AVAHO

PHOENIX – Three decades after he initiated the transformation of the Veterans Health Administration (VHA) into a model research and clinical health care system, former US Department of Veterans Affairs (VA) Under Secretary of Health Kenneth W. Kizer, MD, MPH, urged cancer specialists to embrace this challenging moment as an opportunity for bold innovation.

At the annual meeting of the Association of VA Hematology/Oncology (AVAHO), Kizer acknowledged that the VA faces an “uncertain and turbulent time” in areas such as funding, staffing, community care implementation, and the rollout of a new electronic health record system. 

He also noted the grim rise of global instability, economic turmoil, climate change, infectious diseases, political violence, and mass shootings.

“This can be stressful. It can create negative energy. But this uncertainty can also be liberating, and it can prompt positive energy and innovation, depending on choices that we make,” said Kizer, who also has served as California’s top health official prior to leading the VHA from 1994 to 1999.

From “Bloated Bureaucracy’ to High-Quality Health Care System

Kizer has been credited with revitalizing VHA care through a greater commitment to quality, and harkened to his work with the VA as an example of how bold goals can lead to bold innovation. 

“What were the perceptions of VA health care in 1994? Well, they weren’t very good, frankly,” Kizer recalled. He described the VA as having a reputation at that time as “highly dysfunctional” with “a very bloated and entrenched bureaucracy.” As for quality of care, it “wasn’t viewed as very good.”

The system’s problems were so severe that patients would park motorhomes in VA medical center parking lots as they waited for care. “While they might have an appointment for one day, they may not be seen for 3 or 4 or 5 days. So they would stay in their motorhome until they finally got into their clinic appointment,” Kizer said.

Overall, “the public viewed the VA as this bleak backwater of incompetence and difference and inefficiency, and there were very strong calls to privatize the VA,” Kizer said. 

Kizer asked colleagues about what he should do after he was asked to take the under secretary job. “With one exception, they all said, don’t go near it. Don’t touch it. Walk away. That it’s impossible to change the organization.

“I looked at the VA and I saw an opportunity. When I told [members of the President Bill] Clinton [Administration] yes, my bold aim was that I would like to pursue this was to make VHA a model of excellent health care, an exemplary health care system. Most everyone else thought that I was totally delusional, but sometimes it’s good to be delusional.”

Revolutionary Changes Despite Opposition

Kizer sought reforms in 5 major strategic objectives, all without explicit congressional approval: creating an accountable management structure, decentralizing decision-making, integrating care, implementing universal primary care, and pursuing eligibility reform to create the current 8-tier VA system.

One major innovation was the implementation of community-based outpatient clinics (CBOCs): “Those were strongly opposed initially,” Kizer said. “Everyone, the veteran community in particular, had been led to believe that the only good care was in the hospital.”

The resistance was substantial. “There was a lot of opposition when we said we’re going to move out into the community where you live to make [care] easier to access,” Kizer said.

To make things more difficult, Congress wouldn’t fund the project: “For the first 3 years, every CBOC had to be funded by redirected savings from other things that we could do within the system,” he said. “All of this was through redirected savings and finding ways to save and reinvest.”

Innovation From the Ground Up

Kizer emphasized that many breakthrough innovations came from frontline staff rather than executive mandates. He cited the example of Barcode Medication Administration, which originated from a nurse in Topeka, Kan.

The nurse saw a barcode scanner put to work at a rental car company where it was used to check cars in and out. She wondered, “Why can’t we do this with medications when they’re given on the floor? We followed up on it, pursued those things, tested it out, it worked.”

The results were dramatic. “I was told at a meeting that they had achieved close to 80% reduction in medication errors,” Kizer said. After verifying the results personally, he “authorized $20 million, and we moved forward with it systemwide.”

This experience reinforced his belief in harvesting ideas from staff at all levels. 

Innovation remains part of the VA’s culture “despite what some people would have you believe,” Kizer said. Recently, the VA has made major advances in areas such as patient transportation and the climate crisis, he said. 

Inside the Recipe for Innovation

Boldness, persistence, adaptability, and tolerance for risk are necessary ingredients for high-risk goals, Kizer said. Ambition is also part of the picture. 

He highlighted examples such as the Apollo moon landing, the first sub-4-minute mile, and the first swim across the English Channel by a woman.

In medicine, Kizer pointed to a national patient safety campaign that saved an estimated 122,000 lives. He also mentioned recent progress in organ transplantation such as recommendations from the National Academies of Sciences, Engineering, and Medicine to establish national performance goals and the Organ Procurement and Transplantation Network’s target of 60,000 deceased donor transplants by 2026.

Bold doesn’t mean being reckless or careless, Kizer said. “But it does require innovation. And it does require that you try some new things, some of which aren’t going to work out.”

The key mindset, he explained, is to “embrace the unknown” because “you often really don’t know how you will accomplish the aim when you start. But you’ll figure it out as you go.”

Kizer highlighted 2 opposing strategies to handling challenging times. 

According to him, the “negative energy” approach focuses on frustrations, limitations, and asking “Why is this happening to me?” 

In contrast, a “positive energy” approach expects problems, focuses on available resources and capabilities, and asks, “What are the opportunities that these changes are creating for me?”

Kizer made it crystal clear which option he prefers.

Dr. Kizer disclosed that his comments represent his opinions only, and he noted his ongoing connections to the VA.

PHOENIX – Three decades after he initiated the transformation of the Veterans Health Administration (VHA) into a model research and clinical health care system, former US Department of Veterans Affairs (VA) Under Secretary of Health Kenneth W. Kizer, MD, MPH, urged cancer specialists to embrace this challenging moment as an opportunity for bold innovation.

At the annual meeting of the Association of VA Hematology/Oncology (AVAHO), Kizer acknowledged that the VA faces an “uncertain and turbulent time” in areas such as funding, staffing, community care implementation, and the rollout of a new electronic health record system. 

He also noted the grim rise of global instability, economic turmoil, climate change, infectious diseases, political violence, and mass shootings.

“This can be stressful. It can create negative energy. But this uncertainty can also be liberating, and it can prompt positive energy and innovation, depending on choices that we make,” said Kizer, who also has served as California’s top health official prior to leading the VHA from 1994 to 1999.

From “Bloated Bureaucracy’ to High-Quality Health Care System

Kizer has been credited with revitalizing VHA care through a greater commitment to quality, and harkened to his work with the VA as an example of how bold goals can lead to bold innovation. 

“What were the perceptions of VA health care in 1994? Well, they weren’t very good, frankly,” Kizer recalled. He described the VA as having a reputation at that time as “highly dysfunctional” with “a very bloated and entrenched bureaucracy.” As for quality of care, it “wasn’t viewed as very good.”

The system’s problems were so severe that patients would park motorhomes in VA medical center parking lots as they waited for care. “While they might have an appointment for one day, they may not be seen for 3 or 4 or 5 days. So they would stay in their motorhome until they finally got into their clinic appointment,” Kizer said.

Overall, “the public viewed the VA as this bleak backwater of incompetence and difference and inefficiency, and there were very strong calls to privatize the VA,” Kizer said. 

Kizer asked colleagues about what he should do after he was asked to take the under secretary job. “With one exception, they all said, don’t go near it. Don’t touch it. Walk away. That it’s impossible to change the organization.

“I looked at the VA and I saw an opportunity. When I told [members of the President Bill] Clinton [Administration] yes, my bold aim was that I would like to pursue this was to make VHA a model of excellent health care, an exemplary health care system. Most everyone else thought that I was totally delusional, but sometimes it’s good to be delusional.”

Revolutionary Changes Despite Opposition

Kizer sought reforms in 5 major strategic objectives, all without explicit congressional approval: creating an accountable management structure, decentralizing decision-making, integrating care, implementing universal primary care, and pursuing eligibility reform to create the current 8-tier VA system.

One major innovation was the implementation of community-based outpatient clinics (CBOCs): “Those were strongly opposed initially,” Kizer said. “Everyone, the veteran community in particular, had been led to believe that the only good care was in the hospital.”

The resistance was substantial. “There was a lot of opposition when we said we’re going to move out into the community where you live to make [care] easier to access,” Kizer said.

To make things more difficult, Congress wouldn’t fund the project: “For the first 3 years, every CBOC had to be funded by redirected savings from other things that we could do within the system,” he said. “All of this was through redirected savings and finding ways to save and reinvest.”

Innovation From the Ground Up

Kizer emphasized that many breakthrough innovations came from frontline staff rather than executive mandates. He cited the example of Barcode Medication Administration, which originated from a nurse in Topeka, Kan.

The nurse saw a barcode scanner put to work at a rental car company where it was used to check cars in and out. She wondered, “Why can’t we do this with medications when they’re given on the floor? We followed up on it, pursued those things, tested it out, it worked.”

The results were dramatic. “I was told at a meeting that they had achieved close to 80% reduction in medication errors,” Kizer said. After verifying the results personally, he “authorized $20 million, and we moved forward with it systemwide.”

This experience reinforced his belief in harvesting ideas from staff at all levels. 

Innovation remains part of the VA’s culture “despite what some people would have you believe,” Kizer said. Recently, the VA has made major advances in areas such as patient transportation and the climate crisis, he said. 

Inside the Recipe for Innovation

Boldness, persistence, adaptability, and tolerance for risk are necessary ingredients for high-risk goals, Kizer said. Ambition is also part of the picture. 

He highlighted examples such as the Apollo moon landing, the first sub-4-minute mile, and the first swim across the English Channel by a woman.

In medicine, Kizer pointed to a national patient safety campaign that saved an estimated 122,000 lives. He also mentioned recent progress in organ transplantation such as recommendations from the National Academies of Sciences, Engineering, and Medicine to establish national performance goals and the Organ Procurement and Transplantation Network’s target of 60,000 deceased donor transplants by 2026.

Bold doesn’t mean being reckless or careless, Kizer said. “But it does require innovation. And it does require that you try some new things, some of which aren’t going to work out.”

The key mindset, he explained, is to “embrace the unknown” because “you often really don’t know how you will accomplish the aim when you start. But you’ll figure it out as you go.”

Kizer highlighted 2 opposing strategies to handling challenging times. 

According to him, the “negative energy” approach focuses on frustrations, limitations, and asking “Why is this happening to me?” 

In contrast, a “positive energy” approach expects problems, focuses on available resources and capabilities, and asks, “What are the opportunities that these changes are creating for me?”

Kizer made it crystal clear which option he prefers.

Dr. Kizer disclosed that his comments represent his opinions only, and he noted his ongoing connections to the VA.

PHOENIX – Three decades after he initiated the transformation of the Veterans Health Administration (VHA) into a model research and clinical health care system, former US Department of Veterans Affairs (VA) Under Secretary of Health Kenneth W. Kizer, MD, MPH, urged cancer specialists to embrace this challenging moment as an opportunity for bold innovation.

At the annual meeting of the Association of VA Hematology/Oncology (AVAHO), Kizer acknowledged that the VA faces an “uncertain and turbulent time” in areas such as funding, staffing, community care implementation, and the rollout of a new electronic health record system. 

He also noted the grim rise of global instability, economic turmoil, climate change, infectious diseases, political violence, and mass shootings.

“This can be stressful. It can create negative energy. But this uncertainty can also be liberating, and it can prompt positive energy and innovation, depending on choices that we make,” said Kizer, who also has served as California’s top health official prior to leading the VHA from 1994 to 1999.

From “Bloated Bureaucracy’ to High-Quality Health Care System

Kizer has been credited with revitalizing VHA care through a greater commitment to quality, and harkened to his work with the VA as an example of how bold goals can lead to bold innovation. 

“What were the perceptions of VA health care in 1994? Well, they weren’t very good, frankly,” Kizer recalled. He described the VA as having a reputation at that time as “highly dysfunctional” with “a very bloated and entrenched bureaucracy.” As for quality of care, it “wasn’t viewed as very good.”

The system’s problems were so severe that patients would park motorhomes in VA medical center parking lots as they waited for care. “While they might have an appointment for one day, they may not be seen for 3 or 4 or 5 days. So they would stay in their motorhome until they finally got into their clinic appointment,” Kizer said.

Overall, “the public viewed the VA as this bleak backwater of incompetence and difference and inefficiency, and there were very strong calls to privatize the VA,” Kizer said. 

Kizer asked colleagues about what he should do after he was asked to take the under secretary job. “With one exception, they all said, don’t go near it. Don’t touch it. Walk away. That it’s impossible to change the organization.

“I looked at the VA and I saw an opportunity. When I told [members of the President Bill] Clinton [Administration] yes, my bold aim was that I would like to pursue this was to make VHA a model of excellent health care, an exemplary health care system. Most everyone else thought that I was totally delusional, but sometimes it’s good to be delusional.”

Revolutionary Changes Despite Opposition

Kizer sought reforms in 5 major strategic objectives, all without explicit congressional approval: creating an accountable management structure, decentralizing decision-making, integrating care, implementing universal primary care, and pursuing eligibility reform to create the current 8-tier VA system.

One major innovation was the implementation of community-based outpatient clinics (CBOCs): “Those were strongly opposed initially,” Kizer said. “Everyone, the veteran community in particular, had been led to believe that the only good care was in the hospital.”

The resistance was substantial. “There was a lot of opposition when we said we’re going to move out into the community where you live to make [care] easier to access,” Kizer said.

To make things more difficult, Congress wouldn’t fund the project: “For the first 3 years, every CBOC had to be funded by redirected savings from other things that we could do within the system,” he said. “All of this was through redirected savings and finding ways to save and reinvest.”

Innovation From the Ground Up

Kizer emphasized that many breakthrough innovations came from frontline staff rather than executive mandates. He cited the example of Barcode Medication Administration, which originated from a nurse in Topeka, Kan.

The nurse saw a barcode scanner put to work at a rental car company where it was used to check cars in and out. She wondered, “Why can’t we do this with medications when they’re given on the floor? We followed up on it, pursued those things, tested it out, it worked.”

The results were dramatic. “I was told at a meeting that they had achieved close to 80% reduction in medication errors,” Kizer said. After verifying the results personally, he “authorized $20 million, and we moved forward with it systemwide.”

This experience reinforced his belief in harvesting ideas from staff at all levels. 

Innovation remains part of the VA’s culture “despite what some people would have you believe,” Kizer said. Recently, the VA has made major advances in areas such as patient transportation and the climate crisis, he said. 

Inside the Recipe for Innovation

Boldness, persistence, adaptability, and tolerance for risk are necessary ingredients for high-risk goals, Kizer said. Ambition is also part of the picture. 

He highlighted examples such as the Apollo moon landing, the first sub-4-minute mile, and the first swim across the English Channel by a woman.

In medicine, Kizer pointed to a national patient safety campaign that saved an estimated 122,000 lives. He also mentioned recent progress in organ transplantation such as recommendations from the National Academies of Sciences, Engineering, and Medicine to establish national performance goals and the Organ Procurement and Transplantation Network’s target of 60,000 deceased donor transplants by 2026.

Bold doesn’t mean being reckless or careless, Kizer said. “But it does require innovation. And it does require that you try some new things, some of which aren’t going to work out.”

The key mindset, he explained, is to “embrace the unknown” because “you often really don’t know how you will accomplish the aim when you start. But you’ll figure it out as you go.”

Kizer highlighted 2 opposing strategies to handling challenging times. 

According to him, the “negative energy” approach focuses on frustrations, limitations, and asking “Why is this happening to me?” 

In contrast, a “positive energy” approach expects problems, focuses on available resources and capabilities, and asks, “What are the opportunities that these changes are creating for me?”

Kizer made it crystal clear which option he prefers.

Dr. Kizer disclosed that his comments represent his opinions only, and he noted his ongoing connections to the VA.

The size of the Veterans Health Administration (VHA) workforce continues to contract according to the latest data released by the US Department of Veterans Affairs (VA). Applications for employment are down 44% in fiscal year (FY) 2025 with just 14,485 cumulative new hires and 28,969 losses. In 2024, the VHA had 416,667 workers—it now has 401,224. 

The reductions align with VA Secretary Doug Collins’ goal of downsizing the VA’s workforce by 30,000 employees by the end of 2025. In August, Collins outlined how a federal hiring freeze, deferred resignations, retirements, and normal attrition have eliminated the need for the "large-scale" reduction-in-force he proposed earlier this year.

Compared with July’s numbers, the VHA now employs 139 fewer medical officers/physicians, 418 fewer registered nurses, 107 fewer social workers, and 65 fewer psychologists. Staffing of licensed practical nurses, medical support assistants, and nursing assistants is also down (reduced by 77, 129, and 29, respectively). 

Retention rates for the first 2 years of onboarding hover around 80% for physicians and nurses. However, retention incentives have dropped from 19,484 to 8982 and recruitment incentives from 6069 to 1299.

In voluntary exit surveys, 78% of 6762 medical and dental staff who left said they would work again for the VA, while 79% said they would recommend the VA as an employer. These rates are down from a similar survey in May 2023 when 81% noted that they would work again for the VA and 82% would recommend the VA to others. Personal matters, geographic relocation, and poor working relationships with supervisors or colleagues were among the reasons cited for leaving in August 2025. 

Of 435 psychologists, 69% said they would work again for the VA, and 62% said they would recommend the VA as an employer (71% and 67%, respectively in May 2023). Their reasons for leaving in August 2025 included a lack of trust in senior leaders and policy or technology barriers to getting the work done.

An August survey from the Office of the Inspector General found that VHA facilities reported 4434 staffing shortages this fiscal year—a 50% increase from fiscal year 2024. Most (94%) of the 139 facilities reported severe shortages for medical officers, and 79% of facilities reported severe shortages for nurses. Due to the timing of the questionnaire, the responses did not yet reflect the full impact from workforce reshaping efforts.

The size of the Veterans Health Administration (VHA) workforce continues to contract according to the latest data released by the US Department of Veterans Affairs (VA). Applications for employment are down 44% in fiscal year (FY) 2025 with just 14,485 cumulative new hires and 28,969 losses. In 2024, the VHA had 416,667 workers—it now has 401,224. 

The reductions align with VA Secretary Doug Collins’ goal of downsizing the VA’s workforce by 30,000 employees by the end of 2025. In August, Collins outlined how a federal hiring freeze, deferred resignations, retirements, and normal attrition have eliminated the need for the "large-scale" reduction-in-force he proposed earlier this year.

Compared with July’s numbers, the VHA now employs 139 fewer medical officers/physicians, 418 fewer registered nurses, 107 fewer social workers, and 65 fewer psychologists. Staffing of licensed practical nurses, medical support assistants, and nursing assistants is also down (reduced by 77, 129, and 29, respectively). 

Retention rates for the first 2 years of onboarding hover around 80% for physicians and nurses. However, retention incentives have dropped from 19,484 to 8982 and recruitment incentives from 6069 to 1299.

In voluntary exit surveys, 78% of 6762 medical and dental staff who left said they would work again for the VA, while 79% said they would recommend the VA as an employer. These rates are down from a similar survey in May 2023 when 81% noted that they would work again for the VA and 82% would recommend the VA to others. Personal matters, geographic relocation, and poor working relationships with supervisors or colleagues were among the reasons cited for leaving in August 2025. 

Of 435 psychologists, 69% said they would work again for the VA, and 62% said they would recommend the VA as an employer (71% and 67%, respectively in May 2023). Their reasons for leaving in August 2025 included a lack of trust in senior leaders and policy or technology barriers to getting the work done.

An August survey from the Office of the Inspector General found that VHA facilities reported 4434 staffing shortages this fiscal year—a 50% increase from fiscal year 2024. Most (94%) of the 139 facilities reported severe shortages for medical officers, and 79% of facilities reported severe shortages for nurses. Due to the timing of the questionnaire, the responses did not yet reflect the full impact from workforce reshaping efforts.

The size of the Veterans Health Administration (VHA) workforce continues to contract according to the latest data released by the US Department of Veterans Affairs (VA). Applications for employment are down 44% in fiscal year (FY) 2025 with just 14,485 cumulative new hires and 28,969 losses. In 2024, the VHA had 416,667 workers—it now has 401,224. 

The reductions align with VA Secretary Doug Collins’ goal of downsizing the VA’s workforce by 30,000 employees by the end of 2025. In August, Collins outlined how a federal hiring freeze, deferred resignations, retirements, and normal attrition have eliminated the need for the "large-scale" reduction-in-force he proposed earlier this year.

Compared with July’s numbers, the VHA now employs 139 fewer medical officers/physicians, 418 fewer registered nurses, 107 fewer social workers, and 65 fewer psychologists. Staffing of licensed practical nurses, medical support assistants, and nursing assistants is also down (reduced by 77, 129, and 29, respectively). 

Retention rates for the first 2 years of onboarding hover around 80% for physicians and nurses. However, retention incentives have dropped from 19,484 to 8982 and recruitment incentives from 6069 to 1299.

In voluntary exit surveys, 78% of 6762 medical and dental staff who left said they would work again for the VA, while 79% said they would recommend the VA as an employer. These rates are down from a similar survey in May 2023 when 81% noted that they would work again for the VA and 82% would recommend the VA to others. Personal matters, geographic relocation, and poor working relationships with supervisors or colleagues were among the reasons cited for leaving in August 2025. 

Of 435 psychologists, 69% said they would work again for the VA, and 62% said they would recommend the VA as an employer (71% and 67%, respectively in May 2023). Their reasons for leaving in August 2025 included a lack of trust in senior leaders and policy or technology barriers to getting the work done.

An August survey from the Office of the Inspector General found that VHA facilities reported 4434 staffing shortages this fiscal year—a 50% increase from fiscal year 2024. Most (94%) of the 139 facilities reported severe shortages for medical officers, and 79% of facilities reported severe shortages for nurses. Due to the timing of the questionnaire, the responses did not yet reflect the full impact from workforce reshaping efforts.

TOPLINE:

An artificial intelligence (AI) system flagged high-risk areas on mammograms for potentially missed interval breast cancers (IBCs), which radiologists had also retrospectively identified as abnormal. Moreover, the AI detected a substantial number of IBCs that manual review had overlooked.

METHODOLOGY:

• Researchers conducted a retrospective analysis of 119 IBC screening mammograms of women (mean age, 57.3 years) with a high breast density (Breast Imaging Reporting and Data System [BI-RADS] c/d, 63.0%) using data retrieved from Cancer Registries of Eastern Switzerland and Grisons-Glarus databases.
• A recorded tumour was classified as IBC when an invasive or in situ BC was diagnosed within 24 months after a normal screening mammogram.
• Three radiologists retrospectively assessed the mammograms for visible signs of BC, which were then classified as either potentially missed IBCs or IBCs without retrospective abnormalities on the basis of consensus conference recommendations of radiologists.
• An AI system generated two scores (a scale of 0 to 100): a case score reflecting the likelihood that the mammogram currently harbours cancer and a risk score estimating the probability of a BC diagnosis within 2 years.

