AVAHO

Hematology and oncology patients are a complex patient population that requires timely follow-up to prevent clinical decompensation and delays in treatment. Previous reports have demonstrated that outpatient follow-up within 14 days is associated with decreased 30-day readmissions. The magnitude of this effect is greater for higher-risk patients.¹ Therefore, patients being discharged from the hematology and oncology inpatient service should be seen by a hematology and oncology provider within 14 days of discharge. Patients who do not require close oncologic follow-up should be seen by a primary care provider (PCP) within this timeframe.

Background

The Institute of Medicine (IOM) identified the need to focus on quality improvement and patient safety with a 1999 report, To Err Is Human.² Tremendous strides have been made in the areas of quality improvement and patient safety over the past 2 decades. In a 2013 report, the IOM further identified hematology and oncology care as an area of need due to a combination of growing demand, complexity of cancer and cancer treatment, shrinking workforce, and rising costs. The report concluded that cancer care is not as patient-centered, accessible, coordinated, or evidence based as it could be, with detrimental impacts on patients.³ Patients with cancer have been identified as a high-risk population for hospital readmissions.^4,5 Lack of timely follow-up and failed hand-offs have been identified as factors contributing to poor outcomes at time of discharge.^6-10

Upon internal review of baseline performance data, we identified areas needing improvement in the discharge process. These included time to hematology and oncology follow-up appointment, percent of patients with PCP appointments scheduled at time of discharge, and electronically alerts for the outpatient hematologist/oncologist to discharge summaries. It was determined that patients discharged from the inpatient service were seen a mean 17 days later by their outpatient hematology and oncology provider and the time to the follow-up appointment varied substantially, with some patients being seen several weeks to months after discharge. Furthermore, only 68% of patients had a primary care appointment scheduled at the time of discharge. These data along with review of data reported in the medical literature supported our initiative for improvement in the transition from inpatient to outpatient care for our hematology and oncology patients.

Plan-Do-Study-Act (PDSA) quality improvement methodology was used to create and implement several interventions to standardize the discharge process for this patient population, with the primary goal of decreasing the mean time to hematology and oncology follow-up from 17 days by 12% to fewer than 14 days. Patients who do not require close oncologic follow-up should be seen by a PCP within this timeframe. Otherwise, PCP follow-up within at least 6 months should be made. Secondary aims included (1) an increase in scheduled PCP visits at time of discharge from 68% to > 90%; and (2) an increase in communication of the discharge summary via electronic alerting of the outpatient hematology and oncology physician from 20% to > 90%. Herein, we report our experience and results of this quality improvement initiative

Methods

The Institutional Review Board at Edward Hines Veteran Affairs Hospital in Hines, Illinois reviewed this single-center study and deemed it to be exempt from oversight. Using PDSA quality improvement methodology, a multidisciplinary team of hematology and oncology staff developed and implemented a standardized discharge process. The multidisciplinary team included a robust representation of inpatient and outpatient staff caring for the hematology and oncology patient population, including attending physicians, fellows, residents, advanced practice nurses, registered nurses, clinical pharmacists, patient care coordinators, clinic schedulers, clinical applications coordinators, quality support staff, and a systems redesign coach. Hospital leadership including chief of staff, chief of medicine, and chief of nursing participated as the management guidance team. Several interviews and group meetings were conducted and a multidisciplinary team collaboratively developed and implemented the interventions and monitored the results.

Outcome measures were identified, including time to hematology and oncology clinic visit, primary care follow-up scheduling, and communication of discharge to the outpatient hematology and oncology physician. Baseline data were collected and reviewed. The multidisciplinary team developed a process flow map to understand the steps and resources involved with the transition from inpatient to outpatient care. Gap analysis and root cause analysis were performed. A solutions approach was applied to develop interventions. Table 1 shows a summary of the identified problems, symptoms, associated causes, the interventions aimed to address the problems, and expected outcomes. Rotating resident physicians were trained through online and in-person education. The multidisciplinary team met intermittently to monitor outcomes, provide feedback, further refine interventions, and develop additional interventions.

PDSA Cycle 1

A standardized discharge process was developed in the form of guidelines and expectations. These include an explanation of unique features of the hematology and oncology service and expectations of medication reconciliation with emphasis placed on antiemetics, antimicrobial prophylaxis, and bowel regimen when appropriate, outpatient hematology and oncology follow-up within 14 days, primary care follow-up, communication with the outpatient hematology and oncology physician, written discharge instructions, and bedside teaching when appropriate.

PDSA Cycle 2

Based on team member feedback and further discussions, a discharge checklist was developed. This checklist was available online, reviewed in person, and posted in the team room for rotating residents to use for discharge planning and when discharging patients (Figure 1).

PDSA Cycle 3

Based on ongoing user feedback, group discussions, and data monitoring, the discharge checklist was further refined and updated. An electronic clinical decision support tool was developed and integrated into the electronic medical record (EMR) in the form of a discharge checklist note template directly linked to orders. The tool is a computerized patient record system (CPRS) note template that prompts users to select whether medications or return to clinic orders are needed and offers a menu of frequently used medications. If any of the selections are chosen within the note template, an order is generated automatically in the chart that requires only the user’s signature. Furthermore, the patient care coordinator reviews the prescribed follow-up and works with the medical support assistant to make these appointments. The physician is contacted only when an appointment cannot be made. Therefore, this tool allows many additional actions to be bypassed such as generating medication and return to clinic orders individually and calling schedulers to make follow-up appointments (Figure 2).

Data Analysis

All patients discharged during the 2-month period prior to and discharged after the implementation of the standardized process were reviewed. Patients who followed up with hematology and oncology at another facility, enrolled in hospice, or died during admission were excluded. Follow-up appointment scheduling data and communication between inpatient and outpatient providers were reviewed. Data were analyzed using XmR statistical process control chart and Fisher’s Exact Test using GraphPad. Control limits were calculated for each PDSA cycle as the mean ± the average of the moving range multiplied by 2.66. All data were included in the analysis.

Results

A total of 142 consecutive patients were reviewed from May 1, 2018 to August 31, 2018 and January 1, 2019 to April 30, 2019, including 58 patients prior to the intervention and 84 patients during PDSA cycles. There was a gap in data collection between September 1, 2018 and December 31, 2018 due to limited team member availability. All data were collected by 2 reviewers—a postgraduate year (PGY)-4 chief resident and a PGY-2 internal medicine resident. The median age of patients in the preintervention group was 72 years and 69 years in the postintervention group. All patients were men. Baseline data revealed a mean 17 days to hematology and oncology follow-up. Primary care visits were scheduled for 68% of patients at the time of discharge. The outpatient hematology and oncology physician was alerted electronically to the discharge summary for 20% of the patients (Table 2).

The primary endpoint of time to hematology and oncology follow-up appointment improved to 13 days in PDSA cycles 1 and 2 and 10 days in PDSA cycle 3. The target of mean 14 days to follow-up was achieved. The statistical process control chart shows 5 shifts with clusters of ≥ 7 points below the mean revealing a true signal or change in the data and demonstrating that an improvement was seen (Figure 3). Furthermore, the statistical process control chart demonstrates upper control limit decreased from 58 days at baseline to 21 days in PDSA cycle 3, suggesting a decrease in variation.

Discussion

The 2013 IOM report Delivering High-Quality Cancer Care: Charting a New Course for a System in Crisis found that that cancer care is not as patient-centered, accessible, coordinated, or evidence-based as it could be, with detrimental impacts on patients.³ The document offered a conceptual framework to improve quality of cancer care that includes the translation of evidence into clinical practice, quality measurement, and performance improvement, as well as using advances in information technology to enhance quality measurement and performance improvement. Our quality initiative uses this framework to work toward the goal as stated by the IOM report: to deliver “comprehensive, patient-centered, evidence-based, high-quality cancer care that is accessible and affordable.”³

Two large studies that evaluated risk factors for 15-day and 30-day hospital readmissions identified cancer diagnosis as a risk factor for increased hospital readmission, highlighting the need to identify strategies to improve the discharge process for these patients.^4,5 Timely outpatient follow-up and better patient hand-off may improve clinical outcomes among this high-risk patient population after hospital discharge. Multiple studies have demonstrated that timely follow-up is associated with fewer readmissions.^1,8-10 A study by Forster and colleagues that evaluated postdischarge adverse events (AEs) revealed a 23% incidence of AEs with 12% of these identified as preventable. Postdischarge monitoring was deemed inadequate among these patients, with closer follow-up and improved hand-offs between inpatient and outpatient medical teams identified as possible interventions to improve postdischarge patient monitoring and to prevent AEs.⁷

The present quality initiative to standardize the discharge process for the hematology and oncology service decreased time to hematology and oncology follow-up appointment, improved communication between inpatient and outpatient teams, and decreased process variation. Timelier follow-up for this complex patient population likely will prevent clinical decompensation, delays in treatment, and directly improve patient access to care.

The multidisciplinary nature of this effort was instrumental to successful completion. In a complex health care system, it is challenging to truly understand a problem and identify possible solutions without the perspective of all members of the care team. The involvement of team members with training in quality improvement methodology was important to evaluate and develop interventions in a systematic way. Furthermore, the support and involvement of leadership is important in order to allocate resources appropriately to achieve system changes that improve care. Using quality improvement methodology, the team was able to map our processes and perform gap and root cause analyses. Strategies were identified to improve our performance using a solutions approach. Changes were implemented with continued intermittent meetings for monitoring of progression and discussion of how interventions could be made more efficient, effective, and user friendly. The primary goal was ultimately achieved.

Integration of intervention into the EMR embodies the IOM’s call to use advances in information technology to enhance the quality and delivery of care, quality measurement, and performance improvement.³ This intervention offered the strongest system changes as an electronic clinical decision support tool was developed and embedded into the EMR in the form of a Discharge Checklist Note that is linked to associated orders. This intervention was the most robust, as it provided objective data regarding utilization of the checklist, offered a more efficient way to communicate with team members regarding discharge needs, and streamlined the workflow for the discharging provider. Furthermore, this electronic tool created the ability to measure other important aspects in the care of this patient population that we previously had no mechanism of measuring: timely nursing appointments for routine care of PICC lines and ports.

Limitations

The absence of clinical endpoints was a limitation of this study. The present study was unable to evaluate the effect of the intervention on readmission rates, emergency department visits, hospital length of stay, cost, or mortality. Coordinating this multidisciplinary effort required much time and planning, and additional resources were not available to evaluate these clinical endpoints. Further studies are needed to evaluate whether the increased patient access and closer follow-up would result in improvement in these clinical endpoints. Another consideration for future improvement projects would be to include patients in the multidisciplinary team. The patient perspective would be invaluable in identifying gaps in care delivery and strategies aimed at improving care delivery.

Conclusions

This quality initiative to standardize the discharge process for the hematology and oncology service decreased time to the initial hematology and oncology follow-up appointment, improved communication between inpatient and outpatient teams, and decreased process variation. Timelier follow-up for this complex patient population likely will prevent clinical decompensation, delays in treatment, and directly improve patient access to care.

Acknowledgments

We thank our patients for whom we hope our process improvement efforts will ultimately benefit. We thank all the hematology and oncology staff at Edward Hines Jr. VA Hospital and Loyola University Medical Center residents and fellows who care for our patients and participated in the multidisciplinary team to improve care for our patients. We thank the following professionals for their uncompensated assistance in the coordination and execution of this initiative: Robert Kutter, MS, and Meghan O’Halloran, MD.

Hematology and oncology patients are a complex patient population that requires timely follow-up to prevent clinical decompensation and delays in treatment. Previous reports have demonstrated that outpatient follow-up within 14 days is associated with decreased 30-day readmissions. The magnitude of this effect is greater for higher-risk patients.¹ Therefore, patients being discharged from the hematology and oncology inpatient service should be seen by a hematology and oncology provider within 14 days of discharge. Patients who do not require close oncologic follow-up should be seen by a primary care provider (PCP) within this timeframe.

Background

The Institute of Medicine (IOM) identified the need to focus on quality improvement and patient safety with a 1999 report, To Err Is Human.² Tremendous strides have been made in the areas of quality improvement and patient safety over the past 2 decades. In a 2013 report, the IOM further identified hematology and oncology care as an area of need due to a combination of growing demand, complexity of cancer and cancer treatment, shrinking workforce, and rising costs. The report concluded that cancer care is not as patient-centered, accessible, coordinated, or evidence based as it could be, with detrimental impacts on patients.³ Patients with cancer have been identified as a high-risk population for hospital readmissions.^4,5 Lack of timely follow-up and failed hand-offs have been identified as factors contributing to poor outcomes at time of discharge.^6-10

Upon internal review of baseline performance data, we identified areas needing improvement in the discharge process. These included time to hematology and oncology follow-up appointment, percent of patients with PCP appointments scheduled at time of discharge, and electronically alerts for the outpatient hematologist/oncologist to discharge summaries. It was determined that patients discharged from the inpatient service were seen a mean 17 days later by their outpatient hematology and oncology provider and the time to the follow-up appointment varied substantially, with some patients being seen several weeks to months after discharge. Furthermore, only 68% of patients had a primary care appointment scheduled at the time of discharge. These data along with review of data reported in the medical literature supported our initiative for improvement in the transition from inpatient to outpatient care for our hematology and oncology patients.

Plan-Do-Study-Act (PDSA) quality improvement methodology was used to create and implement several interventions to standardize the discharge process for this patient population, with the primary goal of decreasing the mean time to hematology and oncology follow-up from 17 days by 12% to fewer than 14 days. Patients who do not require close oncologic follow-up should be seen by a PCP within this timeframe. Otherwise, PCP follow-up within at least 6 months should be made. Secondary aims included (1) an increase in scheduled PCP visits at time of discharge from 68% to > 90%; and (2) an increase in communication of the discharge summary via electronic alerting of the outpatient hematology and oncology physician from 20% to > 90%. Herein, we report our experience and results of this quality improvement initiative

Methods

The Institutional Review Board at Edward Hines Veteran Affairs Hospital in Hines, Illinois reviewed this single-center study and deemed it to be exempt from oversight. Using PDSA quality improvement methodology, a multidisciplinary team of hematology and oncology staff developed and implemented a standardized discharge process. The multidisciplinary team included a robust representation of inpatient and outpatient staff caring for the hematology and oncology patient population, including attending physicians, fellows, residents, advanced practice nurses, registered nurses, clinical pharmacists, patient care coordinators, clinic schedulers, clinical applications coordinators, quality support staff, and a systems redesign coach. Hospital leadership including chief of staff, chief of medicine, and chief of nursing participated as the management guidance team. Several interviews and group meetings were conducted and a multidisciplinary team collaboratively developed and implemented the interventions and monitored the results.

Outcome measures were identified, including time to hematology and oncology clinic visit, primary care follow-up scheduling, and communication of discharge to the outpatient hematology and oncology physician. Baseline data were collected and reviewed. The multidisciplinary team developed a process flow map to understand the steps and resources involved with the transition from inpatient to outpatient care. Gap analysis and root cause analysis were performed. A solutions approach was applied to develop interventions. Table 1 shows a summary of the identified problems, symptoms, associated causes, the interventions aimed to address the problems, and expected outcomes. Rotating resident physicians were trained through online and in-person education. The multidisciplinary team met intermittently to monitor outcomes, provide feedback, further refine interventions, and develop additional interventions.

PDSA Cycle 1

A standardized discharge process was developed in the form of guidelines and expectations. These include an explanation of unique features of the hematology and oncology service and expectations of medication reconciliation with emphasis placed on antiemetics, antimicrobial prophylaxis, and bowel regimen when appropriate, outpatient hematology and oncology follow-up within 14 days, primary care follow-up, communication with the outpatient hematology and oncology physician, written discharge instructions, and bedside teaching when appropriate.

PDSA Cycle 2

Based on team member feedback and further discussions, a discharge checklist was developed. This checklist was available online, reviewed in person, and posted in the team room for rotating residents to use for discharge planning and when discharging patients (Figure 1).

PDSA Cycle 3

Based on ongoing user feedback, group discussions, and data monitoring, the discharge checklist was further refined and updated. An electronic clinical decision support tool was developed and integrated into the electronic medical record (EMR) in the form of a discharge checklist note template directly linked to orders. The tool is a computerized patient record system (CPRS) note template that prompts users to select whether medications or return to clinic orders are needed and offers a menu of frequently used medications. If any of the selections are chosen within the note template, an order is generated automatically in the chart that requires only the user’s signature. Furthermore, the patient care coordinator reviews the prescribed follow-up and works with the medical support assistant to make these appointments. The physician is contacted only when an appointment cannot be made. Therefore, this tool allows many additional actions to be bypassed such as generating medication and return to clinic orders individually and calling schedulers to make follow-up appointments (Figure 2).

Data Analysis

All patients discharged during the 2-month period prior to and discharged after the implementation of the standardized process were reviewed. Patients who followed up with hematology and oncology at another facility, enrolled in hospice, or died during admission were excluded. Follow-up appointment scheduling data and communication between inpatient and outpatient providers were reviewed. Data were analyzed using XmR statistical process control chart and Fisher’s Exact Test using GraphPad. Control limits were calculated for each PDSA cycle as the mean ± the average of the moving range multiplied by 2.66. All data were included in the analysis.

Results

A total of 142 consecutive patients were reviewed from May 1, 2018 to August 31, 2018 and January 1, 2019 to April 30, 2019, including 58 patients prior to the intervention and 84 patients during PDSA cycles. There was a gap in data collection between September 1, 2018 and December 31, 2018 due to limited team member availability. All data were collected by 2 reviewers—a postgraduate year (PGY)-4 chief resident and a PGY-2 internal medicine resident. The median age of patients in the preintervention group was 72 years and 69 years in the postintervention group. All patients were men. Baseline data revealed a mean 17 days to hematology and oncology follow-up. Primary care visits were scheduled for 68% of patients at the time of discharge. The outpatient hematology and oncology physician was alerted electronically to the discharge summary for 20% of the patients (Table 2).

The primary endpoint of time to hematology and oncology follow-up appointment improved to 13 days in PDSA cycles 1 and 2 and 10 days in PDSA cycle 3. The target of mean 14 days to follow-up was achieved. The statistical process control chart shows 5 shifts with clusters of ≥ 7 points below the mean revealing a true signal or change in the data and demonstrating that an improvement was seen (Figure 3). Furthermore, the statistical process control chart demonstrates upper control limit decreased from 58 days at baseline to 21 days in PDSA cycle 3, suggesting a decrease in variation.

Discussion

The 2013 IOM report Delivering High-Quality Cancer Care: Charting a New Course for a System in Crisis found that that cancer care is not as patient-centered, accessible, coordinated, or evidence-based as it could be, with detrimental impacts on patients.³ The document offered a conceptual framework to improve quality of cancer care that includes the translation of evidence into clinical practice, quality measurement, and performance improvement, as well as using advances in information technology to enhance quality measurement and performance improvement. Our quality initiative uses this framework to work toward the goal as stated by the IOM report: to deliver “comprehensive, patient-centered, evidence-based, high-quality cancer care that is accessible and affordable.”³

Two large studies that evaluated risk factors for 15-day and 30-day hospital readmissions identified cancer diagnosis as a risk factor for increased hospital readmission, highlighting the need to identify strategies to improve the discharge process for these patients.^4,5 Timely outpatient follow-up and better patient hand-off may improve clinical outcomes among this high-risk patient population after hospital discharge. Multiple studies have demonstrated that timely follow-up is associated with fewer readmissions.^1,8-10 A study by Forster and colleagues that evaluated postdischarge adverse events (AEs) revealed a 23% incidence of AEs with 12% of these identified as preventable. Postdischarge monitoring was deemed inadequate among these patients, with closer follow-up and improved hand-offs between inpatient and outpatient medical teams identified as possible interventions to improve postdischarge patient monitoring and to prevent AEs.⁷

The present quality initiative to standardize the discharge process for the hematology and oncology service decreased time to hematology and oncology follow-up appointment, improved communication between inpatient and outpatient teams, and decreased process variation. Timelier follow-up for this complex patient population likely will prevent clinical decompensation, delays in treatment, and directly improve patient access to care.

The multidisciplinary nature of this effort was instrumental to successful completion. In a complex health care system, it is challenging to truly understand a problem and identify possible solutions without the perspective of all members of the care team. The involvement of team members with training in quality improvement methodology was important to evaluate and develop interventions in a systematic way. Furthermore, the support and involvement of leadership is important in order to allocate resources appropriately to achieve system changes that improve care. Using quality improvement methodology, the team was able to map our processes and perform gap and root cause analyses. Strategies were identified to improve our performance using a solutions approach. Changes were implemented with continued intermittent meetings for monitoring of progression and discussion of how interventions could be made more efficient, effective, and user friendly. The primary goal was ultimately achieved.

Integration of intervention into the EMR embodies the IOM’s call to use advances in information technology to enhance the quality and delivery of care, quality measurement, and performance improvement.³ This intervention offered the strongest system changes as an electronic clinical decision support tool was developed and embedded into the EMR in the form of a Discharge Checklist Note that is linked to associated orders. This intervention was the most robust, as it provided objective data regarding utilization of the checklist, offered a more efficient way to communicate with team members regarding discharge needs, and streamlined the workflow for the discharging provider. Furthermore, this electronic tool created the ability to measure other important aspects in the care of this patient population that we previously had no mechanism of measuring: timely nursing appointments for routine care of PICC lines and ports.

Limitations

The absence of clinical endpoints was a limitation of this study. The present study was unable to evaluate the effect of the intervention on readmission rates, emergency department visits, hospital length of stay, cost, or mortality. Coordinating this multidisciplinary effort required much time and planning, and additional resources were not available to evaluate these clinical endpoints. Further studies are needed to evaluate whether the increased patient access and closer follow-up would result in improvement in these clinical endpoints. Another consideration for future improvement projects would be to include patients in the multidisciplinary team. The patient perspective would be invaluable in identifying gaps in care delivery and strategies aimed at improving care delivery.

Conclusions

This quality initiative to standardize the discharge process for the hematology and oncology service decreased time to the initial hematology and oncology follow-up appointment, improved communication between inpatient and outpatient teams, and decreased process variation. Timelier follow-up for this complex patient population likely will prevent clinical decompensation, delays in treatment, and directly improve patient access to care.

Acknowledgments

We thank our patients for whom we hope our process improvement efforts will ultimately benefit. We thank all the hematology and oncology staff at Edward Hines Jr. VA Hospital and Loyola University Medical Center residents and fellows who care for our patients and participated in the multidisciplinary team to improve care for our patients. We thank the following professionals for their uncompensated assistance in the coordination and execution of this initiative: Robert Kutter, MS, and Meghan O’Halloran, MD.

Hematology and oncology patients are a complex patient population that requires timely follow-up to prevent clinical decompensation and delays in treatment. Previous reports have demonstrated that outpatient follow-up within 14 days is associated with decreased 30-day readmissions. The magnitude of this effect is greater for higher-risk patients.¹ Therefore, patients being discharged from the hematology and oncology inpatient service should be seen by a hematology and oncology provider within 14 days of discharge. Patients who do not require close oncologic follow-up should be seen by a primary care provider (PCP) within this timeframe.

Background

The Institute of Medicine (IOM) identified the need to focus on quality improvement and patient safety with a 1999 report, To Err Is Human.² Tremendous strides have been made in the areas of quality improvement and patient safety over the past 2 decades. In a 2013 report, the IOM further identified hematology and oncology care as an area of need due to a combination of growing demand, complexity of cancer and cancer treatment, shrinking workforce, and rising costs. The report concluded that cancer care is not as patient-centered, accessible, coordinated, or evidence based as it could be, with detrimental impacts on patients.³ Patients with cancer have been identified as a high-risk population for hospital readmissions.^4,5 Lack of timely follow-up and failed hand-offs have been identified as factors contributing to poor outcomes at time of discharge.^6-10

Upon internal review of baseline performance data, we identified areas needing improvement in the discharge process. These included time to hematology and oncology follow-up appointment, percent of patients with PCP appointments scheduled at time of discharge, and electronically alerts for the outpatient hematologist/oncologist to discharge summaries. It was determined that patients discharged from the inpatient service were seen a mean 17 days later by their outpatient hematology and oncology provider and the time to the follow-up appointment varied substantially, with some patients being seen several weeks to months after discharge. Furthermore, only 68% of patients had a primary care appointment scheduled at the time of discharge. These data along with review of data reported in the medical literature supported our initiative for improvement in the transition from inpatient to outpatient care for our hematology and oncology patients.

Plan-Do-Study-Act (PDSA) quality improvement methodology was used to create and implement several interventions to standardize the discharge process for this patient population, with the primary goal of decreasing the mean time to hematology and oncology follow-up from 17 days by 12% to fewer than 14 days. Patients who do not require close oncologic follow-up should be seen by a PCP within this timeframe. Otherwise, PCP follow-up within at least 6 months should be made. Secondary aims included (1) an increase in scheduled PCP visits at time of discharge from 68% to > 90%; and (2) an increase in communication of the discharge summary via electronic alerting of the outpatient hematology and oncology physician from 20% to > 90%. Herein, we report our experience and results of this quality improvement initiative

Methods

The Institutional Review Board at Edward Hines Veteran Affairs Hospital in Hines, Illinois reviewed this single-center study and deemed it to be exempt from oversight. Using PDSA quality improvement methodology, a multidisciplinary team of hematology and oncology staff developed and implemented a standardized discharge process. The multidisciplinary team included a robust representation of inpatient and outpatient staff caring for the hematology and oncology patient population, including attending physicians, fellows, residents, advanced practice nurses, registered nurses, clinical pharmacists, patient care coordinators, clinic schedulers, clinical applications coordinators, quality support staff, and a systems redesign coach. Hospital leadership including chief of staff, chief of medicine, and chief of nursing participated as the management guidance team. Several interviews and group meetings were conducted and a multidisciplinary team collaboratively developed and implemented the interventions and monitored the results.

Outcome measures were identified, including time to hematology and oncology clinic visit, primary care follow-up scheduling, and communication of discharge to the outpatient hematology and oncology physician. Baseline data were collected and reviewed. The multidisciplinary team developed a process flow map to understand the steps and resources involved with the transition from inpatient to outpatient care. Gap analysis and root cause analysis were performed. A solutions approach was applied to develop interventions. Table 1 shows a summary of the identified problems, symptoms, associated causes, the interventions aimed to address the problems, and expected outcomes. Rotating resident physicians were trained through online and in-person education. The multidisciplinary team met intermittently to monitor outcomes, provide feedback, further refine interventions, and develop additional interventions.

PDSA Cycle 1

A standardized discharge process was developed in the form of guidelines and expectations. These include an explanation of unique features of the hematology and oncology service and expectations of medication reconciliation with emphasis placed on antiemetics, antimicrobial prophylaxis, and bowel regimen when appropriate, outpatient hematology and oncology follow-up within 14 days, primary care follow-up, communication with the outpatient hematology and oncology physician, written discharge instructions, and bedside teaching when appropriate.

PDSA Cycle 2

Based on team member feedback and further discussions, a discharge checklist was developed. This checklist was available online, reviewed in person, and posted in the team room for rotating residents to use for discharge planning and when discharging patients (Figure 1).

