B-Cell Lymphoma ICYMI

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.

Noninvasive brain stimulation has the potential to boost a patient’s placebo experience or blunt the nocebo experience, according to results of a new study published in the Proceedings of the National Academy of Sciences (PNAS).

“Placebo and nocebo effects are a critical component of clinical care and efficacy studies,” said senior author Jian Kong, MD, associate professor in the department of psychiatry at Massachusetts General Hospital, Charlestown campus. “Harnessing these effects in clinical practice and research could facilitate the development of new pain management methods,” he said. “Healing may involve multiple components: the self-healing properties of the body; the nonspecific effects of treatment (i.e., placebo effect); and the specific effect of a physical or pharmacologic intervention. Therefore, enhancing the placebo effect may ultimately boost the overall therapeutic effect of existing treatment,” he explained, emphasizing that the results are preliminary and should be interpreted with caution.

The authors noted that reducing nocebo effects could also be a major benefit “since patients discontinue prescribed medications, make unnecessary medical visits, and take additional medications to counteract adverse effects that are actually nocebo effects.”


 

Testing the hypothesis

The randomized, double-blind, sham-controlled study used transcranial direct current stimulation (tDCS), which delivers an electrical current to the brain via scalp electrodes. The aim was to see if stimulating the dorsolateral prefrontal cortex with tDCS could alter the brain’s perception of placebo and nocebo experiences.

The study included 81 participants (37 females, mean age: 27.4 years), who were randomized into one of three tDCS groups (anodal, cathodal, or sham).

All participants were first conditioned to believe that an inert cream was either lidocaine or capsaicin and that this cream could either dull the impact of a painful heat stimulus (placebo analgesia) or exacerbate it (nocebo hyperalgesia). Participants were then placed into a functional MRI scanner where tDCS was initiated. Painful stimuli were then applied to spots on their forearms where they believed they had either lidocaine, capsaicin, or a neutral control cream and they rated the pain using the Gracely Sensory Scale.

Placebo analgesia was defined as the difference between perceived pain intensity where participants believed they had lidocaine cream compared with where they believed they had control cream. Nocebo hyperalgesia was defined as the difference between perceived pain intensity where they believed they had capsaicin cream compared with where they believed they had control cream.

The researchers found that compared with sham tDCS, cathodal tDCS showed significant effects in increasing placebo analgesia and brain responses in the ventromedial prefrontal cortex (vmPFC), while anodal tDCS showed significant effects in inhibiting nocebo hyperalgesia and brain responses in the insula.

“The potential to enhance salubrious placebo effects and/or diminish treatment-interfering nocebo effects may have clinical significance,” the authors noted. “For example, clinical studies have suggested that expectancy is positively associated with chronic pain improvement, and using conditioning-like expectancy manipulation, we have shown that significantly boosting expectancy can improve treatment outcome.”
 

Proof of concept

Asked to comment on the study, Brian E. McGeeney, MD, of the John R. Graham Headache Center at Brigham and Women’s Faulkner Hospital in Boston, said “the findings are a proof of concept that it is possible to use noninvasive brain stimulation to modulate placebo and nocebo pain effects.”

Although the findings do not have immediate clinical application, they are “exciting” and “break new ground in expectancy research,” he said.

“It is important to recognize that the researchers are trying to utilize a purported expectancy mechanism rather than attempting to alter placebo/nocebo by verbal and other cues. It remains to be seen whether the manipulation of brief experimental pain like this can translate into altered chronic pain over time, the main clinical goal. Current tDCS therapy for various reasons is necessarily brief and one can ask whether there are meaningful changes from brief stimulation. Such results can foster speculation as to whether direct strategic placement of intracranial stimulation leads could result in more longstanding similar benefits.”

Dr. Kong holds equity in a startup company (MNT) and a pending patent to develop new peripheral neuromodulation tools, but declares no conflict of interest. All other authors declare no conflict of interest.

Noninvasive brain stimulation has the potential to boost a patient’s placebo experience or blunt the nocebo experience, according to results of a new study published in the Proceedings of the National Academy of Sciences (PNAS).

“Placebo and nocebo effects are a critical component of clinical care and efficacy studies,” said senior author Jian Kong, MD, associate professor in the department of psychiatry at Massachusetts General Hospital, Charlestown campus. “Harnessing these effects in clinical practice and research could facilitate the development of new pain management methods,” he said. “Healing may involve multiple components: the self-healing properties of the body; the nonspecific effects of treatment (i.e., placebo effect); and the specific effect of a physical or pharmacologic intervention. Therefore, enhancing the placebo effect may ultimately boost the overall therapeutic effect of existing treatment,” he explained, emphasizing that the results are preliminary and should be interpreted with caution.

The authors noted that reducing nocebo effects could also be a major benefit “since patients discontinue prescribed medications, make unnecessary medical visits, and take additional medications to counteract adverse effects that are actually nocebo effects.”


 

Testing the hypothesis

The randomized, double-blind, sham-controlled study used transcranial direct current stimulation (tDCS), which delivers an electrical current to the brain via scalp electrodes. The aim was to see if stimulating the dorsolateral prefrontal cortex with tDCS could alter the brain’s perception of placebo and nocebo experiences.

The study included 81 participants (37 females, mean age: 27.4 years), who were randomized into one of three tDCS groups (anodal, cathodal, or sham).

All participants were first conditioned to believe that an inert cream was either lidocaine or capsaicin and that this cream could either dull the impact of a painful heat stimulus (placebo analgesia) or exacerbate it (nocebo hyperalgesia). Participants were then placed into a functional MRI scanner where tDCS was initiated. Painful stimuli were then applied to spots on their forearms where they believed they had either lidocaine, capsaicin, or a neutral control cream and they rated the pain using the Gracely Sensory Scale.

Placebo analgesia was defined as the difference between perceived pain intensity where participants believed they had lidocaine cream compared with where they believed they had control cream. Nocebo hyperalgesia was defined as the difference between perceived pain intensity where they believed they had capsaicin cream compared with where they believed they had control cream.

The researchers found that compared with sham tDCS, cathodal tDCS showed significant effects in increasing placebo analgesia and brain responses in the ventromedial prefrontal cortex (vmPFC), while anodal tDCS showed significant effects in inhibiting nocebo hyperalgesia and brain responses in the insula.

“The potential to enhance salubrious placebo effects and/or diminish treatment-interfering nocebo effects may have clinical significance,” the authors noted. “For example, clinical studies have suggested that expectancy is positively associated with chronic pain improvement, and using conditioning-like expectancy manipulation, we have shown that significantly boosting expectancy can improve treatment outcome.”
 

Proof of concept

Asked to comment on the study, Brian E. McGeeney, MD, of the John R. Graham Headache Center at Brigham and Women’s Faulkner Hospital in Boston, said “the findings are a proof of concept that it is possible to use noninvasive brain stimulation to modulate placebo and nocebo pain effects.”

Although the findings do not have immediate clinical application, they are “exciting” and “break new ground in expectancy research,” he said.

“It is important to recognize that the researchers are trying to utilize a purported expectancy mechanism rather than attempting to alter placebo/nocebo by verbal and other cues. It remains to be seen whether the manipulation of brief experimental pain like this can translate into altered chronic pain over time, the main clinical goal. Current tDCS therapy for various reasons is necessarily brief and one can ask whether there are meaningful changes from brief stimulation. Such results can foster speculation as to whether direct strategic placement of intracranial stimulation leads could result in more longstanding similar benefits.”

Dr. Kong holds equity in a startup company (MNT) and a pending patent to develop new peripheral neuromodulation tools, but declares no conflict of interest. All other authors declare no conflict of interest.

Noninvasive brain stimulation has the potential to boost a patient’s placebo experience or blunt the nocebo experience, according to results of a new study published in the Proceedings of the National Academy of Sciences (PNAS).

“Placebo and nocebo effects are a critical component of clinical care and efficacy studies,” said senior author Jian Kong, MD, associate professor in the department of psychiatry at Massachusetts General Hospital, Charlestown campus. “Harnessing these effects in clinical practice and research could facilitate the development of new pain management methods,” he said. “Healing may involve multiple components: the self-healing properties of the body; the nonspecific effects of treatment (i.e., placebo effect); and the specific effect of a physical or pharmacologic intervention. Therefore, enhancing the placebo effect may ultimately boost the overall therapeutic effect of existing treatment,” he explained, emphasizing that the results are preliminary and should be interpreted with caution.

The authors noted that reducing nocebo effects could also be a major benefit “since patients discontinue prescribed medications, make unnecessary medical visits, and take additional medications to counteract adverse effects that are actually nocebo effects.”


 

Testing the hypothesis

The randomized, double-blind, sham-controlled study used transcranial direct current stimulation (tDCS), which delivers an electrical current to the brain via scalp electrodes. The aim was to see if stimulating the dorsolateral prefrontal cortex with tDCS could alter the brain’s perception of placebo and nocebo experiences.

The study included 81 participants (37 females, mean age: 27.4 years), who were randomized into one of three tDCS groups (anodal, cathodal, or sham).

All participants were first conditioned to believe that an inert cream was either lidocaine or capsaicin and that this cream could either dull the impact of a painful heat stimulus (placebo analgesia) or exacerbate it (nocebo hyperalgesia). Participants were then placed into a functional MRI scanner where tDCS was initiated. Painful stimuli were then applied to spots on their forearms where they believed they had either lidocaine, capsaicin, or a neutral control cream and they rated the pain using the Gracely Sensory Scale.

Placebo analgesia was defined as the difference between perceived pain intensity where participants believed they had lidocaine cream compared with where they believed they had control cream. Nocebo hyperalgesia was defined as the difference between perceived pain intensity where they believed they had capsaicin cream compared with where they believed they had control cream.

The researchers found that compared with sham tDCS, cathodal tDCS showed significant effects in increasing placebo analgesia and brain responses in the ventromedial prefrontal cortex (vmPFC), while anodal tDCS showed significant effects in inhibiting nocebo hyperalgesia and brain responses in the insula.

“The potential to enhance salubrious placebo effects and/or diminish treatment-interfering nocebo effects may have clinical significance,” the authors noted. “For example, clinical studies have suggested that expectancy is positively associated with chronic pain improvement, and using conditioning-like expectancy manipulation, we have shown that significantly boosting expectancy can improve treatment outcome.”
 

Proof of concept

Asked to comment on the study, Brian E. McGeeney, MD, of the John R. Graham Headache Center at Brigham and Women’s Faulkner Hospital in Boston, said “the findings are a proof of concept that it is possible to use noninvasive brain stimulation to modulate placebo and nocebo pain effects.”

Although the findings do not have immediate clinical application, they are “exciting” and “break new ground in expectancy research,” he said.

“It is important to recognize that the researchers are trying to utilize a purported expectancy mechanism rather than attempting to alter placebo/nocebo by verbal and other cues. It remains to be seen whether the manipulation of brief experimental pain like this can translate into altered chronic pain over time, the main clinical goal. Current tDCS therapy for various reasons is necessarily brief and one can ask whether there are meaningful changes from brief stimulation. Such results can foster speculation as to whether direct strategic placement of intracranial stimulation leads could result in more longstanding similar benefits.”

Dr. Kong holds equity in a startup company (MNT) and a pending patent to develop new peripheral neuromodulation tools, but declares no conflict of interest. All other authors declare no conflict of interest.

Survivors of childhood cancers are at increased risk for prescription opioid misuse compared with their peers, a review of a claims database revealed.

Among more than 8,000 patients age 21 or younger who had completed treatment for hematologic, central nervous system, bone, or gonadal cancers, survivors were significantly more likely than were their peers to have an opioid prescription, longer duration of prescription, and higher daily doses of opioids, and to have opioid prescriptions overlapping for a week or more, reported Xu Ji, PhD, of Emory University in Atlanta.

Teenage and young adult patients were at higher risk than were patients younger than 12, and the risk was highest among patients who had been treated for bone malignancies, as well as those who had undergone any hematopoietic stem cell transplant.

“These findings suggest that health care providers who regularly see survivors should explore nonopioid options to help prevent opioid misuse, and screen for potential misuse in those who actually receive opioids,” she said in an oral abstract presented during the annual meeting of the American Society of Pediatric Hematology/Oncology.

“This is a really important topic, and something that’s probably been underinvestigated and underexplored in our patient population,” said session comoderator Sheri Spunt, MD, Endowed Professor of Pediatric Cancer at Stanford (Calif.) University.
 

Database review

Dr. Ji and colleagues used the IBM MarketScan Commercial Claims and Encounters database from 2009 to 2018 to examine prescription opioid use, potential misuse, and substance use disorders in pediatric cancer survivors in the first year after completion of therapy, and to identify factors associated with risk for misuse or substance use disorders. Specifically, the period of interest was the first year after completion of all treatments, including surgery, chemotherapy, radiation, and stem cell transplant (Abstract 2015).

They looked at deidentified records on any opioid prescription and for treatment of any opioid use or substance use disorder (alcohol, psychotherapeutic drugs, marijuana, or illicit drug use disorders).

They defined indicators of potential misuse as either prescriptions for long-acting or extended-release opioids for acute pain conditions; opioid and benzodiazepine prescriptions overlapping by a week or more; opioid prescriptions overlapping by a week or more; high daily opioid dosage (prescribed daily dose of 100 or greater morphine milligram equivalent [MME]; and/or opioid dose escalation (an increase of at least 50% in mean MMEs per month twice consecutively within 1 year).

They compared outcomes between a total of 8,635 survivors and 44,175 controls, matched on a 1:5 basis with survivors by age, sex, and region, and continuous enrollment during the 1-year posttherapy period.

In each of three age categories – 0 to 11 years, 12 to 17 years, and 18 years and older – survivors were significantly more likely to have received an opioid prescription, at 15% for the youngest survivors vs. 2% of controls, 25% vs. 8% for 12- to 17-year-olds, and 28% vs. 12% for those 18 and older (P < .01 for all three comparisons).

Survivors were also significantly more likely to have any indicator of potential misuse (1.6% vs. 0.1%, 4.6% vs. 0.5%, and 7.4% vs. 1.2%, respectively, P < .001 for all) and both the youngest and oldest groups (but not 12- to 17-year-olds) were significantly more like to have opioid or substance use disorder (0.4% vs. 0% for 0-11 years, 5.76% vs. 4.2% for 18 years and older, P < .001 for both).

Among patients with any opioid prescription, survivors were significantly more likely than were controls of any age to have indicators for potential misuse. For example, 13% of survivors aged 18 years and older had prescriptions for high opioid doses, compared with 5% of controls, and 12% had prescription overlap, vs. 2%.

Compared with patients with leukemia, patients treated for bone malignancies had a 6% greater risk for having any indicator of misuse, while patients with other malignancies were at slightly lower risk for misuse than those who completed leukemia therapy.

Patients who received any stem cell transplant had an 8.4% greater risk for misuse compared with patients who had surgery only.
 

Opioids pre- and posttreatment?

“Being someone who takes care of a lot of bone cancer patients, I do see patients with these issues,” Dr. Spunt said.

Audience member Jack H. Staddon, MD, PhD, of the Billings (Montana) Clinic, noted the possibility that opioid use during treatment may have been carried on into the posttreatment period, and asked whether use of narcotics during treatment was an independent risk factor for posttreatment narcotic use or misuse.

The researchers plan to investigate this question in future studies, Dr. Ji replied.

They did not report a study funding source. Dr. Ji and coauthors and Dr. Staddon reported no relevant disclosures.

Survivors of childhood cancers are at increased risk for prescription opioid misuse compared with their peers, a review of a claims database revealed.

Among more than 8,000 patients age 21 or younger who had completed treatment for hematologic, central nervous system, bone, or gonadal cancers, survivors were significantly more likely than were their peers to have an opioid prescription, longer duration of prescription, and higher daily doses of opioids, and to have opioid prescriptions overlapping for a week or more, reported Xu Ji, PhD, of Emory University in Atlanta.

Teenage and young adult patients were at higher risk than were patients younger than 12, and the risk was highest among patients who had been treated for bone malignancies, as well as those who had undergone any hematopoietic stem cell transplant.

“These findings suggest that health care providers who regularly see survivors should explore nonopioid options to help prevent opioid misuse, and screen for potential misuse in those who actually receive opioids,” she said in an oral abstract presented during the annual meeting of the American Society of Pediatric Hematology/Oncology.

“This is a really important topic, and something that’s probably been underinvestigated and underexplored in our patient population,” said session comoderator Sheri Spunt, MD, Endowed Professor of Pediatric Cancer at Stanford (Calif.) University.
 

Database review

Dr. Ji and colleagues used the IBM MarketScan Commercial Claims and Encounters database from 2009 to 2018 to examine prescription opioid use, potential misuse, and substance use disorders in pediatric cancer survivors in the first year after completion of therapy, and to identify factors associated with risk for misuse or substance use disorders. Specifically, the period of interest was the first year after completion of all treatments, including surgery, chemotherapy, radiation, and stem cell transplant (Abstract 2015).

They looked at deidentified records on any opioid prescription and for treatment of any opioid use or substance use disorder (alcohol, psychotherapeutic drugs, marijuana, or illicit drug use disorders).

They defined indicators of potential misuse as either prescriptions for long-acting or extended-release opioids for acute pain conditions; opioid and benzodiazepine prescriptions overlapping by a week or more; opioid prescriptions overlapping by a week or more; high daily opioid dosage (prescribed daily dose of 100 or greater morphine milligram equivalent [MME]; and/or opioid dose escalation (an increase of at least 50% in mean MMEs per month twice consecutively within 1 year).

They compared outcomes between a total of 8,635 survivors and 44,175 controls, matched on a 1:5 basis with survivors by age, sex, and region, and continuous enrollment during the 1-year posttherapy period.

In each of three age categories – 0 to 11 years, 12 to 17 years, and 18 years and older – survivors were significantly more likely to have received an opioid prescription, at 15% for the youngest survivors vs. 2% of controls, 25% vs. 8% for 12- to 17-year-olds, and 28% vs. 12% for those 18 and older (P < .01 for all three comparisons).

Survivors were also significantly more likely to have any indicator of potential misuse (1.6% vs. 0.1%, 4.6% vs. 0.5%, and 7.4% vs. 1.2%, respectively, P < .001 for all) and both the youngest and oldest groups (but not 12- to 17-year-olds) were significantly more like to have opioid or substance use disorder (0.4% vs. 0% for 0-11 years, 5.76% vs. 4.2% for 18 years and older, P < .001 for both).