TAKEAWAY:

• Radiologists classified 68.9% of IBCs as those having no retrospective abnormalities and assigned significantly higher BI-RADS scores to the remaining 31.1% of potentially missed IBCs (P < .05).
• Potentially missed IBCs received significantly higher AI case scores (mean, 54.1 vs 23.1; P < .05) and were assigned to a higher risk category (48.7% vs 14.6%; P < .05) than IBCs without retrospective abnormalities.
• Of all IBC cases, 46.2% received an AI case score > 25, 25.2% scored > 50, and 13.4% scored > 75.
• Potentially missed IBCs scored widely between low and high risk and case scores, whereas IBCs without retrospective abnormalities scored low case and risk scores. Specifically, 73.0% of potentially missed IBCs vs 34.1% of IBCs without retrospective abnormalities had case scores > 25, 51.4% vs 13.4% had case scores > 50, and 29.7% vs 6.1% had case scores > 75.

IN PRACTICE:

“Our research highlights that an AI system can identify BC signs in relevant portions of IBC screening mammograms and thus potentially reduce the number of IBCs in an MSP [mammography screening program] that currently does not utilize an AI system,” the authors of the study concluded, adding that “it can identify some IBCs that are not visible to humans (IBCs without retrospective abnormalities).”

SOURCE:

This study was led by Jonas Subelack, Chair of Health Economics, Policy and Management, School of Medicine, University of St. Gallen, St. Gallen, Switzerland. It was published online in European Radiology.

LIMITATIONS:

The retrospective study design inherently limited causal conclusions. Without access to diagnostic mammograms or the detailed position of BC, researchers could not evaluate whether AI-marked lesions corresponded to later detected BCs.

DISCLOSURES:

This research was funded by the Cancer League of Eastern Switzerland. One author reported receiving consulting and speaker fees from iCAD.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication. 

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

TOPLINE:

An artificial intelligence (AI) system flagged high-risk areas on mammograms for potentially missed interval breast cancers (IBCs), which radiologists had also retrospectively identified as abnormal. Moreover, the AI detected a substantial number of IBCs that manual review had overlooked.

METHODOLOGY:

• Researchers conducted a retrospective analysis of 119 IBC screening mammograms of women (mean age, 57.3 years) with a high breast density (Breast Imaging Reporting and Data System [BI-RADS] c/d, 63.0%) using data retrieved from Cancer Registries of Eastern Switzerland and Grisons-Glarus databases.
• A recorded tumour was classified as IBC when an invasive or in situ BC was diagnosed within 24 months after a normal screening mammogram.
• Three radiologists retrospectively assessed the mammograms for visible signs of BC, which were then classified as either potentially missed IBCs or IBCs without retrospective abnormalities on the basis of consensus conference recommendations of radiologists.
• An AI system generated two scores (a scale of 0 to 100): a case score reflecting the likelihood that the mammogram currently harbours cancer and a risk score estimating the probability of a BC diagnosis within 2 years.

TAKEAWAY:

• Radiologists classified 68.9% of IBCs as those having no retrospective abnormalities and assigned significantly higher BI-RADS scores to the remaining 31.1% of potentially missed IBCs (P < .05).
• Potentially missed IBCs received significantly higher AI case scores (mean, 54.1 vs 23.1; P < .05) and were assigned to a higher risk category (48.7% vs 14.6%; P < .05) than IBCs without retrospective abnormalities.
• Of all IBC cases, 46.2% received an AI case score > 25, 25.2% scored > 50, and 13.4% scored > 75.
• Potentially missed IBCs scored widely between low and high risk and case scores, whereas IBCs without retrospective abnormalities scored low case and risk scores. Specifically, 73.0% of potentially missed IBCs vs 34.1% of IBCs without retrospective abnormalities had case scores > 25, 51.4% vs 13.4% had case scores > 50, and 29.7% vs 6.1% had case scores > 75.

IN PRACTICE:

“Our research highlights that an AI system can identify BC signs in relevant portions of IBC screening mammograms and thus potentially reduce the number of IBCs in an MSP [mammography screening program] that currently does not utilize an AI system,” the authors of the study concluded, adding that “it can identify some IBCs that are not visible to humans (IBCs without retrospective abnormalities).”

SOURCE:

This study was led by Jonas Subelack, Chair of Health Economics, Policy and Management, School of Medicine, University of St. Gallen, St. Gallen, Switzerland. It was published online in European Radiology.

LIMITATIONS:

The retrospective study design inherently limited causal conclusions. Without access to diagnostic mammograms or the detailed position of BC, researchers could not evaluate whether AI-marked lesions corresponded to later detected BCs.

DISCLOSURES:

This research was funded by the Cancer League of Eastern Switzerland. One author reported receiving consulting and speaker fees from iCAD.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication. 

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

TOPLINE:

An artificial intelligence (AI) system flagged high-risk areas on mammograms for potentially missed interval breast cancers (IBCs), which radiologists had also retrospectively identified as abnormal. Moreover, the AI detected a substantial number of IBCs that manual review had overlooked.

METHODOLOGY:

• Researchers conducted a retrospective analysis of 119 IBC screening mammograms of women (mean age, 57.3 years) with a high breast density (Breast Imaging Reporting and Data System [BI-RADS] c/d, 63.0%) using data retrieved from Cancer Registries of Eastern Switzerland and Grisons-Glarus databases.
• A recorded tumour was classified as IBC when an invasive or in situ BC was diagnosed within 24 months after a normal screening mammogram.
• Three radiologists retrospectively assessed the mammograms for visible signs of BC, which were then classified as either potentially missed IBCs or IBCs without retrospective abnormalities on the basis of consensus conference recommendations of radiologists.
• An AI system generated two scores (a scale of 0 to 100): a case score reflecting the likelihood that the mammogram currently harbours cancer and a risk score estimating the probability of a BC diagnosis within 2 years.

TAKEAWAY:

• Radiologists classified 68.9% of IBCs as those having no retrospective abnormalities and assigned significantly higher BI-RADS scores to the remaining 31.1% of potentially missed IBCs (P < .05).
• Potentially missed IBCs received significantly higher AI case scores (mean, 54.1 vs 23.1; P < .05) and were assigned to a higher risk category (48.7% vs 14.6%; P < .05) than IBCs without retrospective abnormalities.
• Of all IBC cases, 46.2% received an AI case score > 25, 25.2% scored > 50, and 13.4% scored > 75.
• Potentially missed IBCs scored widely between low and high risk and case scores, whereas IBCs without retrospective abnormalities scored low case and risk scores. Specifically, 73.0% of potentially missed IBCs vs 34.1% of IBCs without retrospective abnormalities had case scores > 25, 51.4% vs 13.4% had case scores > 50, and 29.7% vs 6.1% had case scores > 75.

IN PRACTICE:

“Our research highlights that an AI system can identify BC signs in relevant portions of IBC screening mammograms and thus potentially reduce the number of IBCs in an MSP [mammography screening program] that currently does not utilize an AI system,” the authors of the study concluded, adding that “it can identify some IBCs that are not visible to humans (IBCs without retrospective abnormalities).”

SOURCE:

This study was led by Jonas Subelack, Chair of Health Economics, Policy and Management, School of Medicine, University of St. Gallen, St. Gallen, Switzerland. It was published online in European Radiology.

LIMITATIONS:

The retrospective study design inherently limited causal conclusions. Without access to diagnostic mammograms or the detailed position of BC, researchers could not evaluate whether AI-marked lesions corresponded to later detected BCs.

DISCLOSURES:

This research was funded by the Cancer League of Eastern Switzerland. One author reported receiving consulting and speaker fees from iCAD.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication. 

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

Prostate cancer is the most common noncutaneous cancer in men, accounting for 29% of all incident cancer cases.¹ Typically, prostate cancer metastasizes to bone and regional lymph nodes.² However, intrathoracic manifestation may occur. This report presents 3 cases of rare intrathoracic manifestations of metastatic prostate cancer with a review of the current literature.

CASE PRESENTATIONS

Case 1

A 71-year-old male who was an active smoker and a long-standing employment as a plumber was diagnosed with rectal cancer in 2022. He completed neoadjuvant capecitabine and radiation therapy followed by a rectosigmoidectomy. Several weeks after surgery, the patient presented to the emergency department (ED) with a dry cough and worsening shortness of breath. Point-of-care ultrasound of the lungs revealed a moderate right pleural effusion with several nodular pleural masses. A chest computed tomography (CT) confirmed these findings (Figure 1). A CT of the abdomen and pelvis revealed prostatomegaly with the medial lobe of the prostate protruding into the bladder; however, no enlarged retroperitoneal, mesenteric or pelvic lymph nodes were noted. The patient underwent a right pleural fluid drainage and pleural mass biopsy. Pleural mass histomorphology as well as immunohistochemical (IHC) stains were consistent with metastatic prostate adenocarcinoma. The pleural fluid cytology also was consistent with metastatic prostate adenocarcinoma.

Immunohistochemistry showed weak positive staining for prostate-specific NK3 homeobox 1 gene (NKX3.1), alpha-methylacyl-CoA racemase gene (AMACR), and prosaposin, and negative transcription termination factor (TTF-1), keratin-7 (CK7), and prosaposin, and negative transcription termination factor (TTF-1), keratin-7 (CK7), keratin-20, and caudal type homeobox 2 gene (CDX2) (Figure 2) 2). The patient's prostate-specific antigen (PSA) was found to be elevated at 33.9 ng/mL (reference range, < 4 ng/mL).

Case 2

A 71-year-old male with a history of alcohol use disorder and a 30-year smoking history presented to the ED with worsening dyspnea on exertion. The patient’s baseline exercise tolerance decreased to walking for only 1 block. He reported unintentional weight loss of about 30 pounds over the prior year, no recent respiratory infections, no prior breathing problems, and no personal or family history of cancer. Chest CT revealed findings of bilateral peribronchial opacities as well as mediastinal and hilar lymphadenopathy (Figure 3). The patient developed hypoxic respiratory failure necessitating intubation, mechanical ventilation, and management in the medical intensive care unit, where he was treated for postobstructive pneumonia. Fiberoptic bronchoscopy revealed endobronchial lesions in the right and left upper lobe that were partially obstructing the airway (Figure 4).

The endobronchial masses were debulked using forceps, and samples were sent for surgical pathology evaluation. Staging was completed using linear endobronchial ultrasound, which revealed an enlarged subcarinal lymph node (S7). The surgical pathology of the endobronchial mass and the subcarinal lymph node cytology were consistent with metastatic adenocarcinoma of the prostate. The tumor cells were positive for AE1/AE3, PSA, and NKX3.1, but were negative for CK7 and TTF-1 (Figure 5). Further imaging revealed an enlarged heterogeneous prostate gland, prominent pelvic nodes, and left retroperitoneal lymphadenopathy, as well as sclerotic foci within the T10 vertebral body and right inferior pubic ramus. PSA was also found to be significantly elevated at 700 ng/mL.

Case 3

An 80-year-old male veteran with a history of prostate cancer and recently diagnosed T2N1M0 head and neck squamous cell carcinoma was referred to the Pulmonary service for evaluation of a pulmonary nodule. His medical history was notable for prostate cancer diagnosed 12 years earlier, with an unknown Gleason score. Initial treatment included prostatectomy followed by whole pelvic radiation therapy a year after, due to elevated PSA in surveillance monitoring. This treatment led to remission. After establishing remission for > 10 years, the patient was started on low-dose testosterone replacement therapy to address complications of radiation therapy, namely hypogonadism.

On evaluation, a chest CT was significant for a large 2-cm right middle lobe nodule (Figure 6). At that time, PSA was noted to be borderline elevated at 4.2 ng/mL, and whole-body imaging did not reveal any lesions elsewhere, specifically no bone metastasis. Biopsies of the right middle lobe lung nodule revealed adenocarcinoma consistent with metastatic prostate cancer. Testosterone therapy was promptly discontinued.

The patient initially refused androgen deprivation therapy owing to the antiandrogenic adverse effects. However, subsequent chest CTs revealed growing lung nodules, which convinced him to proceed with androgen deprivation therapy followed by palliative radiation, and chemotherapy and management of malignant pleural effusion with indwelling small bore pleural catheter for about 10 years. He died from COVID-19 during the pandemic.

DISCUSSION

These cases highlight the importance of including prostate cancer in the differential diagnoses of male patients with intrathoracic abnormalities, even in the absence of metastasis to the more common sites. In a large cohort study of 74,826 patients with metastatic prostate cancer, Gandaglia et al found that the most frequent sites of metastasis were bone (84.0%) and distant lymph nodes (10.6%).2 However, thoracic involvement was observed in 9.1% of cases, with isolated thoracic metastasis being rare. The cases described in this report exemplify exceptionally uncommon occurrences within that 9.1%.

Pleural metastases, as observed in Case 1, are a particularly rare manifestation. In a 10-year retrospective assessment, Vinjamoori et al discovered pleural nodules or masses in only 6 of 82 patients (7.3%) with atypical metastases.³ Adrenal and liver metastases accounted for 15% and 37% of cases with atypical distribution. As such, isolated pleural disease is rare even in atypical presentations.³

As seen in Case 2, endobronchial metastases producing airway obstruction are also rare, with the most common primary cancers associated with endobronchial metastasis being breast, colon, and renal cancer.⁴ The available literature on this presentation is confined to case reports. Hameed et al reported a case of synchronous biopsy-proven endobronchial metastasis from prostate cancer.⁵ These cases highlight the importance of maintaining a high level of clinical awareness when encountering endobronchial lesions in patients with prostate cancer.

Case 3 presents a unique situation of lung metastases without any involvement of the bones. It is well known—and was confirmed by Heidenreich et al—that lung metastases in prostate adenocarcinoma usually coincide with extensive osseous disease.⁶ This instance highlights the importance of watchful monitoring for unusual patterns of cancer recurrence.

Immunohistochemistry stains that are specific to prostate cancer include antibodies against PSA. Prostate-specific membrane antigen is another marker that is far more present in malignant than in benign prostate tissue.

The NKX3.1 gene encodes a homeobox protein, which is a transcription factor and tumor suppressor. In prostate cancer, there is loss of heterozygosity of the gene and stains for the IHC antibody to NKX3.1.⁷

On the other hand, lung cells stain positive for TTF-1, which is produced by surfactant-producing type 2 pneumocytes and club cells in the lung. Antibodies to TTF-1, a common IHC stain, are used to identify adenocarcinoma of lung origin and may carry a prognostic value.⁷

The immunohistochemistry profiles, specifically the presence of prostate-specific markers such as PSA and NKX3.1, played a vital role in making the diagnosis.

In Case 1, weak TTF-1 positivity was noted, an unusual finding in metastatic prostate adenocarcinoma. Marak et al documented a rare case of TTF-1–positive metastatic prostate cancer, illustrating the potential for diagnostic confusion with primary lung malignancies.⁸

The 3 cases described in this report demonstrate the importance of clinical consideration, serial follow-up of PSA levels, using more prostate-specific positron emission tomography tracers (eg, Pylarify) alongside traditional imaging, and tissue biopsy to detect unusual metastases.

CONCLUSIONS

Although thoracic metastases from prostate cancer are rare, these presentations highlight the importance of clinical awareness regarding atypical cases. Pleural disease, endobronchial lesions, and isolated pulmonary nodules might be the first clinical manifestation of metastatic prostate cancer. A high index of suspicion, appropriate imaging, and judicious use of immunohistochemistry are important to ensure accurate diagnosis and optimal patient management.

Prostate cancer is the most common noncutaneous cancer in men, accounting for 29% of all incident cancer cases.¹ Typically, prostate cancer metastasizes to bone and regional lymph nodes.² However, intrathoracic manifestation may occur. This report presents 3 cases of rare intrathoracic manifestations of metastatic prostate cancer with a review of the current literature.

CASE PRESENTATIONS

Case 1

A 71-year-old male who was an active smoker and a long-standing employment as a plumber was diagnosed with rectal cancer in 2022. He completed neoadjuvant capecitabine and radiation therapy followed by a rectosigmoidectomy. Several weeks after surgery, the patient presented to the emergency department (ED) with a dry cough and worsening shortness of breath. Point-of-care ultrasound of the lungs revealed a moderate right pleural effusion with several nodular pleural masses. A chest computed tomography (CT) confirmed these findings (Figure 1). A CT of the abdomen and pelvis revealed prostatomegaly with the medial lobe of the prostate protruding into the bladder; however, no enlarged retroperitoneal, mesenteric or pelvic lymph nodes were noted. The patient underwent a right pleural fluid drainage and pleural mass biopsy. Pleural mass histomorphology as well as immunohistochemical (IHC) stains were consistent with metastatic prostate adenocarcinoma. The pleural fluid cytology also was consistent with metastatic prostate adenocarcinoma.

Immunohistochemistry showed weak positive staining for prostate-specific NK3 homeobox 1 gene (NKX3.1), alpha-methylacyl-CoA racemase gene (AMACR), and prosaposin, and negative transcription termination factor (TTF-1), keratin-7 (CK7), and prosaposin, and negative transcription termination factor (TTF-1), keratin-7 (CK7), keratin-20, and caudal type homeobox 2 gene (CDX2) (Figure 2) 2). The patient's prostate-specific antigen (PSA) was found to be elevated at 33.9 ng/mL (reference range, < 4 ng/mL).

Case 2

A 71-year-old male with a history of alcohol use disorder and a 30-year smoking history presented to the ED with worsening dyspnea on exertion. The patient’s baseline exercise tolerance decreased to walking for only 1 block. He reported unintentional weight loss of about 30 pounds over the prior year, no recent respiratory infections, no prior breathing problems, and no personal or family history of cancer. Chest CT revealed findings of bilateral peribronchial opacities as well as mediastinal and hilar lymphadenopathy (Figure 3). The patient developed hypoxic respiratory failure necessitating intubation, mechanical ventilation, and management in the medical intensive care unit, where he was treated for postobstructive pneumonia. Fiberoptic bronchoscopy revealed endobronchial lesions in the right and left upper lobe that were partially obstructing the airway (Figure 4).

The endobronchial masses were debulked using forceps, and samples were sent for surgical pathology evaluation. Staging was completed using linear endobronchial ultrasound, which revealed an enlarged subcarinal lymph node (S7). The surgical pathology of the endobronchial mass and the subcarinal lymph node cytology were consistent with metastatic adenocarcinoma of the prostate. The tumor cells were positive for AE1/AE3, PSA, and NKX3.1, but were negative for CK7 and TTF-1 (Figure 5). Further imaging revealed an enlarged heterogeneous prostate gland, prominent pelvic nodes, and left retroperitoneal lymphadenopathy, as well as sclerotic foci within the T10 vertebral body and right inferior pubic ramus. PSA was also found to be significantly elevated at 700 ng/mL.

Case 3

An 80-year-old male veteran with a history of prostate cancer and recently diagnosed T2N1M0 head and neck squamous cell carcinoma was referred to the Pulmonary service for evaluation of a pulmonary nodule. His medical history was notable for prostate cancer diagnosed 12 years earlier, with an unknown Gleason score. Initial treatment included prostatectomy followed by whole pelvic radiation therapy a year after, due to elevated PSA in surveillance monitoring. This treatment led to remission. After establishing remission for > 10 years, the patient was started on low-dose testosterone replacement therapy to address complications of radiation therapy, namely hypogonadism.

On evaluation, a chest CT was significant for a large 2-cm right middle lobe nodule (Figure 6). At that time, PSA was noted to be borderline elevated at 4.2 ng/mL, and whole-body imaging did not reveal any lesions elsewhere, specifically no bone metastasis. Biopsies of the right middle lobe lung nodule revealed adenocarcinoma consistent with metastatic prostate cancer. Testosterone therapy was promptly discontinued.

The patient initially refused androgen deprivation therapy owing to the antiandrogenic adverse effects. However, subsequent chest CTs revealed growing lung nodules, which convinced him to proceed with androgen deprivation therapy followed by palliative radiation, and chemotherapy and management of malignant pleural effusion with indwelling small bore pleural catheter for about 10 years. He died from COVID-19 during the pandemic.

DISCUSSION

These cases highlight the importance of including prostate cancer in the differential diagnoses of male patients with intrathoracic abnormalities, even in the absence of metastasis to the more common sites. In a large cohort study of 74,826 patients with metastatic prostate cancer, Gandaglia et al found that the most frequent sites of metastasis were bone (84.0%) and distant lymph nodes (10.6%).2 However, thoracic involvement was observed in 9.1% of cases, with isolated thoracic metastasis being rare. The cases described in this report exemplify exceptionally uncommon occurrences within that 9.1%.

Pleural metastases, as observed in Case 1, are a particularly rare manifestation. In a 10-year retrospective assessment, Vinjamoori et al discovered pleural nodules or masses in only 6 of 82 patients (7.3%) with atypical metastases.³ Adrenal and liver metastases accounted for 15% and 37% of cases with atypical distribution. As such, isolated pleural disease is rare even in atypical presentations.³

As seen in Case 2, endobronchial metastases producing airway obstruction are also rare, with the most common primary cancers associated with endobronchial metastasis being breast, colon, and renal cancer.⁴ The available literature on this presentation is confined to case reports. Hameed et al reported a case of synchronous biopsy-proven endobronchial metastasis from prostate cancer.⁵ These cases highlight the importance of maintaining a high level of clinical awareness when encountering endobronchial lesions in patients with prostate cancer.

Case 3 presents a unique situation of lung metastases without any involvement of the bones. It is well known—and was confirmed by Heidenreich et al—that lung metastases in prostate adenocarcinoma usually coincide with extensive osseous disease.⁶ This instance highlights the importance of watchful monitoring for unusual patterns of cancer recurrence.

Immunohistochemistry stains that are specific to prostate cancer include antibodies against PSA. Prostate-specific membrane antigen is another marker that is far more present in malignant than in benign prostate tissue.

The NKX3.1 gene encodes a homeobox protein, which is a transcription factor and tumor suppressor. In prostate cancer, there is loss of heterozygosity of the gene and stains for the IHC antibody to NKX3.1.⁷

On the other hand, lung cells stain positive for TTF-1, which is produced by surfactant-producing type 2 pneumocytes and club cells in the lung. Antibodies to TTF-1, a common IHC stain, are used to identify adenocarcinoma of lung origin and may carry a prognostic value.⁷

The immunohistochemistry profiles, specifically the presence of prostate-specific markers such as PSA and NKX3.1, played a vital role in making the diagnosis.

In Case 1, weak TTF-1 positivity was noted, an unusual finding in metastatic prostate adenocarcinoma. Marak et al documented a rare case of TTF-1–positive metastatic prostate cancer, illustrating the potential for diagnostic confusion with primary lung malignancies.⁸

The 3 cases described in this report demonstrate the importance of clinical consideration, serial follow-up of PSA levels, using more prostate-specific positron emission tomography tracers (eg, Pylarify) alongside traditional imaging, and tissue biopsy to detect unusual metastases.

CONCLUSIONS

Although thoracic metastases from prostate cancer are rare, these presentations highlight the importance of clinical awareness regarding atypical cases. Pleural disease, endobronchial lesions, and isolated pulmonary nodules might be the first clinical manifestation of metastatic prostate cancer. A high index of suspicion, appropriate imaging, and judicious use of immunohistochemistry are important to ensure accurate diagnosis and optimal patient management.