PDSA Cycle 3

Based on ongoing user feedback, group discussions, and data monitoring, the discharge checklist was further refined and updated. An electronic clinical decision support tool was developed and integrated into the electronic medical record (EMR) in the form of a discharge checklist note template directly linked to orders. The tool is a computerized patient record system (CPRS) note template that prompts users to select whether medications or return to clinic orders are needed and offers a menu of frequently used medications. If any of the selections are chosen within the note template, an order is generated automatically in the chart that requires only the user’s signature. Furthermore, the patient care coordinator reviews the prescribed follow-up and works with the medical support assistant to make these appointments. The physician is contacted only when an appointment cannot be made. Therefore, this tool allows many additional actions to be bypassed such as generating medication and return to clinic orders individually and calling schedulers to make follow-up appointments (Figure 2).

Data Analysis

All patients discharged during the 2-month period prior to and discharged after the implementation of the standardized process were reviewed. Patients who followed up with hematology and oncology at another facility, enrolled in hospice, or died during admission were excluded. Follow-up appointment scheduling data and communication between inpatient and outpatient providers were reviewed. Data were analyzed using XmR statistical process control chart and Fisher’s Exact Test using GraphPad. Control limits were calculated for each PDSA cycle as the mean ± the average of the moving range multiplied by 2.66. All data were included in the analysis.

Results

A total of 142 consecutive patients were reviewed from May 1, 2018 to August 31, 2018 and January 1, 2019 to April 30, 2019, including 58 patients prior to the intervention and 84 patients during PDSA cycles. There was a gap in data collection between September 1, 2018 and December 31, 2018 due to limited team member availability. All data were collected by 2 reviewers—a postgraduate year (PGY)-4 chief resident and a PGY-2 internal medicine resident. The median age of patients in the preintervention group was 72 years and 69 years in the postintervention group. All patients were men. Baseline data revealed a mean 17 days to hematology and oncology follow-up. Primary care visits were scheduled for 68% of patients at the time of discharge. The outpatient hematology and oncology physician was alerted electronically to the discharge summary for 20% of the patients (Table 2).

The primary endpoint of time to hematology and oncology follow-up appointment improved to 13 days in PDSA cycles 1 and 2 and 10 days in PDSA cycle 3. The target of mean 14 days to follow-up was achieved. The statistical process control chart shows 5 shifts with clusters of ≥ 7 points below the mean revealing a true signal or change in the data and demonstrating that an improvement was seen (Figure 3). Furthermore, the statistical process control chart demonstrates upper control limit decreased from 58 days at baseline to 21 days in PDSA cycle 3, suggesting a decrease in variation.

Discussion

The 2013 IOM report Delivering High-Quality Cancer Care: Charting a New Course for a System in Crisis found that that cancer care is not as patient-centered, accessible, coordinated, or evidence-based as it could be, with detrimental impacts on patients.³ The document offered a conceptual framework to improve quality of cancer care that includes the translation of evidence into clinical practice, quality measurement, and performance improvement, as well as using advances in information technology to enhance quality measurement and performance improvement. Our quality initiative uses this framework to work toward the goal as stated by the IOM report: to deliver “comprehensive, patient-centered, evidence-based, high-quality cancer care that is accessible and affordable.”³

Two large studies that evaluated risk factors for 15-day and 30-day hospital readmissions identified cancer diagnosis as a risk factor for increased hospital readmission, highlighting the need to identify strategies to improve the discharge process for these patients.^4,5 Timely outpatient follow-up and better patient hand-off may improve clinical outcomes among this high-risk patient population after hospital discharge. Multiple studies have demonstrated that timely follow-up is associated with fewer readmissions.^1,8-10 A study by Forster and colleagues that evaluated postdischarge adverse events (AEs) revealed a 23% incidence of AEs with 12% of these identified as preventable. Postdischarge monitoring was deemed inadequate among these patients, with closer follow-up and improved hand-offs between inpatient and outpatient medical teams identified as possible interventions to improve postdischarge patient monitoring and to prevent AEs.⁷

The present quality initiative to standardize the discharge process for the hematology and oncology service decreased time to hematology and oncology follow-up appointment, improved communication between inpatient and outpatient teams, and decreased process variation. Timelier follow-up for this complex patient population likely will prevent clinical decompensation, delays in treatment, and directly improve patient access to care.

The multidisciplinary nature of this effort was instrumental to successful completion. In a complex health care system, it is challenging to truly understand a problem and identify possible solutions without the perspective of all members of the care team. The involvement of team members with training in quality improvement methodology was important to evaluate and develop interventions in a systematic way. Furthermore, the support and involvement of leadership is important in order to allocate resources appropriately to achieve system changes that improve care. Using quality improvement methodology, the team was able to map our processes and perform gap and root cause analyses. Strategies were identified to improve our performance using a solutions approach. Changes were implemented with continued intermittent meetings for monitoring of progression and discussion of how interventions could be made more efficient, effective, and user friendly. The primary goal was ultimately achieved.

Integration of intervention into the EMR embodies the IOM’s call to use advances in information technology to enhance the quality and delivery of care, quality measurement, and performance improvement.³ This intervention offered the strongest system changes as an electronic clinical decision support tool was developed and embedded into the EMR in the form of a Discharge Checklist Note that is linked to associated orders. This intervention was the most robust, as it provided objective data regarding utilization of the checklist, offered a more efficient way to communicate with team members regarding discharge needs, and streamlined the workflow for the discharging provider. Furthermore, this electronic tool created the ability to measure other important aspects in the care of this patient population that we previously had no mechanism of measuring: timely nursing appointments for routine care of PICC lines and ports.

Limitations

The absence of clinical endpoints was a limitation of this study. The present study was unable to evaluate the effect of the intervention on readmission rates, emergency department visits, hospital length of stay, cost, or mortality. Coordinating this multidisciplinary effort required much time and planning, and additional resources were not available to evaluate these clinical endpoints. Further studies are needed to evaluate whether the increased patient access and closer follow-up would result in improvement in these clinical endpoints. Another consideration for future improvement projects would be to include patients in the multidisciplinary team. The patient perspective would be invaluable in identifying gaps in care delivery and strategies aimed at improving care delivery.

Conclusions

This quality initiative to standardize the discharge process for the hematology and oncology service decreased time to the initial hematology and oncology follow-up appointment, improved communication between inpatient and outpatient teams, and decreased process variation. Timelier follow-up for this complex patient population likely will prevent clinical decompensation, delays in treatment, and directly improve patient access to care.

Acknowledgments

We thank our patients for whom we hope our process improvement efforts will ultimately benefit. We thank all the hematology and oncology staff at Edward Hines Jr. VA Hospital and Loyola University Medical Center residents and fellows who care for our patients and participated in the multidisciplinary team to improve care for our patients. We thank the following professionals for their uncompensated assistance in the coordination and execution of this initiative: Robert Kutter, MS, and Meghan O’Halloran, MD.

Stereotactic ablative radiotherapy (SABR) has become the standard of care for inoperable early-stage non-small cell lung cancer (NSCLC). Many patients are unable to undergo a biopsy safely because of poor pulmonary function or underlying emphysema and are then empirically treated with radiotherapy if they meet criteria. In these patients, local control can be achieved with SABR with minimal toxicity.¹ Considering that median overall survival (OS) among patients with untreated stage I NSCLC has been reported to be as low as 9 months, early treatment with SABR could lead to increased survival of 29 to 60 months.^2-4

The RTOG 0236 trial showed a median OS of 48 months and the randomized phase III CHISEL trial showed a median OS of 60 months; however, these survival data were reported in patients who were able to safely undergo a biopsy and had confirmed NSCLC.^4,5 For patients without a diagnosis confirmed by biopsy and who are treated with empiric SABR, patient factors that influence radiation toxicity and OS are not well defined.

It is not clear if empiric radiation benefits survival or if treatment causes decline in lung function, considering that underlying chronic lung disease precludes these patients from biopsy. The purpose of this study was to evaluate the factors associated with radiation toxicity with empiric SABR and to evaluate OS in this population without a biopsy-confirmed diagnosis.

Methods

This was a single center retrospective review of patients treated at the radiation oncology department at the Kansas City Veterans Affairs Medical Center from August 2014 to February 2019. Data were collected on 69 patients with pulmonary nodules identified by chest computed tomography (CT) and/or positron emission tomography (PET)-CT that were highly suspicious for primary NSCLC.

These patients were presented at a multidisciplinary meeting that involved pulmonologists, oncologists, radiation oncologists, and thoracic surgeons. Patients were deemed to be poor candidates for biopsy because of severe underlying emphysema, which would put them at high risk for pneumothorax with a percutaneous needle biopsy, or were unable to tolerate general anesthesia for navigational bronchoscopy or surgical biopsy because of poor lung function. These patients were diagnosed with presumed stage I NSCLC using the criteria: minimum of 2 sequential CT scans with enlarging nodule; absence of metastases on PET-CT; the single nodule had to be fluorodeoxyglucose avid with a minimum standardized uptake value of 2.5, and absence of clinical history or physical examination consistent with small cell lung cancer or infection.

After a consensus was reached that patients met these criteria, individuals were referred for empiric SABR. Follow-up visits were at 1 month, 3 months, and every 6 months. Variables analyzed included: patient demographics, pre- and posttreatment pulmonary function tests (PFT) when available, pre-treatment oxygen use, tumor size and location (peripheral, central, or ultra-central), radiation doses, and grade of toxicity as defined by Human and Health Services Common Terminology Criteria for Adverse Events version 5.0 (dyspnea and cough both counted as pulmonary toxicity): acute ≤ 90 days and late > 90 days (Table 1).

Results

The median follow-up was 18 months (range, 1 to 54). The median age was 71 years (range, 59 to 95) (Table 2). Most patients (97.1%) were male. The majority of patients (79.4%) had a 0 or 1 for the Eastern Cooperative Oncology group performance status, indicating fully active or restricted in physically strenuous activity but ambulatory and able to perform light work. All patients were either current or former smokers with an average pack-year history of 69.4. Only 11.6% of patients had operable disease, but received empiric SABR because they declined surgery. Four patients did not have pretreatment spirometry available and 37 did not have pretreatment diffusing capacity for carbon monoxide (DLCO) data.

Most patients had a pretreatment forced expiratory volume during the first seconds (FEV₁) value and DLCO < 60% of predicted (60% and 84% of the patients, respectively). The median tumor diameter was 2 cm. Of the 68.2% of patients who did not have chronic hypoxemic respiratory failure before SABR, 16% developed a new requirement for supplemental oxygen. Sixty-two tumors (89.9%) were peripheral. There were 4 local recurrences (5.7%), 10 regional (different lobe and nodal) failures (14.3%), and 15 distant metastases (21.4%).

Nineteen of 67 patients (26.3%) had acute toxicity of which 9 had acute grade ≥ 2 toxicity; information regarding toxicity was missing on 2 patients. Thirty-two of 65 (49.9%) patients had late toxicity of which 20 (30.8%) had late grade ≥ 2 toxicity. The main factor associated with development of acute toxicity was pretreatment oxygendependence (P = .047). This was not significant when comparing only inoperable patients. Twenty patients (29.9%) developed some type of acute toxicity; pulmonary toxicity was most common (22.4%) (Table 3). All patients with acute toxicity also developed late toxicity except for 1 who died before 3 months. Predominantly, the deaths in our sample were from causes other than the malignancy or treatment, such as sepsis, deconditioning after a fall, cardiovascular complications, etc. Acute toxicity of grade ≥ 2 was significantly associated with late toxicity (P < .001 for both) in both operable and inoperable patients (P < .001).

Discussion

SABR is an effective treatment for inoperable early-stage NSCLC, however its therapeutic ratio in a more frail population who cannot withstand biopsy is not well established. Additionally, the prevalence of benign disease in patients with solitary pulmonary nodules can be between 9% and 21%.⁶ Haidar and colleagues looked at 55 patients who received empiric SABR and found a median OS of 30.2 months with an 8.7% risk of local failure, 13% risk of regional failure with 8.7% acute toxicity, and 13% chronic toxicity.⁷ Data from Harkenrider and colleagues (n = 34) revealed similar results with a 2-year OS of 85%, local control of 97.1%, and regional control of 80%. The authors noted no grade ≥ 3 acute toxicities and an incidence of grade ≥ 3 late toxicities of 8.8%.¹ These findings are concordant with our study results, confirming the safety and efficacy of SABR. Furthermore, a National Cancer Database analysis of observation vs empiric SABR found an OS of 10.1 months and 29 months respectively, with a hazard ratio of 0.64 (P < .001).³ Additionally, Fischer-Valuck and colleagues (n = 88) compared biopsy confirmed vs unbiopsied patients treated with SABR and found no difference in the 3-year local progression-free survival (93.1% vs 94.1%), regional lymph node metastasis and distant metastases free survival (92.5% vs 87.4%), or OS (59.9% vs 58.9%).⁸ With a median OS of ≤ 1 year for untreated stage I NSCLC,these studies support treating patients with empiric SABR.⁴

Other researchers have sought parameters to identify patients for whom radiation therapy would be too toxic. Guckenberger and colleagues aimed to establish a lower limit of pretreatment PFT to exclude patients and found only a 7% incidence of grade ≥ 2 adverse effects and toxicity did not increase with lower pulmonary function.⁹ They concluded that SABR was safe even for patients with poor pulmonary function. Other institutions have confirmed such findings and have been unable to find a cut-off PFT to exclude patients from empiric SABR.^10,11 An analysis from the RTOG 0236 trial also noted that poor baseline PFT could not predict pulmonary toxicity or survival. Additionally, the study demonstrated only minimal decreases in patients’ FEV₁ (5.8%) and DLCO (6%) at 2 years.¹²

Limitations

Our study is limited by caveats of a retrospective study and its small sample size, but is in line with the reported literature (ranging from 33 to 88 patients).^1,7,8 Another limitation is that data on pretreatment DLCO was missing in 37 patients and the lack of statistical robustness in terms of the smaller inoperable cohort, which limits the analyses of these factors in regards to anticipated morbidity from SABR. Also, given this is data collected from the US Department of Veterans Affairs, only 3% of our sample was female.

Conclusions

Empiric SABR for patients with presumed early-stage NSCLC appears to be safe and might positively impact OS. Development of any acute toxicity grade ≥ 2 was significantly associated with dependence on supplemental oxygen before treatment, central tumor location, and development of new oxygen requirement. No association was found in patients with poor pulmonary function before treatment because we could not find a FEV₁ or DLCO cutoff that could preclude patients from empiric SABR. Considering the poor survival of untreated early-stage NSCLC, coupled with the efficacy and safety of empiric SABR for those with presumed disease, definitive SABR should be offered selectively within this patient population.

Acknowledgments

Drs. Park, Whiting and Castillo contributed to data collection. Drs. Park, Govindan and Castillo contributed to the statistical analysis and writing the first draft and final manuscript. Drs. Park, Govindan, Huang, and Reddy contributed to the discussion section.

Stereotactic ablative radiotherapy (SABR) has become the standard of care for inoperable early-stage non-small cell lung cancer (NSCLC). Many patients are unable to undergo a biopsy safely because of poor pulmonary function or underlying emphysema and are then empirically treated with radiotherapy if they meet criteria. In these patients, local control can be achieved with SABR with minimal toxicity.¹ Considering that median overall survival (OS) among patients with untreated stage I NSCLC has been reported to be as low as 9 months, early treatment with SABR could lead to increased survival of 29 to 60 months.^2-4

The RTOG 0236 trial showed a median OS of 48 months and the randomized phase III CHISEL trial showed a median OS of 60 months; however, these survival data were reported in patients who were able to safely undergo a biopsy and had confirmed NSCLC.^4,5 For patients without a diagnosis confirmed by biopsy and who are treated with empiric SABR, patient factors that influence radiation toxicity and OS are not well defined.

It is not clear if empiric radiation benefits survival or if treatment causes decline in lung function, considering that underlying chronic lung disease precludes these patients from biopsy. The purpose of this study was to evaluate the factors associated with radiation toxicity with empiric SABR and to evaluate OS in this population without a biopsy-confirmed diagnosis.

Methods

This was a single center retrospective review of patients treated at the radiation oncology department at the Kansas City Veterans Affairs Medical Center from August 2014 to February 2019. Data were collected on 69 patients with pulmonary nodules identified by chest computed tomography (CT) and/or positron emission tomography (PET)-CT that were highly suspicious for primary NSCLC.

These patients were presented at a multidisciplinary meeting that involved pulmonologists, oncologists, radiation oncologists, and thoracic surgeons. Patients were deemed to be poor candidates for biopsy because of severe underlying emphysema, which would put them at high risk for pneumothorax with a percutaneous needle biopsy, or were unable to tolerate general anesthesia for navigational bronchoscopy or surgical biopsy because of poor lung function. These patients were diagnosed with presumed stage I NSCLC using the criteria: minimum of 2 sequential CT scans with enlarging nodule; absence of metastases on PET-CT; the single nodule had to be fluorodeoxyglucose avid with a minimum standardized uptake value of 2.5, and absence of clinical history or physical examination consistent with small cell lung cancer or infection.

After a consensus was reached that patients met these criteria, individuals were referred for empiric SABR. Follow-up visits were at 1 month, 3 months, and every 6 months. Variables analyzed included: patient demographics, pre- and posttreatment pulmonary function tests (PFT) when available, pre-treatment oxygen use, tumor size and location (peripheral, central, or ultra-central), radiation doses, and grade of toxicity as defined by Human and Health Services Common Terminology Criteria for Adverse Events version 5.0 (dyspnea and cough both counted as pulmonary toxicity): acute ≤ 90 days and late > 90 days (Table 1).

Results

The median follow-up was 18 months (range, 1 to 54). The median age was 71 years (range, 59 to 95) (Table 2). Most patients (97.1%) were male. The majority of patients (79.4%) had a 0 or 1 for the Eastern Cooperative Oncology group performance status, indicating fully active or restricted in physically strenuous activity but ambulatory and able to perform light work. All patients were either current or former smokers with an average pack-year history of 69.4. Only 11.6% of patients had operable disease, but received empiric SABR because they declined surgery. Four patients did not have pretreatment spirometry available and 37 did not have pretreatment diffusing capacity for carbon monoxide (DLCO) data.

Most patients had a pretreatment forced expiratory volume during the first seconds (FEV₁) value and DLCO < 60% of predicted (60% and 84% of the patients, respectively). The median tumor diameter was 2 cm. Of the 68.2% of patients who did not have chronic hypoxemic respiratory failure before SABR, 16% developed a new requirement for supplemental oxygen. Sixty-two tumors (89.9%) were peripheral. There were 4 local recurrences (5.7%), 10 regional (different lobe and nodal) failures (14.3%), and 15 distant metastases (21.4%).

Nineteen of 67 patients (26.3%) had acute toxicity of which 9 had acute grade ≥ 2 toxicity; information regarding toxicity was missing on 2 patients. Thirty-two of 65 (49.9%) patients had late toxicity of which 20 (30.8%) had late grade ≥ 2 toxicity. The main factor associated with development of acute toxicity was pretreatment oxygendependence (P = .047). This was not significant when comparing only inoperable patients. Twenty patients (29.9%) developed some type of acute toxicity; pulmonary toxicity was most common (22.4%) (Table 3). All patients with acute toxicity also developed late toxicity except for 1 who died before 3 months. Predominantly, the deaths in our sample were from causes other than the malignancy or treatment, such as sepsis, deconditioning after a fall, cardiovascular complications, etc. Acute toxicity of grade ≥ 2 was significantly associated with late toxicity (P < .001 for both) in both operable and inoperable patients (P < .001).

Discussion

SABR is an effective treatment for inoperable early-stage NSCLC, however its therapeutic ratio in a more frail population who cannot withstand biopsy is not well established. Additionally, the prevalence of benign disease in patients with solitary pulmonary nodules can be between 9% and 21%.⁶ Haidar and colleagues looked at 55 patients who received empiric SABR and found a median OS of 30.2 months with an 8.7% risk of local failure, 13% risk of regional failure with 8.7% acute toxicity, and 13% chronic toxicity.⁷ Data from Harkenrider and colleagues (n = 34) revealed similar results with a 2-year OS of 85%, local control of 97.1%, and regional control of 80%. The authors noted no grade ≥ 3 acute toxicities and an incidence of grade ≥ 3 late toxicities of 8.8%.¹ These findings are concordant with our study results, confirming the safety and efficacy of SABR. Furthermore, a National Cancer Database analysis of observation vs empiric SABR found an OS of 10.1 months and 29 months respectively, with a hazard ratio of 0.64 (P < .001).³ Additionally, Fischer-Valuck and colleagues (n = 88) compared biopsy confirmed vs unbiopsied patients treated with SABR and found no difference in the 3-year local progression-free survival (93.1% vs 94.1%), regional lymph node metastasis and distant metastases free survival (92.5% vs 87.4%), or OS (59.9% vs 58.9%).⁸ With a median OS of ≤ 1 year for untreated stage I NSCLC,these studies support treating patients with empiric SABR.⁴

Other researchers have sought parameters to identify patients for whom radiation therapy would be too toxic. Guckenberger and colleagues aimed to establish a lower limit of pretreatment PFT to exclude patients and found only a 7% incidence of grade ≥ 2 adverse effects and toxicity did not increase with lower pulmonary function.⁹ They concluded that SABR was safe even for patients with poor pulmonary function. Other institutions have confirmed such findings and have been unable to find a cut-off PFT to exclude patients from empiric SABR.^10,11 An analysis from the RTOG 0236 trial also noted that poor baseline PFT could not predict pulmonary toxicity or survival. Additionally, the study demonstrated only minimal decreases in patients’ FEV₁ (5.8%) and DLCO (6%) at 2 years.¹²

Limitations

Our study is limited by caveats of a retrospective study and its small sample size, but is in line with the reported literature (ranging from 33 to 88 patients).^1,7,8 Another limitation is that data on pretreatment DLCO was missing in 37 patients and the lack of statistical robustness in terms of the smaller inoperable cohort, which limits the analyses of these factors in regards to anticipated morbidity from SABR. Also, given this is data collected from the US Department of Veterans Affairs, only 3% of our sample was female.

Conclusions

Empiric SABR for patients with presumed early-stage NSCLC appears to be safe and might positively impact OS. Development of any acute toxicity grade ≥ 2 was significantly associated with dependence on supplemental oxygen before treatment, central tumor location, and development of new oxygen requirement. No association was found in patients with poor pulmonary function before treatment because we could not find a FEV₁ or DLCO cutoff that could preclude patients from empiric SABR. Considering the poor survival of untreated early-stage NSCLC, coupled with the efficacy and safety of empiric SABR for those with presumed disease, definitive SABR should be offered selectively within this patient population.

Acknowledgments

Drs. Park, Whiting and Castillo contributed to data collection. Drs. Park, Govindan and Castillo contributed to the statistical analysis and writing the first draft and final manuscript. Drs. Park, Govindan, Huang, and Reddy contributed to the discussion section.

Stereotactic ablative radiotherapy (SABR) has become the standard of care for inoperable early-stage non-small cell lung cancer (NSCLC). Many patients are unable to undergo a biopsy safely because of poor pulmonary function or underlying emphysema and are then empirically treated with radiotherapy if they meet criteria. In these patients, local control can be achieved with SABR with minimal toxicity.¹ Considering that median overall survival (OS) among patients with untreated stage I NSCLC has been reported to be as low as 9 months, early treatment with SABR could lead to increased survival of 29 to 60 months.^2-4

The RTOG 0236 trial showed a median OS of 48 months and the randomized phase III CHISEL trial showed a median OS of 60 months; however, these survival data were reported in patients who were able to safely undergo a biopsy and had confirmed NSCLC.^4,5 For patients without a diagnosis confirmed by biopsy and who are treated with empiric SABR, patient factors that influence radiation toxicity and OS are not well defined.

It is not clear if empiric radiation benefits survival or if treatment causes decline in lung function, considering that underlying chronic lung disease precludes these patients from biopsy. The purpose of this study was to evaluate the factors associated with radiation toxicity with empiric SABR and to evaluate OS in this population without a biopsy-confirmed diagnosis.

Methods

This was a single center retrospective review of patients treated at the radiation oncology department at the Kansas City Veterans Affairs Medical Center from August 2014 to February 2019. Data were collected on 69 patients with pulmonary nodules identified by chest computed tomography (CT) and/or positron emission tomography (PET)-CT that were highly suspicious for primary NSCLC.

These patients were presented at a multidisciplinary meeting that involved pulmonologists, oncologists, radiation oncologists, and thoracic surgeons. Patients were deemed to be poor candidates for biopsy because of severe underlying emphysema, which would put them at high risk for pneumothorax with a percutaneous needle biopsy, or were unable to tolerate general anesthesia for navigational bronchoscopy or surgical biopsy because of poor lung function. These patients were diagnosed with presumed stage I NSCLC using the criteria: minimum of 2 sequential CT scans with enlarging nodule; absence of metastases on PET-CT; the single nodule had to be fluorodeoxyglucose avid with a minimum standardized uptake value of 2.5, and absence of clinical history or physical examination consistent with small cell lung cancer or infection.

After a consensus was reached that patients met these criteria, individuals were referred for empiric SABR. Follow-up visits were at 1 month, 3 months, and every 6 months. Variables analyzed included: patient demographics, pre- and posttreatment pulmonary function tests (PFT) when available, pre-treatment oxygen use, tumor size and location (peripheral, central, or ultra-central), radiation doses, and grade of toxicity as defined by Human and Health Services Common Terminology Criteria for Adverse Events version 5.0 (dyspnea and cough both counted as pulmonary toxicity): acute ≤ 90 days and late > 90 days (Table 1).

Results

The median follow-up was 18 months (range, 1 to 54). The median age was 71 years (range, 59 to 95) (Table 2). Most patients (97.1%) were male. The majority of patients (79.4%) had a 0 or 1 for the Eastern Cooperative Oncology group performance status, indicating fully active or restricted in physically strenuous activity but ambulatory and able to perform light work. All patients were either current or former smokers with an average pack-year history of 69.4. Only 11.6% of patients had operable disease, but received empiric SABR because they declined surgery. Four patients did not have pretreatment spirometry available and 37 did not have pretreatment diffusing capacity for carbon monoxide (DLCO) data.

Most patients had a pretreatment forced expiratory volume during the first seconds (FEV₁) value and DLCO < 60% of predicted (60% and 84% of the patients, respectively). The median tumor diameter was 2 cm. Of the 68.2% of patients who did not have chronic hypoxemic respiratory failure before SABR, 16% developed a new requirement for supplemental oxygen. Sixty-two tumors (89.9%) were peripheral. There were 4 local recurrences (5.7%), 10 regional (different lobe and nodal) failures (14.3%), and 15 distant metastases (21.4%).

