The Journal of Community and Supportive Oncology

Over 170,000 cases of metastatic brain tumors are diagnosed in the United States each year; and the length of survival for patients with brain metastases is often quite limited, ranging from a few weeks to several months.¹ The Radiation Therapy Oncology Group (RTOG) Recursive Partitioning Analysis (RPA) and the Graded Prognostic Assessment (GPA) are 2 prognostic indices that have been validated to predict survival and guide the treatment of these patients.^2-5 The RPA and GPA indices were formulated by comparing survival to patient and tumor characteristics compiled from RTOG brain metastasis treatment protocols spanning greater than 3 decades. The RPA has 3 classes of patients enumerated as “I”, “II”, and “III,” with class I patients having the longest predicted survival and class III patients having the worst prognosis. The RPA classes are based upon factors that include patient age and Karnofsky Performance Status (KPS) as well as control of the primary tumor and evidence of extra-cranial metastases (Table 1).² The GPA has 4 classes of patients with a score that may be considered analogous to a grade point average achieved by students in school. The classes are arranged into 4 groupings, which are divided from best to worst prognosis as follows: 3.5 to 4.0, 3.0, 1.5 to 2.5, and 0.0 to 1.0. The GPA employs criteria similar to but slightly different from those used in the RPA, estimating survival by patient age and performance status as well as the number of brain metastases and evidence of extracranial metastases (Table 2).⁴

Treatment options for patients with brain metastases include surgery, stereotactic radiosurgery (SRS), whole brain radiation therapy (WBRT), supportive measures such as corticosteroids, or a combination of these modalities. The survival of the worst prognosis brain metastases patients treated with WBRT and steroids is estimated by the RPA and GPA tools to be 2.3 months and 2.6 months, respectively.^2,4 As noted above, the patient data from which the RPA and GPA indices were created included patients treated on clinical trials. This could have resulted in the selection of patients more fit than average patients and lead to an overestimation of survival when applied to all patients. The clinical trial data used were drawn from over 3 decades, during which supportive care and chemotherapy treatments improved. This could result in an underestimation of survival when applied to patients treated with current systemic therapies and supportive care. It is important for physicians to have an accurate method to predict survival in patients to ensure that appropriate treatments can be recommended.

Over 170,000 cases of metastatic brain tumors are diagnosed in the United States each year; and the length of survival for patients with brain metastases is often quite limited, ranging from a few weeks to several months.¹ The Radiation Therapy Oncology Group (RTOG) Recursive Partitioning Analysis (RPA) and the Graded Prognostic Assessment (GPA) are 2 prognostic indices that have been validated to predict survival and guide the treatment of these patients.^2-5 The RPA and GPA indices were formulated by comparing survival to patient and tumor characteristics compiled from RTOG brain metastasis treatment protocols spanning greater than 3 decades. The RPA has 3 classes of patients enumerated as “I”, “II”, and “III,” with class I patients having the longest predicted survival and class III patients having the worst prognosis. The RPA classes are based upon factors that include patient age and Karnofsky Performance Status (KPS) as well as control of the primary tumor and evidence of extra-cranial metastases (Table 1).² The GPA has 4 classes of patients with a score that may be considered analogous to a grade point average achieved by students in school. The classes are arranged into 4 groupings, which are divided from best to worst prognosis as follows: 3.5 to 4.0, 3.0, 1.5 to 2.5, and 0.0 to 1.0. The GPA employs criteria similar to but slightly different from those used in the RPA, estimating survival by patient age and performance status as well as the number of brain metastases and evidence of extracranial metastases (Table 2).⁴

Treatment options for patients with brain metastases include surgery, stereotactic radiosurgery (SRS), whole brain radiation therapy (WBRT), supportive measures such as corticosteroids, or a combination of these modalities. The survival of the worst prognosis brain metastases patients treated with WBRT and steroids is estimated by the RPA and GPA tools to be 2.3 months and 2.6 months, respectively.^2,4 As noted above, the patient data from which the RPA and GPA indices were created included patients treated on clinical trials. This could have resulted in the selection of patients more fit than average patients and lead to an overestimation of survival when applied to all patients. The clinical trial data used were drawn from over 3 decades, during which supportive care and chemotherapy treatments improved. This could result in an underestimation of survival when applied to patients treated with current systemic therapies and supportive care. It is important for physicians to have an accurate method to predict survival in patients to ensure that appropriate treatments can be recommended.