Prostate cancer is the most common noncutaneous cancer in men, accounting for 29% of all incident cancer cases.¹ Typically, prostate cancer metastasizes to bone and regional lymph nodes.² However, intrathoracic manifestation may occur. This report presents 3 cases of rare intrathoracic manifestations of metastatic prostate cancer with a review of the current literature.

CASE PRESENTATIONS

Case 1

A 71-year-old male who was an active smoker and a long-standing employment as a plumber was diagnosed with rectal cancer in 2022. He completed neoadjuvant capecitabine and radiation therapy followed by a rectosigmoidectomy. Several weeks after surgery, the patient presented to the emergency department (ED) with a dry cough and worsening shortness of breath. Point-of-care ultrasound of the lungs revealed a moderate right pleural effusion with several nodular pleural masses. A chest computed tomography (CT) confirmed these findings (Figure 1). A CT of the abdomen and pelvis revealed prostatomegaly with the medial lobe of the prostate protruding into the bladder; however, no enlarged retroperitoneal, mesenteric or pelvic lymph nodes were noted. The patient underwent a right pleural fluid drainage and pleural mass biopsy. Pleural mass histomorphology as well as immunohistochemical (IHC) stains were consistent with metastatic prostate adenocarcinoma. The pleural fluid cytology also was consistent with metastatic prostate adenocarcinoma.

Immunohistochemistry showed weak positive staining for prostate-specific NK3 homeobox 1 gene (NKX3.1), alpha-methylacyl-CoA racemase gene (AMACR), and prosaposin, and negative transcription termination factor (TTF-1), keratin-7 (CK7), and prosaposin, and negative transcription termination factor (TTF-1), keratin-7 (CK7), keratin-20, and caudal type homeobox 2 gene (CDX2) (Figure 2) 2). The patient's prostate-specific antigen (PSA) was found to be elevated at 33.9 ng/mL (reference range, < 4 ng/mL).

Case 2

A 71-year-old male with a history of alcohol use disorder and a 30-year smoking history presented to the ED with worsening dyspnea on exertion. The patient’s baseline exercise tolerance decreased to walking for only 1 block. He reported unintentional weight loss of about 30 pounds over the prior year, no recent respiratory infections, no prior breathing problems, and no personal or family history of cancer. Chest CT revealed findings of bilateral peribronchial opacities as well as mediastinal and hilar lymphadenopathy (Figure 3). The patient developed hypoxic respiratory failure necessitating intubation, mechanical ventilation, and management in the medical intensive care unit, where he was treated for postobstructive pneumonia. Fiberoptic bronchoscopy revealed endobronchial lesions in the right and left upper lobe that were partially obstructing the airway (Figure 4).

The endobronchial masses were debulked using forceps, and samples were sent for surgical pathology evaluation. Staging was completed using linear endobronchial ultrasound, which revealed an enlarged subcarinal lymph node (S7). The surgical pathology of the endobronchial mass and the subcarinal lymph node cytology were consistent with metastatic adenocarcinoma of the prostate. The tumor cells were positive for AE1/AE3, PSA, and NKX3.1, but were negative for CK7 and TTF-1 (Figure 5). Further imaging revealed an enlarged heterogeneous prostate gland, prominent pelvic nodes, and left retroperitoneal lymphadenopathy, as well as sclerotic foci within the T10 vertebral body and right inferior pubic ramus. PSA was also found to be significantly elevated at 700 ng/mL.

Case 3

An 80-year-old male veteran with a history of prostate cancer and recently diagnosed T2N1M0 head and neck squamous cell carcinoma was referred to the Pulmonary service for evaluation of a pulmonary nodule. His medical history was notable for prostate cancer diagnosed 12 years earlier, with an unknown Gleason score. Initial treatment included prostatectomy followed by whole pelvic radiation therapy a year after, due to elevated PSA in surveillance monitoring. This treatment led to remission. After establishing remission for > 10 years, the patient was started on low-dose testosterone replacement therapy to address complications of radiation therapy, namely hypogonadism.

On evaluation, a chest CT was significant for a large 2-cm right middle lobe nodule (Figure 6). At that time, PSA was noted to be borderline elevated at 4.2 ng/mL, and whole-body imaging did not reveal any lesions elsewhere, specifically no bone metastasis. Biopsies of the right middle lobe lung nodule revealed adenocarcinoma consistent with metastatic prostate cancer. Testosterone therapy was promptly discontinued.

The patient initially refused androgen deprivation therapy owing to the antiandrogenic adverse effects. However, subsequent chest CTs revealed growing lung nodules, which convinced him to proceed with androgen deprivation therapy followed by palliative radiation, and chemotherapy and management of malignant pleural effusion with indwelling small bore pleural catheter for about 10 years. He died from COVID-19 during the pandemic.

DISCUSSION

These cases highlight the importance of including prostate cancer in the differential diagnoses of male patients with intrathoracic abnormalities, even in the absence of metastasis to the more common sites. In a large cohort study of 74,826 patients with metastatic prostate cancer, Gandaglia et al found that the most frequent sites of metastasis were bone (84.0%) and distant lymph nodes (10.6%).2 However, thoracic involvement was observed in 9.1% of cases, with isolated thoracic metastasis being rare. The cases described in this report exemplify exceptionally uncommon occurrences within that 9.1%.

Pleural metastases, as observed in Case 1, are a particularly rare manifestation. In a 10-year retrospective assessment, Vinjamoori et al discovered pleural nodules or masses in only 6 of 82 patients (7.3%) with atypical metastases.³ Adrenal and liver metastases accounted for 15% and 37% of cases with atypical distribution. As such, isolated pleural disease is rare even in atypical presentations.³

As seen in Case 2, endobronchial metastases producing airway obstruction are also rare, with the most common primary cancers associated with endobronchial metastasis being breast, colon, and renal cancer.⁴ The available literature on this presentation is confined to case reports. Hameed et al reported a case of synchronous biopsy-proven endobronchial metastasis from prostate cancer.⁵ These cases highlight the importance of maintaining a high level of clinical awareness when encountering endobronchial lesions in patients with prostate cancer.

Case 3 presents a unique situation of lung metastases without any involvement of the bones. It is well known—and was confirmed by Heidenreich et al—that lung metastases in prostate adenocarcinoma usually coincide with extensive osseous disease.⁶ This instance highlights the importance of watchful monitoring for unusual patterns of cancer recurrence.

Immunohistochemistry stains that are specific to prostate cancer include antibodies against PSA. Prostate-specific membrane antigen is another marker that is far more present in malignant than in benign prostate tissue.

The NKX3.1 gene encodes a homeobox protein, which is a transcription factor and tumor suppressor. In prostate cancer, there is loss of heterozygosity of the gene and stains for the IHC antibody to NKX3.1.⁷

On the other hand, lung cells stain positive for TTF-1, which is produced by surfactant-producing type 2 pneumocytes and club cells in the lung. Antibodies to TTF-1, a common IHC stain, are used to identify adenocarcinoma of lung origin and may carry a prognostic value.⁷

The immunohistochemistry profiles, specifically the presence of prostate-specific markers such as PSA and NKX3.1, played a vital role in making the diagnosis.

In Case 1, weak TTF-1 positivity was noted, an unusual finding in metastatic prostate adenocarcinoma. Marak et al documented a rare case of TTF-1–positive metastatic prostate cancer, illustrating the potential for diagnostic confusion with primary lung malignancies.⁸

The 3 cases described in this report demonstrate the importance of clinical consideration, serial follow-up of PSA levels, using more prostate-specific positron emission tomography tracers (eg, Pylarify) alongside traditional imaging, and tissue biopsy to detect unusual metastases.

CONCLUSIONS

Although thoracic metastases from prostate cancer are rare, these presentations highlight the importance of clinical awareness regarding atypical cases. Pleural disease, endobronchial lesions, and isolated pulmonary nodules might be the first clinical manifestation of metastatic prostate cancer. A high index of suspicion, appropriate imaging, and judicious use of immunohistochemistry are important to ensure accurate diagnosis and optimal patient management.

The veteran population, with its unique and diverse types of exposure and military service experiences, faces distinct health factors compared with the general population. These factors can be categorized into exposures during military service and those occurring postservice. While the latter phase incorporates psychological issues that may arise while transitioning to civilian life, the service period is associated with major physical, chemical, and psychological exposures that can impact veterans’ health. Carcinogenesis related to military exposures is concerning, and different types of malignancies have been associated with military exposures.¹ The 2022 introduction of the Cancer Moonshot initiative served as a breeding ground for multiple projects aimed at investigation of exposure-related carcinogenesis, prompting increased attention and efforts to linking specific exposures to specific malignancies.²

Melanoma is the deadliest skin cancer, accounting for 1.3% of all cancer deaths.³ Although it may only account for 1% to 5% of skin cancer diagnoses, its incidence in the United States’ population has been increasing.^4,5 There were 97,610 estimated new cases of melanoma in 2023, according to the National Cancer Institute.⁶

The incidence of melanoma may be higher in the military population compared with the general population.⁷ Melanoma is the fourth-most common cancer diagnosed in veterans.⁸

Several demographic characteristics of the US military population are associated with higher melanoma incidence and poorer prognosis, including male sex, older age, and White race. Apart from sun exposure—a known risk factor for melanoma development—other factors, such as service branch, seem to contribute to risk, with the highest melanoma rates noted in the Air Force.⁹ According to a study by Chang et al, veterans have a higher risk of stage III (18%) or stage IV (13%) melanoma at initial diagnosis.⁸

Molecular testing of metastatic melanoma is currently the standard of care for guiding the use of US Food and Drug Administration-approved targeted therapies such as BRAF, MEK, and KIT inhibitors. This comparative analysis details the melanoma comprehensive genomic profiles observed at a large US Department of Veterans Affairs (VA) medical center (VAMC) and those reported in reference databases.

Methods

A query to select all metastatic melanomas sent for comprehensive genomic profiling from the Kansas City VAMC (KCVAMC), identified 35 cases from 2019 through 2023 as the study population. The health records of these patients were reviewed to collect demographic information, military service history, melanoma history, other medical, social, and family histories. The comprehensive genomic profiling reports were reviewed to collect the reported pathogenic variants, microsatellite instability (MSI) status, and tumor mutational burden (TMB) for each case.

The Catalogue of Somatic Mutations in Cancer (COSMIC) was used to identify the most commonly mutated genes in melanomas from The Cancer Genome Atlas for the general population.^4,5 The literature was consulted to determine the MSI status and TMB in melanomas from The Cancer Genome Atlas for separate reference populations.^6,7 The frequency of MSI-high (MSI-H) status, TMB ≥ 10 mutations/megabase (mut/Mb), and mutations in each of the 20 most commonly mutated genes was determined and compared between melanomas from The Cancer Genome Atlas and KCVAMC cases. Corresponding P  values were calculated to identify significant differences. Values were calculated for the entire sample as well as a subgroup with Agent Orange (AO) exposure. The study was approved by the KCVAMC Institutional Review Board.

Results

The mean (SD) age of study participants was 72.9 (9.4) years (range, 39-90 years). The mean (SD) duration of military service was 1654 (1421) days (about 4 years, 6 months, and 10 days). Of the 35 patients included, 22 (63%) served during the Vietnam era (November 1, 1965, to April 30, 1975) and 2 (6%) served during the Persian Gulf War era (August 2, 1990, to February 28, 1991). Seventeen veterans (49%) served in the Army, 9 in the Navy (26%), 5 in the Air Force (14%), and 4 in the Marine Corps (11%). Definitive AO exposure was noted in 13 patients (37%) (Table 1).

Of the 35 patients, 24 (69%) had metastatic disease and the primary site of melanoma was unknown in 14 patients (40%). One patient (Patient 32) had an intraocular melanoma. The primary site was the trunk for 11 patients (31%), the face/head for 7 patients (20%) and extremities for 3 patients (9%). Eight patients (23%) were pT3 stage (thickness > 2 mm but < 4 mm), 7 patients (20%) were pT4 stage (thickness > 4 mm), and 5 patients (14%) were pT1 (thickness ≥ 1 mm). One patient had a primary lesion at pT2 stage, and 1 had a Tis stage lesion. Three patients (9%) had a family history of melanoma in a first-degree relative.

The list of genes mutated in melanoma cells in the study population is provided in the eAppendix.^10,11 Twenty-seven patients (77%) had mutations in TERT promoter, 15 (43%) in CDKN2A/B, 13 (37%) in BRAF, 11 (31%) in NF1, 9 (26%) in TP53, and 8 (23%) in NRAS (Table 2). The majority of mutations in TERT promoter were c.- 146C>T (18 of 27 patients [67%]), whereas c.-124C>T was the second-most common (8 of 27 patients [30%]). The 2 observed mutations in the 13 patients with BRAF mutations were V600E and V600K, with almost equal distribution (54% and 46%, respectively). The mean (SD) TMB was 33.2 (39) mut/Mb (range, 1-203 mut/Mb). Ten patients (29%) had a TMB < 10 mut/Mb, whereas 24 (69%) had a TMB > 10 mut/Mb. The TMB could not be determined in 1 case. The frequency of TMB-high tumors in the study population compared with frequency in the reference population is shown in Table 3.¹² Only 3 patients (0.64%) in the reference population had MSI-H tumors, and the microsatellite status could not be determined in those tumors (Table 4).¹³ Table 5 outlines statistically significant findings.

Agent Orange Subgroup

AO was a tactical herbicide used by the US military, named for the orange band around the storage barrels. Possible mutagenic properties of AO have been attributed to its byproduct, dioxin. Among the most common cancers known to be associated with AO exposure are bladder and prostate carcinoma and hematopoietic neoplasms. The association between genetic alterations and AO exposure was studied in veterans with prostate cancer.¹⁴ However, to our knowledge, insufficient information is available to determine whether an association exists between exposure to herbicides used in Vietnam or the contaminant dioxin and melanoma. Because a significant proportion of this study population had a well-documented history of AO exposure (37.1%), we were able to analyze them as a subgroup and to separately compare their mutation frequency with the general population.

Results were notable for different distributions of the most frequently mutated genes in the AO subgroup compared with the whole study population. As such, TERT promoter remained the most frequently mutated gene (92%), followed by CDKN2A/B (46%); however, frequency of mutations in NF1 (46%) outnumbered those of BRAF (31%), the fourth-most common mutation. Moreover, when compared with the general melanoma population, a significantly higher frequency of mutations in the NF1 gene was observed in the AO subgroup—not the entire study population.

Discussion

Given that veterans constitute a distinct population, there is reasonable interest in investigating characteristic health issues related to military service. Skin cancer—melanoma in particular—has been researched recently in a veteran population. The differences in demographics, tumor characteristics, and melanoma- specific survival in veterans compared with the general population have already been assessed. According to Chang et al, compared with the general population, veterans are more likely to present with metastatic disease and have lower 5-year survival rates.⁸

Melanoma is one of the most highly mutated malignancies.¹⁵ Fortunately, the most common mutation in melanoma, BRAF V600E, is now considered therapeutically targetable. However, there are still many mutations that are less often discussed and not well understood. Regardless of therapeutic implications, all mutations observed in melanoma are worth investigating because a tumor’s genomic profile also can provide prognostic and etiologic information. Developing comprehensive descriptions of melanoma mutational profiles in specific populations is critical to advancing etiologic understanding and informing prevention strategies.

Our results demonstrate the high prevalence of TERT promoter mutations with characteristic ultraviolet signature (C>T) in the study population. This aligns with general evidence that TERT promoter mutations are common in cutaneous melanomas: 77% of this study sample and up to 86% of all mutations are TERT promoter mutations, according to Davis et al.¹⁵TERT promoter mutations are positively associated with the initiation, invasion, and metastasis of melanoma. In certain subtypes, there is evidence that the presence of TERT promoter mutations is significantly associated with risk for extranodal metastasis and death.¹⁶ The second-most common mutated gene in the veteran study population was CDKN2A/B (43%), and the third-most mutated gene was BRAF (37%).

In chronically sun-exposed skin NF1, NRAS, and occasionally BRAF V600K mutations tend to predominate. BRAF V600E mutations, on the other hand, are rare in these melanomas.15 In our study population, the most prevalent melanoma site was the trunk (31%), which is considered a location with an intermittent pattern of sun exposure.¹⁷

This study population also had a higher frequency of CDKN2A/B mutations. High frequencies of CDKN2A/B mutations have been reported in familial melanomas, but only 1 patient with CDKN2A/B mutations had a known family history of melanoma.¹⁵ Tumors in the study population showed significantly lower frequency of mutations in ROS1, GRIN2A, KDR, KMT2C (MLL3), KMT2D (MLL2), LRP1B, PTPRT, PTCH1, FAT4, and PREX2 (P < .05).

In this study the subgroup of veterans with AO exposure differed from the whole study population. As such, CDKN2A/B mutations were observed with the same frequency as NF1 mutations (46% each); however, BRAF mutations constituted only 31% of the mutations. In addition, the frequency of NF1 mutations was significantly higher in the AO subgroup compared with the general population, but not in the whole study population.

Our sample also differed from the reference population by showing a significantly higher frequency of TMB-high (ie, ≥ 10 mut/Mb) tumors (71% vs 49%; P = .01).¹² Interestingly, no significant difference in the frequency of TMB-high tumors was observed between the AO subgroup and the reference population (69% vs 49%; P = .16). There also was no statistically significant difference between the frequency of MSI-H tumors in our study population and the reference population (P = .64).¹³

One patient in the study population had uveal melanoma. Mutations encountered in this patient’s tumor differed from the general mutational profile of tumors. None of the 21 mutations depicted in Table 2 were present in this sample.^10,11 On the other hand, those mutations frequently observed in intraocular melanomas, BAP1 and GNA11, were present in this patient.¹⁸ Additionally, this particular melanoma possessed mutations in genes RICTOR, RAD21, and PIK3R1.

Limitations

This study population consisted exclusively of male patients, introducing sex as a potential confounder in analyzing differences between the study population and the general population. As noted in a 2020 systematic review, there were no sex-based differences in the frequency of mutations in BRAF, NRAS, and KIT genes.¹⁹

Regarding NF1 mutations, only NF1-mutated acral and mucosal melanomas were more frequently observed in female patients, whereas nonacral NF1-mutated melanomas were more frequently observed in male patients.²⁰ However, there is currently no clear evidence of whether the mutational landscapes of cutaneous melanoma differ by sex.²¹ Among the 11 cases with NF1-mutatation, site of origin was known in 6, 5 of which originated at nonacral sites. Although the AO subgroup also consisted entirely of male patients, this does not explain the observed increased frequency of NF1 mutations relative to the general population. No such difference was observed between the whole study population, which also consisted exclusively of male patients, and the general population. The similar frequencies of nonacral location in the whole study population (3 acral, 18 nonacral, 14 unknown site of origin) and AO subgroup (1 acral, 7 nonacral, 5 unknown site of origin) preclude location as an explanation.

The Cancer Genome Atlas Network proposed a framework for genomic classification of melanoma into 4 subtypes based on the pattern of the most prevalent significantly mutated genes: mutant BRAF, mutant RAS, mutant NF1, and triple–wild-type. According to that study, BRAF mutations were indeed associated with younger age, in contrast to the NF1-mutant genomic subtype, which was more prevalent in older individuals with higher TMB.²² This emphasizes the need to interpret the potential association of AO exposure and NF1 mutation in melanoma with caution, although additional studies are required to observe the difference between the veteran population and age-matched general population.

On the other hand, Yu et al reported no significant differences of TMB values between patients aged < 60 and ≥ 60 years with melanoma.²³ In short, the observed differences we report in our limited study warrant additional investigation with larger sample sizes, sex-matched controlling, and age-matched controlling. The study was limited by its small sample size and the single location.

Conclusion

The genomic profile of melanomas in the veteran population appears to be similar to that of the general population with a few possible differences. Melanomas in the veteran study population showed a higher frequency of CDKN2A/B mutations; lower frequency of ROS1, GRIN2A, KDR, KMT2C (MLL3), KMT2D (MLL2), LRP1B, PTPRT, PTCH1, FAT4, and PREX2 mutations; and higher TMB. In addition, melanomas in the AO subgroup showed higher frequencies of NF1 mutations. The significance of such findings remains to be determined by further investigation.

The veteran population, with its unique and diverse types of exposure and military service experiences, faces distinct health factors compared with the general population. These factors can be categorized into exposures during military service and those occurring postservice. While the latter phase incorporates psychological issues that may arise while transitioning to civilian life, the service period is associated with major physical, chemical, and psychological exposures that can impact veterans’ health. Carcinogenesis related to military exposures is concerning, and different types of malignancies have been associated with military exposures.¹ The 2022 introduction of the Cancer Moonshot initiative served as a breeding ground for multiple projects aimed at investigation of exposure-related carcinogenesis, prompting increased attention and efforts to linking specific exposures to specific malignancies.²

Melanoma is the deadliest skin cancer, accounting for 1.3% of all cancer deaths.³ Although it may only account for 1% to 5% of skin cancer diagnoses, its incidence in the United States’ population has been increasing.^4,5 There were 97,610 estimated new cases of melanoma in 2023, according to the National Cancer Institute.⁶

The incidence of melanoma may be higher in the military population compared with the general population.⁷ Melanoma is the fourth-most common cancer diagnosed in veterans.⁸

Several demographic characteristics of the US military population are associated with higher melanoma incidence and poorer prognosis, including male sex, older age, and White race. Apart from sun exposure—a known risk factor for melanoma development—other factors, such as service branch, seem to contribute to risk, with the highest melanoma rates noted in the Air Force.⁹ According to a study by Chang et al, veterans have a higher risk of stage III (18%) or stage IV (13%) melanoma at initial diagnosis.⁸

Molecular testing of metastatic melanoma is currently the standard of care for guiding the use of US Food and Drug Administration-approved targeted therapies such as BRAF, MEK, and KIT inhibitors. This comparative analysis details the melanoma comprehensive genomic profiles observed at a large US Department of Veterans Affairs (VA) medical center (VAMC) and those reported in reference databases.

Methods

A query to select all metastatic melanomas sent for comprehensive genomic profiling from the Kansas City VAMC (KCVAMC), identified 35 cases from 2019 through 2023 as the study population. The health records of these patients were reviewed to collect demographic information, military service history, melanoma history, other medical, social, and family histories. The comprehensive genomic profiling reports were reviewed to collect the reported pathogenic variants, microsatellite instability (MSI) status, and tumor mutational burden (TMB) for each case.

The Catalogue of Somatic Mutations in Cancer (COSMIC) was used to identify the most commonly mutated genes in melanomas from The Cancer Genome Atlas for the general population.^4,5 The literature was consulted to determine the MSI status and TMB in melanomas from The Cancer Genome Atlas for separate reference populations.^6,7 The frequency of MSI-high (MSI-H) status, TMB ≥ 10 mutations/megabase (mut/Mb), and mutations in each of the 20 most commonly mutated genes was determined and compared between melanomas from The Cancer Genome Atlas and KCVAMC cases. Corresponding P  values were calculated to identify significant differences. Values were calculated for the entire sample as well as a subgroup with Agent Orange (AO) exposure. The study was approved by the KCVAMC Institutional Review Board.

