alongside results of the RE-SONATE study, which compared dabigatran and placebo in a similar patient population.
Both of these randomized, double-blind studies were sponsored by the makers of dabigatran, Boehringer Ingelheim.
In the RE-MEDY trial, 2856 patients were randomized in a 1:1 ratio to receive dabigatran or warfarin for up to 36 months. Patients either received active dabigatran at 150 mg twice daily and a warfarin-like placebo or active warfarin and a dabigatran-like placebo. The warfarin dose was adjusted to maintain an INR of 2.0 to 3.0.
In the RE-SONATE trial, 1343 patients were randomized to receive treatment for 6 months. They were assigned in a 1:1 ratio to receive dabigatran at 150 mg twice daily or a matching placebo.
Extended follow-up to evaluate the long-term risk of VTE recurrence took place 12 months after the completion of study treatment.
In RE-MEDY, recurrent VTE occurred in 1.8% of patients in the dabigatran arm and 1.3% of patients in the warfarin arm (P=0.01 for noninferiority).
In RE-SONATE, recurrent VTE occurred in 0.4% of patients in the dabigatran arm and 5.6% of patients in the placebo arm (P<0.001 for superiority).
The rate of clinically relevant or major bleeding was lower with dabigatran than with warfarin—at 5.6% and 10.2%, respectively (P<0.001).
But the rate of clinically relevant or major bleeding was higher with dabigatran than with placebo, at 5.3% and 1.8%, respectively (P=0.001).
“[These results] suggest dabigatran is a good option to prevent deep vein thrombosis and pulmonary embolism from happening again after an initial event,” said lead study author Sam Schulman, MD, PhD, of McMaster University in Hamilton, Ontario, Canada.
“They reinforce the efficacy and favorable safety profile of dabigatran seen in the RE-COVER trials, where dabigatran showed similar efficacy and a significant reduction in clinically relevant bleeding versus warfarin in the treatment of acute venous thromboembolism.”
The Society of Hospital Medicine’s "Choosing Wisely" Recommendations for Hospitalists
SHM has joined the American Board of Internal Medicine (ABIM) Foundation’s Choosing Wisely campaign, a multiyear effort to spark national dialogue about waste in the healthcare system and the kinds of common treatments that doctors and patients should think twice about before deciding to pursue. Ad hoc subcommittees of SHM’s Hospital Quality and Patient Safety Committee created lists of five adult and five pediatric treatments that hospitalists and their patients should question (see below). Those lists were shared alongside 15 other medical specialty societies at a Feb. 21 news conference in Washington, D.C.
Adult Hospitalist "Avoid List"
1. Do not place, or leave in place, urinary catheters for incontinence or convenience or monitoring of output for non-critically ill patients (acceptable indications: critical illness, obstruction, hospice, perioperatively for <2 days for urologic procedures; use weights instead to monitor diuresis).
2. Do not prescribe medications for stress ulcer prophylaxis to medical inpatients unless at high risk for GI complications.
3. Avoid transfusions of red blood cells for arbitrary hemoglobin or hematocrit thresholds and in the absence of symptoms or active coronary disease, heart failure or stroke.
4. Do not order continuous telemetry monitoring outside of the ICU without using a protocol that governs continuation.
5. Do not perform repetitive CBC and chemistry testing in the face of clinical and lab stability.
Pediatric HospitalIST "Avoid List"
1. Don’t order chest radiographs in children with uncomplicated asthma or bronchiolitis.
2. Don’t routinely use bronchodilators in children with bronchiolitis.
3. Don’t use systemic corticosteroids in children under 2 years of age with an uncomplicated lower respiratory tract infection.
4. Don’t treat gastroesophageal reflux in infants routinely with acid suppression therapy.
5. Don’t use continuous pulse oximetry routinely in children with acute respiratory illness unless they are on supplemental oxygen.
For complete recommendations and references, visit SHM's website.
SHM has joined the American Board of Internal Medicine (ABIM) Foundation’s Choosing Wisely campaign, a multiyear effort to spark national dialogue about waste in the healthcare system and the kinds of common treatments that doctors and patients should think twice about before deciding to pursue. Ad hoc subcommittees of SHM’s Hospital Quality and Patient Safety Committee created lists of five adult and five pediatric treatments that hospitalists and their patients should question (see below). Those lists were shared alongside 15 other medical specialty societies at a Feb. 21 news conference in Washington, D.C.
Adult Hospitalist "Avoid List"
1. Do not place, or leave in place, urinary catheters for incontinence or convenience or monitoring of output for non-critically ill patients (acceptable indications: critical illness, obstruction, hospice, perioperatively for <2 days for urologic procedures; use weights instead to monitor diuresis).
2. Do not prescribe medications for stress ulcer prophylaxis to medical inpatients unless at high risk for GI complications.
3. Avoid transfusions of red blood cells for arbitrary hemoglobin or hematocrit thresholds and in the absence of symptoms or active coronary disease, heart failure or stroke.
4. Do not order continuous telemetry monitoring outside of the ICU without using a protocol that governs continuation.
5. Do not perform repetitive CBC and chemistry testing in the face of clinical and lab stability.
Pediatric HospitalIST "Avoid List"
1. Don’t order chest radiographs in children with uncomplicated asthma or bronchiolitis.
2. Don’t routinely use bronchodilators in children with bronchiolitis.
3. Don’t use systemic corticosteroids in children under 2 years of age with an uncomplicated lower respiratory tract infection.
4. Don’t treat gastroesophageal reflux in infants routinely with acid suppression therapy.
5. Don’t use continuous pulse oximetry routinely in children with acute respiratory illness unless they are on supplemental oxygen.
For complete recommendations and references, visit SHM's website.
SHM has joined the American Board of Internal Medicine (ABIM) Foundation’s Choosing Wisely campaign, a multiyear effort to spark national dialogue about waste in the healthcare system and the kinds of common treatments that doctors and patients should think twice about before deciding to pursue. Ad hoc subcommittees of SHM’s Hospital Quality and Patient Safety Committee created lists of five adult and five pediatric treatments that hospitalists and their patients should question (see below). Those lists were shared alongside 15 other medical specialty societies at a Feb. 21 news conference in Washington, D.C.
Adult Hospitalist "Avoid List"
1. Do not place, or leave in place, urinary catheters for incontinence or convenience or monitoring of output for non-critically ill patients (acceptable indications: critical illness, obstruction, hospice, perioperatively for <2 days for urologic procedures; use weights instead to monitor diuresis).