Among patients with any opioid prescription, survivors were significantly more likely than were controls of any age to have indicators for potential misuse. For example, 13% of survivors aged 18 years and older had prescriptions for high opioid doses, compared with 5% of controls, and 12% had prescription overlap, vs. 2%.

Compared with patients with leukemia, patients treated for bone malignancies had a 6% greater risk for having any indicator of misuse, while patients with other malignancies were at slightly lower risk for misuse than those who completed leukemia therapy.

Patients who received any stem cell transplant had an 8.4% greater risk for misuse compared with patients who had surgery only.
 

Opioids pre- and posttreatment?

“Being someone who takes care of a lot of bone cancer patients, I do see patients with these issues,” Dr. Spunt said.

Audience member Jack H. Staddon, MD, PhD, of the Billings (Montana) Clinic, noted the possibility that opioid use during treatment may have been carried on into the posttreatment period, and asked whether use of narcotics during treatment was an independent risk factor for posttreatment narcotic use or misuse.

The researchers plan to investigate this question in future studies, Dr. Ji replied.

They did not report a study funding source. Dr. Ji and coauthors and Dr. Staddon reported no relevant disclosures.

Survivors of childhood cancers are at increased risk for prescription opioid misuse compared with their peers, a review of a claims database revealed.

Among more than 8,000 patients age 21 or younger who had completed treatment for hematologic, central nervous system, bone, or gonadal cancers, survivors were significantly more likely than were their peers to have an opioid prescription, longer duration of prescription, and higher daily doses of opioids, and to have opioid prescriptions overlapping for a week or more, reported Xu Ji, PhD, of Emory University in Atlanta.

Teenage and young adult patients were at higher risk than were patients younger than 12, and the risk was highest among patients who had been treated for bone malignancies, as well as those who had undergone any hematopoietic stem cell transplant.

“These findings suggest that health care providers who regularly see survivors should explore nonopioid options to help prevent opioid misuse, and screen for potential misuse in those who actually receive opioids,” she said in an oral abstract presented during the annual meeting of the American Society of Pediatric Hematology/Oncology.

“This is a really important topic, and something that’s probably been underinvestigated and underexplored in our patient population,” said session comoderator Sheri Spunt, MD, Endowed Professor of Pediatric Cancer at Stanford (Calif.) University.
 

Database review

Dr. Ji and colleagues used the IBM MarketScan Commercial Claims and Encounters database from 2009 to 2018 to examine prescription opioid use, potential misuse, and substance use disorders in pediatric cancer survivors in the first year after completion of therapy, and to identify factors associated with risk for misuse or substance use disorders. Specifically, the period of interest was the first year after completion of all treatments, including surgery, chemotherapy, radiation, and stem cell transplant (Abstract 2015).

They looked at deidentified records on any opioid prescription and for treatment of any opioid use or substance use disorder (alcohol, psychotherapeutic drugs, marijuana, or illicit drug use disorders).

They defined indicators of potential misuse as either prescriptions for long-acting or extended-release opioids for acute pain conditions; opioid and benzodiazepine prescriptions overlapping by a week or more; opioid prescriptions overlapping by a week or more; high daily opioid dosage (prescribed daily dose of 100 or greater morphine milligram equivalent [MME]; and/or opioid dose escalation (an increase of at least 50% in mean MMEs per month twice consecutively within 1 year).

They compared outcomes between a total of 8,635 survivors and 44,175 controls, matched on a 1:5 basis with survivors by age, sex, and region, and continuous enrollment during the 1-year posttherapy period.

In each of three age categories – 0 to 11 years, 12 to 17 years, and 18 years and older – survivors were significantly more likely to have received an opioid prescription, at 15% for the youngest survivors vs. 2% of controls, 25% vs. 8% for 12- to 17-year-olds, and 28% vs. 12% for those 18 and older (P < .01 for all three comparisons).

Survivors were also significantly more likely to have any indicator of potential misuse (1.6% vs. 0.1%, 4.6% vs. 0.5%, and 7.4% vs. 1.2%, respectively, P < .001 for all) and both the youngest and oldest groups (but not 12- to 17-year-olds) were significantly more like to have opioid or substance use disorder (0.4% vs. 0% for 0-11 years, 5.76% vs. 4.2% for 18 years and older, P < .001 for both).

Among patients with any opioid prescription, survivors were significantly more likely than were controls of any age to have indicators for potential misuse. For example, 13% of survivors aged 18 years and older had prescriptions for high opioid doses, compared with 5% of controls, and 12% had prescription overlap, vs. 2%.

Compared with patients with leukemia, patients treated for bone malignancies had a 6% greater risk for having any indicator of misuse, while patients with other malignancies were at slightly lower risk for misuse than those who completed leukemia therapy.

Patients who received any stem cell transplant had an 8.4% greater risk for misuse compared with patients who had surgery only.
 

Opioids pre- and posttreatment?

“Being someone who takes care of a lot of bone cancer patients, I do see patients with these issues,” Dr. Spunt said.

Audience member Jack H. Staddon, MD, PhD, of the Billings (Montana) Clinic, noted the possibility that opioid use during treatment may have been carried on into the posttreatment period, and asked whether use of narcotics during treatment was an independent risk factor for posttreatment narcotic use or misuse.

The researchers plan to investigate this question in future studies, Dr. Ji replied.

They did not report a study funding source. Dr. Ji and coauthors and Dr. Staddon reported no relevant disclosures.

Adding ibrutinib to chemotherapy did not improve outcomes for children and young adults with relapsed or refractory mature B-cell non-Hodgkin lymphoma (B-NHL), an interim analysis of the SPARKLE trial showed.

Among 51 patients aged 1-30 years with mature B-NHL that had been diagnosed before age 18, there was no significant difference in the primary endpoint of event-free survival (EFS) between patients assigned on a 2:1 basis to receive either ibrutinib (Imbruvica) plus one of two chemotherapy regimens or to chemotherapy alone. In fact, EFS was shorter among patients assigned to ibrutinib, although a larger proportion of these patients had previously received rituximab, a known factor for poor prognosis, reported Amos Burke, MD, from Cambridge (England) University.

The trial was stopped for futility in May 2020, after a median follow-up of 17.97 months.

“Further studies are required to determine the optimal therapy for patients with relapsed, mature B-NHL, especially those who have received prior rituximab,” he said in an audio walk-through of a scientific poster presented during the annual meeting of the American Society of Pediatric Hematology/Oncology.

“This is a very challenging patient population because they historically have had a very poor survival rate,” commented Paul J. Galardy, MD, a pediatric hematologist/oncologist at the Mayo Clinic in Rochester, Minn., who was not involved in the study.

“The field has struggled to improve outcomes for these patients in part because there are relatively few patients per year with relapsed refractory mature B-cell lymphoma due to the very effective nature of the up-front therapy. This makes new clinical trials difficult to perform,” he said.
 

Poor prognosis

Ibrutinib, an inhibitor of Bruton tyrosine kinase, is approved in the United States for treatment of marginal zone lymphoma, mantle cell lymphoma, and chronic lymphocytic leukemia/small lymphocytic lymphoma, as well as other indications, all in adults only. It has also been shown to have activity against B-NHL in preclinical and early human trials, Dr. Burke said.

Given the poor prognosis of children and young adults with relapsed/refractory mature B-NHL – a 2-year overall survival (OS) of 30% or less with chemoimmunotherapy – the investigators tested whether adding ibrutinib to the standard of care could improve outcomes.

They enrolled patients with relapsed/refractory B-NHL in first relapse or primarily refractory to conventional therapy, with measurable disease (greater than 1 cm) by CT, bone marrow involvement, or cerebrospinal fluid with blasts. The patients were required to have Karnofsky-Lansky performance scores of 50 or greater.

The histologies included Burkitt lymphoma, diffuse large B-cell lymphoma (DLBCL), Burkitt-like lymphoma, Burkitt leukemia, primary mediastinal B-cell lymphoma, and other unspecified types.

Dr. Burke reported results on 48 patients included in the May 2020 analysis, plus 3 additional patients who were enrolled between the data cutoff for the first analysis and the meeting of the independent data monitoring committee where the decision was made to stop the trial.

A total of 35 patients were randomized to receive ibrutinib with either the RICE (rituximab plus ifosfamide, carboplatin, and etoposide) or RVICI (rituximab plus vincristine, ifosfamide, carboplatin, idarubicin, and dexamethasone) regimen. All of these patients received treatment on study.

Of the 18 patients randomized to receive either RICE or RVICI alone, 1 did not receive any cycles of chemoimmunotherapy.

At the data cutoff for the updated analysis in November 2020, 14 patients assigned to ibrutinib and 4 assigned to chemoimmunotherapy alone remained on study; no patients in either arm were still receiving therapy.

A total of 17 patients assigned to the combination arm died and 4 withdrew consent. In the chemoimmunotherapy-alone arm, 10 died and 2 withdrew consent.

In both arms, patients were treated until either completing three cycles of therapy, start of conditioning treatment prior to stem cell transplantation, disease progression, or unacceptable toxicity.

In the ibrutinib arm, the median EFS was 5.36 months, compared with 6.97 months with chemoimmunotherapy alone, translating into a hazard ratio for EFS with ibrutinib of 1.078 (nonsignificant).

The respective median overall survival was 13.44 versus 11.07 months,

Subgroup analysis showed that EFS and OS did not differ significantly by age, histology, background regimen, or central nervous system or bone marrow involvement. ­

Overall response rates were 68.6% in the ibrutinib arm, and 81.3% in the chemoimmunotherapy arm. The respective complete response rates were 8.6% and 18.8%, and partial response rates were 60% and 62.5%.

The overall treatment-emergent adverse event (TEAE) profile was similar between the treatment arms, although six patients in the ibrutinib arm versus one in the chemoimmunotherapy arm experienced a major hemorrhage. One patients in the ibrutinib arm died from pulmonary hemorrhage.

Dr. Burke noted that, although the numbers were small, the failure to see a difference in efficacy between study arms may have been caused in part by a greater number of patients assigned to ibrutinib who had received prior treatment with rituximab (85.7% vs. 56.3%).
 

Not the right partner?

“The results of this study would suggest that ibrutinib is not the right agent. This is not altogether unexpected,” Dr. Galardy said. “The benefit of ibrutinib in adults with mature B-cell lymphoma is primarily based on biological characteristics of lymphomas that develop in older individuals.”

He noted that mature B-cell lymphoma in older adults is often of the activated B-cell subtype, which frequently has mutations that make it sensitive to ibrutinib. In contrast, children, adolescents, and young adults more commonly have the germinal center B-cell subtype that doesn’t have similarly targetable mutations.

He added that, although the reasons for poor prognosis in patients with prior rituximab exposure are unclear, “it is likely that patients who have recurrent or refractory disease after therapy that included rituximab may have developed resistance to this drug. Since both arms of this study included rituximab as a component of the therapy, the patients with prior exposure to this drug may have had reduced benefit of the additional rituximab, compared with those who had not received the drug before.”

The SPARKLE trial was funded by Janssen Research & Development. Dr. Burke disclosed consultancy fees from Janssen and others. Dr. Galardy is an equity holder in Abbott and AbbVie.

Adding ibrutinib to chemotherapy did not improve outcomes for children and young adults with relapsed or refractory mature B-cell non-Hodgkin lymphoma (B-NHL), an interim analysis of the SPARKLE trial showed.

Among 51 patients aged 1-30 years with mature B-NHL that had been diagnosed before age 18, there was no significant difference in the primary endpoint of event-free survival (EFS) between patients assigned on a 2:1 basis to receive either ibrutinib (Imbruvica) plus one of two chemotherapy regimens or to chemotherapy alone. In fact, EFS was shorter among patients assigned to ibrutinib, although a larger proportion of these patients had previously received rituximab, a known factor for poor prognosis, reported Amos Burke, MD, from Cambridge (England) University.

The trial was stopped for futility in May 2020, after a median follow-up of 17.97 months.

“Further studies are required to determine the optimal therapy for patients with relapsed, mature B-NHL, especially those who have received prior rituximab,” he said in an audio walk-through of a scientific poster presented during the annual meeting of the American Society of Pediatric Hematology/Oncology.

“This is a very challenging patient population because they historically have had a very poor survival rate,” commented Paul J. Galardy, MD, a pediatric hematologist/oncologist at the Mayo Clinic in Rochester, Minn., who was not involved in the study.

“The field has struggled to improve outcomes for these patients in part because there are relatively few patients per year with relapsed refractory mature B-cell lymphoma due to the very effective nature of the up-front therapy. This makes new clinical trials difficult to perform,” he said.
 

Poor prognosis

Ibrutinib, an inhibitor of Bruton tyrosine kinase, is approved in the United States for treatment of marginal zone lymphoma, mantle cell lymphoma, and chronic lymphocytic leukemia/small lymphocytic lymphoma, as well as other indications, all in adults only. It has also been shown to have activity against B-NHL in preclinical and early human trials, Dr. Burke said.

Given the poor prognosis of children and young adults with relapsed/refractory mature B-NHL – a 2-year overall survival (OS) of 30% or less with chemoimmunotherapy – the investigators tested whether adding ibrutinib to the standard of care could improve outcomes.

They enrolled patients with relapsed/refractory B-NHL in first relapse or primarily refractory to conventional therapy, with measurable disease (greater than 1 cm) by CT, bone marrow involvement, or cerebrospinal fluid with blasts. The patients were required to have Karnofsky-Lansky performance scores of 50 or greater.

The histologies included Burkitt lymphoma, diffuse large B-cell lymphoma (DLBCL), Burkitt-like lymphoma, Burkitt leukemia, primary mediastinal B-cell lymphoma, and other unspecified types.

Dr. Burke reported results on 48 patients included in the May 2020 analysis, plus 3 additional patients who were enrolled between the data cutoff for the first analysis and the meeting of the independent data monitoring committee where the decision was made to stop the trial.

A total of 35 patients were randomized to receive ibrutinib with either the RICE (rituximab plus ifosfamide, carboplatin, and etoposide) or RVICI (rituximab plus vincristine, ifosfamide, carboplatin, idarubicin, and dexamethasone) regimen. All of these patients received treatment on study.

Of the 18 patients randomized to receive either RICE or RVICI alone, 1 did not receive any cycles of chemoimmunotherapy.

At the data cutoff for the updated analysis in November 2020, 14 patients assigned to ibrutinib and 4 assigned to chemoimmunotherapy alone remained on study; no patients in either arm were still receiving therapy.

A total of 17 patients assigned to the combination arm died and 4 withdrew consent. In the chemoimmunotherapy-alone arm, 10 died and 2 withdrew consent.

In both arms, patients were treated until either completing three cycles of therapy, start of conditioning treatment prior to stem cell transplantation, disease progression, or unacceptable toxicity.

In the ibrutinib arm, the median EFS was 5.36 months, compared with 6.97 months with chemoimmunotherapy alone, translating into a hazard ratio for EFS with ibrutinib of 1.078 (nonsignificant).

The respective median overall survival was 13.44 versus 11.07 months,

Subgroup analysis showed that EFS and OS did not differ significantly by age, histology, background regimen, or central nervous system or bone marrow involvement. ­

Overall response rates were 68.6% in the ibrutinib arm, and 81.3% in the chemoimmunotherapy arm. The respective complete response rates were 8.6% and 18.8%, and partial response rates were 60% and 62.5%.

The overall treatment-emergent adverse event (TEAE) profile was similar between the treatment arms, although six patients in the ibrutinib arm versus one in the chemoimmunotherapy arm experienced a major hemorrhage. One patients in the ibrutinib arm died from pulmonary hemorrhage.

Dr. Burke noted that, although the numbers were small, the failure to see a difference in efficacy between study arms may have been caused in part by a greater number of patients assigned to ibrutinib who had received prior treatment with rituximab (85.7% vs. 56.3%).
 

Not the right partner?

“The results of this study would suggest that ibrutinib is not the right agent. This is not altogether unexpected,” Dr. Galardy said. “The benefit of ibrutinib in adults with mature B-cell lymphoma is primarily based on biological characteristics of lymphomas that develop in older individuals.”

He noted that mature B-cell lymphoma in older adults is often of the activated B-cell subtype, which frequently has mutations that make it sensitive to ibrutinib. In contrast, children, adolescents, and young adults more commonly have the germinal center B-cell subtype that doesn’t have similarly targetable mutations.

He added that, although the reasons for poor prognosis in patients with prior rituximab exposure are unclear, “it is likely that patients who have recurrent or refractory disease after therapy that included rituximab may have developed resistance to this drug. Since both arms of this study included rituximab as a component of the therapy, the patients with prior exposure to this drug may have had reduced benefit of the additional rituximab, compared with those who had not received the drug before.”

The SPARKLE trial was funded by Janssen Research & Development. Dr. Burke disclosed consultancy fees from Janssen and others. Dr. Galardy is an equity holder in Abbott and AbbVie.

Adding ibrutinib to chemotherapy did not improve outcomes for children and young adults with relapsed or refractory mature B-cell non-Hodgkin lymphoma (B-NHL), an interim analysis of the SPARKLE trial showed.

Among 51 patients aged 1-30 years with mature B-NHL that had been diagnosed before age 18, there was no significant difference in the primary endpoint of event-free survival (EFS) between patients assigned on a 2:1 basis to receive either ibrutinib (Imbruvica) plus one of two chemotherapy regimens or to chemotherapy alone. In fact, EFS was shorter among patients assigned to ibrutinib, although a larger proportion of these patients had previously received rituximab, a known factor for poor prognosis, reported Amos Burke, MD, from Cambridge (England) University.

The trial was stopped for futility in May 2020, after a median follow-up of 17.97 months.

“Further studies are required to determine the optimal therapy for patients with relapsed, mature B-NHL, especially those who have received prior rituximab,” he said in an audio walk-through of a scientific poster presented during the annual meeting of the American Society of Pediatric Hematology/Oncology.

“This is a very challenging patient population because they historically have had a very poor survival rate,” commented Paul J. Galardy, MD, a pediatric hematologist/oncologist at the Mayo Clinic in Rochester, Minn., who was not involved in the study.

“The field has struggled to improve outcomes for these patients in part because there are relatively few patients per year with relapsed refractory mature B-cell lymphoma due to the very effective nature of the up-front therapy. This makes new clinical trials difficult to perform,” he said.
 