Results

The mean (SD) age of study participants was 72.9 (9.4) years (range, 39-90 years). The mean (SD) duration of military service was 1654 (1421) days (about 4 years, 6 months, and 10 days). Of the 35 patients included, 22 (63%) served during the Vietnam era (November 1, 1965, to April 30, 1975) and 2 (6%) served during the Persian Gulf War era (August 2, 1990, to February 28, 1991). Seventeen veterans (49%) served in the Army, 9 in the Navy (26%), 5 in the Air Force (14%), and 4 in the Marine Corps (11%). Definitive AO exposure was noted in 13 patients (37%) (Table 1).

Of the 35 patients, 24 (69%) had metastatic disease and the primary site of melanoma was unknown in 14 patients (40%). One patient (Patient 32) had an intraocular melanoma. The primary site was the trunk for 11 patients (31%), the face/head for 7 patients (20%) and extremities for 3 patients (9%). Eight patients (23%) were pT3 stage (thickness > 2 mm but < 4 mm), 7 patients (20%) were pT4 stage (thickness > 4 mm), and 5 patients (14%) were pT1 (thickness ≥ 1 mm). One patient had a primary lesion at pT2 stage, and 1 had a Tis stage lesion. Three patients (9%) had a family history of melanoma in a first-degree relative.

The list of genes mutated in melanoma cells in the study population is provided in the eAppendix.^10,11 Twenty-seven patients (77%) had mutations in TERT promoter, 15 (43%) in CDKN2A/B, 13 (37%) in BRAF, 11 (31%) in NF1, 9 (26%) in TP53, and 8 (23%) in NRAS (Table 2). The majority of mutations in TERT promoter were c.- 146C>T (18 of 27 patients [67%]), whereas c.-124C>T was the second-most common (8 of 27 patients [30%]). The 2 observed mutations in the 13 patients with BRAF mutations were V600E and V600K, with almost equal distribution (54% and 46%, respectively). The mean (SD) TMB was 33.2 (39) mut/Mb (range, 1-203 mut/Mb). Ten patients (29%) had a TMB < 10 mut/Mb, whereas 24 (69%) had a TMB > 10 mut/Mb. The TMB could not be determined in 1 case. The frequency of TMB-high tumors in the study population compared with frequency in the reference population is shown in Table 3.¹² Only 3 patients (0.64%) in the reference population had MSI-H tumors, and the microsatellite status could not be determined in those tumors (Table 4).¹³ Table 5 outlines statistically significant findings.

Agent Orange Subgroup

AO was a tactical herbicide used by the US military, named for the orange band around the storage barrels. Possible mutagenic properties of AO have been attributed to its byproduct, dioxin. Among the most common cancers known to be associated with AO exposure are bladder and prostate carcinoma and hematopoietic neoplasms. The association between genetic alterations and AO exposure was studied in veterans with prostate cancer.¹⁴ However, to our knowledge, insufficient information is available to determine whether an association exists between exposure to herbicides used in Vietnam or the contaminant dioxin and melanoma. Because a significant proportion of this study population had a well-documented history of AO exposure (37.1%), we were able to analyze them as a subgroup and to separately compare their mutation frequency with the general population.

Results were notable for different distributions of the most frequently mutated genes in the AO subgroup compared with the whole study population. As such, TERT promoter remained the most frequently mutated gene (92%), followed by CDKN2A/B (46%); however, frequency of mutations in NF1 (46%) outnumbered those of BRAF (31%), the fourth-most common mutation. Moreover, when compared with the general melanoma population, a significantly higher frequency of mutations in the NF1 gene was observed in the AO subgroup—not the entire study population.

Discussion

Given that veterans constitute a distinct population, there is reasonable interest in investigating characteristic health issues related to military service. Skin cancer—melanoma in particular—has been researched recently in a veteran population. The differences in demographics, tumor characteristics, and melanoma- specific survival in veterans compared with the general population have already been assessed. According to Chang et al, compared with the general population, veterans are more likely to present with metastatic disease and have lower 5-year survival rates.⁸

Melanoma is one of the most highly mutated malignancies.¹⁵ Fortunately, the most common mutation in melanoma, BRAF V600E, is now considered therapeutically targetable. However, there are still many mutations that are less often discussed and not well understood. Regardless of therapeutic implications, all mutations observed in melanoma are worth investigating because a tumor’s genomic profile also can provide prognostic and etiologic information. Developing comprehensive descriptions of melanoma mutational profiles in specific populations is critical to advancing etiologic understanding and informing prevention strategies.

Our results demonstrate the high prevalence of TERT promoter mutations with characteristic ultraviolet signature (C>T) in the study population. This aligns with general evidence that TERT promoter mutations are common in cutaneous melanomas: 77% of this study sample and up to 86% of all mutations are TERT promoter mutations, according to Davis et al.¹⁵TERT promoter mutations are positively associated with the initiation, invasion, and metastasis of melanoma. In certain subtypes, there is evidence that the presence of TERT promoter mutations is significantly associated with risk for extranodal metastasis and death.¹⁶ The second-most common mutated gene in the veteran study population was CDKN2A/B (43%), and the third-most mutated gene was BRAF (37%).

In chronically sun-exposed skin NF1, NRAS, and occasionally BRAF V600K mutations tend to predominate. BRAF V600E mutations, on the other hand, are rare in these melanomas.15 In our study population, the most prevalent melanoma site was the trunk (31%), which is considered a location with an intermittent pattern of sun exposure.¹⁷

This study population also had a higher frequency of CDKN2A/B mutations. High frequencies of CDKN2A/B mutations have been reported in familial melanomas, but only 1 patient with CDKN2A/B mutations had a known family history of melanoma.¹⁵ Tumors in the study population showed significantly lower frequency of mutations in ROS1, GRIN2A, KDR, KMT2C (MLL3), KMT2D (MLL2), LRP1B, PTPRT, PTCH1, FAT4, and PREX2 (P < .05).

In this study the subgroup of veterans with AO exposure differed from the whole study population. As such, CDKN2A/B mutations were observed with the same frequency as NF1 mutations (46% each); however, BRAF mutations constituted only 31% of the mutations. In addition, the frequency of NF1 mutations was significantly higher in the AO subgroup compared with the general population, but not in the whole study population.

Our sample also differed from the reference population by showing a significantly higher frequency of TMB-high (ie, ≥ 10 mut/Mb) tumors (71% vs 49%; P = .01).¹² Interestingly, no significant difference in the frequency of TMB-high tumors was observed between the AO subgroup and the reference population (69% vs 49%; P = .16). There also was no statistically significant difference between the frequency of MSI-H tumors in our study population and the reference population (P = .64).¹³

One patient in the study population had uveal melanoma. Mutations encountered in this patient’s tumor differed from the general mutational profile of tumors. None of the 21 mutations depicted in Table 2 were present in this sample.^10,11 On the other hand, those mutations frequently observed in intraocular melanomas, BAP1 and GNA11, were present in this patient.¹⁸ Additionally, this particular melanoma possessed mutations in genes RICTOR, RAD21, and PIK3R1.

Limitations

This study population consisted exclusively of male patients, introducing sex as a potential confounder in analyzing differences between the study population and the general population. As noted in a 2020 systematic review, there were no sex-based differences in the frequency of mutations in BRAF, NRAS, and KIT genes.¹⁹

Regarding NF1 mutations, only NF1-mutated acral and mucosal melanomas were more frequently observed in female patients, whereas nonacral NF1-mutated melanomas were more frequently observed in male patients.²⁰ However, there is currently no clear evidence of whether the mutational landscapes of cutaneous melanoma differ by sex.²¹ Among the 11 cases with NF1-mutatation, site of origin was known in 6, 5 of which originated at nonacral sites. Although the AO subgroup also consisted entirely of male patients, this does not explain the observed increased frequency of NF1 mutations relative to the general population. No such difference was observed between the whole study population, which also consisted exclusively of male patients, and the general population. The similar frequencies of nonacral location in the whole study population (3 acral, 18 nonacral, 14 unknown site of origin) and AO subgroup (1 acral, 7 nonacral, 5 unknown site of origin) preclude location as an explanation.

The Cancer Genome Atlas Network proposed a framework for genomic classification of melanoma into 4 subtypes based on the pattern of the most prevalent significantly mutated genes: mutant BRAF, mutant RAS, mutant NF1, and triple–wild-type. According to that study, BRAF mutations were indeed associated with younger age, in contrast to the NF1-mutant genomic subtype, which was more prevalent in older individuals with higher TMB.²² This emphasizes the need to interpret the potential association of AO exposure and NF1 mutation in melanoma with caution, although additional studies are required to observe the difference between the veteran population and age-matched general population.

On the other hand, Yu et al reported no significant differences of TMB values between patients aged < 60 and ≥ 60 years with melanoma.²³ In short, the observed differences we report in our limited study warrant additional investigation with larger sample sizes, sex-matched controlling, and age-matched controlling. The study was limited by its small sample size and the single location.

Conclusion

The genomic profile of melanomas in the veteran population appears to be similar to that of the general population with a few possible differences. Melanomas in the veteran study population showed a higher frequency of CDKN2A/B mutations; lower frequency of ROS1, GRIN2A, KDR, KMT2C (MLL3), KMT2D (MLL2), LRP1B, PTPRT, PTCH1, FAT4, and PREX2 mutations; and higher TMB. In addition, melanomas in the AO subgroup showed higher frequencies of NF1 mutations. The significance of such findings remains to be determined by further investigation.

The veteran population, with its unique and diverse types of exposure and military service experiences, faces distinct health factors compared with the general population. These factors can be categorized into exposures during military service and those occurring postservice. While the latter phase incorporates psychological issues that may arise while transitioning to civilian life, the service period is associated with major physical, chemical, and psychological exposures that can impact veterans’ health. Carcinogenesis related to military exposures is concerning, and different types of malignancies have been associated with military exposures.¹ The 2022 introduction of the Cancer Moonshot initiative served as a breeding ground for multiple projects aimed at investigation of exposure-related carcinogenesis, prompting increased attention and efforts to linking specific exposures to specific malignancies.²

Melanoma is the deadliest skin cancer, accounting for 1.3% of all cancer deaths.³ Although it may only account for 1% to 5% of skin cancer diagnoses, its incidence in the United States’ population has been increasing.^4,5 There were 97,610 estimated new cases of melanoma in 2023, according to the National Cancer Institute.⁶

The incidence of melanoma may be higher in the military population compared with the general population.⁷ Melanoma is the fourth-most common cancer diagnosed in veterans.⁸

Several demographic characteristics of the US military population are associated with higher melanoma incidence and poorer prognosis, including male sex, older age, and White race. Apart from sun exposure—a known risk factor for melanoma development—other factors, such as service branch, seem to contribute to risk, with the highest melanoma rates noted in the Air Force.⁹ According to a study by Chang et al, veterans have a higher risk of stage III (18%) or stage IV (13%) melanoma at initial diagnosis.⁸

Molecular testing of metastatic melanoma is currently the standard of care for guiding the use of US Food and Drug Administration-approved targeted therapies such as BRAF, MEK, and KIT inhibitors. This comparative analysis details the melanoma comprehensive genomic profiles observed at a large US Department of Veterans Affairs (VA) medical center (VAMC) and those reported in reference databases.

Methods

A query to select all metastatic melanomas sent for comprehensive genomic profiling from the Kansas City VAMC (KCVAMC), identified 35 cases from 2019 through 2023 as the study population. The health records of these patients were reviewed to collect demographic information, military service history, melanoma history, other medical, social, and family histories. The comprehensive genomic profiling reports were reviewed to collect the reported pathogenic variants, microsatellite instability (MSI) status, and tumor mutational burden (TMB) for each case.

The Catalogue of Somatic Mutations in Cancer (COSMIC) was used to identify the most commonly mutated genes in melanomas from The Cancer Genome Atlas for the general population.^4,5 The literature was consulted to determine the MSI status and TMB in melanomas from The Cancer Genome Atlas for separate reference populations.^6,7 The frequency of MSI-high (MSI-H) status, TMB ≥ 10 mutations/megabase (mut/Mb), and mutations in each of the 20 most commonly mutated genes was determined and compared between melanomas from The Cancer Genome Atlas and KCVAMC cases. Corresponding P  values were calculated to identify significant differences. Values were calculated for the entire sample as well as a subgroup with Agent Orange (AO) exposure. The study was approved by the KCVAMC Institutional Review Board.

Results

The mean (SD) age of study participants was 72.9 (9.4) years (range, 39-90 years). The mean (SD) duration of military service was 1654 (1421) days (about 4 years, 6 months, and 10 days). Of the 35 patients included, 22 (63%) served during the Vietnam era (November 1, 1965, to April 30, 1975) and 2 (6%) served during the Persian Gulf War era (August 2, 1990, to February 28, 1991). Seventeen veterans (49%) served in the Army, 9 in the Navy (26%), 5 in the Air Force (14%), and 4 in the Marine Corps (11%). Definitive AO exposure was noted in 13 patients (37%) (Table 1).

Of the 35 patients, 24 (69%) had metastatic disease and the primary site of melanoma was unknown in 14 patients (40%). One patient (Patient 32) had an intraocular melanoma. The primary site was the trunk for 11 patients (31%), the face/head for 7 patients (20%) and extremities for 3 patients (9%). Eight patients (23%) were pT3 stage (thickness > 2 mm but < 4 mm), 7 patients (20%) were pT4 stage (thickness > 4 mm), and 5 patients (14%) were pT1 (thickness ≥ 1 mm). One patient had a primary lesion at pT2 stage, and 1 had a Tis stage lesion. Three patients (9%) had a family history of melanoma in a first-degree relative.

The list of genes mutated in melanoma cells in the study population is provided in the eAppendix.^10,11 Twenty-seven patients (77%) had mutations in TERT promoter, 15 (43%) in CDKN2A/B, 13 (37%) in BRAF, 11 (31%) in NF1, 9 (26%) in TP53, and 8 (23%) in NRAS (Table 2). The majority of mutations in TERT promoter were c.- 146C>T (18 of 27 patients [67%]), whereas c.-124C>T was the second-most common (8 of 27 patients [30%]). The 2 observed mutations in the 13 patients with BRAF mutations were V600E and V600K, with almost equal distribution (54% and 46%, respectively). The mean (SD) TMB was 33.2 (39) mut/Mb (range, 1-203 mut/Mb). Ten patients (29%) had a TMB < 10 mut/Mb, whereas 24 (69%) had a TMB > 10 mut/Mb. The TMB could not be determined in 1 case. The frequency of TMB-high tumors in the study population compared with frequency in the reference population is shown in Table 3.¹² Only 3 patients (0.64%) in the reference population had MSI-H tumors, and the microsatellite status could not be determined in those tumors (Table 4).¹³ Table 5 outlines statistically significant findings.

Agent Orange Subgroup

AO was a tactical herbicide used by the US military, named for the orange band around the storage barrels. Possible mutagenic properties of AO have been attributed to its byproduct, dioxin. Among the most common cancers known to be associated with AO exposure are bladder and prostate carcinoma and hematopoietic neoplasms. The association between genetic alterations and AO exposure was studied in veterans with prostate cancer.¹⁴ However, to our knowledge, insufficient information is available to determine whether an association exists between exposure to herbicides used in Vietnam or the contaminant dioxin and melanoma. Because a significant proportion of this study population had a well-documented history of AO exposure (37.1%), we were able to analyze them as a subgroup and to separately compare their mutation frequency with the general population.

Results were notable for different distributions of the most frequently mutated genes in the AO subgroup compared with the whole study population. As such, TERT promoter remained the most frequently mutated gene (92%), followed by CDKN2A/B (46%); however, frequency of mutations in NF1 (46%) outnumbered those of BRAF (31%), the fourth-most common mutation. Moreover, when compared with the general melanoma population, a significantly higher frequency of mutations in the NF1 gene was observed in the AO subgroup—not the entire study population.

Discussion

Given that veterans constitute a distinct population, there is reasonable interest in investigating characteristic health issues related to military service. Skin cancer—melanoma in particular—has been researched recently in a veteran population. The differences in demographics, tumor characteristics, and melanoma- specific survival in veterans compared with the general population have already been assessed. According to Chang et al, compared with the general population, veterans are more likely to present with metastatic disease and have lower 5-year survival rates.⁸

Melanoma is one of the most highly mutated malignancies.¹⁵ Fortunately, the most common mutation in melanoma, BRAF V600E, is now considered therapeutically targetable. However, there are still many mutations that are less often discussed and not well understood. Regardless of therapeutic implications, all mutations observed in melanoma are worth investigating because a tumor’s genomic profile also can provide prognostic and etiologic information. Developing comprehensive descriptions of melanoma mutational profiles in specific populations is critical to advancing etiologic understanding and informing prevention strategies.

Our results demonstrate the high prevalence of TERT promoter mutations with characteristic ultraviolet signature (C>T) in the study population. This aligns with general evidence that TERT promoter mutations are common in cutaneous melanomas: 77% of this study sample and up to 86% of all mutations are TERT promoter mutations, according to Davis et al.¹⁵TERT promoter mutations are positively associated with the initiation, invasion, and metastasis of melanoma. In certain subtypes, there is evidence that the presence of TERT promoter mutations is significantly associated with risk for extranodal metastasis and death.¹⁶ The second-most common mutated gene in the veteran study population was CDKN2A/B (43%), and the third-most mutated gene was BRAF (37%).

In chronically sun-exposed skin NF1, NRAS, and occasionally BRAF V600K mutations tend to predominate. BRAF V600E mutations, on the other hand, are rare in these melanomas.15 In our study population, the most prevalent melanoma site was the trunk (31%), which is considered a location with an intermittent pattern of sun exposure.¹⁷

This study population also had a higher frequency of CDKN2A/B mutations. High frequencies of CDKN2A/B mutations have been reported in familial melanomas, but only 1 patient with CDKN2A/B mutations had a known family history of melanoma.¹⁵ Tumors in the study population showed significantly lower frequency of mutations in ROS1, GRIN2A, KDR, KMT2C (MLL3), KMT2D (MLL2), LRP1B, PTPRT, PTCH1, FAT4, and PREX2 (P < .05).

In this study the subgroup of veterans with AO exposure differed from the whole study population. As such, CDKN2A/B mutations were observed with the same frequency as NF1 mutations (46% each); however, BRAF mutations constituted only 31% of the mutations. In addition, the frequency of NF1 mutations was significantly higher in the AO subgroup compared with the general population, but not in the whole study population.

Our sample also differed from the reference population by showing a significantly higher frequency of TMB-high (ie, ≥ 10 mut/Mb) tumors (71% vs 49%; P = .01).¹² Interestingly, no significant difference in the frequency of TMB-high tumors was observed between the AO subgroup and the reference population (69% vs 49%; P = .16). There also was no statistically significant difference between the frequency of MSI-H tumors in our study population and the reference population (P = .64).¹³

One patient in the study population had uveal melanoma. Mutations encountered in this patient’s tumor differed from the general mutational profile of tumors. None of the 21 mutations depicted in Table 2 were present in this sample.^10,11 On the other hand, those mutations frequently observed in intraocular melanomas, BAP1 and GNA11, were present in this patient.¹⁸ Additionally, this particular melanoma possessed mutations in genes RICTOR, RAD21, and PIK3R1.

Limitations

This study population consisted exclusively of male patients, introducing sex as a potential confounder in analyzing differences between the study population and the general population. As noted in a 2020 systematic review, there were no sex-based differences in the frequency of mutations in BRAF, NRAS, and KIT genes.¹⁹

Regarding NF1 mutations, only NF1-mutated acral and mucosal melanomas were more frequently observed in female patients, whereas nonacral NF1-mutated melanomas were more frequently observed in male patients.²⁰ However, there is currently no clear evidence of whether the mutational landscapes of cutaneous melanoma differ by sex.²¹ Among the 11 cases with NF1-mutatation, site of origin was known in 6, 5 of which originated at nonacral sites. Although the AO subgroup also consisted entirely of male patients, this does not explain the observed increased frequency of NF1 mutations relative to the general population. No such difference was observed between the whole study population, which also consisted exclusively of male patients, and the general population. The similar frequencies of nonacral location in the whole study population (3 acral, 18 nonacral, 14 unknown site of origin) and AO subgroup (1 acral, 7 nonacral, 5 unknown site of origin) preclude location as an explanation.

The Cancer Genome Atlas Network proposed a framework for genomic classification of melanoma into 4 subtypes based on the pattern of the most prevalent significantly mutated genes: mutant BRAF, mutant RAS, mutant NF1, and triple–wild-type. According to that study, BRAF mutations were indeed associated with younger age, in contrast to the NF1-mutant genomic subtype, which was more prevalent in older individuals with higher TMB.²² This emphasizes the need to interpret the potential association of AO exposure and NF1 mutation in melanoma with caution, although additional studies are required to observe the difference between the veteran population and age-matched general population.

On the other hand, Yu et al reported no significant differences of TMB values between patients aged < 60 and ≥ 60 years with melanoma.²³ In short, the observed differences we report in our limited study warrant additional investigation with larger sample sizes, sex-matched controlling, and age-matched controlling. The study was limited by its small sample size and the single location.

Conclusion

The genomic profile of melanomas in the veteran population appears to be similar to that of the general population with a few possible differences. Melanomas in the veteran study population showed a higher frequency of CDKN2A/B mutations; lower frequency of ROS1, GRIN2A, KDR, KMT2C (MLL3), KMT2D (MLL2), LRP1B, PTPRT, PTCH1, FAT4, and PREX2 mutations; and higher TMB. In addition, melanomas in the AO subgroup showed higher frequencies of NF1 mutations. The significance of such findings remains to be determined by further investigation.

For patients with cancer, the determining factor in whether they pursue mental health services is often whether their oncologist explicitly says it is a good idea, a psychologist said during the July Association of VA Hematology and Oncology (AVAHO) seminar in Long Beach, California, on treating veterans with renal cell carcinoma (RCC).

Kysa Christie, PhD, of the West Los Angeles Veterans Affairs Medical Center, presented findings from a 2018 study in which researchers asked Swiss patients with cancer whether their oncologist discussed their emotional health with them. 

In terms of boosting intake, it did not matter if oncologists acknowledged distress or pointed out that psychosocial services existed. Instead, a direct recommendation made a difference, increasing the likelihood of using the services over a 4-month period after initial assessment (odds ratio, 6.27).

“What it took was, ‘I really recommend this. This is something that I would want you to try,’” Christie said. 

Oncologists are crucial links between patients and mental health services, Christie said: “If people don’t ask about [distress], you’re not going to see it, but it’s there. Distress is the norm, right? It is not a weakness. It is something that we expect to see.”

Christie noted that an estimated 20% of cancer patients have major depressive disorder, and 35% to 40% have a diagnosable psychiatric condition. RCC shows disproportionately high rates of mental strain. According to Christie, research suggests that about three-fourths of the population report elevated levels of distress as evidenced by patients who scored ≥ 5 on the NCCN Distress Thermometer. Patients with cancer have an estimated 20% higher risk of suicide, especially during the first 12 months after diagnosis and at end of life, she added.