2. Do not prescribe medications for stress ulcer prophylaxis to medical inpatients unless at high risk for GI complications.
3. Avoid transfusions of red blood cells for arbitrary hemoglobin or hematocrit thresholds and in the absence of symptoms or active coronary disease, heart failure or stroke.
4. Do not order continuous telemetry monitoring outside of the ICU without using a protocol that governs continuation.
5. Do not perform repetitive CBC and chemistry testing in the face of clinical and lab stability.
Pediatric HospitalIST "Avoid List"
1. Don’t order chest radiographs in children with uncomplicated asthma or bronchiolitis.
2. Don’t routinely use bronchodilators in children with bronchiolitis.
3. Don’t use systemic corticosteroids in children under 2 years of age with an uncomplicated lower respiratory tract infection.
4. Don’t treat gastroesophageal reflux in infants routinely with acid suppression therapy.
5. Don’t use continuous pulse oximetry routinely in children with acute respiratory illness unless they are on supplemental oxygen.
For complete recommendations and references, visit SHM's website.
Better Choices, Better Healthcare
WASHINGTON, D.C.—SHM joined hands today with 15 other U.S. medical specialty societies in the fight to eliminate wasteful medical tests, drugs, and treatments.
The 10,000-member SHM, which represents more than 40,000 hospitalists, released two lists of common tests and procedures that clinicians and patients should seriously question as part of the ABIM Foundation’s Choosing Wisely campaign. The campaign debuted in April 2012 with nine medical societies providing input on medical decisions that lack evidence, waste finite healthcare resources, or potentially harm patients.
“We acknowledge that there is waste in our system,” says Gregory Maynard, MD, MSc, SFHM, senior vice president of SHM’s Center for Healthcare Improvement and Innovation. “We also believe that if you have an engaged, empowered patient, together you will make better choices, have less waste, and probably also reduce costs.”
SHM’s Hospital Quality and Patient Safety Committee created two lists of five recommendations: one for adult hospitalists and inpatients, and one for pediatric hospitalists and patients. Examples include:
- Do not prescribe medications for stress ulcer prophylaxis to medical inpatients unless they are at high risk for gastrointestinal complications;
- Do not order continuous telemetry monitoring outside the ICU without using a protocol that governs its continuation; and
- Do not order chest radiography in children who have uncomplicated asthma or bronchiolitis.
The “avoid” lists were chosen by SHM because they potentially represent significant, needless waste of healthcare resources, according to John Bulger, DO, MBA, SFHM, chief quality officer at Geisinger Medical Center in Danville, Pa. Dr. Bulger, who chaired SHM’s Choosing Wisely committee, encourages hospitalists to stop and take a long look at the list and think about ways to improve their own practice. He encourages hospitalists to take the recommendations to their hospitals’ quality-improvement (QI) committee and start collecting baseline data, he says. “We should be able to come back a year from now and show that we’ve been able to change practice using these lists,” he says.

—Gregory Maynard, MD, MSc, SFHM, senior vice president of SHM’s Center for Healthcare Improvement and Innovation
HM pioneer Robert Wachter, MD, MHM, who heads the division of hospital medicine at the University of California at San Francisco, chairs the American Board of Internal Medicine, and sits on the board of the ABIM Foundation, agrees.
“I think you’ll be hearing similar kinds of drumbeats about waste from every national organization involved in healthcare,” says Dr. Wachter, author of the Wachter’s World blog. “I think hospitalists should be active and enthusiastic partners in the Choosing Wisely campaign and leaders in American healthcare’s efforts to figure out how to purge waste from the system and decrease unnecessary expense.”
Click here to listen to more of Dr. Wachter’s interview on the Choosing Wisely campaign.
A similar kind of focus on efficiency and cost-effectiveness was part of the initial motivation for developing hospital medicine, Dr. Wachter says. He compares the current national obsession about healthcare waste with the medical quality and patient safety movements of the past decade.
“It’s the right time, the right message, and the right messenger,” he says. “But now we’re a little scared about raised expectations. Delivering on them is going to be more difficult, even, than patient safety was because, ultimately, it will require curtailing some income streams. You can’t reach the final outcome of cutting costs in healthcare without someone making less money.” TH
Larry Beresford is a freelance writer in Oakland, Calif.
CHoosing Wisely
Who: Sponsored by the ABIM Foundation, the campaign includes 25 medical specialty societies.
What: A national quality campaign to educate physicians and patients about wasteful medical tests, procedures, and treatments.
When: Launched April 4, 2012.
Why: Treatments that are commonly ordered but not supported by medical research are not only potentially wasteful of finite healthcare resources, but they also could harm patients.
More: Check out the complete adult and pediatric HM "avoid" lists.
WASHINGTON, D.C.—SHM joined hands today with 15 other U.S. medical specialty societies in the fight to eliminate wasteful medical tests, drugs, and treatments.
The 10,000-member SHM, which represents more than 40,000 hospitalists, released two lists of common tests and procedures that clinicians and patients should seriously question as part of the ABIM Foundation’s Choosing Wisely campaign. The campaign debuted in April 2012 with nine medical societies providing input on medical decisions that lack evidence, waste finite healthcare resources, or potentially harm patients.
“We acknowledge that there is waste in our system,” says Gregory Maynard, MD, MSc, SFHM, senior vice president of SHM’s Center for Healthcare Improvement and Innovation. “We also believe that if you have an engaged, empowered patient, together you will make better choices, have less waste, and probably also reduce costs.”
SHM’s Hospital Quality and Patient Safety Committee created two lists of five recommendations: one for adult hospitalists and inpatients, and one for pediatric hospitalists and patients. Examples include:
- Do not prescribe medications for stress ulcer prophylaxis to medical inpatients unless they are at high risk for gastrointestinal complications;
- Do not order continuous telemetry monitoring outside the ICU without using a protocol that governs its continuation; and
- Do not order chest radiography in children who have uncomplicated asthma or bronchiolitis.
The “avoid” lists were chosen by SHM because they potentially represent significant, needless waste of healthcare resources, according to John Bulger, DO, MBA, SFHM, chief quality officer at Geisinger Medical Center in Danville, Pa. Dr. Bulger, who chaired SHM’s Choosing Wisely committee, encourages hospitalists to stop and take a long look at the list and think about ways to improve their own practice. He encourages hospitalists to take the recommendations to their hospitals’ quality-improvement (QI) committee and start collecting baseline data, he says. “We should be able to come back a year from now and show that we’ve been able to change practice using these lists,” he says.