Over 170,000 cases of metastatic brain tumors are diagnosed in the United States each year; and the length of survival for patients with brain metastases is often quite limited, ranging from a few weeks to several months.¹ The Radiation Therapy Oncology Group (RTOG) Recursive Partitioning Analysis (RPA) and the Graded Prognostic Assessment (GPA) are 2 prognostic indices that have been validated to predict survival and guide the treatment of these patients.^2-5 The RPA and GPA indices were formulated by comparing survival to patient and tumor characteristics compiled from RTOG brain metastasis treatment protocols spanning greater than 3 decades. The RPA has 3 classes of patients enumerated as “I”, “II”, and “III,” with class I patients having the longest predicted survival and class III patients having the worst prognosis. The RPA classes are based upon factors that include patient age and Karnofsky Performance Status (KPS) as well as control of the primary tumor and evidence of extra-cranial metastases (Table 1).² The GPA has 4 classes of patients with a score that may be considered analogous to a grade point average achieved by students in school. The classes are arranged into 4 groupings, which are divided from best to worst prognosis as follows: 3.5 to 4.0, 3.0, 1.5 to 2.5, and 0.0 to 1.0. The GPA employs criteria similar to but slightly different from those used in the RPA, estimating survival by patient age and performance status as well as the number of brain metastases and evidence of extracranial metastases (Table 2).⁴

Treatment options for patients with brain metastases include surgery, stereotactic radiosurgery (SRS), whole brain radiation therapy (WBRT), supportive measures such as corticosteroids, or a combination of these modalities. The survival of the worst prognosis brain metastases patients treated with WBRT and steroids is estimated by the RPA and GPA tools to be 2.3 months and 2.6 months, respectively.^2,4 As noted above, the patient data from which the RPA and GPA indices were created included patients treated on clinical trials. This could have resulted in the selection of patients more fit than average patients and lead to an overestimation of survival when applied to all patients. The clinical trial data used were drawn from over 3 decades, during which supportive care and chemotherapy treatments improved. This could result in an underestimation of survival when applied to patients treated with current systemic therapies and supportive care. It is important for physicians to have an accurate method to predict survival in patients to ensure that appropriate treatments can be recommended.

Palliative care quality indicators should be part of oncology performance assessment initiatives. Palliative care programs should also include initiatives to address the overall quality of palliative care issues, such as pain management, in the settings where the programs are located.1 Measuring quality facilitates justifying palliative care initiatives and documenting their impact, targeting quality improvement efforts, monitoring care for deficiencies, and evaluating providers (Table 1). However, measurement in this field is often not straightforward. Potential challenges include defining the population to measure and data sources, collection and analysis, as well as choosing among many potentially relevant issues and quality measures. This article describes an approach to quality measurement in palliative care, beginning with a description of key frameworks to guide the measurement approach. The article also reviews key steps in designing a quality measurement program, which include defining the quality problem and population to measure and choosing domains and specific measures. Finally, the article addresses other key considerations, such as considering unintended consequences and using data for quality improvement.

Frameworks for evaluating quality

The Donabedian framework of structure (stable elements of the health care system), process (what health care services are provided), and outcome (end results for the patient and family) can be
applied to relevant domains to guide evaluation design (Table 2).^2-8 Key structural elements may include characteristics of programs (eg, palliative clinic availability), providers (eg, multidisciplinary members of the palliative care team), and tools (eg, do-not-resuscitate policies). Processes may include technical aspects of care, such as appropriate prescribing and interpersonal aspects of care (eg, coordination among providers). Outcomes may include patient quality of life or symptoms, perceptions of care, or caregiver outcomes such as burden. Outcomes may also be categorized as overuse (eg, use of chemotherapy at the end of life compared to national benchmarks), underuse (eg, lower rates of hospice care or use of antinausea drugs), or appropriateness of care (eg, accurately documenting patients’ preferences for care).