Poor prognosis

Ibrutinib, an inhibitor of Bruton tyrosine kinase, is approved in the United States for treatment of marginal zone lymphoma, mantle cell lymphoma, and chronic lymphocytic leukemia/small lymphocytic lymphoma, as well as other indications, all in adults only. It has also been shown to have activity against B-NHL in preclinical and early human trials, Dr. Burke said.

Given the poor prognosis of children and young adults with relapsed/refractory mature B-NHL – a 2-year overall survival (OS) of 30% or less with chemoimmunotherapy – the investigators tested whether adding ibrutinib to the standard of care could improve outcomes.

They enrolled patients with relapsed/refractory B-NHL in first relapse or primarily refractory to conventional therapy, with measurable disease (greater than 1 cm) by CT, bone marrow involvement, or cerebrospinal fluid with blasts. The patients were required to have Karnofsky-Lansky performance scores of 50 or greater.

The histologies included Burkitt lymphoma, diffuse large B-cell lymphoma (DLBCL), Burkitt-like lymphoma, Burkitt leukemia, primary mediastinal B-cell lymphoma, and other unspecified types.

Dr. Burke reported results on 48 patients included in the May 2020 analysis, plus 3 additional patients who were enrolled between the data cutoff for the first analysis and the meeting of the independent data monitoring committee where the decision was made to stop the trial.

A total of 35 patients were randomized to receive ibrutinib with either the RICE (rituximab plus ifosfamide, carboplatin, and etoposide) or RVICI (rituximab plus vincristine, ifosfamide, carboplatin, idarubicin, and dexamethasone) regimen. All of these patients received treatment on study.

Of the 18 patients randomized to receive either RICE or RVICI alone, 1 did not receive any cycles of chemoimmunotherapy.

At the data cutoff for the updated analysis in November 2020, 14 patients assigned to ibrutinib and 4 assigned to chemoimmunotherapy alone remained on study; no patients in either arm were still receiving therapy.

A total of 17 patients assigned to the combination arm died and 4 withdrew consent. In the chemoimmunotherapy-alone arm, 10 died and 2 withdrew consent.

In both arms, patients were treated until either completing three cycles of therapy, start of conditioning treatment prior to stem cell transplantation, disease progression, or unacceptable toxicity.

In the ibrutinib arm, the median EFS was 5.36 months, compared with 6.97 months with chemoimmunotherapy alone, translating into a hazard ratio for EFS with ibrutinib of 1.078 (nonsignificant).

The respective median overall survival was 13.44 versus 11.07 months,

Subgroup analysis showed that EFS and OS did not differ significantly by age, histology, background regimen, or central nervous system or bone marrow involvement. ­

Overall response rates were 68.6% in the ibrutinib arm, and 81.3% in the chemoimmunotherapy arm. The respective complete response rates were 8.6% and 18.8%, and partial response rates were 60% and 62.5%.

The overall treatment-emergent adverse event (TEAE) profile was similar between the treatment arms, although six patients in the ibrutinib arm versus one in the chemoimmunotherapy arm experienced a major hemorrhage. One patients in the ibrutinib arm died from pulmonary hemorrhage.

Dr. Burke noted that, although the numbers were small, the failure to see a difference in efficacy between study arms may have been caused in part by a greater number of patients assigned to ibrutinib who had received prior treatment with rituximab (85.7% vs. 56.3%).
 

Not the right partner?

“The results of this study would suggest that ibrutinib is not the right agent. This is not altogether unexpected,” Dr. Galardy said. “The benefit of ibrutinib in adults with mature B-cell lymphoma is primarily based on biological characteristics of lymphomas that develop in older individuals.”

He noted that mature B-cell lymphoma in older adults is often of the activated B-cell subtype, which frequently has mutations that make it sensitive to ibrutinib. In contrast, children, adolescents, and young adults more commonly have the germinal center B-cell subtype that doesn’t have similarly targetable mutations.

He added that, although the reasons for poor prognosis in patients with prior rituximab exposure are unclear, “it is likely that patients who have recurrent or refractory disease after therapy that included rituximab may have developed resistance to this drug. Since both arms of this study included rituximab as a component of the therapy, the patients with prior exposure to this drug may have had reduced benefit of the additional rituximab, compared with those who had not received the drug before.”

The SPARKLE trial was funded by Janssen Research & Development. Dr. Burke disclosed consultancy fees from Janssen and others. Dr. Galardy is an equity holder in Abbott and AbbVie.

Rapid advances in imaging technology have led to better spatial resolution with lower radiation doses to patients. These advances have helped to increase the use of diagnostic chest imaging, particularly in emergency departments and oncology centers, and in screening for coronary artery disease. As a result, there has been an explosion of incidental findings on chest imaging—including indeterminate lung nodules.^1,2

Lung nodules are rounded and well-circumscribed lung opacities (≤ 3 cm in diameter) that may present as solitary or multiple lesions in usually asymptomatic patients. Most lung nodules are benign, the result of an infectious or inflammatory process. Nodules that are ≤ 8 mm in diameter, unless they show increase in size over time, often can be safely followed with imaging surveillance. In contrast, lung nodules > 8 mm could represent an early-stage lung cancer, especially among patients with high-risk for developing lung cancer (ie, those with advanced age, heavy tobacco abuse, or emphysema) and should be further assessed with close imaging surveillance, either chest computed tomography (CT) alone or positron-emission tomography (PET)/CT, or tissue biopsy, based on the underlying likelihood of malignancy.

Patients who receive an early-stage lung cancer diagnosis can be offered curative treatments leading to improved 5-year survival rates.^3,4 Consequently, health care systems need to be able to identify these nodules accurately, in order to categorize and manage them accordingly to the Fleischner radiographic and American College of Chest Physicians clinical guidelines.^5,6 Unfortunately, many hospitals struggle to identify patients with incidental lung nodules found during diagnostic chest and abdominal imaging, due in part to poor adherence to Fleischner guidelines among radiologists for categorizing pulmonary nodules.^7,8

The Veterans Health Administration (VHA) system is interested in effectively detecting patients with incidental lung nodules. Veterans have a higher risk of developing lung cancer when compared with the entire US population, mainly due to a higher incidence of tobacco use.⁶ The prevalence of lung nodules among veterans with significant risk factors for lung cancer is about 60% nationwide, and up to 85% in the Midwest, due to the high prevalence of histoplasmosis.⁷ However, only a small percentage of these nodules represent an early stage primary lung cancer.

Several Veterans Integrated Service Networks (VISNs) in the VHA use a radiology diagnostic code to systematically identify imaging studies with presence of lung nodules. In VISN 23, which includes Minnesota, North Dakota, South Dakota, Iowa, and portions of neighboring states, the code used to identify these radiology studies is 44. However, there is high variability in the reporting and coding of imaging studies among radiologists, which could lead to misclassifying patients with lung nodules.⁸

Some studies suggest that using an automated text search algorithm within radiology reports can be a highly effective strategy to identify patients with lung nodules.^9,10 In this study, we compared the diagnostic performance of a newly developed text search algorithm applied to radiology reports with the current standard practice of using a radiology diagnostic code for identifying patients with lung nodules at the Iowa City US Department of Veterans Affairs (VA) Health Care System (ICVAHCS) hospital in Iowa.

Methods

Since 2014, The ICVAHCS has used a radiology diagnostic code to identify any imaging studies with lung nodules. The radiologist enters “44” at the end of the reading process using the Nuance Powerscribe 360 radiation reporting system. The code is uploaded into the VHA Corporate Data Warehouse (CDW), and it is located within the radiology exam domain. This strategy was created and implemented by the Minneapolis VA Health Care System in Minnesota for all the VA hospitals in VISN 23. A lung nodule registry nurse was provided with a list of radiology studies flagged with this radiology diagnostic code every 2 weeks. A chart review was then performed for all these studies to determine the presence of a lung nodule. When detected, the ordering health care provider was alerted and given recommendations for managing the nodule.

We initially searched for the radiology studies with a presumptive lung nodule using the radiology code 44 within the CDW. Separately, we applied the text search strategy only to radiology reports from chest and abdomen studies (ie, X-rays, CT, magnetic resonance imaging [MRI], and PET) that contained any of the keyword phrases. The text search strategy was modeled based on a natural language processing (NLP) algorithm developed by the Puget Sound VA Healthcare System in Seattle, Washington to identify lung nodules on radiology reports.⁹ Our algorithm included a series of text searches using Microsoft SQL. After several simulations using a random group of radiology reports, we chose the keywords: “lung AND nodul”; “pulm AND nodul”; “pulm AND mass”; “lung AND mass”; and “ground glass”. We selected only chest and abdomen studies because on several simulations using a random group of radiology reports, the vast majority of lung nodules were identified on chest and abdomen imaging studies. Also, it would not have been feasible to chart review the approximately 30,000 total radiology reports that were generated during the study period.

From January 1, 2016 through November 30, 2016, we applied both search strategies independently: radiology diagnostic code for lung nodules to all imaging studies, and text search to all radiology reports of chest and abdomen imaging studies in the CDW (Figure). We also collected demographic (eg, age, sex, race, rurality) and clinical (eg, medical comorbidities, tobacco use) information that were uploaded to the database automatically from CDW using International Statistical Classification of Diseases, Tenth Edition and demographic codes. The VHA uses the Rural-Urban Commuting Areas (RUCA) system to define rurality, which takes into account population density and how closely a community is linked socioeconomically to larger urban centers.¹¹ The protocol was reviewed and approved by the institutional review board of ICVAHCS and the University of Iowa.

Results

We identified 12,983 radiology studies that required manual review during the study period. We confirmed that 8,516 imaging studies had lung nodules, representing 2,912 patients. Subjects with lung nodules were predominantly male (96%), aged between 60 and 79 years (71%), and lived in a rural area (72%). More than 50% of these patients had COPD and over a third were current smokers (Table 1). The text search algorithm identified all of the patients identified by the radiology diagnostic code (n = 1,251). It also identified an additional 1,661 patients with lung nodules that otherwise would have been missed by the radiology code. Compared with those identified only by the text search, those identified by both the radiology coding and text search were older, had lower Charlson comorbidity scores, and were more likely to be a current smoker.

The text search algorithm identified more than twice as many patients with potential lung nodules compared with the radiology diagnostic code (4,071 vs 1,363) (Table 2). However, the text search algorithm was associated with a much higher number of false positives than was the diagnostic code (1,159 vs 112) and a lower PPV (72% [95% CI, 70.6-73.4] vs 92% [95% CI, 90.6-93.4], respectively). The text search algorithm identified 130 patients with lung nodules of moderate to high risk for malignancy (> 8 mm diameter) that were not identified by the radiology code. When the PPV of each search strategy was calculated based on imaging studies with nodules (most patients had > 1 imaging study), the results remained similar (98% for radiology code and 66% for text search). A larger proportion of the lung nodules detected by code 44 vs the text search algorithm were from CT chest studies.

Discussion

In a population of predominantly older male veterans with significant risk factors for lung cancer and high incidence of incidental lung nodules, applying a text search algorithm on radiology reports identified a substantial number of patients with lung nodules, including some with nodules > 8 mm, that were missed by the radiologist-generated code.^9,10 Improving the yield of detection for lung nodules in a population with high risk for lung cancer would increase the likelihood of detecting patients with potentially curable early-stage lung cancers, decreasing lung cancer mortality.

The reasons for the high number of patients with lung nodules missed by the radiology code are unclear. Potential explanations may include the lack of standardization of imaging reports by the radiologists (ie, only 21% of chest CTs used a standardized template describing a lung nodule in our study), a problem well recognized both within and outside VHA.^8,12

The text search algorithm identified more patients with lung nodules but had a higher rate of false positives when compared with the diagnostic code. The high rate of false positives resulted in more charts to review and an increased workload for the lung nodule registry team. The challenges presented by an increased workload should be balanced against the potential harms of missing nodules that develop into advanced cancer.

Text Search Adjustments

Refining the text search criteria algorithm and the chart review process may decrease the rate of false positives significantly without affecting detection of lung nodules. In subsequent simulations, we found that by adding an exclusion criteria to text search algorithm to remove reports with specific keywords we could substantially reduce the number of false positive reports without affecting the detection rate of the lung nodules. These exclusion criteria would exclude any reports that: (1) contain “nodul” within the next 8 words after mentioning “no”; (2) contain “clear” within the next 8 words after mentioning “lung” in the text (eg, “lungs appear to be clear”); (3) contain “clear” within the next 4 words after mentioning “otherwise” in the text (eg, “otherwise appear to be clear”). Based on our study results, we further refined the text search strategy by limiting the search to only chest imaging studies. When we applied the revised algorithm to a random sample of imaging reports, we found all the code 44 radiology reports were still captured, but we were able to reduce the number of radiology reports needing review by about 80%.

Although classification approaches are being refined to improve radiology performance in multiple categories of nodules, this study suggests that alternative approaches based on text algorithms can improve the capture of pulmonary nodules that require surveillance. These algorithms also can be used to augment radiologist reporting systems. This represents an investment in resources to build a team that should include a bioinformatics specialist, lung nodule registry personnel (review charts of the detected imaging studies with lung nodules, populating the lung nodule database, and determining and tracking the need of imaging follow up), a lung nodule clinic nurse coordinator, and a dedicated lung nodule clinic pulmonologist.

Radiology departments could employ this text search approach to identify missed nodules and use an audit and feedback system to train radiologists to code lung nodules consistently at the time of the initial reading to avoid delays in identifying patients with nodules. Alternatively, the more widespread use of a standardized CT chest radiology reports using Fleischner or the American College of Radiology Lung Imaging Reporting and Data System (Lung RADS) templates might improve the detection of patients with lung nodules.^5,13,14

Limitations

This study was performed in a single VHA hospital and the findings may not be generalizable to other settings of care. Second, our study design is susceptible to work-up bias because the results of a diagnostic test (eg, chest or abdomen imaging) affected whether the chart review was used to verify the test result. It was not feasible to review the patient records of all radiology studies done at the facility during the study period, consequently complete 2 × 2 tables could not be created to calculate sensitivity, specificity, and negative predictive value.

Conclusion

A text search algorithm of radiology reports increased the detection of patients with lung nodules when compared with radiology diagnostic coding alone. However, the improved detection was associated with a higher rate of false positives, which requires manually reviewing a larger number of patient’s chart reports. Future research and quality improvement should focus on standardizing the radiology reporting process and improving the efficiency and reliability of follow up and tracking of incidental lung nodules.

Acknowledgments

The work reported here was supported by a grant from the Office of Rural Health (N32-FY16Q1-S1-P01577), US Department of Veterans Affairs, Veterans Health Administration. We also had the support from the Veterans Rural Health Resource Center-Iowa City, and the Health Services Research and Development (HSR&D) Service through the Comprehensive Access and Delivery Research and Evaluation (CADRE) Center (REA 09-220).

Rapid advances in imaging technology have led to better spatial resolution with lower radiation doses to patients. These advances have helped to increase the use of diagnostic chest imaging, particularly in emergency departments and oncology centers, and in screening for coronary artery disease. As a result, there has been an explosion of incidental findings on chest imaging—including indeterminate lung nodules.^1,2

Lung nodules are rounded and well-circumscribed lung opacities (≤ 3 cm in diameter) that may present as solitary or multiple lesions in usually asymptomatic patients. Most lung nodules are benign, the result of an infectious or inflammatory process. Nodules that are ≤ 8 mm in diameter, unless they show increase in size over time, often can be safely followed with imaging surveillance. In contrast, lung nodules > 8 mm could represent an early-stage lung cancer, especially among patients with high-risk for developing lung cancer (ie, those with advanced age, heavy tobacco abuse, or emphysema) and should be further assessed with close imaging surveillance, either chest computed tomography (CT) alone or positron-emission tomography (PET)/CT, or tissue biopsy, based on the underlying likelihood of malignancy.

Patients who receive an early-stage lung cancer diagnosis can be offered curative treatments leading to improved 5-year survival rates.^3,4 Consequently, health care systems need to be able to identify these nodules accurately, in order to categorize and manage them accordingly to the Fleischner radiographic and American College of Chest Physicians clinical guidelines.^5,6 Unfortunately, many hospitals struggle to identify patients with incidental lung nodules found during diagnostic chest and abdominal imaging, due in part to poor adherence to Fleischner guidelines among radiologists for categorizing pulmonary nodules.^7,8

The Veterans Health Administration (VHA) system is interested in effectively detecting patients with incidental lung nodules. Veterans have a higher risk of developing lung cancer when compared with the entire US population, mainly due to a higher incidence of tobacco use.⁶ The prevalence of lung nodules among veterans with significant risk factors for lung cancer is about 60% nationwide, and up to 85% in the Midwest, due to the high prevalence of histoplasmosis.⁷ However, only a small percentage of these nodules represent an early stage primary lung cancer.

Several Veterans Integrated Service Networks (VISNs) in the VHA use a radiology diagnostic code to systematically identify imaging studies with presence of lung nodules. In VISN 23, which includes Minnesota, North Dakota, South Dakota, Iowa, and portions of neighboring states, the code used to identify these radiology studies is 44. However, there is high variability in the reporting and coding of imaging studies among radiologists, which could lead to misclassifying patients with lung nodules.⁸

Some studies suggest that using an automated text search algorithm within radiology reports can be a highly effective strategy to identify patients with lung nodules.^9,10 In this study, we compared the diagnostic performance of a newly developed text search algorithm applied to radiology reports with the current standard practice of using a radiology diagnostic code for identifying patients with lung nodules at the Iowa City US Department of Veterans Affairs (VA) Health Care System (ICVAHCS) hospital in Iowa.

Methods

Since 2014, The ICVAHCS has used a radiology diagnostic code to identify any imaging studies with lung nodules. The radiologist enters “44” at the end of the reading process using the Nuance Powerscribe 360 radiation reporting system. The code is uploaded into the VHA Corporate Data Warehouse (CDW), and it is located within the radiology exam domain. This strategy was created and implemented by the Minneapolis VA Health Care System in Minnesota for all the VA hospitals in VISN 23. A lung nodule registry nurse was provided with a list of radiology studies flagged with this radiology diagnostic code every 2 weeks. A chart review was then performed for all these studies to determine the presence of a lung nodule. When detected, the ordering health care provider was alerted and given recommendations for managing the nodule.