—Gregory Maynard, MD, MSc, SFHM, senior vice president of SHM’s Center for Healthcare Improvement and Innovation
HM pioneer Robert Wachter, MD, MHM, who heads the division of hospital medicine at the University of California at San Francisco, chairs the American Board of Internal Medicine, and sits on the board of the ABIM Foundation, agrees.
“I think you’ll be hearing similar kinds of drumbeats about waste from every national organization involved in healthcare,” says Dr. Wachter, author of the Wachter’s World blog. “I think hospitalists should be active and enthusiastic partners in the Choosing Wisely campaign and leaders in American healthcare’s efforts to figure out how to purge waste from the system and decrease unnecessary expense.”
Click here to listen to more of Dr. Wachter’s interview on the Choosing Wisely campaign.
A similar kind of focus on efficiency and cost-effectiveness was part of the initial motivation for developing hospital medicine, Dr. Wachter says. He compares the current national obsession about healthcare waste with the medical quality and patient safety movements of the past decade.
“It’s the right time, the right message, and the right messenger,” he says. “But now we’re a little scared about raised expectations. Delivering on them is going to be more difficult, even, than patient safety was because, ultimately, it will require curtailing some income streams. You can’t reach the final outcome of cutting costs in healthcare without someone making less money.” TH
Larry Beresford is a freelance writer in Oakland, Calif.
CHoosing Wisely
Who: Sponsored by the ABIM Foundation, the campaign includes 25 medical specialty societies.
What: A national quality campaign to educate physicians and patients about wasteful medical tests, procedures, and treatments.
When: Launched April 4, 2012.
Why: Treatments that are commonly ordered but not supported by medical research are not only potentially wasteful of finite healthcare resources, but they also could harm patients.
More: Check out the complete adult and pediatric HM "avoid" lists.
WASHINGTON, D.C.—SHM joined hands today with 15 other U.S. medical specialty societies in the fight to eliminate wasteful medical tests, drugs, and treatments.
The 10,000-member SHM, which represents more than 40,000 hospitalists, released two lists of common tests and procedures that clinicians and patients should seriously question as part of the ABIM Foundation’s Choosing Wisely campaign. The campaign debuted in April 2012 with nine medical societies providing input on medical decisions that lack evidence, waste finite healthcare resources, or potentially harm patients.
“We acknowledge that there is waste in our system,” says Gregory Maynard, MD, MSc, SFHM, senior vice president of SHM’s Center for Healthcare Improvement and Innovation. “We also believe that if you have an engaged, empowered patient, together you will make better choices, have less waste, and probably also reduce costs.”
SHM’s Hospital Quality and Patient Safety Committee created two lists of five recommendations: one for adult hospitalists and inpatients, and one for pediatric hospitalists and patients. Examples include:
- Do not prescribe medications for stress ulcer prophylaxis to medical inpatients unless they are at high risk for gastrointestinal complications;
- Do not order continuous telemetry monitoring outside the ICU without using a protocol that governs its continuation; and
- Do not order chest radiography in children who have uncomplicated asthma or bronchiolitis.
The “avoid” lists were chosen by SHM because they potentially represent significant, needless waste of healthcare resources, according to John Bulger, DO, MBA, SFHM, chief quality officer at Geisinger Medical Center in Danville, Pa. Dr. Bulger, who chaired SHM’s Choosing Wisely committee, encourages hospitalists to stop and take a long look at the list and think about ways to improve their own practice. He encourages hospitalists to take the recommendations to their hospitals’ quality-improvement (QI) committee and start collecting baseline data, he says. “We should be able to come back a year from now and show that we’ve been able to change practice using these lists,” he says.

—Gregory Maynard, MD, MSc, SFHM, senior vice president of SHM’s Center for Healthcare Improvement and Innovation
HM pioneer Robert Wachter, MD, MHM, who heads the division of hospital medicine at the University of California at San Francisco, chairs the American Board of Internal Medicine, and sits on the board of the ABIM Foundation, agrees.
“I think you’ll be hearing similar kinds of drumbeats about waste from every national organization involved in healthcare,” says Dr. Wachter, author of the Wachter’s World blog. “I think hospitalists should be active and enthusiastic partners in the Choosing Wisely campaign and leaders in American healthcare’s efforts to figure out how to purge waste from the system and decrease unnecessary expense.”
Click here to listen to more of Dr. Wachter’s interview on the Choosing Wisely campaign.
A similar kind of focus on efficiency and cost-effectiveness was part of the initial motivation for developing hospital medicine, Dr. Wachter says. He compares the current national obsession about healthcare waste with the medical quality and patient safety movements of the past decade.
“It’s the right time, the right message, and the right messenger,” he says. “But now we’re a little scared about raised expectations. Delivering on them is going to be more difficult, even, than patient safety was because, ultimately, it will require curtailing some income streams. You can’t reach the final outcome of cutting costs in healthcare without someone making less money.” TH
Larry Beresford is a freelance writer in Oakland, Calif.
CHoosing Wisely
Who: Sponsored by the ABIM Foundation, the campaign includes 25 medical specialty societies.
What: A national quality campaign to educate physicians and patients about wasteful medical tests, procedures, and treatments.
When: Launched April 4, 2012.
Why: Treatments that are commonly ordered but not supported by medical research are not only potentially wasteful of finite healthcare resources, but they also could harm patients.
More: Check out the complete adult and pediatric HM "avoid" lists.
Hospitalists Earn High Marks in Patient Care Survey
The lead author of a new report that says hospitalized Medicare patients are happier in facilities using a greater number of hospitalists didn't expect that would be the case.
The study, "Hospitalist Staffing and Patient Satisfaction in the National Medicare Population," which was recently published in the Journal of Hospital Medicine, sprung from the theory that hospitals using a large number of hospitalists generally would rank lower in patient satisfaction than others. In part, the expectation was tied to the belief that patients might prefer to be seen by their primary-care physician (PCP) rather than a hospitalist.
"What we'd like people to take away is that in our study—and it's only one study—hospitals with higher levels of hospitalist care had modestly higher patient satisfaction scores, especially in the areas of discharge planning and overall satisfaction," says Lena Chen, MD, MS, clinical lecturer in the division of general medicine at the University of Michigan in Ann Arbor. "It suggests that there doesn't need to be a tradeoff between greater use of hospitalist services and patient satisfaction."