Palliative care quality indicators should be part of oncology performance assessment initiatives. Palliative care programs should also include initiatives to address the overall quality of palliative care issues, such as pain management, in the settings where the programs are located.1 Measuring quality facilitates justifying palliative care initiatives and documenting their impact, targeting quality improvement efforts, monitoring care for deficiencies, and evaluating providers (Table 1). However, measurement in this field is often not straightforward. Potential challenges include defining the population to measure and data sources, collection and analysis, as well as choosing among many potentially relevant issues and quality measures. This article describes an approach to quality measurement in palliative care, beginning with a description of key frameworks to guide the measurement approach. The article also reviews key steps in designing a quality measurement program, which include defining the quality problem and population to measure and choosing domains and specific measures. Finally, the article addresses other key considerations, such as considering unintended consequences and using data for quality improvement.

Frameworks for evaluating quality

The Donabedian framework of structure (stable elements of the health care system), process (what health care services are provided), and outcome (end results for the patient and family) can be
applied to relevant domains to guide evaluation design (Table 2).^2-8 Key structural elements may include characteristics of programs (eg, palliative clinic availability), providers (eg, multidisciplinary members of the palliative care team), and tools (eg, do-not-resuscitate policies). Processes may include technical aspects of care, such as appropriate prescribing and interpersonal aspects of care (eg, coordination among providers). Outcomes may include patient quality of life or symptoms, perceptions of care, or caregiver outcomes such as burden. Outcomes may also be categorized as overuse (eg, use of chemotherapy at the end of life compared to national benchmarks), underuse (eg, lower rates of hospice care or use of antinausea drugs), or appropriateness of care (eg, accurately documenting patients’ preferences for care).

Palliative care quality indicators should be part of oncology performance assessment initiatives. Palliative care programs should also include initiatives to address the overall quality of palliative care issues, such as pain management, in the settings where the programs are located.1 Measuring quality facilitates justifying palliative care initiatives and documenting their impact, targeting quality improvement efforts, monitoring care for deficiencies, and evaluating providers (Table 1). However, measurement in this field is often not straightforward. Potential challenges include defining the population to measure and data sources, collection and analysis, as well as choosing among many potentially relevant issues and quality measures. This article describes an approach to quality measurement in palliative care, beginning with a description of key frameworks to guide the measurement approach. The article also reviews key steps in designing a quality measurement program, which include defining the quality problem and population to measure and choosing domains and specific measures. Finally, the article addresses other key considerations, such as considering unintended consequences and using data for quality improvement.

Frameworks for evaluating quality

The Donabedian framework of structure (stable elements of the health care system), process (what health care services are provided), and outcome (end results for the patient and family) can be
applied to relevant domains to guide evaluation design (Table 2).^2-8 Key structural elements may include characteristics of programs (eg, palliative clinic availability), providers (eg, multidisciplinary members of the palliative care team), and tools (eg, do-not-resuscitate policies). Processes may include technical aspects of care, such as appropriate prescribing and interpersonal aspects of care (eg, coordination among providers). Outcomes may include patient quality of life or symptoms, perceptions of care, or caregiver outcomes such as burden. Outcomes may also be categorized as overuse (eg, use of chemotherapy at the end of life compared to national benchmarks), underuse (eg, lower rates of hospice care or use of antinausea drugs), or appropriateness of care (eg, accurately documenting patients’ preferences for care).