“Early during a diagnosis phase, where you’re having a lot of tests being done, you know something is happening. But you don’t know what,” Christie said. “It could be very serious. That’s just a lot of stress to hold and not know how to plan for.”

After diagnosis, routine could set in and lower distress, she said. Then terminal illness may spike it back up again. Does mental health treatment work in patients with cancer?

“There’s a really strong body of evidence-based treatments for depression, anxiety, adjustment disorders, and coping with different cancers,” Christie said. But it is a step too far to expect patients to ask for help while they are juggling appointments, tests, infusions, and more. “It’s a big ask, right? It’s setting people up for failure.”

To help, Christie said she is embedded with a medical oncology team and routinely talks with the staff about which patients may need help. “One thing I like to do is try to have brief visits with veterans and introduce myself when they come to clinic. I treat it like an opt-out rather than an opt-in program: I’ll just pop into the exam room. They don’t have to ask to see me.”

Christie focuses on open-ended questions and talks about resources ranging from support groups and brief appointments to extensive individual therapy. 

Another approach is a strategy known as the “warm handoff,” when an oncologist directly introduces a patient to a mental health professional. “It’s a transfer of care in front of the veteran: It’s much more time-efficient than putting in a referral.”

Christie explained how this can work. A clinician will ask her to meet with a patient during an appointment, perhaps in a couple minutes.

“Then I pop into the room, and the oncologist says, ‘Thanks for joining us. This is Mr. Jones. He has been experiencing feelings of anxiety and sadness, and we’d appreciate your help in exploring some options that might help.’  I turn to the patient and ask, ‘What more would you add?’ Then I either take Mr. Jones back to my office or stay in clinic, and we’re off to the races.”

Christie reported no disclosures.

For patients with cancer, the determining factor in whether they pursue mental health services is often whether their oncologist explicitly says it is a good idea, a psychologist said during the July Association of VA Hematology and Oncology (AVAHO) seminar in Long Beach, California, on treating veterans with renal cell carcinoma (RCC).

Kysa Christie, PhD, of the West Los Angeles Veterans Affairs Medical Center, presented findings from a 2018 study in which researchers asked Swiss patients with cancer whether their oncologist discussed their emotional health with them. 

In terms of boosting intake, it did not matter if oncologists acknowledged distress or pointed out that psychosocial services existed. Instead, a direct recommendation made a difference, increasing the likelihood of using the services over a 4-month period after initial assessment (odds ratio, 6.27).

“What it took was, ‘I really recommend this. This is something that I would want you to try,’” Christie said. 

Oncologists are crucial links between patients and mental health services, Christie said: “If people don’t ask about [distress], you’re not going to see it, but it’s there. Distress is the norm, right? It is not a weakness. It is something that we expect to see.”

Christie noted that an estimated 20% of cancer patients have major depressive disorder, and 35% to 40% have a diagnosable psychiatric condition. RCC shows disproportionately high rates of mental strain. According to Christie, research suggests that about three-fourths of the population report elevated levels of distress as evidenced by patients who scored ≥ 5 on the NCCN Distress Thermometer. Patients with cancer have an estimated 20% higher risk of suicide, especially during the first 12 months after diagnosis and at end of life, she added.

“Early during a diagnosis phase, where you’re having a lot of tests being done, you know something is happening. But you don’t know what,” Christie said. “It could be very serious. That’s just a lot of stress to hold and not know how to plan for.”

After diagnosis, routine could set in and lower distress, she said. Then terminal illness may spike it back up again. Does mental health treatment work in patients with cancer?

“There’s a really strong body of evidence-based treatments for depression, anxiety, adjustment disorders, and coping with different cancers,” Christie said. But it is a step too far to expect patients to ask for help while they are juggling appointments, tests, infusions, and more. “It’s a big ask, right? It’s setting people up for failure.”

To help, Christie said she is embedded with a medical oncology team and routinely talks with the staff about which patients may need help. “One thing I like to do is try to have brief visits with veterans and introduce myself when they come to clinic. I treat it like an opt-out rather than an opt-in program: I’ll just pop into the exam room. They don’t have to ask to see me.”

Christie focuses on open-ended questions and talks about resources ranging from support groups and brief appointments to extensive individual therapy. 

Another approach is a strategy known as the “warm handoff,” when an oncologist directly introduces a patient to a mental health professional. “It’s a transfer of care in front of the veteran: It’s much more time-efficient than putting in a referral.”

Christie explained how this can work. A clinician will ask her to meet with a patient during an appointment, perhaps in a couple minutes.

“Then I pop into the room, and the oncologist says, ‘Thanks for joining us. This is Mr. Jones. He has been experiencing feelings of anxiety and sadness, and we’d appreciate your help in exploring some options that might help.’  I turn to the patient and ask, ‘What more would you add?’ Then I either take Mr. Jones back to my office or stay in clinic, and we’re off to the races.”

Christie reported no disclosures.

For patients with cancer, the determining factor in whether they pursue mental health services is often whether their oncologist explicitly says it is a good idea, a psychologist said during the July Association of VA Hematology and Oncology (AVAHO) seminar in Long Beach, California, on treating veterans with renal cell carcinoma (RCC).

Kysa Christie, PhD, of the West Los Angeles Veterans Affairs Medical Center, presented findings from a 2018 study in which researchers asked Swiss patients with cancer whether their oncologist discussed their emotional health with them. 

In terms of boosting intake, it did not matter if oncologists acknowledged distress or pointed out that psychosocial services existed. Instead, a direct recommendation made a difference, increasing the likelihood of using the services over a 4-month period after initial assessment (odds ratio, 6.27).

“What it took was, ‘I really recommend this. This is something that I would want you to try,’” Christie said. 

Oncologists are crucial links between patients and mental health services, Christie said: “If people don’t ask about [distress], you’re not going to see it, but it’s there. Distress is the norm, right? It is not a weakness. It is something that we expect to see.”

Christie noted that an estimated 20% of cancer patients have major depressive disorder, and 35% to 40% have a diagnosable psychiatric condition. RCC shows disproportionately high rates of mental strain. According to Christie, research suggests that about three-fourths of the population report elevated levels of distress as evidenced by patients who scored ≥ 5 on the NCCN Distress Thermometer. Patients with cancer have an estimated 20% higher risk of suicide, especially during the first 12 months after diagnosis and at end of life, she added.

“Early during a diagnosis phase, where you’re having a lot of tests being done, you know something is happening. But you don’t know what,” Christie said. “It could be very serious. That’s just a lot of stress to hold and not know how to plan for.”

After diagnosis, routine could set in and lower distress, she said. Then terminal illness may spike it back up again. Does mental health treatment work in patients with cancer?

“There’s a really strong body of evidence-based treatments for depression, anxiety, adjustment disorders, and coping with different cancers,” Christie said. But it is a step too far to expect patients to ask for help while they are juggling appointments, tests, infusions, and more. “It’s a big ask, right? It’s setting people up for failure.”

To help, Christie said she is embedded with a medical oncology team and routinely talks with the staff about which patients may need help. “One thing I like to do is try to have brief visits with veterans and introduce myself when they come to clinic. I treat it like an opt-out rather than an opt-in program: I’ll just pop into the exam room. They don’t have to ask to see me.”

Christie focuses on open-ended questions and talks about resources ranging from support groups and brief appointments to extensive individual therapy. 

Another approach is a strategy known as the “warm handoff,” when an oncologist directly introduces a patient to a mental health professional. “It’s a transfer of care in front of the veteran: It’s much more time-efficient than putting in a referral.”

Christie explained how this can work. A clinician will ask her to meet with a patient during an appointment, perhaps in a couple minutes.

“Then I pop into the room, and the oncologist says, ‘Thanks for joining us. This is Mr. Jones. He has been experiencing feelings of anxiety and sadness, and we’d appreciate your help in exploring some options that might help.’  I turn to the patient and ask, ‘What more would you add?’ Then I either take Mr. Jones back to my office or stay in clinic, and we’re off to the races.”

Christie reported no disclosures.

TOPLINE:

Patients living in rural areas and those aged ≥ 65 y had increased odds of receiving mental health care exclusively by phone.

METHODOLOGY:

• Researchers explored factors linked to receiving phone-only mental health care among patients within the Department of Veterans Affairs.
• They included data for 1,156,146 veteran patients with at least one mental health-specific outpatient encounter between October 2021 and September 2022 and at least one between October 2022 and September 2023.
• Patients were categorized as those who received care through phone only (n = 49,125) and those who received care through other methods (n = 1,107,021. Care was received exclusively through video (6.39%), in-person (6.63%), or a combination of in-person, video, and/or phone (86.98%).
• Demographic and clinical predictors, including rurality, age, sex, race, ethnicity, and the number of mental health diagnoses (< 3 vs ≥ 3), were evaluated.

TAKEAWAY:

• The phone-only group had a mean of 6.27 phone visits, whereas those who received care through other methods had a mean of 4.79 phone visits.
• Highly rural patients had 1.50 times higher odds of receiving phone-only mental health care than their urban counterparts (adjusted odds ratio [aOR], 1.50; P < .0001).
• Patients aged 65 years or older were more than twice as likely to receive phone-only care than those younger than 30 years (aOR, ≥ 2.17; P < .0001).
• Having fewer than three mental health diagnoses and more than 50% of mental health visits conducted by medical providers was associated with higher odds of receiving mental health care exclusively by phone (aORs, 2.03 and 1.87, respectively; P < .0001).

IN PRACTICE:

“The results of this work help to characterize the phone-only patient population and can serve to inform future implementation efforts to ensure that patients are receiving care via the modality that best meets their needs,” the authors wrote.

SOURCE:

This study was led by Samantha L. Connolly, PhD, at the VA Boston Healthcare System in Boston. It was published online in The Journal of Rural Health.

LIMITATIONS:

This study focused on a veteran population which may limit the generalizability of the findings to other groups. Additionally, its cross-sectional design restricted the ability to determine cause-and-effect relationships between factors and phone-only care.

DISCLOSURES:

This study was supported by the US Department of Veterans Affairs. The authors declared having no conflicts of interest.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

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

TOPLINE:

Patients living in rural areas and those aged ≥ 65 y had increased odds of receiving mental health care exclusively by phone.

METHODOLOGY:

• Researchers explored factors linked to receiving phone-only mental health care among patients within the Department of Veterans Affairs.
• They included data for 1,156,146 veteran patients with at least one mental health-specific outpatient encounter between October 2021 and September 2022 and at least one between October 2022 and September 2023.
• Patients were categorized as those who received care through phone only (n = 49,125) and those who received care through other methods (n = 1,107,021. Care was received exclusively through video (6.39%), in-person (6.63%), or a combination of in-person, video, and/or phone (86.98%).
• Demographic and clinical predictors, including rurality, age, sex, race, ethnicity, and the number of mental health diagnoses (< 3 vs ≥ 3), were evaluated.

TAKEAWAY:

• The phone-only group had a mean of 6.27 phone visits, whereas those who received care through other methods had a mean of 4.79 phone visits.
• Highly rural patients had 1.50 times higher odds of receiving phone-only mental health care than their urban counterparts (adjusted odds ratio [aOR], 1.50; P < .0001).
• Patients aged 65 years or older were more than twice as likely to receive phone-only care than those younger than 30 years (aOR, ≥ 2.17; P < .0001).
• Having fewer than three mental health diagnoses and more than 50% of mental health visits conducted by medical providers was associated with higher odds of receiving mental health care exclusively by phone (aORs, 2.03 and 1.87, respectively; P < .0001).

IN PRACTICE:

“The results of this work help to characterize the phone-only patient population and can serve to inform future implementation efforts to ensure that patients are receiving care via the modality that best meets their needs,” the authors wrote.

SOURCE:

This study was led by Samantha L. Connolly, PhD, at the VA Boston Healthcare System in Boston. It was published online in The Journal of Rural Health.

LIMITATIONS:

This study focused on a veteran population which may limit the generalizability of the findings to other groups. Additionally, its cross-sectional design restricted the ability to determine cause-and-effect relationships between factors and phone-only care.

DISCLOSURES:

This study was supported by the US Department of Veterans Affairs. The authors declared having no conflicts of interest.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

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

TOPLINE:

Patients living in rural areas and those aged ≥ 65 y had increased odds of receiving mental health care exclusively by phone.

METHODOLOGY:

• Researchers explored factors linked to receiving phone-only mental health care among patients within the Department of Veterans Affairs.
• They included data for 1,156,146 veteran patients with at least one mental health-specific outpatient encounter between October 2021 and September 2022 and at least one between October 2022 and September 2023.
• Patients were categorized as those who received care through phone only (n = 49,125) and those who received care through other methods (n = 1,107,021. Care was received exclusively through video (6.39%), in-person (6.63%), or a combination of in-person, video, and/or phone (86.98%).
• Demographic and clinical predictors, including rurality, age, sex, race, ethnicity, and the number of mental health diagnoses (< 3 vs ≥ 3), were evaluated.

TAKEAWAY:

• The phone-only group had a mean of 6.27 phone visits, whereas those who received care through other methods had a mean of 4.79 phone visits.
• Highly rural patients had 1.50 times higher odds of receiving phone-only mental health care than their urban counterparts (adjusted odds ratio [aOR], 1.50; P < .0001).
• Patients aged 65 years or older were more than twice as likely to receive phone-only care than those younger than 30 years (aOR, ≥ 2.17; P < .0001).
• Having fewer than three mental health diagnoses and more than 50% of mental health visits conducted by medical providers was associated with higher odds of receiving mental health care exclusively by phone (aORs, 2.03 and 1.87, respectively; P < .0001).

IN PRACTICE:

“The results of this work help to characterize the phone-only patient population and can serve to inform future implementation efforts to ensure that patients are receiving care via the modality that best meets their needs,” the authors wrote.

SOURCE:

This study was led by Samantha L. Connolly, PhD, at the VA Boston Healthcare System in Boston. It was published online in The Journal of Rural Health.

LIMITATIONS:

This study focused on a veteran population which may limit the generalizability of the findings to other groups. Additionally, its cross-sectional design restricted the ability to determine cause-and-effect relationships between factors and phone-only care.

DISCLOSURES:

This study was supported by the US Department of Veterans Affairs. The authors declared having no conflicts of interest.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

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

Colorectal cancer (CRC) is the second-leading cause of cancer-related deaths in the United States, with an estimated 52,550 deaths in 2023.¹ However, the disease burden varies among different segments of the population.² While both CRC incidence and mortality have been decreasing due to screening and advances in treatment, there are disparities in incidence and mortality across the sociodemographic spectrum including race, ethnicity, education, and income.^1-4 While CRC incidence is decreasing for older adults, it is increasing among those aged < 55 years.⁵ The incidence of CRC in adults aged 40 to 54 years has increased by 0.5% to 1.3% annually since the mid-1990s.⁶ The US Preventive Services Task Force now recommends starting CRC screening at age 45 years for asymptomatic adults with average risk.⁷

Disparities also exist across geographical boundaries and living environment. Rural Americans faces additional challenges in health and lifestyle that can affect CRC outcomes. Compared to their urban counterparts, rural residents are more likely to be older, have lower levels of education, higher levels of poverty, lack health insurance, and less access to health care practitioners (HCPs).^8-10 Geographic proximity, defined as travel time or physical distance to a health facility, has been recognized as a predictor of inferior outcomes.¹¹ These aspects of rural living may pose challenges for accessing care for CRC screening and treatment.^11-13 National and local studies have shown disparities in CRC screening rates, incidence, and mortality between rural and urban populations.^14-16

It is unclear whether rural/urban disparities persist under the Veterans Health Administration (VHA) health care delivery model. This study examined differences in baseline characteristics and mortality between rural and urban veterans newly diagnosed with CRC. We also focused on a subpopulation aged ≤ 45 years.

Methods

This study extracted national data from the US Department of Veterans Affairs (VA) Corporate Data Warehouse (CDW) hosted in the VA Informatics and Computing Infrastructure (VINCI) environment. VINCI is an initiative to improve access to VA data and facilitate the analysis of these data while ensuring veterans’ privacy and data security.¹⁷ CDW is the VHA business intelligence information repository, which extracts data from clinical and nonclinical sources following prescribed and validated protocols. Data extracted included demographics, diagnosis, and procedure codes for both inpatient and outpatient encounters, vital signs, and vital status. This study used data previously extracted from a national cohort of veterans that encompassed all patients who received a group of commonly prescribed medications, such as statins, proton pump inhibitors, histamine-2 blockers, acetaminophen-containing products, and hydrocortisone-containing skin applications. This cohort encompassed 8,648,754 veterans, from whom 2,460,727 had encounters during fiscal years (FY) 2016 to 2021 (study period). The cohort was used to ensure that subjects were VHA patients, allowing them to adequately capture their clinical profiles.

Patients were identified as rural or urban based on their residence address at the date of their first diagnosis of CRC. The Geospatial Service Support Center (GSSC) aggregates and updates veterans’ residence address records for all enrolled veterans from the National Change of Address database. The data contain 1 record per enrollee. GSSC Geocoded Enrollee File contains enrollee addresses and their rurality indicators, categorized as urban, rural, or highly rural.¹⁸ Rurality is defined by the Rural Urban Commuting Area (RUCA) categories developed by the Department of Agriculture and the Health Resources and Services Administration of the US Department of Health and Human Services.¹⁹ Urban areas had RUCA codes of 1.0 to 1.1, and highly rural areas had RUCA scores of 10.0. All other areas were classified as rural. Since the proportion of veterans from highly rural areas was small, we included residents from highly rural areas in the rural residents’ group.

Inclusion and Exclusion Criteria

All veterans newly diagnosed with CRC from FY 2016 to 2021 were included. We used the ninth and tenth clinical modification revisions of the International Classification of Diseases (ICD-9-CM and ICD-10-CM) to define CRC diagnosis (Supplemental materials).^4,20 To ensure that patients were newly diagnosed with CRC, this study excluded patients with a previous ICD-9-CM code for CRC diagnosis since FY 2003.

Comorbidities were identified using diagnosis and procedure codes from inpatient and outpatient encounters, which were used to calculate the Charlson Comorbidity Index (CCI) at the time of CRC diagnosis using the weighted method described by Schneeweiss et al.²¹ We defined CRC high-risk conditions and CRC screening tests, including flexible sigmoidoscopy and stool tests, as described in previous studies (Supplemental materials).²⁰

The main outcome was total mortality. The date of death was extracted from the VHA Death Ascertainment File, which contains mortality data from the Master Person Index file in CDW and the Social Security Administration Death Master File. We used the date of death from any cause, as cause of death was not available.

A propensity score (PS) was created to match rural (including highly rural) and urban residents at a ratio of 1:1. Using a standard procedure described in prior publications, multivariable logistic regression used all baseline characteristics to estimate the PS and perform nearest-number matching without replacement.^22,23 A caliper of 0.01 maximized the matched cohort size and achieved balance (Supplemental materials). We then examined the balance of baseline characteristics between PS-matched groups.

Analyses

Cox proportional hazards regression analysis estimated the hazard ratio (HR) of death in rural residents compared to urban residents in the PS-matched cohort. The outcome event was the date of death during the study’s follow-up period (defined as period from first CRC diagnosis to death or study end), with censoring at the study’s end date (September 30, 2021). The proportional hazards assumption was assessed by inspecting the Kaplan-Meier curves. Multiple analyses examined the HR of total mortality in the PS-matched cohort, stratified by sex, race, and ethnicity. We also examined the HR of total mortality stratified by duration of follow-up.

Another PS-matching analysis among veterans aged ≤ 45 years was performed using the same techniques described earlier in this article. We performed a Cox proportional hazards regression analysis to compare mortality in PS-matched urban and rural veterans aged ≤ 45 years. The HR of death in all veterans aged ≤ 45 years (before PS-matching) was estimated using Cox proportional hazard regression analysis, adjusting for PS.

Dichotomous variables were compared using X² tests and continuous variables were compared using t tests. Baseline characteristics with missing values were converted into categorical variables and the proportion of subjects with missing values was equalized between treatment groups after PS-matching. For subgroup analysis, we examined the HR of total mortality in each subgroup using separate Cox proportional hazards regression models similar to the primary analysis but adjusted for PS. Due to multiple comparisons in the subgroup analysis, the findings should be considered exploratory. Statistical tests were 2-tailed, and significance was defined as P < .05. Data management and statistical analyses were conducted from June 2022 to January 2023 using STATA, Version 17. The VA Orlando Healthcare System Institutional Review Board approved the study and waived requirements for informed consent because only deidentified data were used.

Results

After excluding 49 patients (Supplemental materials, available at doi:10.12788/fp.0560), we identified 30,219 veterans with newly diagnosed CRC between FY 2016 to 2021 (Table 1). Of these, 19,422 (64.3%) resided in urban areas and 10,797 (35.7%) resided in rural areas (Table 2). The mean (SD) duration from the first CRC diagnosis to death or study end was 832 (640) days, and the median (IQR) was 723 (246–1330) days. Overall, incident CRC diagnoses were numerically highest in FY 2016 and lowest in FY 2020 (Figure 1). Patients with CRC in rural areas vs urban areas were significantly older (mean, 71.2 years vs 70.8 years, respectively; P < .001), more likely to be male (96.7% vs 95.7%, respectively; P < .001), more likely to be White (83.6% vs 67.8%, respectively; P < .001) and more likely to be non-Hispanic (92.2% vs 87.5%, respectively; P < .001). In terms of general health, rural veterans with CRC were more likely to be overweight or obese (81.5% rural vs 78.5% urban; P < .001) but had fewer mean comorbidities as measured by CCI (5.66 rural vs 5.90 urban; P < .001). A higher proportion of rural veterans with CRC had received stool-based (fecal occult blood test or fecal immunochemical test) CRC screening tests (61.6% rural vs 57.2% urban; P < .001). Fewer rural patients presented with systemic symptoms or signs within 1 year of CRC diagnosis (54.4% rural vs 57.5% urban, P < .001). Among urban patients with CRC, 6959 (35.8%) deaths were observed, compared with 3766 (34.9%) among rural patients (P = .10).

There were 21,568 PS-matched veterans: 10,784 in each group. In the PS-matched cohort, baseline characteristics were similar between veterans in urban and rural communities, including age, sex, race/ethnicity, body mass index, and comorbidities. Among rural patients with CRC, 3763 deaths (34.9%) were observed compared with 3702 (34.3%) among urban veterans. There was no significant difference in the HR of mortality between rural and urban CRC residents (HR, 1.01; 95% CI, 0.97-1.06; P = .53) (Figure 2).

Among veterans aged ≤ 45 years, 551 were diagnosed with CRC (391 urban and 160 rural). We PS-matched 142 pairs of urban and rural veterans without residual differences in baseline characteristics (eAppendix 1). There was no significant difference in the HR of mortality between rural and urban veterans aged ≤ 45 years (HR, 0.97; 95% CI, 0.57-1.63; P = .90) (Figure 2). Similarly, no difference in mortality was observed adjusting for PS between all rural and urban veterans aged ≤ 45 years (HR, 1.03; 95% CI, 0.67-1.59; P = .88).