The retrospective cohort study looked at 2,843 acute-care hospitals and split them into groups ranked by the percentage of patients cared for by hospitalists. Those categorized as "nonhospitalist" hospitals had a median of 0% of general medicine patients cared for by hospitalists; a "mixed" hospital had a median of 39.5% of general medicine patients cared for by hospitalists; and a "hospitalist" hospital had a median of 76.5% cared for by hospitalists, according to the report. "Hospitalist" hospitals scored better (65.6%) on global measures of satisfaction than "mixed" (63.9%) or "nonhospitalist" (63.9%) hospitals (P<0.001), the study found. Hospitalist care was not associated with patient satisfaction in the areas of room cleanliness or communication with a physician.
Dr. Chen says she would like to see the research prompt more investigation into why hospitalist care is associated with patient satisfaction.
"We all want to have satisfied patients," she adds. "It would be important to have research that explores what the factors are that lead to greater patient satisfaction. This is a first step, but it's definitely not the end of the road."
Visit our website for more information about patient satisfaction.
The lead author of a new report that says hospitalized Medicare patients are happier in facilities using a greater number of hospitalists didn't expect that would be the case.
The study, "Hospitalist Staffing and Patient Satisfaction in the National Medicare Population," which was recently published in the Journal of Hospital Medicine, sprung from the theory that hospitals using a large number of hospitalists generally would rank lower in patient satisfaction than others. In part, the expectation was tied to the belief that patients might prefer to be seen by their primary-care physician (PCP) rather than a hospitalist.
"What we'd like people to take away is that in our study—and it's only one study—hospitals with higher levels of hospitalist care had modestly higher patient satisfaction scores, especially in the areas of discharge planning and overall satisfaction," says Lena Chen, MD, MS, clinical lecturer in the division of general medicine at the University of Michigan in Ann Arbor. "It suggests that there doesn't need to be a tradeoff between greater use of hospitalist services and patient satisfaction."
The retrospective cohort study looked at 2,843 acute-care hospitals and split them into groups ranked by the percentage of patients cared for by hospitalists. Those categorized as "nonhospitalist" hospitals had a median of 0% of general medicine patients cared for by hospitalists; a "mixed" hospital had a median of 39.5% of general medicine patients cared for by hospitalists; and a "hospitalist" hospital had a median of 76.5% cared for by hospitalists, according to the report. "Hospitalist" hospitals scored better (65.6%) on global measures of satisfaction than "mixed" (63.9%) or "nonhospitalist" (63.9%) hospitals (P<0.001), the study found. Hospitalist care was not associated with patient satisfaction in the areas of room cleanliness or communication with a physician.
Dr. Chen says she would like to see the research prompt more investigation into why hospitalist care is associated with patient satisfaction.
"We all want to have satisfied patients," she adds. "It would be important to have research that explores what the factors are that lead to greater patient satisfaction. This is a first step, but it's definitely not the end of the road."
Visit our website for more information about patient satisfaction.
The lead author of a new report that says hospitalized Medicare patients are happier in facilities using a greater number of hospitalists didn't expect that would be the case.
The study, "Hospitalist Staffing and Patient Satisfaction in the National Medicare Population," which was recently published in the Journal of Hospital Medicine, sprung from the theory that hospitals using a large number of hospitalists generally would rank lower in patient satisfaction than others. In part, the expectation was tied to the belief that patients might prefer to be seen by their primary-care physician (PCP) rather than a hospitalist.
"What we'd like people to take away is that in our study—and it's only one study—hospitals with higher levels of hospitalist care had modestly higher patient satisfaction scores, especially in the areas of discharge planning and overall satisfaction," says Lena Chen, MD, MS, clinical lecturer in the division of general medicine at the University of Michigan in Ann Arbor. "It suggests that there doesn't need to be a tradeoff between greater use of hospitalist services and patient satisfaction."
The retrospective cohort study looked at 2,843 acute-care hospitals and split them into groups ranked by the percentage of patients cared for by hospitalists. Those categorized as "nonhospitalist" hospitals had a median of 0% of general medicine patients cared for by hospitalists; a "mixed" hospital had a median of 39.5% of general medicine patients cared for by hospitalists; and a "hospitalist" hospital had a median of 76.5% cared for by hospitalists, according to the report. "Hospitalist" hospitals scored better (65.6%) on global measures of satisfaction than "mixed" (63.9%) or "nonhospitalist" (63.9%) hospitals (P<0.001), the study found. Hospitalist care was not associated with patient satisfaction in the areas of room cleanliness or communication with a physician.
Dr. Chen says she would like to see the research prompt more investigation into why hospitalist care is associated with patient satisfaction.
"We all want to have satisfied patients," she adds. "It would be important to have research that explores what the factors are that lead to greater patient satisfaction. This is a first step, but it's definitely not the end of the road."
Visit our website for more information about patient satisfaction.
Drugs, Pregnancy, and Lactation: New Weight Loss Drugs
The need for effective weight management medications as an adjunct to diet and exercise has escalated in the United States as obesity has reached epidemic proportions.
However, in recent years, several Food and Drug Administration–approved medications for weight loss have been plagued with safety concerns and many have been removed from the market, leaving clinicians with limited choices for treatment of overweight or obese patients.
In 2012, two new weight loss medications were approved by the FDA – the first new medications approved for this indication in over a decade (N. Engl. J. Med. 2012;367:1577-9).
As of February 2013, one of the two products, a combination product containing the anorexant phentermine and the anticonvulsant topiramate in an extended-release form, is currently available by prescription in the United States. Marketed as Qysmia, the product is intended to be used together with a reduced-calorie diet and increased physical activity for chronic weight management in adults with an initial body mass index of 30 kg/m2 or greater (obese).
The medication is also indicated for adults with a BMI of 27 or greater (overweight) who also have at least one weight-related medical condition such as high blood pressure, type 2 diabetes, or high cholesterol. The recommended starting daily dose contains 3.75 mg of phentermine and 23 mg of topiramate; the maximum dose contains 15 mg of phentermine and 92 mg of topiramate.
In part, due to concerns about the teratogenicity of topiramate, Qysmia has been designated a category X drug, and specific pregnancy prevention measures in the form of a Risk Evaluation and Mitigation Strategy (REMS) have been put in place. The medication can be obtained only by prescription obtained directly from a health care provider, and providers receive training on the risks of birth defects. A prescription for Qysmia can only be filled by specially certified mail order pharmacies in the United States.