Research on symptom management and monitoring of health-related quality of life (HRQOL) among cancer patients has typically focused on the active treatment phase.^1-7 More recently, greater attention has been given to the psychosocial needs and follow-up care plans for survivors.⁸ Several technology-assisted symptom/HRQOL monitoring systems with routine assessments have been shown to be easy to use,^1,3,5,9-16 readily accepted by patients,^{3,9,11,14,15,17,18} helpful in communication between patients and providers, ^3,9,11,13,15 and a means of overcoming numerous barriers to conducting routine assessments.^16,19-23 Real-time clinician feedback at the point-of-care appears to be a crucial component of these systems, giving patients and providers a systematic way of discussing symptoms and aspects of HRQOL that are often addressed only informally or not at all.

To date, 6 randomized controlled trials (RCTs) have assessed the impact of technology-assisted interventions among cancer patients.^6,23-27 There was significant variability across these studies, including differing sample sizes, number of intervention contacts, tumor site (eg, breast, lung, colon), outcomes assessed (eg, symptom distress, communication, and HRQOL), and types of technology used (eg, touch-screen computers, telephone systems). The methodological differences make it difficult to compare these studies, although a common thread was that patients found the systems easy to use and they generally perceived the systems as beneficial.^6,23-27 

Despite the positive response from participants, only 2 of the 6 RCTs demonstrated positive outcomes for the intervention over the control group.^23,25 In a study of 286 cancer patients and 28 oncologists, Velikova et al (2004) found that both the intervention and the attentioncontrol groups had better HRQOL than the control group over a 6-month period.²³ Among the intervention patients, the HRQOL improvement was related to clear use of the HRQOL data by physicians, and to physician/ patient discussion of pain and role function. A positive effect on emotional well-being was associated with feedback of the data to physicians. However, there were no significant differences between the intervention and attention-control groups.

Research on symptom management and monitoring of health-related quality of life (HRQOL) among cancer patients has typically focused on the active treatment phase.^1-7 More recently, greater attention has been given to the psychosocial needs and follow-up care plans for survivors.⁸ Several technology-assisted symptom/HRQOL monitoring systems with routine assessments have been shown to be easy to use,^1,3,5,9-16 readily accepted by patients,^{3,9,11,14,15,17,18} helpful in communication between patients and providers, ^3,9,11,13,15 and a means of overcoming numerous barriers to conducting routine assessments.^16,19-23 Real-time clinician feedback at the point-of-care appears to be a crucial component of these systems, giving patients and providers a systematic way of discussing symptoms and aspects of HRQOL that are often addressed only informally or not at all.

To date, 6 randomized controlled trials (RCTs) have assessed the impact of technology-assisted interventions among cancer patients.^6,23-27 There was significant variability across these studies, including differing sample sizes, number of intervention contacts, tumor site (eg, breast, lung, colon), outcomes assessed (eg, symptom distress, communication, and HRQOL), and types of technology used (eg, touch-screen computers, telephone systems). The methodological differences make it difficult to compare these studies, although a common thread was that patients found the systems easy to use and they generally perceived the systems as beneficial.^6,23-27 

Despite the positive response from participants, only 2 of the 6 RCTs demonstrated positive outcomes for the intervention over the control group.^23,25 In a study of 286 cancer patients and 28 oncologists, Velikova et al (2004) found that both the intervention and the attentioncontrol groups had better HRQOL than the control group over a 6-month period.²³ Among the intervention patients, the HRQOL improvement was related to clear use of the HRQOL data by physicians, and to physician/ patient discussion of pain and role function. A positive effect on emotional well-being was associated with feedback of the data to physicians. However, there were no significant differences between the intervention and attention-control groups.

Research on symptom management and monitoring of health-related quality of life (HRQOL) among cancer patients has typically focused on the active treatment phase.^1-7 More recently, greater attention has been given to the psychosocial needs and follow-up care plans for survivors.⁸ Several technology-assisted symptom/HRQOL monitoring systems with routine assessments have been shown to be easy to use,^1,3,5,9-16 readily accepted by patients,^{3,9,11,14,15,17,18} helpful in communication between patients and providers, ^3,9,11,13,15 and a means of overcoming numerous barriers to conducting routine assessments.^16,19-23 Real-time clinician feedback at the point-of-care appears to be a crucial component of these systems, giving patients and providers a systematic way of discussing symptoms and aspects of HRQOL that are often addressed only informally or not at all.