There was no difference in total mortality between rural and urban veterans in any subgroup except for American Indian or Alaska Native veterans (HR, 2.41; 95% CI, 1.29-4.50; P = .006) (eAppendix 2).

Discussion

This study examined characteristics of patients with CRC between urban and rural areas among veterans who were VHA patients. Similar to other studies, rural veterans with CRC were older, more likely to be White, and were obese, but exhibited fewer comorbidities (lower CCI and lower incidence of congestive heart failure, dementia, hemiplegia, kidney diseases, liver diseases and AIDS, but higher incidence of chronic obstructive lung disease).^8,16 The incidence of CRC in this study population was lowest in FY 2020, which was reported by the Centers for Disease Control and Prevention and is attributed to COVID-19 pandemic disruption of health services.²⁴ The overall mortality in this study was similar to rates reported in other studies from the VA Central Cancer Registry.⁴ In the PS-matched cohort, where baseline characteristics were similar between urban and rural patients with CRC, we found no disparities in CRC-specific mortality between veterans in rural and urban areas. Additionally, when analysis was restricted to veterans aged ≤ 45 years, the results remained consistent.

Subgroup analyses showed no significant difference in mortality between rural and urban areas by sex, race or ethnicity, except rural American Indian or Alaska Native veterans who had double the mortality of their urban counterparts (HR, 2.41; 95% CI, 1.29-4.50; P = .006). This finding is difficult to interpret due to the small number of events and the wide CI. While with a Bonferroni correction the adjusted P value was .08, which is not statistically significant, a previous study found that although CRC incidence was lower overall in American Indian or Alaska Native populations compared to non-Hispanic White populations, CRC incidence was higher among American Indian or Alaska Native individuals in some areas such as Alaska and the Northern Plains.^25,26 Studies have noted that rural American Indian/Alaska Native populations experience greater poverty, less access to broadband internet, and limited access to care, contributing to poorer cancer outcomes and lower survival.²⁷ Thus, the finding of disparity in mortality between rural and urban American Indian or Alaska Native veterans warrants further study.

Other studies have raised concerns that CRC disproportionately affects adults in rural areas with higher mortality rates.^14-16 These disparities arise from sociodemographic factors and modifiable risk factors, including physical activity, dietary patterns, access to cancer screening, and gaps in quality treatment resources.^16,28 These factors operate at multiple levels: from individual, local health system, to community and policy.^2,27 For example, a South Carolina study (1996–2016) found that residents in rural areas were more likely to be diagnosed with advanced CRC, possibly indicating lower rates of CRC screening in rural areas. They also had higher likelihood of death from CRC.¹⁵ However, the study did not include any clinical parameters, such as comorbidities or obesity. A statewide, population-based study in Utah showed that rural men experienced a lower CRC survival in their unadjusted analysis.¹⁶ However, the study was small, with only 3948 urban and 712 rural residents. Additionally, there was no difference in total mortality in the whole cohort (HR, 0.96; 95% CI, 0.86-1.07) or in CRC-specific death (HR, 0.93; 95% CI, 0.81-1.08). A nationwide study also showed that CRC mortality rates were 8% higher in nonmetropolitan or rural areas than in the most urbanized areas containing large metropolitan counties.²⁹ However, this study did not include descriptions of clinical confounders, such as comorbidities, making it difficult to ascertain whether the difference in CRC mortality was due to rurality or differences in baseline risk characteristics.

In this study, the lack of CRC-specific mortality disparities may be attributed to the structures and practices of VHA health care. Recent studies have noted that mortality of several chronic medical conditions treated at the VHA was lower than at non-VHA hospitals.^30,31 One study that measured the quality of nonmetastatic CRC care based on National Comprehensive Cancer Network guidelines showed that > 72% of VHA patients received guideline-concordant care for each diagnostic and therapeutic measure, except for follow-up colonoscopy timing, which appear to be similar or superior to that of the private sector.^30,32,33 Some of the VA initiative for CRC screening may bypass the urban-rurality divide such as the mailed fecal immunochemical test program for CRC. This program was implemented at the onset of the COVID-19 pandemic to avoid disruptions of medical care.³⁴ Rural patients are more likely to undergo fecal immunochemical testing when compared to urban patients in this data. Beyond clinical care, the VHA uses processes to tackle social determinants of health such as housing, food security, and transportation, promoting equal access to health care, and promoting cultural competency among HCPs.^35-37

The results suggest that solutions to CRC disparities between rural and urban areas need to consider known barriers to rural health care, including transportation, diminished rural health care workforce, and other social determinants of health.^9,10,27,38 VHA makes considerable efforts to provide equitable care to all enrolled veterans, including specific programs for rural veterans, including ongoing outreach.³⁹ This study demonstrated lack of disparity in CRC-specific mortality in veterans receiving VHA care, highlighting the importance of these efforts.

Strengths and Limitations

This study used the VHA cohort to compare patient characteristics and mortality between patients with CRC residing in rural and urban areas. The study provides nationwide perspectives on CRC across the geographical spectrum and used a longitudinal cohort with prolonged follow-up to account for comorbidities.

However, the study compared a cohort of rural and urban veterans enrolled in the VHA; hence, the results may not reflect CRC outcomes in veterans without access to VHA care. Rurality has been independently associated with decreased likelihood of meeting CRC screening guidelines among veterans and military service members.³⁸ This study lacked sufficient information to compare CRC staging or treatment modalities among veterans. Although the data cannot identify CRC stage, the proportions of patients with metastatic CRC at diagnosis and CRC location were similar between groups. The study did not have information on their care outside of VHA setting.

This study could not ascertain whether disparities existed in CRC treatment modality since rural residence may result in referral to community-based CRC care, which did not appear in the data. To address these limitations, we used death from any cause as the primary outcome, since death is a hard outcome and is not subject to ascertainment bias. The relatively short follow-up time is another limitation, though subgroup analysis by follow-up did not show significant differences. Despite PS matching, residual unmeasured confounding may exist between urban and rural groups. The predominantly White, male VHA population with high CCI may limit the generalizability of the results.

Conclusions

Rural VHA enrollees had similar survival rates after CRC diagnosis compared to their urban counterparts in a PS-matched analysis. The VHA models of care—including mailed CRC screening tools, several socioeconomic determinants of health (housing, food security, and transportation), and promoting equal access to health care, as well as cultural competency among HCPs—HCPs—may help alleviate disparities across the rural-urban spectrum. The VHA should continue efforts to enroll veterans and provide comprehensive coordinated care in community partnerships.

Colorectal cancer (CRC) is the second-leading cause of cancer-related deaths in the United States, with an estimated 52,550 deaths in 2023.¹ However, the disease burden varies among different segments of the population.² While both CRC incidence and mortality have been decreasing due to screening and advances in treatment, there are disparities in incidence and mortality across the sociodemographic spectrum including race, ethnicity, education, and income.^1-4 While CRC incidence is decreasing for older adults, it is increasing among those aged < 55 years.⁵ The incidence of CRC in adults aged 40 to 54 years has increased by 0.5% to 1.3% annually since the mid-1990s.⁶ The US Preventive Services Task Force now recommends starting CRC screening at age 45 years for asymptomatic adults with average risk.⁷

Disparities also exist across geographical boundaries and living environment. Rural Americans faces additional challenges in health and lifestyle that can affect CRC outcomes. Compared to their urban counterparts, rural residents are more likely to be older, have lower levels of education, higher levels of poverty, lack health insurance, and less access to health care practitioners (HCPs).^8-10 Geographic proximity, defined as travel time or physical distance to a health facility, has been recognized as a predictor of inferior outcomes.¹¹ These aspects of rural living may pose challenges for accessing care for CRC screening and treatment.^11-13 National and local studies have shown disparities in CRC screening rates, incidence, and mortality between rural and urban populations.^14-16

It is unclear whether rural/urban disparities persist under the Veterans Health Administration (VHA) health care delivery model. This study examined differences in baseline characteristics and mortality between rural and urban veterans newly diagnosed with CRC. We also focused on a subpopulation aged ≤ 45 years.

Methods

This study extracted national data from the US Department of Veterans Affairs (VA) Corporate Data Warehouse (CDW) hosted in the VA Informatics and Computing Infrastructure (VINCI) environment. VINCI is an initiative to improve access to VA data and facilitate the analysis of these data while ensuring veterans’ privacy and data security.¹⁷ CDW is the VHA business intelligence information repository, which extracts data from clinical and nonclinical sources following prescribed and validated protocols. Data extracted included demographics, diagnosis, and procedure codes for both inpatient and outpatient encounters, vital signs, and vital status. This study used data previously extracted from a national cohort of veterans that encompassed all patients who received a group of commonly prescribed medications, such as statins, proton pump inhibitors, histamine-2 blockers, acetaminophen-containing products, and hydrocortisone-containing skin applications. This cohort encompassed 8,648,754 veterans, from whom 2,460,727 had encounters during fiscal years (FY) 2016 to 2021 (study period). The cohort was used to ensure that subjects were VHA patients, allowing them to adequately capture their clinical profiles.

Patients were identified as rural or urban based on their residence address at the date of their first diagnosis of CRC. The Geospatial Service Support Center (GSSC) aggregates and updates veterans’ residence address records for all enrolled veterans from the National Change of Address database. The data contain 1 record per enrollee. GSSC Geocoded Enrollee File contains enrollee addresses and their rurality indicators, categorized as urban, rural, or highly rural.¹⁸ Rurality is defined by the Rural Urban Commuting Area (RUCA) categories developed by the Department of Agriculture and the Health Resources and Services Administration of the US Department of Health and Human Services.¹⁹ Urban areas had RUCA codes of 1.0 to 1.1, and highly rural areas had RUCA scores of 10.0. All other areas were classified as rural. Since the proportion of veterans from highly rural areas was small, we included residents from highly rural areas in the rural residents’ group.

Inclusion and Exclusion Criteria

All veterans newly diagnosed with CRC from FY 2016 to 2021 were included. We used the ninth and tenth clinical modification revisions of the International Classification of Diseases (ICD-9-CM and ICD-10-CM) to define CRC diagnosis (Supplemental materials).^4,20 To ensure that patients were newly diagnosed with CRC, this study excluded patients with a previous ICD-9-CM code for CRC diagnosis since FY 2003.

Comorbidities were identified using diagnosis and procedure codes from inpatient and outpatient encounters, which were used to calculate the Charlson Comorbidity Index (CCI) at the time of CRC diagnosis using the weighted method described by Schneeweiss et al.²¹ We defined CRC high-risk conditions and CRC screening tests, including flexible sigmoidoscopy and stool tests, as described in previous studies (Supplemental materials).²⁰

The main outcome was total mortality. The date of death was extracted from the VHA Death Ascertainment File, which contains mortality data from the Master Person Index file in CDW and the Social Security Administration Death Master File. We used the date of death from any cause, as cause of death was not available.

A propensity score (PS) was created to match rural (including highly rural) and urban residents at a ratio of 1:1. Using a standard procedure described in prior publications, multivariable logistic regression used all baseline characteristics to estimate the PS and perform nearest-number matching without replacement.^22,23 A caliper of 0.01 maximized the matched cohort size and achieved balance (Supplemental materials). We then examined the balance of baseline characteristics between PS-matched groups.

Analyses

Cox proportional hazards regression analysis estimated the hazard ratio (HR) of death in rural residents compared to urban residents in the PS-matched cohort. The outcome event was the date of death during the study’s follow-up period (defined as period from first CRC diagnosis to death or study end), with censoring at the study’s end date (September 30, 2021). The proportional hazards assumption was assessed by inspecting the Kaplan-Meier curves. Multiple analyses examined the HR of total mortality in the PS-matched cohort, stratified by sex, race, and ethnicity. We also examined the HR of total mortality stratified by duration of follow-up.

Another PS-matching analysis among veterans aged ≤ 45 years was performed using the same techniques described earlier in this article. We performed a Cox proportional hazards regression analysis to compare mortality in PS-matched urban and rural veterans aged ≤ 45 years. The HR of death in all veterans aged ≤ 45 years (before PS-matching) was estimated using Cox proportional hazard regression analysis, adjusting for PS.

Dichotomous variables were compared using X² tests and continuous variables were compared using t tests. Baseline characteristics with missing values were converted into categorical variables and the proportion of subjects with missing values was equalized between treatment groups after PS-matching. For subgroup analysis, we examined the HR of total mortality in each subgroup using separate Cox proportional hazards regression models similar to the primary analysis but adjusted for PS. Due to multiple comparisons in the subgroup analysis, the findings should be considered exploratory. Statistical tests were 2-tailed, and significance was defined as P < .05. Data management and statistical analyses were conducted from June 2022 to January 2023 using STATA, Version 17. The VA Orlando Healthcare System Institutional Review Board approved the study and waived requirements for informed consent because only deidentified data were used.

Results

After excluding 49 patients (Supplemental materials, available at doi:10.12788/fp.0560), we identified 30,219 veterans with newly diagnosed CRC between FY 2016 to 2021 (Table 1). Of these, 19,422 (64.3%) resided in urban areas and 10,797 (35.7%) resided in rural areas (Table 2). The mean (SD) duration from the first CRC diagnosis to death or study end was 832 (640) days, and the median (IQR) was 723 (246–1330) days. Overall, incident CRC diagnoses were numerically highest in FY 2016 and lowest in FY 2020 (Figure 1). Patients with CRC in rural areas vs urban areas were significantly older (mean, 71.2 years vs 70.8 years, respectively; P < .001), more likely to be male (96.7% vs 95.7%, respectively; P < .001), more likely to be White (83.6% vs 67.8%, respectively; P < .001) and more likely to be non-Hispanic (92.2% vs 87.5%, respectively; P < .001). In terms of general health, rural veterans with CRC were more likely to be overweight or obese (81.5% rural vs 78.5% urban; P < .001) but had fewer mean comorbidities as measured by CCI (5.66 rural vs 5.90 urban; P < .001). A higher proportion of rural veterans with CRC had received stool-based (fecal occult blood test or fecal immunochemical test) CRC screening tests (61.6% rural vs 57.2% urban; P < .001). Fewer rural patients presented with systemic symptoms or signs within 1 year of CRC diagnosis (54.4% rural vs 57.5% urban, P < .001). Among urban patients with CRC, 6959 (35.8%) deaths were observed, compared with 3766 (34.9%) among rural patients (P = .10).

There were 21,568 PS-matched veterans: 10,784 in each group. In the PS-matched cohort, baseline characteristics were similar between veterans in urban and rural communities, including age, sex, race/ethnicity, body mass index, and comorbidities. Among rural patients with CRC, 3763 deaths (34.9%) were observed compared with 3702 (34.3%) among urban veterans. There was no significant difference in the HR of mortality between rural and urban CRC residents (HR, 1.01; 95% CI, 0.97-1.06; P = .53) (Figure 2).

Among veterans aged ≤ 45 years, 551 were diagnosed with CRC (391 urban and 160 rural). We PS-matched 142 pairs of urban and rural veterans without residual differences in baseline characteristics (eAppendix 1). There was no significant difference in the HR of mortality between rural and urban veterans aged ≤ 45 years (HR, 0.97; 95% CI, 0.57-1.63; P = .90) (Figure 2). Similarly, no difference in mortality was observed adjusting for PS between all rural and urban veterans aged ≤ 45 years (HR, 1.03; 95% CI, 0.67-1.59; P = .88).

There was no difference in total mortality between rural and urban veterans in any subgroup except for American Indian or Alaska Native veterans (HR, 2.41; 95% CI, 1.29-4.50; P = .006) (eAppendix 2).

Discussion

This study examined characteristics of patients with CRC between urban and rural areas among veterans who were VHA patients. Similar to other studies, rural veterans with CRC were older, more likely to be White, and were obese, but exhibited fewer comorbidities (lower CCI and lower incidence of congestive heart failure, dementia, hemiplegia, kidney diseases, liver diseases and AIDS, but higher incidence of chronic obstructive lung disease).^8,16 The incidence of CRC in this study population was lowest in FY 2020, which was reported by the Centers for Disease Control and Prevention and is attributed to COVID-19 pandemic disruption of health services.²⁴ The overall mortality in this study was similar to rates reported in other studies from the VA Central Cancer Registry.⁴ In the PS-matched cohort, where baseline characteristics were similar between urban and rural patients with CRC, we found no disparities in CRC-specific mortality between veterans in rural and urban areas. Additionally, when analysis was restricted to veterans aged ≤ 45 years, the results remained consistent.

Subgroup analyses showed no significant difference in mortality between rural and urban areas by sex, race or ethnicity, except rural American Indian or Alaska Native veterans who had double the mortality of their urban counterparts (HR, 2.41; 95% CI, 1.29-4.50; P = .006). This finding is difficult to interpret due to the small number of events and the wide CI. While with a Bonferroni correction the adjusted P value was .08, which is not statistically significant, a previous study found that although CRC incidence was lower overall in American Indian or Alaska Native populations compared to non-Hispanic White populations, CRC incidence was higher among American Indian or Alaska Native individuals in some areas such as Alaska and the Northern Plains.^25,26 Studies have noted that rural American Indian/Alaska Native populations experience greater poverty, less access to broadband internet, and limited access to care, contributing to poorer cancer outcomes and lower survival.²⁷ Thus, the finding of disparity in mortality between rural and urban American Indian or Alaska Native veterans warrants further study.

Other studies have raised concerns that CRC disproportionately affects adults in rural areas with higher mortality rates.^14-16 These disparities arise from sociodemographic factors and modifiable risk factors, including physical activity, dietary patterns, access to cancer screening, and gaps in quality treatment resources.^16,28 These factors operate at multiple levels: from individual, local health system, to community and policy.^2,27 For example, a South Carolina study (1996–2016) found that residents in rural areas were more likely to be diagnosed with advanced CRC, possibly indicating lower rates of CRC screening in rural areas. They also had higher likelihood of death from CRC.¹⁵ However, the study did not include any clinical parameters, such as comorbidities or obesity. A statewide, population-based study in Utah showed that rural men experienced a lower CRC survival in their unadjusted analysis.¹⁶ However, the study was small, with only 3948 urban and 712 rural residents. Additionally, there was no difference in total mortality in the whole cohort (HR, 0.96; 95% CI, 0.86-1.07) or in CRC-specific death (HR, 0.93; 95% CI, 0.81-1.08). A nationwide study also showed that CRC mortality rates were 8% higher in nonmetropolitan or rural areas than in the most urbanized areas containing large metropolitan counties.²⁹ However, this study did not include descriptions of clinical confounders, such as comorbidities, making it difficult to ascertain whether the difference in CRC mortality was due to rurality or differences in baseline risk characteristics.

In this study, the lack of CRC-specific mortality disparities may be attributed to the structures and practices of VHA health care. Recent studies have noted that mortality of several chronic medical conditions treated at the VHA was lower than at non-VHA hospitals.^30,31 One study that measured the quality of nonmetastatic CRC care based on National Comprehensive Cancer Network guidelines showed that > 72% of VHA patients received guideline-concordant care for each diagnostic and therapeutic measure, except for follow-up colonoscopy timing, which appear to be similar or superior to that of the private sector.^30,32,33 Some of the VA initiative for CRC screening may bypass the urban-rurality divide such as the mailed fecal immunochemical test program for CRC. This program was implemented at the onset of the COVID-19 pandemic to avoid disruptions of medical care.³⁴ Rural patients are more likely to undergo fecal immunochemical testing when compared to urban patients in this data. Beyond clinical care, the VHA uses processes to tackle social determinants of health such as housing, food security, and transportation, promoting equal access to health care, and promoting cultural competency among HCPs.^35-37

The results suggest that solutions to CRC disparities between rural and urban areas need to consider known barriers to rural health care, including transportation, diminished rural health care workforce, and other social determinants of health.^9,10,27,38 VHA makes considerable efforts to provide equitable care to all enrolled veterans, including specific programs for rural veterans, including ongoing outreach.³⁹ This study demonstrated lack of disparity in CRC-specific mortality in veterans receiving VHA care, highlighting the importance of these efforts.

Strengths and Limitations

This study used the VHA cohort to compare patient characteristics and mortality between patients with CRC residing in rural and urban areas. The study provides nationwide perspectives on CRC across the geographical spectrum and used a longitudinal cohort with prolonged follow-up to account for comorbidities.

However, the study compared a cohort of rural and urban veterans enrolled in the VHA; hence, the results may not reflect CRC outcomes in veterans without access to VHA care. Rurality has been independently associated with decreased likelihood of meeting CRC screening guidelines among veterans and military service members.³⁸ This study lacked sufficient information to compare CRC staging or treatment modalities among veterans. Although the data cannot identify CRC stage, the proportions of patients with metastatic CRC at diagnosis and CRC location were similar between groups. The study did not have information on their care outside of VHA setting.

This study could not ascertain whether disparities existed in CRC treatment modality since rural residence may result in referral to community-based CRC care, which did not appear in the data. To address these limitations, we used death from any cause as the primary outcome, since death is a hard outcome and is not subject to ascertainment bias. The relatively short follow-up time is another limitation, though subgroup analysis by follow-up did not show significant differences. Despite PS matching, residual unmeasured confounding may exist between urban and rural groups. The predominantly White, male VHA population with high CCI may limit the generalizability of the results.

Conclusions

Rural VHA enrollees had similar survival rates after CRC diagnosis compared to their urban counterparts in a PS-matched analysis. The VHA models of care—including mailed CRC screening tools, several socioeconomic determinants of health (housing, food security, and transportation), and promoting equal access to health care, as well as cultural competency among HCPs—HCPs—may help alleviate disparities across the rural-urban spectrum. The VHA should continue efforts to enroll veterans and provide comprehensive coordinated care in community partnerships.

Colorectal cancer (CRC) is the second-leading cause of cancer-related deaths in the United States, with an estimated 52,550 deaths in 2023.¹ However, the disease burden varies among different segments of the population.² While both CRC incidence and mortality have been decreasing due to screening and advances in treatment, there are disparities in incidence and mortality across the sociodemographic spectrum including race, ethnicity, education, and income.^1-4 While CRC incidence is decreasing for older adults, it is increasing among those aged < 55 years.⁵ The incidence of CRC in adults aged 40 to 54 years has increased by 0.5% to 1.3% annually since the mid-1990s.⁶ The US Preventive Services Task Force now recommends starting CRC screening at age 45 years for asymptomatic adults with average risk.⁷

Disparities also exist across geographical boundaries and living environment. Rural Americans faces additional challenges in health and lifestyle that can affect CRC outcomes. Compared to their urban counterparts, rural residents are more likely to be older, have lower levels of education, higher levels of poverty, lack health insurance, and less access to health care practitioners (HCPs).^8-10 Geographic proximity, defined as travel time or physical distance to a health facility, has been recognized as a predictor of inferior outcomes.¹¹ These aspects of rural living may pose challenges for accessing care for CRC screening and treatment.^11-13 National and local studies have shown disparities in CRC screening rates, incidence, and mortality between rural and urban populations.^14-16

It is unclear whether rural/urban disparities persist under the Veterans Health Administration (VHA) health care delivery model. This study examined differences in baseline characteristics and mortality between rural and urban veterans newly diagnosed with CRC. We also focused on a subpopulation aged ≤ 45 years.