Educational materials indicate that the drug should not be prescribed to women who are pregnant or who are planning on becoming pregnant. Women who are not planning pregnancy but have the potential to become pregnant should have a negative pregnancy test before starting the drug and again every month while taking the drug, and they should use an effective method or combination of methods of contraception. The manufacturer has also initiated a pregnancy surveillance system.
Given the likelihood that many women of reproductive age will use this medication, even with a REMS in place, the potential for unintentional exposure in pregnancy exists. In the inevitable event of an exposed pregnancy, what are the specific risks and their magnitude? The concern about birth defects with this medication stems from previously published data suggesting that topiramate used in monotherapy for other indications, most commonly epilepsy, is associated with an increased risk for oral clefts (cleft lip with or without cleft palate). Although numbers are still small, a few studies have suggested the risk for oral clefts, with the most recent a large pooled case-control analysis from two data sources in the United States (Am. J. Obstet. Gynecol. 2012;207:405e1-7). The pooled estimate of the risk of oral clefts was 5.36 with very wide confidence intervals (1.49-20.07), based on seven exposed children with cleft lip with or without cleft palate. To the extent that this estimate is correct, this translates to an absolute risk of about 5 in 1,000 first-trimester topiramate-exposed pregnancies, compared with a baseline risk of about 1 in 1,000 in unexposed pregnancies.
Published studies of topiramate and oral clefts have not involved sufficient numbers of exposed and affected children to allow examination of a dose threshold; however, the range of recommended doses for seizure prevention in adults treated with topiramate monotherapy (50-400 mg/day) overlaps with the dosing range of topiramate contained in Qysmia. It is important to note that based on the published reports suggesting an increased risk for oral clefts, the pregnancy category for topiramate alone was recently changed from a C to a D, while the pregnancy category for Qysmia is an X. The rationale behind the category D is likely that the benefits of topiramate might outweigh the risks in a pregnant woman with a seizure disorder for whom topiramate is the only effective medication. However, topiramate use for weight loss would typically never be indicated in pregnancy.
The second drug, lorcaserin (Belviq), is a single-ingredient serotonergic medication – a selective agonist of the 5-HT2C receptor. Lorcaserin was approved by the FDA in 2012, but as of February 2013, it is not yet available in the United States. This medication also received a pregnancy category X designation; however, in this situation, it was presumably for the sole reason that intentional weight loss in pregnancy is not recommended. Preclinical data for lorcaserin did not suggest teratogenicity, but maternal exposure in rats late in gestation resulted in lower pup body weight that persisted into adulthood.
To the extent that these new medications are effective in reducing and maintaining BMI within a healthier range in women who are currently overweight or obese, they may lead to improvement in subsequent pregnancy outcomes. However, avoiding exposure to these medications during early pregnancy will be a challenge, even with pregnancy prevention guidance and restricted distribution programs. Postmarketing surveillance for outcomes of inadvertently exposed pregnancies will be essential.
Dr. Chambers is associate professor of pediatrics and family and preventive medicine at the University of California, San Diego. She is director of the California Teratogen Information Service and Clinical Research Program. Dr. Chambers is a past president of the Organization of Teratology Information Specialists and past president of the Teratology Society. She said she had no relevant financial disclosures. To comment, e-mail her at obnews@elsevier.com.
The need for effective weight management medications as an adjunct to diet and exercise has escalated in the United States as obesity has reached epidemic proportions.
However, in recent years, several Food and Drug Administration–approved medications for weight loss have been plagued with safety concerns and many have been removed from the market, leaving clinicians with limited choices for treatment of overweight or obese patients.
In 2012, two new weight loss medications were approved by the FDA – the first new medications approved for this indication in over a decade (N. Engl. J. Med. 2012;367:1577-9).
As of February 2013, one of the two products, a combination product containing the anorexant phentermine and the anticonvulsant topiramate in an extended-release form, is currently available by prescription in the United States. Marketed as Qysmia, the product is intended to be used together with a reduced-calorie diet and increased physical activity for chronic weight management in adults with an initial body mass index of 30 kg/m2 or greater (obese).
The medication is also indicated for adults with a BMI of 27 or greater (overweight) who also have at least one weight-related medical condition such as high blood pressure, type 2 diabetes, or high cholesterol. The recommended starting daily dose contains 3.75 mg of phentermine and 23 mg of topiramate; the maximum dose contains 15 mg of phentermine and 92 mg of topiramate.
In part, due to concerns about the teratogenicity of topiramate, Qysmia has been designated a category X drug, and specific pregnancy prevention measures in the form of a Risk Evaluation and Mitigation Strategy (REMS) have been put in place. The medication can be obtained only by prescription obtained directly from a health care provider, and providers receive training on the risks of birth defects. A prescription for Qysmia can only be filled by specially certified mail order pharmacies in the United States.
Educational materials indicate that the drug should not be prescribed to women who are pregnant or who are planning on becoming pregnant. Women who are not planning pregnancy but have the potential to become pregnant should have a negative pregnancy test before starting the drug and again every month while taking the drug, and they should use an effective method or combination of methods of contraception. The manufacturer has also initiated a pregnancy surveillance system.
Given the likelihood that many women of reproductive age will use this medication, even with a REMS in place, the potential for unintentional exposure in pregnancy exists. In the inevitable event of an exposed pregnancy, what are the specific risks and their magnitude? The concern about birth defects with this medication stems from previously published data suggesting that topiramate used in monotherapy for other indications, most commonly epilepsy, is associated with an increased risk for oral clefts (cleft lip with or without cleft palate). Although numbers are still small, a few studies have suggested the risk for oral clefts, with the most recent a large pooled case-control analysis from two data sources in the United States (Am. J. Obstet. Gynecol. 2012;207:405e1-7). The pooled estimate of the risk of oral clefts was 5.36 with very wide confidence intervals (1.49-20.07), based on seven exposed children with cleft lip with or without cleft palate. To the extent that this estimate is correct, this translates to an absolute risk of about 5 in 1,000 first-trimester topiramate-exposed pregnancies, compared with a baseline risk of about 1 in 1,000 in unexposed pregnancies.
Published studies of topiramate and oral clefts have not involved sufficient numbers of exposed and affected children to allow examination of a dose threshold; however, the range of recommended doses for seizure prevention in adults treated with topiramate monotherapy (50-400 mg/day) overlaps with the dosing range of topiramate contained in Qysmia. It is important to note that based on the published reports suggesting an increased risk for oral clefts, the pregnancy category for topiramate alone was recently changed from a C to a D, while the pregnancy category for Qysmia is an X. The rationale behind the category D is likely that the benefits of topiramate might outweigh the risks in a pregnant woman with a seizure disorder for whom topiramate is the only effective medication. However, topiramate use for weight loss would typically never be indicated in pregnancy.