Nineteen of 67 patients (26.3%) had acute toxicity of which 9 had acute grade ≥ 2 toxicity; information regarding toxicity was missing on 2 patients. Thirty-two of 65 (49.9%) patients had late toxicity of which 20 (30.8%) had late grade ≥ 2 toxicity. The main factor associated with development of acute toxicity was pretreatment oxygendependence (P = .047). This was not significant when comparing only inoperable patients. Twenty patients (29.9%) developed some type of acute toxicity; pulmonary toxicity was most common (22.4%) (Table 3). All patients with acute toxicity also developed late toxicity except for 1 who died before 3 months. Predominantly, the deaths in our sample were from causes other than the malignancy or treatment, such as sepsis, deconditioning after a fall, cardiovascular complications, etc. Acute toxicity of grade ≥ 2 was significantly associated with late toxicity (P < .001 for both) in both operable and inoperable patients (P < .001).

Discussion

SABR is an effective treatment for inoperable early-stage NSCLC, however its therapeutic ratio in a more frail population who cannot withstand biopsy is not well established. Additionally, the prevalence of benign disease in patients with solitary pulmonary nodules can be between 9% and 21%.⁶ Haidar and colleagues looked at 55 patients who received empiric SABR and found a median OS of 30.2 months with an 8.7% risk of local failure, 13% risk of regional failure with 8.7% acute toxicity, and 13% chronic toxicity.⁷ Data from Harkenrider and colleagues (n = 34) revealed similar results with a 2-year OS of 85%, local control of 97.1%, and regional control of 80%. The authors noted no grade ≥ 3 acute toxicities and an incidence of grade ≥ 3 late toxicities of 8.8%.¹ These findings are concordant with our study results, confirming the safety and efficacy of SABR. Furthermore, a National Cancer Database analysis of observation vs empiric SABR found an OS of 10.1 months and 29 months respectively, with a hazard ratio of 0.64 (P < .001).³ Additionally, Fischer-Valuck and colleagues (n = 88) compared biopsy confirmed vs unbiopsied patients treated with SABR and found no difference in the 3-year local progression-free survival (93.1% vs 94.1%), regional lymph node metastasis and distant metastases free survival (92.5% vs 87.4%), or OS (59.9% vs 58.9%).⁸ With a median OS of ≤ 1 year for untreated stage I NSCLC,these studies support treating patients with empiric SABR.⁴

Other researchers have sought parameters to identify patients for whom radiation therapy would be too toxic. Guckenberger and colleagues aimed to establish a lower limit of pretreatment PFT to exclude patients and found only a 7% incidence of grade ≥ 2 adverse effects and toxicity did not increase with lower pulmonary function.⁹ They concluded that SABR was safe even for patients with poor pulmonary function. Other institutions have confirmed such findings and have been unable to find a cut-off PFT to exclude patients from empiric SABR.^10,11 An analysis from the RTOG 0236 trial also noted that poor baseline PFT could not predict pulmonary toxicity or survival. Additionally, the study demonstrated only minimal decreases in patients’ FEV₁ (5.8%) and DLCO (6%) at 2 years.¹²

Limitations

Our study is limited by caveats of a retrospective study and its small sample size, but is in line with the reported literature (ranging from 33 to 88 patients).^1,7,8 Another limitation is that data on pretreatment DLCO was missing in 37 patients and the lack of statistical robustness in terms of the smaller inoperable cohort, which limits the analyses of these factors in regards to anticipated morbidity from SABR. Also, given this is data collected from the US Department of Veterans Affairs, only 3% of our sample was female.

Conclusions

Empiric SABR for patients with presumed early-stage NSCLC appears to be safe and might positively impact OS. Development of any acute toxicity grade ≥ 2 was significantly associated with dependence on supplemental oxygen before treatment, central tumor location, and development of new oxygen requirement. No association was found in patients with poor pulmonary function before treatment because we could not find a FEV₁ or DLCO cutoff that could preclude patients from empiric SABR. Considering the poor survival of untreated early-stage NSCLC, coupled with the efficacy and safety of empiric SABR for those with presumed disease, definitive SABR should be offered selectively within this patient population.

Acknowledgments

Drs. Park, Whiting and Castillo contributed to data collection. Drs. Park, Govindan and Castillo contributed to the statistical analysis and writing the first draft and final manuscript. Drs. Park, Govindan, Huang, and Reddy contributed to the discussion section.

Evaluation of oral antineoplastic agent (OAN) adherence patterns have identified correlations between nonadherence or over-adherence and poorer disease-related outcomes. Multiple studies have focused on imatinib use in chronic myeloid leukemia (CML) due to its continuous, long-term use. A study by Ganesan and colleagues found that nonadherence to imatinib showed a significant decrease in 5-year event-free survival between 76.7% of adherent participants compared with 59.8% of nonadherent participants.¹ This study found that 44% of patients who were adherent to imatinib achieved complete cytogenetic response vs only 26% of patients who were nonadherent. In another study of imatinib for CML, major molecular response (MMR) was strongly correlated with adherence and no patients with adherence < 80% were able to achieve MMR.² Similarly, in studies of tamoxifen for breast cancer, < 80% adherence resulted in a 10% decrease in survival when compared to those who were more adherent.^3,4

In addition to the clinical implications of nonadherence, there can be a significant cost associated with suboptimal use of these medications. The price of a single dose of OAN medication may cost as much as $440.⁵

The benefits of multidisciplinary care teams have been identified in many studies.^6,7 While studies are limited in oncology, pharmacists provide vital contributions to the oncology multidisciplinary team when managing OANs as these health care professionals have expert knowledge of the medications, potential adverse events (AEs), and necessary monitoring parameters.⁸ In one study, patients seen by the pharmacist-led oral chemotherapy management program experienced improved clinical outcomes and response to therapy when compared with preintervention patients (early molecular response, 88.9% vs 54.8%, P = .01; major molecular response, 83.3% vs 57.6%, P = .06).⁹ During the study, 318 AEs were reported, leading to 235 pharmacist interventions to ameliorate AEs and improve adherence.

The primary objective of this study was to measure the impact of a pharmacist-driven OAN renewal clinic on medication adherence. The secondary objective was to estimate cost-savings of this new service.

Methods 

Prior to July 2014, several limitations were identified related to OAN prescribing and monitoring at the Richard L. Roudebush Veterans Affairs Medical Center in Indianapolis, Indiana (RLRVAMC). The prescription ordering process relied primarily on the patient to initiate refills, rather than the prescriber OAN prescriptions also lacked consistency for number of refills or quantities dispensed. Furthermore, ordering of antineoplastic products was not limited to hematology/oncology providers. Patients were identified with significant supply on hand at the time of medication discontinuation, creating concerns for medication waste, tolerability, and nonadherence.

As a result, opportunities were identified to improve the prescribing process, recommended monitoring, toxicity and tolerability evaluation, medication reconciliation, and medication adherence. In July of 2014, the RLRVAMC adopted a new chemotherapy order entry system capable of restricting prescriptions to hematology/oncology providers and limiting dispensed quantities and refill amounts. A comprehensive pharmacist driven OAN renewal clinic was implemented on September 1, 2014 with the goal of improving long-term adherence and tolerability, in addition to minimizing medication waste.

Study Design and Setting

This was a pre/post retrospective cohort, quality improvement study of patients enrolled in the RLRVAMC OAN pharmacist renewal clinic. The study was deemed exempt from institutional review board (IRB) by the US Department of Veterans Affairs (VA) Research and Development Department.

Study Population

Patients were included in the preimplementation group if they had received at least 2 prescriptions of an eligible OAN. Therapy for the preimplementation group was required to be a monthly duration > 21 days and between the dates of September 1, 2013 and August 31, 2014. Patients were included in the postimplementation group if they had received at least 2 prescriptions of the studied OANs between September 1, 2014 and January 31, 2015. Patients were excluded if they had filled < 2 prescriptions of OAN; were managed by a non-VA oncologist or hematologist; or received an OAN other than those listed in Table 1.

Data Collection

For all patients in both the pre- and postimplementation cohorts, a standardized data collection tool was used to collect the following via electronic health record review by a PGY2 oncology resident: age, race, gender, oral antineoplastic agent, refill dates, days’ supply, estimated unit cost per dose cancer diagnosis, distance from the RLRVAMC, copay status, presence of hospitalizations/ED visits/dosage reductions, discontinuation rates, reasons for discontinuation, and total number of current prescriptions. The presence or absence of dosage reductions were collected to identify concerns for tolerability, but only the original dose for the preimplementation group and dosage at time of clinic enrollment for the postimplementation group was included in the analysis.

Outcomes and Statistical Analyses

The primary outcome was medication adherence defined as the median medication possession ratio (MPR) before and after implementation of the clinic. Secondary outcomes included the proportion of patients who were adherent from before implementation to after implementation and estimated cost-savings of this clinic after implementation. MPR was used to estimate medication adherence by taking the cumulative day supply of medication on hand divided by the number of days on therapy.¹² Number of days on therapy was determined by taking the difference on the start date of the new medication regimen and the discontinuation date of the same regimen. Patients were grouped by adherence into one of the following categories: < 0.8, 0.8 to 0.89, 0.9 to 1, and > 1.1. Patients were considered adherent if they reported taking ≥ 90% (MPR ≥ 0.9) of prescribed doses, adopted from the study by Anderson and colleagues.¹² A patient with an MPR > 1, likely due to filling prior to the anticipated refill date, was considered 100% adherent (MPR = 1). If a patient switched OAN during the study, both agents were included as separate entities.

A conservative estimate of cost-savings was made by multiplying the RLRVAMC cost per unit of medication at time of initial prescription fill by the number of units taken each day multiplied by the total days’ supply on hand at time of therapy discontinuation. Patients with an MPR < 1 at time of therapy discontinuation were assumed to have zero remaining units on hand and zero cost savings was estimated. Waste, for purposes of cost-savings, was calculated for all MPR values > 1. Additional supply anticipated to be on hand from dose reductions was not included in the estimated cost of unused medication.

Descriptive statistics compared demographic characteristics between the pre- and postimplementation groups. MPR data were not normally distributed, which required the use of nonparametric Mann-Whitney U tests to compare pre- and postMPRs. Pearson χ² compared the proportion of adherent patients between groups while descriptive statistics were used to estimate cost savings. Significance was determined based on a P value < .05. IBM SPSS Statistics software was used for all statistical analyses. As this was a complete sample of all eligible subjects, no sample size calculation was performed.

Results

In the preimplementation period, 246 patients received an OAN and 61 patients received an OAN in the postimplementation period (Figure 1). Of the 246 patients in the preimplementation period, 98 were eligible and included in the preimplementation group. Similarly, of the 61 patients in the postimplementation period, 35 patients met inclusion criteria for the postimplementation group. The study population was predominantly male with an average age of approximately 70 years in both groups (Table 3). More than 70% of the population in each group was White. No statistically significant differences between groups were identified. The most commonly prescribed OAN in the preimplementation group were abiraterone, imatinib, and enzalutamide (Table 3). In the postimplementation group, the most commonly prescribed agents were abiraterone, imatinib, pazopanib, and dasatinib. No significant differences were observed in prescribing of individual agents between the pre- and postimplementation groups or other characteristics that may affect adherence including patient copay status, number of concomitant medications, and driving distance from the RLRVAMC.

Thirty-six (36.7%) patients in the preimplementation group were considered nonadherent (MPR < 0.9) and 18 (18.4%) had an MPR < 0.8. Fifteen (15.3%) patients in the preimplementation clinic were considered overadherent (MPR > 1.1). Forty-seven (47.9%) patients in the preimplementation group were considered adherent (MPR 0.9 - 1.1) while all 35 (100%) patients in the postimplementation group were considered adherent (MPR 0.9 - 1.1). No non- or overadherent patients were identified in the postimplementation group (Figure 2). The median MPR for all patients in the preimplementation group was 0.94 compared with 1.06 (P < .001) in the postimplementation group.

Discussion

OANs are widely used therapies, with more than 25 million doses administered per year in the United States alone.¹² The use of these agents will continue to grow as more targeted agents become available and patients request more convenient treatment options. The role for hematology/oncology clinical pharmacy services must adapt to this increased usage of OANs, including increasing pharmacist involvement in medication education, adherence and tolerability assessments, and proactive drug interaction monitoring.However, additional research is needed to determine optimal management strategies.

Our study aimed to compare OAN adherence among patients at a tertiary care VA hospital before and after implementation of a renewal clinic. The preimplementation population had a median MPR of 0.94 compared with 1.06 in the postimplementation group (P < .001). Although an ideal MPR is 1.0, we aimed for a slightly higher MPR to allow a supply buffer in the event of prescription delivery delays, as more than 90% of prescriptions are mailed to patients from a regional mail-order pharmacy. Importantly, the median MPRs do not adequately convey the impact from this clinic. The proportion of patients who were considered adherent to OANs increased from 47.9% in the preimplementation to 100% in the postimplementation period. These finding suggest that the clinical pharmacist role to assess and encourage adherence through monitoring tolerability of these OANs improved the overall medication taking experience of these patients.

Upon initial evaluation of adherence pre- and postimplementation, median adherence rates in both groups appeared to be above goal at 0.94 and 1.06 respectively. Patients in the postimplementation group intentionally received a 5- to 7-day supply buffer to account for potential prescription delivery delays due to holidays and inclement weather. This would indicate that the patients in the postimplementation group would have 15% oversupply due to the 5-day supply buffer. After correcting for patients with confounding reasons for excess (dose reductions, breaks in treatment, etc.), the median MPR in the prerefill clinic group decreased to 0.9 and the MPR in the postrefill clinic group increased slightly to 1.08. Although the median adherence rate in both the pre- and postimplementation groups were above goal of 0.90, 36% of the patients in the preimplementation group were considered nonadherent (MPR < 0.9) compared with no patients in the postimplementation group. Therefore, our intervention to improve patient adherence appeared to be beneficial at our institution.

In addition to improving adherence, one of the goals of the renewal clinic was to minimize excess supply at the time of therapy discontinuation. This was accomplished by aligning medication fills with medical visits and objective monitoring, as well as limiting supply to no more than 30 days. Of the patients in the postimplementation group, only 1 patient had remaining medication at the time of therapy discontinuation compared with 14 patients in the preimplementation group. The estimated cost savings from excess supply was $36,335. Limiting the amount of unused supply not only saves money for the patient and the institution, but also decreases opportunity for improper hazardous waste disposal and unnecessary exposure of hazardous materials to others.

Our results show the pharmacist intervention in the coordination of renewals improved adherence, minimized medication waste, and saved money. The cost of pharmacist time participating in the refill clinic was not calculated. Each visit was completed in approximately 5 minutes, with subsequent documentation and coordination taking an additional 5 to 10 minutes. During the launch of this service, the oncology pharmacy resident provided all coverage of the clinic. Oversite of the resident was provided by hematology/oncology clinical pharmacy specialists. We have continued to utilize pharmacy resident coverage since that time to meet education needs and keep the estimated cost per visit low. Another option in the case that pharmacy residents are not available would be utilization of a pharmacy technician, intern, or professional student to conduct the adherence and tolerability phone assessments. Our escalation protocol allows intervention by clinical pharmacy specialist and/or other health care providers when necessary. Trainees have only required basic training on how to use the protocol.

Limitations

Due to this study’s retrospective design, an inherent limitation is dependence on prescriber and refill records for documentation of initiation and discontinuation dates. Therefore, only the association of impact of pharmacist intervention on medication adherence can be determined as opposed to causation. We did not take into account discrepancies in day supply secondary to ‘held’ therapies, dose reductions, or doses supplied during an inpatient admission, which may alter estimates of MPR and cost-savings data. Patients in the postimplementation group intentionally received a 5 to 7-day supply buffer to account for potential prescription delivery delays due to holidays and inclement weather. This would indicate that the patients in the postimplementation group would have 15% oversupply due to the 5-day supply buffer, thereby skewing MPR values. This study did not account for cost avoidance resulting from early identification and management of toxicity. Finally, the postimplementation data only spans 4 months and a longer duration of time is needed to more accurately determine sustainability of renewal clinic interventions and provide comprehensive evaluation of cost-avoidance.

Conclusion

Implementation of an OAN renewal clinic was associated with an increase in MPR, improved proportion of patients considered adherent, and an estimated $36,335 cost-savings. However, prospective evaluation and a longer study duration are needed to determine causality of improved adherence and cost-savings associated with a pharmacist-driven OAN renewal clinic.

Evaluation of oral antineoplastic agent (OAN) adherence patterns have identified correlations between nonadherence or over-adherence and poorer disease-related outcomes. Multiple studies have focused on imatinib use in chronic myeloid leukemia (CML) due to its continuous, long-term use. A study by Ganesan and colleagues found that nonadherence to imatinib showed a significant decrease in 5-year event-free survival between 76.7% of adherent participants compared with 59.8% of nonadherent participants.¹ This study found that 44% of patients who were adherent to imatinib achieved complete cytogenetic response vs only 26% of patients who were nonadherent. In another study of imatinib for CML, major molecular response (MMR) was strongly correlated with adherence and no patients with adherence < 80% were able to achieve MMR.² Similarly, in studies of tamoxifen for breast cancer, < 80% adherence resulted in a 10% decrease in survival when compared to those who were more adherent.^3,4

In addition to the clinical implications of nonadherence, there can be a significant cost associated with suboptimal use of these medications. The price of a single dose of OAN medication may cost as much as $440.⁵

The benefits of multidisciplinary care teams have been identified in many studies.^6,7 While studies are limited in oncology, pharmacists provide vital contributions to the oncology multidisciplinary team when managing OANs as these health care professionals have expert knowledge of the medications, potential adverse events (AEs), and necessary monitoring parameters.⁸ In one study, patients seen by the pharmacist-led oral chemotherapy management program experienced improved clinical outcomes and response to therapy when compared with preintervention patients (early molecular response, 88.9% vs 54.8%, P = .01; major molecular response, 83.3% vs 57.6%, P = .06).⁹ During the study, 318 AEs were reported, leading to 235 pharmacist interventions to ameliorate AEs and improve adherence.

The primary objective of this study was to measure the impact of a pharmacist-driven OAN renewal clinic on medication adherence. The secondary objective was to estimate cost-savings of this new service.

Methods 

Prior to July 2014, several limitations were identified related to OAN prescribing and monitoring at the Richard L. Roudebush Veterans Affairs Medical Center in Indianapolis, Indiana (RLRVAMC). The prescription ordering process relied primarily on the patient to initiate refills, rather than the prescriber OAN prescriptions also lacked consistency for number of refills or quantities dispensed. Furthermore, ordering of antineoplastic products was not limited to hematology/oncology providers. Patients were identified with significant supply on hand at the time of medication discontinuation, creating concerns for medication waste, tolerability, and nonadherence.

As a result, opportunities were identified to improve the prescribing process, recommended monitoring, toxicity and tolerability evaluation, medication reconciliation, and medication adherence. In July of 2014, the RLRVAMC adopted a new chemotherapy order entry system capable of restricting prescriptions to hematology/oncology providers and limiting dispensed quantities and refill amounts. A comprehensive pharmacist driven OAN renewal clinic was implemented on September 1, 2014 with the goal of improving long-term adherence and tolerability, in addition to minimizing medication waste.

Study Design and Setting

This was a pre/post retrospective cohort, quality improvement study of patients enrolled in the RLRVAMC OAN pharmacist renewal clinic. The study was deemed exempt from institutional review board (IRB) by the US Department of Veterans Affairs (VA) Research and Development Department.

Study Population

Patients were included in the preimplementation group if they had received at least 2 prescriptions of an eligible OAN. Therapy for the preimplementation group was required to be a monthly duration > 21 days and between the dates of September 1, 2013 and August 31, 2014. Patients were included in the postimplementation group if they had received at least 2 prescriptions of the studied OANs between September 1, 2014 and January 31, 2015. Patients were excluded if they had filled < 2 prescriptions of OAN; were managed by a non-VA oncologist or hematologist; or received an OAN other than those listed in Table 1.

Data Collection

For all patients in both the pre- and postimplementation cohorts, a standardized data collection tool was used to collect the following via electronic health record review by a PGY2 oncology resident: age, race, gender, oral antineoplastic agent, refill dates, days’ supply, estimated unit cost per dose cancer diagnosis, distance from the RLRVAMC, copay status, presence of hospitalizations/ED visits/dosage reductions, discontinuation rates, reasons for discontinuation, and total number of current prescriptions. The presence or absence of dosage reductions were collected to identify concerns for tolerability, but only the original dose for the preimplementation group and dosage at time of clinic enrollment for the postimplementation group was included in the analysis.

Outcomes and Statistical Analyses

The primary outcome was medication adherence defined as the median medication possession ratio (MPR) before and after implementation of the clinic. Secondary outcomes included the proportion of patients who were adherent from before implementation to after implementation and estimated cost-savings of this clinic after implementation. MPR was used to estimate medication adherence by taking the cumulative day supply of medication on hand divided by the number of days on therapy.¹² Number of days on therapy was determined by taking the difference on the start date of the new medication regimen and the discontinuation date of the same regimen. Patients were grouped by adherence into one of the following categories: < 0.8, 0.8 to 0.89, 0.9 to 1, and > 1.1. Patients were considered adherent if they reported taking ≥ 90% (MPR ≥ 0.9) of prescribed doses, adopted from the study by Anderson and colleagues.¹² A patient with an MPR > 1, likely due to filling prior to the anticipated refill date, was considered 100% adherent (MPR = 1). If a patient switched OAN during the study, both agents were included as separate entities.

A conservative estimate of cost-savings was made by multiplying the RLRVAMC cost per unit of medication at time of initial prescription fill by the number of units taken each day multiplied by the total days’ supply on hand at time of therapy discontinuation. Patients with an MPR < 1 at time of therapy discontinuation were assumed to have zero remaining units on hand and zero cost savings was estimated. Waste, for purposes of cost-savings, was calculated for all MPR values > 1. Additional supply anticipated to be on hand from dose reductions was not included in the estimated cost of unused medication.

Descriptive statistics compared demographic characteristics between the pre- and postimplementation groups. MPR data were not normally distributed, which required the use of nonparametric Mann-Whitney U tests to compare pre- and postMPRs. Pearson χ² compared the proportion of adherent patients between groups while descriptive statistics were used to estimate cost savings. Significance was determined based on a P value < .05. IBM SPSS Statistics software was used for all statistical analyses. As this was a complete sample of all eligible subjects, no sample size calculation was performed.

Results

In the preimplementation period, 246 patients received an OAN and 61 patients received an OAN in the postimplementation period (Figure 1). Of the 246 patients in the preimplementation period, 98 were eligible and included in the preimplementation group. Similarly, of the 61 patients in the postimplementation period, 35 patients met inclusion criteria for the postimplementation group. The study population was predominantly male with an average age of approximately 70 years in both groups (Table 3). More than 70% of the population in each group was White. No statistically significant differences between groups were identified. The most commonly prescribed OAN in the preimplementation group were abiraterone, imatinib, and enzalutamide (Table 3). In the postimplementation group, the most commonly prescribed agents were abiraterone, imatinib, pazopanib, and dasatinib. No significant differences were observed in prescribing of individual agents between the pre- and postimplementation groups or other characteristics that may affect adherence including patient copay status, number of concomitant medications, and driving distance from the RLRVAMC.

Thirty-six (36.7%) patients in the preimplementation group were considered nonadherent (MPR < 0.9) and 18 (18.4%) had an MPR < 0.8. Fifteen (15.3%) patients in the preimplementation clinic were considered overadherent (MPR > 1.1). Forty-seven (47.9%) patients in the preimplementation group were considered adherent (MPR 0.9 - 1.1) while all 35 (100%) patients in the postimplementation group were considered adherent (MPR 0.9 - 1.1). No non- or overadherent patients were identified in the postimplementation group (Figure 2). The median MPR for all patients in the preimplementation group was 0.94 compared with 1.06 (P < .001) in the postimplementation group.

Discussion

OANs are widely used therapies, with more than 25 million doses administered per year in the United States alone.¹² The use of these agents will continue to grow as more targeted agents become available and patients request more convenient treatment options. The role for hematology/oncology clinical pharmacy services must adapt to this increased usage of OANs, including increasing pharmacist involvement in medication education, adherence and tolerability assessments, and proactive drug interaction monitoring.However, additional research is needed to determine optimal management strategies.

Our study aimed to compare OAN adherence among patients at a tertiary care VA hospital before and after implementation of a renewal clinic. The preimplementation population had a median MPR of 0.94 compared with 1.06 in the postimplementation group (P < .001). Although an ideal MPR is 1.0, we aimed for a slightly higher MPR to allow a supply buffer in the event of prescription delivery delays, as more than 90% of prescriptions are mailed to patients from a regional mail-order pharmacy. Importantly, the median MPRs do not adequately convey the impact from this clinic. The proportion of patients who were considered adherent to OANs increased from 47.9% in the preimplementation to 100% in the postimplementation period. These finding suggest that the clinical pharmacist role to assess and encourage adherence through monitoring tolerability of these OANs improved the overall medication taking experience of these patients.

Upon initial evaluation of adherence pre- and postimplementation, median adherence rates in both groups appeared to be above goal at 0.94 and 1.06 respectively. Patients in the postimplementation group intentionally received a 5- to 7-day supply buffer to account for potential prescription delivery delays due to holidays and inclement weather. This would indicate that the patients in the postimplementation group would have 15% oversupply due to the 5-day supply buffer. After correcting for patients with confounding reasons for excess (dose reductions, breaks in treatment, etc.), the median MPR in the prerefill clinic group decreased to 0.9 and the MPR in the postrefill clinic group increased slightly to 1.08. Although the median adherence rate in both the pre- and postimplementation groups were above goal of 0.90, 36% of the patients in the preimplementation group were considered nonadherent (MPR < 0.9) compared with no patients in the postimplementation group. Therefore, our intervention to improve patient adherence appeared to be beneficial at our institution.

In addition to improving adherence, one of the goals of the renewal clinic was to minimize excess supply at the time of therapy discontinuation. This was accomplished by aligning medication fills with medical visits and objective monitoring, as well as limiting supply to no more than 30 days. Of the patients in the postimplementation group, only 1 patient had remaining medication at the time of therapy discontinuation compared with 14 patients in the preimplementation group. The estimated cost savings from excess supply was $36,335. Limiting the amount of unused supply not only saves money for the patient and the institution, but also decreases opportunity for improper hazardous waste disposal and unnecessary exposure of hazardous materials to others.

Our results show the pharmacist intervention in the coordination of renewals improved adherence, minimized medication waste, and saved money. The cost of pharmacist time participating in the refill clinic was not calculated. Each visit was completed in approximately 5 minutes, with subsequent documentation and coordination taking an additional 5 to 10 minutes. During the launch of this service, the oncology pharmacy resident provided all coverage of the clinic. Oversite of the resident was provided by hematology/oncology clinical pharmacy specialists. We have continued to utilize pharmacy resident coverage since that time to meet education needs and keep the estimated cost per visit low. Another option in the case that pharmacy residents are not available would be utilization of a pharmacy technician, intern, or professional student to conduct the adherence and tolerability phone assessments. Our escalation protocol allows intervention by clinical pharmacy specialist and/or other health care providers when necessary. Trainees have only required basic training on how to use the protocol.

