Gastroenterology (GI) subspecialty training is carefully designed to develop expertise in digestive diseases and gastrointestinal endoscopy, while facilitating the transition from generalist to subspecialty consultant. The concept of effective consultation extends far beyond clinical expertise and has been explored repeatedly, beginning with Goldman’s “Ten Commandments” in 1983.^1,2 How should these best practices be specifically applied to GI? More importantly, what kind of experience would you want if you were the referring provider or the patient themselves?

Below are five essential tips to guide your development as a high-impact GI consultant with a reputation for excellence. 
 

1. Be Kind

Survey studies of medical/surgical residents and attending hospitalists have demonstrated that willingness to accept consultation requests was the single factor consistently rated as most important in determining the quality of the consultation interaction.^3,4 Unfortunately, nearly 65% of respondents reported encountering pushback when requesting subspecialty consultation. It is critical to recognize that when you receive a GI consult request, the requester has already decided that it is needed. Whether that request comports with our individual notion of “necessary” or “important,” this is a colleague’s request for help. There are myriad reasons why a request may be made, but they are unified in this principle.

Effective teamwork in healthcare settings enhances clinical performance and patient safety. Positive relationships with colleagues and healthcare team members also mitigate the emotional basis for physician burnout.⁵ Be kind and courteous to those who seek your assistance. Move beyond the notion of the “bad” or “soft” consult and seek instead to understand how you can help.

A requesting physician may phrase the consult question vaguely or may know that the patient is having a GI-related issue, but simply lack the specific knowledge to know what is needed. In these instances, it is our role to listen and help guide them to the correct thought process to ensure the best care of the patient. These important interactions establish our reputation, create our referral bases, and directly affect our sense of personal satisfaction.

 

2. Be Timely

GI presents an appealing breadth of pathology, but this also corresponds to a wide variety of indications for consultation and, therefore, urgency of need. In a busy clinical practice, not all requests can be urgently prioritized. However, it is the consultant’s responsibility to identify patients that require urgent evaluation and intervention to avert a potential adverse outcome.

We are well-trained in the medical triage of consultations. There are explicit guidelines for assessing urgency for GI bleeding, foreign body ingestion, choledocholithiasis, and many other indications. However, there are often special contextual circumstances that will elevate the urgency of a seemingly non-urgent consult request. Does the patient have an upcoming surgery or treatment that will depend on your input? Are they facing an imminent loss of insurance coverage? Is their non-severe GI disease leading to more severe impact on non-GI organ systems? The referring provider knows the patient better than you – seek to understand the context of the consult request.
 

Timeliness also applies to our communication. Communicate recommendations directly to the consulting service as soon as the patient is seen. When a colleague reaches out with a concern about a patient, make sure to take that request seriously. If you are unable to address the concern immediately, at least provide acknowledgment and an estimated timeline for response. As the maxim states, the effectiveness of a consultant is just as dependent on availability as it is on ability.

 

3. Be Specific

The same survey studies indicate that the second most critical aspect of successful subspecialty consultation is delivering clear recommendations. Accordingly, I always urge my trainees to challenge me when we leave a consult interaction if they feel that our plan is vague or imprecise.

Specificity in consult recommendations is an essential way to demonstrate your expertise and provide value. Clear and definitive recommendations enhance others’ perception of your skill, reduce the need for additional clarifying communication, and lead to more efficient, higher quality care. Avoid vague language, such as asking the requester to “consider” a test or intervention. When recommending medication, specify the dose, frequency, duration, and expected timeline of effect. Rather than recommending “cross-sectional imaging,” specify what modality and protocol. Instead of recommending “adequate resuscitation,” specify your target endpoints. If you engage in multidisciplinary discussion, ensure you strive for a specific group consensus plan and communicate this to all members of the team.

Specificity also applies to the quality of your documentation. Ensure that your clinical notes outline your rationale for your recommended plan, specific contingencies based on results of recommended testing, and a plan for follow-up care. When referring for open-access endoscopy, specifically outline what to look for and which specimens or endoscopic interventions are needed. Be precise in your procedure documentation – avoid vague terms such as small/medium/large and instead quantify in terms of millimeter/centimeter measurement. If you do not adopt specific classification schemes (e.g. Prague classification, Paris classification, Eosinophilic Esophagitis Endoscopic Reference Score, etc.), ensure you provide enough descriptive language to convey an adequate understanding of the findings.

 

4. Be Helpful

A consultant’s primary directive is to be of service to the consulting provider and the patient. As an educational leader, I am often asked what attributes separate a high-performing trainee from an average one. My feeling is that the most critical attribute is a sense of ownership over patient care.

As a consultant, when others feel we are exhibiting engagement and ownership in a patient’s care, they perceive that we are working together as an effective healthcare team. Interestingly, survey studies of inpatient care show that primary services do not necessarily value assistance with orders or care coordination – they consider these as core aspects of their daily work. What they did value was ongoing daily progress notes/communication, regardless of patient acuity or consulting specialty. This is a potent signal that our continued engagement (both inpatient and outpatient) is perceived as helpful.

Helpfulness is further aided by ensuring mutual understanding. While survey data indicate that sharing specific literature citations may not always be perceived positively, explaining the consultant’s rationale for their recommendations is highly valued. Take the time to tactfully explain your assessment of the patient and why you arrived at your specific recommendations. If your recommendations differ from what the requester expected (e.g. a procedure was expected but is not offered), ensure you explain why and answer questions they may have. This fosters mutual respect and proactively averts conflict or discontent from misunderstanding.

Multidisciplinary collaboration is another important avenue for aiding our patients and colleagues. Studies across a wide range of disease processes (including GI bleeding, IBD, etc.) and medical settings have demonstrated that multidisciplinary collaboration unequivocally improves patient outcomes.⁶ The success of these collaborations relies on our willingness to fully engage in these conversations, despite the fact that they may often be logistically challenging. 

We all know how difficult it can be to locate and organize multiple medical specialists with complex varying clinical schedules and busy personal lives. Choosing to do so demonstrates a dedication to providing the highest level of care and elevates both patient and physician satisfaction. Having chosen to cultivate several ongoing multidisciplinary conferences/collaborations, I can attest to the notion that the outcome is well worth the effort.

 

5. Be Honest

While we always strive to provide the answers for our patients and colleagues, we must also acknowledge our limitations. Be honest with yourself when you encounter a scenario that pushes beyond the boundaries of your knowledge and comfort. Be willing to admit when you yourself need to consult others or seek an outside referral to provide the care a patient needs. Aspiring physicians often espouse that a devotion to lifelong learning is a key driver of their desire to pursue a career in medicine. These scenarios provide a key opportunity to expand our knowledge while doing what is right for our patients.

Be equally honest about your comfort with “curbside” consultations. Studies show that subspecialists receive on average of 3-4 such requests per week.⁷ The perception of these interactions is starkly discrepant between the requester and recipient. While over 80% of surveyed primary nonsurgical services felt that curbside consultations were helpful in patient care, a similar proportion of subspecialists expressed concern that insufficient clinical information was provided, even leading to a fear of litigation. While straightforward, informal conversations on narrow, well-defined questions can be helpful and efficient, the consultant should always feel comfortable seeking an opportunity for formal consultation when the details are unclear or the case/question is complex.

 

Closing Thoughts

Being an effective GI consultant isn’t just about what you know—it’s about how you apply it, how you communicate it, and how you make others feel in the process.

The attributes outlined above are not ancillary traits—they are essential components of high-quality consultation. When consistently applied, they enhance collaboration, improve patient outcomes, and reinforce trust within the healthcare system. By committing to them, you establish your reputation of excellence and play a role in elevating the field of gastroenterology more broadly.

Dr. Kahn is based in the Division of Gastroenterology and Hepatology at Mayo Clinic, Scottsdale, Arizona. He reports no conflicts of interest in regard to this article.

References

1. Goldman L, et al. Ten commandments for effective consultations. Arch Intern Med. 1983 Sep.

2. Salerno SM, et al. Principles of effective consultation: an update for the 21st-century consultant. Arch Intern Med. 2007 Feb. doi: 10.1001/archinte.167.3.271.

3. Adams TN, et al. Hospitalist Perspective of Interactions with Medicine Subspecialty Consult Services. J Hosp Med. 2018 May. doi: 10.12788/jhm.2882.

4. Matsuo T, et al. Essential consultants’ skills and attitudes (Willing CONSULT): a cross-sectional survey. BMC Med Educ. 2021 Jul. doi: 10.1186/s12909-021-02810-9.

5. Welp A, Manser T. Integrating teamwork, clinician occupational well-being and patient safety - development of a conceptual framework based on a systematic review. BMC Health Serv Res. 2016 Jul. doi: 10.1186/s12913-016-1535-y.

6. Webster CS, et al. Interprofessional Learning in Multidisciplinary Healthcare Teams Is Associated With Reduced Patient Mortality: A Quantitative Systematic Review and Meta-analysis. J Patient Saf. 2024 Jan. doi: 10.1097/PTS.0000000000001170.

7. Lin M, et al. Curbside Consultations: The Good, the Bad, and the Ugly. Clin Gastroenterol Hepatol. 2016 Jan. doi: 10.1016/j.cgh.2015.09.026.

Gastroenterology (GI) subspecialty training is carefully designed to develop expertise in digestive diseases and gastrointestinal endoscopy, while facilitating the transition from generalist to subspecialty consultant. The concept of effective consultation extends far beyond clinical expertise and has been explored repeatedly, beginning with Goldman’s “Ten Commandments” in 1983.^1,2 How should these best practices be specifically applied to GI? More importantly, what kind of experience would you want if you were the referring provider or the patient themselves?

Below are five essential tips to guide your development as a high-impact GI consultant with a reputation for excellence. 
 

1. Be Kind

Survey studies of medical/surgical residents and attending hospitalists have demonstrated that willingness to accept consultation requests was the single factor consistently rated as most important in determining the quality of the consultation interaction.^3,4 Unfortunately, nearly 65% of respondents reported encountering pushback when requesting subspecialty consultation. It is critical to recognize that when you receive a GI consult request, the requester has already decided that it is needed. Whether that request comports with our individual notion of “necessary” or “important,” this is a colleague’s request for help. There are myriad reasons why a request may be made, but they are unified in this principle.

Effective teamwork in healthcare settings enhances clinical performance and patient safety. Positive relationships with colleagues and healthcare team members also mitigate the emotional basis for physician burnout.⁵ Be kind and courteous to those who seek your assistance. Move beyond the notion of the “bad” or “soft” consult and seek instead to understand how you can help.

A requesting physician may phrase the consult question vaguely or may know that the patient is having a GI-related issue, but simply lack the specific knowledge to know what is needed. In these instances, it is our role to listen and help guide them to the correct thought process to ensure the best care of the patient. These important interactions establish our reputation, create our referral bases, and directly affect our sense of personal satisfaction.

 

2. Be Timely

GI presents an appealing breadth of pathology, but this also corresponds to a wide variety of indications for consultation and, therefore, urgency of need. In a busy clinical practice, not all requests can be urgently prioritized. However, it is the consultant’s responsibility to identify patients that require urgent evaluation and intervention to avert a potential adverse outcome.

We are well-trained in the medical triage of consultations. There are explicit guidelines for assessing urgency for GI bleeding, foreign body ingestion, choledocholithiasis, and many other indications. However, there are often special contextual circumstances that will elevate the urgency of a seemingly non-urgent consult request. Does the patient have an upcoming surgery or treatment that will depend on your input? Are they facing an imminent loss of insurance coverage? Is their non-severe GI disease leading to more severe impact on non-GI organ systems? The referring provider knows the patient better than you – seek to understand the context of the consult request.
 

Timeliness also applies to our communication. Communicate recommendations directly to the consulting service as soon as the patient is seen. When a colleague reaches out with a concern about a patient, make sure to take that request seriously. If you are unable to address the concern immediately, at least provide acknowledgment and an estimated timeline for response. As the maxim states, the effectiveness of a consultant is just as dependent on availability as it is on ability.

 

3. Be Specific

The same survey studies indicate that the second most critical aspect of successful subspecialty consultation is delivering clear recommendations. Accordingly, I always urge my trainees to challenge me when we leave a consult interaction if they feel that our plan is vague or imprecise.

Specificity in consult recommendations is an essential way to demonstrate your expertise and provide value. Clear and definitive recommendations enhance others’ perception of your skill, reduce the need for additional clarifying communication, and lead to more efficient, higher quality care. Avoid vague language, such as asking the requester to “consider” a test or intervention. When recommending medication, specify the dose, frequency, duration, and expected timeline of effect. Rather than recommending “cross-sectional imaging,” specify what modality and protocol. Instead of recommending “adequate resuscitation,” specify your target endpoints. If you engage in multidisciplinary discussion, ensure you strive for a specific group consensus plan and communicate this to all members of the team.

Specificity also applies to the quality of your documentation. Ensure that your clinical notes outline your rationale for your recommended plan, specific contingencies based on results of recommended testing, and a plan for follow-up care. When referring for open-access endoscopy, specifically outline what to look for and which specimens or endoscopic interventions are needed. Be precise in your procedure documentation – avoid vague terms such as small/medium/large and instead quantify in terms of millimeter/centimeter measurement. If you do not adopt specific classification schemes (e.g. Prague classification, Paris classification, Eosinophilic Esophagitis Endoscopic Reference Score, etc.), ensure you provide enough descriptive language to convey an adequate understanding of the findings.

 

4. Be Helpful

A consultant’s primary directive is to be of service to the consulting provider and the patient. As an educational leader, I am often asked what attributes separate a high-performing trainee from an average one. My feeling is that the most critical attribute is a sense of ownership over patient care.

As a consultant, when others feel we are exhibiting engagement and ownership in a patient’s care, they perceive that we are working together as an effective healthcare team. Interestingly, survey studies of inpatient care show that primary services do not necessarily value assistance with orders or care coordination – they consider these as core aspects of their daily work. What they did value was ongoing daily progress notes/communication, regardless of patient acuity or consulting specialty. This is a potent signal that our continued engagement (both inpatient and outpatient) is perceived as helpful.

Helpfulness is further aided by ensuring mutual understanding. While survey data indicate that sharing specific literature citations may not always be perceived positively, explaining the consultant’s rationale for their recommendations is highly valued. Take the time to tactfully explain your assessment of the patient and why you arrived at your specific recommendations. If your recommendations differ from what the requester expected (e.g. a procedure was expected but is not offered), ensure you explain why and answer questions they may have. This fosters mutual respect and proactively averts conflict or discontent from misunderstanding.

Multidisciplinary collaboration is another important avenue for aiding our patients and colleagues. Studies across a wide range of disease processes (including GI bleeding, IBD, etc.) and medical settings have demonstrated that multidisciplinary collaboration unequivocally improves patient outcomes.⁶ The success of these collaborations relies on our willingness to fully engage in these conversations, despite the fact that they may often be logistically challenging. 

We all know how difficult it can be to locate and organize multiple medical specialists with complex varying clinical schedules and busy personal lives. Choosing to do so demonstrates a dedication to providing the highest level of care and elevates both patient and physician satisfaction. Having chosen to cultivate several ongoing multidisciplinary conferences/collaborations, I can attest to the notion that the outcome is well worth the effort.

 

5. Be Honest

While we always strive to provide the answers for our patients and colleagues, we must also acknowledge our limitations. Be honest with yourself when you encounter a scenario that pushes beyond the boundaries of your knowledge and comfort. Be willing to admit when you yourself need to consult others or seek an outside referral to provide the care a patient needs. Aspiring physicians often espouse that a devotion to lifelong learning is a key driver of their desire to pursue a career in medicine. These scenarios provide a key opportunity to expand our knowledge while doing what is right for our patients.

Be equally honest about your comfort with “curbside” consultations. Studies show that subspecialists receive on average of 3-4 such requests per week.⁷ The perception of these interactions is starkly discrepant between the requester and recipient. While over 80% of surveyed primary nonsurgical services felt that curbside consultations were helpful in patient care, a similar proportion of subspecialists expressed concern that insufficient clinical information was provided, even leading to a fear of litigation. While straightforward, informal conversations on narrow, well-defined questions can be helpful and efficient, the consultant should always feel comfortable seeking an opportunity for formal consultation when the details are unclear or the case/question is complex.

 

Closing Thoughts

Being an effective GI consultant isn’t just about what you know—it’s about how you apply it, how you communicate it, and how you make others feel in the process.

The attributes outlined above are not ancillary traits—they are essential components of high-quality consultation. When consistently applied, they enhance collaboration, improve patient outcomes, and reinforce trust within the healthcare system. By committing to them, you establish your reputation of excellence and play a role in elevating the field of gastroenterology more broadly.

Dr. Kahn is based in the Division of Gastroenterology and Hepatology at Mayo Clinic, Scottsdale, Arizona. He reports no conflicts of interest in regard to this article.

References

1. Goldman L, et al. Ten commandments for effective consultations. Arch Intern Med. 1983 Sep.

2. Salerno SM, et al. Principles of effective consultation: an update for the 21st-century consultant. Arch Intern Med. 2007 Feb. doi: 10.1001/archinte.167.3.271.

3. Adams TN, et al. Hospitalist Perspective of Interactions with Medicine Subspecialty Consult Services. J Hosp Med. 2018 May. doi: 10.12788/jhm.2882.

4. Matsuo T, et al. Essential consultants’ skills and attitudes (Willing CONSULT): a cross-sectional survey. BMC Med Educ. 2021 Jul. doi: 10.1186/s12909-021-02810-9.

5. Welp A, Manser T. Integrating teamwork, clinician occupational well-being and patient safety - development of a conceptual framework based on a systematic review. BMC Health Serv Res. 2016 Jul. doi: 10.1186/s12913-016-1535-y.

6. Webster CS, et al. Interprofessional Learning in Multidisciplinary Healthcare Teams Is Associated With Reduced Patient Mortality: A Quantitative Systematic Review and Meta-analysis. J Patient Saf. 2024 Jan. doi: 10.1097/PTS.0000000000001170.

7. Lin M, et al. Curbside Consultations: The Good, the Bad, and the Ugly. Clin Gastroenterol Hepatol. 2016 Jan. doi: 10.1016/j.cgh.2015.09.026.

Gastroenterology (GI) subspecialty training is carefully designed to develop expertise in digestive diseases and gastrointestinal endoscopy, while facilitating the transition from generalist to subspecialty consultant. The concept of effective consultation extends far beyond clinical expertise and has been explored repeatedly, beginning with Goldman’s “Ten Commandments” in 1983.^1,2 How should these best practices be specifically applied to GI? More importantly, what kind of experience would you want if you were the referring provider or the patient themselves?

Below are five essential tips to guide your development as a high-impact GI consultant with a reputation for excellence. 
 

1. Be Kind

Survey studies of medical/surgical residents and attending hospitalists have demonstrated that willingness to accept consultation requests was the single factor consistently rated as most important in determining the quality of the consultation interaction.^3,4 Unfortunately, nearly 65% of respondents reported encountering pushback when requesting subspecialty consultation. It is critical to recognize that when you receive a GI consult request, the requester has already decided that it is needed. Whether that request comports with our individual notion of “necessary” or “important,” this is a colleague’s request for help. There are myriad reasons why a request may be made, but they are unified in this principle.

Effective teamwork in healthcare settings enhances clinical performance and patient safety. Positive relationships with colleagues and healthcare team members also mitigate the emotional basis for physician burnout.⁵ Be kind and courteous to those who seek your assistance. Move beyond the notion of the “bad” or “soft” consult and seek instead to understand how you can help.

A requesting physician may phrase the consult question vaguely or may know that the patient is having a GI-related issue, but simply lack the specific knowledge to know what is needed. In these instances, it is our role to listen and help guide them to the correct thought process to ensure the best care of the patient. These important interactions establish our reputation, create our referral bases, and directly affect our sense of personal satisfaction.

 

2. Be Timely

GI presents an appealing breadth of pathology, but this also corresponds to a wide variety of indications for consultation and, therefore, urgency of need. In a busy clinical practice, not all requests can be urgently prioritized. However, it is the consultant’s responsibility to identify patients that require urgent evaluation and intervention to avert a potential adverse outcome.

We are well-trained in the medical triage of consultations. There are explicit guidelines for assessing urgency for GI bleeding, foreign body ingestion, choledocholithiasis, and many other indications. However, there are often special contextual circumstances that will elevate the urgency of a seemingly non-urgent consult request. Does the patient have an upcoming surgery or treatment that will depend on your input? Are they facing an imminent loss of insurance coverage? Is their non-severe GI disease leading to more severe impact on non-GI organ systems? The referring provider knows the patient better than you – seek to understand the context of the consult request.
 

Timeliness also applies to our communication. Communicate recommendations directly to the consulting service as soon as the patient is seen. When a colleague reaches out with a concern about a patient, make sure to take that request seriously. If you are unable to address the concern immediately, at least provide acknowledgment and an estimated timeline for response. As the maxim states, the effectiveness of a consultant is just as dependent on availability as it is on ability.

 

3. Be Specific

The same survey studies indicate that the second most critical aspect of successful subspecialty consultation is delivering clear recommendations. Accordingly, I always urge my trainees to challenge me when we leave a consult interaction if they feel that our plan is vague or imprecise.

Specificity in consult recommendations is an essential way to demonstrate your expertise and provide value. Clear and definitive recommendations enhance others’ perception of your skill, reduce the need for additional clarifying communication, and lead to more efficient, higher quality care. Avoid vague language, such as asking the requester to “consider” a test or intervention. When recommending medication, specify the dose, frequency, duration, and expected timeline of effect. Rather than recommending “cross-sectional imaging,” specify what modality and protocol. Instead of recommending “adequate resuscitation,” specify your target endpoints. If you engage in multidisciplinary discussion, ensure you strive for a specific group consensus plan and communicate this to all members of the team.

Specificity also applies to the quality of your documentation. Ensure that your clinical notes outline your rationale for your recommended plan, specific contingencies based on results of recommended testing, and a plan for follow-up care. When referring for open-access endoscopy, specifically outline what to look for and which specimens or endoscopic interventions are needed. Be precise in your procedure documentation – avoid vague terms such as small/medium/large and instead quantify in terms of millimeter/centimeter measurement. If you do not adopt specific classification schemes (e.g. Prague classification, Paris classification, Eosinophilic Esophagitis Endoscopic Reference Score, etc.), ensure you provide enough descriptive language to convey an adequate understanding of the findings.

 

4. Be Helpful

A consultant’s primary directive is to be of service to the consulting provider and the patient. As an educational leader, I am often asked what attributes separate a high-performing trainee from an average one. My feeling is that the most critical attribute is a sense of ownership over patient care.

As a consultant, when others feel we are exhibiting engagement and ownership in a patient’s care, they perceive that we are working together as an effective healthcare team. Interestingly, survey studies of inpatient care show that primary services do not necessarily value assistance with orders or care coordination – they consider these as core aspects of their daily work. What they did value was ongoing daily progress notes/communication, regardless of patient acuity or consulting specialty. This is a potent signal that our continued engagement (both inpatient and outpatient) is perceived as helpful.

Helpfulness is further aided by ensuring mutual understanding. While survey data indicate that sharing specific literature citations may not always be perceived positively, explaining the consultant’s rationale for their recommendations is highly valued. Take the time to tactfully explain your assessment of the patient and why you arrived at your specific recommendations. If your recommendations differ from what the requester expected (e.g. a procedure was expected but is not offered), ensure you explain why and answer questions they may have. This fosters mutual respect and proactively averts conflict or discontent from misunderstanding.

Multidisciplinary collaboration is another important avenue for aiding our patients and colleagues. Studies across a wide range of disease processes (including GI bleeding, IBD, etc.) and medical settings have demonstrated that multidisciplinary collaboration unequivocally improves patient outcomes.⁶ The success of these collaborations relies on our willingness to fully engage in these conversations, despite the fact that they may often be logistically challenging. 

We all know how difficult it can be to locate and organize multiple medical specialists with complex varying clinical schedules and busy personal lives. Choosing to do so demonstrates a dedication to providing the highest level of care and elevates both patient and physician satisfaction. Having chosen to cultivate several ongoing multidisciplinary conferences/collaborations, I can attest to the notion that the outcome is well worth the effort.

 

5. Be Honest

While we always strive to provide the answers for our patients and colleagues, we must also acknowledge our limitations. Be honest with yourself when you encounter a scenario that pushes beyond the boundaries of your knowledge and comfort. Be willing to admit when you yourself need to consult others or seek an outside referral to provide the care a patient needs. Aspiring physicians often espouse that a devotion to lifelong learning is a key driver of their desire to pursue a career in medicine. These scenarios provide a key opportunity to expand our knowledge while doing what is right for our patients.

Be equally honest about your comfort with “curbside” consultations. Studies show that subspecialists receive on average of 3-4 such requests per week.⁷ The perception of these interactions is starkly discrepant between the requester and recipient. While over 80% of surveyed primary nonsurgical services felt that curbside consultations were helpful in patient care, a similar proportion of subspecialists expressed concern that insufficient clinical information was provided, even leading to a fear of litigation. While straightforward, informal conversations on narrow, well-defined questions can be helpful and efficient, the consultant should always feel comfortable seeking an opportunity for formal consultation when the details are unclear or the case/question is complex.

 

Closing Thoughts

Being an effective GI consultant isn’t just about what you know—it’s about how you apply it, how you communicate it, and how you make others feel in the process.

The attributes outlined above are not ancillary traits—they are essential components of high-quality consultation. When consistently applied, they enhance collaboration, improve patient outcomes, and reinforce trust within the healthcare system. By committing to them, you establish your reputation of excellence and play a role in elevating the field of gastroenterology more broadly.

Dr. Kahn is based in the Division of Gastroenterology and Hepatology at Mayo Clinic, Scottsdale, Arizona. He reports no conflicts of interest in regard to this article.

References

1. Goldman L, et al. Ten commandments for effective consultations. Arch Intern Med. 1983 Sep.

2. Salerno SM, et al. Principles of effective consultation: an update for the 21st-century consultant. Arch Intern Med. 2007 Feb. doi: 10.1001/archinte.167.3.271.