The second drug, lorcaserin (Belviq), is a single-ingredient serotonergic medication – a selective agonist of the 5-HT2C receptor. Lorcaserin was approved by the FDA in 2012, but as of February 2013, it is not yet available in the United States. This medication also received a pregnancy category X designation; however, in this situation, it was presumably for the sole reason that intentional weight loss in pregnancy is not recommended. Preclinical data for lorcaserin did not suggest teratogenicity, but maternal exposure in rats late in gestation resulted in lower pup body weight that persisted into adulthood.
To the extent that these new medications are effective in reducing and maintaining BMI within a healthier range in women who are currently overweight or obese, they may lead to improvement in subsequent pregnancy outcomes. However, avoiding exposure to these medications during early pregnancy will be a challenge, even with pregnancy prevention guidance and restricted distribution programs. Postmarketing surveillance for outcomes of inadvertently exposed pregnancies will be essential.
Dr. Chambers is associate professor of pediatrics and family and preventive medicine at the University of California, San Diego. She is director of the California Teratogen Information Service and Clinical Research Program. Dr. Chambers is a past president of the Organization of Teratology Information Specialists and past president of the Teratology Society. She said she had no relevant financial disclosures. To comment, e-mail her at obnews@elsevier.com.
The need for effective weight management medications as an adjunct to diet and exercise has escalated in the United States as obesity has reached epidemic proportions.
However, in recent years, several Food and Drug Administration–approved medications for weight loss have been plagued with safety concerns and many have been removed from the market, leaving clinicians with limited choices for treatment of overweight or obese patients.
In 2012, two new weight loss medications were approved by the FDA – the first new medications approved for this indication in over a decade (N. Engl. J. Med. 2012;367:1577-9).
As of February 2013, one of the two products, a combination product containing the anorexant phentermine and the anticonvulsant topiramate in an extended-release form, is currently available by prescription in the United States. Marketed as Qysmia, the product is intended to be used together with a reduced-calorie diet and increased physical activity for chronic weight management in adults with an initial body mass index of 30 kg/m2 or greater (obese).
The medication is also indicated for adults with a BMI of 27 or greater (overweight) who also have at least one weight-related medical condition such as high blood pressure, type 2 diabetes, or high cholesterol. The recommended starting daily dose contains 3.75 mg of phentermine and 23 mg of topiramate; the maximum dose contains 15 mg of phentermine and 92 mg of topiramate.
In part, due to concerns about the teratogenicity of topiramate, Qysmia has been designated a category X drug, and specific pregnancy prevention measures in the form of a Risk Evaluation and Mitigation Strategy (REMS) have been put in place. The medication can be obtained only by prescription obtained directly from a health care provider, and providers receive training on the risks of birth defects. A prescription for Qysmia can only be filled by specially certified mail order pharmacies in the United States.
Educational materials indicate that the drug should not be prescribed to women who are pregnant or who are planning on becoming pregnant. Women who are not planning pregnancy but have the potential to become pregnant should have a negative pregnancy test before starting the drug and again every month while taking the drug, and they should use an effective method or combination of methods of contraception. The manufacturer has also initiated a pregnancy surveillance system.
Given the likelihood that many women of reproductive age will use this medication, even with a REMS in place, the potential for unintentional exposure in pregnancy exists. In the inevitable event of an exposed pregnancy, what are the specific risks and their magnitude? The concern about birth defects with this medication stems from previously published data suggesting that topiramate used in monotherapy for other indications, most commonly epilepsy, is associated with an increased risk for oral clefts (cleft lip with or without cleft palate). Although numbers are still small, a few studies have suggested the risk for oral clefts, with the most recent a large pooled case-control analysis from two data sources in the United States (Am. J. Obstet. Gynecol. 2012;207:405e1-7). The pooled estimate of the risk of oral clefts was 5.36 with very wide confidence intervals (1.49-20.07), based on seven exposed children with cleft lip with or without cleft palate. To the extent that this estimate is correct, this translates to an absolute risk of about 5 in 1,000 first-trimester topiramate-exposed pregnancies, compared with a baseline risk of about 1 in 1,000 in unexposed pregnancies.
Published studies of topiramate and oral clefts have not involved sufficient numbers of exposed and affected children to allow examination of a dose threshold; however, the range of recommended doses for seizure prevention in adults treated with topiramate monotherapy (50-400 mg/day) overlaps with the dosing range of topiramate contained in Qysmia. It is important to note that based on the published reports suggesting an increased risk for oral clefts, the pregnancy category for topiramate alone was recently changed from a C to a D, while the pregnancy category for Qysmia is an X. The rationale behind the category D is likely that the benefits of topiramate might outweigh the risks in a pregnant woman with a seizure disorder for whom topiramate is the only effective medication. However, topiramate use for weight loss would typically never be indicated in pregnancy.
The second drug, lorcaserin (Belviq), is a single-ingredient serotonergic medication – a selective agonist of the 5-HT2C receptor. Lorcaserin was approved by the FDA in 2012, but as of February 2013, it is not yet available in the United States. This medication also received a pregnancy category X designation; however, in this situation, it was presumably for the sole reason that intentional weight loss in pregnancy is not recommended. Preclinical data for lorcaserin did not suggest teratogenicity, but maternal exposure in rats late in gestation resulted in lower pup body weight that persisted into adulthood.
To the extent that these new medications are effective in reducing and maintaining BMI within a healthier range in women who are currently overweight or obese, they may lead to improvement in subsequent pregnancy outcomes. However, avoiding exposure to these medications during early pregnancy will be a challenge, even with pregnancy prevention guidance and restricted distribution programs. Postmarketing surveillance for outcomes of inadvertently exposed pregnancies will be essential.
Dr. Chambers is associate professor of pediatrics and family and preventive medicine at the University of California, San Diego. She is director of the California Teratogen Information Service and Clinical Research Program. Dr. Chambers is a past president of the Organization of Teratology Information Specialists and past president of the Teratology Society. She said she had no relevant financial disclosures. To comment, e-mail her at obnews@elsevier.com.