Limitations

Due to this study’s retrospective design, an inherent limitation is dependence on prescriber and refill records for documentation of initiation and discontinuation dates. Therefore, only the association of impact of pharmacist intervention on medication adherence can be determined as opposed to causation. We did not take into account discrepancies in day supply secondary to ‘held’ therapies, dose reductions, or doses supplied during an inpatient admission, which may alter estimates of MPR and cost-savings data. Patients in the postimplementation group intentionally received a 5 to 7-day supply buffer to account for potential prescription delivery delays due to holidays and inclement weather. This would indicate that the patients in the postimplementation group would have 15% oversupply due to the 5-day supply buffer, thereby skewing MPR values. This study did not account for cost avoidance resulting from early identification and management of toxicity. Finally, the postimplementation data only spans 4 months and a longer duration of time is needed to more accurately determine sustainability of renewal clinic interventions and provide comprehensive evaluation of cost-avoidance.

Conclusion

Implementation of an OAN renewal clinic was associated with an increase in MPR, improved proportion of patients considered adherent, and an estimated $36,335 cost-savings. However, prospective evaluation and a longer study duration are needed to determine causality of improved adherence and cost-savings associated with a pharmacist-driven OAN renewal clinic.

Evaluation of oral antineoplastic agent (OAN) adherence patterns have identified correlations between nonadherence or over-adherence and poorer disease-related outcomes. Multiple studies have focused on imatinib use in chronic myeloid leukemia (CML) due to its continuous, long-term use. A study by Ganesan and colleagues found that nonadherence to imatinib showed a significant decrease in 5-year event-free survival between 76.7% of adherent participants compared with 59.8% of nonadherent participants.¹ This study found that 44% of patients who were adherent to imatinib achieved complete cytogenetic response vs only 26% of patients who were nonadherent. In another study of imatinib for CML, major molecular response (MMR) was strongly correlated with adherence and no patients with adherence < 80% were able to achieve MMR.² Similarly, in studies of tamoxifen for breast cancer, < 80% adherence resulted in a 10% decrease in survival when compared to those who were more adherent.^3,4

In addition to the clinical implications of nonadherence, there can be a significant cost associated with suboptimal use of these medications. The price of a single dose of OAN medication may cost as much as $440.⁵

The benefits of multidisciplinary care teams have been identified in many studies.^6,7 While studies are limited in oncology, pharmacists provide vital contributions to the oncology multidisciplinary team when managing OANs as these health care professionals have expert knowledge of the medications, potential adverse events (AEs), and necessary monitoring parameters.⁸ In one study, patients seen by the pharmacist-led oral chemotherapy management program experienced improved clinical outcomes and response to therapy when compared with preintervention patients (early molecular response, 88.9% vs 54.8%, P = .01; major molecular response, 83.3% vs 57.6%, P = .06).⁹ During the study, 318 AEs were reported, leading to 235 pharmacist interventions to ameliorate AEs and improve adherence.

The primary objective of this study was to measure the impact of a pharmacist-driven OAN renewal clinic on medication adherence. The secondary objective was to estimate cost-savings of this new service.

Methods 

Prior to July 2014, several limitations were identified related to OAN prescribing and monitoring at the Richard L. Roudebush Veterans Affairs Medical Center in Indianapolis, Indiana (RLRVAMC). The prescription ordering process relied primarily on the patient to initiate refills, rather than the prescriber OAN prescriptions also lacked consistency for number of refills or quantities dispensed. Furthermore, ordering of antineoplastic products was not limited to hematology/oncology providers. Patients were identified with significant supply on hand at the time of medication discontinuation, creating concerns for medication waste, tolerability, and nonadherence.

As a result, opportunities were identified to improve the prescribing process, recommended monitoring, toxicity and tolerability evaluation, medication reconciliation, and medication adherence. In July of 2014, the RLRVAMC adopted a new chemotherapy order entry system capable of restricting prescriptions to hematology/oncology providers and limiting dispensed quantities and refill amounts. A comprehensive pharmacist driven OAN renewal clinic was implemented on September 1, 2014 with the goal of improving long-term adherence and tolerability, in addition to minimizing medication waste.

Study Design and Setting

This was a pre/post retrospective cohort, quality improvement study of patients enrolled in the RLRVAMC OAN pharmacist renewal clinic. The study was deemed exempt from institutional review board (IRB) by the US Department of Veterans Affairs (VA) Research and Development Department.

Study Population

Patients were included in the preimplementation group if they had received at least 2 prescriptions of an eligible OAN. Therapy for the preimplementation group was required to be a monthly duration > 21 days and between the dates of September 1, 2013 and August 31, 2014. Patients were included in the postimplementation group if they had received at least 2 prescriptions of the studied OANs between September 1, 2014 and January 31, 2015. Patients were excluded if they had filled < 2 prescriptions of OAN; were managed by a non-VA oncologist or hematologist; or received an OAN other than those listed in Table 1.

Data Collection

For all patients in both the pre- and postimplementation cohorts, a standardized data collection tool was used to collect the following via electronic health record review by a PGY2 oncology resident: age, race, gender, oral antineoplastic agent, refill dates, days’ supply, estimated unit cost per dose cancer diagnosis, distance from the RLRVAMC, copay status, presence of hospitalizations/ED visits/dosage reductions, discontinuation rates, reasons for discontinuation, and total number of current prescriptions. The presence or absence of dosage reductions were collected to identify concerns for tolerability, but only the original dose for the preimplementation group and dosage at time of clinic enrollment for the postimplementation group was included in the analysis.

Outcomes and Statistical Analyses

The primary outcome was medication adherence defined as the median medication possession ratio (MPR) before and after implementation of the clinic. Secondary outcomes included the proportion of patients who were adherent from before implementation to after implementation and estimated cost-savings of this clinic after implementation. MPR was used to estimate medication adherence by taking the cumulative day supply of medication on hand divided by the number of days on therapy.¹² Number of days on therapy was determined by taking the difference on the start date of the new medication regimen and the discontinuation date of the same regimen. Patients were grouped by adherence into one of the following categories: < 0.8, 0.8 to 0.89, 0.9 to 1, and > 1.1. Patients were considered adherent if they reported taking ≥ 90% (MPR ≥ 0.9) of prescribed doses, adopted from the study by Anderson and colleagues.¹² A patient with an MPR > 1, likely due to filling prior to the anticipated refill date, was considered 100% adherent (MPR = 1). If a patient switched OAN during the study, both agents were included as separate entities.

A conservative estimate of cost-savings was made by multiplying the RLRVAMC cost per unit of medication at time of initial prescription fill by the number of units taken each day multiplied by the total days’ supply on hand at time of therapy discontinuation. Patients with an MPR < 1 at time of therapy discontinuation were assumed to have zero remaining units on hand and zero cost savings was estimated. Waste, for purposes of cost-savings, was calculated for all MPR values > 1. Additional supply anticipated to be on hand from dose reductions was not included in the estimated cost of unused medication.

Descriptive statistics compared demographic characteristics between the pre- and postimplementation groups. MPR data were not normally distributed, which required the use of nonparametric Mann-Whitney U tests to compare pre- and postMPRs. Pearson χ² compared the proportion of adherent patients between groups while descriptive statistics were used to estimate cost savings. Significance was determined based on a P value < .05. IBM SPSS Statistics software was used for all statistical analyses. As this was a complete sample of all eligible subjects, no sample size calculation was performed.

Results

In the preimplementation period, 246 patients received an OAN and 61 patients received an OAN in the postimplementation period (Figure 1). Of the 246 patients in the preimplementation period, 98 were eligible and included in the preimplementation group. Similarly, of the 61 patients in the postimplementation period, 35 patients met inclusion criteria for the postimplementation group. The study population was predominantly male with an average age of approximately 70 years in both groups (Table 3). More than 70% of the population in each group was White. No statistically significant differences between groups were identified. The most commonly prescribed OAN in the preimplementation group were abiraterone, imatinib, and enzalutamide (Table 3). In the postimplementation group, the most commonly prescribed agents were abiraterone, imatinib, pazopanib, and dasatinib. No significant differences were observed in prescribing of individual agents between the pre- and postimplementation groups or other characteristics that may affect adherence including patient copay status, number of concomitant medications, and driving distance from the RLRVAMC.

Thirty-six (36.7%) patients in the preimplementation group were considered nonadherent (MPR < 0.9) and 18 (18.4%) had an MPR < 0.8. Fifteen (15.3%) patients in the preimplementation clinic were considered overadherent (MPR > 1.1). Forty-seven (47.9%) patients in the preimplementation group were considered adherent (MPR 0.9 - 1.1) while all 35 (100%) patients in the postimplementation group were considered adherent (MPR 0.9 - 1.1). No non- or overadherent patients were identified in the postimplementation group (Figure 2). The median MPR for all patients in the preimplementation group was 0.94 compared with 1.06 (P < .001) in the postimplementation group.

Discussion

OANs are widely used therapies, with more than 25 million doses administered per year in the United States alone.¹² The use of these agents will continue to grow as more targeted agents become available and patients request more convenient treatment options. The role for hematology/oncology clinical pharmacy services must adapt to this increased usage of OANs, including increasing pharmacist involvement in medication education, adherence and tolerability assessments, and proactive drug interaction monitoring.However, additional research is needed to determine optimal management strategies.

Our study aimed to compare OAN adherence among patients at a tertiary care VA hospital before and after implementation of a renewal clinic. The preimplementation population had a median MPR of 0.94 compared with 1.06 in the postimplementation group (P < .001). Although an ideal MPR is 1.0, we aimed for a slightly higher MPR to allow a supply buffer in the event of prescription delivery delays, as more than 90% of prescriptions are mailed to patients from a regional mail-order pharmacy. Importantly, the median MPRs do not adequately convey the impact from this clinic. The proportion of patients who were considered adherent to OANs increased from 47.9% in the preimplementation to 100% in the postimplementation period. These finding suggest that the clinical pharmacist role to assess and encourage adherence through monitoring tolerability of these OANs improved the overall medication taking experience of these patients.

Upon initial evaluation of adherence pre- and postimplementation, median adherence rates in both groups appeared to be above goal at 0.94 and 1.06 respectively. Patients in the postimplementation group intentionally received a 5- to 7-day supply buffer to account for potential prescription delivery delays due to holidays and inclement weather. This would indicate that the patients in the postimplementation group would have 15% oversupply due to the 5-day supply buffer. After correcting for patients with confounding reasons for excess (dose reductions, breaks in treatment, etc.), the median MPR in the prerefill clinic group decreased to 0.9 and the MPR in the postrefill clinic group increased slightly to 1.08. Although the median adherence rate in both the pre- and postimplementation groups were above goal of 0.90, 36% of the patients in the preimplementation group were considered nonadherent (MPR < 0.9) compared with no patients in the postimplementation group. Therefore, our intervention to improve patient adherence appeared to be beneficial at our institution.

In addition to improving adherence, one of the goals of the renewal clinic was to minimize excess supply at the time of therapy discontinuation. This was accomplished by aligning medication fills with medical visits and objective monitoring, as well as limiting supply to no more than 30 days. Of the patients in the postimplementation group, only 1 patient had remaining medication at the time of therapy discontinuation compared with 14 patients in the preimplementation group. The estimated cost savings from excess supply was $36,335. Limiting the amount of unused supply not only saves money for the patient and the institution, but also decreases opportunity for improper hazardous waste disposal and unnecessary exposure of hazardous materials to others.

Our results show the pharmacist intervention in the coordination of renewals improved adherence, minimized medication waste, and saved money. The cost of pharmacist time participating in the refill clinic was not calculated. Each visit was completed in approximately 5 minutes, with subsequent documentation and coordination taking an additional 5 to 10 minutes. During the launch of this service, the oncology pharmacy resident provided all coverage of the clinic. Oversite of the resident was provided by hematology/oncology clinical pharmacy specialists. We have continued to utilize pharmacy resident coverage since that time to meet education needs and keep the estimated cost per visit low. Another option in the case that pharmacy residents are not available would be utilization of a pharmacy technician, intern, or professional student to conduct the adherence and tolerability phone assessments. Our escalation protocol allows intervention by clinical pharmacy specialist and/or other health care providers when necessary. Trainees have only required basic training on how to use the protocol.

Limitations

Due to this study’s retrospective design, an inherent limitation is dependence on prescriber and refill records for documentation of initiation and discontinuation dates. Therefore, only the association of impact of pharmacist intervention on medication adherence can be determined as opposed to causation. We did not take into account discrepancies in day supply secondary to ‘held’ therapies, dose reductions, or doses supplied during an inpatient admission, which may alter estimates of MPR and cost-savings data. Patients in the postimplementation group intentionally received a 5 to 7-day supply buffer to account for potential prescription delivery delays due to holidays and inclement weather. This would indicate that the patients in the postimplementation group would have 15% oversupply due to the 5-day supply buffer, thereby skewing MPR values. This study did not account for cost avoidance resulting from early identification and management of toxicity. Finally, the postimplementation data only spans 4 months and a longer duration of time is needed to more accurately determine sustainability of renewal clinic interventions and provide comprehensive evaluation of cost-avoidance.

Conclusion

Implementation of an OAN renewal clinic was associated with an increase in MPR, improved proportion of patients considered adherent, and an estimated $36,335 cost-savings. However, prospective evaluation and a longer study duration are needed to determine causality of improved adherence and cost-savings associated with a pharmacist-driven OAN renewal clinic.

Age is a well recognized risk factor for cancer development. The population of older Americans is growing, and by 2030, 20% of the US population will be aged ≥ 65 years.¹ While 25% of all new cancer cases are diagnosed in people aged 65 to 74 years, more than half of cancers occur in individuals aged ≥ 70 years, with even higher rates in those aged ≥ 75 years.² Although cancer rates have declined slightly overall among people aged ≥ 65 years, this population still has an 11-fold increased incidence of cancer compared with that of younger individuals.³ With a rapidly growing older population, there will be increasing demand for cancer care.

Treatment of cancer in older individuals often is complicated by medical comorbidities, frailty, and poor functional status. Distinguishing patients who can tolerate aggressive therapy from those who require less intensive therapy can be challenging. Age-related physiologic changes predispose older adults to an increased risk of therapy-related toxicities, resulting in suboptimal therapeutic benefit and substantial morbidity. For example, cardiovascular changes can lead to reduction of the cardiac functional reserve, which can increase the risk of congestive heart failure. Similarly, decline in renal function leads to an increased potential for nephrotoxicity.⁴ Although patients may be of the same chronologic age, their performance, functional, and biologic status may be quite variable; thus, tolerance to aggressive treatment is not easily predicted. The comprehensive geriatric assessment (CGA) may be used as a global assessment tool to risk stratify older patients prior to oncologic treatment decisions.⁵

Health care providers (HCPs), including physician assistants, nurse practitioners, clinical nurse specialists, nurses, and physicians, routinely participate in every aspect of cancer care by ordering and interpreting diagnostic tests, addressing comorbidities, managing symptoms, and discussing cancer treatment recommendations. HCPs in oncology will continue to play a vital role in the coordination and management of older patients with cancer. However, in general, CGA has not been a consistent part of oncology practices, and few HCPs are familiar with the benefits of CGA screening tools.

What Is Geriatric Assessment? 

Geriatric assessment is a multidisciplinary, multidimensional process aimed at detecting medical, psychosocial, and functional issues of older adults that are not identified by traditional performance status measures alone. It provides guidance for management of identified problems and improvement in quality of life.⁶ CGA was developed by geriatricians and multidisciplinary care teams to evaluate the domains of functional, nutritional, cognitive, psychosocial, and economic status; comorbidities; geriatric syndromes; and mood, and it has been tested in both clinics and hospitals.⁷ Although such assessment requires additional time and resources, its goals are to identify areas of vulnerability, assist in clinical decisions of treatable health problems, and guide therapeutic interventions.⁶ In oncology practice, the assessment not only addresses these global issues, but also is critical in predicting toxicity and survival outcomes in older oncology patients.

Components of CGA 

Advancing age brings many physiologic, psychosocial, and functional challenges, and a cancer diagnosis only adds to these issues. CGA provides a system of assessing older and/or frail patients with cancer through specific domains to identify issues that are not apparent on routine evaluation in a clinic setting before and during chemotherapy treatments. These domains include comorbidity, polypharmacy, functional status, cognition, psychological and social status, and nutrition.⁸

Comorbidity

The prevalence of multiple medical problems and comorbidities, including cancer, among people aged > 65 years is increasing.⁹ Studies have shown that two-thirds of patients with cancer had ≥ 2 medical conditions, and nearly one quarter had ≥ 4 medical conditions.¹⁰ In older adults, common comorbidities include cardiovascular disease, hypertension, diabetes mellitus, and dementia. These comorbidities can impact treatment decisions, increase the risk of disease, impact treatment-related complications, and affect a patient’s life expectancy.¹¹ Assessing comorbidities is essential to CGA and is done using the Charlson Comorbidity Index and/or the Cumulative Illness Rating Scale.¹²

The Charlson Comorbidity Index was originally designed to predict 1-year mortality on the basis of a weighted composite score for the following categories: cardiovascular, endocrine, pulmonary, neurologic, renal, hepatic, gastrointestinal, and neoplastic disease.¹³ It is now the most widely used comorbidity index and has been adapted and verified as applicable and valid for predicting the outcomes and risk of death from many comorbid diseases.¹⁴ The Cumulative Illness Rating Scale has been validated as a predictor for readmission for hospitalized older adults, hospitalization within 1 year in a residential setting, and long-term mortality when assessed in inpatient and residential settings.¹⁵

Polypharmacy

Polypharmacy (use of ≥ 5 medications) is common in older patients regardless of cancer diagnosis and is often instead defined as “the use of multiple drugs or more than are medically necessary.”¹⁶ The use of multiple medications, including those not indicated for existing medical conditions (such as over‐the‐counter, herbal, and complementary/alternative medicines, which patients often fail to declare to their specialist, doctor, or pharmacist) adds to the potential negative aspects of polypharmacy that affect older patients.¹⁷

Patients with cancer usually are prescribed an extensive number of medicines, both for the disease and for supportive care, which can increase the chance of drug-drug interactions and adverse reactions.¹⁸ While these issues certainly affect quality of life, they also may influence chemotherapy treatment and potentially impact survival. Studies have shown that the presence of polypharmacy has been associated with higher numbers of comorbidities, increased use of inappropriate medications, poor performance status, decline in functional status, and poor survival.¹⁸

Functional Status

Although Eastern Cooperative Oncology Group (ECOG) performance status and Karnofsky Performance Status are commonly used by oncologists, these guidelines are limited in focus and do not reliably measure functional status in older patients. Functional status is determined by the ability to perform daily acts of self-care, which includes assessment of activities of daily living (ADLs) and instrumental activities of daily living (IADLs). ADLs refer to such tasks as bathing, dressing, eating, mobility, balance, and toileting.¹⁹ IADLs include the ability to perform activities required to live within a community and include shopping, transportation, managing finances, medication management, cooking, and cleaning.¹¹

Physical functionality also can be assessed by measures such as gait speed, grip strength, balance, and lower extremity strength. These are more sensitive and shown to be associated with worse clinical outcomes.²⁰ Grip strength and gait speed, as assessed by the Timed Up and Go test or the Short Physical Performance Battery measure strength and balance.¹² Reduction in gait speed and/or grip strength are associated with adverse clinical outcomes and increased risk of mortality.²¹ Patients with cancer who have difficulty with ADLs are at increased risk for falls, which can limit their functional independence, compromise cancer therapy, and increase the risk of chemotherapy toxicities.¹¹ Impaired hearing and poor vision are added factors that can be barriers to cancer treatment.

Cognition

Cognitive impairment in patients with cancer is becoming more of an issue for oncology HCPs as both cancer and cognitive decline are more common with advancing age. Cognition in cancer patients is important for understanding their diagnosis, prognosis, treatment options, and adherence. Impaired cognition can affect decision making regarding treatment options and administration. Cognition can be assessed through validated screening tools such as the Mini-Mental State Examination and Mini-Cog.¹¹

Psychological and Social Status

A cancer diagnosis has a major impact on the mental and emotional state of patients and family members. Clinically significant anxiety has been reported in approximately 21% of older patients with cancer, and the incidence of depression ranges from 17 to 26%.²² In older patients with, psychologic distress can impact cancer treatment, resulting in less definitive therapy and poorer outcomes.²³ All patients with cancer should be screened for psychologic distress using standardized methods, such as the Geriatric Depression Scale or the General Anxiety Disorder-7 scale.²⁴ A positive screen should lead to additional assessments that evaluate the severity of depression and other comorbid psychological problems and medical conditions.

Social isolation and loneliness are factors that can affect both depression and anxiety. Older patients with cancer are at risk for decreased social activities and are already challenged with issues related to home care, comorbidities, functional status, and caregiver support.²³ Therefore, it is important to assess the social interactions of an older and/or frail patient with cancer and use social work assistance to address needs for supportive services.

Nutrition

Nutrition is important in any patient with cancer undergoing chemotherapy treatment. However, it is of greater importance in older adults, as malnutrition and weight loss are negative prognostic factors that correlate with poor tolerance to chemotherapy treatment, decline in quality of life, and increased mortality.²⁵ The Mini-Nutritional Assessment is a widely used validated tool to assess nutritional status and risk of malnutrition.¹¹ This tool can help identify those older and/or frail patients with cancer with impaired nutritional status and aid in instituting corrective measures to treat or prevent malnutrition.

Effectiveness of CGA

Multiple randomized controlled clinical trials assessing the effectiveness of CGA have been conducted over the past 3 decades with overall positive outcomes related to its value.²⁶ Benefits of CGA can include overall improved medical care, avoidance of hospitalization or nursing home placement, identification of cognitive impairment, and prevention of geriatric syndrome (a range of conditions representing multiple organ impairment in older adults).²⁷

In oncology, CGA is particularly beneficial, as it can identify issues in nearly 70% of patients that may not be apparent through traditional oncology assessment.²⁸ A systematic review of 36 studies assessing the prognostic value of CGA in elderly patients with cancer receiving chemotherapy concluded that impaired performance and functional status as well as a frail and vulnerable profile are important predictors of severe chemotherapy-related toxicity and are associated with a higher risk of mortality.²⁹ Therefore, CGA should be an integral part of the evaluation of older and/or frail patients with cancer prior to chemotherapy consideration.

Several screening tools have been developed using information from CGA to assess the risk of severe toxicities. The most commonly used tools for predicting toxicity include the Cancer and Aging Research Group (CARG) chemotoxicity calculator and the Chemotherapy Risk Assessment Scale for High-Age Patients (CRASH).^30,31 Although these tools are readily available to facilitate CGA, and despite their proven beneficial outcome and recommended usage by national guidelines, implementation of these tools in routine oncology practice has been challenging and slow to spread. Unless these recommended interventions are effectively implemented, the benefits of CGA cannot be realized. With the expected surge in the number of older patients with cancer, hopefully this will change.

Geriatric Assessment Screening Tools

A screening tool recommended for use in older and/or frail patients with cancer allows for a brief assessment to help clinicians identify patients in need of further evaluation by CGA and to provides information on treatment-related toxicities, functional decline, and survival.³² The predictive value and utility of geriatric assessment screening tools have been repeatedly proven to identify older and/or frail adults at risk for treatment-related toxicities.¹² The CARG and the CRASH are validated screening tools used in identifying patients at higher risk for chemotherapy toxicity. These screening tools are intended to provide guidance to the clinical oncology practitioner on risk stratification of chemotherapy toxicity in older patients with cancer.³³

Both of these screening tools provide similar predictive performance for chemotherapy toxicity in older patients with cancer.³⁴ However, the CARG tool seems to have the advantage of using more data that had already been obtained during regular office visits and is clear and easy to use clinically. The CRASH tool is slightly more involved, as it uses multiple geriatric instruments to determine the predictive risk of both hematologic and nonhematologic toxicities of chemotherapy.

CARG Chemotoxicity Calculator

Hurria and colleagues originally developed the CARG tool from data obtained through a prospective multicenter study involving 500 patients with cancer aged ≥ 65 years.³⁵ They concluded that chemotherapy-related toxicity is common in older adults, with 53% of patients sustaining grade 3 or 4 treatment-related toxicities and 2% treatment-related mortality.¹² This predictive model for chemotherapy-related toxicity used 11 variables, both objective (obtained during a regular clinical encounter: age, tumor type, chemotherapy dosing, number of drugs, creatinine, and hemoglobin) and subjective (completed by patient: number of falls, social support, the ability to take medications, hearing impairment, and physical performance), to determine at-risk patients (Table 1).³¹

Compared with standard performance status measures in oncology practice, the CARG model was better able to predict chemotherapy-related toxicities. In 2016, Hurria and colleagues published the results of an updated external validation study with a cohort of 250 older patients with cancer receiving chemotherapy that confirmed the prediction of chemotherapy toxicity using the CARG screening tool in this population.³¹ An appealing feature of this tool is the free online accessibility and the expedited manner in which screening can be conducted.

CRASH Score

The CRASH score was derived from the results of a prospective, multicenter study of 518 patients aged ≥ 70 years who were assessed on 24 parameters prior to starting chemotherapy.³⁰ A total of 64% of patients experienced significant toxicities, including 32% with grade 4 hematologic toxicity and 56% with grade 3 or 4 nonhematologic toxicity. The hematologic and nonhematologic toxicity risks are the 2 categories that comprise the CRASH score. Both baseline patient variables and chemotherapy regimen are incorporated into an 8-item assessment profile that determines the risk categories (Table 2).³⁰

Increased risk of hematologic toxicities was associated with increased diastolic blood pressure, increased lactate dehydrogenase, need for assistance with IADL, and increased toxicity potential of the chemotherapy regimen. Nonhematologic toxicities were associated with ECOG performance score, Mini Mental Status Examination and Mini-Nutritional Assessment, and increased toxicity of the chemotherapy regimen.¹² Patient scores are stratified into 4 risk categories: low, medium-low, medium-high, and high.³⁰ Like the CARG tool, the CRASH screening tool also is available as a free online resource and can be used in everyday clinical practice to assess older and/or frail adults with cancer.

Conclusions 

In older adults, cancer may significantly impact the natural course of concurrent comorbidities due to physiologic and functional changes. These vulnerabilities predispose older patients with cancer to an increased risk of adverse outcomes, including treatment-related toxicities.³⁶ Given the rapidly aging population, it is critical for oncology clinical teams to be prepared to assess for, prevent, and manage issues for older adults that could impact outcomes, including complications and toxicities from chemotherapy.³⁵ Studies have reported that 78 to 93% of older oncology patients have at least 1 geriatric impairment that could potentially impact oncology treatment plans.^37,38 This supports the utility of CGA as a global assessment tool to risk stratify older and/or frail patients prior to deciding on subsequent oncologic treatment approaches.⁵ In fact, major cooperative groups sponsored by the National Cancer Institute, such as the Alliance for Clinical Trials in Oncology, are including CGA as part of some of their treatment trials. CGA was conducted as part of a multicenter cooperative group study in older patients with acute myeloid leukemia prior to inpatient intensive induction chemotherapy and was determined to be feasible and useful in clinical trials and practice.³⁹

Despite the increasing evidence for benefits of CGA, it has not been a consistent part of oncology practices, and few HCPs are familiar with the benefits of CGA screening tools. Although oncology providers routinely participate in every aspect of cancer care and play a vital role in the coordination and management of older patients with cancer, CGA implementation into routine clinical practice has been slow in part due to lack of knowledge and training regarding the use of GA tools.