3. Adams TN, et al. Hospitalist Perspective of Interactions with Medicine Subspecialty Consult Services. J Hosp Med. 2018 May. doi: 10.12788/jhm.2882.

4. Matsuo T, et al. Essential consultants’ skills and attitudes (Willing CONSULT): a cross-sectional survey. BMC Med Educ. 2021 Jul. doi: 10.1186/s12909-021-02810-9.

5. Welp A, Manser T. Integrating teamwork, clinician occupational well-being and patient safety - development of a conceptual framework based on a systematic review. BMC Health Serv Res. 2016 Jul. doi: 10.1186/s12913-016-1535-y.

6. Webster CS, et al. Interprofessional Learning in Multidisciplinary Healthcare Teams Is Associated With Reduced Patient Mortality: A Quantitative Systematic Review and Meta-analysis. J Patient Saf. 2024 Jan. doi: 10.1097/PTS.0000000000001170.

7. Lin M, et al. Curbside Consultations: The Good, the Bad, and the Ugly. Clin Gastroenterol Hepatol. 2016 Jan. doi: 10.1016/j.cgh.2015.09.026.

Acute pancreatitis can be associated with multiorgan system failure, including respiratory failure, which has a high mortality rate. Acute respiratory distress syndrome (ARDS) is a known complication of severe, acute pancreatitis, and is fatal in up to 40% of cases. Mortality rates exceed 80% in patients with PaO₂/FiO₂ < 100 mm Hg.² Although ARDS is typically associated with bilateral pulmonary infiltrates, severe hypoxemia in pancreatitis may not be visible in radiography in up to 50% of cases.¹

Hypertriglyceridemia is the third-most common cause of acute pancreatitis, with an incidence of 2% to 10% among patients diagnosed with acute pancreatitis.^3.4 Elevated serum triglycerides have been proposed to trigger acute pancreatitis by increasing plasma viscosity, which leads to ischemia and inflammation of the pancreas.⁴ In severe cases of hypertriglyceridemia-induced acute pancreatitis, plasmapheresis is used to rapidly reduce serum chylomicron and triglyceride levels.³    

This case report discusses a patient with acute pancreatitis whose hypoxemia coincided with the severity of hypertriglyceridemia, but without radiographic evidence of pulmonary infiltrates or other known pulmonary causes.

Case Presentation

A 60-year-old male presented to the emergency department with several hours of diffuse abdominal pain, nausea, and vomiting. The patient reported that his symptoms began after eating fried chicken. He reported no dyspnea, fever, chills, or other symptoms. His medical history included type 2 diabetes (hemoglobin A_1c, 11.1%), Hashimoto hypothyroidism, severe obstructive sleep apnea not on continuous positive airway pressure (apnea-hypoxia index, 59/h), and obesity (body mass index, 52). Initial vital signs were afebrile, heart rate of 90 beats/min, and oxygen saturation (SpO₂) of 85% on 6L oxygen via nasal cannula. He was admitted to the intensive care unit and quickly maximized on high flow nasal cannula, ultimately requiring endotracheal intubation and mechanical ventilation.

Initial laboratory studies were remarkable for serum sodium of 120 mmol/L (reference range, 136-146 mmol/L), creatinine of 1.65 mg/dL (reference range, 0.52-1.28 mg/dL), anion gap of 18 mEq/L (reference range, 3-11 mEq/L), lipase level of 1115 U/L (reference range, 11-82 U/L), glucose level of 334 mg/dL (reference range, 70-110 mg/dL), white blood count of 13.1 K/uL (reference range, 4.5-11.0 K/uL), lactate level of 3.8 mmol/L (reference range, 0.5-2.2 mmol/L), triglyceride level of 1605 mg/dL (reference range, 40-160 mg/dL), cholesterol level of 565 mg/dL (reference range, < 200 mg/dL), aminotransferase of 21 U/L (reference range, 13-36 U/L), alanine aminotransferase of < 3 U/L (reference range, 7-45 U/L), and total bilirubin level of 1.6 mg/dL (reference range, 0.2-1 mg/dL).     

The patient had an initial arterial blood gas pH of 7.26, partial pressure of CO₂ and O₂ of 64.1 mm Hg and 74.1 mm Hg, respectively, on volume control with a tidal volume of 500 mL, positive end-expiratory pressure of 10 cm H₂O, respiratory rate of 26 breaths/min, and FiO₂ was 100%, which yielded a PaO₂/FiO₂ of 74 mm Hg. The patient was maintained in steep reverse-Trendelenburg position with moderate improvement in his SpO₂.    

Chest X-ray and computed tomography angiogram did not reveal pleural effusions, pulmonary infiltrates, or pulmonary embolism (Figure 1). Computed tomography of the abdomen and pelvis demonstrated severe acute interstitial edematous pancreatitis with no evidence of pancreatic necrosis or evidence of gallstones (Figure 2). A transthoracic echocardiogram with bubble was negative for intracardiac right to left shunting.    

The leading diagnosis was ARDS secondary to acute pancreatitis with hypoxemia exacerbated by morbid obesity and untreated obstructive sleep apnea leading to hypoventilation.

Treatment

The patient was intubated and restricted to nothing by mouth and provided fluid resuscitation with crystalloids. On hospital day 1, he remained intubated and on mechanical ventilation, started on plasmapheresis and continued insulin infusion for severe hypertriglyceridemia. The patient’s PaO₂/FiO₂ ratio remained persistently < 100 mm Hg despite maximal ventilatory support. After 3 sessions of plasmapheresis, the serum triglyceride levels and oxygen requirements improved (Figure 3).

Due to prolonged intubation, the patient ultimately required a tracheostomy. By hospital day 48, the patient was successfully weaned off mechanical ventilation. His tracheostomy was decannulated uneventfully on hospital day 55 and the stoma was closed. The patient was discharged to a skilled nursing home for rehabilitation and received intensive physical therapy for deconditioning from prolonged hospitalization.

Discussion

Respiratory insufficiency is a common and potentially lethal complication observed in one-third of patients with acute pancreatitis.¹ Radiographic evidence of pleural effusions, atelectasis and pulmonary infiltrates are often present. Acute lung injury (ALI) and ARDS are the most severe pulmonary complications of acute pancreatitis.⁵ It has been proposed that ALI and ARDS are driven by a hyperinflammatory state, which has multiple downstream effects. Pulmonary parenchymal and vascular damage has been associated with activated proinflammatory cytokines, trypsin, phospholipase A, and free fatty acids (Figure 4).¹

Hypoxemia secondary to acute pancreatitis may occur without initial radiographic findings and has been observed in up to half of patients.¹ Hypoxemia in ARDS occurs due to ventilation-perfusion defects causing gas exchange impairments which may be worsened further by high distending volumes and pressures on mechanical ventilation, dyssynchronous breathing, and/or lung derecruitment.⁶ Patients who require intubation for pancreatitis-associated ALI or ARDS eventually exhibit imaging findings consistent with their disease.¹ The patient in this case exhibited severe hypoxemia for several days despite persistently negative radiographic studies. His history of obstructive sleep apnea and a body mass index of 52 may have contributed to respiratory failure; however, assessment of other contributors to the acute and profound hypoxemia yielded largely unremarkable results. The patient did not have a history or evidence of heart failure and his hypoxemia did not improve with diuresis. He tested positive for COVID-19 on admission and was briefly treated with remdesivir and dexamethasone, but it was determined that the test was likely a false positive due to negative subsequent tests and elevated cycle thresholds (> 40). A concomitant COVID-19 infection likely did not contribute to his symptoms.    

Ventilation-perfusion mismatch is a well-recognized complication of pancreatitis, which results in right-to-left shunting.⁵ While we considered whether an intracardiac shunt may have contributed to the patient’s hypoxemia, a transthoracic echocardiogram with bubble contrast was negative.    

The patient had a peak serum triglyceride of > 6000 mg/dl, which meets the criteria for very severe hypertriglyceridemia.⁷ As observed in prior reports, the extent of the hypertriglyceridemia in this patient resulted in pronounced lipemic blood, which was appreciable by the eye and necessitated several rounds of centrifugation to analyze the laboratory studies.⁸ In this case, plasmapheresis was used to rapidly treat the hypertriglyceridemia, thereby reducing inflammation and further damage to the pancreas.⁹    

It is possible the patient’s hypertriglyceridemia may have been associated with his hypoxemia. His hypoxemia was most pronounced approximately 24 hours postadmission, which coincided with the peak of the hypertriglyceridemia. It remains unclear whether the severity of triglyceride elevation could accurately predict the severity of respiratory insufficiency. Hypoxemia is thought to modulate triglyceride metabolism through stimulation of intracellular lipolysis, upregulation of very low-density lipoproteins production in the liver, and inhibition of triglyceride-rich lipoprotein metabolism.¹⁰ Evidence from rodent studies supports the idea that acute hypoxemia increases triglycerides, and the degree of hypoxemia correlates with the elevated triglyceride levels.¹¹ However, this has not been consistently observed in humans and may vary by prandial state.^12,13 Thus, dysfunction of lipid metabolism may be a relevant clinical indicator of hypoxemia; further work is needed to elucidate this association.

Patient Perspective

The patient continues to undergo extensive rehabilitation following his prolonged illness and hospitalization. He expressed gratitude for the care received. However, he has limited and distorted recollection of the events during his hospitalization and stated that it felt “like an extraterrestrial state.”

Conclusions

This report describes a case of marked hypoxemia in the setting of acute pancreatitis. Pulmonary insufficiency in acute pancreatitis is commonly associated with imaging findings such as atelectasis, pleural effusions, and pulmonary infiltrates; however, up to half of cases initially lack any radiographic findings. Plasmapheresis is an effective treatment for hypertriglyceridemia-induced pancreatitis to both directly reduce circulating triglycerides and inflammation. Plasmapheresis also represents a promising therapy for the prevention of further episodes of pancreatitis in patients with recurrent pancreatitis. We propose a feedback mechanism through which pancreatitis induces severe hypoxemia, which may modulate lipid metabolism and severe hypertriglyceridemia correlates with respiratory failure.

Acute pancreatitis can be associated with multiorgan system failure, including respiratory failure, which has a high mortality rate. Acute respiratory distress syndrome (ARDS) is a known complication of severe, acute pancreatitis, and is fatal in up to 40% of cases. Mortality rates exceed 80% in patients with PaO₂/FiO₂ < 100 mm Hg.² Although ARDS is typically associated with bilateral pulmonary infiltrates, severe hypoxemia in pancreatitis may not be visible in radiography in up to 50% of cases.¹

Hypertriglyceridemia is the third-most common cause of acute pancreatitis, with an incidence of 2% to 10% among patients diagnosed with acute pancreatitis.^3.4 Elevated serum triglycerides have been proposed to trigger acute pancreatitis by increasing plasma viscosity, which leads to ischemia and inflammation of the pancreas.⁴ In severe cases of hypertriglyceridemia-induced acute pancreatitis, plasmapheresis is used to rapidly reduce serum chylomicron and triglyceride levels.³    

This case report discusses a patient with acute pancreatitis whose hypoxemia coincided with the severity of hypertriglyceridemia, but without radiographic evidence of pulmonary infiltrates or other known pulmonary causes.

Case Presentation

A 60-year-old male presented to the emergency department with several hours of diffuse abdominal pain, nausea, and vomiting. The patient reported that his symptoms began after eating fried chicken. He reported no dyspnea, fever, chills, or other symptoms. His medical history included type 2 diabetes (hemoglobin A_1c, 11.1%), Hashimoto hypothyroidism, severe obstructive sleep apnea not on continuous positive airway pressure (apnea-hypoxia index, 59/h), and obesity (body mass index, 52). Initial vital signs were afebrile, heart rate of 90 beats/min, and oxygen saturation (SpO₂) of 85% on 6L oxygen via nasal cannula. He was admitted to the intensive care unit and quickly maximized on high flow nasal cannula, ultimately requiring endotracheal intubation and mechanical ventilation.

Initial laboratory studies were remarkable for serum sodium of 120 mmol/L (reference range, 136-146 mmol/L), creatinine of 1.65 mg/dL (reference range, 0.52-1.28 mg/dL), anion gap of 18 mEq/L (reference range, 3-11 mEq/L), lipase level of 1115 U/L (reference range, 11-82 U/L), glucose level of 334 mg/dL (reference range, 70-110 mg/dL), white blood count of 13.1 K/uL (reference range, 4.5-11.0 K/uL), lactate level of 3.8 mmol/L (reference range, 0.5-2.2 mmol/L), triglyceride level of 1605 mg/dL (reference range, 40-160 mg/dL), cholesterol level of 565 mg/dL (reference range, < 200 mg/dL), aminotransferase of 21 U/L (reference range, 13-36 U/L), alanine aminotransferase of < 3 U/L (reference range, 7-45 U/L), and total bilirubin level of 1.6 mg/dL (reference range, 0.2-1 mg/dL).     

The patient had an initial arterial blood gas pH of 7.26, partial pressure of CO₂ and O₂ of 64.1 mm Hg and 74.1 mm Hg, respectively, on volume control with a tidal volume of 500 mL, positive end-expiratory pressure of 10 cm H₂O, respiratory rate of 26 breaths/min, and FiO₂ was 100%, which yielded a PaO₂/FiO₂ of 74 mm Hg. The patient was maintained in steep reverse-Trendelenburg position with moderate improvement in his SpO₂.    

Chest X-ray and computed tomography angiogram did not reveal pleural effusions, pulmonary infiltrates, or pulmonary embolism (Figure 1). Computed tomography of the abdomen and pelvis demonstrated severe acute interstitial edematous pancreatitis with no evidence of pancreatic necrosis or evidence of gallstones (Figure 2). A transthoracic echocardiogram with bubble was negative for intracardiac right to left shunting.    

The leading diagnosis was ARDS secondary to acute pancreatitis with hypoxemia exacerbated by morbid obesity and untreated obstructive sleep apnea leading to hypoventilation.

Treatment

The patient was intubated and restricted to nothing by mouth and provided fluid resuscitation with crystalloids. On hospital day 1, he remained intubated and on mechanical ventilation, started on plasmapheresis and continued insulin infusion for severe hypertriglyceridemia. The patient’s PaO₂/FiO₂ ratio remained persistently < 100 mm Hg despite maximal ventilatory support. After 3 sessions of plasmapheresis, the serum triglyceride levels and oxygen requirements improved (Figure 3).

Due to prolonged intubation, the patient ultimately required a tracheostomy. By hospital day 48, the patient was successfully weaned off mechanical ventilation. His tracheostomy was decannulated uneventfully on hospital day 55 and the stoma was closed. The patient was discharged to a skilled nursing home for rehabilitation and received intensive physical therapy for deconditioning from prolonged hospitalization.

Discussion

Respiratory insufficiency is a common and potentially lethal complication observed in one-third of patients with acute pancreatitis.¹ Radiographic evidence of pleural effusions, atelectasis and pulmonary infiltrates are often present. Acute lung injury (ALI) and ARDS are the most severe pulmonary complications of acute pancreatitis.⁵ It has been proposed that ALI and ARDS are driven by a hyperinflammatory state, which has multiple downstream effects. Pulmonary parenchymal and vascular damage has been associated with activated proinflammatory cytokines, trypsin, phospholipase A, and free fatty acids (Figure 4).¹

Hypoxemia secondary to acute pancreatitis may occur without initial radiographic findings and has been observed in up to half of patients.¹ Hypoxemia in ARDS occurs due to ventilation-perfusion defects causing gas exchange impairments which may be worsened further by high distending volumes and pressures on mechanical ventilation, dyssynchronous breathing, and/or lung derecruitment.⁶ Patients who require intubation for pancreatitis-associated ALI or ARDS eventually exhibit imaging findings consistent with their disease.¹ The patient in this case exhibited severe hypoxemia for several days despite persistently negative radiographic studies. His history of obstructive sleep apnea and a body mass index of 52 may have contributed to respiratory failure; however, assessment of other contributors to the acute and profound hypoxemia yielded largely unremarkable results. The patient did not have a history or evidence of heart failure and his hypoxemia did not improve with diuresis. He tested positive for COVID-19 on admission and was briefly treated with remdesivir and dexamethasone, but it was determined that the test was likely a false positive due to negative subsequent tests and elevated cycle thresholds (> 40). A concomitant COVID-19 infection likely did not contribute to his symptoms.    

Ventilation-perfusion mismatch is a well-recognized complication of pancreatitis, which results in right-to-left shunting.⁵ While we considered whether an intracardiac shunt may have contributed to the patient’s hypoxemia, a transthoracic echocardiogram with bubble contrast was negative.    

The patient had a peak serum triglyceride of > 6000 mg/dl, which meets the criteria for very severe hypertriglyceridemia.⁷ As observed in prior reports, the extent of the hypertriglyceridemia in this patient resulted in pronounced lipemic blood, which was appreciable by the eye and necessitated several rounds of centrifugation to analyze the laboratory studies.⁸ In this case, plasmapheresis was used to rapidly treat the hypertriglyceridemia, thereby reducing inflammation and further damage to the pancreas.⁹    

It is possible the patient’s hypertriglyceridemia may have been associated with his hypoxemia. His hypoxemia was most pronounced approximately 24 hours postadmission, which coincided with the peak of the hypertriglyceridemia. It remains unclear whether the severity of triglyceride elevation could accurately predict the severity of respiratory insufficiency. Hypoxemia is thought to modulate triglyceride metabolism through stimulation of intracellular lipolysis, upregulation of very low-density lipoproteins production in the liver, and inhibition of triglyceride-rich lipoprotein metabolism.¹⁰ Evidence from rodent studies supports the idea that acute hypoxemia increases triglycerides, and the degree of hypoxemia correlates with the elevated triglyceride levels.¹¹ However, this has not been consistently observed in humans and may vary by prandial state.^12,13 Thus, dysfunction of lipid metabolism may be a relevant clinical indicator of hypoxemia; further work is needed to elucidate this association.

Patient Perspective

The patient continues to undergo extensive rehabilitation following his prolonged illness and hospitalization. He expressed gratitude for the care received. However, he has limited and distorted recollection of the events during his hospitalization and stated that it felt “like an extraterrestrial state.”

Conclusions

This report describes a case of marked hypoxemia in the setting of acute pancreatitis. Pulmonary insufficiency in acute pancreatitis is commonly associated with imaging findings such as atelectasis, pleural effusions, and pulmonary infiltrates; however, up to half of cases initially lack any radiographic findings. Plasmapheresis is an effective treatment for hypertriglyceridemia-induced pancreatitis to both directly reduce circulating triglycerides and inflammation. Plasmapheresis also represents a promising therapy for the prevention of further episodes of pancreatitis in patients with recurrent pancreatitis. We propose a feedback mechanism through which pancreatitis induces severe hypoxemia, which may modulate lipid metabolism and severe hypertriglyceridemia correlates with respiratory failure.

Acute pancreatitis can be associated with multiorgan system failure, including respiratory failure, which has a high mortality rate. Acute respiratory distress syndrome (ARDS) is a known complication of severe, acute pancreatitis, and is fatal in up to 40% of cases. Mortality rates exceed 80% in patients with PaO₂/FiO₂ < 100 mm Hg.² Although ARDS is typically associated with bilateral pulmonary infiltrates, severe hypoxemia in pancreatitis may not be visible in radiography in up to 50% of cases.¹

Hypertriglyceridemia is the third-most common cause of acute pancreatitis, with an incidence of 2% to 10% among patients diagnosed with acute pancreatitis.^3.4 Elevated serum triglycerides have been proposed to trigger acute pancreatitis by increasing plasma viscosity, which leads to ischemia and inflammation of the pancreas.⁴ In severe cases of hypertriglyceridemia-induced acute pancreatitis, plasmapheresis is used to rapidly reduce serum chylomicron and triglyceride levels.³    

This case report discusses a patient with acute pancreatitis whose hypoxemia coincided with the severity of hypertriglyceridemia, but without radiographic evidence of pulmonary infiltrates or other known pulmonary causes.

Case Presentation

A 60-year-old male presented to the emergency department with several hours of diffuse abdominal pain, nausea, and vomiting. The patient reported that his symptoms began after eating fried chicken. He reported no dyspnea, fever, chills, or other symptoms. His medical history included type 2 diabetes (hemoglobin A_1c, 11.1%), Hashimoto hypothyroidism, severe obstructive sleep apnea not on continuous positive airway pressure (apnea-hypoxia index, 59/h), and obesity (body mass index, 52). Initial vital signs were afebrile, heart rate of 90 beats/min, and oxygen saturation (SpO₂) of 85% on 6L oxygen via nasal cannula. He was admitted to the intensive care unit and quickly maximized on high flow nasal cannula, ultimately requiring endotracheal intubation and mechanical ventilation.

Initial laboratory studies were remarkable for serum sodium of 120 mmol/L (reference range, 136-146 mmol/L), creatinine of 1.65 mg/dL (reference range, 0.52-1.28 mg/dL), anion gap of 18 mEq/L (reference range, 3-11 mEq/L), lipase level of 1115 U/L (reference range, 11-82 U/L), glucose level of 334 mg/dL (reference range, 70-110 mg/dL), white blood count of 13.1 K/uL (reference range, 4.5-11.0 K/uL), lactate level of 3.8 mmol/L (reference range, 0.5-2.2 mmol/L), triglyceride level of 1605 mg/dL (reference range, 40-160 mg/dL), cholesterol level of 565 mg/dL (reference range, < 200 mg/dL), aminotransferase of 21 U/L (reference range, 13-36 U/L), alanine aminotransferase of < 3 U/L (reference range, 7-45 U/L), and total bilirubin level of 1.6 mg/dL (reference range, 0.2-1 mg/dL).     

The patient had an initial arterial blood gas pH of 7.26, partial pressure of CO₂ and O₂ of 64.1 mm Hg and 74.1 mm Hg, respectively, on volume control with a tidal volume of 500 mL, positive end-expiratory pressure of 10 cm H₂O, respiratory rate of 26 breaths/min, and FiO₂ was 100%, which yielded a PaO₂/FiO₂ of 74 mm Hg. The patient was maintained in steep reverse-Trendelenburg position with moderate improvement in his SpO₂.    

Chest X-ray and computed tomography angiogram did not reveal pleural effusions, pulmonary infiltrates, or pulmonary embolism (Figure 1). Computed tomography of the abdomen and pelvis demonstrated severe acute interstitial edematous pancreatitis with no evidence of pancreatic necrosis or evidence of gallstones (Figure 2). A transthoracic echocardiogram with bubble was negative for intracardiac right to left shunting.    

The leading diagnosis was ARDS secondary to acute pancreatitis with hypoxemia exacerbated by morbid obesity and untreated obstructive sleep apnea leading to hypoventilation.

Treatment

The patient was intubated and restricted to nothing by mouth and provided fluid resuscitation with crystalloids. On hospital day 1, he remained intubated and on mechanical ventilation, started on plasmapheresis and continued insulin infusion for severe hypertriglyceridemia. The patient’s PaO₂/FiO₂ ratio remained persistently < 100 mm Hg despite maximal ventilatory support. After 3 sessions of plasmapheresis, the serum triglyceride levels and oxygen requirements improved (Figure 3).

Due to prolonged intubation, the patient ultimately required a tracheostomy. By hospital day 48, the patient was successfully weaned off mechanical ventilation. His tracheostomy was decannulated uneventfully on hospital day 55 and the stoma was closed. The patient was discharged to a skilled nursing home for rehabilitation and received intensive physical therapy for deconditioning from prolonged hospitalization.

Discussion

Respiratory insufficiency is a common and potentially lethal complication observed in one-third of patients with acute pancreatitis.¹ Radiographic evidence of pleural effusions, atelectasis and pulmonary infiltrates are often present. Acute lung injury (ALI) and ARDS are the most severe pulmonary complications of acute pancreatitis.⁵ It has been proposed that ALI and ARDS are driven by a hyperinflammatory state, which has multiple downstream effects. Pulmonary parenchymal and vascular damage has been associated with activated proinflammatory cytokines, trypsin, phospholipase A, and free fatty acids (Figure 4).¹

Hypoxemia secondary to acute pancreatitis may occur without initial radiographic findings and has been observed in up to half of patients.¹ Hypoxemia in ARDS occurs due to ventilation-perfusion defects causing gas exchange impairments which may be worsened further by high distending volumes and pressures on mechanical ventilation, dyssynchronous breathing, and/or lung derecruitment.⁶ Patients who require intubation for pancreatitis-associated ALI or ARDS eventually exhibit imaging findings consistent with their disease.¹ The patient in this case exhibited severe hypoxemia for several days despite persistently negative radiographic studies. His history of obstructive sleep apnea and a body mass index of 52 may have contributed to respiratory failure; however, assessment of other contributors to the acute and profound hypoxemia yielded largely unremarkable results. The patient did not have a history or evidence of heart failure and his hypoxemia did not improve with diuresis. He tested positive for COVID-19 on admission and was briefly treated with remdesivir and dexamethasone, but it was determined that the test was likely a false positive due to negative subsequent tests and elevated cycle thresholds (> 40). A concomitant COVID-19 infection likely did not contribute to his symptoms.    

Ventilation-perfusion mismatch is a well-recognized complication of pancreatitis, which results in right-to-left shunting.⁵ While we considered whether an intracardiac shunt may have contributed to the patient’s hypoxemia, a transthoracic echocardiogram with bubble contrast was negative.    