Company suspends enrollment in drug trials
Credit: Esther Dyson
After 2 deaths among patients receiving the BCL-2 inhibitor ABT-199, the company developing the drug has suspended enrollment in 5 trials and stopped dose-escalation of the drug.
The patients died of tumor lysis syndrome, a complication that likely stems from the drug’s potency, according to Tracy Sorrentino, a spokeswoman for the company, AbbVie.
Research has suggested the risk of tumor lysis syndrome might be eliminated by altering the dose of ABT-199, Sorrentino said.
But until that is confirmed, AbbVie has stopped dose-escalation in patients receiving ABT-199 and voluntarily suspended enrollment in phase 1 trials of the drug.
The trials are testing ABT-199, both alone and in combination, as a treatment for chronic lymphocytic leukemia, non-Hodgkin lymphoma, and small lymphocytic lymphoma.
Though enrollment has stopped for these trials, dosing of active patients in ABT-199 trials will continue. In addition, a study testing ABT-199 in women with systemic lupus erythematosus is still enrolling patients.
Sorrentino said AbbVie has “every expectation” the suspended enrollment is temporary, and refining the dose of ABT-199 may eliminate the problem. In fact, the company is still planning to begin phase 3 trials of the drug later this year.
Credit: Esther Dyson
After 2 deaths among patients receiving the BCL-2 inhibitor ABT-199, the company developing the drug has suspended enrollment in 5 trials and stopped dose-escalation of the drug.
The patients died of tumor lysis syndrome, a complication that likely stems from the drug’s potency, according to Tracy Sorrentino, a spokeswoman for the company, AbbVie.
Research has suggested the risk of tumor lysis syndrome might be eliminated by altering the dose of ABT-199, Sorrentino said.
But until that is confirmed, AbbVie has stopped dose-escalation in patients receiving ABT-199 and voluntarily suspended enrollment in phase 1 trials of the drug.
The trials are testing ABT-199, both alone and in combination, as a treatment for chronic lymphocytic leukemia, non-Hodgkin lymphoma, and small lymphocytic lymphoma.
Though enrollment has stopped for these trials, dosing of active patients in ABT-199 trials will continue. In addition, a study testing ABT-199 in women with systemic lupus erythematosus is still enrolling patients.
Sorrentino said AbbVie has “every expectation” the suspended enrollment is temporary, and refining the dose of ABT-199 may eliminate the problem. In fact, the company is still planning to begin phase 3 trials of the drug later this year.
Credit: Esther Dyson
After 2 deaths among patients receiving the BCL-2 inhibitor ABT-199, the company developing the drug has suspended enrollment in 5 trials and stopped dose-escalation of the drug.
The patients died of tumor lysis syndrome, a complication that likely stems from the drug’s potency, according to Tracy Sorrentino, a spokeswoman for the company, AbbVie.
Research has suggested the risk of tumor lysis syndrome might be eliminated by altering the dose of ABT-199, Sorrentino said.
But until that is confirmed, AbbVie has stopped dose-escalation in patients receiving ABT-199 and voluntarily suspended enrollment in phase 1 trials of the drug.
The trials are testing ABT-199, both alone and in combination, as a treatment for chronic lymphocytic leukemia, non-Hodgkin lymphoma, and small lymphocytic lymphoma.
Though enrollment has stopped for these trials, dosing of active patients in ABT-199 trials will continue. In addition, a study testing ABT-199 in women with systemic lupus erythematosus is still enrolling patients.
Sorrentino said AbbVie has “every expectation” the suspended enrollment is temporary, and refining the dose of ABT-199 may eliminate the problem. In fact, the company is still planning to begin phase 3 trials of the drug later this year.
Neonates at highest risk for Ebstein’s malformation treatment
LOS ANGELES – Neonates in particular are at risk for poor outcomes from Ebstein’s malformation, showing significantly higher mortality than infants, children, and adults, according to a database study presented by Dr. Ryan R. Davies at the annual meeting of the Society of Thoracic Surgeons.
Ebstein’s malformation is a rare congenital cardiac anomaly. Surgery for Ebstein’s involves a range of procedures, and with low institutional volumes, the only available data on treatment have been limited to individual reports demonstrating highly variable approaches.
Dr. Davies of the Nemours/A.I. duPont Hospital for Children, Wilmington, Del., and his colleagues performed a retrospective study of procedures performed on patients with a primary diagnosis of Ebstein’s malformation (2002-2009) in the STS Congenital Heart Surgery Database.
A total of 595 operations on patients with Ebstein’s were included: 116 on neonates (19%), 122 on infants (21%), 264 on children (44%), and 93 on adults (16%). The authors found that average annual institutional case volumes were low (median, 1 case/year), and procedures varied according to age. Neonates had a high rate of palliative procedures: tricuspid valve (TV) closure (16%) and systemic-to-pulmonary shunts with or without TV closure (37%) and isolated TV closure (8.6%), with Ebstein’s repair or TV valvuloplasty performed in 32%.
Infants usually underwent superior cavopulmonary connections (52%).
Among older patients, procedures were mostly in three categories: TV surgery (children, 55%; adults, 69%), arrhythmia procedures (children, 9%; adults, 17%), and Fontan (children, 16%). In-hospital mortality was higher among neonatal patients (23%) than in infants (4%), children (0.8%), and adults (1.1%).
Among neonates, 36 subsequent procedures were performed during the same hospitalization in 27 patients (23%); including TV closure (11%); shunt (15%); Ebstein’s repair (17%) or TV replacement (15%); and heart transplantation (7.4%). Mortality was similar among neonates who had a second procedure and those who did not (27% vs. 23%, respectively). ECMO (extracorporeal membrane oxygenation) was used in 9% of neonates but in less than 2% of patients in other age groups.
"This study represents a broad overview of the diverse options for surgical treatment of Ebstein’s anomaly. It shows the challenges faced in caring for extremely ill neonatal patients. We have also shown that repair of Ebstein’s anomaly is performed infrequently at most centers, limiting the ability of individual series to define optimal management strategies," Dr. Davies said in an interview.
"Unfortunately, currently available databases do not contain information that may be important in defining such strategies (both surgical and nonsurgical), including anatomic and physiologic variables – whether they are neonates presenting in severe heart failure or older patients presenting for tricuspid valve repair or replacement," he added.
"We feel that in this setting, a prospective multi-institutional study would be of significant value. It should include operative and nonoperative patients, as well as precise diagnostic information and procedural details, to evaluate long-term outcomes including survival, reoperation and other reinterventions, as well as neurodevelopmental outcomes, functional health status, and quality of life," Dr. Davies concluded.