Oncology providers can easily incorporate CGA screening tools into the history and physical examination process for older patients with cancer, which will add an important dimension to these patient evaluations. Oncology providers are not only well positioned to administer these screening tools, but also can lead the field in developing innovative ways for effective implementation in busy routine oncology clinics. However, to be successful, oncology providers must be knowledgeable about these tools and understand their utility in guiding treatment decisions and improving quality of care in older patients with cancer.

Age is a well recognized risk factor for cancer development. The population of older Americans is growing, and by 2030, 20% of the US population will be aged ≥ 65 years.¹ While 25% of all new cancer cases are diagnosed in people aged 65 to 74 years, more than half of cancers occur in individuals aged ≥ 70 years, with even higher rates in those aged ≥ 75 years.² Although cancer rates have declined slightly overall among people aged ≥ 65 years, this population still has an 11-fold increased incidence of cancer compared with that of younger individuals.³ With a rapidly growing older population, there will be increasing demand for cancer care.

Treatment of cancer in older individuals often is complicated by medical comorbidities, frailty, and poor functional status. Distinguishing patients who can tolerate aggressive therapy from those who require less intensive therapy can be challenging. Age-related physiologic changes predispose older adults to an increased risk of therapy-related toxicities, resulting in suboptimal therapeutic benefit and substantial morbidity. For example, cardiovascular changes can lead to reduction of the cardiac functional reserve, which can increase the risk of congestive heart failure. Similarly, decline in renal function leads to an increased potential for nephrotoxicity.⁴ Although patients may be of the same chronologic age, their performance, functional, and biologic status may be quite variable; thus, tolerance to aggressive treatment is not easily predicted. The comprehensive geriatric assessment (CGA) may be used as a global assessment tool to risk stratify older patients prior to oncologic treatment decisions.⁵

Health care providers (HCPs), including physician assistants, nurse practitioners, clinical nurse specialists, nurses, and physicians, routinely participate in every aspect of cancer care by ordering and interpreting diagnostic tests, addressing comorbidities, managing symptoms, and discussing cancer treatment recommendations. HCPs in oncology will continue to play a vital role in the coordination and management of older patients with cancer. However, in general, CGA has not been a consistent part of oncology practices, and few HCPs are familiar with the benefits of CGA screening tools.

What Is Geriatric Assessment? 

Geriatric assessment is a multidisciplinary, multidimensional process aimed at detecting medical, psychosocial, and functional issues of older adults that are not identified by traditional performance status measures alone. It provides guidance for management of identified problems and improvement in quality of life.⁶ CGA was developed by geriatricians and multidisciplinary care teams to evaluate the domains of functional, nutritional, cognitive, psychosocial, and economic status; comorbidities; geriatric syndromes; and mood, and it has been tested in both clinics and hospitals.⁷ Although such assessment requires additional time and resources, its goals are to identify areas of vulnerability, assist in clinical decisions of treatable health problems, and guide therapeutic interventions.⁶ In oncology practice, the assessment not only addresses these global issues, but also is critical in predicting toxicity and survival outcomes in older oncology patients.

Components of CGA 

Advancing age brings many physiologic, psychosocial, and functional challenges, and a cancer diagnosis only adds to these issues. CGA provides a system of assessing older and/or frail patients with cancer through specific domains to identify issues that are not apparent on routine evaluation in a clinic setting before and during chemotherapy treatments. These domains include comorbidity, polypharmacy, functional status, cognition, psychological and social status, and nutrition.⁸

Comorbidity

The prevalence of multiple medical problems and comorbidities, including cancer, among people aged > 65 years is increasing.⁹ Studies have shown that two-thirds of patients with cancer had ≥ 2 medical conditions, and nearly one quarter had ≥ 4 medical conditions.¹⁰ In older adults, common comorbidities include cardiovascular disease, hypertension, diabetes mellitus, and dementia. These comorbidities can impact treatment decisions, increase the risk of disease, impact treatment-related complications, and affect a patient’s life expectancy.¹¹ Assessing comorbidities is essential to CGA and is done using the Charlson Comorbidity Index and/or the Cumulative Illness Rating Scale.¹²

The Charlson Comorbidity Index was originally designed to predict 1-year mortality on the basis of a weighted composite score for the following categories: cardiovascular, endocrine, pulmonary, neurologic, renal, hepatic, gastrointestinal, and neoplastic disease.¹³ It is now the most widely used comorbidity index and has been adapted and verified as applicable and valid for predicting the outcomes and risk of death from many comorbid diseases.¹⁴ The Cumulative Illness Rating Scale has been validated as a predictor for readmission for hospitalized older adults, hospitalization within 1 year in a residential setting, and long-term mortality when assessed in inpatient and residential settings.¹⁵

Polypharmacy

Polypharmacy (use of ≥ 5 medications) is common in older patients regardless of cancer diagnosis and is often instead defined as “the use of multiple drugs or more than are medically necessary.”¹⁶ The use of multiple medications, including those not indicated for existing medical conditions (such as over‐the‐counter, herbal, and complementary/alternative medicines, which patients often fail to declare to their specialist, doctor, or pharmacist) adds to the potential negative aspects of polypharmacy that affect older patients.¹⁷

Patients with cancer usually are prescribed an extensive number of medicines, both for the disease and for supportive care, which can increase the chance of drug-drug interactions and adverse reactions.¹⁸ While these issues certainly affect quality of life, they also may influence chemotherapy treatment and potentially impact survival. Studies have shown that the presence of polypharmacy has been associated with higher numbers of comorbidities, increased use of inappropriate medications, poor performance status, decline in functional status, and poor survival.¹⁸

Functional Status

Although Eastern Cooperative Oncology Group (ECOG) performance status and Karnofsky Performance Status are commonly used by oncologists, these guidelines are limited in focus and do not reliably measure functional status in older patients. Functional status is determined by the ability to perform daily acts of self-care, which includes assessment of activities of daily living (ADLs) and instrumental activities of daily living (IADLs). ADLs refer to such tasks as bathing, dressing, eating, mobility, balance, and toileting.¹⁹ IADLs include the ability to perform activities required to live within a community and include shopping, transportation, managing finances, medication management, cooking, and cleaning.¹¹

Physical functionality also can be assessed by measures such as gait speed, grip strength, balance, and lower extremity strength. These are more sensitive and shown to be associated with worse clinical outcomes.²⁰ Grip strength and gait speed, as assessed by the Timed Up and Go test or the Short Physical Performance Battery measure strength and balance.¹² Reduction in gait speed and/or grip strength are associated with adverse clinical outcomes and increased risk of mortality.²¹ Patients with cancer who have difficulty with ADLs are at increased risk for falls, which can limit their functional independence, compromise cancer therapy, and increase the risk of chemotherapy toxicities.¹¹ Impaired hearing and poor vision are added factors that can be barriers to cancer treatment.

Cognition

Cognitive impairment in patients with cancer is becoming more of an issue for oncology HCPs as both cancer and cognitive decline are more common with advancing age. Cognition in cancer patients is important for understanding their diagnosis, prognosis, treatment options, and adherence. Impaired cognition can affect decision making regarding treatment options and administration. Cognition can be assessed through validated screening tools such as the Mini-Mental State Examination and Mini-Cog.¹¹

Psychological and Social Status

A cancer diagnosis has a major impact on the mental and emotional state of patients and family members. Clinically significant anxiety has been reported in approximately 21% of older patients with cancer, and the incidence of depression ranges from 17 to 26%.²² In older patients with, psychologic distress can impact cancer treatment, resulting in less definitive therapy and poorer outcomes.²³ All patients with cancer should be screened for psychologic distress using standardized methods, such as the Geriatric Depression Scale or the General Anxiety Disorder-7 scale.²⁴ A positive screen should lead to additional assessments that evaluate the severity of depression and other comorbid psychological problems and medical conditions.

Social isolation and loneliness are factors that can affect both depression and anxiety. Older patients with cancer are at risk for decreased social activities and are already challenged with issues related to home care, comorbidities, functional status, and caregiver support.²³ Therefore, it is important to assess the social interactions of an older and/or frail patient with cancer and use social work assistance to address needs for supportive services.

Nutrition

Nutrition is important in any patient with cancer undergoing chemotherapy treatment. However, it is of greater importance in older adults, as malnutrition and weight loss are negative prognostic factors that correlate with poor tolerance to chemotherapy treatment, decline in quality of life, and increased mortality.²⁵ The Mini-Nutritional Assessment is a widely used validated tool to assess nutritional status and risk of malnutrition.¹¹ This tool can help identify those older and/or frail patients with cancer with impaired nutritional status and aid in instituting corrective measures to treat or prevent malnutrition.

Effectiveness of CGA

Multiple randomized controlled clinical trials assessing the effectiveness of CGA have been conducted over the past 3 decades with overall positive outcomes related to its value.²⁶ Benefits of CGA can include overall improved medical care, avoidance of hospitalization or nursing home placement, identification of cognitive impairment, and prevention of geriatric syndrome (a range of conditions representing multiple organ impairment in older adults).²⁷

In oncology, CGA is particularly beneficial, as it can identify issues in nearly 70% of patients that may not be apparent through traditional oncology assessment.²⁸ A systematic review of 36 studies assessing the prognostic value of CGA in elderly patients with cancer receiving chemotherapy concluded that impaired performance and functional status as well as a frail and vulnerable profile are important predictors of severe chemotherapy-related toxicity and are associated with a higher risk of mortality.²⁹ Therefore, CGA should be an integral part of the evaluation of older and/or frail patients with cancer prior to chemotherapy consideration.

Several screening tools have been developed using information from CGA to assess the risk of severe toxicities. The most commonly used tools for predicting toxicity include the Cancer and Aging Research Group (CARG) chemotoxicity calculator and the Chemotherapy Risk Assessment Scale for High-Age Patients (CRASH).^30,31 Although these tools are readily available to facilitate CGA, and despite their proven beneficial outcome and recommended usage by national guidelines, implementation of these tools in routine oncology practice has been challenging and slow to spread. Unless these recommended interventions are effectively implemented, the benefits of CGA cannot be realized. With the expected surge in the number of older patients with cancer, hopefully this will change.

Geriatric Assessment Screening Tools

A screening tool recommended for use in older and/or frail patients with cancer allows for a brief assessment to help clinicians identify patients in need of further evaluation by CGA and to provides information on treatment-related toxicities, functional decline, and survival.³² The predictive value and utility of geriatric assessment screening tools have been repeatedly proven to identify older and/or frail adults at risk for treatment-related toxicities.¹² The CARG and the CRASH are validated screening tools used in identifying patients at higher risk for chemotherapy toxicity. These screening tools are intended to provide guidance to the clinical oncology practitioner on risk stratification of chemotherapy toxicity in older patients with cancer.³³

Both of these screening tools provide similar predictive performance for chemotherapy toxicity in older patients with cancer.³⁴ However, the CARG tool seems to have the advantage of using more data that had already been obtained during regular office visits and is clear and easy to use clinically. The CRASH tool is slightly more involved, as it uses multiple geriatric instruments to determine the predictive risk of both hematologic and nonhematologic toxicities of chemotherapy.

CARG Chemotoxicity Calculator

Hurria and colleagues originally developed the CARG tool from data obtained through a prospective multicenter study involving 500 patients with cancer aged ≥ 65 years.³⁵ They concluded that chemotherapy-related toxicity is common in older adults, with 53% of patients sustaining grade 3 or 4 treatment-related toxicities and 2% treatment-related mortality.¹² This predictive model for chemotherapy-related toxicity used 11 variables, both objective (obtained during a regular clinical encounter: age, tumor type, chemotherapy dosing, number of drugs, creatinine, and hemoglobin) and subjective (completed by patient: number of falls, social support, the ability to take medications, hearing impairment, and physical performance), to determine at-risk patients (Table 1).³¹

Compared with standard performance status measures in oncology practice, the CARG model was better able to predict chemotherapy-related toxicities. In 2016, Hurria and colleagues published the results of an updated external validation study with a cohort of 250 older patients with cancer receiving chemotherapy that confirmed the prediction of chemotherapy toxicity using the CARG screening tool in this population.³¹ An appealing feature of this tool is the free online accessibility and the expedited manner in which screening can be conducted.

CRASH Score

The CRASH score was derived from the results of a prospective, multicenter study of 518 patients aged ≥ 70 years who were assessed on 24 parameters prior to starting chemotherapy.³⁰ A total of 64% of patients experienced significant toxicities, including 32% with grade 4 hematologic toxicity and 56% with grade 3 or 4 nonhematologic toxicity. The hematologic and nonhematologic toxicity risks are the 2 categories that comprise the CRASH score. Both baseline patient variables and chemotherapy regimen are incorporated into an 8-item assessment profile that determines the risk categories (Table 2).³⁰

Increased risk of hematologic toxicities was associated with increased diastolic blood pressure, increased lactate dehydrogenase, need for assistance with IADL, and increased toxicity potential of the chemotherapy regimen. Nonhematologic toxicities were associated with ECOG performance score, Mini Mental Status Examination and Mini-Nutritional Assessment, and increased toxicity of the chemotherapy regimen.¹² Patient scores are stratified into 4 risk categories: low, medium-low, medium-high, and high.³⁰ Like the CARG tool, the CRASH screening tool also is available as a free online resource and can be used in everyday clinical practice to assess older and/or frail adults with cancer.

Conclusions 

In older adults, cancer may significantly impact the natural course of concurrent comorbidities due to physiologic and functional changes. These vulnerabilities predispose older patients with cancer to an increased risk of adverse outcomes, including treatment-related toxicities.³⁶ Given the rapidly aging population, it is critical for oncology clinical teams to be prepared to assess for, prevent, and manage issues for older adults that could impact outcomes, including complications and toxicities from chemotherapy.³⁵ Studies have reported that 78 to 93% of older oncology patients have at least 1 geriatric impairment that could potentially impact oncology treatment plans.^37,38 This supports the utility of CGA as a global assessment tool to risk stratify older and/or frail patients prior to deciding on subsequent oncologic treatment approaches.⁵ In fact, major cooperative groups sponsored by the National Cancer Institute, such as the Alliance for Clinical Trials in Oncology, are including CGA as part of some of their treatment trials. CGA was conducted as part of a multicenter cooperative group study in older patients with acute myeloid leukemia prior to inpatient intensive induction chemotherapy and was determined to be feasible and useful in clinical trials and practice.³⁹

Despite the increasing evidence for benefits of CGA, it has not been a consistent part of oncology practices, and few HCPs are familiar with the benefits of CGA screening tools. Although oncology providers routinely participate in every aspect of cancer care and play a vital role in the coordination and management of older patients with cancer, CGA implementation into routine clinical practice has been slow in part due to lack of knowledge and training regarding the use of GA tools.

Oncology providers can easily incorporate CGA screening tools into the history and physical examination process for older patients with cancer, which will add an important dimension to these patient evaluations. Oncology providers are not only well positioned to administer these screening tools, but also can lead the field in developing innovative ways for effective implementation in busy routine oncology clinics. However, to be successful, oncology providers must be knowledgeable about these tools and understand their utility in guiding treatment decisions and improving quality of care in older patients with cancer.

Age is a well recognized risk factor for cancer development. The population of older Americans is growing, and by 2030, 20% of the US population will be aged ≥ 65 years.¹ While 25% of all new cancer cases are diagnosed in people aged 65 to 74 years, more than half of cancers occur in individuals aged ≥ 70 years, with even higher rates in those aged ≥ 75 years.² Although cancer rates have declined slightly overall among people aged ≥ 65 years, this population still has an 11-fold increased incidence of cancer compared with that of younger individuals.³ With a rapidly growing older population, there will be increasing demand for cancer care.

Treatment of cancer in older individuals often is complicated by medical comorbidities, frailty, and poor functional status. Distinguishing patients who can tolerate aggressive therapy from those who require less intensive therapy can be challenging. Age-related physiologic changes predispose older adults to an increased risk of therapy-related toxicities, resulting in suboptimal therapeutic benefit and substantial morbidity. For example, cardiovascular changes can lead to reduction of the cardiac functional reserve, which can increase the risk of congestive heart failure. Similarly, decline in renal function leads to an increased potential for nephrotoxicity.⁴ Although patients may be of the same chronologic age, their performance, functional, and biologic status may be quite variable; thus, tolerance to aggressive treatment is not easily predicted. The comprehensive geriatric assessment (CGA) may be used as a global assessment tool to risk stratify older patients prior to oncologic treatment decisions.⁵

Health care providers (HCPs), including physician assistants, nurse practitioners, clinical nurse specialists, nurses, and physicians, routinely participate in every aspect of cancer care by ordering and interpreting diagnostic tests, addressing comorbidities, managing symptoms, and discussing cancer treatment recommendations. HCPs in oncology will continue to play a vital role in the coordination and management of older patients with cancer. However, in general, CGA has not been a consistent part of oncology practices, and few HCPs are familiar with the benefits of CGA screening tools.

What Is Geriatric Assessment? 

Geriatric assessment is a multidisciplinary, multidimensional process aimed at detecting medical, psychosocial, and functional issues of older adults that are not identified by traditional performance status measures alone. It provides guidance for management of identified problems and improvement in quality of life.⁶ CGA was developed by geriatricians and multidisciplinary care teams to evaluate the domains of functional, nutritional, cognitive, psychosocial, and economic status; comorbidities; geriatric syndromes; and mood, and it has been tested in both clinics and hospitals.⁷ Although such assessment requires additional time and resources, its goals are to identify areas of vulnerability, assist in clinical decisions of treatable health problems, and guide therapeutic interventions.⁶ In oncology practice, the assessment not only addresses these global issues, but also is critical in predicting toxicity and survival outcomes in older oncology patients.

Components of CGA 

Advancing age brings many physiologic, psychosocial, and functional challenges, and a cancer diagnosis only adds to these issues. CGA provides a system of assessing older and/or frail patients with cancer through specific domains to identify issues that are not apparent on routine evaluation in a clinic setting before and during chemotherapy treatments. These domains include comorbidity, polypharmacy, functional status, cognition, psychological and social status, and nutrition.⁸

Comorbidity

The prevalence of multiple medical problems and comorbidities, including cancer, among people aged > 65 years is increasing.⁹ Studies have shown that two-thirds of patients with cancer had ≥ 2 medical conditions, and nearly one quarter had ≥ 4 medical conditions.¹⁰ In older adults, common comorbidities include cardiovascular disease, hypertension, diabetes mellitus, and dementia. These comorbidities can impact treatment decisions, increase the risk of disease, impact treatment-related complications, and affect a patient’s life expectancy.¹¹ Assessing comorbidities is essential to CGA and is done using the Charlson Comorbidity Index and/or the Cumulative Illness Rating Scale.¹²

The Charlson Comorbidity Index was originally designed to predict 1-year mortality on the basis of a weighted composite score for the following categories: cardiovascular, endocrine, pulmonary, neurologic, renal, hepatic, gastrointestinal, and neoplastic disease.¹³ It is now the most widely used comorbidity index and has been adapted and verified as applicable and valid for predicting the outcomes and risk of death from many comorbid diseases.¹⁴ The Cumulative Illness Rating Scale has been validated as a predictor for readmission for hospitalized older adults, hospitalization within 1 year in a residential setting, and long-term mortality when assessed in inpatient and residential settings.¹⁵

Polypharmacy

Polypharmacy (use of ≥ 5 medications) is common in older patients regardless of cancer diagnosis and is often instead defined as “the use of multiple drugs or more than are medically necessary.”¹⁶ The use of multiple medications, including those not indicated for existing medical conditions (such as over‐the‐counter, herbal, and complementary/alternative medicines, which patients often fail to declare to their specialist, doctor, or pharmacist) adds to the potential negative aspects of polypharmacy that affect older patients.¹⁷

Patients with cancer usually are prescribed an extensive number of medicines, both for the disease and for supportive care, which can increase the chance of drug-drug interactions and adverse reactions.¹⁸ While these issues certainly affect quality of life, they also may influence chemotherapy treatment and potentially impact survival. Studies have shown that the presence of polypharmacy has been associated with higher numbers of comorbidities, increased use of inappropriate medications, poor performance status, decline in functional status, and poor survival.¹⁸

Functional Status

Although Eastern Cooperative Oncology Group (ECOG) performance status and Karnofsky Performance Status are commonly used by oncologists, these guidelines are limited in focus and do not reliably measure functional status in older patients. Functional status is determined by the ability to perform daily acts of self-care, which includes assessment of activities of daily living (ADLs) and instrumental activities of daily living (IADLs). ADLs refer to such tasks as bathing, dressing, eating, mobility, balance, and toileting.¹⁹ IADLs include the ability to perform activities required to live within a community and include shopping, transportation, managing finances, medication management, cooking, and cleaning.¹¹

Physical functionality also can be assessed by measures such as gait speed, grip strength, balance, and lower extremity strength. These are more sensitive and shown to be associated with worse clinical outcomes.²⁰ Grip strength and gait speed, as assessed by the Timed Up and Go test or the Short Physical Performance Battery measure strength and balance.¹² Reduction in gait speed and/or grip strength are associated with adverse clinical outcomes and increased risk of mortality.²¹ Patients with cancer who have difficulty with ADLs are at increased risk for falls, which can limit their functional independence, compromise cancer therapy, and increase the risk of chemotherapy toxicities.¹¹ Impaired hearing and poor vision are added factors that can be barriers to cancer treatment.

Cognition

Cognitive impairment in patients with cancer is becoming more of an issue for oncology HCPs as both cancer and cognitive decline are more common with advancing age. Cognition in cancer patients is important for understanding their diagnosis, prognosis, treatment options, and adherence. Impaired cognition can affect decision making regarding treatment options and administration. Cognition can be assessed through validated screening tools such as the Mini-Mental State Examination and Mini-Cog.¹¹

Psychological and Social Status

A cancer diagnosis has a major impact on the mental and emotional state of patients and family members. Clinically significant anxiety has been reported in approximately 21% of older patients with cancer, and the incidence of depression ranges from 17 to 26%.²² In older patients with, psychologic distress can impact cancer treatment, resulting in less definitive therapy and poorer outcomes.²³ All patients with cancer should be screened for psychologic distress using standardized methods, such as the Geriatric Depression Scale or the General Anxiety Disorder-7 scale.²⁴ A positive screen should lead to additional assessments that evaluate the severity of depression and other comorbid psychological problems and medical conditions.

Social isolation and loneliness are factors that can affect both depression and anxiety. Older patients with cancer are at risk for decreased social activities and are already challenged with issues related to home care, comorbidities, functional status, and caregiver support.²³ Therefore, it is important to assess the social interactions of an older and/or frail patient with cancer and use social work assistance to address needs for supportive services.

Nutrition

Nutrition is important in any patient with cancer undergoing chemotherapy treatment. However, it is of greater importance in older adults, as malnutrition and weight loss are negative prognostic factors that correlate with poor tolerance to chemotherapy treatment, decline in quality of life, and increased mortality.²⁵ The Mini-Nutritional Assessment is a widely used validated tool to assess nutritional status and risk of malnutrition.¹¹ This tool can help identify those older and/or frail patients with cancer with impaired nutritional status and aid in instituting corrective measures to treat or prevent malnutrition.

Effectiveness of CGA

Multiple randomized controlled clinical trials assessing the effectiveness of CGA have been conducted over the past 3 decades with overall positive outcomes related to its value.²⁶ Benefits of CGA can include overall improved medical care, avoidance of hospitalization or nursing home placement, identification of cognitive impairment, and prevention of geriatric syndrome (a range of conditions representing multiple organ impairment in older adults).²⁷

In oncology, CGA is particularly beneficial, as it can identify issues in nearly 70% of patients that may not be apparent through traditional oncology assessment.²⁸ A systematic review of 36 studies assessing the prognostic value of CGA in elderly patients with cancer receiving chemotherapy concluded that impaired performance and functional status as well as a frail and vulnerable profile are important predictors of severe chemotherapy-related toxicity and are associated with a higher risk of mortality.²⁹ Therefore, CGA should be an integral part of the evaluation of older and/or frail patients with cancer prior to chemotherapy consideration.

Several screening tools have been developed using information from CGA to assess the risk of severe toxicities. The most commonly used tools for predicting toxicity include the Cancer and Aging Research Group (CARG) chemotoxicity calculator and the Chemotherapy Risk Assessment Scale for High-Age Patients (CRASH).^30,31 Although these tools are readily available to facilitate CGA, and despite their proven beneficial outcome and recommended usage by national guidelines, implementation of these tools in routine oncology practice has been challenging and slow to spread. Unless these recommended interventions are effectively implemented, the benefits of CGA cannot be realized. With the expected surge in the number of older patients with cancer, hopefully this will change.

Geriatric Assessment Screening Tools

A screening tool recommended for use in older and/or frail patients with cancer allows for a brief assessment to help clinicians identify patients in need of further evaluation by CGA and to provides information on treatment-related toxicities, functional decline, and survival.³² The predictive value and utility of geriatric assessment screening tools have been repeatedly proven to identify older and/or frail adults at risk for treatment-related toxicities.¹² The CARG and the CRASH are validated screening tools used in identifying patients at higher risk for chemotherapy toxicity. These screening tools are intended to provide guidance to the clinical oncology practitioner on risk stratification of chemotherapy toxicity in older patients with cancer.³³

Both of these screening tools provide similar predictive performance for chemotherapy toxicity in older patients with cancer.³⁴ However, the CARG tool seems to have the advantage of using more data that had already been obtained during regular office visits and is clear and easy to use clinically. The CRASH tool is slightly more involved, as it uses multiple geriatric instruments to determine the predictive risk of both hematologic and nonhematologic toxicities of chemotherapy.

CARG Chemotoxicity Calculator

Hurria and colleagues originally developed the CARG tool from data obtained through a prospective multicenter study involving 500 patients with cancer aged ≥ 65 years.³⁵ They concluded that chemotherapy-related toxicity is common in older adults, with 53% of patients sustaining grade 3 or 4 treatment-related toxicities and 2% treatment-related mortality.¹² This predictive model for chemotherapy-related toxicity used 11 variables, both objective (obtained during a regular clinical encounter: age, tumor type, chemotherapy dosing, number of drugs, creatinine, and hemoglobin) and subjective (completed by patient: number of falls, social support, the ability to take medications, hearing impairment, and physical performance), to determine at-risk patients (Table 1).³¹

Compared with standard performance status measures in oncology practice, the CARG model was better able to predict chemotherapy-related toxicities. In 2016, Hurria and colleagues published the results of an updated external validation study with a cohort of 250 older patients with cancer receiving chemotherapy that confirmed the prediction of chemotherapy toxicity using the CARG screening tool in this population.³¹ An appealing feature of this tool is the free online accessibility and the expedited manner in which screening can be conducted.