We initially searched for the radiology studies with a presumptive lung nodule using the radiology code 44 within the CDW. Separately, we applied the text search strategy only to radiology reports from chest and abdomen studies (ie, X-rays, CT, magnetic resonance imaging [MRI], and PET) that contained any of the keyword phrases. The text search strategy was modeled based on a natural language processing (NLP) algorithm developed by the Puget Sound VA Healthcare System in Seattle, Washington to identify lung nodules on radiology reports.⁹ Our algorithm included a series of text searches using Microsoft SQL. After several simulations using a random group of radiology reports, we chose the keywords: “lung AND nodul”; “pulm AND nodul”; “pulm AND mass”; “lung AND mass”; and “ground glass”. We selected only chest and abdomen studies because on several simulations using a random group of radiology reports, the vast majority of lung nodules were identified on chest and abdomen imaging studies. Also, it would not have been feasible to chart review the approximately 30,000 total radiology reports that were generated during the study period.

From January 1, 2016 through November 30, 2016, we applied both search strategies independently: radiology diagnostic code for lung nodules to all imaging studies, and text search to all radiology reports of chest and abdomen imaging studies in the CDW (Figure). We also collected demographic (eg, age, sex, race, rurality) and clinical (eg, medical comorbidities, tobacco use) information that were uploaded to the database automatically from CDW using International Statistical Classification of Diseases, Tenth Edition and demographic codes. The VHA uses the Rural-Urban Commuting Areas (RUCA) system to define rurality, which takes into account population density and how closely a community is linked socioeconomically to larger urban centers.¹¹ The protocol was reviewed and approved by the institutional review board of ICVAHCS and the University of Iowa.

Results

We identified 12,983 radiology studies that required manual review during the study period. We confirmed that 8,516 imaging studies had lung nodules, representing 2,912 patients. Subjects with lung nodules were predominantly male (96%), aged between 60 and 79 years (71%), and lived in a rural area (72%). More than 50% of these patients had COPD and over a third were current smokers (Table 1). The text search algorithm identified all of the patients identified by the radiology diagnostic code (n = 1,251). It also identified an additional 1,661 patients with lung nodules that otherwise would have been missed by the radiology code. Compared with those identified only by the text search, those identified by both the radiology coding and text search were older, had lower Charlson comorbidity scores, and were more likely to be a current smoker.

The text search algorithm identified more than twice as many patients with potential lung nodules compared with the radiology diagnostic code (4,071 vs 1,363) (Table 2). However, the text search algorithm was associated with a much higher number of false positives than was the diagnostic code (1,159 vs 112) and a lower PPV (72% [95% CI, 70.6-73.4] vs 92% [95% CI, 90.6-93.4], respectively). The text search algorithm identified 130 patients with lung nodules of moderate to high risk for malignancy (> 8 mm diameter) that were not identified by the radiology code. When the PPV of each search strategy was calculated based on imaging studies with nodules (most patients had > 1 imaging study), the results remained similar (98% for radiology code and 66% for text search). A larger proportion of the lung nodules detected by code 44 vs the text search algorithm were from CT chest studies.

Discussion

In a population of predominantly older male veterans with significant risk factors for lung cancer and high incidence of incidental lung nodules, applying a text search algorithm on radiology reports identified a substantial number of patients with lung nodules, including some with nodules > 8 mm, that were missed by the radiologist-generated code.^9,10 Improving the yield of detection for lung nodules in a population with high risk for lung cancer would increase the likelihood of detecting patients with potentially curable early-stage lung cancers, decreasing lung cancer mortality.

The reasons for the high number of patients with lung nodules missed by the radiology code are unclear. Potential explanations may include the lack of standardization of imaging reports by the radiologists (ie, only 21% of chest CTs used a standardized template describing a lung nodule in our study), a problem well recognized both within and outside VHA.^8,12

The text search algorithm identified more patients with lung nodules but had a higher rate of false positives when compared with the diagnostic code. The high rate of false positives resulted in more charts to review and an increased workload for the lung nodule registry team. The challenges presented by an increased workload should be balanced against the potential harms of missing nodules that develop into advanced cancer.

Text Search Adjustments

Refining the text search criteria algorithm and the chart review process may decrease the rate of false positives significantly without affecting detection of lung nodules. In subsequent simulations, we found that by adding an exclusion criteria to text search algorithm to remove reports with specific keywords we could substantially reduce the number of false positive reports without affecting the detection rate of the lung nodules. These exclusion criteria would exclude any reports that: (1) contain “nodul” within the next 8 words after mentioning “no”; (2) contain “clear” within the next 8 words after mentioning “lung” in the text (eg, “lungs appear to be clear”); (3) contain “clear” within the next 4 words after mentioning “otherwise” in the text (eg, “otherwise appear to be clear”). Based on our study results, we further refined the text search strategy by limiting the search to only chest imaging studies. When we applied the revised algorithm to a random sample of imaging reports, we found all the code 44 radiology reports were still captured, but we were able to reduce the number of radiology reports needing review by about 80%.

Although classification approaches are being refined to improve radiology performance in multiple categories of nodules, this study suggests that alternative approaches based on text algorithms can improve the capture of pulmonary nodules that require surveillance. These algorithms also can be used to augment radiologist reporting systems. This represents an investment in resources to build a team that should include a bioinformatics specialist, lung nodule registry personnel (review charts of the detected imaging studies with lung nodules, populating the lung nodule database, and determining and tracking the need of imaging follow up), a lung nodule clinic nurse coordinator, and a dedicated lung nodule clinic pulmonologist.

Radiology departments could employ this text search approach to identify missed nodules and use an audit and feedback system to train radiologists to code lung nodules consistently at the time of the initial reading to avoid delays in identifying patients with nodules. Alternatively, the more widespread use of a standardized CT chest radiology reports using Fleischner or the American College of Radiology Lung Imaging Reporting and Data System (Lung RADS) templates might improve the detection of patients with lung nodules.^5,13,14

Limitations

This study was performed in a single VHA hospital and the findings may not be generalizable to other settings of care. Second, our study design is susceptible to work-up bias because the results of a diagnostic test (eg, chest or abdomen imaging) affected whether the chart review was used to verify the test result. It was not feasible to review the patient records of all radiology studies done at the facility during the study period, consequently complete 2 × 2 tables could not be created to calculate sensitivity, specificity, and negative predictive value.

Conclusion

A text search algorithm of radiology reports increased the detection of patients with lung nodules when compared with radiology diagnostic coding alone. However, the improved detection was associated with a higher rate of false positives, which requires manually reviewing a larger number of patient’s chart reports. Future research and quality improvement should focus on standardizing the radiology reporting process and improving the efficiency and reliability of follow up and tracking of incidental lung nodules.

Acknowledgments

The work reported here was supported by a grant from the Office of Rural Health (N32-FY16Q1-S1-P01577), US Department of Veterans Affairs, Veterans Health Administration. We also had the support from the Veterans Rural Health Resource Center-Iowa City, and the Health Services Research and Development (HSR&D) Service through the Comprehensive Access and Delivery Research and Evaluation (CADRE) Center (REA 09-220).

Rapid advances in imaging technology have led to better spatial resolution with lower radiation doses to patients. These advances have helped to increase the use of diagnostic chest imaging, particularly in emergency departments and oncology centers, and in screening for coronary artery disease. As a result, there has been an explosion of incidental findings on chest imaging—including indeterminate lung nodules.^1,2

Lung nodules are rounded and well-circumscribed lung opacities (≤ 3 cm in diameter) that may present as solitary or multiple lesions in usually asymptomatic patients. Most lung nodules are benign, the result of an infectious or inflammatory process. Nodules that are ≤ 8 mm in diameter, unless they show increase in size over time, often can be safely followed with imaging surveillance. In contrast, lung nodules > 8 mm could represent an early-stage lung cancer, especially among patients with high-risk for developing lung cancer (ie, those with advanced age, heavy tobacco abuse, or emphysema) and should be further assessed with close imaging surveillance, either chest computed tomography (CT) alone or positron-emission tomography (PET)/CT, or tissue biopsy, based on the underlying likelihood of malignancy.

Patients who receive an early-stage lung cancer diagnosis can be offered curative treatments leading to improved 5-year survival rates.^3,4 Consequently, health care systems need to be able to identify these nodules accurately, in order to categorize and manage them accordingly to the Fleischner radiographic and American College of Chest Physicians clinical guidelines.^5,6 Unfortunately, many hospitals struggle to identify patients with incidental lung nodules found during diagnostic chest and abdominal imaging, due in part to poor adherence to Fleischner guidelines among radiologists for categorizing pulmonary nodules.^7,8

The Veterans Health Administration (VHA) system is interested in effectively detecting patients with incidental lung nodules. Veterans have a higher risk of developing lung cancer when compared with the entire US population, mainly due to a higher incidence of tobacco use.⁶ The prevalence of lung nodules among veterans with significant risk factors for lung cancer is about 60% nationwide, and up to 85% in the Midwest, due to the high prevalence of histoplasmosis.⁷ However, only a small percentage of these nodules represent an early stage primary lung cancer.

Several Veterans Integrated Service Networks (VISNs) in the VHA use a radiology diagnostic code to systematically identify imaging studies with presence of lung nodules. In VISN 23, which includes Minnesota, North Dakota, South Dakota, Iowa, and portions of neighboring states, the code used to identify these radiology studies is 44. However, there is high variability in the reporting and coding of imaging studies among radiologists, which could lead to misclassifying patients with lung nodules.⁸

Some studies suggest that using an automated text search algorithm within radiology reports can be a highly effective strategy to identify patients with lung nodules.^9,10 In this study, we compared the diagnostic performance of a newly developed text search algorithm applied to radiology reports with the current standard practice of using a radiology diagnostic code for identifying patients with lung nodules at the Iowa City US Department of Veterans Affairs (VA) Health Care System (ICVAHCS) hospital in Iowa.

Methods

Since 2014, The ICVAHCS has used a radiology diagnostic code to identify any imaging studies with lung nodules. The radiologist enters “44” at the end of the reading process using the Nuance Powerscribe 360 radiation reporting system. The code is uploaded into the VHA Corporate Data Warehouse (CDW), and it is located within the radiology exam domain. This strategy was created and implemented by the Minneapolis VA Health Care System in Minnesota for all the VA hospitals in VISN 23. A lung nodule registry nurse was provided with a list of radiology studies flagged with this radiology diagnostic code every 2 weeks. A chart review was then performed for all these studies to determine the presence of a lung nodule. When detected, the ordering health care provider was alerted and given recommendations for managing the nodule.

We initially searched for the radiology studies with a presumptive lung nodule using the radiology code 44 within the CDW. Separately, we applied the text search strategy only to radiology reports from chest and abdomen studies (ie, X-rays, CT, magnetic resonance imaging [MRI], and PET) that contained any of the keyword phrases. The text search strategy was modeled based on a natural language processing (NLP) algorithm developed by the Puget Sound VA Healthcare System in Seattle, Washington to identify lung nodules on radiology reports.⁹ Our algorithm included a series of text searches using Microsoft SQL. After several simulations using a random group of radiology reports, we chose the keywords: “lung AND nodul”; “pulm AND nodul”; “pulm AND mass”; “lung AND mass”; and “ground glass”. We selected only chest and abdomen studies because on several simulations using a random group of radiology reports, the vast majority of lung nodules were identified on chest and abdomen imaging studies. Also, it would not have been feasible to chart review the approximately 30,000 total radiology reports that were generated during the study period.

From January 1, 2016 through November 30, 2016, we applied both search strategies independently: radiology diagnostic code for lung nodules to all imaging studies, and text search to all radiology reports of chest and abdomen imaging studies in the CDW (Figure). We also collected demographic (eg, age, sex, race, rurality) and clinical (eg, medical comorbidities, tobacco use) information that were uploaded to the database automatically from CDW using International Statistical Classification of Diseases, Tenth Edition and demographic codes. The VHA uses the Rural-Urban Commuting Areas (RUCA) system to define rurality, which takes into account population density and how closely a community is linked socioeconomically to larger urban centers.¹¹ The protocol was reviewed and approved by the institutional review board of ICVAHCS and the University of Iowa.

Results

We identified 12,983 radiology studies that required manual review during the study period. We confirmed that 8,516 imaging studies had lung nodules, representing 2,912 patients. Subjects with lung nodules were predominantly male (96%), aged between 60 and 79 years (71%), and lived in a rural area (72%). More than 50% of these patients had COPD and over a third were current smokers (Table 1). The text search algorithm identified all of the patients identified by the radiology diagnostic code (n = 1,251). It also identified an additional 1,661 patients with lung nodules that otherwise would have been missed by the radiology code. Compared with those identified only by the text search, those identified by both the radiology coding and text search were older, had lower Charlson comorbidity scores, and were more likely to be a current smoker.

The text search algorithm identified more than twice as many patients with potential lung nodules compared with the radiology diagnostic code (4,071 vs 1,363) (Table 2). However, the text search algorithm was associated with a much higher number of false positives than was the diagnostic code (1,159 vs 112) and a lower PPV (72% [95% CI, 70.6-73.4] vs 92% [95% CI, 90.6-93.4], respectively). The text search algorithm identified 130 patients with lung nodules of moderate to high risk for malignancy (> 8 mm diameter) that were not identified by the radiology code. When the PPV of each search strategy was calculated based on imaging studies with nodules (most patients had > 1 imaging study), the results remained similar (98% for radiology code and 66% for text search). A larger proportion of the lung nodules detected by code 44 vs the text search algorithm were from CT chest studies.

Discussion

In a population of predominantly older male veterans with significant risk factors for lung cancer and high incidence of incidental lung nodules, applying a text search algorithm on radiology reports identified a substantial number of patients with lung nodules, including some with nodules > 8 mm, that were missed by the radiologist-generated code.^9,10 Improving the yield of detection for lung nodules in a population with high risk for lung cancer would increase the likelihood of detecting patients with potentially curable early-stage lung cancers, decreasing lung cancer mortality.

The reasons for the high number of patients with lung nodules missed by the radiology code are unclear. Potential explanations may include the lack of standardization of imaging reports by the radiologists (ie, only 21% of chest CTs used a standardized template describing a lung nodule in our study), a problem well recognized both within and outside VHA.^8,12

The text search algorithm identified more patients with lung nodules but had a higher rate of false positives when compared with the diagnostic code. The high rate of false positives resulted in more charts to review and an increased workload for the lung nodule registry team. The challenges presented by an increased workload should be balanced against the potential harms of missing nodules that develop into advanced cancer.

Text Search Adjustments

Refining the text search criteria algorithm and the chart review process may decrease the rate of false positives significantly without affecting detection of lung nodules. In subsequent simulations, we found that by adding an exclusion criteria to text search algorithm to remove reports with specific keywords we could substantially reduce the number of false positive reports without affecting the detection rate of the lung nodules. These exclusion criteria would exclude any reports that: (1) contain “nodul” within the next 8 words after mentioning “no”; (2) contain “clear” within the next 8 words after mentioning “lung” in the text (eg, “lungs appear to be clear”); (3) contain “clear” within the next 4 words after mentioning “otherwise” in the text (eg, “otherwise appear to be clear”). Based on our study results, we further refined the text search strategy by limiting the search to only chest imaging studies. When we applied the revised algorithm to a random sample of imaging reports, we found all the code 44 radiology reports were still captured, but we were able to reduce the number of radiology reports needing review by about 80%.

Although classification approaches are being refined to improve radiology performance in multiple categories of nodules, this study suggests that alternative approaches based on text algorithms can improve the capture of pulmonary nodules that require surveillance. These algorithms also can be used to augment radiologist reporting systems. This represents an investment in resources to build a team that should include a bioinformatics specialist, lung nodule registry personnel (review charts of the detected imaging studies with lung nodules, populating the lung nodule database, and determining and tracking the need of imaging follow up), a lung nodule clinic nurse coordinator, and a dedicated lung nodule clinic pulmonologist.

Radiology departments could employ this text search approach to identify missed nodules and use an audit and feedback system to train radiologists to code lung nodules consistently at the time of the initial reading to avoid delays in identifying patients with nodules. Alternatively, the more widespread use of a standardized CT chest radiology reports using Fleischner or the American College of Radiology Lung Imaging Reporting and Data System (Lung RADS) templates might improve the detection of patients with lung nodules.^5,13,14

Limitations

This study was performed in a single VHA hospital and the findings may not be generalizable to other settings of care. Second, our study design is susceptible to work-up bias because the results of a diagnostic test (eg, chest or abdomen imaging) affected whether the chart review was used to verify the test result. It was not feasible to review the patient records of all radiology studies done at the facility during the study period, consequently complete 2 × 2 tables could not be created to calculate sensitivity, specificity, and negative predictive value.

Conclusion

A text search algorithm of radiology reports increased the detection of patients with lung nodules when compared with radiology diagnostic coding alone. However, the improved detection was associated with a higher rate of false positives, which requires manually reviewing a larger number of patient’s chart reports. Future research and quality improvement should focus on standardizing the radiology reporting process and improving the efficiency and reliability of follow up and tracking of incidental lung nodules.

Acknowledgments

The work reported here was supported by a grant from the Office of Rural Health (N32-FY16Q1-S1-P01577), US Department of Veterans Affairs, Veterans Health Administration. We also had the support from the Veterans Rural Health Resource Center-Iowa City, and the Health Services Research and Development (HSR&D) Service through the Comprehensive Access and Delivery Research and Evaluation (CADRE) Center (REA 09-220).

Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is a well-established technique for the evaluation of coronary artery disease (CAD).¹ To improve image quality, low-resolution computed tomography (CT) is used commonly for anatomical correct and artifact attenuation during SPECT MPI.² The low resolution, unenhanced CT images are considered low quality and are, therefore, labeled by the manufacturer as nondiagnostic. The CT portion of the MPI in many centers is used only for image fusion and attenuation correction, and these images are not routinely reviewed or reported by cardiologists.

Incidental findings by these low-resolution CT were frequent. However, clinically significant findings, including lung cancer, although relatively infrequent, were serious enough for major clinical management.^3-5 Currently, there are no consensus recommendations for reviewing low-resolution CT images or the interpretation of such incidental findings during cardiac MPI.⁶ Clinically, low-dose CT were used for early detection and screening of lung cancer and were associated with reduced lung-cancer and any cause mortality in National Lung Screening Trial (NLST).^7,8 Therefore, low-dose CT is recommended for lung cancer screening of high-risk patients by the US Preventive Service Task Force (USPSTF).⁹ In the veteran population, current and past smoking history are more common when compared with the general population; therefore, veterans are potentially at increased risk of lung cancer.¹⁰ In this study, we did not intend to use low-resolution CT for lung cancer screening or detection but rather to identify and report incidental findings of pulmonary/hilar malignancy detected during cardiac MPI.