Dr. Davies and his colleagues reported having no relevant disclosures.
LOS ANGELES – Neonates in particular are at risk for poor outcomes from Ebstein’s malformation, showing significantly higher mortality than infants, children, and adults, according to a database study presented by Dr. Ryan R. Davies at the annual meeting of the Society of Thoracic Surgeons.
Ebstein’s malformation is a rare congenital cardiac anomaly. Surgery for Ebstein’s involves a range of procedures, and with low institutional volumes, the only available data on treatment have been limited to individual reports demonstrating highly variable approaches.
Dr. Davies of the Nemours/A.I. duPont Hospital for Children, Wilmington, Del., and his colleagues performed a retrospective study of procedures performed on patients with a primary diagnosis of Ebstein’s malformation (2002-2009) in the STS Congenital Heart Surgery Database.
A total of 595 operations on patients with Ebstein’s were included: 116 on neonates (19%), 122 on infants (21%), 264 on children (44%), and 93 on adults (16%). The authors found that average annual institutional case volumes were low (median, 1 case/year), and procedures varied according to age. Neonates had a high rate of palliative procedures: tricuspid valve (TV) closure (16%) and systemic-to-pulmonary shunts with or without TV closure (37%) and isolated TV closure (8.6%), with Ebstein’s repair or TV valvuloplasty performed in 32%.
Infants usually underwent superior cavopulmonary connections (52%).
Among older patients, procedures were mostly in three categories: TV surgery (children, 55%; adults, 69%), arrhythmia procedures (children, 9%; adults, 17%), and Fontan (children, 16%). In-hospital mortality was higher among neonatal patients (23%) than in infants (4%), children (0.8%), and adults (1.1%).
Among neonates, 36 subsequent procedures were performed during the same hospitalization in 27 patients (23%); including TV closure (11%); shunt (15%); Ebstein’s repair (17%) or TV replacement (15%); and heart transplantation (7.4%). Mortality was similar among neonates who had a second procedure and those who did not (27% vs. 23%, respectively). ECMO (extracorporeal membrane oxygenation) was used in 9% of neonates but in less than 2% of patients in other age groups.
"This study represents a broad overview of the diverse options for surgical treatment of Ebstein’s anomaly. It shows the challenges faced in caring for extremely ill neonatal patients. We have also shown that repair of Ebstein’s anomaly is performed infrequently at most centers, limiting the ability of individual series to define optimal management strategies," Dr. Davies said in an interview.
"Unfortunately, currently available databases do not contain information that may be important in defining such strategies (both surgical and nonsurgical), including anatomic and physiologic variables – whether they are neonates presenting in severe heart failure or older patients presenting for tricuspid valve repair or replacement," he added.
"We feel that in this setting, a prospective multi-institutional study would be of significant value. It should include operative and nonoperative patients, as well as precise diagnostic information and procedural details, to evaluate long-term outcomes including survival, reoperation and other reinterventions, as well as neurodevelopmental outcomes, functional health status, and quality of life," Dr. Davies concluded.
Dr. Davies and his colleagues reported having no relevant disclosures.
LOS ANGELES – Neonates in particular are at risk for poor outcomes from Ebstein’s malformation, showing significantly higher mortality than infants, children, and adults, according to a database study presented by Dr. Ryan R. Davies at the annual meeting of the Society of Thoracic Surgeons.
Ebstein’s malformation is a rare congenital cardiac anomaly. Surgery for Ebstein’s involves a range of procedures, and with low institutional volumes, the only available data on treatment have been limited to individual reports demonstrating highly variable approaches.
Dr. Davies of the Nemours/A.I. duPont Hospital for Children, Wilmington, Del., and his colleagues performed a retrospective study of procedures performed on patients with a primary diagnosis of Ebstein’s malformation (2002-2009) in the STS Congenital Heart Surgery Database.
A total of 595 operations on patients with Ebstein’s were included: 116 on neonates (19%), 122 on infants (21%), 264 on children (44%), and 93 on adults (16%). The authors found that average annual institutional case volumes were low (median, 1 case/year), and procedures varied according to age. Neonates had a high rate of palliative procedures: tricuspid valve (TV) closure (16%) and systemic-to-pulmonary shunts with or without TV closure (37%) and isolated TV closure (8.6%), with Ebstein’s repair or TV valvuloplasty performed in 32%.
Infants usually underwent superior cavopulmonary connections (52%).
Among older patients, procedures were mostly in three categories: TV surgery (children, 55%; adults, 69%), arrhythmia procedures (children, 9%; adults, 17%), and Fontan (children, 16%). In-hospital mortality was higher among neonatal patients (23%) than in infants (4%), children (0.8%), and adults (1.1%).
Among neonates, 36 subsequent procedures were performed during the same hospitalization in 27 patients (23%); including TV closure (11%); shunt (15%); Ebstein’s repair (17%) or TV replacement (15%); and heart transplantation (7.4%). Mortality was similar among neonates who had a second procedure and those who did not (27% vs. 23%, respectively). ECMO (extracorporeal membrane oxygenation) was used in 9% of neonates but in less than 2% of patients in other age groups.
"This study represents a broad overview of the diverse options for surgical treatment of Ebstein’s anomaly. It shows the challenges faced in caring for extremely ill neonatal patients. We have also shown that repair of Ebstein’s anomaly is performed infrequently at most centers, limiting the ability of individual series to define optimal management strategies," Dr. Davies said in an interview.
"Unfortunately, currently available databases do not contain information that may be important in defining such strategies (both surgical and nonsurgical), including anatomic and physiologic variables – whether they are neonates presenting in severe heart failure or older patients presenting for tricuspid valve repair or replacement," he added.
"We feel that in this setting, a prospective multi-institutional study would be of significant value. It should include operative and nonoperative patients, as well as precise diagnostic information and procedural details, to evaluate long-term outcomes including survival, reoperation and other reinterventions, as well as neurodevelopmental outcomes, functional health status, and quality of life," Dr. Davies concluded.
Dr. Davies and his colleagues reported having no relevant disclosures.
AT THE STS ANNUAL MEETING
Major Finding: In-hospital mortality was higher among neonatal patients (23%) than in infants (4%), children (0.8%), and adults (1.1%).
Data Source: A retrospective database analysis of 595 operations on patients with Ebstein’s malformation.
Disclosures: Dr. Davies and his colleagues reported having no relevant disclosures.