The patient had a peak serum triglyceride of > 6000 mg/dl, which meets the criteria for very severe hypertriglyceridemia.⁷ As observed in prior reports, the extent of the hypertriglyceridemia in this patient resulted in pronounced lipemic blood, which was appreciable by the eye and necessitated several rounds of centrifugation to analyze the laboratory studies.⁸ In this case, plasmapheresis was used to rapidly treat the hypertriglyceridemia, thereby reducing inflammation and further damage to the pancreas.⁹    

It is possible the patient’s hypertriglyceridemia may have been associated with his hypoxemia. His hypoxemia was most pronounced approximately 24 hours postadmission, which coincided with the peak of the hypertriglyceridemia. It remains unclear whether the severity of triglyceride elevation could accurately predict the severity of respiratory insufficiency. Hypoxemia is thought to modulate triglyceride metabolism through stimulation of intracellular lipolysis, upregulation of very low-density lipoproteins production in the liver, and inhibition of triglyceride-rich lipoprotein metabolism.¹⁰ Evidence from rodent studies supports the idea that acute hypoxemia increases triglycerides, and the degree of hypoxemia correlates with the elevated triglyceride levels.¹¹ However, this has not been consistently observed in humans and may vary by prandial state.^12,13 Thus, dysfunction of lipid metabolism may be a relevant clinical indicator of hypoxemia; further work is needed to elucidate this association.

Patient Perspective

The patient continues to undergo extensive rehabilitation following his prolonged illness and hospitalization. He expressed gratitude for the care received. However, he has limited and distorted recollection of the events during his hospitalization and stated that it felt “like an extraterrestrial state.”

Conclusions

This report describes a case of marked hypoxemia in the setting of acute pancreatitis. Pulmonary insufficiency in acute pancreatitis is commonly associated with imaging findings such as atelectasis, pleural effusions, and pulmonary infiltrates; however, up to half of cases initially lack any radiographic findings. Plasmapheresis is an effective treatment for hypertriglyceridemia-induced pancreatitis to both directly reduce circulating triglycerides and inflammation. Plasmapheresis also represents a promising therapy for the prevention of further episodes of pancreatitis in patients with recurrent pancreatitis. We propose a feedback mechanism through which pancreatitis induces severe hypoxemia, which may modulate lipid metabolism and severe hypertriglyceridemia correlates with respiratory failure.

Cancer patients experience depression at rates > 5 times that of the general population.^1-11 Despite an increase in palliative care use, depression rates continued to rise.^2-4 Between 5% to 16% of outpatients, 4% to 14% of inpatients, and up to 49% of patients receiving palliative care experience depression.⁵ This issue also impacts families and caregivers.¹ A 2021 meta-analysis found that 23% of active military personnel and 20% of veterans experience depression.¹¹

Antidepressants approved by the US Food and Drug Administration (FDA) target the serotonin, norepinephrine, or dopamine systems and include boxed warnings about an increased risk of suicidal thoughts in adults aged 18 to 24 years.^12,13 These medications are categorized into several classes: monoamine oxidase inhibitors (MAOIs), tricyclic antidepressants (TCAs), tetracyclic antidepressants (TeCAs), norepinephrine-dopamine reuptake inhibitors (NDRIs), selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNRIs), serotonin receptor modulators (SRMs), serotonin-melatonin receptor antagonists (SMRAs), and N—methyl-D-aspartate receptor antagonists (NMDARAs).^14,15 The first FDA-approved antidepressants, iproniazid (an MAOI) and imipramine (a TCA) laid the foundation for the development of newer classes like SSRIs and SNRIs.^15-17

Older antidepressants such as MAOIs and TCAs are used less due to their adverse effects (AEs) and drug interactions. MAOIs, such as iproniazid, selegiline, moclobemide, tranylcypromine, isocarboxazid, and phenelzine, have numerous AEs and drug interactions, making them unsuitable for first- or second-line treatment of depression.^14,18-21 TCAs such as doxepin, amitriptyline, nortriptyline, imipramine, desipramine, clomipramine, trimipramine, protriptyline, maprotiline, and amoxapine have a narrow therapeutic index requiring careful monitoring for signs of toxicity such as QRS widening, tremors, or confusion. Despite the issues, TCAs are generally classified as second-line agents for major depressive disorder (MDD). TCAs have off-label uses for migraine prophylaxis, treatment of obsessive-compulsive disorder (OCD), insomnia, and chronic pain management first-line.^14,22-29

Newer antidepressants, including TeCAs and NDRIs, are typically more effective, but also come with safety concerns. TeCAs like mirtazapine interact with several medications, including MAOIs, serotonin-increasing drugs, alcohol, cannabidiol, and marijuana. Mirtazapine is FDA-approved for the treatment of moderate to severe depression in adults. It is also used off-label to treat insomnia, panic disorder, posttraumatic stress disorder (PTSD), generalized anxiety disorder (GAD), social anxiety disorder (SAD), headaches, and migraines. Compared to other antidepressants, mirtazapine is effective for all stages of depression and addresses a broad range of related symptoms.^14,30-34 NDRIs, such as bupropion, also interact with various medications, including MAOIs, other antidepressants, stimulants, and alcohol. Bupropion is FDA-approved for smoking cessation and to treat depression and SAD. It is also used off-label for depression- related bipolar disorder or sexual dysfunction, attention-deficit/hyperactivity disorder (ADHD), and obesity.^14,35-42

SSRIs, SNRIs, and SRMs should be used with caution. SSRIs such as sertraline, citalopram, escitalopram, fluoxetine, paroxetine, and fluvoxamine are first-line treatments for depression and various psychiatric disorders due to their safety and efficacy. Common AEs of SSRIs include sexual dysfunction, sleep disturbances, weight changes, and gastrointestinal (GI) issues. SSRIs can prolong the QT interval, posing a risk of life-threatening arrhythmia, and may interact with other medications, necessitating treatment adjustments. The FDA approved SSRIs for MDD, GAD, bulimia nervosa, bipolar depression, OCD, panic disorder, premenstrual dysphoric disorder, treatment-resistant depression, PTSD, and SAD. Off-label uses include binge eating disorder, body dysmorphic disorder, fibromyalgia, premature ejaculation, paraphilias, autism, Raynaud phenomenon, and vasomotor symptoms associated with menopause. Among SSRIs, sertraline and escitalopram are noted for their effectiveness and tolerability.^14,43-53

SNRIs, including duloxetine, venlafaxine, desvenlafaxine, milnacipran, and levomilnacipran, may increase bleeding risk, especially when taken with blood thinners. They can also elevate blood pressure, which may worsen if combined with stimulants. SNRIs may interact with other medications that affect serotonin levels, increasing the risk of serotonin syndrome when taken with triptans, pain medications, or other antidepressants.¹⁴ Desvenlafaxine has been approved by the FDA (but not by the European Medicines Agency).^54-56 Duloxetine is FDA-approved for the treatment of depression, neuropathic pain, anxiety disorders, fibromyalgia, and musculoskeletal disorders. It is used off-label to treat chemotherapy-induced peripheral neuropathy and stress urinary incontinence.^57-61 Venlafaxine is FDA-approved for depression, SAD, and panic disorder, and is prescribed off-label to treat ADHD, neuropathy, fibromyalgia, cataplexy, and PTSD, either alone or in combination with other medications.^62,63 Milnacipran is not approved for MDD; levomilnacipran received approval in 2013.⁶⁴

SRMs such as trazodone, nefazodone, vilazodone, and vortioxetine also function as serotonin reuptake inhibitors.^14,15 Trazodone is FDA-approved for MDD. It has been used off-label to treat anxiety, Alzheimer disease, substance misuse, bulimia nervosa, insomnia, fibromyalgia, and PTSD when first-line SSRIs are ineffective. A notable AE of trazodone is orthostatic hypotension, which can lead to dizziness and increase the risk of falls, especially in geriatric patients.^65-70 Nefazodone was discontinued in Europe in 2003 due to rare cases of liver toxicity but remains available in the US.^71-74 Vilazodone and vortioxetine are FDA-approved.

The latest classes of antidepressants include SMRAs and NMDARAs.¹⁴ Agomelatine, an SMRA, was approved in Europe in 2009 but rejected by the FDA in 2011 due to liver toxicity.⁷⁵ NMDARAs like esketamine and a combination of dextromethorphan and bupropion received FDA approval in 2019 and 2022, respectively.^76,77

This retrospective study analyzes noncancer drugs used during systemic chemotherapy based on a dataset of 14 antineoplastic agents. It sought to identify the most dispensed noncancer drug groups, discuss findings, compare patients with and without antidepressant prescriptions, and examine trends in antidepressant use from 2002 to 2023. This analysis expands on prior research.^78-81

Methods

The Walter Reed National Military Medical Center Institutional Review Board approved the study protocol and ensured compliance with the Health Insurance Portability and Accountability Act as an exempt protocol. The Joint Pathology Center (JPC) of the US Department of Defense (DoD) Cancer Registry Program and Military Health System (MHS) data experts from the Comprehensive Ambulatory/Professional Encounter Record (CAPER) and Pharmacy Data Transaction Service (PDTS) provided data for the analysis.

Data Sources

The JPC DoD Cancer Registry Program contains data from 1998 to 2024. CAPER and PDTS are part of the MHS Data Repository/Management Analysis and Reporting Tool database. Each observation in CAPER represents an ambulatory encounter at a military treatment facility (MTF). CAPER records are available from 2003 to 2024. PDTS records are available from 2002 to 2004. Each observation in PDTS represents a prescription filled for an MHS beneficiary, excluding those filled at international civilian pharmacies and inpatient pharmacy prescriptions.

This cross-sectional analysis requested data extraction for specific cancer drugs from the DoD Cancer Registry, focusing on treatment details, diagnosis dates, patient demographics, and physicians’ comments on AEs. After identifying patients, CAPER was used to identify additional health conditions. PDTS was used to compile a list of prescription medications filled during systemic cancer treatment or < 2 years postdiagnosis.

The 2016 Surveillance, Epidemiology, and End Results Program Coding and Staging Manual and International Classification of Diseases for Oncology, 3rd edition, 1st revision, were used to decode disease and cancer types.^82,83 Data sorting and analysis were performed using Microsoft Excel. The percentage for the total was calculated by using the number of patients or data available within the subgroup divided by the total number of patients or data variables. To compare the mean number of dispensed antidepressants to those without antidepressants, a 2-tailed, 2-sample z test was used to calculate the P value and determine statistical significance (P < .05) using socscistatistics.com.

Data were extracted 3 times between 2021 and 2023. The initial 2021 protocol focused on erlotinib and gefitinib. A modified protocol in 2022 added paclitaxel, cisplatin, docetaxel, pemetrexed, and crizotinib; further modification in 2023 included 8 new antineoplastic agents and 2 anticoagulants. Sotorasib has not been prescribed in the MHS, and JPC lacks records for noncancer drugs. The 2023 dataset comprised 2210 patients with cancer treated with 14 antineoplastic agents; 2104 had documented diagnoses and 2113 had recorded prescriptions. Data for erlotinib, gefitinib, and paclitaxel have been published previously.^78,79

Results

Of 2113 patients with recorded prescriptions, 1297 patients (61.4%) received 109 cancer drugs, including 96 antineoplastics, 7 disease-modifying antirheumatic agents, 4 biologic response modifiers, and 2 calcitonin gene-related peptides. Fourteen antineoplastic agents had complete data from JPC, while others were noted for combination therapies or treatment switches from the PDTS (Table 1). Seventy-six cancer drugs were prescribed with antidepressants in 489 patients (eAppendix).

The JPC provided 2242 entries for 2210 patients, ranging in age from 2 months to 88 years (mean, 56 years), documenting treatment from September 1988 to January 2023. Thirty-two patients had duplicate entries due to multiple cancer locations or occurrences. Of the 2242 patients, 1541 (68.7%) were aged > 50 years, 975 patients (43.5%) had cancers that were stage III or IV, and 1267 (56.5%) had cancers that were stage 0, I, II, or not applicable/unknown. There were 51 different types of cancer: breast, lung, testicular, endometrial, and ovarian were most common (n ≥ 100 patients). Forty-two cancer types were documented among 750 patients prescribed antidepressants (Table 2).

The CAPER database recorded 8882 unique diagnoses for 2104 patients, while PDTS noted 1089 unique prescriptions within 273 therapeutic codes for 2113 patients. Nine therapeutic codes (opiate agonists, adrenals, cathartics-laxatives, nonsteroidal anti-inflammatory agents, antihistamines for GI conditions, 5-HT3 receptor antagonists, analgesics and antipyretic miscellanea, antineoplastic agents, and proton-pump inhibitors) and 8 drugs (dexamethasone, prochlorperazine, ondansetron, docusate, acetaminophen, ibuprofen, oxycodone, and polyethylene glycol 3350) were associated with > 1000 patients (≥ 50%). Patients had between 1 and 275 unique health conditions and filled 1 to 108 prescriptions. The mean (SD) number of diagnoses and prescriptions was 50 (28) and 29 (12), respectively. Of the 273 therapeutic codes, 30 groups were analyzed, with others categorized into miscellaneous groups such as lotions, vaccines, and devices. Significant differences in mean number of prescriptions were found for patients taking antidepressants compared to those not (P < .05), except for anticonvulsants and antipsychotics (P = .12 and .09, respectively) (Table 3).

Antidepressants

Of the 2113 patients with recorded prescriptions, 750 (35.5%) were dispensed 17 different antidepressants. Among these 17 antidepressants, 183 (8.7%) patients received duloxetine, 158 (7.5%) received venlafaxine, 118 (5.6%) received trazodone, and 107 (5.1%) received sertraline (Figure 1, Table 4). Of the 750 patients, 509 (67.9%) received 1 antidepressant, 168 (22.4%) received 2, 60 (8.0%) received 3, and 13 (1.7%) received > 3. Combinations varied, but only duloxetine and trazodone were prescribed to > 10 patients.

Antidepressants were prescribed annually at an overall mean (SD) rate of 23% (5%) from 2003 to 2022 (Figure 2). Patients on antidepressants during systemic therapy had a greater number of diagnosed medical conditions and received more prescription medications compared to those not taking antidepressants (P < .001) (Figure 3). The 745 patients taking antidepressants in CAPER data had between 1 and 275 diagnosed medical issues, with a mean (SD) of 55 (31) vs a range of 1 to 209 and a mean (SD) of 46 (26) for the 1359 patients not taking antidepressants. The 750 patients on antidepressants in PDTS data had between 8 and 108 prescriptions dispensed, with a mean (SD) of 32 (12), vs a range of 1 to 65 prescriptions and a mean (SD) of 29 (12) for 1363 patients not taking antidepressants.

Discussion

The JPC DoD Cancer Registry includes information on cancer types, stages, treatment regimens, and physicians’ notes, while noncancer drugs are sourced from the PDTS database. The pharmacy uses a different documentation system, leading to varied classifications.

Database reliance has its drawbacks. For example, megestrol is coded as a cancer drug, although it’s primarily used for endometrial or gynecologic cancers. Many drugs have multiple therapeutic codes assigned to them, including 10 antineoplastic agents: diclofenac, Bacillus Calmette-Guérin (BCG), megestrol acetate, tamoxifen, anastrozole, letrozole, leuprolide, goserelin, degarelix, and fluorouracil. Diclofenac, BCG, and mitomycin have been repurposed for cancer treatment.^84-87 From 2003 to 2023, diclofenac was prescribed to 350 patients for mild-to-moderate pain, with only 2 patients receiving it for cancer in 2018. FDA-approved for bladder cancer in 1990, BCG was prescribed for cancer treatment for 1 patient in 2021 after being used for vaccines between 2003 and 2018. Tamoxifen, used for hormone receptor-positive breast cancer from 2004 to 2017 with 53 patients, switched to estrogen agonist-antagonists from 2017 to 2023 with 123 patients. Only a few of the 168 patients were prescribed tamoxifen using both codes.^88-91 Anastrozole and letrozole were coded as antiestrogens for 7 and 18 patients, respectively, while leuprolide and goserelin were coded as gonadotropins for 59 and 18 patients. Degarelix was coded as antigonadotropins, fluorouracil as skin and mucous membrane agents miscellaneous, and megestrol acetate as progestins for 7, 6, and 3 patients, respectively. Duloxetine was given to 186 patients, primarily for depression from 2005 to 2023, with 7 patients treated for fibromyalgia from 2022 to 2023.

Antidepressants Observed

Tables 1 and 5 provide insight into the FDA approval of 14 antineoplastics and antidepressants and their CYP metabolic pathways.^92-122 In Table 4, the most prescribed antidepressant classes are SNRIs, SRMs, SSRIs, TeCAs, NDRIs, and TCAs. This trend highlights a preference for newer medications with weak CYP inhibition. A total of 349 patients were prescribed SSRIs, 343 SNRIs, 119 SRMs, 109 TCAs, 83 TeCAs, and 79 NDRIs. MAOIs, SMRAs, and NMDARAs were not observed in this dataset. While there are instances of dextromethorphan-bupropion and sertraline-escitalopram being dispensed together, it remains unclear whether these were NMDARA combinations.

Among the 14 specific antineoplastic agents, 10 are metabolized by CYP isoenzymes, primarily CYP3A4. Duloxetine neither inhibits nor is metabolized by CYP3A4, a reason it is often recommended, following venlafaxine.

Both duloxetine and venlafaxine are used off-label for chemotherapy-induced peripheral neuropathy related to paclitaxel and docetaxel. According to the CYP metabolized pathway, duloxetine tends to have more favorable DDIs than venlafaxine. In PDTS data, 371 patients were treated with paclitaxel and 180 with docetaxel, with respective antidepressant prescriptions of 156 and 70. Of the 156 patients dispensed paclitaxel, 62 (40%) were dispensed with duloxetine compared to 43 (28%) with venlafaxine. Of the 70 patients dispensed docetaxel, 23 (33%) received duloxetine vs 24 (34%) with venlafaxine.

Of 85 patients prescribed duloxetine, 75 received it with either paclitaxel or docetaxel (5 received both). Five patients had documented AEs (1 neuropathy related). Of 67 patients prescribed venlafaxine, 66 received it with either paclitaxel or docetaxel. Two patients had documented AEs (1 was neuropathy related, the same patient who received duloxetine). Of the 687 patients treated with paclitaxel and 337 with docetaxel in all databases, 4 experienced neuropathic AEs from both medications.⁷⁹

Antidepressants can increase the risk of bleeding, especially when combined with blood thinners, and may elevate blood pressure, particularly alongside stimulants. Of the 554 patients prescribed 9 different anticoagulants, enoxaparin, apixaban, and rivaroxaban were the most common (each > 100 patients). Among these, 201 patients (36%) received both anticoagulants and antidepressants: duloxetine for 64 patients, venlafaxine for 30, trazodone for 35, and sertraline for 26. There were no data available to assess bleeding rates related to the evaluation of DDIs between these medication classes.

Antidepressants can be prescribed for erectile dysfunction. Of the 148 patients prescribed an antidepressant for erectile dysfunction, duloxetine, trazodone, and mirtazapine were the most common. Antidepressant preferences varied by cancer type. Duloxetine was the only antidepressant used for all types of cancer. Venlafaxine, duloxetine, trazodone, sertraline, and escitalopram were the most prescribed antidepressants for breast cancer, while duloxetine, mirtazapine, citalopram, sertraline, and trazodone were the most prescribed for lung cancer. Sertraline, duloxetine, trazodone, amitriptyline, and escitalopram were most common for testicular cancer. Duloxetine, venlafaxine, trazodone, amitriptyline, and sertraline were the most prescribed for endometrial cancer, while duloxetine, venlafaxine, amitriptyline, citalopram, and sertraline were most prescribed for ovarian cancer.

The broadness of International Statistical Classification of Diseases, Tenth Revision codes made it challenging to identify nondepression diagnoses in the analyzed population. However, if all antidepressants were prescribed to treat depression, service members with cancer exhibited a higher depression rate (35%) than the general population (25%). Of 2104 patients, 191 (9.1%) had mood disorders, and 706 (33.6%) had mental disorders: 346 (49.0%) had 1 diagnosis, and 360 (51.0%) had multiple diagnoses. The percentage of diagnoses varied yearly, with notable drops in 2003, 2007, 2011, 2014, and 2018, and peaks in 2006, 2008, 2013, 2017, and 2022. This fluctuation was influenced by events like the establishment of PDTS in 2002, the 2008 economic recession, a hospital relocation in 2011, the 2014 Ebola outbreak, and the COVID-19 pandemic. Although the number of patients receiving antidepressants increased from 2019 to 2022, the overall percentage of patients receiving them did not significantly change from 2003 to 2022, aligning with previous research.^5,125

Many medications have potential uses beyond what is detailed in the prescribing information. Antidepressants can relieve pain, while pain medications may help with depression. Opioids were once thought to effectively treat depression, but this perspective has changed with a greater understanding of their risks, including misuse.^126-131 Pain is a severe and often unbearable AE of cancer. Of 2113 patients, 92% received opioids; 34% received both opioids and antidepressants; 2% received only antidepressants; and 7% received neither. This study didn’t clarify whether those on opioids alone recognized their depression or if those on both were aware of their dependence. While SSRIs are generally not addictive, they can lead to physical dependence, and any medication can be abused if not managed properly.^132-134

Conclusions

This retrospective study analyzes data from antineoplastic agents used in systemic cancer treatment between 1988 and 2023, with a particular focus on the use of antidepressants. Data on antidepressant prescriptions are incomplete and specific to these agents, which means the findings cannot be generalized to all antidepressants. Hence, the results indicate that patients taking antidepressants had more diagnosed health issues and received more medications compared to patients who were not on these drugs.

This study underscores the need for further research into the effects of antidepressants on cancer treatment, utilizing all data from the DoD Cancer Registry. Future research should explore DDIs between antidepressants and other cancer and noncancer medications, as this study did not assess AE documentation, unlike in studies involving erlotinib, gefitinib, and paclitaxel.^78,79 Further investigation is needed to evaluate the impact of discontinuing antidepressant use during cancer treatment. This comprehensive overview provides insights for clinicians to help them make informed decisions regarding the prescription of antidepressants in the context of cancer treatment.

Cancer patients experience depression at rates > 5 times that of the general population.^1-11 Despite an increase in palliative care use, depression rates continued to rise.^2-4 Between 5% to 16% of outpatients, 4% to 14% of inpatients, and up to 49% of patients receiving palliative care experience depression.⁵ This issue also impacts families and caregivers.¹ A 2021 meta-analysis found that 23% of active military personnel and 20% of veterans experience depression.¹¹

Antidepressants approved by the US Food and Drug Administration (FDA) target the serotonin, norepinephrine, or dopamine systems and include boxed warnings about an increased risk of suicidal thoughts in adults aged 18 to 24 years.^12,13 These medications are categorized into several classes: monoamine oxidase inhibitors (MAOIs), tricyclic antidepressants (TCAs), tetracyclic antidepressants (TeCAs), norepinephrine-dopamine reuptake inhibitors (NDRIs), selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNRIs), serotonin receptor modulators (SRMs), serotonin-melatonin receptor antagonists (SMRAs), and N—methyl-D-aspartate receptor antagonists (NMDARAs).^14,15 The first FDA-approved antidepressants, iproniazid (an MAOI) and imipramine (a TCA) laid the foundation for the development of newer classes like SSRIs and SNRIs.^15-17

Older antidepressants such as MAOIs and TCAs are used less due to their adverse effects (AEs) and drug interactions. MAOIs, such as iproniazid, selegiline, moclobemide, tranylcypromine, isocarboxazid, and phenelzine, have numerous AEs and drug interactions, making them unsuitable for first- or second-line treatment of depression.^14,18-21 TCAs such as doxepin, amitriptyline, nortriptyline, imipramine, desipramine, clomipramine, trimipramine, protriptyline, maprotiline, and amoxapine have a narrow therapeutic index requiring careful monitoring for signs of toxicity such as QRS widening, tremors, or confusion. Despite the issues, TCAs are generally classified as second-line agents for major depressive disorder (MDD). TCAs have off-label uses for migraine prophylaxis, treatment of obsessive-compulsive disorder (OCD), insomnia, and chronic pain management first-line.^14,22-29

Newer antidepressants, including TeCAs and NDRIs, are typically more effective, but also come with safety concerns. TeCAs like mirtazapine interact with several medications, including MAOIs, serotonin-increasing drugs, alcohol, cannabidiol, and marijuana. Mirtazapine is FDA-approved for the treatment of moderate to severe depression in adults. It is also used off-label to treat insomnia, panic disorder, posttraumatic stress disorder (PTSD), generalized anxiety disorder (GAD), social anxiety disorder (SAD), headaches, and migraines. Compared to other antidepressants, mirtazapine is effective for all stages of depression and addresses a broad range of related symptoms.^14,30-34 NDRIs, such as bupropion, also interact with various medications, including MAOIs, other antidepressants, stimulants, and alcohol. Bupropion is FDA-approved for smoking cessation and to treat depression and SAD. It is also used off-label for depression- related bipolar disorder or sexual dysfunction, attention-deficit/hyperactivity disorder (ADHD), and obesity.^14,35-42

SSRIs, SNRIs, and SRMs should be used with caution. SSRIs such as sertraline, citalopram, escitalopram, fluoxetine, paroxetine, and fluvoxamine are first-line treatments for depression and various psychiatric disorders due to their safety and efficacy. Common AEs of SSRIs include sexual dysfunction, sleep disturbances, weight changes, and gastrointestinal (GI) issues. SSRIs can prolong the QT interval, posing a risk of life-threatening arrhythmia, and may interact with other medications, necessitating treatment adjustments. The FDA approved SSRIs for MDD, GAD, bulimia nervosa, bipolar depression, OCD, panic disorder, premenstrual dysphoric disorder, treatment-resistant depression, PTSD, and SAD. Off-label uses include binge eating disorder, body dysmorphic disorder, fibromyalgia, premature ejaculation, paraphilias, autism, Raynaud phenomenon, and vasomotor symptoms associated with menopause. Among SSRIs, sertraline and escitalopram are noted for their effectiveness and tolerability.^14,43-53

SNRIs, including duloxetine, venlafaxine, desvenlafaxine, milnacipran, and levomilnacipran, may increase bleeding risk, especially when taken with blood thinners. They can also elevate blood pressure, which may worsen if combined with stimulants. SNRIs may interact with other medications that affect serotonin levels, increasing the risk of serotonin syndrome when taken with triptans, pain medications, or other antidepressants.¹⁴ Desvenlafaxine has been approved by the FDA (but not by the European Medicines Agency).^54-56 Duloxetine is FDA-approved for the treatment of depression, neuropathic pain, anxiety disorders, fibromyalgia, and musculoskeletal disorders. It is used off-label to treat chemotherapy-induced peripheral neuropathy and stress urinary incontinence.^57-61 Venlafaxine is FDA-approved for depression, SAD, and panic disorder, and is prescribed off-label to treat ADHD, neuropathy, fibromyalgia, cataplexy, and PTSD, either alone or in combination with other medications.^62,63 Milnacipran is not approved for MDD; levomilnacipran received approval in 2013.⁶⁴

SRMs such as trazodone, nefazodone, vilazodone, and vortioxetine also function as serotonin reuptake inhibitors.^14,15 Trazodone is FDA-approved for MDD. It has been used off-label to treat anxiety, Alzheimer disease, substance misuse, bulimia nervosa, insomnia, fibromyalgia, and PTSD when first-line SSRIs are ineffective. A notable AE of trazodone is orthostatic hypotension, which can lead to dizziness and increase the risk of falls, especially in geriatric patients.^65-70 Nefazodone was discontinued in Europe in 2003 due to rare cases of liver toxicity but remains available in the US.^71-74 Vilazodone and vortioxetine are FDA-approved.