To date, 6 randomized controlled trials (RCTs) have assessed the impact of technology-assisted interventions among cancer patients.^6,23-27 There was significant variability across these studies, including differing sample sizes, number of intervention contacts, tumor site (eg, breast, lung, colon), outcomes assessed (eg, symptom distress, communication, and HRQOL), and types of technology used (eg, touch-screen computers, telephone systems). The methodological differences make it difficult to compare these studies, although a common thread was that patients found the systems easy to use and they generally perceived the systems as beneficial.^6,23-27 

Despite the positive response from participants, only 2 of the 6 RCTs demonstrated positive outcomes for the intervention over the control group.^23,25 In a study of 286 cancer patients and 28 oncologists, Velikova et al (2004) found that both the intervention and the attentioncontrol groups had better HRQOL than the control group over a 6-month period.²³ Among the intervention patients, the HRQOL improvement was related to clear use of the HRQOL data by physicians, and to physician/ patient discussion of pain and role function. A positive effect on emotional well-being was associated with feedback of the data to physicians. However, there were no significant differences between the intervention and attention-control groups.

Background Patients with late-stage cancer are living longer, making it important to understand factors that contribute to maintaining quality of life (QOL) and completing advanced illness behaviors (eg, advance directives).

Objective To examine whether illness perceptions—the cognitive beliefs that patients form about their cancer—may be more important guides to adjustment than clinical characteristics of the cancer.

Methods In a cross-sectional study, 105 female patients diagnosed with stage III (n 66) or IV (n 39) breast (n 44), gynecological (n 38), or lung (n 23) cancer completed self-report measures of illness perceptions, QOL, and advanced illness behaviors. Clinical data was obtained from medical records.

Results Despite modest associations, patients’ beliefs about the cancer were clearly unique from the clinical characteristics of the cancer. Illness perception variables accounted for a large portion of the variance (PS .01) for QOL and advanced illness behaviors, whereas clinical characteristics did not. QOL scores were predicted by patients’ reports of experiencing more cancer related symptoms (ie, illness identity), believing that their cancer is central to their self-identity, and higher income. Higher completion of advanced illness behaviors was predicted by higher income, the cancer being recurrent, and participants perceiving their cancer as more severe but also more understandable.

Limitations This study was limited by a cross-sectional design, small sample size, and focus on female patients.

Conclusion Addressing patients’ beliefs about their cancer diagnosis may provide important targets for intervention to improve QOL and illness behaviors in patients with late-stage cancer.

Click on the PDF icon at the top of this introduction to read the full article.

Background Patients with late-stage cancer are living longer, making it important to understand factors that contribute to maintaining quality of life (QOL) and completing advanced illness behaviors (eg, advance directives).

Objective To examine whether illness perceptions—the cognitive beliefs that patients form about their cancer—may be more important guides to adjustment than clinical characteristics of the cancer.

Methods In a cross-sectional study, 105 female patients diagnosed with stage III (n 66) or IV (n 39) breast (n 44), gynecological (n 38), or lung (n 23) cancer completed self-report measures of illness perceptions, QOL, and advanced illness behaviors. Clinical data was obtained from medical records.

Results Despite modest associations, patients’ beliefs about the cancer were clearly unique from the clinical characteristics of the cancer. Illness perception variables accounted for a large portion of the variance (PS .01) for QOL and advanced illness behaviors, whereas clinical characteristics did not. QOL scores were predicted by patients’ reports of experiencing more cancer related symptoms (ie, illness identity), believing that their cancer is central to their self-identity, and higher income. Higher completion of advanced illness behaviors was predicted by higher income, the cancer being recurrent, and participants perceiving their cancer as more severe but also more understandable.

Limitations This study was limited by a cross-sectional design, small sample size, and focus on female patients.