Methods

The Siemens’ (Munich, Germany) Symbia Intevo Excel SPECT/CT MPI cameras with dedicated cardiac collimators were used at both the Dwight D. Eisenhower VA Medical Center (VAMC) in Leavenworth, Kansas and Colmery-O'Neil VAMC in Topeka, Kansas. The integrated CT scanner (x-ray tube current 30 to 240 mA; voltage 110 Kv with a 40 kW power generator) has the capability to image up to a 2-slice/rotation, each of 5.0 mm per slice with a scan time of about 30 seconds. The SPECT/CT gamma camera has a low energy (140 KeV), high resolution, parallel hole collimator with IQ SPECT capabilities.

The radiation dose received by the patients were expressed in dose length product (DLP), which reflects the total energy absorbed by the patient and represents integrated dose in terms of the total scan length. Additionally, each patients received 2 injections of Technetium Tc 99m sestamibi (1-day Protocol: 10 mCi rest injection, 30 mCi stress injection: 2-day Protocol for patients weighing > 350 pounds: 30 mCi at rest injection and 30 mCi at stress injection) for myocardial perfusion imaging.

All CT images and cardiac MPI findings were reviewed and reported contemporaneously by 1 of 2 experienced, board-certified radiologists who were blinded to patients’ clinical information except the indication for the cardiac stress testing. When suspicious pulmonary/hilar nodules or masses were detected, these findings and recommendations for further evaluation were conveyed to primary care provider or ordering physician via the electronic health record system.

All CT images were reviewed with cardiac MPI from September 1, 2017 to August 31, 2018. When pulmonary/hilar malignancies were identified, the health records were reviewed. Patients with known history of prior pulmonary malignancy were excluded from the study.

Results

A total of 1,098 patients underwent cardiac MPI during the study period. When the CT imaging and cardiac MPI were reviewed, incidental findings led to the diagnosis of lung cancer in 5 patients and hilar mantle cell lymphoma in 1 patient. Their clinical characteristics, CT findings, and types of malignancies for these 6 patients are summarized in the Table and Figure. Only 0.55% (6 of 1,098) patients were found to have incidental pulmonary/hilar malignancy with the cardiac evaluation low-resolution CT. Four patients with prior, known history of lung cancer were excluded from the study.

For the 6 patients found to have cancer, the average CT radiation dose during the cardiac MPI was 100 mGy-cm (range, 77 -133 mCy-cm). The subsequent chest CT with or without contrast delivered a radiation dose of 726.4 mGy-cm (range, 279.4 - 1,075 mGy-cm).

Discussion

Although incidental findings on low-resolution CT during cardiac MPI are frequent, clinically significant findings are less common. However, some incidental findings may be of important clinical significance.^3-5 A multicenter analysis by Coward and colleagues reported that 2.4% findings on low-resolution CT were significant enough to warrant follow-up tests, but only 0.2% were deemed potentially detrimental to patient outcomes (ie, pathology confirmed malignancies).¹² Thus, the authors suggested that routine reporting of incidental findings on low-dose CT images was not beneficial.^12,13

Currently, the majority of cardiac MPIs are reviewed and interpreted by nuclear cardiologists, the use of hybrid SPECT/CT for attenuation correction give rise of issue of reviewing and interpreting these CT images during cardiac MPI. Since low-dose, low-resolution CT are considered nondiagnostic, these images are not routinely and readily reviewed by cardiologists who are not trained or skilled in CT interpretations.

Studies of high-resolution cardiac CT (including multidetector CT with contrast) suggest that incidental extracardiac findings should always be reported as there was a 0.7% incidence of previously unknown malignancies, while others have argued against “performing large field reconstructionsfor the explicit purpose of screening as it will lead to additional cost, liability and anxiety without proven benefits.”^14-16 A review of incidental findings of cardiac CT by Earls suggested that all cardiac CT should be reconstructed in the maximal field of view available and images should be adequately reviewed to detect pathological findings.¹⁷ This led to an interesting discussion by Douglas and colleagues regarding the role of cardiologists and radiologists in this issue.¹⁸ Currently there is no uniform or consensus recommendations regarding incidental findings during cardiac CT imaging. Guidances range from no recommendations to optional reporting or mandatory reporting.^19-23

Risk Factors for Veterans

Lung cancer is the second most common cancer and the leading cause of cancer-related death in the US.²⁴ Smoking is the most important risk factor for lung cancer and CAD.²⁵ Current or past smoking are more common among the veterans.¹⁰ According to a report for the US Centers for Disease Control and Prevention report, about 29.2% US veterans use tobacco products between 2010-2015, which is similar to the rate reported in 1997.²⁶

When low-dose CT was used for lung cancer screening, it was associated with a 20.0% reduction in lung cancer mortality and a 6.7% reduction in any cause mortality.⁷ Currently, the US Preventive Services Task Force (USPSTF) recommends annual low-dose CT screening for lung cancer in high-risk adults that includes patients aged 55 to 80 years who have a 30-pack-year smoking history and currently smoke or have quit within the past 15 years.⁸

It is likely that the cardiac patients in this study might have pulmonary malignancy mortality similar to those reported in the NLST. While other studies have shown a low incidence (0.2%) of detection of malignancy by low-resolution CT during cardiac MPI,^12,13 in this study we found pulmonary or hilar malignancy in 0.55% of patients.The higher incidence of malignancy in our study might be due in part to differences in the patient population studied (ie, our veterans patients have a higher proportion of current or past smoking history).¹⁰

The CT used in this study is part of the cardiac imaging process. Therefore, there was no additional radiation exposure besides that of the cardiac MPI for patients. Despite the limitations of low-resolution CT, which may miss small lesions, this study showed 0.55% incidence of incidental detection of pulmonary/hilar malignancy. This is comparable with 0.65%/year of diagnosing lung cancer using low-dose CT for lung cancer screening in NLST.⁸

Two of the 5 study patients who were found to have lung cancer, had quit smoking > 15 years previously and thus would not be considered as high-risk for lung cancer screening according to USPSTF guideline. These patients would not have been candidates for annual low-dose CT lung cancer screening. This study suggests that it is appropriate and necessary to review the low-resolution CT images for incidental findings during cardiac MPI.

Limitations

The study was retrospective in nature and limited by its small number of patients. The CT modality used in the study also has limitations, including low resolution, respiratory motion artifacts, and scans that did not include the entire chest area. Therefore, small and apical lesions may have been missed. However, both sets of CT at rest and after stress were reviewed to reduce or minimize the effects of respiratory motion artifacts. The true prevalence or incidence of pulmonary/hilar malignancies may have been higher than reported here. Our study population of veterans may not be representative of the general population with regards to gender (as most of our veteran patient population are of male gender, vs general population), smoking history, or lung cancer risk, thus the results should be interpreted with caution.

Conclusion

Low-resolution CTs used for attenuation correction during cardiac MPI should be routinely reviewed and interpreted by a physician or radiologist skilled in CT interpretation in order to identify incidental findings of pulmonary/hilar malignancy. This would require close collaboration between cardiologists and radiologists in the field to ensure unfragmented and high-quality patient care.

Acknowledgements

We want to thank all the staffs in cardiology and radiology department on both campuses for their dedication for our patients. Special thanks to Laura Knox, Radiation Safety Officer, Nuclear Medicine Supervisor for her technical assistance.

Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is a well-established technique for the evaluation of coronary artery disease (CAD).¹ To improve image quality, low-resolution computed tomography (CT) is used commonly for anatomical correct and artifact attenuation during SPECT MPI.² The low resolution, unenhanced CT images are considered low quality and are, therefore, labeled by the manufacturer as nondiagnostic. The CT portion of the MPI in many centers is used only for image fusion and attenuation correction, and these images are not routinely reviewed or reported by cardiologists.

Incidental findings by these low-resolution CT were frequent. However, clinically significant findings, including lung cancer, although relatively infrequent, were serious enough for major clinical management.^3-5 Currently, there are no consensus recommendations for reviewing low-resolution CT images or the interpretation of such incidental findings during cardiac MPI.⁶ Clinically, low-dose CT were used for early detection and screening of lung cancer and were associated with reduced lung-cancer and any cause mortality in National Lung Screening Trial (NLST).^7,8 Therefore, low-dose CT is recommended for lung cancer screening of high-risk patients by the US Preventive Service Task Force (USPSTF).⁹ In the veteran population, current and past smoking history are more common when compared with the general population; therefore, veterans are potentially at increased risk of lung cancer.¹⁰ In this study, we did not intend to use low-resolution CT for lung cancer screening or detection but rather to identify and report incidental findings of pulmonary/hilar malignancy detected during cardiac MPI.

Methods

The Siemens’ (Munich, Germany) Symbia Intevo Excel SPECT/CT MPI cameras with dedicated cardiac collimators were used at both the Dwight D. Eisenhower VA Medical Center (VAMC) in Leavenworth, Kansas and Colmery-O'Neil VAMC in Topeka, Kansas. The integrated CT scanner (x-ray tube current 30 to 240 mA; voltage 110 Kv with a 40 kW power generator) has the capability to image up to a 2-slice/rotation, each of 5.0 mm per slice with a scan time of about 30 seconds. The SPECT/CT gamma camera has a low energy (140 KeV), high resolution, parallel hole collimator with IQ SPECT capabilities.

The radiation dose received by the patients were expressed in dose length product (DLP), which reflects the total energy absorbed by the patient and represents integrated dose in terms of the total scan length. Additionally, each patients received 2 injections of Technetium Tc 99m sestamibi (1-day Protocol: 10 mCi rest injection, 30 mCi stress injection: 2-day Protocol for patients weighing > 350 pounds: 30 mCi at rest injection and 30 mCi at stress injection) for myocardial perfusion imaging.

All CT images and cardiac MPI findings were reviewed and reported contemporaneously by 1 of 2 experienced, board-certified radiologists who were blinded to patients’ clinical information except the indication for the cardiac stress testing. When suspicious pulmonary/hilar nodules or masses were detected, these findings and recommendations for further evaluation were conveyed to primary care provider or ordering physician via the electronic health record system.

All CT images were reviewed with cardiac MPI from September 1, 2017 to August 31, 2018. When pulmonary/hilar malignancies were identified, the health records were reviewed. Patients with known history of prior pulmonary malignancy were excluded from the study.

Results

A total of 1,098 patients underwent cardiac MPI during the study period. When the CT imaging and cardiac MPI were reviewed, incidental findings led to the diagnosis of lung cancer in 5 patients and hilar mantle cell lymphoma in 1 patient. Their clinical characteristics, CT findings, and types of malignancies for these 6 patients are summarized in the Table and Figure. Only 0.55% (6 of 1,098) patients were found to have incidental pulmonary/hilar malignancy with the cardiac evaluation low-resolution CT. Four patients with prior, known history of lung cancer were excluded from the study.

For the 6 patients found to have cancer, the average CT radiation dose during the cardiac MPI was 100 mGy-cm (range, 77 -133 mCy-cm). The subsequent chest CT with or without contrast delivered a radiation dose of 726.4 mGy-cm (range, 279.4 - 1,075 mGy-cm).

Discussion

Although incidental findings on low-resolution CT during cardiac MPI are frequent, clinically significant findings are less common. However, some incidental findings may be of important clinical significance.^3-5 A multicenter analysis by Coward and colleagues reported that 2.4% findings on low-resolution CT were significant enough to warrant follow-up tests, but only 0.2% were deemed potentially detrimental to patient outcomes (ie, pathology confirmed malignancies).¹² Thus, the authors suggested that routine reporting of incidental findings on low-dose CT images was not beneficial.^12,13

Currently, the majority of cardiac MPIs are reviewed and interpreted by nuclear cardiologists, the use of hybrid SPECT/CT for attenuation correction give rise of issue of reviewing and interpreting these CT images during cardiac MPI. Since low-dose, low-resolution CT are considered nondiagnostic, these images are not routinely and readily reviewed by cardiologists who are not trained or skilled in CT interpretations.

Studies of high-resolution cardiac CT (including multidetector CT with contrast) suggest that incidental extracardiac findings should always be reported as there was a 0.7% incidence of previously unknown malignancies, while others have argued against “performing large field reconstructionsfor the explicit purpose of screening as it will lead to additional cost, liability and anxiety without proven benefits.”^14-16 A review of incidental findings of cardiac CT by Earls suggested that all cardiac CT should be reconstructed in the maximal field of view available and images should be adequately reviewed to detect pathological findings.¹⁷ This led to an interesting discussion by Douglas and colleagues regarding the role of cardiologists and radiologists in this issue.¹⁸ Currently there is no uniform or consensus recommendations regarding incidental findings during cardiac CT imaging. Guidances range from no recommendations to optional reporting or mandatory reporting.^19-23

Risk Factors for Veterans

Lung cancer is the second most common cancer and the leading cause of cancer-related death in the US.²⁴ Smoking is the most important risk factor for lung cancer and CAD.²⁵ Current or past smoking are more common among the veterans.¹⁰ According to a report for the US Centers for Disease Control and Prevention report, about 29.2% US veterans use tobacco products between 2010-2015, which is similar to the rate reported in 1997.²⁶

When low-dose CT was used for lung cancer screening, it was associated with a 20.0% reduction in lung cancer mortality and a 6.7% reduction in any cause mortality.⁷ Currently, the US Preventive Services Task Force (USPSTF) recommends annual low-dose CT screening for lung cancer in high-risk adults that includes patients aged 55 to 80 years who have a 30-pack-year smoking history and currently smoke or have quit within the past 15 years.⁸

It is likely that the cardiac patients in this study might have pulmonary malignancy mortality similar to those reported in the NLST. While other studies have shown a low incidence (0.2%) of detection of malignancy by low-resolution CT during cardiac MPI,^12,13 in this study we found pulmonary or hilar malignancy in 0.55% of patients.The higher incidence of malignancy in our study might be due in part to differences in the patient population studied (ie, our veterans patients have a higher proportion of current or past smoking history).¹⁰

The CT used in this study is part of the cardiac imaging process. Therefore, there was no additional radiation exposure besides that of the cardiac MPI for patients. Despite the limitations of low-resolution CT, which may miss small lesions, this study showed 0.55% incidence of incidental detection of pulmonary/hilar malignancy. This is comparable with 0.65%/year of diagnosing lung cancer using low-dose CT for lung cancer screening in NLST.⁸

Two of the 5 study patients who were found to have lung cancer, had quit smoking > 15 years previously and thus would not be considered as high-risk for lung cancer screening according to USPSTF guideline. These patients would not have been candidates for annual low-dose CT lung cancer screening. This study suggests that it is appropriate and necessary to review the low-resolution CT images for incidental findings during cardiac MPI.

Limitations

The study was retrospective in nature and limited by its small number of patients. The CT modality used in the study also has limitations, including low resolution, respiratory motion artifacts, and scans that did not include the entire chest area. Therefore, small and apical lesions may have been missed. However, both sets of CT at rest and after stress were reviewed to reduce or minimize the effects of respiratory motion artifacts. The true prevalence or incidence of pulmonary/hilar malignancies may have been higher than reported here. Our study population of veterans may not be representative of the general population with regards to gender (as most of our veteran patient population are of male gender, vs general population), smoking history, or lung cancer risk, thus the results should be interpreted with caution.

Conclusion

Low-resolution CTs used for attenuation correction during cardiac MPI should be routinely reviewed and interpreted by a physician or radiologist skilled in CT interpretation in order to identify incidental findings of pulmonary/hilar malignancy. This would require close collaboration between cardiologists and radiologists in the field to ensure unfragmented and high-quality patient care.

Acknowledgements

We want to thank all the staffs in cardiology and radiology department on both campuses for their dedication for our patients. Special thanks to Laura Knox, Radiation Safety Officer, Nuclear Medicine Supervisor for her technical assistance.

Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is a well-established technique for the evaluation of coronary artery disease (CAD).¹ To improve image quality, low-resolution computed tomography (CT) is used commonly for anatomical correct and artifact attenuation during SPECT MPI.² The low resolution, unenhanced CT images are considered low quality and are, therefore, labeled by the manufacturer as nondiagnostic. The CT portion of the MPI in many centers is used only for image fusion and attenuation correction, and these images are not routinely reviewed or reported by cardiologists.

Incidental findings by these low-resolution CT were frequent. However, clinically significant findings, including lung cancer, although relatively infrequent, were serious enough for major clinical management.^3-5 Currently, there are no consensus recommendations for reviewing low-resolution CT images or the interpretation of such incidental findings during cardiac MPI.⁶ Clinically, low-dose CT were used for early detection and screening of lung cancer and were associated with reduced lung-cancer and any cause mortality in National Lung Screening Trial (NLST).^7,8 Therefore, low-dose CT is recommended for lung cancer screening of high-risk patients by the US Preventive Service Task Force (USPSTF).⁹ In the veteran population, current and past smoking history are more common when compared with the general population; therefore, veterans are potentially at increased risk of lung cancer.¹⁰ In this study, we did not intend to use low-resolution CT for lung cancer screening or detection but rather to identify and report incidental findings of pulmonary/hilar malignancy detected during cardiac MPI.

Methods

The Siemens’ (Munich, Germany) Symbia Intevo Excel SPECT/CT MPI cameras with dedicated cardiac collimators were used at both the Dwight D. Eisenhower VA Medical Center (VAMC) in Leavenworth, Kansas and Colmery-O'Neil VAMC in Topeka, Kansas. The integrated CT scanner (x-ray tube current 30 to 240 mA; voltage 110 Kv with a 40 kW power generator) has the capability to image up to a 2-slice/rotation, each of 5.0 mm per slice with a scan time of about 30 seconds. The SPECT/CT gamma camera has a low energy (140 KeV), high resolution, parallel hole collimator with IQ SPECT capabilities.

The radiation dose received by the patients were expressed in dose length product (DLP), which reflects the total energy absorbed by the patient and represents integrated dose in terms of the total scan length. Additionally, each patients received 2 injections of Technetium Tc 99m sestamibi (1-day Protocol: 10 mCi rest injection, 30 mCi stress injection: 2-day Protocol for patients weighing > 350 pounds: 30 mCi at rest injection and 30 mCi at stress injection) for myocardial perfusion imaging.

All CT images and cardiac MPI findings were reviewed and reported contemporaneously by 1 of 2 experienced, board-certified radiologists who were blinded to patients’ clinical information except the indication for the cardiac stress testing. When suspicious pulmonary/hilar nodules or masses were detected, these findings and recommendations for further evaluation were conveyed to primary care provider or ordering physician via the electronic health record system.