Methods

This study extracted national data from the US Department of Veterans Affairs (VA) Corporate Data Warehouse (CDW) hosted in the VA Informatics and Computing Infrastructure (VINCI) environment. VINCI is an initiative to improve access to VA data and facilitate the analysis of these data while ensuring veterans’ privacy and data security.¹⁷ CDW is the VHA business intelligence information repository, which extracts data from clinical and nonclinical sources following prescribed and validated protocols. Data extracted included demographics, diagnosis, and procedure codes for both inpatient and outpatient encounters, vital signs, and vital status. This study used data previously extracted from a national cohort of veterans that encompassed all patients who received a group of commonly prescribed medications, such as statins, proton pump inhibitors, histamine-2 blockers, acetaminophen-containing products, and hydrocortisone-containing skin applications. This cohort encompassed 8,648,754 veterans, from whom 2,460,727 had encounters during fiscal years (FY) 2016 to 2021 (study period). The cohort was used to ensure that subjects were VHA patients, allowing them to adequately capture their clinical profiles.

Patients were identified as rural or urban based on their residence address at the date of their first diagnosis of CRC. The Geospatial Service Support Center (GSSC) aggregates and updates veterans’ residence address records for all enrolled veterans from the National Change of Address database. The data contain 1 record per enrollee. GSSC Geocoded Enrollee File contains enrollee addresses and their rurality indicators, categorized as urban, rural, or highly rural.¹⁸ Rurality is defined by the Rural Urban Commuting Area (RUCA) categories developed by the Department of Agriculture and the Health Resources and Services Administration of the US Department of Health and Human Services.¹⁹ Urban areas had RUCA codes of 1.0 to 1.1, and highly rural areas had RUCA scores of 10.0. All other areas were classified as rural. Since the proportion of veterans from highly rural areas was small, we included residents from highly rural areas in the rural residents’ group.

Inclusion and Exclusion Criteria

All veterans newly diagnosed with CRC from FY 2016 to 2021 were included. We used the ninth and tenth clinical modification revisions of the International Classification of Diseases (ICD-9-CM and ICD-10-CM) to define CRC diagnosis (Supplemental materials).^4,20 To ensure that patients were newly diagnosed with CRC, this study excluded patients with a previous ICD-9-CM code for CRC diagnosis since FY 2003.

Comorbidities were identified using diagnosis and procedure codes from inpatient and outpatient encounters, which were used to calculate the Charlson Comorbidity Index (CCI) at the time of CRC diagnosis using the weighted method described by Schneeweiss et al.²¹ We defined CRC high-risk conditions and CRC screening tests, including flexible sigmoidoscopy and stool tests, as described in previous studies (Supplemental materials).²⁰

The main outcome was total mortality. The date of death was extracted from the VHA Death Ascertainment File, which contains mortality data from the Master Person Index file in CDW and the Social Security Administration Death Master File. We used the date of death from any cause, as cause of death was not available.

A propensity score (PS) was created to match rural (including highly rural) and urban residents at a ratio of 1:1. Using a standard procedure described in prior publications, multivariable logistic regression used all baseline characteristics to estimate the PS and perform nearest-number matching without replacement.^22,23 A caliper of 0.01 maximized the matched cohort size and achieved balance (Supplemental materials). We then examined the balance of baseline characteristics between PS-matched groups.

Analyses

Cox proportional hazards regression analysis estimated the hazard ratio (HR) of death in rural residents compared to urban residents in the PS-matched cohort. The outcome event was the date of death during the study’s follow-up period (defined as period from first CRC diagnosis to death or study end), with censoring at the study’s end date (September 30, 2021). The proportional hazards assumption was assessed by inspecting the Kaplan-Meier curves. Multiple analyses examined the HR of total mortality in the PS-matched cohort, stratified by sex, race, and ethnicity. We also examined the HR of total mortality stratified by duration of follow-up.

Another PS-matching analysis among veterans aged ≤ 45 years was performed using the same techniques described earlier in this article. We performed a Cox proportional hazards regression analysis to compare mortality in PS-matched urban and rural veterans aged ≤ 45 years. The HR of death in all veterans aged ≤ 45 years (before PS-matching) was estimated using Cox proportional hazard regression analysis, adjusting for PS.

Dichotomous variables were compared using X² tests and continuous variables were compared using t tests. Baseline characteristics with missing values were converted into categorical variables and the proportion of subjects with missing values was equalized between treatment groups after PS-matching. For subgroup analysis, we examined the HR of total mortality in each subgroup using separate Cox proportional hazards regression models similar to the primary analysis but adjusted for PS. Due to multiple comparisons in the subgroup analysis, the findings should be considered exploratory. Statistical tests were 2-tailed, and significance was defined as P < .05. Data management and statistical analyses were conducted from June 2022 to January 2023 using STATA, Version 17. The VA Orlando Healthcare System Institutional Review Board approved the study and waived requirements for informed consent because only deidentified data were used.

Results

After excluding 49 patients (Supplemental materials, available at doi:10.12788/fp.0560), we identified 30,219 veterans with newly diagnosed CRC between FY 2016 to 2021 (Table 1). Of these, 19,422 (64.3%) resided in urban areas and 10,797 (35.7%) resided in rural areas (Table 2). The mean (SD) duration from the first CRC diagnosis to death or study end was 832 (640) days, and the median (IQR) was 723 (246–1330) days. Overall, incident CRC diagnoses were numerically highest in FY 2016 and lowest in FY 2020 (Figure 1). Patients with CRC in rural areas vs urban areas were significantly older (mean, 71.2 years vs 70.8 years, respectively; P < .001), more likely to be male (96.7% vs 95.7%, respectively; P < .001), more likely to be White (83.6% vs 67.8%, respectively; P < .001) and more likely to be non-Hispanic (92.2% vs 87.5%, respectively; P < .001). In terms of general health, rural veterans with CRC were more likely to be overweight or obese (81.5% rural vs 78.5% urban; P < .001) but had fewer mean comorbidities as measured by CCI (5.66 rural vs 5.90 urban; P < .001). A higher proportion of rural veterans with CRC had received stool-based (fecal occult blood test or fecal immunochemical test) CRC screening tests (61.6% rural vs 57.2% urban; P < .001). Fewer rural patients presented with systemic symptoms or signs within 1 year of CRC diagnosis (54.4% rural vs 57.5% urban, P < .001). Among urban patients with CRC, 6959 (35.8%) deaths were observed, compared with 3766 (34.9%) among rural patients (P = .10).

There were 21,568 PS-matched veterans: 10,784 in each group. In the PS-matched cohort, baseline characteristics were similar between veterans in urban and rural communities, including age, sex, race/ethnicity, body mass index, and comorbidities. Among rural patients with CRC, 3763 deaths (34.9%) were observed compared with 3702 (34.3%) among urban veterans. There was no significant difference in the HR of mortality between rural and urban CRC residents (HR, 1.01; 95% CI, 0.97-1.06; P = .53) (Figure 2).

Among veterans aged ≤ 45 years, 551 were diagnosed with CRC (391 urban and 160 rural). We PS-matched 142 pairs of urban and rural veterans without residual differences in baseline characteristics (eAppendix 1). There was no significant difference in the HR of mortality between rural and urban veterans aged ≤ 45 years (HR, 0.97; 95% CI, 0.57-1.63; P = .90) (Figure 2). Similarly, no difference in mortality was observed adjusting for PS between all rural and urban veterans aged ≤ 45 years (HR, 1.03; 95% CI, 0.67-1.59; P = .88).

There was no difference in total mortality between rural and urban veterans in any subgroup except for American Indian or Alaska Native veterans (HR, 2.41; 95% CI, 1.29-4.50; P = .006) (eAppendix 2).

Discussion

This study examined characteristics of patients with CRC between urban and rural areas among veterans who were VHA patients. Similar to other studies, rural veterans with CRC were older, more likely to be White, and were obese, but exhibited fewer comorbidities (lower CCI and lower incidence of congestive heart failure, dementia, hemiplegia, kidney diseases, liver diseases and AIDS, but higher incidence of chronic obstructive lung disease).^8,16 The incidence of CRC in this study population was lowest in FY 2020, which was reported by the Centers for Disease Control and Prevention and is attributed to COVID-19 pandemic disruption of health services.²⁴ The overall mortality in this study was similar to rates reported in other studies from the VA Central Cancer Registry.⁴ In the PS-matched cohort, where baseline characteristics were similar between urban and rural patients with CRC, we found no disparities in CRC-specific mortality between veterans in rural and urban areas. Additionally, when analysis was restricted to veterans aged ≤ 45 years, the results remained consistent.

Subgroup analyses showed no significant difference in mortality between rural and urban areas by sex, race or ethnicity, except rural American Indian or Alaska Native veterans who had double the mortality of their urban counterparts (HR, 2.41; 95% CI, 1.29-4.50; P = .006). This finding is difficult to interpret due to the small number of events and the wide CI. While with a Bonferroni correction the adjusted P value was .08, which is not statistically significant, a previous study found that although CRC incidence was lower overall in American Indian or Alaska Native populations compared to non-Hispanic White populations, CRC incidence was higher among American Indian or Alaska Native individuals in some areas such as Alaska and the Northern Plains.^25,26 Studies have noted that rural American Indian/Alaska Native populations experience greater poverty, less access to broadband internet, and limited access to care, contributing to poorer cancer outcomes and lower survival.²⁷ Thus, the finding of disparity in mortality between rural and urban American Indian or Alaska Native veterans warrants further study.

Other studies have raised concerns that CRC disproportionately affects adults in rural areas with higher mortality rates.^14-16 These disparities arise from sociodemographic factors and modifiable risk factors, including physical activity, dietary patterns, access to cancer screening, and gaps in quality treatment resources.^16,28 These factors operate at multiple levels: from individual, local health system, to community and policy.^2,27 For example, a South Carolina study (1996–2016) found that residents in rural areas were more likely to be diagnosed with advanced CRC, possibly indicating lower rates of CRC screening in rural areas. They also had higher likelihood of death from CRC.¹⁵ However, the study did not include any clinical parameters, such as comorbidities or obesity. A statewide, population-based study in Utah showed that rural men experienced a lower CRC survival in their unadjusted analysis.¹⁶ However, the study was small, with only 3948 urban and 712 rural residents. Additionally, there was no difference in total mortality in the whole cohort (HR, 0.96; 95% CI, 0.86-1.07) or in CRC-specific death (HR, 0.93; 95% CI, 0.81-1.08). A nationwide study also showed that CRC mortality rates were 8% higher in nonmetropolitan or rural areas than in the most urbanized areas containing large metropolitan counties.²⁹ However, this study did not include descriptions of clinical confounders, such as comorbidities, making it difficult to ascertain whether the difference in CRC mortality was due to rurality or differences in baseline risk characteristics.

In this study, the lack of CRC-specific mortality disparities may be attributed to the structures and practices of VHA health care. Recent studies have noted that mortality of several chronic medical conditions treated at the VHA was lower than at non-VHA hospitals.^30,31 One study that measured the quality of nonmetastatic CRC care based on National Comprehensive Cancer Network guidelines showed that > 72% of VHA patients received guideline-concordant care for each diagnostic and therapeutic measure, except for follow-up colonoscopy timing, which appear to be similar or superior to that of the private sector.^30,32,33 Some of the VA initiative for CRC screening may bypass the urban-rurality divide such as the mailed fecal immunochemical test program for CRC. This program was implemented at the onset of the COVID-19 pandemic to avoid disruptions of medical care.³⁴ Rural patients are more likely to undergo fecal immunochemical testing when compared to urban patients in this data. Beyond clinical care, the VHA uses processes to tackle social determinants of health such as housing, food security, and transportation, promoting equal access to health care, and promoting cultural competency among HCPs.^35-37

The results suggest that solutions to CRC disparities between rural and urban areas need to consider known barriers to rural health care, including transportation, diminished rural health care workforce, and other social determinants of health.^9,10,27,38 VHA makes considerable efforts to provide equitable care to all enrolled veterans, including specific programs for rural veterans, including ongoing outreach.³⁹ This study demonstrated lack of disparity in CRC-specific mortality in veterans receiving VHA care, highlighting the importance of these efforts.

Strengths and Limitations

This study used the VHA cohort to compare patient characteristics and mortality between patients with CRC residing in rural and urban areas. The study provides nationwide perspectives on CRC across the geographical spectrum and used a longitudinal cohort with prolonged follow-up to account for comorbidities.

However, the study compared a cohort of rural and urban veterans enrolled in the VHA; hence, the results may not reflect CRC outcomes in veterans without access to VHA care. Rurality has been independently associated with decreased likelihood of meeting CRC screening guidelines among veterans and military service members.³⁸ This study lacked sufficient information to compare CRC staging or treatment modalities among veterans. Although the data cannot identify CRC stage, the proportions of patients with metastatic CRC at diagnosis and CRC location were similar between groups. The study did not have information on their care outside of VHA setting.

This study could not ascertain whether disparities existed in CRC treatment modality since rural residence may result in referral to community-based CRC care, which did not appear in the data. To address these limitations, we used death from any cause as the primary outcome, since death is a hard outcome and is not subject to ascertainment bias. The relatively short follow-up time is another limitation, though subgroup analysis by follow-up did not show significant differences. Despite PS matching, residual unmeasured confounding may exist between urban and rural groups. The predominantly White, male VHA population with high CCI may limit the generalizability of the results.

Conclusions

Rural VHA enrollees had similar survival rates after CRC diagnosis compared to their urban counterparts in a PS-matched analysis. The VHA models of care—including mailed CRC screening tools, several socioeconomic determinants of health (housing, food security, and transportation), and promoting equal access to health care, as well as cultural competency among HCPs—HCPs—may help alleviate disparities across the rural-urban spectrum. The VHA should continue efforts to enroll veterans and provide comprehensive coordinated care in community partnerships.

The annual issue of Cancer Data Trends, produced in collaboration with the Association of VA Hematology/Oncology (AVAHO), highlights the latest research in some of the top cancers impacting US veterans. 

In this issue: 

1. Access, Race, and "Colon Age": Improving CRC Screening
2. Lung Cancer: Mortality Trends in Veterans and New Treatments
3. Racial Disparities, Germline Testing, and Improved Overall Survival in Prostate Cancer
4. Breast and Uterine Cancer: Screening Guidelines, Genetic Testing, and Mortality Trends
5. HCC Updates: Quality Care Framework and Risk Stratification Data
6. Rising Kidney Cancer Cases and Emerging Treatments for Veterans
7. Advances in Blood Cancer Care for Veterans
8. AI-Based Risk Stratification for Oropharyngeal Carcinomas: AIROC
9. Brain Cancer: Epidemiology, TBI, and New Treatments

The annual issue of Cancer Data Trends, produced in collaboration with the Association of VA Hematology/Oncology (AVAHO), highlights the latest research in some of the top cancers impacting US veterans. 

In this issue: 

1. Access, Race, and "Colon Age": Improving CRC Screening
2. Lung Cancer: Mortality Trends in Veterans and New Treatments
3. Racial Disparities, Germline Testing, and Improved Overall Survival in Prostate Cancer
4. Breast and Uterine Cancer: Screening Guidelines, Genetic Testing, and Mortality Trends
5. HCC Updates: Quality Care Framework and Risk Stratification Data
6. Rising Kidney Cancer Cases and Emerging Treatments for Veterans
7. Advances in Blood Cancer Care for Veterans
8. AI-Based Risk Stratification for Oropharyngeal Carcinomas: AIROC
9. Brain Cancer: Epidemiology, TBI, and New Treatments

The annual issue of Cancer Data Trends, produced in collaboration with the Association of VA Hematology/Oncology (AVAHO), highlights the latest research in some of the top cancers impacting US veterans. 

In this issue: 

1. Access, Race, and "Colon Age": Improving CRC Screening
2. Lung Cancer: Mortality Trends in Veterans and New Treatments
3. Racial Disparities, Germline Testing, and Improved Overall Survival in Prostate Cancer
4. Breast and Uterine Cancer: Screening Guidelines, Genetic Testing, and Mortality Trends
5. HCC Updates: Quality Care Framework and Risk Stratification Data
6. Rising Kidney Cancer Cases and Emerging Treatments for Veterans
7. Advances in Blood Cancer Care for Veterans
8. AI-Based Risk Stratification for Oropharyngeal Carcinomas: AIROC
9. Brain Cancer: Epidemiology, TBI, and New Treatments

Pulmonary embolism (PE) is a common cause of morbidity and mortality in the general population.¹ The incidence of PE has been reported to range from 39 to 115 per 100,000 persons per year and has remained stable.² Although mortality rates have declined, they remain high.³ The clinical presentation is nonspecific, making diagnosis and management challenging. A crucial and difficult aspect in the management of patients with PE is weighing the risks vs benefits of treatment, including thrombolytic therapy and other invasive procedures, which carry inherent risks. These factors have led to the development of PE response teams (PERTs) in some hospitals to implement effective multidisciplinary protocols that facilitate prompt diagnosis, management, and follow-up.⁴

CASE PRESENTATIONS

Case 1

New onset seizures and cardiac arrest in the treatment of saddle PE. A 54-year-old male who worked as a draftsman and truck driver with a history of hypertension and nephrolithiasis presented to the emergency department (ED) with progressive shortness of breath for 2 weeks. On the morning of ED presentation the patient experienced an episode of severe shortness of breath, lightheadedness, and chest pressure. He reported no other symptoms such as palpitations, nausea, vomiting, abdominal discomfort, or extremity pain or swelling. He reported no recent travel, immunization, falls, or surgery. Upon evaluation, the patient was found to be in no acute distress, with stable vital signs and laboratory results except for 2 elevated results: > 20 μg/mL D-dimer (reference range, < 0.5 μg/mL) and N-terminal prohormone brain natriuretic peptide (proBNP) level, 3455 pg/mL (reference range, < 125 pg/mL for patients aged < 75 years). Electrocardiogram showed T-wave inversions in leads V2 to V4. Imaging revealed a saddle PE and left popliteal deep venous thrombosis (Figure 1). The patient received an anticoagulation loading dose and was started on heparin drip upon admission to the medical intensive care unit (MICU) for further management and monitoring. The Interventional Radiology Service recommended full anticoagulation with consideration of reperfusion therapies if deterioration developed.

While in the MICU, point-of-care ultrasound findings were confirmed with official echocardiogram by the cardiology service, which demonstrated a preserved ejection fraction of 60% to 65%, a D-shaped left ventricle with septal wall hypokinesis secondary to right heart strain (Figure 2), a markedly elevated right ventricular systolic pressure (RVSP) of 73 mm Hg, and a mean pulmonary artery pressure (mPAP) of 38 mm Hg. The patient’s blood pressure progressively decreased, heart rate increased, and he required increased oxygen supplementation. The case was discussed with the Pharmacy Service, and since the patient had no contraindications to thrombolytic therapy, the appropriate dosage was calculated and 100 mg intravenous (IV) tissue plasminogen activator (tPA) was administered over 2 hours.

About 40 minutes into tPA infusion, the patient suddenly experienced marked shortness of breath, diaphoresis, and anxiety with seizure-like involuntary movements; as a result, the infusion was stopped. He also had episodes of posturing, mental status decline, and briefly going in and out of consciousness, which lasted about 3 minutes before he lost consciousness and pulse. High-quality advanced cardiac life support was initiated, followed by endotracheal intubation. Despite a secured airway and return of spontaneous circulation, the patient remained hypotensive and continued to have seizure-like activity.

The patient was administered a total of 8 mg of lorazepam, sedated with propofol, initiated at 5 μg/kg/min, titrated to stop seizure activity at 15μg/kg/min, and later maintained at 10 μg/kg/min, for a RASS of -1, and started on norepinephrine 0.1 μg/kg/min for acute stabilization. Head computed tomography without contrast showed no acute intracranial pathology as etiology of seizures. Seizure etiology differential at this time was broad; however, hypoxemia due to PE and medication adverse effects were strongly suspected.

The patient’s condition improved, and vasopressor therapy was tapered off the next day. Four days later, the patient was weaned from mechanical ventilation and transferred to the step-down unit. Echocardiogram obtained 48 hours after tPA infusion showed essentially normal left ventricular function (60%-65%), a RVSP of 17 mm Hg and mPAP of 13 mm Hg. The patient’s ProBNP levels markedly decreased to 137 pg/mL. Postextubation, the neurologic examination was at baseline. The Neurology Service recommended temporary treatment with levetiracetam, 1000 mg every 12 hours, and the Hematology Service recommended transitioning to direct oral anticoagulation with follow-up. The patient presented significant clinical and respiratory improvement and was referred for home-based physical rehabilitation as recommended by the physical medicine and rehabilitation service before being discharged.

Case 2

Localized tPA infusion for bilateral PEs via infusion catheters. A 91-year-old male with no history of smoking and a medical history of hypertension, diabetes mellitus, prostate cancer (> 20 years postradiotherapy) and severe osteoarthritis was receiving treatment in the medical ward for medication-induced liver injury secondary to an antibiotic for a urinary tract infection. During the night the patient developed hypotension (86/46 mm Hg), shortness of breath, tachypnea, desaturation, nonradiating retrosternal chest pain, and tachycardia. The hypotension resolved after a 500-mL 0.9 normal saline bolus, and hypoxemia improved with supplemental oxygen via Venturi mask. Chest computed tomography angiography was performed immediately and revealed extensive bilateral acute PE, located most proximally in the right main pulmonary artery (PA) and on the left in the proximal lobar branches, with associated right heart strain. The patient was started on IV heparin with a bolus of 5000 units (80 u/kg) followed by a drip with a partial thromboplastin time goal of 62-103 seconds and transferred to MICU.

Laboratory findings were notable for proBNP that increased from 115 pg/mL to 4470 pg/mL (reference range, < 450 pg/mL for patients aged 75 years) and elevated troponin levels at 218 ng/L to 295 ng/L (reference range, < 22 ng/L), exhibiting chemical evidence of right heart strain. Initial echocardiogram showed mid-right ventricular free wall akinesis with a hypercontractile apex, suggestive of PE (McConnell’s sign) (Figure 3). Interventional Radiology Service was consulted and recommended tPA infusion given that the patient had bilateral PEs and stable blood pressure.

Pulmonary angiogram showed elevated pressures in the right PA of 64/21 mm Hg and the left PA pressures of 63/20 mm Hg. Mechanical disruption of the larger right lower PA thrombus was achieved via a pigtail catheter followed by bilateral catheter bolus infusions of 2 mg tPA (alteplase) and a continuous tPA infusion 0.5 mg/h for 24 hours, in conjunction with a heparin infusion.

After 24 hours of tPA infusion, the catheters were removed, with posttreatment pulmonary angiography demonstrating right and left PA pressures of 42/15 mm Hg and 40/16 mm Hg, respectively. Pre- and postlocalized tPA infusion treatment images are provided for visual comparison (Figure 4). An echocardiogram performed after tPA infusion showed no signs of pulmonary hypertension. The Hematology Service provided recommendations regarding anticoagulation, and after completion of tPA infusion, the patient was transitioned to an unfractioned heparin infusion and subsequently to direct oral anticoagulation prior to transfer back to the medical ward, hemodynamically stable and asymptomatic.