Conclusion Addressing patients’ beliefs about their cancer diagnosis may provide important targets for intervention to improve QOL and illness behaviors in patients with late-stage cancer.

Click on the PDF icon at the top of this introduction to read the full article.

Background Patients with late-stage cancer are living longer, making it important to understand factors that contribute to maintaining quality of life (QOL) and completing advanced illness behaviors (eg, advance directives).

Objective To examine whether illness perceptions—the cognitive beliefs that patients form about their cancer—may be more important guides to adjustment than clinical characteristics of the cancer.

Methods In a cross-sectional study, 105 female patients diagnosed with stage III (n 66) or IV (n 39) breast (n 44), gynecological (n 38), or lung (n 23) cancer completed self-report measures of illness perceptions, QOL, and advanced illness behaviors. Clinical data was obtained from medical records.

Results Despite modest associations, patients’ beliefs about the cancer were clearly unique from the clinical characteristics of the cancer. Illness perception variables accounted for a large portion of the variance (PS .01) for QOL and advanced illness behaviors, whereas clinical characteristics did not. QOL scores were predicted by patients’ reports of experiencing more cancer related symptoms (ie, illness identity), believing that their cancer is central to their self-identity, and higher income. Higher completion of advanced illness behaviors was predicted by higher income, the cancer being recurrent, and participants perceiving their cancer as more severe but also more understandable.

Limitations This study was limited by a cross-sectional design, small sample size, and focus on female patients.

Conclusion Addressing patients’ beliefs about their cancer diagnosis may provide important targets for intervention to improve QOL and illness behaviors in patients with late-stage cancer.

Click on the PDF icon at the top of this introduction to read the full article.

Despite the advent of modern chemo- and radioimmunotherapies, the disease course in most mature B-cell malignancies (with the exception of diffuse large B-cell lymphoma [DLBCL] and Burkitt lymphoma) is highlighted by frequent relapses, progressively shorter remissions, and eventual emergence of therapy resistance. An effective salvage therapy in this setting remains an area of unmet medical need. Bruton’s tyrosine kinase (BTK) is a critical component of B-cell–receptor signaling that mediates interactions with the tumor microenvironment and promotes survival and proliferation of malignant B-cells.1,2 The BTK protein itself is a Tec family tyrosine kinase that is activated by spleen tyrosine kinase following B-cell-receptor stimulation and which is then required for downstream events including calcium release, activation of the NFB and NFAT pathways, cell survival and proliferation.1 The fundamental role of BTK in B-cell function is underscored by the human disease X-linked agammaglobulinemia, which is caused by loss of function mutations in BTK.3 These mutations result in the virtual absence of all B cells and immunoglobulins, leading to recurrent bacterial infections. Ibrutinib (formally known as PCI-32765) is the first-in-class BTK inhibitor to enter clinical trials. In a multicenter phase 1 dose-escalating study, 56 patients with relapsed or refractory B-cell lymphomas received escalated doses of oral ibrutinib either on an intermittent or continuous daily dosing schedule.4 The most common adverse effects were grade 1-2 nonhematologic toxicities, which included rash, nausea, fatigue, diarrhea, muscle spasms/myalgia, and arthralgia. An overall response rate (ORR) of 60% was achieved across all histological types with the best efficacy seen in patients with mantle cell lymphoma (MCL; 78%) and chronic lymphocytic leukemia (CLL; 68%).

Click on the PDF icon at the top of this article to read the full article.