The latest classes of antidepressants include SMRAs and NMDARAs.¹⁴ Agomelatine, an SMRA, was approved in Europe in 2009 but rejected by the FDA in 2011 due to liver toxicity.⁷⁵ NMDARAs like esketamine and a combination of dextromethorphan and bupropion received FDA approval in 2019 and 2022, respectively.^76,77

This retrospective study analyzes noncancer drugs used during systemic chemotherapy based on a dataset of 14 antineoplastic agents. It sought to identify the most dispensed noncancer drug groups, discuss findings, compare patients with and without antidepressant prescriptions, and examine trends in antidepressant use from 2002 to 2023. This analysis expands on prior research.^78-81

Methods

The Walter Reed National Military Medical Center Institutional Review Board approved the study protocol and ensured compliance with the Health Insurance Portability and Accountability Act as an exempt protocol. The Joint Pathology Center (JPC) of the US Department of Defense (DoD) Cancer Registry Program and Military Health System (MHS) data experts from the Comprehensive Ambulatory/Professional Encounter Record (CAPER) and Pharmacy Data Transaction Service (PDTS) provided data for the analysis.

Data Sources

The JPC DoD Cancer Registry Program contains data from 1998 to 2024. CAPER and PDTS are part of the MHS Data Repository/Management Analysis and Reporting Tool database. Each observation in CAPER represents an ambulatory encounter at a military treatment facility (MTF). CAPER records are available from 2003 to 2024. PDTS records are available from 2002 to 2004. Each observation in PDTS represents a prescription filled for an MHS beneficiary, excluding those filled at international civilian pharmacies and inpatient pharmacy prescriptions.

This cross-sectional analysis requested data extraction for specific cancer drugs from the DoD Cancer Registry, focusing on treatment details, diagnosis dates, patient demographics, and physicians’ comments on AEs. After identifying patients, CAPER was used to identify additional health conditions. PDTS was used to compile a list of prescription medications filled during systemic cancer treatment or < 2 years postdiagnosis.

The 2016 Surveillance, Epidemiology, and End Results Program Coding and Staging Manual and International Classification of Diseases for Oncology, 3rd edition, 1st revision, were used to decode disease and cancer types.^82,83 Data sorting and analysis were performed using Microsoft Excel. The percentage for the total was calculated by using the number of patients or data available within the subgroup divided by the total number of patients or data variables. To compare the mean number of dispensed antidepressants to those without antidepressants, a 2-tailed, 2-sample z test was used to calculate the P value and determine statistical significance (P < .05) using socscistatistics.com.

Data were extracted 3 times between 2021 and 2023. The initial 2021 protocol focused on erlotinib and gefitinib. A modified protocol in 2022 added paclitaxel, cisplatin, docetaxel, pemetrexed, and crizotinib; further modification in 2023 included 8 new antineoplastic agents and 2 anticoagulants. Sotorasib has not been prescribed in the MHS, and JPC lacks records for noncancer drugs. The 2023 dataset comprised 2210 patients with cancer treated with 14 antineoplastic agents; 2104 had documented diagnoses and 2113 had recorded prescriptions. Data for erlotinib, gefitinib, and paclitaxel have been published previously.^78,79

Results

Of 2113 patients with recorded prescriptions, 1297 patients (61.4%) received 109 cancer drugs, including 96 antineoplastics, 7 disease-modifying antirheumatic agents, 4 biologic response modifiers, and 2 calcitonin gene-related peptides. Fourteen antineoplastic agents had complete data from JPC, while others were noted for combination therapies or treatment switches from the PDTS (Table 1). Seventy-six cancer drugs were prescribed with antidepressants in 489 patients (eAppendix).

The JPC provided 2242 entries for 2210 patients, ranging in age from 2 months to 88 years (mean, 56 years), documenting treatment from September 1988 to January 2023. Thirty-two patients had duplicate entries due to multiple cancer locations or occurrences. Of the 2242 patients, 1541 (68.7%) were aged > 50 years, 975 patients (43.5%) had cancers that were stage III or IV, and 1267 (56.5%) had cancers that were stage 0, I, II, or not applicable/unknown. There were 51 different types of cancer: breast, lung, testicular, endometrial, and ovarian were most common (n ≥ 100 patients). Forty-two cancer types were documented among 750 patients prescribed antidepressants (Table 2).

The CAPER database recorded 8882 unique diagnoses for 2104 patients, while PDTS noted 1089 unique prescriptions within 273 therapeutic codes for 2113 patients. Nine therapeutic codes (opiate agonists, adrenals, cathartics-laxatives, nonsteroidal anti-inflammatory agents, antihistamines for GI conditions, 5-HT3 receptor antagonists, analgesics and antipyretic miscellanea, antineoplastic agents, and proton-pump inhibitors) and 8 drugs (dexamethasone, prochlorperazine, ondansetron, docusate, acetaminophen, ibuprofen, oxycodone, and polyethylene glycol 3350) were associated with > 1000 patients (≥ 50%). Patients had between 1 and 275 unique health conditions and filled 1 to 108 prescriptions. The mean (SD) number of diagnoses and prescriptions was 50 (28) and 29 (12), respectively. Of the 273 therapeutic codes, 30 groups were analyzed, with others categorized into miscellaneous groups such as lotions, vaccines, and devices. Significant differences in mean number of prescriptions were found for patients taking antidepressants compared to those not (P < .05), except for anticonvulsants and antipsychotics (P = .12 and .09, respectively) (Table 3).

Antidepressants

Of the 2113 patients with recorded prescriptions, 750 (35.5%) were dispensed 17 different antidepressants. Among these 17 antidepressants, 183 (8.7%) patients received duloxetine, 158 (7.5%) received venlafaxine, 118 (5.6%) received trazodone, and 107 (5.1%) received sertraline (Figure 1, Table 4). Of the 750 patients, 509 (67.9%) received 1 antidepressant, 168 (22.4%) received 2, 60 (8.0%) received 3, and 13 (1.7%) received > 3. Combinations varied, but only duloxetine and trazodone were prescribed to > 10 patients.

Antidepressants were prescribed annually at an overall mean (SD) rate of 23% (5%) from 2003 to 2022 (Figure 2). Patients on antidepressants during systemic therapy had a greater number of diagnosed medical conditions and received more prescription medications compared to those not taking antidepressants (P < .001) (Figure 3). The 745 patients taking antidepressants in CAPER data had between 1 and 275 diagnosed medical issues, with a mean (SD) of 55 (31) vs a range of 1 to 209 and a mean (SD) of 46 (26) for the 1359 patients not taking antidepressants. The 750 patients on antidepressants in PDTS data had between 8 and 108 prescriptions dispensed, with a mean (SD) of 32 (12), vs a range of 1 to 65 prescriptions and a mean (SD) of 29 (12) for 1363 patients not taking antidepressants.

Discussion

The JPC DoD Cancer Registry includes information on cancer types, stages, treatment regimens, and physicians’ notes, while noncancer drugs are sourced from the PDTS database. The pharmacy uses a different documentation system, leading to varied classifications.

Database reliance has its drawbacks. For example, megestrol is coded as a cancer drug, although it’s primarily used for endometrial or gynecologic cancers. Many drugs have multiple therapeutic codes assigned to them, including 10 antineoplastic agents: diclofenac, Bacillus Calmette-Guérin (BCG), megestrol acetate, tamoxifen, anastrozole, letrozole, leuprolide, goserelin, degarelix, and fluorouracil. Diclofenac, BCG, and mitomycin have been repurposed for cancer treatment.^84-87 From 2003 to 2023, diclofenac was prescribed to 350 patients for mild-to-moderate pain, with only 2 patients receiving it for cancer in 2018. FDA-approved for bladder cancer in 1990, BCG was prescribed for cancer treatment for 1 patient in 2021 after being used for vaccines between 2003 and 2018. Tamoxifen, used for hormone receptor-positive breast cancer from 2004 to 2017 with 53 patients, switched to estrogen agonist-antagonists from 2017 to 2023 with 123 patients. Only a few of the 168 patients were prescribed tamoxifen using both codes.^88-91 Anastrozole and letrozole were coded as antiestrogens for 7 and 18 patients, respectively, while leuprolide and goserelin were coded as gonadotropins for 59 and 18 patients. Degarelix was coded as antigonadotropins, fluorouracil as skin and mucous membrane agents miscellaneous, and megestrol acetate as progestins for 7, 6, and 3 patients, respectively. Duloxetine was given to 186 patients, primarily for depression from 2005 to 2023, with 7 patients treated for fibromyalgia from 2022 to 2023.

Antidepressants Observed

Tables 1 and 5 provide insight into the FDA approval of 14 antineoplastics and antidepressants and their CYP metabolic pathways.^92-122 In Table 4, the most prescribed antidepressant classes are SNRIs, SRMs, SSRIs, TeCAs, NDRIs, and TCAs. This trend highlights a preference for newer medications with weak CYP inhibition. A total of 349 patients were prescribed SSRIs, 343 SNRIs, 119 SRMs, 109 TCAs, 83 TeCAs, and 79 NDRIs. MAOIs, SMRAs, and NMDARAs were not observed in this dataset. While there are instances of dextromethorphan-bupropion and sertraline-escitalopram being dispensed together, it remains unclear whether these were NMDARA combinations.

Among the 14 specific antineoplastic agents, 10 are metabolized by CYP isoenzymes, primarily CYP3A4. Duloxetine neither inhibits nor is metabolized by CYP3A4, a reason it is often recommended, following venlafaxine.

Both duloxetine and venlafaxine are used off-label for chemotherapy-induced peripheral neuropathy related to paclitaxel and docetaxel. According to the CYP metabolized pathway, duloxetine tends to have more favorable DDIs than venlafaxine. In PDTS data, 371 patients were treated with paclitaxel and 180 with docetaxel, with respective antidepressant prescriptions of 156 and 70. Of the 156 patients dispensed paclitaxel, 62 (40%) were dispensed with duloxetine compared to 43 (28%) with venlafaxine. Of the 70 patients dispensed docetaxel, 23 (33%) received duloxetine vs 24 (34%) with venlafaxine.

Of 85 patients prescribed duloxetine, 75 received it with either paclitaxel or docetaxel (5 received both). Five patients had documented AEs (1 neuropathy related). Of 67 patients prescribed venlafaxine, 66 received it with either paclitaxel or docetaxel. Two patients had documented AEs (1 was neuropathy related, the same patient who received duloxetine). Of the 687 patients treated with paclitaxel and 337 with docetaxel in all databases, 4 experienced neuropathic AEs from both medications.⁷⁹

Antidepressants can increase the risk of bleeding, especially when combined with blood thinners, and may elevate blood pressure, particularly alongside stimulants. Of the 554 patients prescribed 9 different anticoagulants, enoxaparin, apixaban, and rivaroxaban were the most common (each > 100 patients). Among these, 201 patients (36%) received both anticoagulants and antidepressants: duloxetine for 64 patients, venlafaxine for 30, trazodone for 35, and sertraline for 26. There were no data available to assess bleeding rates related to the evaluation of DDIs between these medication classes.

Antidepressants can be prescribed for erectile dysfunction. Of the 148 patients prescribed an antidepressant for erectile dysfunction, duloxetine, trazodone, and mirtazapine were the most common. Antidepressant preferences varied by cancer type. Duloxetine was the only antidepressant used for all types of cancer. Venlafaxine, duloxetine, trazodone, sertraline, and escitalopram were the most prescribed antidepressants for breast cancer, while duloxetine, mirtazapine, citalopram, sertraline, and trazodone were the most prescribed for lung cancer. Sertraline, duloxetine, trazodone, amitriptyline, and escitalopram were most common for testicular cancer. Duloxetine, venlafaxine, trazodone, amitriptyline, and sertraline were the most prescribed for endometrial cancer, while duloxetine, venlafaxine, amitriptyline, citalopram, and sertraline were most prescribed for ovarian cancer.

The broadness of International Statistical Classification of Diseases, Tenth Revision codes made it challenging to identify nondepression diagnoses in the analyzed population. However, if all antidepressants were prescribed to treat depression, service members with cancer exhibited a higher depression rate (35%) than the general population (25%). Of 2104 patients, 191 (9.1%) had mood disorders, and 706 (33.6%) had mental disorders: 346 (49.0%) had 1 diagnosis, and 360 (51.0%) had multiple diagnoses. The percentage of diagnoses varied yearly, with notable drops in 2003, 2007, 2011, 2014, and 2018, and peaks in 2006, 2008, 2013, 2017, and 2022. This fluctuation was influenced by events like the establishment of PDTS in 2002, the 2008 economic recession, a hospital relocation in 2011, the 2014 Ebola outbreak, and the COVID-19 pandemic. Although the number of patients receiving antidepressants increased from 2019 to 2022, the overall percentage of patients receiving them did not significantly change from 2003 to 2022, aligning with previous research.^5,125

Many medications have potential uses beyond what is detailed in the prescribing information. Antidepressants can relieve pain, while pain medications may help with depression. Opioids were once thought to effectively treat depression, but this perspective has changed with a greater understanding of their risks, including misuse.^126-131 Pain is a severe and often unbearable AE of cancer. Of 2113 patients, 92% received opioids; 34% received both opioids and antidepressants; 2% received only antidepressants; and 7% received neither. This study didn’t clarify whether those on opioids alone recognized their depression or if those on both were aware of their dependence. While SSRIs are generally not addictive, they can lead to physical dependence, and any medication can be abused if not managed properly.^132-134

Conclusions

This retrospective study analyzes data from antineoplastic agents used in systemic cancer treatment between 1988 and 2023, with a particular focus on the use of antidepressants. Data on antidepressant prescriptions are incomplete and specific to these agents, which means the findings cannot be generalized to all antidepressants. Hence, the results indicate that patients taking antidepressants had more diagnosed health issues and received more medications compared to patients who were not on these drugs.

This study underscores the need for further research into the effects of antidepressants on cancer treatment, utilizing all data from the DoD Cancer Registry. Future research should explore DDIs between antidepressants and other cancer and noncancer medications, as this study did not assess AE documentation, unlike in studies involving erlotinib, gefitinib, and paclitaxel.^78,79 Further investigation is needed to evaluate the impact of discontinuing antidepressant use during cancer treatment. This comprehensive overview provides insights for clinicians to help them make informed decisions regarding the prescription of antidepressants in the context of cancer treatment.

Cancer patients experience depression at rates > 5 times that of the general population.^1-11 Despite an increase in palliative care use, depression rates continued to rise.^2-4 Between 5% to 16% of outpatients, 4% to 14% of inpatients, and up to 49% of patients receiving palliative care experience depression.⁵ This issue also impacts families and caregivers.¹ A 2021 meta-analysis found that 23% of active military personnel and 20% of veterans experience depression.¹¹

Antidepressants approved by the US Food and Drug Administration (FDA) target the serotonin, norepinephrine, or dopamine systems and include boxed warnings about an increased risk of suicidal thoughts in adults aged 18 to 24 years.^12,13 These medications are categorized into several classes: monoamine oxidase inhibitors (MAOIs), tricyclic antidepressants (TCAs), tetracyclic antidepressants (TeCAs), norepinephrine-dopamine reuptake inhibitors (NDRIs), selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNRIs), serotonin receptor modulators (SRMs), serotonin-melatonin receptor antagonists (SMRAs), and N—methyl-D-aspartate receptor antagonists (NMDARAs).^14,15 The first FDA-approved antidepressants, iproniazid (an MAOI) and imipramine (a TCA) laid the foundation for the development of newer classes like SSRIs and SNRIs.^15-17

Older antidepressants such as MAOIs and TCAs are used less due to their adverse effects (AEs) and drug interactions. MAOIs, such as iproniazid, selegiline, moclobemide, tranylcypromine, isocarboxazid, and phenelzine, have numerous AEs and drug interactions, making them unsuitable for first- or second-line treatment of depression.^14,18-21 TCAs such as doxepin, amitriptyline, nortriptyline, imipramine, desipramine, clomipramine, trimipramine, protriptyline, maprotiline, and amoxapine have a narrow therapeutic index requiring careful monitoring for signs of toxicity such as QRS widening, tremors, or confusion. Despite the issues, TCAs are generally classified as second-line agents for major depressive disorder (MDD). TCAs have off-label uses for migraine prophylaxis, treatment of obsessive-compulsive disorder (OCD), insomnia, and chronic pain management first-line.^14,22-29

Newer antidepressants, including TeCAs and NDRIs, are typically more effective, but also come with safety concerns. TeCAs like mirtazapine interact with several medications, including MAOIs, serotonin-increasing drugs, alcohol, cannabidiol, and marijuana. Mirtazapine is FDA-approved for the treatment of moderate to severe depression in adults. It is also used off-label to treat insomnia, panic disorder, posttraumatic stress disorder (PTSD), generalized anxiety disorder (GAD), social anxiety disorder (SAD), headaches, and migraines. Compared to other antidepressants, mirtazapine is effective for all stages of depression and addresses a broad range of related symptoms.^14,30-34 NDRIs, such as bupropion, also interact with various medications, including MAOIs, other antidepressants, stimulants, and alcohol. Bupropion is FDA-approved for smoking cessation and to treat depression and SAD. It is also used off-label for depression- related bipolar disorder or sexual dysfunction, attention-deficit/hyperactivity disorder (ADHD), and obesity.^14,35-42

SSRIs, SNRIs, and SRMs should be used with caution. SSRIs such as sertraline, citalopram, escitalopram, fluoxetine, paroxetine, and fluvoxamine are first-line treatments for depression and various psychiatric disorders due to their safety and efficacy. Common AEs of SSRIs include sexual dysfunction, sleep disturbances, weight changes, and gastrointestinal (GI) issues. SSRIs can prolong the QT interval, posing a risk of life-threatening arrhythmia, and may interact with other medications, necessitating treatment adjustments. The FDA approved SSRIs for MDD, GAD, bulimia nervosa, bipolar depression, OCD, panic disorder, premenstrual dysphoric disorder, treatment-resistant depression, PTSD, and SAD. Off-label uses include binge eating disorder, body dysmorphic disorder, fibromyalgia, premature ejaculation, paraphilias, autism, Raynaud phenomenon, and vasomotor symptoms associated with menopause. Among SSRIs, sertraline and escitalopram are noted for their effectiveness and tolerability.^14,43-53

SNRIs, including duloxetine, venlafaxine, desvenlafaxine, milnacipran, and levomilnacipran, may increase bleeding risk, especially when taken with blood thinners. They can also elevate blood pressure, which may worsen if combined with stimulants. SNRIs may interact with other medications that affect serotonin levels, increasing the risk of serotonin syndrome when taken with triptans, pain medications, or other antidepressants.¹⁴ Desvenlafaxine has been approved by the FDA (but not by the European Medicines Agency).^54-56 Duloxetine is FDA-approved for the treatment of depression, neuropathic pain, anxiety disorders, fibromyalgia, and musculoskeletal disorders. It is used off-label to treat chemotherapy-induced peripheral neuropathy and stress urinary incontinence.^57-61 Venlafaxine is FDA-approved for depression, SAD, and panic disorder, and is prescribed off-label to treat ADHD, neuropathy, fibromyalgia, cataplexy, and PTSD, either alone or in combination with other medications.^62,63 Milnacipran is not approved for MDD; levomilnacipran received approval in 2013.⁶⁴

SRMs such as trazodone, nefazodone, vilazodone, and vortioxetine also function as serotonin reuptake inhibitors.^14,15 Trazodone is FDA-approved for MDD. It has been used off-label to treat anxiety, Alzheimer disease, substance misuse, bulimia nervosa, insomnia, fibromyalgia, and PTSD when first-line SSRIs are ineffective. A notable AE of trazodone is orthostatic hypotension, which can lead to dizziness and increase the risk of falls, especially in geriatric patients.^65-70 Nefazodone was discontinued in Europe in 2003 due to rare cases of liver toxicity but remains available in the US.^71-74 Vilazodone and vortioxetine are FDA-approved.

The latest classes of antidepressants include SMRAs and NMDARAs.¹⁴ Agomelatine, an SMRA, was approved in Europe in 2009 but rejected by the FDA in 2011 due to liver toxicity.⁷⁵ NMDARAs like esketamine and a combination of dextromethorphan and bupropion received FDA approval in 2019 and 2022, respectively.^76,77

This retrospective study analyzes noncancer drugs used during systemic chemotherapy based on a dataset of 14 antineoplastic agents. It sought to identify the most dispensed noncancer drug groups, discuss findings, compare patients with and without antidepressant prescriptions, and examine trends in antidepressant use from 2002 to 2023. This analysis expands on prior research.^78-81

Methods

The Walter Reed National Military Medical Center Institutional Review Board approved the study protocol and ensured compliance with the Health Insurance Portability and Accountability Act as an exempt protocol. The Joint Pathology Center (JPC) of the US Department of Defense (DoD) Cancer Registry Program and Military Health System (MHS) data experts from the Comprehensive Ambulatory/Professional Encounter Record (CAPER) and Pharmacy Data Transaction Service (PDTS) provided data for the analysis.

Data Sources

The JPC DoD Cancer Registry Program contains data from 1998 to 2024. CAPER and PDTS are part of the MHS Data Repository/Management Analysis and Reporting Tool database. Each observation in CAPER represents an ambulatory encounter at a military treatment facility (MTF). CAPER records are available from 2003 to 2024. PDTS records are available from 2002 to 2004. Each observation in PDTS represents a prescription filled for an MHS beneficiary, excluding those filled at international civilian pharmacies and inpatient pharmacy prescriptions.

This cross-sectional analysis requested data extraction for specific cancer drugs from the DoD Cancer Registry, focusing on treatment details, diagnosis dates, patient demographics, and physicians’ comments on AEs. After identifying patients, CAPER was used to identify additional health conditions. PDTS was used to compile a list of prescription medications filled during systemic cancer treatment or < 2 years postdiagnosis.

The 2016 Surveillance, Epidemiology, and End Results Program Coding and Staging Manual and International Classification of Diseases for Oncology, 3rd edition, 1st revision, were used to decode disease and cancer types.^82,83 Data sorting and analysis were performed using Microsoft Excel. The percentage for the total was calculated by using the number of patients or data available within the subgroup divided by the total number of patients or data variables. To compare the mean number of dispensed antidepressants to those without antidepressants, a 2-tailed, 2-sample z test was used to calculate the P value and determine statistical significance (P < .05) using socscistatistics.com.

Data were extracted 3 times between 2021 and 2023. The initial 2021 protocol focused on erlotinib and gefitinib. A modified protocol in 2022 added paclitaxel, cisplatin, docetaxel, pemetrexed, and crizotinib; further modification in 2023 included 8 new antineoplastic agents and 2 anticoagulants. Sotorasib has not been prescribed in the MHS, and JPC lacks records for noncancer drugs. The 2023 dataset comprised 2210 patients with cancer treated with 14 antineoplastic agents; 2104 had documented diagnoses and 2113 had recorded prescriptions. Data for erlotinib, gefitinib, and paclitaxel have been published previously.^78,79

Results

Of 2113 patients with recorded prescriptions, 1297 patients (61.4%) received 109 cancer drugs, including 96 antineoplastics, 7 disease-modifying antirheumatic agents, 4 biologic response modifiers, and 2 calcitonin gene-related peptides. Fourteen antineoplastic agents had complete data from JPC, while others were noted for combination therapies or treatment switches from the PDTS (Table 1). Seventy-six cancer drugs were prescribed with antidepressants in 489 patients (eAppendix).

The JPC provided 2242 entries for 2210 patients, ranging in age from 2 months to 88 years (mean, 56 years), documenting treatment from September 1988 to January 2023. Thirty-two patients had duplicate entries due to multiple cancer locations or occurrences. Of the 2242 patients, 1541 (68.7%) were aged > 50 years, 975 patients (43.5%) had cancers that were stage III or IV, and 1267 (56.5%) had cancers that were stage 0, I, II, or not applicable/unknown. There were 51 different types of cancer: breast, lung, testicular, endometrial, and ovarian were most common (n ≥ 100 patients). Forty-two cancer types were documented among 750 patients prescribed antidepressants (Table 2).