All CT images were reviewed with cardiac MPI from September 1, 2017 to August 31, 2018. When pulmonary/hilar malignancies were identified, the health records were reviewed. Patients with known history of prior pulmonary malignancy were excluded from the study.

Results

A total of 1,098 patients underwent cardiac MPI during the study period. When the CT imaging and cardiac MPI were reviewed, incidental findings led to the diagnosis of lung cancer in 5 patients and hilar mantle cell lymphoma in 1 patient. Their clinical characteristics, CT findings, and types of malignancies for these 6 patients are summarized in the Table and Figure. Only 0.55% (6 of 1,098) patients were found to have incidental pulmonary/hilar malignancy with the cardiac evaluation low-resolution CT. Four patients with prior, known history of lung cancer were excluded from the study.

For the 6 patients found to have cancer, the average CT radiation dose during the cardiac MPI was 100 mGy-cm (range, 77 -133 mCy-cm). The subsequent chest CT with or without contrast delivered a radiation dose of 726.4 mGy-cm (range, 279.4 - 1,075 mGy-cm).

Discussion

Although incidental findings on low-resolution CT during cardiac MPI are frequent, clinically significant findings are less common. However, some incidental findings may be of important clinical significance.^3-5 A multicenter analysis by Coward and colleagues reported that 2.4% findings on low-resolution CT were significant enough to warrant follow-up tests, but only 0.2% were deemed potentially detrimental to patient outcomes (ie, pathology confirmed malignancies).¹² Thus, the authors suggested that routine reporting of incidental findings on low-dose CT images was not beneficial.^12,13

Currently, the majority of cardiac MPIs are reviewed and interpreted by nuclear cardiologists, the use of hybrid SPECT/CT for attenuation correction give rise of issue of reviewing and interpreting these CT images during cardiac MPI. Since low-dose, low-resolution CT are considered nondiagnostic, these images are not routinely and readily reviewed by cardiologists who are not trained or skilled in CT interpretations.

Studies of high-resolution cardiac CT (including multidetector CT with contrast) suggest that incidental extracardiac findings should always be reported as there was a 0.7% incidence of previously unknown malignancies, while others have argued against “performing large field reconstructionsfor the explicit purpose of screening as it will lead to additional cost, liability and anxiety without proven benefits.”^14-16 A review of incidental findings of cardiac CT by Earls suggested that all cardiac CT should be reconstructed in the maximal field of view available and images should be adequately reviewed to detect pathological findings.¹⁷ This led to an interesting discussion by Douglas and colleagues regarding the role of cardiologists and radiologists in this issue.¹⁸ Currently there is no uniform or consensus recommendations regarding incidental findings during cardiac CT imaging. Guidances range from no recommendations to optional reporting or mandatory reporting.^19-23

Risk Factors for Veterans

Lung cancer is the second most common cancer and the leading cause of cancer-related death in the US.²⁴ Smoking is the most important risk factor for lung cancer and CAD.²⁵ Current or past smoking are more common among the veterans.¹⁰ According to a report for the US Centers for Disease Control and Prevention report, about 29.2% US veterans use tobacco products between 2010-2015, which is similar to the rate reported in 1997.²⁶

When low-dose CT was used for lung cancer screening, it was associated with a 20.0% reduction in lung cancer mortality and a 6.7% reduction in any cause mortality.⁷ Currently, the US Preventive Services Task Force (USPSTF) recommends annual low-dose CT screening for lung cancer in high-risk adults that includes patients aged 55 to 80 years who have a 30-pack-year smoking history and currently smoke or have quit within the past 15 years.⁸

It is likely that the cardiac patients in this study might have pulmonary malignancy mortality similar to those reported in the NLST. While other studies have shown a low incidence (0.2%) of detection of malignancy by low-resolution CT during cardiac MPI,^12,13 in this study we found pulmonary or hilar malignancy in 0.55% of patients.The higher incidence of malignancy in our study might be due in part to differences in the patient population studied (ie, our veterans patients have a higher proportion of current or past smoking history).¹⁰

The CT used in this study is part of the cardiac imaging process. Therefore, there was no additional radiation exposure besides that of the cardiac MPI for patients. Despite the limitations of low-resolution CT, which may miss small lesions, this study showed 0.55% incidence of incidental detection of pulmonary/hilar malignancy. This is comparable with 0.65%/year of diagnosing lung cancer using low-dose CT for lung cancer screening in NLST.⁸

Two of the 5 study patients who were found to have lung cancer, had quit smoking > 15 years previously and thus would not be considered as high-risk for lung cancer screening according to USPSTF guideline. These patients would not have been candidates for annual low-dose CT lung cancer screening. This study suggests that it is appropriate and necessary to review the low-resolution CT images for incidental findings during cardiac MPI.

Limitations

The study was retrospective in nature and limited by its small number of patients. The CT modality used in the study also has limitations, including low resolution, respiratory motion artifacts, and scans that did not include the entire chest area. Therefore, small and apical lesions may have been missed. However, both sets of CT at rest and after stress were reviewed to reduce or minimize the effects of respiratory motion artifacts. The true prevalence or incidence of pulmonary/hilar malignancies may have been higher than reported here. Our study population of veterans may not be representative of the general population with regards to gender (as most of our veteran patient population are of male gender, vs general population), smoking history, or lung cancer risk, thus the results should be interpreted with caution.

Conclusion

Low-resolution CTs used for attenuation correction during cardiac MPI should be routinely reviewed and interpreted by a physician or radiologist skilled in CT interpretation in order to identify incidental findings of pulmonary/hilar malignancy. This would require close collaboration between cardiologists and radiologists in the field to ensure unfragmented and high-quality patient care.

Acknowledgements

We want to thank all the staffs in cardiology and radiology department on both campuses for their dedication for our patients. Special thanks to Laura Knox, Radiation Safety Officer, Nuclear Medicine Supervisor for her technical assistance.

It was estimated that physicians would diagnose a form of invasive cancer > 1.7 million times in 2019. As the second most common cause of death in the US, > 600,000 people were projected to die from cancer in 2019.¹ Many individuals with cancer endure distress, which the National Comprehensive Cancer Network (NCCN) defines as a “multifactorial unpleasant experience of a psychological (ie, cognitive, behavioral, emotional), social, spiritual, and/or physical nature that may interfere with the ability to cope effectively with cancer, its physical symptoms, and its treatment.”^2,3 Distress in people living with cancer has been attributed to various psychosocial concerns, such as family problems, whichinclude dealing with partners and children; emotional problems, such as depression and anxiety; and physical symptoms, such as pain and fatigue.^4-9 Certain factors associated with distress may increase a patient’s risk for suicide.⁴

Veterans are at particularly high risk for suicide.¹⁰ In 2014, veterans accounted for 18% of completed suicides in the US but only were 8.5% of the total population that same year.¹⁰ Yet, little research has been done on the relationship between distress and suicide in veterans living with cancer. Aboumrad and colleagues found that 45% of veterans with cancer who completed suicide reported family issues and 41% endorsed chronic pain.¹¹ This study recommended continued efforts to assess and treat distress to lessen risk of suicide in veterans living with cancer; however, to date, only 1 study has specifically evaluated distress and problems endorsed among veterans living with cancer.⁷

Suicide prevention is of the highest priority to the US Department of Veterans Affairs (VA).¹² Consistent with the VA mission to end veteran suicide, the current study aimed to better understand the relationship between distress and suicide within a sample of veterans living with cancer. Findings would additionally be used to tailor clinical assessments and interventions for veterans living with cancer.

This study had 3 primary goals. First, we sought to understand demographic and clinical factors associated with low, moderate, and severe levels of distress in veterans living with cancer who were referred for psychology services. Second, the study investigated the most commonly endorsed problems by veterans living with cancer. Finally, we examined which problems were related to suicidal ideation (SI). It was hypothesized that veterans who reported severe distress would be significantly more likely to endorse SI when compared with veterans who reported mild or moderate distress. Based on existing literature, it was further hypothesized that family, emotional, and physical problems would be significantly associated with SI.^7,11

Methods

The current study was conducted at James A. Haley Veterans’ Hospital (JAHVH) in Tampa, Florida. Inclusion criteria included veterans who were diagnosed with cancer, attended an outpatient psychology-oncology evaluation, and completed mental health screening measures provided during their evaluation. Exclusion criteria included veterans who: were seen in response to an inpatient consult, were seen solely for a stem cell transplant evaluation, or did not complete the screening measures.

Measures

A veteran’s demographic (eg, age, sex, ethnicity) and clinical (eg, cancer type, stage of disease, recurrence, cancer treatments received) information was abstracted from their VA medical records. Marital status was assessed during a clinical interview and documented as part of the standardized suicide risk assessment.

The Distress Thermometer (DT) is a subjective measure developed by the NCCN.² The DT provides a visual representation of a thermometer and asks patients to rate their level of distress over the past week with 0 indicating no distress and 10 indicating extreme distress. A distress rating of 4 or higher is clinically significant.^4,6 Distress may be categorized into 3 levels of severity: mild distress (< 4), moderate distress (4-7), or severe distress (8-10). The DT has been found to have good face validity, sensitivity and specificity, and is user-friendly.^2,6,7,13

The measurement additionally lists 39 problems nested within 5 domains: practical, family, emotional, spiritual/religious, and physical. Patients may endorse listed items under each problem domain by indicating yes or no. Endorsement of various items are intended to provide more detailed information about sources of distress. Due to the predominantly male and mostly older population included in this study the ability to have children measure was removed from the family problem domain.

SI was assessed in 2 ways. First, by patients’ self-report through item-9 of the Patient Health Questionnaire-9 (PHQ-9).¹⁴ Item-9 asks “over the last 2 weeks, how often have you been bothered by thoughts that you would be better off dead or of hurting yourself in some way?” Responses range from 0 (not at all) to 3 (nearly every day).¹⁴ Responses > 0 were considered a positive screen for SI. The process of administering the PHQ-9 item-9 is part of a national VA directive for standardizing assessment of suicide risk (Steve Young, personal communication, May 23, 2018). The PHQ-9 has been found to have good construct validity when used with both medical samples and the general population along with good internal and test-retest reliability.^14,15 Second, all veterans also were asked directly about SI during clinical interview, the results of which were documented in health records using a standardized format for risk assessment.

Procedure

Participants were a sample of veterans who were referred for psychology-oncology services. The NCCN DT and Problems List were administered prior to the start of clinical interviews, which followed a checklist and included standardized assessments of SI and history of a suicide attempt(s). A licensed clinical psychologist or a postdoctoral resident conducted these assessments under the supervision of a licensed psychologist. Data gathered during the clinical interview and from the DT and problems list were documented in health records, which were retrospectively reviewed for relevant information (eg, cancer diagnosis, SI). Therefore, informed consent was waived. This study was approved by the JAHVH Institutional Review Board.

Analysis

Data were analyzed using SPSS Version 25. Data analysis proceeded in 3 steps. First, descriptive statistics included the demographic and clinical factors present in the current sample. Difference between those with and without suicidal ideation were compared using F-statistic for continuous variables and χ² analyses for categorical variables. Second, to examine relationships between each DT problem domain and SI, χ² analyses were conducted. Third, DT problem domains that had a significant relationship with SI were entered in a logistic regression. This analysis determined which items, if any, from a DT problem domain predicted SI. In the logistic regression model, history of suicide attempts was entered into the first block, as history of suicide attempts is a well-established risk factor for subsequent suicidal ideation. In the second block, other variables that were significantly related to suicidal ideation in the second step of analyses were included. Before interpreting the results of the logistic regression, model fit was tested using the Hosmer-Lemeshow test. Significance of each individual predictor variable in the model is reported using the Wald χ² statistic; each Wald statistic is compared with a χ² distribution with 1 degree of freedom (df). Results of logistic regression models also provide information about the effect of each predictor variable in the regression equation (beta weight), odds a veteran who endorsed each predictor variable in the model would also endorse SI (as indicated by the odds ratio), and an estimate of the amount of variance accounted for by each predictor variable (using Nagelkerke’s pseudo R², which ranges in value from 0 to 1 with higher values indicating more variance explained). For all analyses, P value of .05 (2-tailed) was used for statistical significance.

Results

The sample consisted of 174 veterans (Table 1). The majority (77.6%) were male with a mean age of nearly 62 years (range, 29-87). Most identified as white (74.1%) with half reporting they were either married or living with a partner.

Prostate cancer (19.0%) was the most common type of cancer among study participants followed by head and neck (18.4%), lymphoma/leukemia (11.5%), lung (11.5%), and breast (10.9%); 31.6% had metastatic disease and 14.9% had recurrent disease. Chemotherapy (42.5%) was the most common treatment modality, followed by surgery (38.5%) and radiation (31.6%). The sample was distributed among the 3 distress DT categories: mild (18.4%), moderate (42.5%), and severe (39.1%).

Problems Endorsed

Treatment decisions (44.3%) and insurance/financial concerns (35.1%) were the most frequently endorsed practical problems (Figure 1). Family health issues (33.9%) and dealing with partner (23.0%) were the most frequently endorsed family problems (Figure 2). Worry (73.0%) and depression (69.5%) were the most frequent emotional problems; of note, all emotional problems were endorsed by at least 50% of veterans (Figure 3). Fatigue (71.3%), sleep (70.7%), and pain (69%), were the most frequently endorsed physical problems (Figure 4). Spiritual/religious problems were endorsed by 15% of veterans.

Suicidal Ideation

Overall, 25.3% of veterans endorsed SI. About 20% of veterans reported a history of ≥ 1 suicide attempts in their lifetime. A significant relationship among distress categories and SI was found (χ² = 18.36, P < .001). Veterans with severe distress were more likely to endorse SI (42.7%) when compared with veterans with mild (9.4%) or moderate (16.2%) distress.

Similarly, a significant relationship among distress categories and a history of a suicide attempt(s) was found (χ² = 6.08, P = .048). Veterans with severe distress were more likely to have attempted suicide (29.4%) when compared with veterans with mild (12.5%) or moderate (14.9%) distress.

Discussion

This study aimed to understand factors associated with low, moderate, and severe levels of distress in veterans living with cancer who were referred for psychology services. As hypothesized, veterans who endorsed severe distress were significantly more likely to endorse SI. They also were more likely to have a history of a suicide attempt(s) when compared with those with mild or moderate distress.

A second aim of this study was to understand the most commonly endorsed problems. Consistent with prior literature, treatment decisions were the most commonly endorsed practical problem; worry and depression were the most common emotional problems; and fatigue, sleep, and pain were the most common physical problems.⁷

A finding unique to the current study is that family health issues and dealing with partner were specified as the most common family problems. However, a study by Smith and colleagues did not provide information about the rank of most frequently reported problems within this domain.⁷

The third aim was to understand which problems were related to SI. It was hypothesized that family, emotional, and physical problems would be related to SI. However, results indicated that only family problems (specifically, problems dealing with a partner) were significantly associated with SI among veterans living with cancer.

Contrary to expectations, emotional and physical problems were not found to have a significant relationship with SI. This is likely because veterans endorsed items nested within these problem domains with similar frequency. The lack of significant findings does not suggest that emotional and physical problems are not significant predictors of SI for veterans living with cancer, but that no specific emotional or physical symptom stood out as a predictor of suicidal ideation above the others.

The finding of a significant relationship between family problems (specifically, problems dealing with a partner) and SI in this study is consistent with findings of Aboumrad and colleagues in a study that examined root-cause analyses of completed suicides by veterans living with cancer.¹¹ They found that nearly half the sample endorsed family problems prior to their death, and a small but notable percentage of veterans who completed suicide reported divorce as a stressor prior to their death.

This finding may be explained by Thomas Joiner's interpersonal-psychological theory of suicidal behavior (IPT), which suggests that completed suicide may result from a thwarted sense of belonging, perceived burdensomeness, and acquired capability for suicide.¹⁶ Problems dealing with a partner may impact a veteran’s sense of belonging or social connectedness. Problems dealing with a partner also may be attributed to perceived burdens due to limitations imposed by living with cancer and/or undergoing treatment. In both circumstances, the veteran’s social support system may be negatively impacted, and perceived social support is a well-established protective factor against suicide.¹⁷

Partner distress is a second consideration. It is likely that veterans’ partners experienced their own distress in response to the veteran’s cancer diagnosis and/or treatment. The partner’s cause, severity, and expression of distress may contribute to problems for the couple.

Finally, the latter point of the IPT refers to acquired capability, or the ability to inflict deadly harm to oneself.¹⁸ A military sample was found to have more acquired capability for suicide when compared with a college undergraduate sample.¹⁹ A history of a suicide attempt(s) and male gender have been found to significantly predict acquired capability to complete suicide.¹⁸ Furthermore, because veterans living with cancer often are in pain, fear of pain associated with suicide may be reduced and, therefore, acquired capability increased. This suggests that male veterans living with cancer who are in pain, have a history of a suicide attempt(s), and current problems with their partner may be an extremely vulnerable population at-risk for suicide. Results from the current study emphasize the importance of veterans having access to mental health and crisis resources for problems dealing with their partner. Partner problems may foreshadow a potentially lethal type of distress.

Strengths

This study’s aims are consistent with the VA’s mission to end veteran suicide and contributes to literature in several important ways.¹² First, veterans living with cancer are an understudied population. The current study addresses a gap in existing literature by researching veterans living with cancer and aims to better understand the relationship between cancer-related distress and SI. Second, to the best of the authors’ knowledge, this study is the first to find that problems dealing with a partner significantly increases a veteran’s risk for SI above a history of a suicide attempt(s). Risk assessments now may be more comprehensive through inclusion of this distress factor.

It is recommended that future research use IPT to further investigate the relationship between problems dealing with a partner and SI.¹⁶ Future research may do so by including specific measures to assess for the tenants of the theory, including measurements of burdensomeness and belongingness. An expanded knowledge base about what makes problems dealing with a partner a significant suicide risk factor (eg, increased conflict, lack of support, etc.) would better enable clinicians to intervene effectively. Effective intervention may lessen suicidal behaviors or deaths from suicides within the Veteran population.

Limitations

One limitation is the focus on patients who accepted a mental health referral. This study design may limit the generalizability of results to veterans who would not accept mental health treatment. The homogenous sample of veterans is a second limitation. Most participants were male, white, and had a mean age of 62 years. These demographics are representative of the veterans that most typically utilize VA services; however, more research is needed on veterans living with cancer who are female and of diverse racial and ethnic backgrounds. There are likely differences in problems endorsed and factors associated with SI based on age, race, sex, and other socioeconomic factors. A third limitation is the cross-sectional, retrospective nature of this study. Future studies are advised to assess for distress at multiple time points. This is consistent with NCCN Standards of Care for Distress Management.² Longitudinal data would enable more findings about distress and SI throughout the course of cancer diagnosis and treatment, therefore enhancing clinical implications and informing future research.