Despite the advent of modern chemo- and radioimmunotherapies, the disease course in most mature B-cell malignancies (with the exception of diffuse large B-cell lymphoma [DLBCL] and Burkitt lymphoma) is highlighted by frequent relapses, progressively shorter remissions, and eventual emergence of therapy resistance. An effective salvage therapy in this setting remains an area of unmet medical need. Bruton’s tyrosine kinase (BTK) is a critical component of B-cell–receptor signaling that mediates interactions with the tumor microenvironment and promotes survival and proliferation of malignant B-cells.1,2 The BTK protein itself is a Tec family tyrosine kinase that is activated by spleen tyrosine kinase following B-cell-receptor stimulation and which is then required for downstream events including calcium release, activation of the NFB and NFAT pathways, cell survival and proliferation.1 The fundamental role of BTK in B-cell function is underscored by the human disease X-linked agammaglobulinemia, which is caused by loss of function mutations in BTK.3 These mutations result in the virtual absence of all B cells and immunoglobulins, leading to recurrent bacterial infections. Ibrutinib (formally known as PCI-32765) is the first-in-class BTK inhibitor to enter clinical trials. In a multicenter phase 1 dose-escalating study, 56 patients with relapsed or refractory B-cell lymphomas received escalated doses of oral ibrutinib either on an intermittent or continuous daily dosing schedule.4 The most common adverse effects were grade 1-2 nonhematologic toxicities, which included rash, nausea, fatigue, diarrhea, muscle spasms/myalgia, and arthralgia. An overall response rate (ORR) of 60% was achieved across all histological types with the best efficacy seen in patients with mantle cell lymphoma (MCL; 78%) and chronic lymphocytic leukemia (CLL; 68%).

Click on the PDF icon at the top of this article to read the full article.

Despite the advent of modern chemo- and radioimmunotherapies, the disease course in most mature B-cell malignancies (with the exception of diffuse large B-cell lymphoma [DLBCL] and Burkitt lymphoma) is highlighted by frequent relapses, progressively shorter remissions, and eventual emergence of therapy resistance. An effective salvage therapy in this setting remains an area of unmet medical need. Bruton’s tyrosine kinase (BTK) is a critical component of B-cell–receptor signaling that mediates interactions with the tumor microenvironment and promotes survival and proliferation of malignant B-cells.1,2 The BTK protein itself is a Tec family tyrosine kinase that is activated by spleen tyrosine kinase following B-cell-receptor stimulation and which is then required for downstream events including calcium release, activation of the NFB and NFAT pathways, cell survival and proliferation.1 The fundamental role of BTK in B-cell function is underscored by the human disease X-linked agammaglobulinemia, which is caused by loss of function mutations in BTK.3 These mutations result in the virtual absence of all B cells and immunoglobulins, leading to recurrent bacterial infections. Ibrutinib (formally known as PCI-32765) is the first-in-class BTK inhibitor to enter clinical trials. In a multicenter phase 1 dose-escalating study, 56 patients with relapsed or refractory B-cell lymphomas received escalated doses of oral ibrutinib either on an intermittent or continuous daily dosing schedule.4 The most common adverse effects were grade 1-2 nonhematologic toxicities, which included rash, nausea, fatigue, diarrhea, muscle spasms/myalgia, and arthralgia. An overall response rate (ORR) of 60% was achieved across all histological types with the best efficacy seen in patients with mantle cell lymphoma (MCL; 78%) and chronic lymphocytic leukemia (CLL; 68%).

Click on the PDF icon at the top of this article to read the full article.

Background Romiplostim increases platelet counts in ITP and is typically injected at clinic visits.

Objective To estimate the efficacy and safety of romiplostim self-administration, we evaluated data from an open-label extension study in a post hoc analysis.

Methods Patients received weekly romiplostim with dose adjustments to target platelet counts of 50-200 x 10⁹/L. Patients with a stable dose and platelet counts of 50-200 x 10⁹/L for 3 or more weeks could begin self-administration if investigators deemed it appropriate, returning to study sites every 4 weeks.

Results Of 292 patients, 239 (82%) initiated self-administration for a median of 74 (Q1-Q3:56-164) weeks. Twenty-eight of the 239 (12%) discontinued self-administration (investigator or sponsor decision: 19, patient request: 6, noncompliance: 3). The median average weekly dose for patients self-administering romiplostim was 4.1  g/kg. The romiplostim dose was adjusted in 40 (17%) of the 239 patients in the first 8 weeks of self-administration; 84 (35%) in the first 6 months. Patients had a platelet response (more than 50 x 10⁹/L) for a mean of 75.1% of weeks. The adverse event (AE) rate was 18.3/100 patient-weeks, with 0.8 serious AEs/100 patient-weeks. Fourteen AEs led to withdrawal; none related to self-administration.