The CAPER database recorded 8882 unique diagnoses for 2104 patients, while PDTS noted 1089 unique prescriptions within 273 therapeutic codes for 2113 patients. Nine therapeutic codes (opiate agonists, adrenals, cathartics-laxatives, nonsteroidal anti-inflammatory agents, antihistamines for GI conditions, 5-HT3 receptor antagonists, analgesics and antipyretic miscellanea, antineoplastic agents, and proton-pump inhibitors) and 8 drugs (dexamethasone, prochlorperazine, ondansetron, docusate, acetaminophen, ibuprofen, oxycodone, and polyethylene glycol 3350) were associated with > 1000 patients (≥ 50%). Patients had between 1 and 275 unique health conditions and filled 1 to 108 prescriptions. The mean (SD) number of diagnoses and prescriptions was 50 (28) and 29 (12), respectively. Of the 273 therapeutic codes, 30 groups were analyzed, with others categorized into miscellaneous groups such as lotions, vaccines, and devices. Significant differences in mean number of prescriptions were found for patients taking antidepressants compared to those not (P < .05), except for anticonvulsants and antipsychotics (P = .12 and .09, respectively) (Table 3).

Antidepressants

Of the 2113 patients with recorded prescriptions, 750 (35.5%) were dispensed 17 different antidepressants. Among these 17 antidepressants, 183 (8.7%) patients received duloxetine, 158 (7.5%) received venlafaxine, 118 (5.6%) received trazodone, and 107 (5.1%) received sertraline (Figure 1, Table 4). Of the 750 patients, 509 (67.9%) received 1 antidepressant, 168 (22.4%) received 2, 60 (8.0%) received 3, and 13 (1.7%) received > 3. Combinations varied, but only duloxetine and trazodone were prescribed to > 10 patients.

Antidepressants were prescribed annually at an overall mean (SD) rate of 23% (5%) from 2003 to 2022 (Figure 2). Patients on antidepressants during systemic therapy had a greater number of diagnosed medical conditions and received more prescription medications compared to those not taking antidepressants (P < .001) (Figure 3). The 745 patients taking antidepressants in CAPER data had between 1 and 275 diagnosed medical issues, with a mean (SD) of 55 (31) vs a range of 1 to 209 and a mean (SD) of 46 (26) for the 1359 patients not taking antidepressants. The 750 patients on antidepressants in PDTS data had between 8 and 108 prescriptions dispensed, with a mean (SD) of 32 (12), vs a range of 1 to 65 prescriptions and a mean (SD) of 29 (12) for 1363 patients not taking antidepressants.

Discussion

The JPC DoD Cancer Registry includes information on cancer types, stages, treatment regimens, and physicians’ notes, while noncancer drugs are sourced from the PDTS database. The pharmacy uses a different documentation system, leading to varied classifications.

Database reliance has its drawbacks. For example, megestrol is coded as a cancer drug, although it’s primarily used for endometrial or gynecologic cancers. Many drugs have multiple therapeutic codes assigned to them, including 10 antineoplastic agents: diclofenac, Bacillus Calmette-Guérin (BCG), megestrol acetate, tamoxifen, anastrozole, letrozole, leuprolide, goserelin, degarelix, and fluorouracil. Diclofenac, BCG, and mitomycin have been repurposed for cancer treatment.^84-87 From 2003 to 2023, diclofenac was prescribed to 350 patients for mild-to-moderate pain, with only 2 patients receiving it for cancer in 2018. FDA-approved for bladder cancer in 1990, BCG was prescribed for cancer treatment for 1 patient in 2021 after being used for vaccines between 2003 and 2018. Tamoxifen, used for hormone receptor-positive breast cancer from 2004 to 2017 with 53 patients, switched to estrogen agonist-antagonists from 2017 to 2023 with 123 patients. Only a few of the 168 patients were prescribed tamoxifen using both codes.^88-91 Anastrozole and letrozole were coded as antiestrogens for 7 and 18 patients, respectively, while leuprolide and goserelin were coded as gonadotropins for 59 and 18 patients. Degarelix was coded as antigonadotropins, fluorouracil as skin and mucous membrane agents miscellaneous, and megestrol acetate as progestins for 7, 6, and 3 patients, respectively. Duloxetine was given to 186 patients, primarily for depression from 2005 to 2023, with 7 patients treated for fibromyalgia from 2022 to 2023.

Antidepressants Observed

Tables 1 and 5 provide insight into the FDA approval of 14 antineoplastics and antidepressants and their CYP metabolic pathways.^92-122 In Table 4, the most prescribed antidepressant classes are SNRIs, SRMs, SSRIs, TeCAs, NDRIs, and TCAs. This trend highlights a preference for newer medications with weak CYP inhibition. A total of 349 patients were prescribed SSRIs, 343 SNRIs, 119 SRMs, 109 TCAs, 83 TeCAs, and 79 NDRIs. MAOIs, SMRAs, and NMDARAs were not observed in this dataset. While there are instances of dextromethorphan-bupropion and sertraline-escitalopram being dispensed together, it remains unclear whether these were NMDARA combinations.

Among the 14 specific antineoplastic agents, 10 are metabolized by CYP isoenzymes, primarily CYP3A4. Duloxetine neither inhibits nor is metabolized by CYP3A4, a reason it is often recommended, following venlafaxine.

Both duloxetine and venlafaxine are used off-label for chemotherapy-induced peripheral neuropathy related to paclitaxel and docetaxel. According to the CYP metabolized pathway, duloxetine tends to have more favorable DDIs than venlafaxine. In PDTS data, 371 patients were treated with paclitaxel and 180 with docetaxel, with respective antidepressant prescriptions of 156 and 70. Of the 156 patients dispensed paclitaxel, 62 (40%) were dispensed with duloxetine compared to 43 (28%) with venlafaxine. Of the 70 patients dispensed docetaxel, 23 (33%) received duloxetine vs 24 (34%) with venlafaxine.

Of 85 patients prescribed duloxetine, 75 received it with either paclitaxel or docetaxel (5 received both). Five patients had documented AEs (1 neuropathy related). Of 67 patients prescribed venlafaxine, 66 received it with either paclitaxel or docetaxel. Two patients had documented AEs (1 was neuropathy related, the same patient who received duloxetine). Of the 687 patients treated with paclitaxel and 337 with docetaxel in all databases, 4 experienced neuropathic AEs from both medications.⁷⁹

Antidepressants can increase the risk of bleeding, especially when combined with blood thinners, and may elevate blood pressure, particularly alongside stimulants. Of the 554 patients prescribed 9 different anticoagulants, enoxaparin, apixaban, and rivaroxaban were the most common (each > 100 patients). Among these, 201 patients (36%) received both anticoagulants and antidepressants: duloxetine for 64 patients, venlafaxine for 30, trazodone for 35, and sertraline for 26. There were no data available to assess bleeding rates related to the evaluation of DDIs between these medication classes.

Antidepressants can be prescribed for erectile dysfunction. Of the 148 patients prescribed an antidepressant for erectile dysfunction, duloxetine, trazodone, and mirtazapine were the most common. Antidepressant preferences varied by cancer type. Duloxetine was the only antidepressant used for all types of cancer. Venlafaxine, duloxetine, trazodone, sertraline, and escitalopram were the most prescribed antidepressants for breast cancer, while duloxetine, mirtazapine, citalopram, sertraline, and trazodone were the most prescribed for lung cancer. Sertraline, duloxetine, trazodone, amitriptyline, and escitalopram were most common for testicular cancer. Duloxetine, venlafaxine, trazodone, amitriptyline, and sertraline were the most prescribed for endometrial cancer, while duloxetine, venlafaxine, amitriptyline, citalopram, and sertraline were most prescribed for ovarian cancer.

The broadness of International Statistical Classification of Diseases, Tenth Revision codes made it challenging to identify nondepression diagnoses in the analyzed population. However, if all antidepressants were prescribed to treat depression, service members with cancer exhibited a higher depression rate (35%) than the general population (25%). Of 2104 patients, 191 (9.1%) had mood disorders, and 706 (33.6%) had mental disorders: 346 (49.0%) had 1 diagnosis, and 360 (51.0%) had multiple diagnoses. The percentage of diagnoses varied yearly, with notable drops in 2003, 2007, 2011, 2014, and 2018, and peaks in 2006, 2008, 2013, 2017, and 2022. This fluctuation was influenced by events like the establishment of PDTS in 2002, the 2008 economic recession, a hospital relocation in 2011, the 2014 Ebola outbreak, and the COVID-19 pandemic. Although the number of patients receiving antidepressants increased from 2019 to 2022, the overall percentage of patients receiving them did not significantly change from 2003 to 2022, aligning with previous research.^5,125

Many medications have potential uses beyond what is detailed in the prescribing information. Antidepressants can relieve pain, while pain medications may help with depression. Opioids were once thought to effectively treat depression, but this perspective has changed with a greater understanding of their risks, including misuse.^126-131 Pain is a severe and often unbearable AE of cancer. Of 2113 patients, 92% received opioids; 34% received both opioids and antidepressants; 2% received only antidepressants; and 7% received neither. This study didn’t clarify whether those on opioids alone recognized their depression or if those on both were aware of their dependence. While SSRIs are generally not addictive, they can lead to physical dependence, and any medication can be abused if not managed properly.^132-134

Conclusions

This retrospective study analyzes data from antineoplastic agents used in systemic cancer treatment between 1988 and 2023, with a particular focus on the use of antidepressants. Data on antidepressant prescriptions are incomplete and specific to these agents, which means the findings cannot be generalized to all antidepressants. Hence, the results indicate that patients taking antidepressants had more diagnosed health issues and received more medications compared to patients who were not on these drugs.

This study underscores the need for further research into the effects of antidepressants on cancer treatment, utilizing all data from the DoD Cancer Registry. Future research should explore DDIs between antidepressants and other cancer and noncancer medications, as this study did not assess AE documentation, unlike in studies involving erlotinib, gefitinib, and paclitaxel.^78,79 Further investigation is needed to evaluate the impact of discontinuing antidepressant use during cancer treatment. This comprehensive overview provides insights for clinicians to help them make informed decisions regarding the prescription of antidepressants in the context of cancer treatment.

Screening for colorectal cancer (CRC) with colonoscopy enables the identification and removal of CRC precursors (colonic adenomas) and has been associated with reduced risk of CRC incidence and mortality.^1-3 Furthermore, there is consensus that diet and lifestyle may be associated with forestalling CRC pathogenesis at the intermediate adenoma stages.^4-7 However, studies have shown that US veterans have poorer diet quality and a higher risk for neoplasia compared with nonveterans, reinforcing the need for tailored clinical approaches.^8,9 Combining screening with conversations about modifiable environmental and lifestyle risk factors, such as poor diet, is a highly relevant and possibly easily leveraged prevention for those at high risk. However, there is limited evidence for any particular dietary patterns or dietary features that are most important over time.⁷

Several dietary components have been shown to be associated with CRC risk,10 either as potentially chemopreventive (fiber, fruits and vegetables,¹¹ dairy,¹² supplemental vitamin D,¹³ calcium,¹⁴ and multivitamins¹⁵) or carcinogenic (red meat¹⁶ and alcohol¹⁷). Previous studies of veterans have similarly shown that higher intake of fiber and vitamin D reduced risk, and red meat is associated with an increased risk for finding CRC precursors during colonoscopy.¹⁸ However, these dietary categories are often analyzed in isolation. Studying healthy dietary patterns in aggregate may be more clinically relevant and easier to implement for prevention of CRC and its precursors.^19-21 Healthy dietary patterns, such as the US Dietary Guidelines for Americans represented by the Healthy Eating Index (HEI), the Mediterranean diet (MD), and the Dietary Approaches to Stop Hypertension (DASH) diet, have been associated with lower risk for chronic disease.^22-24 Despite the extant literature, no known studies have compared these dietary patterns for associations with risk of CRC precursor or CRC development among US veterans undergoing long-term screening and follow-up after a baseline colonoscopy.

The objective of this study was to test for associations between baseline scores of healthy dietary patterns and the most severe colonoscopy findings (MSCFs) over ≥ 10 years following a baseline screening colonoscopy in veterans.

Methods

Participants in the Cooperative Studies Program (CSP) #380 cohort study included 3121 asymptomatic veterans aged 50 to 75 years at baseline who had consented to initial screening colonoscopy between 1994 and 1997, with subsequent follow-up and surveillance.²⁵ Prior to their colonoscopy, all participants completed a baseline study survey that included questions about cancer risk factors including family history of CRC, diet, physical activity, and medication use.

Included in this cross-sectional analysis were data from a sample of veteran participants of the CSP #380 cohort with 1 baseline colonoscopy, follow-up surveillance through 2009, a cancer risk factor survey collected at baseline, and complete demographic and clinical indicator data. Excluded from the analysis were 67 participants with insufficient responses to the dietary food frequency questionnaire (FFQ) and 31 participants with missing body mass index (BMI), 3023 veterans.

Measures

MSCF. The outcome of interest in this study was the MSCF recorded across all participant colonoscopies during the study period. MSCF was categorized as either (1) no neoplasia; (2) < 2 nonadvanced adenomas, including small adenomas (diameter < 10 mm) with tubular histology; or (3) advanced neoplasia (AN), which is characterized by adenomas > 10 mm in diameter, with villous histology, with high-grade dysplasia, or CRC.

Dietary patterns. Dietary pattern scores representing dietary quality and calculated based on recommendations of the US Dietary Guidelines for Americans using the HEI, MD, and DASH diets were independent variables.^26-28 These 3 dietary patterns were chosen for their hypothesized relationship with CRC risk, but each weighs food categories differently (Appendix 1).^22-24,29 Dietary pattern scores were calculated using the CSP #380 self-reported responses to 129 baseline survey questions adapted from a well-established and previously validated semiquantitative FFQ.³⁰ The form was administered by mail twice to a sample of 127 participants at baseline and at 1 year. During this interval, men completed 1-week diet records twice, spaced about 6 months apart. Mean values for intake of most nutrients assessed by the 2 methods were similar. Intraclass correlation coefficients for the baseline and 1-year FFQ-assessed nutrient intakes that ranged from 0.47 for vitamin E (without supplements) to 0.80 for vitamin C (with supplements). Correlation coefficients between the energy-adjusted nutrient intakes were measured by diet records and the 1-year FFQ, which asked about diet during the year encompassing the diet records. Higher raw and percent scores indicated better alignment with recommendations from each respective dietary pattern. Percent scores were calculated as a standardizing method and used in analyses for ease of comparing the dietary patterns. Scoring can be found in Appendix 2.

Demographic characteristics and clinical indicators. Demographic characteristics included age categories, sex, and race/ethnicity. Clinical indicators included BMI, the number of comorbid conditions used to calculate the Charlson Comorbidity Index, family history of CRC in first-degree relatives, number of follow-up colonoscopies across the study period, and food-based vitamin D intake.³¹ These variables were chosen for their applicability found in previous CSP #380 cohort studies.^18,32,33 Self-reported race and ethnicity were collapsed due to small numbers in some groups. The authors acknowledge these are distinct concepts and the variable has limited utility other than for controlling for systemic racism in the model.

Statistical Analyses

Descriptive statistics were used to describe distributional assumptions for all variables, including demographics, clinical indicators, colonoscopy results, and dietary patterns. Pairwise correlations between the total dietary pattern scores and food category scores were calculated with Pearson correlation (r).

Multinomial logistic regression models were created using SAS procedure LOGISTIC with the outcome of the categorical MSCF (no neoplasia, nonadvanced adenoma, or AN).³⁴ A model was created for each independent predictor variable of interest (ie, the HEI, MD, or DASH percentage-standardized dietary pattern score and each food category comprising each dietary pattern score). All models were adjusted for age, sex, race/ethnicity, BMI, number of comorbidities, family history of CRC, number of follow-up colonoscopies, and estimated daily food-derived vitamin D intake. The demographic and clinical indicators were included in the models as they are known to be associated with CRC risk.¹⁸ The number of colonoscopies was included to control for surveillance intensity presuming risk for AN is reduced as polyps are removed. Because colonoscopy findings from an initial screening have unique clinical implications compared with follow- up and surveillance, MSCF was observed in 2 ways in sensitivity analyses: (1) baseline and (2) aggregate follow-up and surveillance only, excluding baseline findings.

Adjusted odds ratios (aORs) and 95% CIs for each of the MSCF outcomes with a reference finding of no neoplasia for the models are presented. We chose not to adjust for multiple comparisons across the different dietary patterns given the correlation between dietary pattern total and category scores but did adjust for multiple comparisons for dietary categories within each dietary pattern. Tests for statistical significance used α= .05 for the dietary pattern total scores and P values for the dietary category scores for each dietary pattern controlled for false discovery rate using the MULTTEST SAS procedure.35 All data manipulations and analyses were performed using SAS version 9.4.

Results

The study included 3023 patients. All were aged 50 to 75 years, 2923 (96.7%) were male and 2532 (83.8%) were non-Hispanic White (Table 1). Most participants were overweight or obese (n = 2535 [83.8%]), 2024 (67.0%) had ≤ 2 comorbidities, and 2602 (86.1%) had no family history of CRC. The MSCF for 1628 patients (53.9%) was no neoplasia, 966 patients (32.0%) was nonadvanced adenoma, and 429 participants (14.2%) had AN.

Mean percent scores were 58.5% for HEI, 38.2% for MD, and 63.1% for the DASH diet, with higher percentages indicating greater alignment with the recommendations for each diet (Table 2). All 3 dietary patterns scores standardized to percentages were strongly and significantly correlated in pairwise comparisons: HEI:MD, r = 0.62 (P < .001); HEI:DASH, r = 0.60 (P < .001); and MD:DASH, r = 0.72 (P < .001). Likewise, food category scores were significantly correlated across dietary patterns. For example, whole grain and fiber values from each dietary score were strongly correlated in pairwise comparisons: HEI Whole Grain:MD Grain, r = 0.64 (P < .001); HEI Whole Grain:DASH Fiber, r = 0.71 (P < .001); and MD Grain:DASH Fiber, r = 0.70 (P < .001).

Associations between individual participants' dietary pattern scores and the outcome of their pooled MSCF from baseline screening and ≥ 10 years of surveillance are presented in Table 3. For each single-point increases in dietary pattern scores (reflecting better dietary quality), aORs for nonadvanced adenoma vs no neoplasia were slightly lower but not statistically significantly: HEI, aOR, 1.00 (95% CI, 0.99-1.01); MD, aOR, 0.98 (95% CI, 0.94-1.02); and DASH, aOR, 0.99 (95% CI, 0.99-1.00). aORs for AN vs no neoplasia were slightly lower for each dietary pattern assessed, and only the MD and DASH scores were significantly different from 1.00: HEI, aOR, 1.00 (95% CI, 0.99-1.01); MD, aOR, 0.95 (95% CI, 0.90-1.00); and DASH, aOR, 0.99 (95% CI, 0.98-1.00).

We observed lower odds for nonadvanced adenoma and AN among all these dietary patterns when there was greater alignment with the recommended intake of whole grains and fiber. In separate models conducted using food categories comprising the dietary patterns as independent variables and after correcting for multiple tests, higher scores for the HEI Refined Grain category were associated with higher odds for nonadvanced adenoma (aOR, 1.03 [95% CI, 1.01-1.05]; P = .01) and AN (aOR, 1.05 [95% CI, 1.02-1.08]; P < .001). Higher scores for the HEI Whole Grain category were associated with lower odds for nonadvanced adenoma (aOR, 0.97 [95% CI, 0.95-0.99]; P = .01) and AN (aOR, 0.96 [95% CI, 0.93-0.99]; P = .01). Higher scores for the MD Grain category were significantly associated with lower odds for nonadvanced adenoma (aOR, 0.44 [95% CI, 0.26-0.75]; P = .002) and AN (aOR, 0.29 [95% CI, 0.14-0.62]; P = .001). The DASH Grains category also was significantly associated with lower odds for AN (aOR, 0.86 [95% CI, 0.78-0.95]; P = .002).

Discussion

In this study of 3023 veterans undergoing first-time screening colonoscopy and ≥ 10 years of surveillance, we found that healthy dietary patterns, as assessed by the MD and DASH diet, were significantly associated with lower risk of AN. Additionally, we identified lower odds for AN and nonadvanced adenoma compared with no neoplasia for higher grain scores for all the dietary patterns studied. Other food categories that comprise the dietary pattern scores had mixed associations with the MSCF outcomes. Several other studies have examined associations between dietary patterns and risk for CRC but to our knowledge, no studies have explored these associations among US veterans.

These results also indicate study participants had better than average (based on a 50% threshold) dietary quality according to the HEI and DASH diet scoring methods we used, but poor dietary quality according to the MD scoring method. The mean HEI scores for the present study were higher than a US Department of Agriculture study by Dong et al that compared dietary quality between veterans and nonveterans using the HEI, for which veterans’ expected HEI score was 45.6 of 100.⁸ This could be explained by the fact that the participants needed to be healthy to be eligible and those with healthier behaviors overall may have self-selected into the study due to motivation for screening during a time when screening was not yet commonplace. ³⁶ Similarly, participants of the present study had higher adherence to the DASH diet (63.1%) than adolescents with diabetes in a study by Günther et al. Conversely, firefighters who were coached to use a Mediterranean-style dietary pattern and dietary had higher adherence to MD than did participants in this study.²⁷

A closer examination of specific food category component scores that comprise the 3 distinct dietary patterns revealed mixed results from the multinomial modeling, which may have to do with the guideline thresholds used to calculate the dietary scores. When analyzed separately in the logistic regression models for their associations with nonadvanced adenomas and AN compared with no neoplasia, higher MD and DASH fruit scores (but not HEI fruit scores) were found to be significant. Other studies have had mixed findings when attempting to test for associations of fruit intake with adenoma recurrence.^10,37

This study had some unexpected findings. Vegetable intake was not associated with nonadvanced adenomas or AN risk. Studies of food categories have consistently found vegetable (specifically cruciferous ones) intake to be linked with lower odds for cancers.³⁸ Likewise, the red meat category, which was only a unique food category in the MD score, was not associated with nonadvanced adenomas or AN. Despite consistent literature suggesting higher intake of red meat and processed meats increases CRC risk, in 2019 the Nutritional Recommendations Consortium indicated that the evidence was weak.^39,40 This study showed higher DASH diet scores for low-fat dairy, which were maximized when participants reported at least 50% of their dairy servings per day as being low-fat, had lower odds for AN. Yet, the MD scores for low-fat dairy had no association with either outcome; their calculation was based on total number of servings per week. This difference in findings suggests the fat intake ratio may be more relevant to CRC risk than intake quantity.

The literature is mixed regarding fatty acid intake and CRC risk, which may be relevant to both dairy and meat intake. One systematic review and meta-analysis found dietary fat and types of fatty acid intake had no association with CRC risk.⁴¹ However, a more recent meta-analysis that assessed both dietary intake and plasma levels of fatty acids did find some statistically significant differences for various types of fatty acids and CRC risk.⁴²

The findings in the present study that grain intake is associated with lower odds for more severe colonoscopy findings among veterans are notable.⁴³ Lieberman et al, using the CSP #380 data, found that cereal fiber intake was associated with a lower odds for AN compared with hyperplastic polyps (OR, 0.98 [95% CI, 0.96- 1.00]).¹⁸ Similarly, Hullings et al determined that older adults in the highest quintile of cereal fiber intake had significantly lower odds of CRC than those in lower odds for CRC when compared with lowest quintile (OR, 0.89 [95% CI, 0.83- 0.96]; P < .001).⁴⁴ These findings support existing guidance that prioritizes whole grains as a key source of dietary fiber for CRC prevention.

A recent literature review on fiber, fat, and CRC risk suggested a consensus regarding one protective mechanism: dietary fiber from grains modulates the gut microbiota by promoting butyrate synthesis.⁴⁵ Butyrate is a short-chain fatty acid that supports energy production in colonocytes and has tumor-suppressing properties.⁴⁶ Our findings suggest there could be more to learn about the relationship between butyrate production and reduction of CRC risk through metabolomic studies that use measurements of plasma butyrate. These studies may examine associations between not just a singular food or food category, but rather food patterns that include fruits, vegetables, nuts and seeds, and whole grains known to promote butyrate production and plasma butyrate.⁴⁷

Improved understanding of mechanisms and risk-modifying lifestyle factors such as dietary patterns may enhance prevention strategies. Identifying the collective chemopreventive characteristics of a specific dietary pattern (eg, MD) will be helpful to clinicians and health care staff to promote healthy eating to reduce cancer risk. More studies are needed to understand whether such promotion is more clinically applicable and effective for patients, as compared with eating more or less of specific foods (eg, more whole grains, less red meat). Furthermore, considering important environmental factors collectively beyond dietary patterns may offer a way to better tailor screening and implement a variety of lifestyle interventions. In the literature, this is often referred to as a teachable moment when patients’ attentions are captured and may position them to be more receptive to guidance.⁴⁸

Limitations

This study has several important limitations and leaves opportunities for future studies that explore the role of dietary patterns and AN or CRC risk. First, the FFQ data used to calculate dietary pattern scores used in analysis were only captured at baseline, and there are nearly 3 decades across the study period. However, it is widely assumed that the diets of older adults, like those included in this study, remain stable over time which is appropriate given our sample population was aged 50 to 75 years when the baseline FFQ data were collected.^49-51 Additionally, while the HEI is a well-documented, standard scoring method for dietary quality, there are multitudes of dietary pattern scoring approaches for MD and DASH.^23,52,53 Finally, findings from this study using the sample of veterans may not be generalizable to a broader population. Future longitudinal studies that test for a clinically significant change threshold are warranted.

Conclusion

Results of this study suggest future research should further explore the effects of dietary patterns, particularly intake of specific food groups in combination, as opposed to individual nutrients or food items, on AN and CRC risk. Possible studies might explore these dietary patterns for their mechanistic role in altering the microbiome metabolism, which may influence CRC outcomes or include diet in a more comprehensive, holistic risk score that could be used to predict colonic neoplasia risk or in intervention studies that assess the effects of dietary changes on long-term CRC prevention. We suggest there are differences in people’s dietary intake patterns that might be important to consider when implementing tailored approaches to CRC risk mitigation.