Conclusion

This is among the first of studies to investigate distress and factors associated with SI in veterans living with cancer who were referred for psychology services. The prevalence of distress caused by psychosocial factors (including treatment decisions, worry, and depression) highlights the importance of including mental health services as part of comprehensive cancer treatment.

Distress due to treatment decisions may be attributed to a litany of factors such as a veteran’s consideration of adverse effects, effectiveness of treatments, changes to quality of life or functioning, and inclusion of alternative or complimentary treatments. These types of decisions often are reported to be difficult conversations to have with family members or loved ones, who are likely experiencing distress of their own. The role of a mental health provider to assist veterans in exploring their treatment decisions and the implications of such decisions appears important to lessening distress.

Early intervention for emotional symptoms would likely benefit veterans’ management of distress and may lessen suicide risk as depression is known to place veterans at-risk for SI.²⁰ This underscores the importance of timely distress assessment to prevent mild emotional distress from progressing to potentially severe or life-threatening emotional distress. For veterans with a psychiatric history, timely assessment and intervention is essential because psychiatric history is an established suicide risk factor that may be exacerbated by cancer-related distress.¹²

Furthermore, management of intolerable physical symptoms may lessen risk for suicide.⁴ Under medical guidance, fatigue may be improved using exercise.²¹ Behavioral intervention is commonly used as first-line treatment for sleep problems.²² While pain may be lessened through medication or nonpharmacological interventions.²³

Considering the numerous ways that distress may present itself (eg, practical, emotional, or physical) and increase risk for SI, it is essential that all veterans living with cancer are assessed for distress and SI, regardless of their presentation. Although veterans may not outwardly express distress, this does not indicate the absence of either distress or risk for suicide. For example, a veteran may be distressed due to financial concerns, transportation issues, and the health of his/her partner or spouse. This veteran may not exhibit visible symptoms of distress, as would be expected when the source of distress is emotional (eg, depression, anxiety). However, this veteran is equally vulnerable to impairing distress and SI as someone who exhibits emotional distress. Distress assessments should be further developed to capture both the visible and less apparent sources of distress, while also serving the imperative function of screening for suicide. Other researchers also have noted the necessity of this development.²⁴ Currently, the NCCN DT and Problems List does not include any assessment of SI or behavior.

Finally, this study identified a potentially critical factor to include in distress assessment: problems dealing with a partner. Problems dealing with a partner have been noted as a source of distress in existing literature, but this is the first study to find problems dealing with a partner to be a predictor of SI in veterans living with cancer.^4-6

Because partners often attend appointments with veterans, it is not surprising that problems dealing with their partner are not disclosed more readily. It is recommended that clinicians ask veterans about potential problems with their partner when they are alone. Directly gathering information about such problems while assessing for distress may assist health care workers in providing the most effective, accurate type of intervention in a timely manner, and potentially mitigate risk for suicide.

As recommended by the NCCN and numerous researchers, findings from the current study underscore the importance of accurate, timely assessment of distress.^2,4,8 This study makes several important recommendations about how distress assessment may be strengthened and further developed, specifically for the veteran population. This study also expands the current knowledge base of what is known about veterans living with cancer, and has begun to fill a gap in the existing literature. Consistent with the VA mission to end veteran suicide, results suggest that veterans living with cancer should be regularly screened for distress, asked about distress related to their partner, and assessed for SI. Continued efforts to enhance assessment of and response to distress may lessen suicide risk in veterans with cancer.¹¹

Acknowledgements

This study is the result of work supported with resources and the use of facilities at the James A. Haley Veterans’ Hospital.

It was estimated that physicians would diagnose a form of invasive cancer > 1.7 million times in 2019. As the second most common cause of death in the US, > 600,000 people were projected to die from cancer in 2019.¹ Many individuals with cancer endure distress, which the National Comprehensive Cancer Network (NCCN) defines as a “multifactorial unpleasant experience of a psychological (ie, cognitive, behavioral, emotional), social, spiritual, and/or physical nature that may interfere with the ability to cope effectively with cancer, its physical symptoms, and its treatment.”^2,3 Distress in people living with cancer has been attributed to various psychosocial concerns, such as family problems, whichinclude dealing with partners and children; emotional problems, such as depression and anxiety; and physical symptoms, such as pain and fatigue.^4-9 Certain factors associated with distress may increase a patient’s risk for suicide.⁴

Veterans are at particularly high risk for suicide.¹⁰ In 2014, veterans accounted for 18% of completed suicides in the US but only were 8.5% of the total population that same year.¹⁰ Yet, little research has been done on the relationship between distress and suicide in veterans living with cancer. Aboumrad and colleagues found that 45% of veterans with cancer who completed suicide reported family issues and 41% endorsed chronic pain.¹¹ This study recommended continued efforts to assess and treat distress to lessen risk of suicide in veterans living with cancer; however, to date, only 1 study has specifically evaluated distress and problems endorsed among veterans living with cancer.⁷

Suicide prevention is of the highest priority to the US Department of Veterans Affairs (VA).¹² Consistent with the VA mission to end veteran suicide, the current study aimed to better understand the relationship between distress and suicide within a sample of veterans living with cancer. Findings would additionally be used to tailor clinical assessments and interventions for veterans living with cancer.

This study had 3 primary goals. First, we sought to understand demographic and clinical factors associated with low, moderate, and severe levels of distress in veterans living with cancer who were referred for psychology services. Second, the study investigated the most commonly endorsed problems by veterans living with cancer. Finally, we examined which problems were related to suicidal ideation (SI). It was hypothesized that veterans who reported severe distress would be significantly more likely to endorse SI when compared with veterans who reported mild or moderate distress. Based on existing literature, it was further hypothesized that family, emotional, and physical problems would be significantly associated with SI.^7,11

Methods

The current study was conducted at James A. Haley Veterans’ Hospital (JAHVH) in Tampa, Florida. Inclusion criteria included veterans who were diagnosed with cancer, attended an outpatient psychology-oncology evaluation, and completed mental health screening measures provided during their evaluation. Exclusion criteria included veterans who: were seen in response to an inpatient consult, were seen solely for a stem cell transplant evaluation, or did not complete the screening measures.

Measures

A veteran’s demographic (eg, age, sex, ethnicity) and clinical (eg, cancer type, stage of disease, recurrence, cancer treatments received) information was abstracted from their VA medical records. Marital status was assessed during a clinical interview and documented as part of the standardized suicide risk assessment.

The Distress Thermometer (DT) is a subjective measure developed by the NCCN.² The DT provides a visual representation of a thermometer and asks patients to rate their level of distress over the past week with 0 indicating no distress and 10 indicating extreme distress. A distress rating of 4 or higher is clinically significant.^4,6 Distress may be categorized into 3 levels of severity: mild distress (< 4), moderate distress (4-7), or severe distress (8-10). The DT has been found to have good face validity, sensitivity and specificity, and is user-friendly.^2,6,7,13

The measurement additionally lists 39 problems nested within 5 domains: practical, family, emotional, spiritual/religious, and physical. Patients may endorse listed items under each problem domain by indicating yes or no. Endorsement of various items are intended to provide more detailed information about sources of distress. Due to the predominantly male and mostly older population included in this study the ability to have children measure was removed from the family problem domain.

SI was assessed in 2 ways. First, by patients’ self-report through item-9 of the Patient Health Questionnaire-9 (PHQ-9).¹⁴ Item-9 asks “over the last 2 weeks, how often have you been bothered by thoughts that you would be better off dead or of hurting yourself in some way?” Responses range from 0 (not at all) to 3 (nearly every day).¹⁴ Responses > 0 were considered a positive screen for SI. The process of administering the PHQ-9 item-9 is part of a national VA directive for standardizing assessment of suicide risk (Steve Young, personal communication, May 23, 2018). The PHQ-9 has been found to have good construct validity when used with both medical samples and the general population along with good internal and test-retest reliability.^14,15 Second, all veterans also were asked directly about SI during clinical interview, the results of which were documented in health records using a standardized format for risk assessment.

Procedure

Participants were a sample of veterans who were referred for psychology-oncology services. The NCCN DT and Problems List were administered prior to the start of clinical interviews, which followed a checklist and included standardized assessments of SI and history of a suicide attempt(s). A licensed clinical psychologist or a postdoctoral resident conducted these assessments under the supervision of a licensed psychologist. Data gathered during the clinical interview and from the DT and problems list were documented in health records, which were retrospectively reviewed for relevant information (eg, cancer diagnosis, SI). Therefore, informed consent was waived. This study was approved by the JAHVH Institutional Review Board.

Analysis

Data were analyzed using SPSS Version 25. Data analysis proceeded in 3 steps. First, descriptive statistics included the demographic and clinical factors present in the current sample. Difference between those with and without suicidal ideation were compared using F-statistic for continuous variables and χ² analyses for categorical variables. Second, to examine relationships between each DT problem domain and SI, χ² analyses were conducted. Third, DT problem domains that had a significant relationship with SI were entered in a logistic regression. This analysis determined which items, if any, from a DT problem domain predicted SI. In the logistic regression model, history of suicide attempts was entered into the first block, as history of suicide attempts is a well-established risk factor for subsequent suicidal ideation. In the second block, other variables that were significantly related to suicidal ideation in the second step of analyses were included. Before interpreting the results of the logistic regression, model fit was tested using the Hosmer-Lemeshow test. Significance of each individual predictor variable in the model is reported using the Wald χ² statistic; each Wald statistic is compared with a χ² distribution with 1 degree of freedom (df). Results of logistic regression models also provide information about the effect of each predictor variable in the regression equation (beta weight), odds a veteran who endorsed each predictor variable in the model would also endorse SI (as indicated by the odds ratio), and an estimate of the amount of variance accounted for by each predictor variable (using Nagelkerke’s pseudo R², which ranges in value from 0 to 1 with higher values indicating more variance explained). For all analyses, P value of .05 (2-tailed) was used for statistical significance.

Results

The sample consisted of 174 veterans (Table 1). The majority (77.6%) were male with a mean age of nearly 62 years (range, 29-87). Most identified as white (74.1%) with half reporting they were either married or living with a partner.

Prostate cancer (19.0%) was the most common type of cancer among study participants followed by head and neck (18.4%), lymphoma/leukemia (11.5%), lung (11.5%), and breast (10.9%); 31.6% had metastatic disease and 14.9% had recurrent disease. Chemotherapy (42.5%) was the most common treatment modality, followed by surgery (38.5%) and radiation (31.6%). The sample was distributed among the 3 distress DT categories: mild (18.4%), moderate (42.5%), and severe (39.1%).

Problems Endorsed

Treatment decisions (44.3%) and insurance/financial concerns (35.1%) were the most frequently endorsed practical problems (Figure 1). Family health issues (33.9%) and dealing with partner (23.0%) were the most frequently endorsed family problems (Figure 2). Worry (73.0%) and depression (69.5%) were the most frequent emotional problems; of note, all emotional problems were endorsed by at least 50% of veterans (Figure 3). Fatigue (71.3%), sleep (70.7%), and pain (69%), were the most frequently endorsed physical problems (Figure 4). Spiritual/religious problems were endorsed by 15% of veterans.

Suicidal Ideation

Overall, 25.3% of veterans endorsed SI. About 20% of veterans reported a history of ≥ 1 suicide attempts in their lifetime. A significant relationship among distress categories and SI was found (χ² = 18.36, P < .001). Veterans with severe distress were more likely to endorse SI (42.7%) when compared with veterans with mild (9.4%) or moderate (16.2%) distress.

Similarly, a significant relationship among distress categories and a history of a suicide attempt(s) was found (χ² = 6.08, P = .048). Veterans with severe distress were more likely to have attempted suicide (29.4%) when compared with veterans with mild (12.5%) or moderate (14.9%) distress.

Discussion

This study aimed to understand factors associated with low, moderate, and severe levels of distress in veterans living with cancer who were referred for psychology services. As hypothesized, veterans who endorsed severe distress were significantly more likely to endorse SI. They also were more likely to have a history of a suicide attempt(s) when compared with those with mild or moderate distress.

A second aim of this study was to understand the most commonly endorsed problems. Consistent with prior literature, treatment decisions were the most commonly endorsed practical problem; worry and depression were the most common emotional problems; and fatigue, sleep, and pain were the most common physical problems.⁷

A finding unique to the current study is that family health issues and dealing with partner were specified as the most common family problems. However, a study by Smith and colleagues did not provide information about the rank of most frequently reported problems within this domain.⁷

The third aim was to understand which problems were related to SI. It was hypothesized that family, emotional, and physical problems would be related to SI. However, results indicated that only family problems (specifically, problems dealing with a partner) were significantly associated with SI among veterans living with cancer.

Contrary to expectations, emotional and physical problems were not found to have a significant relationship with SI. This is likely because veterans endorsed items nested within these problem domains with similar frequency. The lack of significant findings does not suggest that emotional and physical problems are not significant predictors of SI for veterans living with cancer, but that no specific emotional or physical symptom stood out as a predictor of suicidal ideation above the others.

The finding of a significant relationship between family problems (specifically, problems dealing with a partner) and SI in this study is consistent with findings of Aboumrad and colleagues in a study that examined root-cause analyses of completed suicides by veterans living with cancer.¹¹ They found that nearly half the sample endorsed family problems prior to their death, and a small but notable percentage of veterans who completed suicide reported divorce as a stressor prior to their death.

This finding may be explained by Thomas Joiner's interpersonal-psychological theory of suicidal behavior (IPT), which suggests that completed suicide may result from a thwarted sense of belonging, perceived burdensomeness, and acquired capability for suicide.¹⁶ Problems dealing with a partner may impact a veteran’s sense of belonging or social connectedness. Problems dealing with a partner also may be attributed to perceived burdens due to limitations imposed by living with cancer and/or undergoing treatment. In both circumstances, the veteran’s social support system may be negatively impacted, and perceived social support is a well-established protective factor against suicide.¹⁷

Partner distress is a second consideration. It is likely that veterans’ partners experienced their own distress in response to the veteran’s cancer diagnosis and/or treatment. The partner’s cause, severity, and expression of distress may contribute to problems for the couple.

Finally, the latter point of the IPT refers to acquired capability, or the ability to inflict deadly harm to oneself.¹⁸ A military sample was found to have more acquired capability for suicide when compared with a college undergraduate sample.¹⁹ A history of a suicide attempt(s) and male gender have been found to significantly predict acquired capability to complete suicide.¹⁸ Furthermore, because veterans living with cancer often are in pain, fear of pain associated with suicide may be reduced and, therefore, acquired capability increased. This suggests that male veterans living with cancer who are in pain, have a history of a suicide attempt(s), and current problems with their partner may be an extremely vulnerable population at-risk for suicide. Results from the current study emphasize the importance of veterans having access to mental health and crisis resources for problems dealing with their partner. Partner problems may foreshadow a potentially lethal type of distress.

Strengths

This study’s aims are consistent with the VA’s mission to end veteran suicide and contributes to literature in several important ways.¹² First, veterans living with cancer are an understudied population. The current study addresses a gap in existing literature by researching veterans living with cancer and aims to better understand the relationship between cancer-related distress and SI. Second, to the best of the authors’ knowledge, this study is the first to find that problems dealing with a partner significantly increases a veteran’s risk for SI above a history of a suicide attempt(s). Risk assessments now may be more comprehensive through inclusion of this distress factor.

It is recommended that future research use IPT to further investigate the relationship between problems dealing with a partner and SI.¹⁶ Future research may do so by including specific measures to assess for the tenants of the theory, including measurements of burdensomeness and belongingness. An expanded knowledge base about what makes problems dealing with a partner a significant suicide risk factor (eg, increased conflict, lack of support, etc.) would better enable clinicians to intervene effectively. Effective intervention may lessen suicidal behaviors or deaths from suicides within the Veteran population.

Limitations

One limitation is the focus on patients who accepted a mental health referral. This study design may limit the generalizability of results to veterans who would not accept mental health treatment. The homogenous sample of veterans is a second limitation. Most participants were male, white, and had a mean age of 62 years. These demographics are representative of the veterans that most typically utilize VA services; however, more research is needed on veterans living with cancer who are female and of diverse racial and ethnic backgrounds. There are likely differences in problems endorsed and factors associated with SI based on age, race, sex, and other socioeconomic factors. A third limitation is the cross-sectional, retrospective nature of this study. Future studies are advised to assess for distress at multiple time points. This is consistent with NCCN Standards of Care for Distress Management.² Longitudinal data would enable more findings about distress and SI throughout the course of cancer diagnosis and treatment, therefore enhancing clinical implications and informing future research.

Conclusion

This is among the first of studies to investigate distress and factors associated with SI in veterans living with cancer who were referred for psychology services. The prevalence of distress caused by psychosocial factors (including treatment decisions, worry, and depression) highlights the importance of including mental health services as part of comprehensive cancer treatment.

Distress due to treatment decisions may be attributed to a litany of factors such as a veteran’s consideration of adverse effects, effectiveness of treatments, changes to quality of life or functioning, and inclusion of alternative or complimentary treatments. These types of decisions often are reported to be difficult conversations to have with family members or loved ones, who are likely experiencing distress of their own. The role of a mental health provider to assist veterans in exploring their treatment decisions and the implications of such decisions appears important to lessening distress.

Early intervention for emotional symptoms would likely benefit veterans’ management of distress and may lessen suicide risk as depression is known to place veterans at-risk for SI.²⁰ This underscores the importance of timely distress assessment to prevent mild emotional distress from progressing to potentially severe or life-threatening emotional distress. For veterans with a psychiatric history, timely assessment and intervention is essential because psychiatric history is an established suicide risk factor that may be exacerbated by cancer-related distress.¹²

Furthermore, management of intolerable physical symptoms may lessen risk for suicide.⁴ Under medical guidance, fatigue may be improved using exercise.²¹ Behavioral intervention is commonly used as first-line treatment for sleep problems.²² While pain may be lessened through medication or nonpharmacological interventions.²³

Considering the numerous ways that distress may present itself (eg, practical, emotional, or physical) and increase risk for SI, it is essential that all veterans living with cancer are assessed for distress and SI, regardless of their presentation. Although veterans may not outwardly express distress, this does not indicate the absence of either distress or risk for suicide. For example, a veteran may be distressed due to financial concerns, transportation issues, and the health of his/her partner or spouse. This veteran may not exhibit visible symptoms of distress, as would be expected when the source of distress is emotional (eg, depression, anxiety). However, this veteran is equally vulnerable to impairing distress and SI as someone who exhibits emotional distress. Distress assessments should be further developed to capture both the visible and less apparent sources of distress, while also serving the imperative function of screening for suicide. Other researchers also have noted the necessity of this development.²⁴ Currently, the NCCN DT and Problems List does not include any assessment of SI or behavior.