CRASH Score

The CRASH score was derived from the results of a prospective, multicenter study of 518 patients aged ≥ 70 years who were assessed on 24 parameters prior to starting chemotherapy.³⁰ A total of 64% of patients experienced significant toxicities, including 32% with grade 4 hematologic toxicity and 56% with grade 3 or 4 nonhematologic toxicity. The hematologic and nonhematologic toxicity risks are the 2 categories that comprise the CRASH score. Both baseline patient variables and chemotherapy regimen are incorporated into an 8-item assessment profile that determines the risk categories (Table 2).³⁰

Increased risk of hematologic toxicities was associated with increased diastolic blood pressure, increased lactate dehydrogenase, need for assistance with IADL, and increased toxicity potential of the chemotherapy regimen. Nonhematologic toxicities were associated with ECOG performance score, Mini Mental Status Examination and Mini-Nutritional Assessment, and increased toxicity of the chemotherapy regimen.¹² Patient scores are stratified into 4 risk categories: low, medium-low, medium-high, and high.³⁰ Like the CARG tool, the CRASH screening tool also is available as a free online resource and can be used in everyday clinical practice to assess older and/or frail adults with cancer.

Conclusions 

In older adults, cancer may significantly impact the natural course of concurrent comorbidities due to physiologic and functional changes. These vulnerabilities predispose older patients with cancer to an increased risk of adverse outcomes, including treatment-related toxicities.³⁶ Given the rapidly aging population, it is critical for oncology clinical teams to be prepared to assess for, prevent, and manage issues for older adults that could impact outcomes, including complications and toxicities from chemotherapy.³⁵ Studies have reported that 78 to 93% of older oncology patients have at least 1 geriatric impairment that could potentially impact oncology treatment plans.^37,38 This supports the utility of CGA as a global assessment tool to risk stratify older and/or frail patients prior to deciding on subsequent oncologic treatment approaches.⁵ In fact, major cooperative groups sponsored by the National Cancer Institute, such as the Alliance for Clinical Trials in Oncology, are including CGA as part of some of their treatment trials. CGA was conducted as part of a multicenter cooperative group study in older patients with acute myeloid leukemia prior to inpatient intensive induction chemotherapy and was determined to be feasible and useful in clinical trials and practice.³⁹

Despite the increasing evidence for benefits of CGA, it has not been a consistent part of oncology practices, and few HCPs are familiar with the benefits of CGA screening tools. Although oncology providers routinely participate in every aspect of cancer care and play a vital role in the coordination and management of older patients with cancer, CGA implementation into routine clinical practice has been slow in part due to lack of knowledge and training regarding the use of GA tools.

Oncology providers can easily incorporate CGA screening tools into the history and physical examination process for older patients with cancer, which will add an important dimension to these patient evaluations. Oncology providers are not only well positioned to administer these screening tools, but also can lead the field in developing innovative ways for effective implementation in busy routine oncology clinics. However, to be successful, oncology providers must be knowledgeable about these tools and understand their utility in guiding treatment decisions and improving quality of care in older patients with cancer.

The number of women seeking care from the Veterans Health Administration (VHA) is increasing.¹ In 2015, there were 2 million women veterans in the United States, which is 9.4% of the total veteran population. This group is expected to increase at an average of about 18,000 women per year for the next 10 years.² The percentage of women veterans who are US Department of Veterans Affairs (VA) users aged 45 to 64 years rose 46% from 2000 to 2015.^1,3-4 It is estimated that 15% of veterans who used VA services in 2020 were women.¹ Nineteen percent of women veterans are Black.¹ The median age of women veterans in 2015 was 50 years.⁵ Breast cancer is the leading cancer affecting female veterans, and data suggest they have an increased risk of breast cancer based on unique service-related exposures.^1,6-9

In the US, about 10 million women are eligible for breast cancer preventive therapy, including, but not limited to, medications, surgery, or lifestyle changes.¹⁰ Secondary prevention options include change in surveillance that can reduce their risk or identify cancer at an earlier stage when treatment is more effective. The United States Preventive Services Task Force, the National Comprehensive Cancer Network, the American Society for Clinical Oncology, the National Institute for Health and Care Excellence, and the Oncology Nursing Society recommend screening women aged ≥ 35 years to assess breast cancer risk.^11-18 If a woman is at increased risk, she may be a candidate for chemoprevention, prozphylactic surgery, and possibly an enhanced screening regimen.

Urban and minority women are an understudied population. Most veterans (75%) live in urban or suburban settings.^19,20 Urban veteran women constitute an important potential study population.

Chemoprevention measures have been underused because of factors involving both women and their health care providers. A large proportion of women are unaware of their higher risk status due to lack of adequate screening and risk assessment.^21,22 In addition to patient lack of awareness of their high-risk status, primary care physicians are also reluctant to prescribe chemopreventive agents due to a lack of comfort or familiarity with the risks and benefits.^23-26 The STAR2015, BCPT2005, IBIS2014, MAP3 2011, IBIS-I 2014, and IBIS II 2014 studies clearly demonstrate a 49 to 62% reduction in risk for women using chemoprevention such as selective estrogen receptor modulators or aromatase inhibitors, respectively.^27-32 Yet only 4 to 9% of high-risk women not enrolled in a clinical trial are using chemoprevention.^33-39

The possibility of developing breast cancer also may be increased because of a positive family history or being a member of a family in which there is a known susceptibility gene mutation.⁴⁰ Based on these risk factors, women may be eligible for tailored follow-up and genetic counseling.^41-44

Nationally, 7 to 10% of the civilian US population will experience posttraumatic stress disorder (PTSD).⁴⁵ The rates are remarkably higher for women veterans, with roughly 20% diagnosed with PTSD.^46,47 Anxiety and PTSD have been implicated in poor adherence to medical advice.^48,49

In 2014, a national VA multidisciplinary group focused on breast cancer prevention, detection, treatment, and research to address breast health in the growing population of women veterans. High-risk breast cancer screenings are not routinely carried out by the VA in primary care, women’s health, or oncology services. Furthermore, the recording of screening questionnaire results was not synchronized until a standard questionnaire was created and approved as a template by this group in the VA electronic medical record (EMR) in 2015.

Several prediction models can identify which women are at an increased risk of developing breast cancer. The most commonly used risk assessment model, the Gail breast cancer risk assessment tool (BCRAT), has been refined to include women of additional ethnicities (https://www.cancer.gov/bcrisktool).

This pilot project was launched to identify an effective manner to screen women veterans regarding their risk of developing breast cancer and refer them for chemoprevention education or genetic counseling as appropriate.

Methods

A high-risk breast cancer screening questionnaire based on the Gail BCRAT and including lifestyle questions was developed and included as a note template in the VA EMR. The James J. Peters VA Medical Center, Bronx, NY (JJPVAMC) and the Washington DC VA Medical Center (DCVAMC) ran a pilot study between 2015 and 2018 using this breast cancer screening questionnaire to collect data from women veterans. Quality Executive Committee and institutional review board approvals were granted respectively.

Eligibility criteria included women aged ≥ 35 years with no personal history of breast cancer. Most patients were self-referred, but participants also were recruited during VA Breast Cancer Awareness month events, health fairs, or at informational tables in the hospital lobbies. After completing the 20 multiple choice questionnaire with a study team member, either in person or over the phone, a 5-year and lifetime risk of invasive breast cancer was calculated using the Gail BCRAT. A woman is considered high risk and eligible for chemoprevention if her 5-year risk is > 1.66% or her lifetime risk is ≥ 20%. Eligibility for genetic counseling is based on the Breast Cancer Referral Screening Tool, which includes a personal or family history of breast or ovarian cancer and Jewish ancestry.

All patients were notified of their average or high risk status by a clinician. Those who were deemed to be average risk received a follow-up letter in the mail with instructions (eg, to follow-up with a yearly mammogram). Those who were deemed to be high risk for developing breast cancer were asked to come in for an appointment with the study principal investigator (a VA oncologist/breast cancer specialist) to discuss prevention options, further screening, or referrals to genetic counseling. Depending on a patient’s other health factors, a woman at high risk for developing breast cancer also may be a candidate for chemoprevention with tamoxifen, raloxifene, exemestane, anastrozole, or letrozole.

Data on the participant’s lifestyle, including exercise, diet, and smoking, were evaluated to determine whether these factors had an impact on risk status.

Results

The JJP and DC VAMCs screened 103 women veterans between 2015 and 2018. Four patients were excluded for nonveteran (spousal) status, leaving 99 women veterans with a mean age of 54 years. The most common self-reported races were Black (60%), non-Hispanic White (14%), and Hispanic or Latino (13%) (Table 1).

Women veterans in our study were nearly 3-times more likely than the general population were to receive a high-risk Gail Score/BCRAT (35% vs 13%, respectively).^50,51 Of this subset, 46% had breast biopsies, and 86% had a positive family history. Thirty-one percent of Black women in our study were high risk, while nationally, 8.2 to 13.3% of Black women aged 50 to 59 years are considered high risk.^50,51 Of the Black high-risk group with a high Gail/BCRAT score, 94% had a positive family history, and 33% had a history of breast biopsy (Table 2).

Of the 35 high-risk patients 26 (74%) patients accepted consultations for chemoprevention and 5 (19%) started chemoprevention. Of this high-risk group, 13 (37%) patients were referred for genetic counseling (Table 3).⁴⁴ The prevalence of PTSD was present in 31% of high-risk women and 29% of the cohort (Figure).The lifestyle questions indicated that, among all participants, 79% had an overweight or obese body mass index; 58% exercised weekly; 51% consumed alcohol; 14% were smokers; and 21% consumed 3 to 4 servings of fruits/vegetables daily.

Discussion

Breast cancer is the most common cancer in women.⁵² The number of women with breast cancer in the VHA has more than tripled from 1995 to 2012.¹ The lifetime risk of developing breast cancer in the general population is about 13%.⁵⁰ This rate can be affected by risk factors including age, hormone exposure, family history, radiation exposure, and lifestyle factors, such as weight and alcohol use.^6,52-56 In the United States, invasive breast cancer affects 1 in 8 women.^50,52,57

Our screened population showed nearly 3 times as many women veterans were at an increased risk for breast cancer when compared with historical averages in US women. This difference may be based on a high rate of prior breast biopsies or positive family history, although a provocative study using the Surveillance, Epidemiology, and End Results database showed military women to have higher rates of breast cancer as well.⁹ Historically, Blacks are vastly understudied in clinical research with only 5% representation on a national level.^5,58 The urban locations of both pilot sites (Washington, DC and Bronx, NY) allowed for the inclusion of minority patients in our study. We found that the rates of breast cancer in Black women veterans to be higher than seen nationally, possibly prompting further screening initiatives for this understudied population.

Our pilot study’s chemoprevention utilization (19%) was double the < 10% seen in the national population.^33-35 The presence of a knowledgeable breast health practitioner to recruit study participants and offer personalized counseling to women veterans is a likely factor in overcoming barriers to chemopreventive acceptance. These participants may have been motivated to seek care for their high-risk status given a strong family history and prior breast biopsies.

Interestingly, a 3-fold higher PTSD rate was seen in this pilot population (29%) when compared with PTSD rates in the general female population (7-10%) and still one-third higher than the general population of women veterans (20%).^45-47 Mental health, anxiety, and PTSD have been barriers to patients who sought treatment and have been implicated in poor adherence to medical advice.^48,49 Cancer screening can induce anxiety in patients, and it may be amplified in patients with PTSD. It was remarkable that although adherence with screening recommendations is decreased when PTSD is present, our patient population demonstrated a higher rate of screening adherence.

Women who are seen at the VA often use multiple clinical specialties, and their EMR can be accessed across VA medical centers nationwide. Therefore, identifying women veterans who meet screening criteria is easily attainable within the VA.

When comparing high-risk with average risk women, the lifestyle results (BMI, smoking history, exercise and consumption of fruits, vegetables and alcohol) were essentially the same. Lifestyle factors were similar to national population rates and were unlikely to impact risk levels.

Limitations

Study limitations included a high number of self-referrals and the large percentage of patients with a family history of breast cancer, making them more likely to seek screening. The higher-than-average risk of breast cancer may be driven by a high rate of breast biopsies and a strong family history. Lifestyle metrics could not be accurately compared to other national assessments of lifestyle factors due to the difference in data points that we used or the format of our questions.

Conclusions

As the number of women veterans increases and the incidence of breast cancer in women veterans rise, chemoprevention options should follow national guidelines. To our knowledge, this is the only oncology study with 60% Black women veterans. This study had a higher participation rate for Black women veterans than is typically seen in national research studies and shows the VA to be a germane source for further understanding of an understudied population that may benefit from increased screening for breast cancer.

A team-based, multidisciplinary model that meets the unique healthcare needs of women veterans results in a patient-centric delivery of care for assessing breast cancer risk status and prevention options. This model can be replicated nationally by directing primary care physicians and women’s health practitioners to a risk-assessment questionnaire and referring high-risk women for appropriate preventative care. Given that these results show chemoprevention adherence rates doubled those seen nationally, perhaps techniques used within this VA pilot study may be adapted to decrease breast cancer incidence nationally.

Since the rate of PTSD among women veterans is triple the national average, we would expect adherence rates to be lower in our patient cohort. However, the multidisciplinary approach we used in this study (eg, 1:1 consultation with oncologist; genetic counseling referrals; mental health support available), may have improved adherence rates. Perhaps the high rates of PTSD seen in the VA patient population can be a useful way to explore patient adherence rates in those with mental illness and medical conditions.

Future research with a larger cohort may lead to greater insight into the correlation between PTSD and adherence to treatment. Exploring the connection between breast cancer, epigenetics, and specific military service-related exposures could be an area of analysis among this veteran population exhibiting increased breast cancer rates. VAMCs are situated in rural, suburban, and urban locations across the United States and offers a diverse socioeconomic and ethnic patient population for inclusion in clinical investigations. Women veterans make up a small subpopulation of women in the United States, but it is worth considering VA patients as an untapped resource for research collaboration.

Acknowledgements

The authors thank Steven Sanchez and Marissa Vallette, PhD, Breast Health Research Group. This research project was approved by the James J. Peters VA Medical Center Quality Executive Committee and the Washington, DC VA Medical Center Institutional Review Board. This work was supported by the US Department of Veterans Affairs. This work did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author disclosures

The authors report no actual or potential conflicts of interest with regard to this article.

Disclaimer

The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the US Government, or any of its agencies.

The number of women seeking care from the Veterans Health Administration (VHA) is increasing.¹ In 2015, there were 2 million women veterans in the United States, which is 9.4% of the total veteran population. This group is expected to increase at an average of about 18,000 women per year for the next 10 years.² The percentage of women veterans who are US Department of Veterans Affairs (VA) users aged 45 to 64 years rose 46% from 2000 to 2015.^1,3-4 It is estimated that 15% of veterans who used VA services in 2020 were women.¹ Nineteen percent of women veterans are Black.¹ The median age of women veterans in 2015 was 50 years.⁵ Breast cancer is the leading cancer affecting female veterans, and data suggest they have an increased risk of breast cancer based on unique service-related exposures.^1,6-9

In the US, about 10 million women are eligible for breast cancer preventive therapy, including, but not limited to, medications, surgery, or lifestyle changes.¹⁰ Secondary prevention options include change in surveillance that can reduce their risk or identify cancer at an earlier stage when treatment is more effective. The United States Preventive Services Task Force, the National Comprehensive Cancer Network, the American Society for Clinical Oncology, the National Institute for Health and Care Excellence, and the Oncology Nursing Society recommend screening women aged ≥ 35 years to assess breast cancer risk.^11-18 If a woman is at increased risk, she may be a candidate for chemoprevention, prozphylactic surgery, and possibly an enhanced screening regimen.

Urban and minority women are an understudied population. Most veterans (75%) live in urban or suburban settings.^19,20 Urban veteran women constitute an important potential study population.

Chemoprevention measures have been underused because of factors involving both women and their health care providers. A large proportion of women are unaware of their higher risk status due to lack of adequate screening and risk assessment.^21,22 In addition to patient lack of awareness of their high-risk status, primary care physicians are also reluctant to prescribe chemopreventive agents due to a lack of comfort or familiarity with the risks and benefits.^23-26 The STAR2015, BCPT2005, IBIS2014, MAP3 2011, IBIS-I 2014, and IBIS II 2014 studies clearly demonstrate a 49 to 62% reduction in risk for women using chemoprevention such as selective estrogen receptor modulators or aromatase inhibitors, respectively.^27-32 Yet only 4 to 9% of high-risk women not enrolled in a clinical trial are using chemoprevention.^33-39

The possibility of developing breast cancer also may be increased because of a positive family history or being a member of a family in which there is a known susceptibility gene mutation.⁴⁰ Based on these risk factors, women may be eligible for tailored follow-up and genetic counseling.^41-44

Nationally, 7 to 10% of the civilian US population will experience posttraumatic stress disorder (PTSD).⁴⁵ The rates are remarkably higher for women veterans, with roughly 20% diagnosed with PTSD.^46,47 Anxiety and PTSD have been implicated in poor adherence to medical advice.^48,49

In 2014, a national VA multidisciplinary group focused on breast cancer prevention, detection, treatment, and research to address breast health in the growing population of women veterans. High-risk breast cancer screenings are not routinely carried out by the VA in primary care, women’s health, or oncology services. Furthermore, the recording of screening questionnaire results was not synchronized until a standard questionnaire was created and approved as a template by this group in the VA electronic medical record (EMR) in 2015.

Several prediction models can identify which women are at an increased risk of developing breast cancer. The most commonly used risk assessment model, the Gail breast cancer risk assessment tool (BCRAT), has been refined to include women of additional ethnicities (https://www.cancer.gov/bcrisktool).

This pilot project was launched to identify an effective manner to screen women veterans regarding their risk of developing breast cancer and refer them for chemoprevention education or genetic counseling as appropriate.

Methods

A high-risk breast cancer screening questionnaire based on the Gail BCRAT and including lifestyle questions was developed and included as a note template in the VA EMR. The James J. Peters VA Medical Center, Bronx, NY (JJPVAMC) and the Washington DC VA Medical Center (DCVAMC) ran a pilot study between 2015 and 2018 using this breast cancer screening questionnaire to collect data from women veterans. Quality Executive Committee and institutional review board approvals were granted respectively.

Eligibility criteria included women aged ≥ 35 years with no personal history of breast cancer. Most patients were self-referred, but participants also were recruited during VA Breast Cancer Awareness month events, health fairs, or at informational tables in the hospital lobbies. After completing the 20 multiple choice questionnaire with a study team member, either in person or over the phone, a 5-year and lifetime risk of invasive breast cancer was calculated using the Gail BCRAT. A woman is considered high risk and eligible for chemoprevention if her 5-year risk is > 1.66% or her lifetime risk is ≥ 20%. Eligibility for genetic counseling is based on the Breast Cancer Referral Screening Tool, which includes a personal or family history of breast or ovarian cancer and Jewish ancestry.

All patients were notified of their average or high risk status by a clinician. Those who were deemed to be average risk received a follow-up letter in the mail with instructions (eg, to follow-up with a yearly mammogram). Those who were deemed to be high risk for developing breast cancer were asked to come in for an appointment with the study principal investigator (a VA oncologist/breast cancer specialist) to discuss prevention options, further screening, or referrals to genetic counseling. Depending on a patient’s other health factors, a woman at high risk for developing breast cancer also may be a candidate for chemoprevention with tamoxifen, raloxifene, exemestane, anastrozole, or letrozole.

Data on the participant’s lifestyle, including exercise, diet, and smoking, were evaluated to determine whether these factors had an impact on risk status.

Results

The JJP and DC VAMCs screened 103 women veterans between 2015 and 2018. Four patients were excluded for nonveteran (spousal) status, leaving 99 women veterans with a mean age of 54 years. The most common self-reported races were Black (60%), non-Hispanic White (14%), and Hispanic or Latino (13%) (Table 1).

Women veterans in our study were nearly 3-times more likely than the general population were to receive a high-risk Gail Score/BCRAT (35% vs 13%, respectively).^50,51 Of this subset, 46% had breast biopsies, and 86% had a positive family history. Thirty-one percent of Black women in our study were high risk, while nationally, 8.2 to 13.3% of Black women aged 50 to 59 years are considered high risk.^50,51 Of the Black high-risk group with a high Gail/BCRAT score, 94% had a positive family history, and 33% had a history of breast biopsy (Table 2).

Of the 35 high-risk patients 26 (74%) patients accepted consultations for chemoprevention and 5 (19%) started chemoprevention. Of this high-risk group, 13 (37%) patients were referred for genetic counseling (Table 3).⁴⁴ The prevalence of PTSD was present in 31% of high-risk women and 29% of the cohort (Figure).The lifestyle questions indicated that, among all participants, 79% had an overweight or obese body mass index; 58% exercised weekly; 51% consumed alcohol; 14% were smokers; and 21% consumed 3 to 4 servings of fruits/vegetables daily.

Discussion

Breast cancer is the most common cancer in women.⁵² The number of women with breast cancer in the VHA has more than tripled from 1995 to 2012.¹ The lifetime risk of developing breast cancer in the general population is about 13%.⁵⁰ This rate can be affected by risk factors including age, hormone exposure, family history, radiation exposure, and lifestyle factors, such as weight and alcohol use.^6,52-56 In the United States, invasive breast cancer affects 1 in 8 women.^50,52,57

Our screened population showed nearly 3 times as many women veterans were at an increased risk for breast cancer when compared with historical averages in US women. This difference may be based on a high rate of prior breast biopsies or positive family history, although a provocative study using the Surveillance, Epidemiology, and End Results database showed military women to have higher rates of breast cancer as well.⁹ Historically, Blacks are vastly understudied in clinical research with only 5% representation on a national level.^5,58 The urban locations of both pilot sites (Washington, DC and Bronx, NY) allowed for the inclusion of minority patients in our study. We found that the rates of breast cancer in Black women veterans to be higher than seen nationally, possibly prompting further screening initiatives for this understudied population.

Our pilot study’s chemoprevention utilization (19%) was double the < 10% seen in the national population.^33-35 The presence of a knowledgeable breast health practitioner to recruit study participants and offer personalized counseling to women veterans is a likely factor in overcoming barriers to chemopreventive acceptance. These participants may have been motivated to seek care for their high-risk status given a strong family history and prior breast biopsies.

Interestingly, a 3-fold higher PTSD rate was seen in this pilot population (29%) when compared with PTSD rates in the general female population (7-10%) and still one-third higher than the general population of women veterans (20%).^45-47 Mental health, anxiety, and PTSD have been barriers to patients who sought treatment and have been implicated in poor adherence to medical advice.^48,49 Cancer screening can induce anxiety in patients, and it may be amplified in patients with PTSD. It was remarkable that although adherence with screening recommendations is decreased when PTSD is present, our patient population demonstrated a higher rate of screening adherence.

Women who are seen at the VA often use multiple clinical specialties, and their EMR can be accessed across VA medical centers nationwide. Therefore, identifying women veterans who meet screening criteria is easily attainable within the VA.

When comparing high-risk with average risk women, the lifestyle results (BMI, smoking history, exercise and consumption of fruits, vegetables and alcohol) were essentially the same. Lifestyle factors were similar to national population rates and were unlikely to impact risk levels.

Limitations

Study limitations included a high number of self-referrals and the large percentage of patients with a family history of breast cancer, making them more likely to seek screening. The higher-than-average risk of breast cancer may be driven by a high rate of breast biopsies and a strong family history. Lifestyle metrics could not be accurately compared to other national assessments of lifestyle factors due to the difference in data points that we used or the format of our questions.

Conclusions

As the number of women veterans increases and the incidence of breast cancer in women veterans rise, chemoprevention options should follow national guidelines. To our knowledge, this is the only oncology study with 60% Black women veterans. This study had a higher participation rate for Black women veterans than is typically seen in national research studies and shows the VA to be a germane source for further understanding of an understudied population that may benefit from increased screening for breast cancer.

A team-based, multidisciplinary model that meets the unique healthcare needs of women veterans results in a patient-centric delivery of care for assessing breast cancer risk status and prevention options. This model can be replicated nationally by directing primary care physicians and women’s health practitioners to a risk-assessment questionnaire and referring high-risk women for appropriate preventative care. Given that these results show chemoprevention adherence rates doubled those seen nationally, perhaps techniques used within this VA pilot study may be adapted to decrease breast cancer incidence nationally.

Since the rate of PTSD among women veterans is triple the national average, we would expect adherence rates to be lower in our patient cohort. However, the multidisciplinary approach we used in this study (eg, 1:1 consultation with oncologist; genetic counseling referrals; mental health support available), may have improved adherence rates. Perhaps the high rates of PTSD seen in the VA patient population can be a useful way to explore patient adherence rates in those with mental illness and medical conditions.

Future research with a larger cohort may lead to greater insight into the correlation between PTSD and adherence to treatment. Exploring the connection between breast cancer, epigenetics, and specific military service-related exposures could be an area of analysis among this veteran population exhibiting increased breast cancer rates. VAMCs are situated in rural, suburban, and urban locations across the United States and offers a diverse socioeconomic and ethnic patient population for inclusion in clinical investigations. Women veterans make up a small subpopulation of women in the United States, but it is worth considering VA patients as an untapped resource for research collaboration.

Acknowledgements

The authors thank Steven Sanchez and Marissa Vallette, PhD, Breast Health Research Group. This research project was approved by the James J. Peters VA Medical Center Quality Executive Committee and the Washington, DC VA Medical Center Institutional Review Board. This work was supported by the US Department of Veterans Affairs. This work did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author disclosures

The authors report no actual or potential conflicts of interest with regard to this article.

Disclaimer

The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the US Government, or any of its agencies.

The number of women seeking care from the Veterans Health Administration (VHA) is increasing.¹ In 2015, there were 2 million women veterans in the United States, which is 9.4% of the total veteran population. This group is expected to increase at an average of about 18,000 women per year for the next 10 years.² The percentage of women veterans who are US Department of Veterans Affairs (VA) users aged 45 to 64 years rose 46% from 2000 to 2015.^1,3-4 It is estimated that 15% of veterans who used VA services in 2020 were women.¹ Nineteen percent of women veterans are Black.¹ The median age of women veterans in 2015 was 50 years.⁵ Breast cancer is the leading cancer affecting female veterans, and data suggest they have an increased risk of breast cancer based on unique service-related exposures.^1,6-9

In the US, about 10 million women are eligible for breast cancer preventive therapy, including, but not limited to, medications, surgery, or lifestyle changes.¹⁰ Secondary prevention options include change in surveillance that can reduce their risk or identify cancer at an earlier stage when treatment is more effective. The United States Preventive Services Task Force, the National Comprehensive Cancer Network, the American Society for Clinical Oncology, the National Institute for Health and Care Excellence, and the Oncology Nursing Society recommend screening women aged ≥ 35 years to assess breast cancer risk.^11-18 If a woman is at increased risk, she may be a candidate for chemoprevention, prozphylactic surgery, and possibly an enhanced screening regimen.

Urban and minority women are an understudied population. Most veterans (75%) live in urban or suburban settings.^19,20 Urban veteran women constitute an important potential study population.