Screening for colorectal cancer (CRC) with colonoscopy enables the identification and removal of CRC precursors (colonic adenomas) and has been associated with reduced risk of CRC incidence and mortality.^1-3 Furthermore, there is consensus that diet and lifestyle may be associated with forestalling CRC pathogenesis at the intermediate adenoma stages.^4-7 However, studies have shown that US veterans have poorer diet quality and a higher risk for neoplasia compared with nonveterans, reinforcing the need for tailored clinical approaches.^8,9 Combining screening with conversations about modifiable environmental and lifestyle risk factors, such as poor diet, is a highly relevant and possibly easily leveraged prevention for those at high risk. However, there is limited evidence for any particular dietary patterns or dietary features that are most important over time.⁷

Several dietary components have been shown to be associated with CRC risk,10 either as potentially chemopreventive (fiber, fruits and vegetables,¹¹ dairy,¹² supplemental vitamin D,¹³ calcium,¹⁴ and multivitamins¹⁵) or carcinogenic (red meat¹⁶ and alcohol¹⁷). Previous studies of veterans have similarly shown that higher intake of fiber and vitamin D reduced risk, and red meat is associated with an increased risk for finding CRC precursors during colonoscopy.¹⁸ However, these dietary categories are often analyzed in isolation. Studying healthy dietary patterns in aggregate may be more clinically relevant and easier to implement for prevention of CRC and its precursors.^19-21 Healthy dietary patterns, such as the US Dietary Guidelines for Americans represented by the Healthy Eating Index (HEI), the Mediterranean diet (MD), and the Dietary Approaches to Stop Hypertension (DASH) diet, have been associated with lower risk for chronic disease.^22-24 Despite the extant literature, no known studies have compared these dietary patterns for associations with risk of CRC precursor or CRC development among US veterans undergoing long-term screening and follow-up after a baseline colonoscopy.

The objective of this study was to test for associations between baseline scores of healthy dietary patterns and the most severe colonoscopy findings (MSCFs) over ≥ 10 years following a baseline screening colonoscopy in veterans.

Methods

Participants in the Cooperative Studies Program (CSP) #380 cohort study included 3121 asymptomatic veterans aged 50 to 75 years at baseline who had consented to initial screening colonoscopy between 1994 and 1997, with subsequent follow-up and surveillance.²⁵ Prior to their colonoscopy, all participants completed a baseline study survey that included questions about cancer risk factors including family history of CRC, diet, physical activity, and medication use.

Included in this cross-sectional analysis were data from a sample of veteran participants of the CSP #380 cohort with 1 baseline colonoscopy, follow-up surveillance through 2009, a cancer risk factor survey collected at baseline, and complete demographic and clinical indicator data. Excluded from the analysis were 67 participants with insufficient responses to the dietary food frequency questionnaire (FFQ) and 31 participants with missing body mass index (BMI), 3023 veterans.

Measures

MSCF. The outcome of interest in this study was the MSCF recorded across all participant colonoscopies during the study period. MSCF was categorized as either (1) no neoplasia; (2) < 2 nonadvanced adenomas, including small adenomas (diameter < 10 mm) with tubular histology; or (3) advanced neoplasia (AN), which is characterized by adenomas > 10 mm in diameter, with villous histology, with high-grade dysplasia, or CRC.

Dietary patterns. Dietary pattern scores representing dietary quality and calculated based on recommendations of the US Dietary Guidelines for Americans using the HEI, MD, and DASH diets were independent variables.^26-28 These 3 dietary patterns were chosen for their hypothesized relationship with CRC risk, but each weighs food categories differently (Appendix 1).^22-24,29 Dietary pattern scores were calculated using the CSP #380 self-reported responses to 129 baseline survey questions adapted from a well-established and previously validated semiquantitative FFQ.³⁰ The form was administered by mail twice to a sample of 127 participants at baseline and at 1 year. During this interval, men completed 1-week diet records twice, spaced about 6 months apart. Mean values for intake of most nutrients assessed by the 2 methods were similar. Intraclass correlation coefficients for the baseline and 1-year FFQ-assessed nutrient intakes that ranged from 0.47 for vitamin E (without supplements) to 0.80 for vitamin C (with supplements). Correlation coefficients between the energy-adjusted nutrient intakes were measured by diet records and the 1-year FFQ, which asked about diet during the year encompassing the diet records. Higher raw and percent scores indicated better alignment with recommendations from each respective dietary pattern. Percent scores were calculated as a standardizing method and used in analyses for ease of comparing the dietary patterns. Scoring can be found in Appendix 2.

Demographic characteristics and clinical indicators. Demographic characteristics included age categories, sex, and race/ethnicity. Clinical indicators included BMI, the number of comorbid conditions used to calculate the Charlson Comorbidity Index, family history of CRC in first-degree relatives, number of follow-up colonoscopies across the study period, and food-based vitamin D intake.³¹ These variables were chosen for their applicability found in previous CSP #380 cohort studies.^18,32,33 Self-reported race and ethnicity were collapsed due to small numbers in some groups. The authors acknowledge these are distinct concepts and the variable has limited utility other than for controlling for systemic racism in the model.

Statistical Analyses

Descriptive statistics were used to describe distributional assumptions for all variables, including demographics, clinical indicators, colonoscopy results, and dietary patterns. Pairwise correlations between the total dietary pattern scores and food category scores were calculated with Pearson correlation (r).

Multinomial logistic regression models were created using SAS procedure LOGISTIC with the outcome of the categorical MSCF (no neoplasia, nonadvanced adenoma, or AN).³⁴ A model was created for each independent predictor variable of interest (ie, the HEI, MD, or DASH percentage-standardized dietary pattern score and each food category comprising each dietary pattern score). All models were adjusted for age, sex, race/ethnicity, BMI, number of comorbidities, family history of CRC, number of follow-up colonoscopies, and estimated daily food-derived vitamin D intake. The demographic and clinical indicators were included in the models as they are known to be associated with CRC risk.¹⁸ The number of colonoscopies was included to control for surveillance intensity presuming risk for AN is reduced as polyps are removed. Because colonoscopy findings from an initial screening have unique clinical implications compared with follow- up and surveillance, MSCF was observed in 2 ways in sensitivity analyses: (1) baseline and (2) aggregate follow-up and surveillance only, excluding baseline findings.

Adjusted odds ratios (aORs) and 95% CIs for each of the MSCF outcomes with a reference finding of no neoplasia for the models are presented. We chose not to adjust for multiple comparisons across the different dietary patterns given the correlation between dietary pattern total and category scores but did adjust for multiple comparisons for dietary categories within each dietary pattern. Tests for statistical significance used α= .05 for the dietary pattern total scores and P values for the dietary category scores for each dietary pattern controlled for false discovery rate using the MULTTEST SAS procedure.35 All data manipulations and analyses were performed using SAS version 9.4.

Results

The study included 3023 patients. All were aged 50 to 75 years, 2923 (96.7%) were male and 2532 (83.8%) were non-Hispanic White (Table 1). Most participants were overweight or obese (n = 2535 [83.8%]), 2024 (67.0%) had ≤ 2 comorbidities, and 2602 (86.1%) had no family history of CRC. The MSCF for 1628 patients (53.9%) was no neoplasia, 966 patients (32.0%) was nonadvanced adenoma, and 429 participants (14.2%) had AN.

Mean percent scores were 58.5% for HEI, 38.2% for MD, and 63.1% for the DASH diet, with higher percentages indicating greater alignment with the recommendations for each diet (Table 2). All 3 dietary patterns scores standardized to percentages were strongly and significantly correlated in pairwise comparisons: HEI:MD, r = 0.62 (P < .001); HEI:DASH, r = 0.60 (P < .001); and MD:DASH, r = 0.72 (P < .001). Likewise, food category scores were significantly correlated across dietary patterns. For example, whole grain and fiber values from each dietary score were strongly correlated in pairwise comparisons: HEI Whole Grain:MD Grain, r = 0.64 (P < .001); HEI Whole Grain:DASH Fiber, r = 0.71 (P < .001); and MD Grain:DASH Fiber, r = 0.70 (P < .001).

Associations between individual participants' dietary pattern scores and the outcome of their pooled MSCF from baseline screening and ≥ 10 years of surveillance are presented in Table 3. For each single-point increases in dietary pattern scores (reflecting better dietary quality), aORs for nonadvanced adenoma vs no neoplasia were slightly lower but not statistically significantly: HEI, aOR, 1.00 (95% CI, 0.99-1.01); MD, aOR, 0.98 (95% CI, 0.94-1.02); and DASH, aOR, 0.99 (95% CI, 0.99-1.00). aORs for AN vs no neoplasia were slightly lower for each dietary pattern assessed, and only the MD and DASH scores were significantly different from 1.00: HEI, aOR, 1.00 (95% CI, 0.99-1.01); MD, aOR, 0.95 (95% CI, 0.90-1.00); and DASH, aOR, 0.99 (95% CI, 0.98-1.00).

We observed lower odds for nonadvanced adenoma and AN among all these dietary patterns when there was greater alignment with the recommended intake of whole grains and fiber. In separate models conducted using food categories comprising the dietary patterns as independent variables and after correcting for multiple tests, higher scores for the HEI Refined Grain category were associated with higher odds for nonadvanced adenoma (aOR, 1.03 [95% CI, 1.01-1.05]; P = .01) and AN (aOR, 1.05 [95% CI, 1.02-1.08]; P < .001). Higher scores for the HEI Whole Grain category were associated with lower odds for nonadvanced adenoma (aOR, 0.97 [95% CI, 0.95-0.99]; P = .01) and AN (aOR, 0.96 [95% CI, 0.93-0.99]; P = .01). Higher scores for the MD Grain category were significantly associated with lower odds for nonadvanced adenoma (aOR, 0.44 [95% CI, 0.26-0.75]; P = .002) and AN (aOR, 0.29 [95% CI, 0.14-0.62]; P = .001). The DASH Grains category also was significantly associated with lower odds for AN (aOR, 0.86 [95% CI, 0.78-0.95]; P = .002).

Discussion

In this study of 3023 veterans undergoing first-time screening colonoscopy and ≥ 10 years of surveillance, we found that healthy dietary patterns, as assessed by the MD and DASH diet, were significantly associated with lower risk of AN. Additionally, we identified lower odds for AN and nonadvanced adenoma compared with no neoplasia for higher grain scores for all the dietary patterns studied. Other food categories that comprise the dietary pattern scores had mixed associations with the MSCF outcomes. Several other studies have examined associations between dietary patterns and risk for CRC but to our knowledge, no studies have explored these associations among US veterans.

These results also indicate study participants had better than average (based on a 50% threshold) dietary quality according to the HEI and DASH diet scoring methods we used, but poor dietary quality according to the MD scoring method. The mean HEI scores for the present study were higher than a US Department of Agriculture study by Dong et al that compared dietary quality between veterans and nonveterans using the HEI, for which veterans’ expected HEI score was 45.6 of 100.⁸ This could be explained by the fact that the participants needed to be healthy to be eligible and those with healthier behaviors overall may have self-selected into the study due to motivation for screening during a time when screening was not yet commonplace. ³⁶ Similarly, participants of the present study had higher adherence to the DASH diet (63.1%) than adolescents with diabetes in a study by Günther et al. Conversely, firefighters who were coached to use a Mediterranean-style dietary pattern and dietary had higher adherence to MD than did participants in this study.²⁷

A closer examination of specific food category component scores that comprise the 3 distinct dietary patterns revealed mixed results from the multinomial modeling, which may have to do with the guideline thresholds used to calculate the dietary scores. When analyzed separately in the logistic regression models for their associations with nonadvanced adenomas and AN compared with no neoplasia, higher MD and DASH fruit scores (but not HEI fruit scores) were found to be significant. Other studies have had mixed findings when attempting to test for associations of fruit intake with adenoma recurrence.^10,37

This study had some unexpected findings. Vegetable intake was not associated with nonadvanced adenomas or AN risk. Studies of food categories have consistently found vegetable (specifically cruciferous ones) intake to be linked with lower odds for cancers.³⁸ Likewise, the red meat category, which was only a unique food category in the MD score, was not associated with nonadvanced adenomas or AN. Despite consistent literature suggesting higher intake of red meat and processed meats increases CRC risk, in 2019 the Nutritional Recommendations Consortium indicated that the evidence was weak.^39,40 This study showed higher DASH diet scores for low-fat dairy, which were maximized when participants reported at least 50% of their dairy servings per day as being low-fat, had lower odds for AN. Yet, the MD scores for low-fat dairy had no association with either outcome; their calculation was based on total number of servings per week. This difference in findings suggests the fat intake ratio may be more relevant to CRC risk than intake quantity.

The literature is mixed regarding fatty acid intake and CRC risk, which may be relevant to both dairy and meat intake. One systematic review and meta-analysis found dietary fat and types of fatty acid intake had no association with CRC risk.⁴¹ However, a more recent meta-analysis that assessed both dietary intake and plasma levels of fatty acids did find some statistically significant differences for various types of fatty acids and CRC risk.⁴²

The findings in the present study that grain intake is associated with lower odds for more severe colonoscopy findings among veterans are notable.⁴³ Lieberman et al, using the CSP #380 data, found that cereal fiber intake was associated with a lower odds for AN compared with hyperplastic polyps (OR, 0.98 [95% CI, 0.96- 1.00]).¹⁸ Similarly, Hullings et al determined that older adults in the highest quintile of cereal fiber intake had significantly lower odds of CRC than those in lower odds for CRC when compared with lowest quintile (OR, 0.89 [95% CI, 0.83- 0.96]; P < .001).⁴⁴ These findings support existing guidance that prioritizes whole grains as a key source of dietary fiber for CRC prevention.

A recent literature review on fiber, fat, and CRC risk suggested a consensus regarding one protective mechanism: dietary fiber from grains modulates the gut microbiota by promoting butyrate synthesis.⁴⁵ Butyrate is a short-chain fatty acid that supports energy production in colonocytes and has tumor-suppressing properties.⁴⁶ Our findings suggest there could be more to learn about the relationship between butyrate production and reduction of CRC risk through metabolomic studies that use measurements of plasma butyrate. These studies may examine associations between not just a singular food or food category, but rather food patterns that include fruits, vegetables, nuts and seeds, and whole grains known to promote butyrate production and plasma butyrate.⁴⁷

Improved understanding of mechanisms and risk-modifying lifestyle factors such as dietary patterns may enhance prevention strategies. Identifying the collective chemopreventive characteristics of a specific dietary pattern (eg, MD) will be helpful to clinicians and health care staff to promote healthy eating to reduce cancer risk. More studies are needed to understand whether such promotion is more clinically applicable and effective for patients, as compared with eating more or less of specific foods (eg, more whole grains, less red meat). Furthermore, considering important environmental factors collectively beyond dietary patterns may offer a way to better tailor screening and implement a variety of lifestyle interventions. In the literature, this is often referred to as a teachable moment when patients’ attentions are captured and may position them to be more receptive to guidance.⁴⁸

Limitations

This study has several important limitations and leaves opportunities for future studies that explore the role of dietary patterns and AN or CRC risk. First, the FFQ data used to calculate dietary pattern scores used in analysis were only captured at baseline, and there are nearly 3 decades across the study period. However, it is widely assumed that the diets of older adults, like those included in this study, remain stable over time which is appropriate given our sample population was aged 50 to 75 years when the baseline FFQ data were collected.^49-51 Additionally, while the HEI is a well-documented, standard scoring method for dietary quality, there are multitudes of dietary pattern scoring approaches for MD and DASH.^23,52,53 Finally, findings from this study using the sample of veterans may not be generalizable to a broader population. Future longitudinal studies that test for a clinically significant change threshold are warranted.

Conclusion

Results of this study suggest future research should further explore the effects of dietary patterns, particularly intake of specific food groups in combination, as opposed to individual nutrients or food items, on AN and CRC risk. Possible studies might explore these dietary patterns for their mechanistic role in altering the microbiome metabolism, which may influence CRC outcomes or include diet in a more comprehensive, holistic risk score that could be used to predict colonic neoplasia risk or in intervention studies that assess the effects of dietary changes on long-term CRC prevention. We suggest there are differences in people’s dietary intake patterns that might be important to consider when implementing tailored approaches to CRC risk mitigation.

Screening for colorectal cancer (CRC) with colonoscopy enables the identification and removal of CRC precursors (colonic adenomas) and has been associated with reduced risk of CRC incidence and mortality.^1-3 Furthermore, there is consensus that diet and lifestyle may be associated with forestalling CRC pathogenesis at the intermediate adenoma stages.^4-7 However, studies have shown that US veterans have poorer diet quality and a higher risk for neoplasia compared with nonveterans, reinforcing the need for tailored clinical approaches.^8,9 Combining screening with conversations about modifiable environmental and lifestyle risk factors, such as poor diet, is a highly relevant and possibly easily leveraged prevention for those at high risk. However, there is limited evidence for any particular dietary patterns or dietary features that are most important over time.⁷

Several dietary components have been shown to be associated with CRC risk,10 either as potentially chemopreventive (fiber, fruits and vegetables,¹¹ dairy,¹² supplemental vitamin D,¹³ calcium,¹⁴ and multivitamins¹⁵) or carcinogenic (red meat¹⁶ and alcohol¹⁷). Previous studies of veterans have similarly shown that higher intake of fiber and vitamin D reduced risk, and red meat is associated with an increased risk for finding CRC precursors during colonoscopy.¹⁸ However, these dietary categories are often analyzed in isolation. Studying healthy dietary patterns in aggregate may be more clinically relevant and easier to implement for prevention of CRC and its precursors.^19-21 Healthy dietary patterns, such as the US Dietary Guidelines for Americans represented by the Healthy Eating Index (HEI), the Mediterranean diet (MD), and the Dietary Approaches to Stop Hypertension (DASH) diet, have been associated with lower risk for chronic disease.^22-24 Despite the extant literature, no known studies have compared these dietary patterns for associations with risk of CRC precursor or CRC development among US veterans undergoing long-term screening and follow-up after a baseline colonoscopy.

The objective of this study was to test for associations between baseline scores of healthy dietary patterns and the most severe colonoscopy findings (MSCFs) over ≥ 10 years following a baseline screening colonoscopy in veterans.

Methods

Participants in the Cooperative Studies Program (CSP) #380 cohort study included 3121 asymptomatic veterans aged 50 to 75 years at baseline who had consented to initial screening colonoscopy between 1994 and 1997, with subsequent follow-up and surveillance.²⁵ Prior to their colonoscopy, all participants completed a baseline study survey that included questions about cancer risk factors including family history of CRC, diet, physical activity, and medication use.

Included in this cross-sectional analysis were data from a sample of veteran participants of the CSP #380 cohort with 1 baseline colonoscopy, follow-up surveillance through 2009, a cancer risk factor survey collected at baseline, and complete demographic and clinical indicator data. Excluded from the analysis were 67 participants with insufficient responses to the dietary food frequency questionnaire (FFQ) and 31 participants with missing body mass index (BMI), 3023 veterans.

Measures

MSCF. The outcome of interest in this study was the MSCF recorded across all participant colonoscopies during the study period. MSCF was categorized as either (1) no neoplasia; (2) < 2 nonadvanced adenomas, including small adenomas (diameter < 10 mm) with tubular histology; or (3) advanced neoplasia (AN), which is characterized by adenomas > 10 mm in diameter, with villous histology, with high-grade dysplasia, or CRC.

Dietary patterns. Dietary pattern scores representing dietary quality and calculated based on recommendations of the US Dietary Guidelines for Americans using the HEI, MD, and DASH diets were independent variables.^26-28 These 3 dietary patterns were chosen for their hypothesized relationship with CRC risk, but each weighs food categories differently (Appendix 1).^22-24,29 Dietary pattern scores were calculated using the CSP #380 self-reported responses to 129 baseline survey questions adapted from a well-established and previously validated semiquantitative FFQ.³⁰ The form was administered by mail twice to a sample of 127 participants at baseline and at 1 year. During this interval, men completed 1-week diet records twice, spaced about 6 months apart. Mean values for intake of most nutrients assessed by the 2 methods were similar. Intraclass correlation coefficients for the baseline and 1-year FFQ-assessed nutrient intakes that ranged from 0.47 for vitamin E (without supplements) to 0.80 for vitamin C (with supplements). Correlation coefficients between the energy-adjusted nutrient intakes were measured by diet records and the 1-year FFQ, which asked about diet during the year encompassing the diet records. Higher raw and percent scores indicated better alignment with recommendations from each respective dietary pattern. Percent scores were calculated as a standardizing method and used in analyses for ease of comparing the dietary patterns. Scoring can be found in Appendix 2.

Demographic characteristics and clinical indicators. Demographic characteristics included age categories, sex, and race/ethnicity. Clinical indicators included BMI, the number of comorbid conditions used to calculate the Charlson Comorbidity Index, family history of CRC in first-degree relatives, number of follow-up colonoscopies across the study period, and food-based vitamin D intake.³¹ These variables were chosen for their applicability found in previous CSP #380 cohort studies.^18,32,33 Self-reported race and ethnicity were collapsed due to small numbers in some groups. The authors acknowledge these are distinct concepts and the variable has limited utility other than for controlling for systemic racism in the model.

Statistical Analyses

Descriptive statistics were used to describe distributional assumptions for all variables, including demographics, clinical indicators, colonoscopy results, and dietary patterns. Pairwise correlations between the total dietary pattern scores and food category scores were calculated with Pearson correlation (r).

Multinomial logistic regression models were created using SAS procedure LOGISTIC with the outcome of the categorical MSCF (no neoplasia, nonadvanced adenoma, or AN).³⁴ A model was created for each independent predictor variable of interest (ie, the HEI, MD, or DASH percentage-standardized dietary pattern score and each food category comprising each dietary pattern score). All models were adjusted for age, sex, race/ethnicity, BMI, number of comorbidities, family history of CRC, number of follow-up colonoscopies, and estimated daily food-derived vitamin D intake. The demographic and clinical indicators were included in the models as they are known to be associated with CRC risk.¹⁸ The number of colonoscopies was included to control for surveillance intensity presuming risk for AN is reduced as polyps are removed. Because colonoscopy findings from an initial screening have unique clinical implications compared with follow- up and surveillance, MSCF was observed in 2 ways in sensitivity analyses: (1) baseline and (2) aggregate follow-up and surveillance only, excluding baseline findings.

Adjusted odds ratios (aORs) and 95% CIs for each of the MSCF outcomes with a reference finding of no neoplasia for the models are presented. We chose not to adjust for multiple comparisons across the different dietary patterns given the correlation between dietary pattern total and category scores but did adjust for multiple comparisons for dietary categories within each dietary pattern. Tests for statistical significance used α= .05 for the dietary pattern total scores and P values for the dietary category scores for each dietary pattern controlled for false discovery rate using the MULTTEST SAS procedure.35 All data manipulations and analyses were performed using SAS version 9.4.

Results

The study included 3023 patients. All were aged 50 to 75 years, 2923 (96.7%) were male and 2532 (83.8%) were non-Hispanic White (Table 1). Most participants were overweight or obese (n = 2535 [83.8%]), 2024 (67.0%) had ≤ 2 comorbidities, and 2602 (86.1%) had no family history of CRC. The MSCF for 1628 patients (53.9%) was no neoplasia, 966 patients (32.0%) was nonadvanced adenoma, and 429 participants (14.2%) had AN.

Mean percent scores were 58.5% for HEI, 38.2% for MD, and 63.1% for the DASH diet, with higher percentages indicating greater alignment with the recommendations for each diet (Table 2). All 3 dietary patterns scores standardized to percentages were strongly and significantly correlated in pairwise comparisons: HEI:MD, r = 0.62 (P < .001); HEI:DASH, r = 0.60 (P < .001); and MD:DASH, r = 0.72 (P < .001). Likewise, food category scores were significantly correlated across dietary patterns. For example, whole grain and fiber values from each dietary score were strongly correlated in pairwise comparisons: HEI Whole Grain:MD Grain, r = 0.64 (P < .001); HEI Whole Grain:DASH Fiber, r = 0.71 (P < .001); and MD Grain:DASH Fiber, r = 0.70 (P < .001).

Associations between individual participants' dietary pattern scores and the outcome of their pooled MSCF from baseline screening and ≥ 10 years of surveillance are presented in Table 3. For each single-point increases in dietary pattern scores (reflecting better dietary quality), aORs for nonadvanced adenoma vs no neoplasia were slightly lower but not statistically significantly: HEI, aOR, 1.00 (95% CI, 0.99-1.01); MD, aOR, 0.98 (95% CI, 0.94-1.02); and DASH, aOR, 0.99 (95% CI, 0.99-1.00). aORs for AN vs no neoplasia were slightly lower for each dietary pattern assessed, and only the MD and DASH scores were significantly different from 1.00: HEI, aOR, 1.00 (95% CI, 0.99-1.01); MD, aOR, 0.95 (95% CI, 0.90-1.00); and DASH, aOR, 0.99 (95% CI, 0.98-1.00).

We observed lower odds for nonadvanced adenoma and AN among all these dietary patterns when there was greater alignment with the recommended intake of whole grains and fiber. In separate models conducted using food categories comprising the dietary patterns as independent variables and after correcting for multiple tests, higher scores for the HEI Refined Grain category were associated with higher odds for nonadvanced adenoma (aOR, 1.03 [95% CI, 1.01-1.05]; P = .01) and AN (aOR, 1.05 [95% CI, 1.02-1.08]; P < .001). Higher scores for the HEI Whole Grain category were associated with lower odds for nonadvanced adenoma (aOR, 0.97 [95% CI, 0.95-0.99]; P = .01) and AN (aOR, 0.96 [95% CI, 0.93-0.99]; P = .01). Higher scores for the MD Grain category were significantly associated with lower odds for nonadvanced adenoma (aOR, 0.44 [95% CI, 0.26-0.75]; P = .002) and AN (aOR, 0.29 [95% CI, 0.14-0.62]; P = .001). The DASH Grains category also was significantly associated with lower odds for AN (aOR, 0.86 [95% CI, 0.78-0.95]; P = .002).