Finally, this study identified a potentially critical factor to include in distress assessment: problems dealing with a partner. Problems dealing with a partner have been noted as a source of distress in existing literature, but this is the first study to find problems dealing with a partner to be a predictor of SI in veterans living with cancer.^4-6

Because partners often attend appointments with veterans, it is not surprising that problems dealing with their partner are not disclosed more readily. It is recommended that clinicians ask veterans about potential problems with their partner when they are alone. Directly gathering information about such problems while assessing for distress may assist health care workers in providing the most effective, accurate type of intervention in a timely manner, and potentially mitigate risk for suicide.

As recommended by the NCCN and numerous researchers, findings from the current study underscore the importance of accurate, timely assessment of distress.^2,4,8 This study makes several important recommendations about how distress assessment may be strengthened and further developed, specifically for the veteran population. This study also expands the current knowledge base of what is known about veterans living with cancer, and has begun to fill a gap in the existing literature. Consistent with the VA mission to end veteran suicide, results suggest that veterans living with cancer should be regularly screened for distress, asked about distress related to their partner, and assessed for SI. Continued efforts to enhance assessment of and response to distress may lessen suicide risk in veterans with cancer.¹¹

Acknowledgements

This study is the result of work supported with resources and the use of facilities at the James A. Haley Veterans’ Hospital.

It was estimated that physicians would diagnose a form of invasive cancer > 1.7 million times in 2019. As the second most common cause of death in the US, > 600,000 people were projected to die from cancer in 2019.¹ Many individuals with cancer endure distress, which the National Comprehensive Cancer Network (NCCN) defines as a “multifactorial unpleasant experience of a psychological (ie, cognitive, behavioral, emotional), social, spiritual, and/or physical nature that may interfere with the ability to cope effectively with cancer, its physical symptoms, and its treatment.”^2,3 Distress in people living with cancer has been attributed to various psychosocial concerns, such as family problems, whichinclude dealing with partners and children; emotional problems, such as depression and anxiety; and physical symptoms, such as pain and fatigue.^4-9 Certain factors associated with distress may increase a patient’s risk for suicide.⁴

Veterans are at particularly high risk for suicide.¹⁰ In 2014, veterans accounted for 18% of completed suicides in the US but only were 8.5% of the total population that same year.¹⁰ Yet, little research has been done on the relationship between distress and suicide in veterans living with cancer. Aboumrad and colleagues found that 45% of veterans with cancer who completed suicide reported family issues and 41% endorsed chronic pain.¹¹ This study recommended continued efforts to assess and treat distress to lessen risk of suicide in veterans living with cancer; however, to date, only 1 study has specifically evaluated distress and problems endorsed among veterans living with cancer.⁷

Suicide prevention is of the highest priority to the US Department of Veterans Affairs (VA).¹² Consistent with the VA mission to end veteran suicide, the current study aimed to better understand the relationship between distress and suicide within a sample of veterans living with cancer. Findings would additionally be used to tailor clinical assessments and interventions for veterans living with cancer.

This study had 3 primary goals. First, we sought to understand demographic and clinical factors associated with low, moderate, and severe levels of distress in veterans living with cancer who were referred for psychology services. Second, the study investigated the most commonly endorsed problems by veterans living with cancer. Finally, we examined which problems were related to suicidal ideation (SI). It was hypothesized that veterans who reported severe distress would be significantly more likely to endorse SI when compared with veterans who reported mild or moderate distress. Based on existing literature, it was further hypothesized that family, emotional, and physical problems would be significantly associated with SI.^7,11

Methods

The current study was conducted at James A. Haley Veterans’ Hospital (JAHVH) in Tampa, Florida. Inclusion criteria included veterans who were diagnosed with cancer, attended an outpatient psychology-oncology evaluation, and completed mental health screening measures provided during their evaluation. Exclusion criteria included veterans who: were seen in response to an inpatient consult, were seen solely for a stem cell transplant evaluation, or did not complete the screening measures.

Measures

A veteran’s demographic (eg, age, sex, ethnicity) and clinical (eg, cancer type, stage of disease, recurrence, cancer treatments received) information was abstracted from their VA medical records. Marital status was assessed during a clinical interview and documented as part of the standardized suicide risk assessment.

The Distress Thermometer (DT) is a subjective measure developed by the NCCN.² The DT provides a visual representation of a thermometer and asks patients to rate their level of distress over the past week with 0 indicating no distress and 10 indicating extreme distress. A distress rating of 4 or higher is clinically significant.^4,6 Distress may be categorized into 3 levels of severity: mild distress (< 4), moderate distress (4-7), or severe distress (8-10). The DT has been found to have good face validity, sensitivity and specificity, and is user-friendly.^2,6,7,13

The measurement additionally lists 39 problems nested within 5 domains: practical, family, emotional, spiritual/religious, and physical. Patients may endorse listed items under each problem domain by indicating yes or no. Endorsement of various items are intended to provide more detailed information about sources of distress. Due to the predominantly male and mostly older population included in this study the ability to have children measure was removed from the family problem domain.

SI was assessed in 2 ways. First, by patients’ self-report through item-9 of the Patient Health Questionnaire-9 (PHQ-9).¹⁴ Item-9 asks “over the last 2 weeks, how often have you been bothered by thoughts that you would be better off dead or of hurting yourself in some way?” Responses range from 0 (not at all) to 3 (nearly every day).¹⁴ Responses > 0 were considered a positive screen for SI. The process of administering the PHQ-9 item-9 is part of a national VA directive for standardizing assessment of suicide risk (Steve Young, personal communication, May 23, 2018). The PHQ-9 has been found to have good construct validity when used with both medical samples and the general population along with good internal and test-retest reliability.^14,15 Second, all veterans also were asked directly about SI during clinical interview, the results of which were documented in health records using a standardized format for risk assessment.

Procedure

Participants were a sample of veterans who were referred for psychology-oncology services. The NCCN DT and Problems List were administered prior to the start of clinical interviews, which followed a checklist and included standardized assessments of SI and history of a suicide attempt(s). A licensed clinical psychologist or a postdoctoral resident conducted these assessments under the supervision of a licensed psychologist. Data gathered during the clinical interview and from the DT and problems list were documented in health records, which were retrospectively reviewed for relevant information (eg, cancer diagnosis, SI). Therefore, informed consent was waived. This study was approved by the JAHVH Institutional Review Board.

Analysis

Data were analyzed using SPSS Version 25. Data analysis proceeded in 3 steps. First, descriptive statistics included the demographic and clinical factors present in the current sample. Difference between those with and without suicidal ideation were compared using F-statistic for continuous variables and χ² analyses for categorical variables. Second, to examine relationships between each DT problem domain and SI, χ² analyses were conducted. Third, DT problem domains that had a significant relationship with SI were entered in a logistic regression. This analysis determined which items, if any, from a DT problem domain predicted SI. In the logistic regression model, history of suicide attempts was entered into the first block, as history of suicide attempts is a well-established risk factor for subsequent suicidal ideation. In the second block, other variables that were significantly related to suicidal ideation in the second step of analyses were included. Before interpreting the results of the logistic regression, model fit was tested using the Hosmer-Lemeshow test. Significance of each individual predictor variable in the model is reported using the Wald χ² statistic; each Wald statistic is compared with a χ² distribution with 1 degree of freedom (df). Results of logistic regression models also provide information about the effect of each predictor variable in the regression equation (beta weight), odds a veteran who endorsed each predictor variable in the model would also endorse SI (as indicated by the odds ratio), and an estimate of the amount of variance accounted for by each predictor variable (using Nagelkerke’s pseudo R², which ranges in value from 0 to 1 with higher values indicating more variance explained). For all analyses, P value of .05 (2-tailed) was used for statistical significance.

Results

The sample consisted of 174 veterans (Table 1). The majority (77.6%) were male with a mean age of nearly 62 years (range, 29-87). Most identified as white (74.1%) with half reporting they were either married or living with a partner.

Prostate cancer (19.0%) was the most common type of cancer among study participants followed by head and neck (18.4%), lymphoma/leukemia (11.5%), lung (11.5%), and breast (10.9%); 31.6% had metastatic disease and 14.9% had recurrent disease. Chemotherapy (42.5%) was the most common treatment modality, followed by surgery (38.5%) and radiation (31.6%). The sample was distributed among the 3 distress DT categories: mild (18.4%), moderate (42.5%), and severe (39.1%).

Problems Endorsed

Treatment decisions (44.3%) and insurance/financial concerns (35.1%) were the most frequently endorsed practical problems (Figure 1). Family health issues (33.9%) and dealing with partner (23.0%) were the most frequently endorsed family problems (Figure 2). Worry (73.0%) and depression (69.5%) were the most frequent emotional problems; of note, all emotional problems were endorsed by at least 50% of veterans (Figure 3). Fatigue (71.3%), sleep (70.7%), and pain (69%), were the most frequently endorsed physical problems (Figure 4). Spiritual/religious problems were endorsed by 15% of veterans.

Suicidal Ideation

Overall, 25.3% of veterans endorsed SI. About 20% of veterans reported a history of ≥ 1 suicide attempts in their lifetime. A significant relationship among distress categories and SI was found (χ² = 18.36, P < .001). Veterans with severe distress were more likely to endorse SI (42.7%) when compared with veterans with mild (9.4%) or moderate (16.2%) distress.

Similarly, a significant relationship among distress categories and a history of a suicide attempt(s) was found (χ² = 6.08, P = .048). Veterans with severe distress were more likely to have attempted suicide (29.4%) when compared with veterans with mild (12.5%) or moderate (14.9%) distress.

Discussion

This study aimed to understand factors associated with low, moderate, and severe levels of distress in veterans living with cancer who were referred for psychology services. As hypothesized, veterans who endorsed severe distress were significantly more likely to endorse SI. They also were more likely to have a history of a suicide attempt(s) when compared with those with mild or moderate distress.

A second aim of this study was to understand the most commonly endorsed problems. Consistent with prior literature, treatment decisions were the most commonly endorsed practical problem; worry and depression were the most common emotional problems; and fatigue, sleep, and pain were the most common physical problems.⁷

A finding unique to the current study is that family health issues and dealing with partner were specified as the most common family problems. However, a study by Smith and colleagues did not provide information about the rank of most frequently reported problems within this domain.⁷

The third aim was to understand which problems were related to SI. It was hypothesized that family, emotional, and physical problems would be related to SI. However, results indicated that only family problems (specifically, problems dealing with a partner) were significantly associated with SI among veterans living with cancer.

Contrary to expectations, emotional and physical problems were not found to have a significant relationship with SI. This is likely because veterans endorsed items nested within these problem domains with similar frequency. The lack of significant findings does not suggest that emotional and physical problems are not significant predictors of SI for veterans living with cancer, but that no specific emotional or physical symptom stood out as a predictor of suicidal ideation above the others.

The finding of a significant relationship between family problems (specifically, problems dealing with a partner) and SI in this study is consistent with findings of Aboumrad and colleagues in a study that examined root-cause analyses of completed suicides by veterans living with cancer.¹¹ They found that nearly half the sample endorsed family problems prior to their death, and a small but notable percentage of veterans who completed suicide reported divorce as a stressor prior to their death.

This finding may be explained by Thomas Joiner's interpersonal-psychological theory of suicidal behavior (IPT), which suggests that completed suicide may result from a thwarted sense of belonging, perceived burdensomeness, and acquired capability for suicide.¹⁶ Problems dealing with a partner may impact a veteran’s sense of belonging or social connectedness. Problems dealing with a partner also may be attributed to perceived burdens due to limitations imposed by living with cancer and/or undergoing treatment. In both circumstances, the veteran’s social support system may be negatively impacted, and perceived social support is a well-established protective factor against suicide.¹⁷

Partner distress is a second consideration. It is likely that veterans’ partners experienced their own distress in response to the veteran’s cancer diagnosis and/or treatment. The partner’s cause, severity, and expression of distress may contribute to problems for the couple.

Finally, the latter point of the IPT refers to acquired capability, or the ability to inflict deadly harm to oneself.¹⁸ A military sample was found to have more acquired capability for suicide when compared with a college undergraduate sample.¹⁹ A history of a suicide attempt(s) and male gender have been found to significantly predict acquired capability to complete suicide.¹⁸ Furthermore, because veterans living with cancer often are in pain, fear of pain associated with suicide may be reduced and, therefore, acquired capability increased. This suggests that male veterans living with cancer who are in pain, have a history of a suicide attempt(s), and current problems with their partner may be an extremely vulnerable population at-risk for suicide. Results from the current study emphasize the importance of veterans having access to mental health and crisis resources for problems dealing with their partner. Partner problems may foreshadow a potentially lethal type of distress.

Strengths

This study’s aims are consistent with the VA’s mission to end veteran suicide and contributes to literature in several important ways.¹² First, veterans living with cancer are an understudied population. The current study addresses a gap in existing literature by researching veterans living with cancer and aims to better understand the relationship between cancer-related distress and SI. Second, to the best of the authors’ knowledge, this study is the first to find that problems dealing with a partner significantly increases a veteran’s risk for SI above a history of a suicide attempt(s). Risk assessments now may be more comprehensive through inclusion of this distress factor.

It is recommended that future research use IPT to further investigate the relationship between problems dealing with a partner and SI.¹⁶ Future research may do so by including specific measures to assess for the tenants of the theory, including measurements of burdensomeness and belongingness. An expanded knowledge base about what makes problems dealing with a partner a significant suicide risk factor (eg, increased conflict, lack of support, etc.) would better enable clinicians to intervene effectively. Effective intervention may lessen suicidal behaviors or deaths from suicides within the Veteran population.

Limitations

One limitation is the focus on patients who accepted a mental health referral. This study design may limit the generalizability of results to veterans who would not accept mental health treatment. The homogenous sample of veterans is a second limitation. Most participants were male, white, and had a mean age of 62 years. These demographics are representative of the veterans that most typically utilize VA services; however, more research is needed on veterans living with cancer who are female and of diverse racial and ethnic backgrounds. There are likely differences in problems endorsed and factors associated with SI based on age, race, sex, and other socioeconomic factors. A third limitation is the cross-sectional, retrospective nature of this study. Future studies are advised to assess for distress at multiple time points. This is consistent with NCCN Standards of Care for Distress Management.² Longitudinal data would enable more findings about distress and SI throughout the course of cancer diagnosis and treatment, therefore enhancing clinical implications and informing future research.

Conclusion

This is among the first of studies to investigate distress and factors associated with SI in veterans living with cancer who were referred for psychology services. The prevalence of distress caused by psychosocial factors (including treatment decisions, worry, and depression) highlights the importance of including mental health services as part of comprehensive cancer treatment.

Distress due to treatment decisions may be attributed to a litany of factors such as a veteran’s consideration of adverse effects, effectiveness of treatments, changes to quality of life or functioning, and inclusion of alternative or complimentary treatments. These types of decisions often are reported to be difficult conversations to have with family members or loved ones, who are likely experiencing distress of their own. The role of a mental health provider to assist veterans in exploring their treatment decisions and the implications of such decisions appears important to lessening distress.

Early intervention for emotional symptoms would likely benefit veterans’ management of distress and may lessen suicide risk as depression is known to place veterans at-risk for SI.²⁰ This underscores the importance of timely distress assessment to prevent mild emotional distress from progressing to potentially severe or life-threatening emotional distress. For veterans with a psychiatric history, timely assessment and intervention is essential because psychiatric history is an established suicide risk factor that may be exacerbated by cancer-related distress.¹²

Furthermore, management of intolerable physical symptoms may lessen risk for suicide.⁴ Under medical guidance, fatigue may be improved using exercise.²¹ Behavioral intervention is commonly used as first-line treatment for sleep problems.²² While pain may be lessened through medication or nonpharmacological interventions.²³

Considering the numerous ways that distress may present itself (eg, practical, emotional, or physical) and increase risk for SI, it is essential that all veterans living with cancer are assessed for distress and SI, regardless of their presentation. Although veterans may not outwardly express distress, this does not indicate the absence of either distress or risk for suicide. For example, a veteran may be distressed due to financial concerns, transportation issues, and the health of his/her partner or spouse. This veteran may not exhibit visible symptoms of distress, as would be expected when the source of distress is emotional (eg, depression, anxiety). However, this veteran is equally vulnerable to impairing distress and SI as someone who exhibits emotional distress. Distress assessments should be further developed to capture both the visible and less apparent sources of distress, while also serving the imperative function of screening for suicide. Other researchers also have noted the necessity of this development.²⁴ Currently, the NCCN DT and Problems List does not include any assessment of SI or behavior.

Finally, this study identified a potentially critical factor to include in distress assessment: problems dealing with a partner. Problems dealing with a partner have been noted as a source of distress in existing literature, but this is the first study to find problems dealing with a partner to be a predictor of SI in veterans living with cancer.^4-6

Because partners often attend appointments with veterans, it is not surprising that problems dealing with their partner are not disclosed more readily. It is recommended that clinicians ask veterans about potential problems with their partner when they are alone. Directly gathering information about such problems while assessing for distress may assist health care workers in providing the most effective, accurate type of intervention in a timely manner, and potentially mitigate risk for suicide.

As recommended by the NCCN and numerous researchers, findings from the current study underscore the importance of accurate, timely assessment of distress.^2,4,8 This study makes several important recommendations about how distress assessment may be strengthened and further developed, specifically for the veteran population. This study also expands the current knowledge base of what is known about veterans living with cancer, and has begun to fill a gap in the existing literature. Consistent with the VA mission to end veteran suicide, results suggest that veterans living with cancer should be regularly screened for distress, asked about distress related to their partner, and assessed for SI. Continued efforts to enhance assessment of and response to distress may lessen suicide risk in veterans with cancer.¹¹

Acknowledgements

This study is the result of work supported with resources and the use of facilities at the James A. Haley Veterans’ Hospital.