Limitations The analysis was post hoc. Lack of a randomized comparator group may have resulted in differences between patient populations. No distinctions could be made between constant and intermittent self-administration or between adverse events occurring during self-administration or administration at the study site.

Conclusions Patients were able to maintain platelet responses for a mean of 75% of the time without new safety issues while self-administering romiplostim.

To read the full article, click on the PDF icon at the top of this introduction.

Background Romiplostim increases platelet counts in ITP and is typically injected at clinic visits.

Objective To estimate the efficacy and safety of romiplostim self-administration, we evaluated data from an open-label extension study in a post hoc analysis.

Methods Patients received weekly romiplostim with dose adjustments to target platelet counts of 50-200 x 10⁹/L. Patients with a stable dose and platelet counts of 50-200 x 10⁹/L for 3 or more weeks could begin self-administration if investigators deemed it appropriate, returning to study sites every 4 weeks.

Results Of 292 patients, 239 (82%) initiated self-administration for a median of 74 (Q1-Q3:56-164) weeks. Twenty-eight of the 239 (12%) discontinued self-administration (investigator or sponsor decision: 19, patient request: 6, noncompliance: 3). The median average weekly dose for patients self-administering romiplostim was 4.1  g/kg. The romiplostim dose was adjusted in 40 (17%) of the 239 patients in the first 8 weeks of self-administration; 84 (35%) in the first 6 months. Patients had a platelet response (more than 50 x 10⁹/L) for a mean of 75.1% of weeks. The adverse event (AE) rate was 18.3/100 patient-weeks, with 0.8 serious AEs/100 patient-weeks. Fourteen AEs led to withdrawal; none related to self-administration.

Limitations The analysis was post hoc. Lack of a randomized comparator group may have resulted in differences between patient populations. No distinctions could be made between constant and intermittent self-administration or between adverse events occurring during self-administration or administration at the study site.

Conclusions Patients were able to maintain platelet responses for a mean of 75% of the time without new safety issues while self-administering romiplostim.

To read the full article, click on the PDF icon at the top of this introduction.

Background Romiplostim increases platelet counts in ITP and is typically injected at clinic visits.

Objective To estimate the efficacy and safety of romiplostim self-administration, we evaluated data from an open-label extension study in a post hoc analysis.

Methods Patients received weekly romiplostim with dose adjustments to target platelet counts of 50-200 x 10⁹/L. Patients with a stable dose and platelet counts of 50-200 x 10⁹/L for 3 or more weeks could begin self-administration if investigators deemed it appropriate, returning to study sites every 4 weeks.

Results Of 292 patients, 239 (82%) initiated self-administration for a median of 74 (Q1-Q3:56-164) weeks. Twenty-eight of the 239 (12%) discontinued self-administration (investigator or sponsor decision: 19, patient request: 6, noncompliance: 3). The median average weekly dose for patients self-administering romiplostim was 4.1  g/kg. The romiplostim dose was adjusted in 40 (17%) of the 239 patients in the first 8 weeks of self-administration; 84 (35%) in the first 6 months. Patients had a platelet response (more than 50 x 10⁹/L) for a mean of 75.1% of weeks. The adverse event (AE) rate was 18.3/100 patient-weeks, with 0.8 serious AEs/100 patient-weeks. Fourteen AEs led to withdrawal; none related to self-administration.

Limitations The analysis was post hoc. Lack of a randomized comparator group may have resulted in differences between patient populations. No distinctions could be made between constant and intermittent self-administration or between adverse events occurring during self-administration or administration at the study site.

Conclusions Patients were able to maintain platelet responses for a mean of 75% of the time without new safety issues while self-administering romiplostim.

To read the full article, click on the PDF icon at the top of this introduction.