Chemoprevention measures have been underused because of factors involving both women and their health care providers. A large proportion of women are unaware of their higher risk status due to lack of adequate screening and risk assessment.^21,22 In addition to patient lack of awareness of their high-risk status, primary care physicians are also reluctant to prescribe chemopreventive agents due to a lack of comfort or familiarity with the risks and benefits.^23-26 The STAR2015, BCPT2005, IBIS2014, MAP3 2011, IBIS-I 2014, and IBIS II 2014 studies clearly demonstrate a 49 to 62% reduction in risk for women using chemoprevention such as selective estrogen receptor modulators or aromatase inhibitors, respectively.^27-32 Yet only 4 to 9% of high-risk women not enrolled in a clinical trial are using chemoprevention.^33-39

The possibility of developing breast cancer also may be increased because of a positive family history or being a member of a family in which there is a known susceptibility gene mutation.⁴⁰ Based on these risk factors, women may be eligible for tailored follow-up and genetic counseling.^41-44

Nationally, 7 to 10% of the civilian US population will experience posttraumatic stress disorder (PTSD).⁴⁵ The rates are remarkably higher for women veterans, with roughly 20% diagnosed with PTSD.^46,47 Anxiety and PTSD have been implicated in poor adherence to medical advice.^48,49

In 2014, a national VA multidisciplinary group focused on breast cancer prevention, detection, treatment, and research to address breast health in the growing population of women veterans. High-risk breast cancer screenings are not routinely carried out by the VA in primary care, women’s health, or oncology services. Furthermore, the recording of screening questionnaire results was not synchronized until a standard questionnaire was created and approved as a template by this group in the VA electronic medical record (EMR) in 2015.

Several prediction models can identify which women are at an increased risk of developing breast cancer. The most commonly used risk assessment model, the Gail breast cancer risk assessment tool (BCRAT), has been refined to include women of additional ethnicities (https://www.cancer.gov/bcrisktool).

This pilot project was launched to identify an effective manner to screen women veterans regarding their risk of developing breast cancer and refer them for chemoprevention education or genetic counseling as appropriate.

Methods

A high-risk breast cancer screening questionnaire based on the Gail BCRAT and including lifestyle questions was developed and included as a note template in the VA EMR. The James J. Peters VA Medical Center, Bronx, NY (JJPVAMC) and the Washington DC VA Medical Center (DCVAMC) ran a pilot study between 2015 and 2018 using this breast cancer screening questionnaire to collect data from women veterans. Quality Executive Committee and institutional review board approvals were granted respectively.

Eligibility criteria included women aged ≥ 35 years with no personal history of breast cancer. Most patients were self-referred, but participants also were recruited during VA Breast Cancer Awareness month events, health fairs, or at informational tables in the hospital lobbies. After completing the 20 multiple choice questionnaire with a study team member, either in person or over the phone, a 5-year and lifetime risk of invasive breast cancer was calculated using the Gail BCRAT. A woman is considered high risk and eligible for chemoprevention if her 5-year risk is > 1.66% or her lifetime risk is ≥ 20%. Eligibility for genetic counseling is based on the Breast Cancer Referral Screening Tool, which includes a personal or family history of breast or ovarian cancer and Jewish ancestry.

All patients were notified of their average or high risk status by a clinician. Those who were deemed to be average risk received a follow-up letter in the mail with instructions (eg, to follow-up with a yearly mammogram). Those who were deemed to be high risk for developing breast cancer were asked to come in for an appointment with the study principal investigator (a VA oncologist/breast cancer specialist) to discuss prevention options, further screening, or referrals to genetic counseling. Depending on a patient’s other health factors, a woman at high risk for developing breast cancer also may be a candidate for chemoprevention with tamoxifen, raloxifene, exemestane, anastrozole, or letrozole.

Data on the participant’s lifestyle, including exercise, diet, and smoking, were evaluated to determine whether these factors had an impact on risk status.

Results

The JJP and DC VAMCs screened 103 women veterans between 2015 and 2018. Four patients were excluded for nonveteran (spousal) status, leaving 99 women veterans with a mean age of 54 years. The most common self-reported races were Black (60%), non-Hispanic White (14%), and Hispanic or Latino (13%) (Table 1).

Women veterans in our study were nearly 3-times more likely than the general population were to receive a high-risk Gail Score/BCRAT (35% vs 13%, respectively).^50,51 Of this subset, 46% had breast biopsies, and 86% had a positive family history. Thirty-one percent of Black women in our study were high risk, while nationally, 8.2 to 13.3% of Black women aged 50 to 59 years are considered high risk.^50,51 Of the Black high-risk group with a high Gail/BCRAT score, 94% had a positive family history, and 33% had a history of breast biopsy (Table 2).

Of the 35 high-risk patients 26 (74%) patients accepted consultations for chemoprevention and 5 (19%) started chemoprevention. Of this high-risk group, 13 (37%) patients were referred for genetic counseling (Table 3).⁴⁴ The prevalence of PTSD was present in 31% of high-risk women and 29% of the cohort (Figure).The lifestyle questions indicated that, among all participants, 79% had an overweight or obese body mass index; 58% exercised weekly; 51% consumed alcohol; 14% were smokers; and 21% consumed 3 to 4 servings of fruits/vegetables daily.

Discussion

Breast cancer is the most common cancer in women.⁵² The number of women with breast cancer in the VHA has more than tripled from 1995 to 2012.¹ The lifetime risk of developing breast cancer in the general population is about 13%.⁵⁰ This rate can be affected by risk factors including age, hormone exposure, family history, radiation exposure, and lifestyle factors, such as weight and alcohol use.^6,52-56 In the United States, invasive breast cancer affects 1 in 8 women.^50,52,57

Our screened population showed nearly 3 times as many women veterans were at an increased risk for breast cancer when compared with historical averages in US women. This difference may be based on a high rate of prior breast biopsies or positive family history, although a provocative study using the Surveillance, Epidemiology, and End Results database showed military women to have higher rates of breast cancer as well.⁹ Historically, Blacks are vastly understudied in clinical research with only 5% representation on a national level.^5,58 The urban locations of both pilot sites (Washington, DC and Bronx, NY) allowed for the inclusion of minority patients in our study. We found that the rates of breast cancer in Black women veterans to be higher than seen nationally, possibly prompting further screening initiatives for this understudied population.

Our pilot study’s chemoprevention utilization (19%) was double the < 10% seen in the national population.^33-35 The presence of a knowledgeable breast health practitioner to recruit study participants and offer personalized counseling to women veterans is a likely factor in overcoming barriers to chemopreventive acceptance. These participants may have been motivated to seek care for their high-risk status given a strong family history and prior breast biopsies.

Interestingly, a 3-fold higher PTSD rate was seen in this pilot population (29%) when compared with PTSD rates in the general female population (7-10%) and still one-third higher than the general population of women veterans (20%).^45-47 Mental health, anxiety, and PTSD have been barriers to patients who sought treatment and have been implicated in poor adherence to medical advice.^48,49 Cancer screening can induce anxiety in patients, and it may be amplified in patients with PTSD. It was remarkable that although adherence with screening recommendations is decreased when PTSD is present, our patient population demonstrated a higher rate of screening adherence.

Women who are seen at the VA often use multiple clinical specialties, and their EMR can be accessed across VA medical centers nationwide. Therefore, identifying women veterans who meet screening criteria is easily attainable within the VA.

When comparing high-risk with average risk women, the lifestyle results (BMI, smoking history, exercise and consumption of fruits, vegetables and alcohol) were essentially the same. Lifestyle factors were similar to national population rates and were unlikely to impact risk levels.

Limitations

Study limitations included a high number of self-referrals and the large percentage of patients with a family history of breast cancer, making them more likely to seek screening. The higher-than-average risk of breast cancer may be driven by a high rate of breast biopsies and a strong family history. Lifestyle metrics could not be accurately compared to other national assessments of lifestyle factors due to the difference in data points that we used or the format of our questions.

Conclusions

As the number of women veterans increases and the incidence of breast cancer in women veterans rise, chemoprevention options should follow national guidelines. To our knowledge, this is the only oncology study with 60% Black women veterans. This study had a higher participation rate for Black women veterans than is typically seen in national research studies and shows the VA to be a germane source for further understanding of an understudied population that may benefit from increased screening for breast cancer.

A team-based, multidisciplinary model that meets the unique healthcare needs of women veterans results in a patient-centric delivery of care for assessing breast cancer risk status and prevention options. This model can be replicated nationally by directing primary care physicians and women’s health practitioners to a risk-assessment questionnaire and referring high-risk women for appropriate preventative care. Given that these results show chemoprevention adherence rates doubled those seen nationally, perhaps techniques used within this VA pilot study may be adapted to decrease breast cancer incidence nationally.

Since the rate of PTSD among women veterans is triple the national average, we would expect adherence rates to be lower in our patient cohort. However, the multidisciplinary approach we used in this study (eg, 1:1 consultation with oncologist; genetic counseling referrals; mental health support available), may have improved adherence rates. Perhaps the high rates of PTSD seen in the VA patient population can be a useful way to explore patient adherence rates in those with mental illness and medical conditions.

Future research with a larger cohort may lead to greater insight into the correlation between PTSD and adherence to treatment. Exploring the connection between breast cancer, epigenetics, and specific military service-related exposures could be an area of analysis among this veteran population exhibiting increased breast cancer rates. VAMCs are situated in rural, suburban, and urban locations across the United States and offers a diverse socioeconomic and ethnic patient population for inclusion in clinical investigations. Women veterans make up a small subpopulation of women in the United States, but it is worth considering VA patients as an untapped resource for research collaboration.

Acknowledgements

The authors thank Steven Sanchez and Marissa Vallette, PhD, Breast Health Research Group. This research project was approved by the James J. Peters VA Medical Center Quality Executive Committee and the Washington, DC VA Medical Center Institutional Review Board. This work was supported by the US Department of Veterans Affairs. This work did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author disclosures

The authors report no actual or potential conflicts of interest with regard to this article.

Disclaimer

The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the US Government, or any of its agencies.

Hyperfractionated radiotherapy with concomitant chemotherapy (HFCRT) proved to be the most effective treatment for locally advanced squamous cell head and neck cancer in a network meta-analysis of nearly 30,000 patients.

Of the 16 treatment options compared and ranked, HFCRT topped the list for overall survival, event-free survival, locoregional control, and cancer-specific death.

The results also suggested that taxane-based induction chemotherapy followed by locoregional therapy, especially with concomitant chemotherapy, “is another good option in selected patients with a good performance status and minor comorbidities,” according to investigator Claire Petit, MD, PhD, of Centre hospitalier de l’Université de Montréal in Canada, and colleagues.

The investigators concluded that further intensifying chemoradiotherapy with these approaches “could improve outcomes over chemoradiotherapy.”

The findings, published in The Lancet Oncology, “could help to guide clinical decision-making in locally advanced head and neck cancer with a high risk of locoregional failure, especially human papillomavirus–negative tumours,” the authors wrote.

However, Jared Weiss, MD, of the University of North Carolina, Chapel Hill, cautioned that this “study is an individual patient data network meta-analysis, not a randomized controlled trial. As the authors note, it can help frame existing data but cannot define standard of care.”

Still, “it does support the efficacy of two commonly considered intensification strategies for high-risk patients – hyperfractionation of the radiation and the addition of preceding induction chemotherapy. Both of these intensifications substantially increase the time commitment from the patient, and many patients find this unacceptable. But, for select patients, hyperfractionation and induction chemotherapy have a role and may be considered for patients at high risk of treatment failure,” Dr. Weiss said.
 

Study details

The goal of this study was to find the best option among many chemoradiation approaches for head and neck cancer. The investigators pulled together and reanalyzed individual patient data from recently updated meta-analyses.

The current analysis included 115 randomized trials that enrolled patients between Jan. 1, 1980, and April 30, 2012. This encompassed 28,978 patients with 20,579 progression events and 19,253 deaths over a median follow-up of 6.6 years.

Treatments were ranked by P score, with higher scores indicating more effective therapies.

For overall survival, HFCRT had a P score of 97%. The hazard ratio (HR) was 0.63 for the comparison with locoregional therapy alone (surgery, radiotherapy, or both). The absolute benefit at 5 years, compared with locoregional therapy alone, was 16.7% with HFCRT.

The P score for the second most effective treatment option – induction chemotherapy with taxane, cisplatin, and fluorouracil followed by locoregional therapy (IC_TaxPF-LRT) – was 89%, with a hazard ratio of 0.69 and an absolute benefit at 5 years of 13.4%, versus locoregional therapy.

The HR of HFCRT versus the accepted standard of care worldwide – locoregional therapy with concomitant platinum-based chemotherapy and radiotherapy (CLRT_P) – was 0.82 in favor of HFCRT for overall survival and 0.80 for event-free survival.

For overall survival, the P score for CLRT_P was 78%. Three other treatment options had a better P score than CLRT_P but not a better HR (0.77). These included IC_TaxPF-LRT (P score, 89%; HR, 0.69), accelerated radiotherapy with concomitant chemotherapy (P score, 82%; HR, 0.75), and IC_TaxPF-LRT followed by CLRTP (P score, 80%; HR, 0.75).

In the end, the investigators found “superiority of HFCRT over other treatments,” but noted it can be difficult to implement HFCRT in the era of intensity-modulated radiotherapy for head and neck cancer. Even so, HFCRT “could be considered as an option for tertiary centres with a high throughput of patients,” the investigators wrote.

The team noted that one of the limitations of this study is that cancer care has improved substantially since the very earliest trials that were included in the analysis. This introduces potential confounders, including that patients in older trials might have been understaged so that even an experimental local therapy would have been less effective.

Toxicity wasn’t part of this analysis but must be taken into account when making therapeutic decisions, “especially because HFCRT and induction chemotherapy based on taxane, cisplatin, and fluorouracil are known to be toxic,” the investigators wrote.

This research was funded by the French Institut National du Cancer, French Ligue Nationale Contre le Cancer, and Fondation ARC. The authors disclosed relationships with numerous companies, including AbbVie, Lilly, and Merck. Dr. Weiss did not report any relevant conflicts.

aotto@mdedge.com

Hyperfractionated radiotherapy with concomitant chemotherapy (HFCRT) proved to be the most effective treatment for locally advanced squamous cell head and neck cancer in a network meta-analysis of nearly 30,000 patients.

Of the 16 treatment options compared and ranked, HFCRT topped the list for overall survival, event-free survival, locoregional control, and cancer-specific death.

The results also suggested that taxane-based induction chemotherapy followed by locoregional therapy, especially with concomitant chemotherapy, “is another good option in selected patients with a good performance status and minor comorbidities,” according to investigator Claire Petit, MD, PhD, of Centre hospitalier de l’Université de Montréal in Canada, and colleagues.

The investigators concluded that further intensifying chemoradiotherapy with these approaches “could improve outcomes over chemoradiotherapy.”

The findings, published in The Lancet Oncology, “could help to guide clinical decision-making in locally advanced head and neck cancer with a high risk of locoregional failure, especially human papillomavirus–negative tumours,” the authors wrote.

However, Jared Weiss, MD, of the University of North Carolina, Chapel Hill, cautioned that this “study is an individual patient data network meta-analysis, not a randomized controlled trial. As the authors note, it can help frame existing data but cannot define standard of care.”

Still, “it does support the efficacy of two commonly considered intensification strategies for high-risk patients – hyperfractionation of the radiation and the addition of preceding induction chemotherapy. Both of these intensifications substantially increase the time commitment from the patient, and many patients find this unacceptable. But, for select patients, hyperfractionation and induction chemotherapy have a role and may be considered for patients at high risk of treatment failure,” Dr. Weiss said.
 

Study details

The goal of this study was to find the best option among many chemoradiation approaches for head and neck cancer. The investigators pulled together and reanalyzed individual patient data from recently updated meta-analyses.

The current analysis included 115 randomized trials that enrolled patients between Jan. 1, 1980, and April 30, 2012. This encompassed 28,978 patients with 20,579 progression events and 19,253 deaths over a median follow-up of 6.6 years.

Treatments were ranked by P score, with higher scores indicating more effective therapies.

For overall survival, HFCRT had a P score of 97%. The hazard ratio (HR) was 0.63 for the comparison with locoregional therapy alone (surgery, radiotherapy, or both). The absolute benefit at 5 years, compared with locoregional therapy alone, was 16.7% with HFCRT.

The P score for the second most effective treatment option – induction chemotherapy with taxane, cisplatin, and fluorouracil followed by locoregional therapy (IC_TaxPF-LRT) – was 89%, with a hazard ratio of 0.69 and an absolute benefit at 5 years of 13.4%, versus locoregional therapy.

The HR of HFCRT versus the accepted standard of care worldwide – locoregional therapy with concomitant platinum-based chemotherapy and radiotherapy (CLRT_P) – was 0.82 in favor of HFCRT for overall survival and 0.80 for event-free survival.

For overall survival, the P score for CLRT_P was 78%. Three other treatment options had a better P score than CLRT_P but not a better HR (0.77). These included IC_TaxPF-LRT (P score, 89%; HR, 0.69), accelerated radiotherapy with concomitant chemotherapy (P score, 82%; HR, 0.75), and IC_TaxPF-LRT followed by CLRTP (P score, 80%; HR, 0.75).

In the end, the investigators found “superiority of HFCRT over other treatments,” but noted it can be difficult to implement HFCRT in the era of intensity-modulated radiotherapy for head and neck cancer. Even so, HFCRT “could be considered as an option for tertiary centres with a high throughput of patients,” the investigators wrote.

The team noted that one of the limitations of this study is that cancer care has improved substantially since the very earliest trials that were included in the analysis. This introduces potential confounders, including that patients in older trials might have been understaged so that even an experimental local therapy would have been less effective.

Toxicity wasn’t part of this analysis but must be taken into account when making therapeutic decisions, “especially because HFCRT and induction chemotherapy based on taxane, cisplatin, and fluorouracil are known to be toxic,” the investigators wrote.

This research was funded by the French Institut National du Cancer, French Ligue Nationale Contre le Cancer, and Fondation ARC. The authors disclosed relationships with numerous companies, including AbbVie, Lilly, and Merck. Dr. Weiss did not report any relevant conflicts.

aotto@mdedge.com

Hyperfractionated radiotherapy with concomitant chemotherapy (HFCRT) proved to be the most effective treatment for locally advanced squamous cell head and neck cancer in a network meta-analysis of nearly 30,000 patients.

Of the 16 treatment options compared and ranked, HFCRT topped the list for overall survival, event-free survival, locoregional control, and cancer-specific death.

The results also suggested that taxane-based induction chemotherapy followed by locoregional therapy, especially with concomitant chemotherapy, “is another good option in selected patients with a good performance status and minor comorbidities,” according to investigator Claire Petit, MD, PhD, of Centre hospitalier de l’Université de Montréal in Canada, and colleagues.

The investigators concluded that further intensifying chemoradiotherapy with these approaches “could improve outcomes over chemoradiotherapy.”

The findings, published in The Lancet Oncology, “could help to guide clinical decision-making in locally advanced head and neck cancer with a high risk of locoregional failure, especially human papillomavirus–negative tumours,” the authors wrote.

However, Jared Weiss, MD, of the University of North Carolina, Chapel Hill, cautioned that this “study is an individual patient data network meta-analysis, not a randomized controlled trial. As the authors note, it can help frame existing data but cannot define standard of care.”

Still, “it does support the efficacy of two commonly considered intensification strategies for high-risk patients – hyperfractionation of the radiation and the addition of preceding induction chemotherapy. Both of these intensifications substantially increase the time commitment from the patient, and many patients find this unacceptable. But, for select patients, hyperfractionation and induction chemotherapy have a role and may be considered for patients at high risk of treatment failure,” Dr. Weiss said.
 

Study details

The goal of this study was to find the best option among many chemoradiation approaches for head and neck cancer. The investigators pulled together and reanalyzed individual patient data from recently updated meta-analyses.

The current analysis included 115 randomized trials that enrolled patients between Jan. 1, 1980, and April 30, 2012. This encompassed 28,978 patients with 20,579 progression events and 19,253 deaths over a median follow-up of 6.6 years.

Treatments were ranked by P score, with higher scores indicating more effective therapies.

For overall survival, HFCRT had a P score of 97%. The hazard ratio (HR) was 0.63 for the comparison with locoregional therapy alone (surgery, radiotherapy, or both). The absolute benefit at 5 years, compared with locoregional therapy alone, was 16.7% with HFCRT.

The P score for the second most effective treatment option – induction chemotherapy with taxane, cisplatin, and fluorouracil followed by locoregional therapy (IC_TaxPF-LRT) – was 89%, with a hazard ratio of 0.69 and an absolute benefit at 5 years of 13.4%, versus locoregional therapy.

The HR of HFCRT versus the accepted standard of care worldwide – locoregional therapy with concomitant platinum-based chemotherapy and radiotherapy (CLRT_P) – was 0.82 in favor of HFCRT for overall survival and 0.80 for event-free survival.

For overall survival, the P score for CLRT_P was 78%. Three other treatment options had a better P score than CLRT_P but not a better HR (0.77). These included IC_TaxPF-LRT (P score, 89%; HR, 0.69), accelerated radiotherapy with concomitant chemotherapy (P score, 82%; HR, 0.75), and IC_TaxPF-LRT followed by CLRTP (P score, 80%; HR, 0.75).

In the end, the investigators found “superiority of HFCRT over other treatments,” but noted it can be difficult to implement HFCRT in the era of intensity-modulated radiotherapy for head and neck cancer. Even so, HFCRT “could be considered as an option for tertiary centres with a high throughput of patients,” the investigators wrote.

The team noted that one of the limitations of this study is that cancer care has improved substantially since the very earliest trials that were included in the analysis. This introduces potential confounders, including that patients in older trials might have been understaged so that even an experimental local therapy would have been less effective.

Toxicity wasn’t part of this analysis but must be taken into account when making therapeutic decisions, “especially because HFCRT and induction chemotherapy based on taxane, cisplatin, and fluorouracil are known to be toxic,” the investigators wrote.

This research was funded by the French Institut National du Cancer, French Ligue Nationale Contre le Cancer, and Fondation ARC. The authors disclosed relationships with numerous companies, including AbbVie, Lilly, and Merck. Dr. Weiss did not report any relevant conflicts.

aotto@mdedge.com

The obesity epidemic in the United States may be slowing improvements in cancer mortality, according to a new analysis of over 50 million cancer and heart disease deaths.

“By integrating 20 years of cancer mortality data, we demonstrated that trends in obesity-associated cancer mortality showed signs of recent deceleration, consistent with recent findings for heart disease mortality,” Christy L. Avery, PhD, and associates wrote in JAMA Network Open.

Improvements in mortality related to heart disease slowed after 2011, a phenomenon that has been associated with rising obesity rates. The age-adjusted mortality rate (AAMR) declined at an average of 3.8 deaths per 100,000 persons from 1999 to 2011 but only 0.7 deaths per 100,000 from 2011 to 2018, based on data from the Centers for Disease Control and Prevention’s Wide-Ranging Online Data for Epidemiologic Research (WONDER).

To understand trends in cancer mortality and their possible connection with obesity, data for 1999-2018 from the WONDER database were divided into obesity-associated and non–obesity-associated categories and compared with heart disease mortality, they explained. The database included more than 50 million deaths that matched inclusion criteria.

The analysis showed there was difference between obesity-associated and non–obesity-associated cancers that was obscured when all cancer deaths were considered together. The average annual change in AAMR for obesity-associated cancers slowed from –1.19 deaths per 100,000 in 1999-2011 to –0.83 in 2011-2018, Dr. Avery and associates reported.

For non–obesity-associated cancers, the annual change in AAMR increased from –1.62 per 100,000 for 1999-2011 to –2.29 for 2011-2018, following the trend for all cancers: –1.48 per 100,000 during 1999-2011 and –1.77 in 2011-2018, they said.

“The largest mortality decreases were observed for melanoma of the skin and lung cancer, two cancers not associated with obesity. For obesity-associated cancers, stable or increasing mortality rates have been observed for liver and pancreatic cancer among both men and women as well as for uterine cancer among women,” the investigators wrote.

Demographically, however, the slowing improvement in mortality for obesity-associated cancers did not follow the trend for heart disease. The deceleration for cancer was more pronounced for women and for non-Hispanic Whites and not seen at all in non-Hispanic Asian/Pacific Islander individuals. “For heart disease, evidence of a deceleration was consistent across sex, race, and ethnicity,” they said.

There are “longstanding disparities in obesity” among various populations in the United States, and the recent trend of obesity occurring earlier in life may be having an effect. “Whether the findings of decelerating mortality rates potentially signal a changing profile of cancer and heart disease mortality as the consequences of the obesity epidemic are realized remains to be seen,” they concluded.

The investigators reported receiving grants from the National Institutes of Health during the conduct of the study, but no other disclosures were reported.

rfranki@mdedge.com

The obesity epidemic in the United States may be slowing improvements in cancer mortality, according to a new analysis of over 50 million cancer and heart disease deaths.

“By integrating 20 years of cancer mortality data, we demonstrated that trends in obesity-associated cancer mortality showed signs of recent deceleration, consistent with recent findings for heart disease mortality,” Christy L. Avery, PhD, and associates wrote in JAMA Network Open.

Improvements in mortality related to heart disease slowed after 2011, a phenomenon that has been associated with rising obesity rates. The age-adjusted mortality rate (AAMR) declined at an average of 3.8 deaths per 100,000 persons from 1999 to 2011 but only 0.7 deaths per 100,000 from 2011 to 2018, based on data from the Centers for Disease Control and Prevention’s Wide-Ranging Online Data for Epidemiologic Research (WONDER).

To understand trends in cancer mortality and their possible connection with obesity, data for 1999-2018 from the WONDER database were divided into obesity-associated and non–obesity-associated categories and compared with heart disease mortality, they explained. The database included more than 50 million deaths that matched inclusion criteria.

The analysis showed there was difference between obesity-associated and non–obesity-associated cancers that was obscured when all cancer deaths were considered together. The average annual change in AAMR for obesity-associated cancers slowed from –1.19 deaths per 100,000 in 1999-2011 to –0.83 in 2011-2018, Dr. Avery and associates reported.

For non–obesity-associated cancers, the annual change in AAMR increased from –1.62 per 100,000 for 1999-2011 to –2.29 for 2011-2018, following the trend for all cancers: –1.48 per 100,000 during 1999-2011 and –1.77 in 2011-2018, they said.

“The largest mortality decreases were observed for melanoma of the skin and lung cancer, two cancers not associated with obesity. For obesity-associated cancers, stable or increasing mortality rates have been observed for liver and pancreatic cancer among both men and women as well as for uterine cancer among women,” the investigators wrote.

Demographically, however, the slowing improvement in mortality for obesity-associated cancers did not follow the trend for heart disease. The deceleration for cancer was more pronounced for women and for non-Hispanic Whites and not seen at all in non-Hispanic Asian/Pacific Islander individuals. “For heart disease, evidence of a deceleration was consistent across sex, race, and ethnicity,” they said.