DISCUSSION

PE management can be a straightforward decision when the patient meets criteria for hemodynamic instability, or with small PE burden. In contrast, management can be more challenging in intermediate-risk (submassive) PE when patients remain hemodynamically stable but show signs of cardiopulmonary stress, such as right heart strain, elevated troponins, or increased proBNP levels.² In these situations, case-by- case evaluation is warranted. A PERT can assess the most beneficial treatment approach by considering factors such as right ventricular dysfunction, hemodynamic status, clot burden, and clinical deterioration despite appropriate anticoagulation. The evidence supporting the benefits these organized teams can provide is growing. These case reports emphasize the need for a multidisciplinary and systematic approach in these complex cases, especially in the management of intermediate-risk PE patients.

Currently, the Veterans Affairs Caribbean Healthcare System does not have an organized PERT, although a multidisciplinary approach was applied in the management of these patients. A systematic, structured team could have decreased time to interventions and alleviated the burden of physician decision-making. Having such a team would streamline the diagnostic pathway for patients presenting from a ward or emergency department with suspected PE.

We present 2 cases of patients found to have a high clot burden from PEs. The patients were initially hemodynamically stable (intermediate-risk PE), but later required systemic or localized thrombolysis due to hemodynamic deterioration despite adequate anticoagulation. Despite similar diagnoses and etiologies, these patients were successfully managed using different approaches, yielding positive outcomes. This reflects the complexity and variability in diagnosing and managing intermediate-risk PE in patients with different comorbidities and clot burden effects. In Case 1, our multidisciplinary approach was obtained via consults to selected services such as interventional radiology, cardiology, and direct involvement of pharmacy. An organized PERT conceivably would have allowed quicker discussions among these services, including hematology, to provide recommendations and collaborative support upon the patient’s arrival to the ED. Additionally, with a PERT team, a systematic approach to these patients could have allowed for an earlier official echocardiogram report for evaluation of right heart strain and develop an adequate therapeutic plan in a timely manner.

In Case 2, consultation with the Interventional Radiology Service yielded a better therapeutic plan, utilizing localized tPA infusion for this older adult patient with increased risk of bleeding with systemic tPA infusion. Having a PERT presents an opportunity to optimize PE management through early recognition, diagnosis, and treatment by institutional consensus from an interdisciplinary team.^5,6 These response teams may improve outcomes and prognosis for patients with PE, especially where diagnosis and management is not clear.

The definite etiology of seizure activity in the first case pre- and postcardiac arrest, in the context of no acute intracranial process, remains unknown. Reports have emerged about postreperfusion seizures in acute ischemic stroke, as well as cases of seizures masquerading as PE as the primary presentation. ^7,8 However, there were no reports of patients developing seizures post tPA infusion for the treatment of PE. This report may shed light into possible complications secondary to tPA infusion, raising awareness among physicians and encouraging further investigation into its possible etiologies.

CONCLUSIONS

Management of PE can be challenging in patients that meet criteria for intermediate risk. PERTs are a tool that allow for a multidisciplinary, standardized and systematic approach with a diagnostic and treatment algorithm that conceivably would result in a better consensus and therapeutic approach.

Pulmonary embolism (PE) is a common cause of morbidity and mortality in the general population.¹ The incidence of PE has been reported to range from 39 to 115 per 100,000 persons per year and has remained stable.² Although mortality rates have declined, they remain high.³ The clinical presentation is nonspecific, making diagnosis and management challenging. A crucial and difficult aspect in the management of patients with PE is weighing the risks vs benefits of treatment, including thrombolytic therapy and other invasive procedures, which carry inherent risks. These factors have led to the development of PE response teams (PERTs) in some hospitals to implement effective multidisciplinary protocols that facilitate prompt diagnosis, management, and follow-up.⁴

CASE PRESENTATIONS

Case 1

New onset seizures and cardiac arrest in the treatment of saddle PE. A 54-year-old male who worked as a draftsman and truck driver with a history of hypertension and nephrolithiasis presented to the emergency department (ED) with progressive shortness of breath for 2 weeks. On the morning of ED presentation the patient experienced an episode of severe shortness of breath, lightheadedness, and chest pressure. He reported no other symptoms such as palpitations, nausea, vomiting, abdominal discomfort, or extremity pain or swelling. He reported no recent travel, immunization, falls, or surgery. Upon evaluation, the patient was found to be in no acute distress, with stable vital signs and laboratory results except for 2 elevated results: > 20 μg/mL D-dimer (reference range, < 0.5 μg/mL) and N-terminal prohormone brain natriuretic peptide (proBNP) level, 3455 pg/mL (reference range, < 125 pg/mL for patients aged < 75 years). Electrocardiogram showed T-wave inversions in leads V2 to V4. Imaging revealed a saddle PE and left popliteal deep venous thrombosis (Figure 1). The patient received an anticoagulation loading dose and was started on heparin drip upon admission to the medical intensive care unit (MICU) for further management and monitoring. The Interventional Radiology Service recommended full anticoagulation with consideration of reperfusion therapies if deterioration developed.

While in the MICU, point-of-care ultrasound findings were confirmed with official echocardiogram by the cardiology service, which demonstrated a preserved ejection fraction of 60% to 65%, a D-shaped left ventricle with septal wall hypokinesis secondary to right heart strain (Figure 2), a markedly elevated right ventricular systolic pressure (RVSP) of 73 mm Hg, and a mean pulmonary artery pressure (mPAP) of 38 mm Hg. The patient’s blood pressure progressively decreased, heart rate increased, and he required increased oxygen supplementation. The case was discussed with the Pharmacy Service, and since the patient had no contraindications to thrombolytic therapy, the appropriate dosage was calculated and 100 mg intravenous (IV) tissue plasminogen activator (tPA) was administered over 2 hours.

About 40 minutes into tPA infusion, the patient suddenly experienced marked shortness of breath, diaphoresis, and anxiety with seizure-like involuntary movements; as a result, the infusion was stopped. He also had episodes of posturing, mental status decline, and briefly going in and out of consciousness, which lasted about 3 minutes before he lost consciousness and pulse. High-quality advanced cardiac life support was initiated, followed by endotracheal intubation. Despite a secured airway and return of spontaneous circulation, the patient remained hypotensive and continued to have seizure-like activity.

The patient was administered a total of 8 mg of lorazepam, sedated with propofol, initiated at 5 μg/kg/min, titrated to stop seizure activity at 15μg/kg/min, and later maintained at 10 μg/kg/min, for a RASS of -1, and started on norepinephrine 0.1 μg/kg/min for acute stabilization. Head computed tomography without contrast showed no acute intracranial pathology as etiology of seizures. Seizure etiology differential at this time was broad; however, hypoxemia due to PE and medication adverse effects were strongly suspected.

The patient’s condition improved, and vasopressor therapy was tapered off the next day. Four days later, the patient was weaned from mechanical ventilation and transferred to the step-down unit. Echocardiogram obtained 48 hours after tPA infusion showed essentially normal left ventricular function (60%-65%), a RVSP of 17 mm Hg and mPAP of 13 mm Hg. The patient’s ProBNP levels markedly decreased to 137 pg/mL. Postextubation, the neurologic examination was at baseline. The Neurology Service recommended temporary treatment with levetiracetam, 1000 mg every 12 hours, and the Hematology Service recommended transitioning to direct oral anticoagulation with follow-up. The patient presented significant clinical and respiratory improvement and was referred for home-based physical rehabilitation as recommended by the physical medicine and rehabilitation service before being discharged.

Case 2

Localized tPA infusion for bilateral PEs via infusion catheters. A 91-year-old male with no history of smoking and a medical history of hypertension, diabetes mellitus, prostate cancer (> 20 years postradiotherapy) and severe osteoarthritis was receiving treatment in the medical ward for medication-induced liver injury secondary to an antibiotic for a urinary tract infection. During the night the patient developed hypotension (86/46 mm Hg), shortness of breath, tachypnea, desaturation, nonradiating retrosternal chest pain, and tachycardia. The hypotension resolved after a 500-mL 0.9 normal saline bolus, and hypoxemia improved with supplemental oxygen via Venturi mask. Chest computed tomography angiography was performed immediately and revealed extensive bilateral acute PE, located most proximally in the right main pulmonary artery (PA) and on the left in the proximal lobar branches, with associated right heart strain. The patient was started on IV heparin with a bolus of 5000 units (80 u/kg) followed by a drip with a partial thromboplastin time goal of 62-103 seconds and transferred to MICU.

Laboratory findings were notable for proBNP that increased from 115 pg/mL to 4470 pg/mL (reference range, < 450 pg/mL for patients aged 75 years) and elevated troponin levels at 218 ng/L to 295 ng/L (reference range, < 22 ng/L), exhibiting chemical evidence of right heart strain. Initial echocardiogram showed mid-right ventricular free wall akinesis with a hypercontractile apex, suggestive of PE (McConnell’s sign) (Figure 3). Interventional Radiology Service was consulted and recommended tPA infusion given that the patient had bilateral PEs and stable blood pressure.

Pulmonary angiogram showed elevated pressures in the right PA of 64/21 mm Hg and the left PA pressures of 63/20 mm Hg. Mechanical disruption of the larger right lower PA thrombus was achieved via a pigtail catheter followed by bilateral catheter bolus infusions of 2 mg tPA (alteplase) and a continuous tPA infusion 0.5 mg/h for 24 hours, in conjunction with a heparin infusion.

After 24 hours of tPA infusion, the catheters were removed, with posttreatment pulmonary angiography demonstrating right and left PA pressures of 42/15 mm Hg and 40/16 mm Hg, respectively. Pre- and postlocalized tPA infusion treatment images are provided for visual comparison (Figure 4). An echocardiogram performed after tPA infusion showed no signs of pulmonary hypertension. The Hematology Service provided recommendations regarding anticoagulation, and after completion of tPA infusion, the patient was transitioned to an unfractioned heparin infusion and subsequently to direct oral anticoagulation prior to transfer back to the medical ward, hemodynamically stable and asymptomatic.

DISCUSSION

PE management can be a straightforward decision when the patient meets criteria for hemodynamic instability, or with small PE burden. In contrast, management can be more challenging in intermediate-risk (submassive) PE when patients remain hemodynamically stable but show signs of cardiopulmonary stress, such as right heart strain, elevated troponins, or increased proBNP levels.² In these situations, case-by- case evaluation is warranted. A PERT can assess the most beneficial treatment approach by considering factors such as right ventricular dysfunction, hemodynamic status, clot burden, and clinical deterioration despite appropriate anticoagulation. The evidence supporting the benefits these organized teams can provide is growing. These case reports emphasize the need for a multidisciplinary and systematic approach in these complex cases, especially in the management of intermediate-risk PE patients.

Currently, the Veterans Affairs Caribbean Healthcare System does not have an organized PERT, although a multidisciplinary approach was applied in the management of these patients. A systematic, structured team could have decreased time to interventions and alleviated the burden of physician decision-making. Having such a team would streamline the diagnostic pathway for patients presenting from a ward or emergency department with suspected PE.

We present 2 cases of patients found to have a high clot burden from PEs. The patients were initially hemodynamically stable (intermediate-risk PE), but later required systemic or localized thrombolysis due to hemodynamic deterioration despite adequate anticoagulation. Despite similar diagnoses and etiologies, these patients were successfully managed using different approaches, yielding positive outcomes. This reflects the complexity and variability in diagnosing and managing intermediate-risk PE in patients with different comorbidities and clot burden effects. In Case 1, our multidisciplinary approach was obtained via consults to selected services such as interventional radiology, cardiology, and direct involvement of pharmacy. An organized PERT conceivably would have allowed quicker discussions among these services, including hematology, to provide recommendations and collaborative support upon the patient’s arrival to the ED. Additionally, with a PERT team, a systematic approach to these patients could have allowed for an earlier official echocardiogram report for evaluation of right heart strain and develop an adequate therapeutic plan in a timely manner.

In Case 2, consultation with the Interventional Radiology Service yielded a better therapeutic plan, utilizing localized tPA infusion for this older adult patient with increased risk of bleeding with systemic tPA infusion. Having a PERT presents an opportunity to optimize PE management through early recognition, diagnosis, and treatment by institutional consensus from an interdisciplinary team.^5,6 These response teams may improve outcomes and prognosis for patients with PE, especially where diagnosis and management is not clear.

The definite etiology of seizure activity in the first case pre- and postcardiac arrest, in the context of no acute intracranial process, remains unknown. Reports have emerged about postreperfusion seizures in acute ischemic stroke, as well as cases of seizures masquerading as PE as the primary presentation. ^7,8 However, there were no reports of patients developing seizures post tPA infusion for the treatment of PE. This report may shed light into possible complications secondary to tPA infusion, raising awareness among physicians and encouraging further investigation into its possible etiologies.

CONCLUSIONS

Management of PE can be challenging in patients that meet criteria for intermediate risk. PERTs are a tool that allow for a multidisciplinary, standardized and systematic approach with a diagnostic and treatment algorithm that conceivably would result in a better consensus and therapeutic approach.

Pulmonary embolism (PE) is a common cause of morbidity and mortality in the general population.¹ The incidence of PE has been reported to range from 39 to 115 per 100,000 persons per year and has remained stable.² Although mortality rates have declined, they remain high.³ The clinical presentation is nonspecific, making diagnosis and management challenging. A crucial and difficult aspect in the management of patients with PE is weighing the risks vs benefits of treatment, including thrombolytic therapy and other invasive procedures, which carry inherent risks. These factors have led to the development of PE response teams (PERTs) in some hospitals to implement effective multidisciplinary protocols that facilitate prompt diagnosis, management, and follow-up.⁴

CASE PRESENTATIONS

Case 1

New onset seizures and cardiac arrest in the treatment of saddle PE. A 54-year-old male who worked as a draftsman and truck driver with a history of hypertension and nephrolithiasis presented to the emergency department (ED) with progressive shortness of breath for 2 weeks. On the morning of ED presentation the patient experienced an episode of severe shortness of breath, lightheadedness, and chest pressure. He reported no other symptoms such as palpitations, nausea, vomiting, abdominal discomfort, or extremity pain or swelling. He reported no recent travel, immunization, falls, or surgery. Upon evaluation, the patient was found to be in no acute distress, with stable vital signs and laboratory results except for 2 elevated results: > 20 μg/mL D-dimer (reference range, < 0.5 μg/mL) and N-terminal prohormone brain natriuretic peptide (proBNP) level, 3455 pg/mL (reference range, < 125 pg/mL for patients aged < 75 years). Electrocardiogram showed T-wave inversions in leads V2 to V4. Imaging revealed a saddle PE and left popliteal deep venous thrombosis (Figure 1). The patient received an anticoagulation loading dose and was started on heparin drip upon admission to the medical intensive care unit (MICU) for further management and monitoring. The Interventional Radiology Service recommended full anticoagulation with consideration of reperfusion therapies if deterioration developed.

While in the MICU, point-of-care ultrasound findings were confirmed with official echocardiogram by the cardiology service, which demonstrated a preserved ejection fraction of 60% to 65%, a D-shaped left ventricle with septal wall hypokinesis secondary to right heart strain (Figure 2), a markedly elevated right ventricular systolic pressure (RVSP) of 73 mm Hg, and a mean pulmonary artery pressure (mPAP) of 38 mm Hg. The patient’s blood pressure progressively decreased, heart rate increased, and he required increased oxygen supplementation. The case was discussed with the Pharmacy Service, and since the patient had no contraindications to thrombolytic therapy, the appropriate dosage was calculated and 100 mg intravenous (IV) tissue plasminogen activator (tPA) was administered over 2 hours.

About 40 minutes into tPA infusion, the patient suddenly experienced marked shortness of breath, diaphoresis, and anxiety with seizure-like involuntary movements; as a result, the infusion was stopped. He also had episodes of posturing, mental status decline, and briefly going in and out of consciousness, which lasted about 3 minutes before he lost consciousness and pulse. High-quality advanced cardiac life support was initiated, followed by endotracheal intubation. Despite a secured airway and return of spontaneous circulation, the patient remained hypotensive and continued to have seizure-like activity.

The patient was administered a total of 8 mg of lorazepam, sedated with propofol, initiated at 5 μg/kg/min, titrated to stop seizure activity at 15μg/kg/min, and later maintained at 10 μg/kg/min, for a RASS of -1, and started on norepinephrine 0.1 μg/kg/min for acute stabilization. Head computed tomography without contrast showed no acute intracranial pathology as etiology of seizures. Seizure etiology differential at this time was broad; however, hypoxemia due to PE and medication adverse effects were strongly suspected.

The patient’s condition improved, and vasopressor therapy was tapered off the next day. Four days later, the patient was weaned from mechanical ventilation and transferred to the step-down unit. Echocardiogram obtained 48 hours after tPA infusion showed essentially normal left ventricular function (60%-65%), a RVSP of 17 mm Hg and mPAP of 13 mm Hg. The patient’s ProBNP levels markedly decreased to 137 pg/mL. Postextubation, the neurologic examination was at baseline. The Neurology Service recommended temporary treatment with levetiracetam, 1000 mg every 12 hours, and the Hematology Service recommended transitioning to direct oral anticoagulation with follow-up. The patient presented significant clinical and respiratory improvement and was referred for home-based physical rehabilitation as recommended by the physical medicine and rehabilitation service before being discharged.

Case 2

Localized tPA infusion for bilateral PEs via infusion catheters. A 91-year-old male with no history of smoking and a medical history of hypertension, diabetes mellitus, prostate cancer (> 20 years postradiotherapy) and severe osteoarthritis was receiving treatment in the medical ward for medication-induced liver injury secondary to an antibiotic for a urinary tract infection. During the night the patient developed hypotension (86/46 mm Hg), shortness of breath, tachypnea, desaturation, nonradiating retrosternal chest pain, and tachycardia. The hypotension resolved after a 500-mL 0.9 normal saline bolus, and hypoxemia improved with supplemental oxygen via Venturi mask. Chest computed tomography angiography was performed immediately and revealed extensive bilateral acute PE, located most proximally in the right main pulmonary artery (PA) and on the left in the proximal lobar branches, with associated right heart strain. The patient was started on IV heparin with a bolus of 5000 units (80 u/kg) followed by a drip with a partial thromboplastin time goal of 62-103 seconds and transferred to MICU.

Laboratory findings were notable for proBNP that increased from 115 pg/mL to 4470 pg/mL (reference range, < 450 pg/mL for patients aged 75 years) and elevated troponin levels at 218 ng/L to 295 ng/L (reference range, < 22 ng/L), exhibiting chemical evidence of right heart strain. Initial echocardiogram showed mid-right ventricular free wall akinesis with a hypercontractile apex, suggestive of PE (McConnell’s sign) (Figure 3). Interventional Radiology Service was consulted and recommended tPA infusion given that the patient had bilateral PEs and stable blood pressure.

Pulmonary angiogram showed elevated pressures in the right PA of 64/21 mm Hg and the left PA pressures of 63/20 mm Hg. Mechanical disruption of the larger right lower PA thrombus was achieved via a pigtail catheter followed by bilateral catheter bolus infusions of 2 mg tPA (alteplase) and a continuous tPA infusion 0.5 mg/h for 24 hours, in conjunction with a heparin infusion.

After 24 hours of tPA infusion, the catheters were removed, with posttreatment pulmonary angiography demonstrating right and left PA pressures of 42/15 mm Hg and 40/16 mm Hg, respectively. Pre- and postlocalized tPA infusion treatment images are provided for visual comparison (Figure 4). An echocardiogram performed after tPA infusion showed no signs of pulmonary hypertension. The Hematology Service provided recommendations regarding anticoagulation, and after completion of tPA infusion, the patient was transitioned to an unfractioned heparin infusion and subsequently to direct oral anticoagulation prior to transfer back to the medical ward, hemodynamically stable and asymptomatic.

DISCUSSION

PE management can be a straightforward decision when the patient meets criteria for hemodynamic instability, or with small PE burden. In contrast, management can be more challenging in intermediate-risk (submassive) PE when patients remain hemodynamically stable but show signs of cardiopulmonary stress, such as right heart strain, elevated troponins, or increased proBNP levels.² In these situations, case-by- case evaluation is warranted. A PERT can assess the most beneficial treatment approach by considering factors such as right ventricular dysfunction, hemodynamic status, clot burden, and clinical deterioration despite appropriate anticoagulation. The evidence supporting the benefits these organized teams can provide is growing. These case reports emphasize the need for a multidisciplinary and systematic approach in these complex cases, especially in the management of intermediate-risk PE patients.

Currently, the Veterans Affairs Caribbean Healthcare System does not have an organized PERT, although a multidisciplinary approach was applied in the management of these patients. A systematic, structured team could have decreased time to interventions and alleviated the burden of physician decision-making. Having such a team would streamline the diagnostic pathway for patients presenting from a ward or emergency department with suspected PE.

We present 2 cases of patients found to have a high clot burden from PEs. The patients were initially hemodynamically stable (intermediate-risk PE), but later required systemic or localized thrombolysis due to hemodynamic deterioration despite adequate anticoagulation. Despite similar diagnoses and etiologies, these patients were successfully managed using different approaches, yielding positive outcomes. This reflects the complexity and variability in diagnosing and managing intermediate-risk PE in patients with different comorbidities and clot burden effects. In Case 1, our multidisciplinary approach was obtained via consults to selected services such as interventional radiology, cardiology, and direct involvement of pharmacy. An organized PERT conceivably would have allowed quicker discussions among these services, including hematology, to provide recommendations and collaborative support upon the patient’s arrival to the ED. Additionally, with a PERT team, a systematic approach to these patients could have allowed for an earlier official echocardiogram report for evaluation of right heart strain and develop an adequate therapeutic plan in a timely manner.

In Case 2, consultation with the Interventional Radiology Service yielded a better therapeutic plan, utilizing localized tPA infusion for this older adult patient with increased risk of bleeding with systemic tPA infusion. Having a PERT presents an opportunity to optimize PE management through early recognition, diagnosis, and treatment by institutional consensus from an interdisciplinary team.^5,6 These response teams may improve outcomes and prognosis for patients with PE, especially where diagnosis and management is not clear.

The definite etiology of seizure activity in the first case pre- and postcardiac arrest, in the context of no acute intracranial process, remains unknown. Reports have emerged about postreperfusion seizures in acute ischemic stroke, as well as cases of seizures masquerading as PE as the primary presentation. ^7,8 However, there were no reports of patients developing seizures post tPA infusion for the treatment of PE. This report may shed light into possible complications secondary to tPA infusion, raising awareness among physicians and encouraging further investigation into its possible etiologies.

CONCLUSIONS

Management of PE can be challenging in patients that meet criteria for intermediate risk. PERTs are a tool that allow for a multidisciplinary, standardized and systematic approach with a diagnostic and treatment algorithm that conceivably would result in a better consensus and therapeutic approach.