Discussion

In this study of 3023 veterans undergoing first-time screening colonoscopy and ≥ 10 years of surveillance, we found that healthy dietary patterns, as assessed by the MD and DASH diet, were significantly associated with lower risk of AN. Additionally, we identified lower odds for AN and nonadvanced adenoma compared with no neoplasia for higher grain scores for all the dietary patterns studied. Other food categories that comprise the dietary pattern scores had mixed associations with the MSCF outcomes. Several other studies have examined associations between dietary patterns and risk for CRC but to our knowledge, no studies have explored these associations among US veterans.

These results also indicate study participants had better than average (based on a 50% threshold) dietary quality according to the HEI and DASH diet scoring methods we used, but poor dietary quality according to the MD scoring method. The mean HEI scores for the present study were higher than a US Department of Agriculture study by Dong et al that compared dietary quality between veterans and nonveterans using the HEI, for which veterans’ expected HEI score was 45.6 of 100.⁸ This could be explained by the fact that the participants needed to be healthy to be eligible and those with healthier behaviors overall may have self-selected into the study due to motivation for screening during a time when screening was not yet commonplace. ³⁶ Similarly, participants of the present study had higher adherence to the DASH diet (63.1%) than adolescents with diabetes in a study by Günther et al. Conversely, firefighters who were coached to use a Mediterranean-style dietary pattern and dietary had higher adherence to MD than did participants in this study.²⁷

A closer examination of specific food category component scores that comprise the 3 distinct dietary patterns revealed mixed results from the multinomial modeling, which may have to do with the guideline thresholds used to calculate the dietary scores. When analyzed separately in the logistic regression models for their associations with nonadvanced adenomas and AN compared with no neoplasia, higher MD and DASH fruit scores (but not HEI fruit scores) were found to be significant. Other studies have had mixed findings when attempting to test for associations of fruit intake with adenoma recurrence.^10,37

This study had some unexpected findings. Vegetable intake was not associated with nonadvanced adenomas or AN risk. Studies of food categories have consistently found vegetable (specifically cruciferous ones) intake to be linked with lower odds for cancers.³⁸ Likewise, the red meat category, which was only a unique food category in the MD score, was not associated with nonadvanced adenomas or AN. Despite consistent literature suggesting higher intake of red meat and processed meats increases CRC risk, in 2019 the Nutritional Recommendations Consortium indicated that the evidence was weak.^39,40 This study showed higher DASH diet scores for low-fat dairy, which were maximized when participants reported at least 50% of their dairy servings per day as being low-fat, had lower odds for AN. Yet, the MD scores for low-fat dairy had no association with either outcome; their calculation was based on total number of servings per week. This difference in findings suggests the fat intake ratio may be more relevant to CRC risk than intake quantity.

The literature is mixed regarding fatty acid intake and CRC risk, which may be relevant to both dairy and meat intake. One systematic review and meta-analysis found dietary fat and types of fatty acid intake had no association with CRC risk.⁴¹ However, a more recent meta-analysis that assessed both dietary intake and plasma levels of fatty acids did find some statistically significant differences for various types of fatty acids and CRC risk.⁴²

The findings in the present study that grain intake is associated with lower odds for more severe colonoscopy findings among veterans are notable.⁴³ Lieberman et al, using the CSP #380 data, found that cereal fiber intake was associated with a lower odds for AN compared with hyperplastic polyps (OR, 0.98 [95% CI, 0.96- 1.00]).¹⁸ Similarly, Hullings et al determined that older adults in the highest quintile of cereal fiber intake had significantly lower odds of CRC than those in lower odds for CRC when compared with lowest quintile (OR, 0.89 [95% CI, 0.83- 0.96]; P < .001).⁴⁴ These findings support existing guidance that prioritizes whole grains as a key source of dietary fiber for CRC prevention.

A recent literature review on fiber, fat, and CRC risk suggested a consensus regarding one protective mechanism: dietary fiber from grains modulates the gut microbiota by promoting butyrate synthesis.⁴⁵ Butyrate is a short-chain fatty acid that supports energy production in colonocytes and has tumor-suppressing properties.⁴⁶ Our findings suggest there could be more to learn about the relationship between butyrate production and reduction of CRC risk through metabolomic studies that use measurements of plasma butyrate. These studies may examine associations between not just a singular food or food category, but rather food patterns that include fruits, vegetables, nuts and seeds, and whole grains known to promote butyrate production and plasma butyrate.⁴⁷

Improved understanding of mechanisms and risk-modifying lifestyle factors such as dietary patterns may enhance prevention strategies. Identifying the collective chemopreventive characteristics of a specific dietary pattern (eg, MD) will be helpful to clinicians and health care staff to promote healthy eating to reduce cancer risk. More studies are needed to understand whether such promotion is more clinically applicable and effective for patients, as compared with eating more or less of specific foods (eg, more whole grains, less red meat). Furthermore, considering important environmental factors collectively beyond dietary patterns may offer a way to better tailor screening and implement a variety of lifestyle interventions. In the literature, this is often referred to as a teachable moment when patients’ attentions are captured and may position them to be more receptive to guidance.⁴⁸

Limitations

This study has several important limitations and leaves opportunities for future studies that explore the role of dietary patterns and AN or CRC risk. First, the FFQ data used to calculate dietary pattern scores used in analysis were only captured at baseline, and there are nearly 3 decades across the study period. However, it is widely assumed that the diets of older adults, like those included in this study, remain stable over time which is appropriate given our sample population was aged 50 to 75 years when the baseline FFQ data were collected.^49-51 Additionally, while the HEI is a well-documented, standard scoring method for dietary quality, there are multitudes of dietary pattern scoring approaches for MD and DASH.^23,52,53 Finally, findings from this study using the sample of veterans may not be generalizable to a broader population. Future longitudinal studies that test for a clinically significant change threshold are warranted.

Conclusion

Results of this study suggest future research should further explore the effects of dietary patterns, particularly intake of specific food groups in combination, as opposed to individual nutrients or food items, on AN and CRC risk. Possible studies might explore these dietary patterns for their mechanistic role in altering the microbiome metabolism, which may influence CRC outcomes or include diet in a more comprehensive, holistic risk score that could be used to predict colonic neoplasia risk or in intervention studies that assess the effects of dietary changes on long-term CRC prevention. We suggest there are differences in people’s dietary intake patterns that might be important to consider when implementing tailored approaches to CRC risk mitigation.

During an adenovirus (AdV) outbreak among recruits and staff at the Marine Corps Recruit Depot (MCRD) in San Diego, an investigation revealed that the earlier individuals working at the site received vaccination, the better. The clinical team found that accelerating the vaccination schedule could help prevent further outbreaks, medical separations, and training disruption.

From July 1, 2024, through September 23, 2024, a total of 212 trainees and staff developed AdV and 28 were hospitalized. Nine patients were hospitalized with AdV pneumo­nia within a 2-week period; 3 were admitted to the intensive care unit. Outpatient acute respiratory disease (ARD) cases also increased, with recruits accounting for nearly 97% of the AdV outbreak cases.

AdV is a frequent cause of illness among military recruits. Research has found that up to 80% of cases of febrile ARD in recruits are due to AdV, and 20% result in hospitalization. 

The military developed and implemented a live, oral vaccine against AdV serotypes 4 and 7 (most common in recruits) starting in the 1970s, reducing febrile respiratory illness in recruit training sites by 50% and AdV infection by > 90%. However, the manufacturer halted production of the vaccine in 1995. By 1999, vaccine supply was depleted, and ARD cases rose. A replacement vaccine introduced in 2011 proved 99% effective, leading to a dramatic 100-fold decline in AdV disease among military trainees. 

While the vaccine is effective, outbreaks are still possible among closely congregating groups like military trainees. AdV pneumonia cases spiked as the virus spread through the training companies and into new companies when they arrived at the MCRD in early July 2024. Most new infections were in recruits who had missed the AdV vaccination day.

Early symptoms of AdV may be very mild, and some recruits were likely already symptomatic when vaccinated. Aggressive environmental cleaning, separation of sick and well recruits, masking, and other nonpharmaceutical interventions did not slow the spread.

The preventive medicine and public health teams noted that AdV vaccination was being administered 11 days postarrival, to allow for pregnancy testing, and for assessing vaccine titers. US Department of Defense regula­tions do not dictate precise vaccination schedules. Implementation of the regulation varies among military train­ing sites. 

After reviewing other training sites’ vaccine timing schedules (most required vaccination by day 6 postarrival) and determin­ing the time required for immu­nity, the medical teams at MCRD recommended shifting AdV vac­cine administration, along with other standard vaccines, from day 11 to day 1 postarrival. Two weeks after the schedule change, overall incidence began declining rapidly.

Nearly 75% of patients had coinfections with other respiratory patho­gens, most notably seasonal coronaviruses, COVID-19, and rhinovirus/enterovirus, suggesting that infection with AdV may increase susceptibility to other viruses, a finding that has not been identified in previous AdV out­breaks. Newly increased testing sensitiv­ity associated with multiplex respiratory pathogen PCR availability may have been a factor in coinfection identification during this outbreak.

AdV is a significant medical threat to military recruits. Early vaccination, the investigators advise, should remain “a central tenet for preven­tion and control of communicable diseases in these high-risk, congregate settings.”

During an adenovirus (AdV) outbreak among recruits and staff at the Marine Corps Recruit Depot (MCRD) in San Diego, an investigation revealed that the earlier individuals working at the site received vaccination, the better. The clinical team found that accelerating the vaccination schedule could help prevent further outbreaks, medical separations, and training disruption.

From July 1, 2024, through September 23, 2024, a total of 212 trainees and staff developed AdV and 28 were hospitalized. Nine patients were hospitalized with AdV pneumo­nia within a 2-week period; 3 were admitted to the intensive care unit. Outpatient acute respiratory disease (ARD) cases also increased, with recruits accounting for nearly 97% of the AdV outbreak cases.

AdV is a frequent cause of illness among military recruits. Research has found that up to 80% of cases of febrile ARD in recruits are due to AdV, and 20% result in hospitalization. 

The military developed and implemented a live, oral vaccine against AdV serotypes 4 and 7 (most common in recruits) starting in the 1970s, reducing febrile respiratory illness in recruit training sites by 50% and AdV infection by > 90%. However, the manufacturer halted production of the vaccine in 1995. By 1999, vaccine supply was depleted, and ARD cases rose. A replacement vaccine introduced in 2011 proved 99% effective, leading to a dramatic 100-fold decline in AdV disease among military trainees. 

While the vaccine is effective, outbreaks are still possible among closely congregating groups like military trainees. AdV pneumonia cases spiked as the virus spread through the training companies and into new companies when they arrived at the MCRD in early July 2024. Most new infections were in recruits who had missed the AdV vaccination day.

Early symptoms of AdV may be very mild, and some recruits were likely already symptomatic when vaccinated. Aggressive environmental cleaning, separation of sick and well recruits, masking, and other nonpharmaceutical interventions did not slow the spread.

The preventive medicine and public health teams noted that AdV vaccination was being administered 11 days postarrival, to allow for pregnancy testing, and for assessing vaccine titers. US Department of Defense regula­tions do not dictate precise vaccination schedules. Implementation of the regulation varies among military train­ing sites. 

After reviewing other training sites’ vaccine timing schedules (most required vaccination by day 6 postarrival) and determin­ing the time required for immu­nity, the medical teams at MCRD recommended shifting AdV vac­cine administration, along with other standard vaccines, from day 11 to day 1 postarrival. Two weeks after the schedule change, overall incidence began declining rapidly.

Nearly 75% of patients had coinfections with other respiratory patho­gens, most notably seasonal coronaviruses, COVID-19, and rhinovirus/enterovirus, suggesting that infection with AdV may increase susceptibility to other viruses, a finding that has not been identified in previous AdV out­breaks. Newly increased testing sensitiv­ity associated with multiplex respiratory pathogen PCR availability may have been a factor in coinfection identification during this outbreak.

AdV is a significant medical threat to military recruits. Early vaccination, the investigators advise, should remain “a central tenet for preven­tion and control of communicable diseases in these high-risk, congregate settings.”

During an adenovirus (AdV) outbreak among recruits and staff at the Marine Corps Recruit Depot (MCRD) in San Diego, an investigation revealed that the earlier individuals working at the site received vaccination, the better. The clinical team found that accelerating the vaccination schedule could help prevent further outbreaks, medical separations, and training disruption.

From July 1, 2024, through September 23, 2024, a total of 212 trainees and staff developed AdV and 28 were hospitalized. Nine patients were hospitalized with AdV pneumo­nia within a 2-week period; 3 were admitted to the intensive care unit. Outpatient acute respiratory disease (ARD) cases also increased, with recruits accounting for nearly 97% of the AdV outbreak cases.

AdV is a frequent cause of illness among military recruits. Research has found that up to 80% of cases of febrile ARD in recruits are due to AdV, and 20% result in hospitalization. 

The military developed and implemented a live, oral vaccine against AdV serotypes 4 and 7 (most common in recruits) starting in the 1970s, reducing febrile respiratory illness in recruit training sites by 50% and AdV infection by > 90%. However, the manufacturer halted production of the vaccine in 1995. By 1999, vaccine supply was depleted, and ARD cases rose. A replacement vaccine introduced in 2011 proved 99% effective, leading to a dramatic 100-fold decline in AdV disease among military trainees. 

While the vaccine is effective, outbreaks are still possible among closely congregating groups like military trainees. AdV pneumonia cases spiked as the virus spread through the training companies and into new companies when they arrived at the MCRD in early July 2024. Most new infections were in recruits who had missed the AdV vaccination day.

Early symptoms of AdV may be very mild, and some recruits were likely already symptomatic when vaccinated. Aggressive environmental cleaning, separation of sick and well recruits, masking, and other nonpharmaceutical interventions did not slow the spread.

The preventive medicine and public health teams noted that AdV vaccination was being administered 11 days postarrival, to allow for pregnancy testing, and for assessing vaccine titers. US Department of Defense regula­tions do not dictate precise vaccination schedules. Implementation of the regulation varies among military train­ing sites. 

After reviewing other training sites’ vaccine timing schedules (most required vaccination by day 6 postarrival) and determin­ing the time required for immu­nity, the medical teams at MCRD recommended shifting AdV vac­cine administration, along with other standard vaccines, from day 11 to day 1 postarrival. Two weeks after the schedule change, overall incidence began declining rapidly.

Nearly 75% of patients had coinfections with other respiratory patho­gens, most notably seasonal coronaviruses, COVID-19, and rhinovirus/enterovirus, suggesting that infection with AdV may increase susceptibility to other viruses, a finding that has not been identified in previous AdV out­breaks. Newly increased testing sensitiv­ity associated with multiplex respiratory pathogen PCR availability may have been a factor in coinfection identification during this outbreak.

AdV is a significant medical threat to military recruits. Early vaccination, the investigators advise, should remain “a central tenet for preven­tion and control of communicable diseases in these high-risk, congregate settings.”

Early-onset gastrointestinal (GI) cancers diagnosed before age 50 are rising at alarming rates worldwide, underscoring the need for enhanced prevention strategies and early detection, said the authors of a JAMA review.

In the US, early-onset GI cancers are increasing faster than any other type of early-onset cancer, including breast cancer. The trend is not limited to colorectal cancer (CRC). Gastric, pancreatic, esophageal, as well as many biliary tract and appendix cancers, are also on the rise in young adults, Kimmie Ng, MD, MPH, and Thejus Jayakrishnan, MD, both with Dana-Farber Cancer Institute, Boston, noted in their article.

The increase in early-onset GI cancers follows a “birth cohort effect,” with generational variation in risk, suggesting a potential association with changes in environmental exposures, Ng explained in an accompanying JAMA podcast.

All these GI cancers link strongly to multiple modifiable risk factors, and it is a “top area of investigation to determine exactly what environmental exposures are at play,” Ng added.

For many of these GI cancers, obesity has been the “leading hypothesis” given that rising rates seem to parallel the increase in incidence of these early-onset GI cancers, Ng explained.

“But we also have evidence, particularly strong for colorectal cancer, that dietary patterns, such as consuming a Western diet, as well as sedentary behavior and lifestyles seem to be associated with a significantly higher risk of developing these cancers at an age under 50,” Ng said.

 

Rising Incidence 

Globally, among early-onset GI cancers reported in 2022, CRC was the most common (54%), followed by gastric cancer (24%), esophageal cancer (13%), and pancreatic cancer (9%).

In the US in 2022, 20,805 individuals were diagnosed with early-onset CRC, 2689 with early-onset gastric cancer, 2657 with early-onset pancreatic cancer, and 875 with early-onset esophageal cancer.

Since the mid-1990s, CRC among adults of all ages in the US declined by 1.3%-4.2% annually but early-onset CRC increased by roughly 2% per year in both men and women, and currently makes up about 14% of all CRC cases.

Early-onset pancreatic cancer and esophageal cancer each currently make up about 5% of all cases of these cancers in the US.

Between 2010 and 2019, the number of newly diagnosed cases of early-onset GI cancers rose by nearly about 15%, with Black, Hispanic, Indigenous ancestry, and women disproportionately affected, Ng and coauthors noted in a related review published in the British Journal of Surgery.

 

Modifiable and Nonmodifiable Risk Factors 

Along with obesity and poor diet, other modifiable risk factors for early-onset GI cancers include sedentary lifestyle, cigarette smoking, and alcohol consumption.

Nonmodifiable risk factors include family history, hereditary cancer syndromes such as Lynch syndrome and inflammatory bowel disease.

Roughly 15%-30% of early-onset GI cancers have pathogenic germline variants in genes such as DNA mismatch repair genes and BRCA1/2.

All individuals with early-onset GI cancers should undergo germline and somatic genetic testing to guide treatment, screen for other cancers (eg, endometrial cancer in Lynch syndrome), and assess familial risk, Ng and Jayakrishnan advised.

 

Treatment Challenges

Treatment for early-onset GI cancers is generally similar to later-onset GI cancers and prognosis for patients with early-onset GI cancers is “similar to or worse” than that for patients with later-onset GI cancers, highlighting the need for improved methods of prevention and early detection, the authors said.

Ng noted that younger cancer patients often face more challenges after diagnosis than older patients and benefit from multidisciplinary care, including referral for fertility counseling and preservation if appropriate, and psychosocial support.

“It is very difficult and challenging to receive a cancer diagnosis no matter what age you are, but when a person is diagnosed in their 20s, 30s, or 40s, there are unique challenges,” Ng said.

Studies have documented “much higher levels of psychosocial distress, depression and anxiety” in early-onset cancer patients, “and they also often experience more financial toxicity, disruptions in their education as well as their career and there may be fertility concerns,” Ng added.

 

Diagnostic Delays and Screening

Currently, screening is not recommended for most early-onset GI cancers — with the exception of CRC, with screening recommended for average-risk adults in the US starting at age 45.

Yet, despite this recommendation, fewer than 1 in 5 (19.7%) US adults aged 45-49 years were screened in 2021, indicating a significant gap in early detection efforts.

High-risk individuals, such as those with Lynch syndrome, a first-degree relative with CRC, or advanced colorectal adenoma, should begin CRC screening earlier, at an age determined by the specific risk factor.

“Studies have shown significant delays in diagnosis among younger patients. It’s important that prompt diagnosis happens so that these patients do not end up being diagnosed with advanced or metastatic stages of cancer, as they often are,” Ng said.

“Screening adherence is absolutely critical,” co-author Jayakrishnan added in a news release.

“We have strong evidence that colorectal cancer screening saves lives by reducing both the number of people who develop colorectal cancer and the number of people who die from it. Each missed screening is a lost opportunity to detect cancer early when it is more treatable, or to prevent cancer altogether by identifying and removing precancerous polyps,” Jayakrishnan said.This research had no funding. Ng reported receipt of nonfinancial support from Pharmavite, institutional grants from Janssen, and personal fees from Bayer, Seagen, GlaxoSmithKline, Pfizer, CytomX, Jazz Pharmaceuticals, Revolution Medicines, Redesign Health, AbbVie, Etiome, and CRICO. Ng is an associate editor of JAMA but was not involved in any of the decisions regarding review of the manuscript or its acceptance. Jayakrishnan had no disclosures.

A version of this article appeared on Medscape.com.

Early-onset gastrointestinal (GI) cancers diagnosed before age 50 are rising at alarming rates worldwide, underscoring the need for enhanced prevention strategies and early detection, said the authors of a JAMA review.

In the US, early-onset GI cancers are increasing faster than any other type of early-onset cancer, including breast cancer. The trend is not limited to colorectal cancer (CRC). Gastric, pancreatic, esophageal, as well as many biliary tract and appendix cancers, are also on the rise in young adults, Kimmie Ng, MD, MPH, and Thejus Jayakrishnan, MD, both with Dana-Farber Cancer Institute, Boston, noted in their article.

The increase in early-onset GI cancers follows a “birth cohort effect,” with generational variation in risk, suggesting a potential association with changes in environmental exposures, Ng explained in an accompanying JAMA podcast.

All these GI cancers link strongly to multiple modifiable risk factors, and it is a “top area of investigation to determine exactly what environmental exposures are at play,” Ng added.

For many of these GI cancers, obesity has been the “leading hypothesis” given that rising rates seem to parallel the increase in incidence of these early-onset GI cancers, Ng explained.

“But we also have evidence, particularly strong for colorectal cancer, that dietary patterns, such as consuming a Western diet, as well as sedentary behavior and lifestyles seem to be associated with a significantly higher risk of developing these cancers at an age under 50,” Ng said.

 

Rising Incidence 

Globally, among early-onset GI cancers reported in 2022, CRC was the most common (54%), followed by gastric cancer (24%), esophageal cancer (13%), and pancreatic cancer (9%).

In the US in 2022, 20,805 individuals were diagnosed with early-onset CRC, 2689 with early-onset gastric cancer, 2657 with early-onset pancreatic cancer, and 875 with early-onset esophageal cancer.

Since the mid-1990s, CRC among adults of all ages in the US declined by 1.3%-4.2% annually but early-onset CRC increased by roughly 2% per year in both men and women, and currently makes up about 14% of all CRC cases.

Early-onset pancreatic cancer and esophageal cancer each currently make up about 5% of all cases of these cancers in the US.

Between 2010 and 2019, the number of newly diagnosed cases of early-onset GI cancers rose by nearly about 15%, with Black, Hispanic, Indigenous ancestry, and women disproportionately affected, Ng and coauthors noted in a related review published in the British Journal of Surgery.

 

Modifiable and Nonmodifiable Risk Factors 

Along with obesity and poor diet, other modifiable risk factors for early-onset GI cancers include sedentary lifestyle, cigarette smoking, and alcohol consumption.

Nonmodifiable risk factors include family history, hereditary cancer syndromes such as Lynch syndrome and inflammatory bowel disease.

Roughly 15%-30% of early-onset GI cancers have pathogenic germline variants in genes such as DNA mismatch repair genes and BRCA1/2.

All individuals with early-onset GI cancers should undergo germline and somatic genetic testing to guide treatment, screen for other cancers (eg, endometrial cancer in Lynch syndrome), and assess familial risk, Ng and Jayakrishnan advised.

 

Treatment Challenges

Treatment for early-onset GI cancers is generally similar to later-onset GI cancers and prognosis for patients with early-onset GI cancers is “similar to or worse” than that for patients with later-onset GI cancers, highlighting the need for improved methods of prevention and early detection, the authors said.

Ng noted that younger cancer patients often face more challenges after diagnosis than older patients and benefit from multidisciplinary care, including referral for fertility counseling and preservation if appropriate, and psychosocial support.

“It is very difficult and challenging to receive a cancer diagnosis no matter what age you are, but when a person is diagnosed in their 20s, 30s, or 40s, there are unique challenges,” Ng said.

Studies have documented “much higher levels of psychosocial distress, depression and anxiety” in early-onset cancer patients, “and they also often experience more financial toxicity, disruptions in their education as well as their career and there may be fertility concerns,” Ng added.

 

Diagnostic Delays and Screening

Currently, screening is not recommended for most early-onset GI cancers — with the exception of CRC, with screening recommended for average-risk adults in the US starting at age 45.

Yet, despite this recommendation, fewer than 1 in 5 (19.7%) US adults aged 45-49 years were screened in 2021, indicating a significant gap in early detection efforts.

High-risk individuals, such as those with Lynch syndrome, a first-degree relative with CRC, or advanced colorectal adenoma, should begin CRC screening earlier, at an age determined by the specific risk factor.

“Studies have shown significant delays in diagnosis among younger patients. It’s important that prompt diagnosis happens so that these patients do not end up being diagnosed with advanced or metastatic stages of cancer, as they often are,” Ng said.

“Screening adherence is absolutely critical,” co-author Jayakrishnan added in a news release.

“We have strong evidence that colorectal cancer screening saves lives by reducing both the number of people who develop colorectal cancer and the number of people who die from it. Each missed screening is a lost opportunity to detect cancer early when it is more treatable, or to prevent cancer altogether by identifying and removing precancerous polyps,” Jayakrishnan said.This research had no funding. Ng reported receipt of nonfinancial support from Pharmavite, institutional grants from Janssen, and personal fees from Bayer, Seagen, GlaxoSmithKline, Pfizer, CytomX, Jazz Pharmaceuticals, Revolution Medicines, Redesign Health, AbbVie, Etiome, and CRICO. Ng is an associate editor of JAMA but was not involved in any of the decisions regarding review of the manuscript or its acceptance. Jayakrishnan had no disclosures.

A version of this article appeared on Medscape.com.

Early-onset gastrointestinal (GI) cancers diagnosed before age 50 are rising at alarming rates worldwide, underscoring the need for enhanced prevention strategies and early detection, said the authors of a JAMA review.

In the US, early-onset GI cancers are increasing faster than any other type of early-onset cancer, including breast cancer. The trend is not limited to colorectal cancer (CRC). Gastric, pancreatic, esophageal, as well as many biliary tract and appendix cancers, are also on the rise in young adults, Kimmie Ng, MD, MPH, and Thejus Jayakrishnan, MD, both with Dana-Farber Cancer Institute, Boston, noted in their article.