There are “longstanding disparities in obesity” among various populations in the United States, and the recent trend of obesity occurring earlier in life may be having an effect. “Whether the findings of decelerating mortality rates potentially signal a changing profile of cancer and heart disease mortality as the consequences of the obesity epidemic are realized remains to be seen,” they concluded.

The investigators reported receiving grants from the National Institutes of Health during the conduct of the study, but no other disclosures were reported.

rfranki@mdedge.com

The obesity epidemic in the United States may be slowing improvements in cancer mortality, according to a new analysis of over 50 million cancer and heart disease deaths.

“By integrating 20 years of cancer mortality data, we demonstrated that trends in obesity-associated cancer mortality showed signs of recent deceleration, consistent with recent findings for heart disease mortality,” Christy L. Avery, PhD, and associates wrote in JAMA Network Open.

Improvements in mortality related to heart disease slowed after 2011, a phenomenon that has been associated with rising obesity rates. The age-adjusted mortality rate (AAMR) declined at an average of 3.8 deaths per 100,000 persons from 1999 to 2011 but only 0.7 deaths per 100,000 from 2011 to 2018, based on data from the Centers for Disease Control and Prevention’s Wide-Ranging Online Data for Epidemiologic Research (WONDER).

To understand trends in cancer mortality and their possible connection with obesity, data for 1999-2018 from the WONDER database were divided into obesity-associated and non–obesity-associated categories and compared with heart disease mortality, they explained. The database included more than 50 million deaths that matched inclusion criteria.

The analysis showed there was difference between obesity-associated and non–obesity-associated cancers that was obscured when all cancer deaths were considered together. The average annual change in AAMR for obesity-associated cancers slowed from –1.19 deaths per 100,000 in 1999-2011 to –0.83 in 2011-2018, Dr. Avery and associates reported.

For non–obesity-associated cancers, the annual change in AAMR increased from –1.62 per 100,000 for 1999-2011 to –2.29 for 2011-2018, following the trend for all cancers: –1.48 per 100,000 during 1999-2011 and –1.77 in 2011-2018, they said.

“The largest mortality decreases were observed for melanoma of the skin and lung cancer, two cancers not associated with obesity. For obesity-associated cancers, stable or increasing mortality rates have been observed for liver and pancreatic cancer among both men and women as well as for uterine cancer among women,” the investigators wrote.

Demographically, however, the slowing improvement in mortality for obesity-associated cancers did not follow the trend for heart disease. The deceleration for cancer was more pronounced for women and for non-Hispanic Whites and not seen at all in non-Hispanic Asian/Pacific Islander individuals. “For heart disease, evidence of a deceleration was consistent across sex, race, and ethnicity,” they said.

There are “longstanding disparities in obesity” among various populations in the United States, and the recent trend of obesity occurring earlier in life may be having an effect. “Whether the findings of decelerating mortality rates potentially signal a changing profile of cancer and heart disease mortality as the consequences of the obesity epidemic are realized remains to be seen,” they concluded.

The investigators reported receiving grants from the National Institutes of Health during the conduct of the study, but no other disclosures were reported.

rfranki@mdedge.com

The Food and Drug Administration granted accelerated approval to pembrolizumab (Keytruda) in combination with other agents for the first-line treatment of patients with locally advanced unresectable or metastatic HER2-positive gastric or gastroesophageal junction (GEJ) adenocarcinoma.

The checkpoint inhibitor is to be used in conjunction with trastuzumab (Herceptin) and fluoropyrimidine- and platinum-containing chemotherapy.

Previously, pembrolizumab was approved as a single agent for these cancers for patients whose tumors express PD-L1 and whose disease progressed after two or more lines of treatment that included chemotherapy and HER2-targeted therapy.

The new approval comes about a year after the FDA’s first-ever approval of a checkpoint inhibitor (nivolumab [Opdivo] in combination with chemotherapies) for the frontline treatment of gastric cancers, as reported by this news organization.

The new approval is based on interim data from the first 264 patients of the ongoing KEYNOTE-811 trial, a randomized, double-blind, placebo-controlled trial involving patients with HER2-positive advanced gastric or GEJ adenocarcinoma who had not previously received systemic therapy for their metastatic disease.

Patients were randomly assigned (1:1) to receive either pembrolizumab at 200 mg or placebo every 3 weeks in combination with trastuzumab and either fluorouracil plus cisplatin or capecitabine plus oxaliplatin.

The overall response rate, which is the primary outcome, was 74% in the pembrolizumab arm and 52% in the placebo arm (one-sided P < .0001).

The median duration of response was 10.6 months in the pembrolizumab arm and 9.5 months in the placebo arm.

The adverse-reaction profile for patients receiving pembrolizumab is consistent with the known pembrolizumab safety profile, the FDA said in a statement.

The recommended pembrolizumab dose in this setting is 200 mg every 3 weeks or 400 mg every 6 weeks.

The FDA’s review, which was granted priority status, used the Real-Time Oncology Review pilot program, which allows streamlined data submission prior to the filing of the full clinical application, and Assessment Aid, a voluntary submission that facilitates the FDA’s assessment.

A version of this article first appeared on Medscape.com.

The Food and Drug Administration granted accelerated approval to pembrolizumab (Keytruda) in combination with other agents for the first-line treatment of patients with locally advanced unresectable or metastatic HER2-positive gastric or gastroesophageal junction (GEJ) adenocarcinoma.

The checkpoint inhibitor is to be used in conjunction with trastuzumab (Herceptin) and fluoropyrimidine- and platinum-containing chemotherapy.

Previously, pembrolizumab was approved as a single agent for these cancers for patients whose tumors express PD-L1 and whose disease progressed after two or more lines of treatment that included chemotherapy and HER2-targeted therapy.

The new approval comes about a year after the FDA’s first-ever approval of a checkpoint inhibitor (nivolumab [Opdivo] in combination with chemotherapies) for the frontline treatment of gastric cancers, as reported by this news organization.

The new approval is based on interim data from the first 264 patients of the ongoing KEYNOTE-811 trial, a randomized, double-blind, placebo-controlled trial involving patients with HER2-positive advanced gastric or GEJ adenocarcinoma who had not previously received systemic therapy for their metastatic disease.

Patients were randomly assigned (1:1) to receive either pembrolizumab at 200 mg or placebo every 3 weeks in combination with trastuzumab and either fluorouracil plus cisplatin or capecitabine plus oxaliplatin.

The overall response rate, which is the primary outcome, was 74% in the pembrolizumab arm and 52% in the placebo arm (one-sided P < .0001).

The median duration of response was 10.6 months in the pembrolizumab arm and 9.5 months in the placebo arm.

The adverse-reaction profile for patients receiving pembrolizumab is consistent with the known pembrolizumab safety profile, the FDA said in a statement.

The recommended pembrolizumab dose in this setting is 200 mg every 3 weeks or 400 mg every 6 weeks.

The FDA’s review, which was granted priority status, used the Real-Time Oncology Review pilot program, which allows streamlined data submission prior to the filing of the full clinical application, and Assessment Aid, a voluntary submission that facilitates the FDA’s assessment.

A version of this article first appeared on Medscape.com.

The Food and Drug Administration granted accelerated approval to pembrolizumab (Keytruda) in combination with other agents for the first-line treatment of patients with locally advanced unresectable or metastatic HER2-positive gastric or gastroesophageal junction (GEJ) adenocarcinoma.

The checkpoint inhibitor is to be used in conjunction with trastuzumab (Herceptin) and fluoropyrimidine- and platinum-containing chemotherapy.

Previously, pembrolizumab was approved as a single agent for these cancers for patients whose tumors express PD-L1 and whose disease progressed after two or more lines of treatment that included chemotherapy and HER2-targeted therapy.

The new approval comes about a year after the FDA’s first-ever approval of a checkpoint inhibitor (nivolumab [Opdivo] in combination with chemotherapies) for the frontline treatment of gastric cancers, as reported by this news organization.

The new approval is based on interim data from the first 264 patients of the ongoing KEYNOTE-811 trial, a randomized, double-blind, placebo-controlled trial involving patients with HER2-positive advanced gastric or GEJ adenocarcinoma who had not previously received systemic therapy for their metastatic disease.

Patients were randomly assigned (1:1) to receive either pembrolizumab at 200 mg or placebo every 3 weeks in combination with trastuzumab and either fluorouracil plus cisplatin or capecitabine plus oxaliplatin.

The overall response rate, which is the primary outcome, was 74% in the pembrolizumab arm and 52% in the placebo arm (one-sided P < .0001).

The median duration of response was 10.6 months in the pembrolizumab arm and 9.5 months in the placebo arm.

The adverse-reaction profile for patients receiving pembrolizumab is consistent with the known pembrolizumab safety profile, the FDA said in a statement.

The recommended pembrolizumab dose in this setting is 200 mg every 3 weeks or 400 mg every 6 weeks.

The FDA’s review, which was granted priority status, used the Real-Time Oncology Review pilot program, which allows streamlined data submission prior to the filing of the full clinical application, and Assessment Aid, a voluntary submission that facilitates the FDA’s assessment.

A version of this article first appeared on Medscape.com.

The use of neoadjuvant endocrine therapy (NET) increased significantly during the first 8 months of the COVID-19 pandemic for women with estrogen receptor–positive (ER+) breast cancer. These patients would normally undergo surgery first, but because of operating room restrictions, those surgeries were delayed because of the pandemic, according to a new study.
 

“We hypothesized that by offering a nontoxic therapy, we would be able to ‘hold over’ patients until such time when personal protective equipment supplies were renewed and we could get into the operating room,” lead author Lee Wilke, MD, professor of surgery, University of Wisconsin, Madison, said in an interview.

“And while a small number of women with ER+ tumors get NET anyway, we found over one-third of patients with ER+ breast cancer were treated with NET due to COVID-19 during the first 8 months of last year,” she said.

“One year later, 31% of the same patient population is still getting NET,” she added.

The study was presented during the online annual meeting of the American Society of Breast Surgeons (ASBrS).
 

COVID-specific registry

Dr. Wilke believes that this study presents an accurate snapshot of changes in treatment caused by the pandemic.

She and her colleagues compared data collected in the ASBrS Mastery Program registry to data collected in an embedded but separate COVID-19 segment. The data were for the period from March 1 to Oct. 28, 2020.

Almost three-quarters of the surgeons who entered patients into the COVID-19 segment were from urban areas; 95% reported stopping mammographic screening during part of this period.

The preliminary analysis focused on data collected from 2,476 patients in the COVID-19 segment and 2,303 patients within the Mastery registry.

For patients with ER+/HER2- breast cancer, NET was described as a usual approach in 6.5% of patients in the COVID-19 registry. In the Mastery registry, 7.8% of patients received NET.

Compared with surgery first/usual practice, which served as the reference, older patients were more likely to receive NET first because of the COVID-19 pandemic than younger patients, and they were more likely to receive NET first if they lived in the Northeast or the Southeast compared to other regions of the United States. Dr. Wilke pointed out that the Northeast and the Southeast were hardest hit by COVID-19 early on in the pandemic.

Genomic testing was carried out in a small subgroup of patients; 24% of those patients underwent testing on the core biopsy specimen because of COVID-19, the investigators noted. Genomic testing on a core biopsy specimen helps determine whether it’s feasible to forgo chemotherapy and use NET instead or whether the patient should proceed directly to surgery. The authors noted that almost 11% of patients required a change in the usual surgical approach because of COVID-19. Such changes were made primarily to avoid hospitalizations during the early phase of the pandemic for patients who were to undergo mastectomy or reconstruction.

“Patients who needed standard approaches still got them,” Dr. Wilke emphasized in a statement. For example, women with aggressive triple-negative and HER2+ tumors were treated with neoadjuvant chemotherapy, she added. “However, NET is a very good approach for a moderate subset of patients, and we think we will see it being used more often in the U.S. now,” Dr. Wilke observed.

“But especially early during the pandemic, these revised treatments were necessary because access to hospital ORs was limited or unavailable, so our algorithmic-based treatment guidelines allowed us to offer high-quality, evidence-based care fine-tuned for a patient’s specific cancer profile,” she affirmed.

Dr. Wilke has disclosed no relevant financial relationships.

A version of this article first appeared on Medscape.com.

The use of neoadjuvant endocrine therapy (NET) increased significantly during the first 8 months of the COVID-19 pandemic for women with estrogen receptor–positive (ER+) breast cancer. These patients would normally undergo surgery first, but because of operating room restrictions, those surgeries were delayed because of the pandemic, according to a new study.
 

“We hypothesized that by offering a nontoxic therapy, we would be able to ‘hold over’ patients until such time when personal protective equipment supplies were renewed and we could get into the operating room,” lead author Lee Wilke, MD, professor of surgery, University of Wisconsin, Madison, said in an interview.

“And while a small number of women with ER+ tumors get NET anyway, we found over one-third of patients with ER+ breast cancer were treated with NET due to COVID-19 during the first 8 months of last year,” she said.

“One year later, 31% of the same patient population is still getting NET,” she added.

The study was presented during the online annual meeting of the American Society of Breast Surgeons (ASBrS).
 

COVID-specific registry

Dr. Wilke believes that this study presents an accurate snapshot of changes in treatment caused by the pandemic.

She and her colleagues compared data collected in the ASBrS Mastery Program registry to data collected in an embedded but separate COVID-19 segment. The data were for the period from March 1 to Oct. 28, 2020.

Almost three-quarters of the surgeons who entered patients into the COVID-19 segment were from urban areas; 95% reported stopping mammographic screening during part of this period.

The preliminary analysis focused on data collected from 2,476 patients in the COVID-19 segment and 2,303 patients within the Mastery registry.

For patients with ER+/HER2- breast cancer, NET was described as a usual approach in 6.5% of patients in the COVID-19 registry. In the Mastery registry, 7.8% of patients received NET.

Compared with surgery first/usual practice, which served as the reference, older patients were more likely to receive NET first because of the COVID-19 pandemic than younger patients, and they were more likely to receive NET first if they lived in the Northeast or the Southeast compared to other regions of the United States. Dr. Wilke pointed out that the Northeast and the Southeast were hardest hit by COVID-19 early on in the pandemic.

Genomic testing was carried out in a small subgroup of patients; 24% of those patients underwent testing on the core biopsy specimen because of COVID-19, the investigators noted. Genomic testing on a core biopsy specimen helps determine whether it’s feasible to forgo chemotherapy and use NET instead or whether the patient should proceed directly to surgery. The authors noted that almost 11% of patients required a change in the usual surgical approach because of COVID-19. Such changes were made primarily to avoid hospitalizations during the early phase of the pandemic for patients who were to undergo mastectomy or reconstruction.

“Patients who needed standard approaches still got them,” Dr. Wilke emphasized in a statement. For example, women with aggressive triple-negative and HER2+ tumors were treated with neoadjuvant chemotherapy, she added. “However, NET is a very good approach for a moderate subset of patients, and we think we will see it being used more often in the U.S. now,” Dr. Wilke observed.

“But especially early during the pandemic, these revised treatments were necessary because access to hospital ORs was limited or unavailable, so our algorithmic-based treatment guidelines allowed us to offer high-quality, evidence-based care fine-tuned for a patient’s specific cancer profile,” she affirmed.

Dr. Wilke has disclosed no relevant financial relationships.

A version of this article first appeared on Medscape.com.

The use of neoadjuvant endocrine therapy (NET) increased significantly during the first 8 months of the COVID-19 pandemic for women with estrogen receptor–positive (ER+) breast cancer. These patients would normally undergo surgery first, but because of operating room restrictions, those surgeries were delayed because of the pandemic, according to a new study.
 

“We hypothesized that by offering a nontoxic therapy, we would be able to ‘hold over’ patients until such time when personal protective equipment supplies were renewed and we could get into the operating room,” lead author Lee Wilke, MD, professor of surgery, University of Wisconsin, Madison, said in an interview.

“And while a small number of women with ER+ tumors get NET anyway, we found over one-third of patients with ER+ breast cancer were treated with NET due to COVID-19 during the first 8 months of last year,” she said.

“One year later, 31% of the same patient population is still getting NET,” she added.

The study was presented during the online annual meeting of the American Society of Breast Surgeons (ASBrS).
 

COVID-specific registry

Dr. Wilke believes that this study presents an accurate snapshot of changes in treatment caused by the pandemic.

She and her colleagues compared data collected in the ASBrS Mastery Program registry to data collected in an embedded but separate COVID-19 segment. The data were for the period from March 1 to Oct. 28, 2020.

Almost three-quarters of the surgeons who entered patients into the COVID-19 segment were from urban areas; 95% reported stopping mammographic screening during part of this period.

The preliminary analysis focused on data collected from 2,476 patients in the COVID-19 segment and 2,303 patients within the Mastery registry.

For patients with ER+/HER2- breast cancer, NET was described as a usual approach in 6.5% of patients in the COVID-19 registry. In the Mastery registry, 7.8% of patients received NET.

Compared with surgery first/usual practice, which served as the reference, older patients were more likely to receive NET first because of the COVID-19 pandemic than younger patients, and they were more likely to receive NET first if they lived in the Northeast or the Southeast compared to other regions of the United States. Dr. Wilke pointed out that the Northeast and the Southeast were hardest hit by COVID-19 early on in the pandemic.

Genomic testing was carried out in a small subgroup of patients; 24% of those patients underwent testing on the core biopsy specimen because of COVID-19, the investigators noted. Genomic testing on a core biopsy specimen helps determine whether it’s feasible to forgo chemotherapy and use NET instead or whether the patient should proceed directly to surgery. The authors noted that almost 11% of patients required a change in the usual surgical approach because of COVID-19. Such changes were made primarily to avoid hospitalizations during the early phase of the pandemic for patients who were to undergo mastectomy or reconstruction.

“Patients who needed standard approaches still got them,” Dr. Wilke emphasized in a statement. For example, women with aggressive triple-negative and HER2+ tumors were treated with neoadjuvant chemotherapy, she added. “However, NET is a very good approach for a moderate subset of patients, and we think we will see it being used more often in the U.S. now,” Dr. Wilke observed.

“But especially early during the pandemic, these revised treatments were necessary because access to hospital ORs was limited or unavailable, so our algorithmic-based treatment guidelines allowed us to offer high-quality, evidence-based care fine-tuned for a patient’s specific cancer profile,” she affirmed.

Dr. Wilke has disclosed no relevant financial relationships.

A version of this article first appeared on Medscape.com.

Women had about a twofold higher risk than that of men of developing dermatologic adverse events while taking immune checkpoint inhibitors for metastatic melanoma in a review of 235 patients at Dana Farber Cancer Center, Boston.
 

Overall, 62.4% of the 93 women in the review and 48.6% of the 142 men experienced confirmed skin reactions, for an odds ratio (OR) of 2.11 for women compared with men (P = .01).

“Clinicians should consider these results in counseling female patients regarding an elevated risk of dermatologic adverse events” when taking checkpoint inhibitors, said investigators led by Harvard University medical student Jordan Said, who presented the results at the American Academy of Dermatology Virtual Meeting Experience.

Autoimmune-like adverse events are common with checkpoint inhibitors. Dermatologic side effects occur in about half of people receiving monotherapy and more than that among patients receiving combination therapy.

Skin reactions can include psoriasiform dermatitis, lichenoid reactions, vitiligo, and bullous pemphigoid and may require hospitalization and prolonged steroid treatment.

Not much is known about risk factors for these reactions. A higher incidence among women has been previously reported. A 2019 study found a higher risk for pneumonitis and endocrinopathy, including hypophysitis, among women who underwent treatment for non–small cell lung cancer or metastatic melanoma.

The 2019 study found that the risk was higher among premenopausal women than postmenopausal women, which led some to suggest that estrogen may play a role.

The results of the Dana Farber review argue against that notion. In their review, the investigators found that the risk was similarly elevated among the 27 premenopausal women (OR, 1.97; P = .40) and the 66 postmenopausal women (OR, 2.17, P = .05). In the study, women who were aged 52 years or older at the start of treatment were considered to be postmenopausal.

“This suggests that factors beyond sex hormones are likely contributory” to the difference in risk between men and women. It’s known that women are at higher risk for autoimmune disease overall, which might be related to the increased odds of autoimmune-like reactions, and it may be that sex-related differences in innate and adoptive immunity are at work, Mr. Said noted.

When asked for comment, Douglas Johnson, MD, assistant professor of hematology/oncology at Vanderbilt University, Nashville, Tenn., said that although some studies have reported a greater risk for side effects among women, others have not. “Additional research is needed to determine the interactions between sex and effects of immune checkpoint inhibitors, as well as many other possible triggers of immune-related adverse events,” he said.

“Continued work in this area will be so important to help determine how to best counsel women and to ensure early recognition and intervention for dermatologic side effects,” said Bernice Kwong, MD, director of the Supportive Dermato-Oncology Program at Stanford (Calif.) University.

The patients in the review were treated from 2011 to 2016 and underwent at least monthly evaluations by their medical teams. They were taking either nivolumab, pembrolizumab, or ipilimumab or a nivolumab/ipilimumab combination.

The median age of the men in the study was 65 years; the median age of women was 60 years. Almost 98% of the participants were White. The majority received one to three infusions, most commonly with pembrolizumab monotherapy.

No funding for the study was reported. Mr. Said has disclosed no relevant financial relationships.

A version of this article first appeared on Medscape.com.

Women had about a twofold higher risk than that of men of developing dermatologic adverse events while taking immune checkpoint inhibitors for metastatic melanoma in a review of 235 patients at Dana Farber Cancer Center, Boston.
 

Overall, 62.4% of the 93 women in the review and 48.6% of the 142 men experienced confirmed skin reactions, for an odds ratio (OR) of 2.11 for women compared with men (P = .01).

“Clinicians should consider these results in counseling female patients regarding an elevated risk of dermatologic adverse events” when taking checkpoint inhibitors, said investigators led by Harvard University medical student Jordan Said, who presented the results at the American Academy of Dermatology Virtual Meeting Experience.

Autoimmune-like adverse events are common with checkpoint inhibitors. Dermatologic side effects occur in about half of people receiving monotherapy and more than that among patients receiving combination therapy.

Skin reactions can include psoriasiform dermatitis, lichenoid reactions, vitiligo, and bullous pemphigoid and may require hospitalization and prolonged steroid treatment.

Not much is known about risk factors for these reactions. A higher incidence among women has been previously reported. A 2019 study found a higher risk for pneumonitis and endocrinopathy, including hypophysitis, among women who underwent treatment for non–small cell lung cancer or metastatic melanoma.

The 2019 study found that the risk was higher among premenopausal women than postmenopausal women, which led some to suggest that estrogen may play a role.

The results of the Dana Farber review argue against that notion. In their review, the investigators found that the risk was similarly elevated among the 27 premenopausal women (OR, 1.97; P = .40) and the 66 postmenopausal women (OR, 2.17, P = .05). In the study, women who were aged 52 years or older at the start of treatment were considered to be postmenopausal.

“This suggests that factors beyond sex hormones are likely contributory” to the difference in risk between men and women. It’s known that women are at higher risk for autoimmune disease overall, which might be related to the increased odds of autoimmune-like reactions, and it may be that sex-related differences in innate and adoptive immunity are at work, Mr. Said noted.

When asked for comment, Douglas Johnson, MD, assistant professor of hematology/oncology at Vanderbilt University, Nashville, Tenn., said that although some studies have reported a greater risk for side effects among women, others have not. “Additional research is needed to determine the interactions between sex and effects of immune checkpoint inhibitors, as well as many other possible triggers of immune-related adverse events,” he said.

“Continued work in this area will be so important to help determine how to best counsel women and to ensure early recognition and intervention for dermatologic side effects,” said Bernice Kwong, MD, director of the Supportive Dermato-Oncology Program at Stanford (Calif.) University.

The patients in the review were treated from 2011 to 2016 and underwent at least monthly evaluations by their medical teams. They were taking either nivolumab, pembrolizumab, or ipilimumab or a nivolumab/ipilimumab combination.

The median age of the men in the study was 65 years; the median age of women was 60 years. Almost 98% of the participants were White. The majority received one to three infusions, most commonly with pembrolizumab monotherapy.

No funding for the study was reported. Mr. Said has disclosed no relevant financial relationships.

A version of this article first appeared on Medscape.com.

Women had about a twofold higher risk than that of men of developing dermatologic adverse events while taking immune checkpoint inhibitors for metastatic melanoma in a review of 235 patients at Dana Farber Cancer Center, Boston.
 

Overall, 62.4% of the 93 women in the review and 48.6% of the 142 men experienced confirmed skin reactions, for an odds ratio (OR) of 2.11 for women compared with men (P = .01).

“Clinicians should consider these results in counseling female patients regarding an elevated risk of dermatologic adverse events” when taking checkpoint inhibitors, said investigators led by Harvard University medical student Jordan Said, who presented the results at the American Academy of Dermatology Virtual Meeting Experience.

Autoimmune-like adverse events are common with checkpoint inhibitors. Dermatologic side effects occur in about half of people receiving monotherapy and more than that among patients receiving combination therapy.

Skin reactions can include psoriasiform dermatitis, lichenoid reactions, vitiligo, and bullous pemphigoid and may require hospitalization and prolonged steroid treatment.

Not much is known about risk factors for these reactions. A higher incidence among women has been previously reported. A 2019 study found a higher risk for pneumonitis and endocrinopathy, including hypophysitis, among women who underwent treatment for non–small cell lung cancer or metastatic melanoma.

The 2019 study found that the risk was higher among premenopausal women than postmenopausal women, which led some to suggest that estrogen may play a role.

The results of the Dana Farber review argue against that notion. In their review, the investigators found that the risk was similarly elevated among the 27 premenopausal women (OR, 1.97; P = .40) and the 66 postmenopausal women (OR, 2.17, P = .05). In the study, women who were aged 52 years or older at the start of treatment were considered to be postmenopausal.

“This suggests that factors beyond sex hormones are likely contributory” to the difference in risk between men and women. It’s known that women are at higher risk for autoimmune disease overall, which might be related to the increased odds of autoimmune-like reactions, and it may be that sex-related differences in innate and adoptive immunity are at work, Mr. Said noted.

When asked for comment, Douglas Johnson, MD, assistant professor of hematology/oncology at Vanderbilt University, Nashville, Tenn., said that although some studies have reported a greater risk for side effects among women, others have not. “Additional research is needed to determine the interactions between sex and effects of immune checkpoint inhibitors, as well as many other possible triggers of immune-related adverse events,” he said.

“Continued work in this area will be so important to help determine how to best counsel women and to ensure early recognition and intervention for dermatologic side effects,” said Bernice Kwong, MD, director of the Supportive Dermato-Oncology Program at Stanford (Calif.) University.

The patients in the review were treated from 2011 to 2016 and underwent at least monthly evaluations by their medical teams. They were taking either nivolumab, pembrolizumab, or ipilimumab or a nivolumab/ipilimumab combination.

The median age of the men in the study was 65 years; the median age of women was 60 years. Almost 98% of the participants were White. The majority received one to three infusions, most commonly with pembrolizumab monotherapy.

No funding for the study was reported. Mr. Said has disclosed no relevant financial relationships.

A version of this article first appeared on Medscape.com.