The increase in early-onset GI cancers follows a “birth cohort effect,” with generational variation in risk, suggesting a potential association with changes in environmental exposures, Ng explained in an accompanying JAMA podcast.

All these GI cancers link strongly to multiple modifiable risk factors, and it is a “top area of investigation to determine exactly what environmental exposures are at play,” Ng added.

For many of these GI cancers, obesity has been the “leading hypothesis” given that rising rates seem to parallel the increase in incidence of these early-onset GI cancers, Ng explained.

“But we also have evidence, particularly strong for colorectal cancer, that dietary patterns, such as consuming a Western diet, as well as sedentary behavior and lifestyles seem to be associated with a significantly higher risk of developing these cancers at an age under 50,” Ng said.

 

Rising Incidence 

Globally, among early-onset GI cancers reported in 2022, CRC was the most common (54%), followed by gastric cancer (24%), esophageal cancer (13%), and pancreatic cancer (9%).

In the US in 2022, 20,805 individuals were diagnosed with early-onset CRC, 2689 with early-onset gastric cancer, 2657 with early-onset pancreatic cancer, and 875 with early-onset esophageal cancer.

Since the mid-1990s, CRC among adults of all ages in the US declined by 1.3%-4.2% annually but early-onset CRC increased by roughly 2% per year in both men and women, and currently makes up about 14% of all CRC cases.

Early-onset pancreatic cancer and esophageal cancer each currently make up about 5% of all cases of these cancers in the US.

Between 2010 and 2019, the number of newly diagnosed cases of early-onset GI cancers rose by nearly about 15%, with Black, Hispanic, Indigenous ancestry, and women disproportionately affected, Ng and coauthors noted in a related review published in the British Journal of Surgery.

 

Modifiable and Nonmodifiable Risk Factors 

Along with obesity and poor diet, other modifiable risk factors for early-onset GI cancers include sedentary lifestyle, cigarette smoking, and alcohol consumption.

Nonmodifiable risk factors include family history, hereditary cancer syndromes such as Lynch syndrome and inflammatory bowel disease.

Roughly 15%-30% of early-onset GI cancers have pathogenic germline variants in genes such as DNA mismatch repair genes and BRCA1/2.

All individuals with early-onset GI cancers should undergo germline and somatic genetic testing to guide treatment, screen for other cancers (eg, endometrial cancer in Lynch syndrome), and assess familial risk, Ng and Jayakrishnan advised.

 

Treatment Challenges

Treatment for early-onset GI cancers is generally similar to later-onset GI cancers and prognosis for patients with early-onset GI cancers is “similar to or worse” than that for patients with later-onset GI cancers, highlighting the need for improved methods of prevention and early detection, the authors said.

Ng noted that younger cancer patients often face more challenges after diagnosis than older patients and benefit from multidisciplinary care, including referral for fertility counseling and preservation if appropriate, and psychosocial support.

“It is very difficult and challenging to receive a cancer diagnosis no matter what age you are, but when a person is diagnosed in their 20s, 30s, or 40s, there are unique challenges,” Ng said.

Studies have documented “much higher levels of psychosocial distress, depression and anxiety” in early-onset cancer patients, “and they also often experience more financial toxicity, disruptions in their education as well as their career and there may be fertility concerns,” Ng added.

 

Diagnostic Delays and Screening

Currently, screening is not recommended for most early-onset GI cancers — with the exception of CRC, with screening recommended for average-risk adults in the US starting at age 45.

Yet, despite this recommendation, fewer than 1 in 5 (19.7%) US adults aged 45-49 years were screened in 2021, indicating a significant gap in early detection efforts.

High-risk individuals, such as those with Lynch syndrome, a first-degree relative with CRC, or advanced colorectal adenoma, should begin CRC screening earlier, at an age determined by the specific risk factor.

“Studies have shown significant delays in diagnosis among younger patients. It’s important that prompt diagnosis happens so that these patients do not end up being diagnosed with advanced or metastatic stages of cancer, as they often are,” Ng said.

“Screening adherence is absolutely critical,” co-author Jayakrishnan added in a news release.

“We have strong evidence that colorectal cancer screening saves lives by reducing both the number of people who develop colorectal cancer and the number of people who die from it. Each missed screening is a lost opportunity to detect cancer early when it is more treatable, or to prevent cancer altogether by identifying and removing precancerous polyps,” Jayakrishnan said.This research had no funding. Ng reported receipt of nonfinancial support from Pharmavite, institutional grants from Janssen, and personal fees from Bayer, Seagen, GlaxoSmithKline, Pfizer, CytomX, Jazz Pharmaceuticals, Revolution Medicines, Redesign Health, AbbVie, Etiome, and CRICO. Ng is an associate editor of JAMA but was not involved in any of the decisions regarding review of the manuscript or its acceptance. Jayakrishnan had no disclosures.

A version of this article appeared on Medscape.com.

Like diners saving on drinks, endoscopists can safely forgo sterile water in favor of tap, reducing both environmental and financial costs, according to a recent narrative review.

“No direct evidence supports the recommendation and widespread use of sterile water during gastrointestinal endosco-py procedures,” lead author Deepak Agrawal, MD, chief of gastroenterology & hepatology at the Dell Medical School, University Texas at Austin, and colleagues, wrote in Gastro Hep Advances. “Guidelines recommending sterile water during endoscopy are based on limited evidence and mostly expert opinions.”

After reviewing the literature back to 1975, Dr. Agrawal and colleagues considered the use of sterile water in endoscopy via three frameworks: medical evidence and guidelines, environmental and broader health effects, and financial costs.

Only 2 studies – both from the 1990s – directly compared sterile and tap water use in endoscopy. Neither showed an increased risk of infection from tap water. In fact, some cultures from allegedly sterile water bottles grew pathogenic bacteria, while no patient complications were reported in either study.

“The recommendations for sterile water contradict observations in other medical care scenarios, for example, for the irrigation of open wounds,” Dr. Agrawal and colleagues noted. “Similarly, there is no benefit in using sterile water for enteral feeds in immunosuppressed patients, and tap water enemas are routinely acceptable for colon cleansing before sigmoidoscopies in all patients, irrespective of immune status.”

Current guidelines, including the 2021 US multisociety guideline on reprocessing flexible GI endoscopes and accessories, recommend sterile water for procedures involving mucosal penetration but acknowledge low-quality supporting evidence. These recommendations are based on outdated studies, some unrelated to GI endoscopy, Dr. Agrawal and colleagues pointed out, and rely heavily on cross-referenced opinion statements rather than clinical data.

They went on to suggest a concerning possibility: all those plastic bottles may actually cause more health problems than prevent them. The review estimates that the production and transportation of sterile water bottles contributes over 6,000 metric tons of emissions per year from US endoscopy units alone. What’s more, as discarded bottles break down, they release greenhouse gases and microplastics, the latter of which have been linked to cardiovascular disease, inflammatory bowel disease, and endocrine disruption.

Dr. Agrawal and colleagues also underscored the financial toxicity of sterile water bottles. Considering a 1-liter bottle of sterile water costs $3-10, an endoscopy unit performing 30 procedures per day spends approximately $1,000-3,000 per month on bottled water alone. Scaled nationally, the routine use of sterile water costs tens of millions of dollars each year, not counting indirect expenses associated with stocking and waste disposal.

Considering the dubious clinical upside against the apparent environmental and financial downsides, Dr. Agrawal and colleagues urged endoscopy units to rethink routine sterile water use. 

They proposed a pragmatic model: start the day with a new sterile or reusable bottle, refill with tap water for subsequent cases, and recycle the bottle at day’s end. Institutions should ensure their tap water meets safety standards, they added, such as those outlined in the Joint Commission’s 2022 R3 Report on standards for water management.

Dr. Agrawal and colleagues also called on GI societies to revise existing guidance to reflect today’s clinical and environmental realities. Until strong evidence supports the need for sterile water, they wrote, the smarter, safer, and more sustainable option may be simply turning on the tap.

The investigators disclosed relationships with Guardant, Exact Sciences, Freenome, and others.
 

Like diners saving on drinks, endoscopists can safely forgo sterile water in favor of tap, reducing both environmental and financial costs, according to a recent narrative review.

“No direct evidence supports the recommendation and widespread use of sterile water during gastrointestinal endosco-py procedures,” lead author Deepak Agrawal, MD, chief of gastroenterology & hepatology at the Dell Medical School, University Texas at Austin, and colleagues, wrote in Gastro Hep Advances. “Guidelines recommending sterile water during endoscopy are based on limited evidence and mostly expert opinions.”

After reviewing the literature back to 1975, Dr. Agrawal and colleagues considered the use of sterile water in endoscopy via three frameworks: medical evidence and guidelines, environmental and broader health effects, and financial costs.

Only 2 studies – both from the 1990s – directly compared sterile and tap water use in endoscopy. Neither showed an increased risk of infection from tap water. In fact, some cultures from allegedly sterile water bottles grew pathogenic bacteria, while no patient complications were reported in either study.

“The recommendations for sterile water contradict observations in other medical care scenarios, for example, for the irrigation of open wounds,” Dr. Agrawal and colleagues noted. “Similarly, there is no benefit in using sterile water for enteral feeds in immunosuppressed patients, and tap water enemas are routinely acceptable for colon cleansing before sigmoidoscopies in all patients, irrespective of immune status.”

Current guidelines, including the 2021 US multisociety guideline on reprocessing flexible GI endoscopes and accessories, recommend sterile water for procedures involving mucosal penetration but acknowledge low-quality supporting evidence. These recommendations are based on outdated studies, some unrelated to GI endoscopy, Dr. Agrawal and colleagues pointed out, and rely heavily on cross-referenced opinion statements rather than clinical data.

They went on to suggest a concerning possibility: all those plastic bottles may actually cause more health problems than prevent them. The review estimates that the production and transportation of sterile water bottles contributes over 6,000 metric tons of emissions per year from US endoscopy units alone. What’s more, as discarded bottles break down, they release greenhouse gases and microplastics, the latter of which have been linked to cardiovascular disease, inflammatory bowel disease, and endocrine disruption.

Dr. Agrawal and colleagues also underscored the financial toxicity of sterile water bottles. Considering a 1-liter bottle of sterile water costs $3-10, an endoscopy unit performing 30 procedures per day spends approximately $1,000-3,000 per month on bottled water alone. Scaled nationally, the routine use of sterile water costs tens of millions of dollars each year, not counting indirect expenses associated with stocking and waste disposal.

Considering the dubious clinical upside against the apparent environmental and financial downsides, Dr. Agrawal and colleagues urged endoscopy units to rethink routine sterile water use. 

They proposed a pragmatic model: start the day with a new sterile or reusable bottle, refill with tap water for subsequent cases, and recycle the bottle at day’s end. Institutions should ensure their tap water meets safety standards, they added, such as those outlined in the Joint Commission’s 2022 R3 Report on standards for water management.

Dr. Agrawal and colleagues also called on GI societies to revise existing guidance to reflect today’s clinical and environmental realities. Until strong evidence supports the need for sterile water, they wrote, the smarter, safer, and more sustainable option may be simply turning on the tap.

The investigators disclosed relationships with Guardant, Exact Sciences, Freenome, and others.
 

Like diners saving on drinks, endoscopists can safely forgo sterile water in favor of tap, reducing both environmental and financial costs, according to a recent narrative review.

“No direct evidence supports the recommendation and widespread use of sterile water during gastrointestinal endosco-py procedures,” lead author Deepak Agrawal, MD, chief of gastroenterology & hepatology at the Dell Medical School, University Texas at Austin, and colleagues, wrote in Gastro Hep Advances. “Guidelines recommending sterile water during endoscopy are based on limited evidence and mostly expert opinions.”

After reviewing the literature back to 1975, Dr. Agrawal and colleagues considered the use of sterile water in endoscopy via three frameworks: medical evidence and guidelines, environmental and broader health effects, and financial costs.

Only 2 studies – both from the 1990s – directly compared sterile and tap water use in endoscopy. Neither showed an increased risk of infection from tap water. In fact, some cultures from allegedly sterile water bottles grew pathogenic bacteria, while no patient complications were reported in either study.

“The recommendations for sterile water contradict observations in other medical care scenarios, for example, for the irrigation of open wounds,” Dr. Agrawal and colleagues noted. “Similarly, there is no benefit in using sterile water for enteral feeds in immunosuppressed patients, and tap water enemas are routinely acceptable for colon cleansing before sigmoidoscopies in all patients, irrespective of immune status.”

Current guidelines, including the 2021 US multisociety guideline on reprocessing flexible GI endoscopes and accessories, recommend sterile water for procedures involving mucosal penetration but acknowledge low-quality supporting evidence. These recommendations are based on outdated studies, some unrelated to GI endoscopy, Dr. Agrawal and colleagues pointed out, and rely heavily on cross-referenced opinion statements rather than clinical data.

They went on to suggest a concerning possibility: all those plastic bottles may actually cause more health problems than prevent them. The review estimates that the production and transportation of sterile water bottles contributes over 6,000 metric tons of emissions per year from US endoscopy units alone. What’s more, as discarded bottles break down, they release greenhouse gases and microplastics, the latter of which have been linked to cardiovascular disease, inflammatory bowel disease, and endocrine disruption.

Dr. Agrawal and colleagues also underscored the financial toxicity of sterile water bottles. Considering a 1-liter bottle of sterile water costs $3-10, an endoscopy unit performing 30 procedures per day spends approximately $1,000-3,000 per month on bottled water alone. Scaled nationally, the routine use of sterile water costs tens of millions of dollars each year, not counting indirect expenses associated with stocking and waste disposal.

Considering the dubious clinical upside against the apparent environmental and financial downsides, Dr. Agrawal and colleagues urged endoscopy units to rethink routine sterile water use. 

They proposed a pragmatic model: start the day with a new sterile or reusable bottle, refill with tap water for subsequent cases, and recycle the bottle at day’s end. Institutions should ensure their tap water meets safety standards, they added, such as those outlined in the Joint Commission’s 2022 R3 Report on standards for water management.

Dr. Agrawal and colleagues also called on GI societies to revise existing guidance to reflect today’s clinical and environmental realities. Until strong evidence supports the need for sterile water, they wrote, the smarter, safer, and more sustainable option may be simply turning on the tap.

The investigators disclosed relationships with Guardant, Exact Sciences, Freenome, and others.
 

Among hospitalized patients with cirrhosis, a machine learning (ML) model enhanced mortality prediction compared with traditional methods and was consistent across country income levels in a large global study.

“This highly inclusive, representative, and globally derived model has been externally validated,” Jasmohan Bajaj, MD, AGAF, professor of medicine at Virginia Commonwealth University in Richmond, Virginia, told GI & Hepatology News. “This gives us a crystal ball. It helps hospital teams, transplant centers, gastroenterology and intensive care unit services triage and prioritize patients more effectively.”

The study supporting the model, which Bajaj said “could be used at this stage,” was published online in Gastroenterology. The model is available for downloading at https://silveys.shinyapps.io/app_cleared/.

 

CLEARED Cohort Analyzed

Wide variations across the world regarding available resources, outpatient services, reasons for admission, and etiologies of cirrhosis can influence patient outcomes, according to Bajaj and colleagues. Therefore, they sought to use ML approaches to improve prognostication for all countries.

They analyzed admission-day data from the prospective Chronic Liver Disease Evolution And Registry for Events and Decompensation (CLEARED) consortium, which includes inpatients with cirrhosis enrolled from six continents. The analysis compared ML approaches with logistical regression to predict inpatient mortality.

The researchers performed internal validation (75/25 split) and subdivision using World-Bank income status: low/low-middle (L-LMIC), upper middle (UMIC), and high (HIC). They determined that the ML model with the best area-under-the-curve (AUC) would be externally validated in a US-Veteran cirrhosis inpatient population.

The CLEARED cohort included 7239 cirrhosis inpatients (mean age, 56 years; 64% men; median MELD-Na, 25) from 115 centers globally; 22.5% of centers belonged to LMICs, 41% to UMICs, and 34% to HICs.

A total of 808 patients (11.1%) died in the hospital.

Random-Forest analysis showed the best AUC (0.815) with high calibration. This was significantly better than parametric logistic regression (AUC, 0.774) and LASSO (AUC, 0.787) models.

Random-Forest also was better than logistic regression regardless of country income-level: HIC (AUC,0.806), UMIC (AUC, 0.867), and L-LMICs (AUC, 0.768).

Of the top 15 important variables selected from Random-Forest, admission for acute kidney injury, hepatic encephalopathy, high MELD-Na/white blood count, and not being in high income country were variables most predictive of mortality.

In contrast, higher albumin, hemoglobin, diuretic use on admission, viral etiology, and being in a high-income country were most protective.

The Random-Forest model was validated in 28,670 veterans (mean age, 67 years; 96% men; median MELD-Na,15), with an inpatient mortality of 4% (1158 patients).

The final Random-Forest model, using 48 of the 67 original covariates, attained a strong AUC of 0.859. A refit version using only the top 15 variables achieved a comparable AUC of 0.851.

 

Clinical Relevance

“Cirrhosis and resultant organ failures remain a dynamic and multidisciplinary problem,” Bajaj noted. “Machine learning techniques are one part of multi-faceted management strategy that is required in this population.”

If patients fall into the high-risk category, he said, “careful consultation with patients, families, and clinical teams is needed before providing information, including where this model was derived from. The results of these discussions could be instructive regarding decisions for transfer, more aggressive monitoring/ICU transfer, palliative care or transplant assessments.”

Meena B. Bansal, MD, system chief, Division of Liver Diseases, Mount Sinai Health System in New York City, called the tool “very promising.” However, she told GI & Hepatology News, “it was validated on a VA [Veterans Affairs] cohort, which is a bit different than the cohort of patients seen at Mount Sinai. Therefore, validation in more academic tertiary care medical centers with high volume liver transplant would be helpful.”

 

Furthermore, said Bansal, who was not involved in the study, “they excluded those that receiving a liver transplant, and while only a small number, this is an important limitation.”

Nevertheless, she added, “Artificial intelligence has great potential in predictive risk models and will likely be a tool that assists for risk stratification, clinical management, and hopefully improved clinical outcomes.”

This study was partly supported by a VA Merit review to Bajaj and the National Center for Advancing Translational Sciences, National Institutes of Health. No conflicts of interest were reported by any author.

A version of this article appeared on Medscape.com.

Among hospitalized patients with cirrhosis, a machine learning (ML) model enhanced mortality prediction compared with traditional methods and was consistent across country income levels in a large global study.

“This highly inclusive, representative, and globally derived model has been externally validated,” Jasmohan Bajaj, MD, AGAF, professor of medicine at Virginia Commonwealth University in Richmond, Virginia, told GI & Hepatology News. “This gives us a crystal ball. It helps hospital teams, transplant centers, gastroenterology and intensive care unit services triage and prioritize patients more effectively.”

The study supporting the model, which Bajaj said “could be used at this stage,” was published online in Gastroenterology. The model is available for downloading at https://silveys.shinyapps.io/app_cleared/.

 

CLEARED Cohort Analyzed

Wide variations across the world regarding available resources, outpatient services, reasons for admission, and etiologies of cirrhosis can influence patient outcomes, according to Bajaj and colleagues. Therefore, they sought to use ML approaches to improve prognostication for all countries.

They analyzed admission-day data from the prospective Chronic Liver Disease Evolution And Registry for Events and Decompensation (CLEARED) consortium, which includes inpatients with cirrhosis enrolled from six continents. The analysis compared ML approaches with logistical regression to predict inpatient mortality.

The researchers performed internal validation (75/25 split) and subdivision using World-Bank income status: low/low-middle (L-LMIC), upper middle (UMIC), and high (HIC). They determined that the ML model with the best area-under-the-curve (AUC) would be externally validated in a US-Veteran cirrhosis inpatient population.

The CLEARED cohort included 7239 cirrhosis inpatients (mean age, 56 years; 64% men; median MELD-Na, 25) from 115 centers globally; 22.5% of centers belonged to LMICs, 41% to UMICs, and 34% to HICs.

A total of 808 patients (11.1%) died in the hospital.

Random-Forest analysis showed the best AUC (0.815) with high calibration. This was significantly better than parametric logistic regression (AUC, 0.774) and LASSO (AUC, 0.787) models.

Random-Forest also was better than logistic regression regardless of country income-level: HIC (AUC,0.806), UMIC (AUC, 0.867), and L-LMICs (AUC, 0.768).

Of the top 15 important variables selected from Random-Forest, admission for acute kidney injury, hepatic encephalopathy, high MELD-Na/white blood count, and not being in high income country were variables most predictive of mortality.

In contrast, higher albumin, hemoglobin, diuretic use on admission, viral etiology, and being in a high-income country were most protective.

The Random-Forest model was validated in 28,670 veterans (mean age, 67 years; 96% men; median MELD-Na,15), with an inpatient mortality of 4% (1158 patients).

The final Random-Forest model, using 48 of the 67 original covariates, attained a strong AUC of 0.859. A refit version using only the top 15 variables achieved a comparable AUC of 0.851.

 

Clinical Relevance

“Cirrhosis and resultant organ failures remain a dynamic and multidisciplinary problem,” Bajaj noted. “Machine learning techniques are one part of multi-faceted management strategy that is required in this population.”

If patients fall into the high-risk category, he said, “careful consultation with patients, families, and clinical teams is needed before providing information, including where this model was derived from. The results of these discussions could be instructive regarding decisions for transfer, more aggressive monitoring/ICU transfer, palliative care or transplant assessments.”

Meena B. Bansal, MD, system chief, Division of Liver Diseases, Mount Sinai Health System in New York City, called the tool “very promising.” However, she told GI & Hepatology News, “it was validated on a VA [Veterans Affairs] cohort, which is a bit different than the cohort of patients seen at Mount Sinai. Therefore, validation in more academic tertiary care medical centers with high volume liver transplant would be helpful.”

 

Furthermore, said Bansal, who was not involved in the study, “they excluded those that receiving a liver transplant, and while only a small number, this is an important limitation.”

Nevertheless, she added, “Artificial intelligence has great potential in predictive risk models and will likely be a tool that assists for risk stratification, clinical management, and hopefully improved clinical outcomes.”

This study was partly supported by a VA Merit review to Bajaj and the National Center for Advancing Translational Sciences, National Institutes of Health. No conflicts of interest were reported by any author.

A version of this article appeared on Medscape.com.

Among hospitalized patients with cirrhosis, a machine learning (ML) model enhanced mortality prediction compared with traditional methods and was consistent across country income levels in a large global study.

“This highly inclusive, representative, and globally derived model has been externally validated,” Jasmohan Bajaj, MD, AGAF, professor of medicine at Virginia Commonwealth University in Richmond, Virginia, told GI & Hepatology News. “This gives us a crystal ball. It helps hospital teams, transplant centers, gastroenterology and intensive care unit services triage and prioritize patients more effectively.”

The study supporting the model, which Bajaj said “could be used at this stage,” was published online in Gastroenterology. The model is available for downloading at https://silveys.shinyapps.io/app_cleared/.

 

CLEARED Cohort Analyzed

Wide variations across the world regarding available resources, outpatient services, reasons for admission, and etiologies of cirrhosis can influence patient outcomes, according to Bajaj and colleagues. Therefore, they sought to use ML approaches to improve prognostication for all countries.

They analyzed admission-day data from the prospective Chronic Liver Disease Evolution And Registry for Events and Decompensation (CLEARED) consortium, which includes inpatients with cirrhosis enrolled from six continents. The analysis compared ML approaches with logistical regression to predict inpatient mortality.

The researchers performed internal validation (75/25 split) and subdivision using World-Bank income status: low/low-middle (L-LMIC), upper middle (UMIC), and high (HIC). They determined that the ML model with the best area-under-the-curve (AUC) would be externally validated in a US-Veteran cirrhosis inpatient population.

The CLEARED cohort included 7239 cirrhosis inpatients (mean age, 56 years; 64% men; median MELD-Na, 25) from 115 centers globally; 22.5% of centers belonged to LMICs, 41% to UMICs, and 34% to HICs.

A total of 808 patients (11.1%) died in the hospital.

Random-Forest analysis showed the best AUC (0.815) with high calibration. This was significantly better than parametric logistic regression (AUC, 0.774) and LASSO (AUC, 0.787) models.

Random-Forest also was better than logistic regression regardless of country income-level: HIC (AUC,0.806), UMIC (AUC, 0.867), and L-LMICs (AUC, 0.768).

Of the top 15 important variables selected from Random-Forest, admission for acute kidney injury, hepatic encephalopathy, high MELD-Na/white blood count, and not being in high income country were variables most predictive of mortality.

In contrast, higher albumin, hemoglobin, diuretic use on admission, viral etiology, and being in a high-income country were most protective.

The Random-Forest model was validated in 28,670 veterans (mean age, 67 years; 96% men; median MELD-Na,15), with an inpatient mortality of 4% (1158 patients).

The final Random-Forest model, using 48 of the 67 original covariates, attained a strong AUC of 0.859. A refit version using only the top 15 variables achieved a comparable AUC of 0.851.

 

Clinical Relevance

“Cirrhosis and resultant organ failures remain a dynamic and multidisciplinary problem,” Bajaj noted. “Machine learning techniques are one part of multi-faceted management strategy that is required in this population.”

If patients fall into the high-risk category, he said, “careful consultation with patients, families, and clinical teams is needed before providing information, including where this model was derived from. The results of these discussions could be instructive regarding decisions for transfer, more aggressive monitoring/ICU transfer, palliative care or transplant assessments.”

Meena B. Bansal, MD, system chief, Division of Liver Diseases, Mount Sinai Health System in New York City, called the tool “very promising.” However, she told GI & Hepatology News, “it was validated on a VA [Veterans Affairs] cohort, which is a bit different than the cohort of patients seen at Mount Sinai. Therefore, validation in more academic tertiary care medical centers with high volume liver transplant would be helpful.”

 

Furthermore, said Bansal, who was not involved in the study, “they excluded those that receiving a liver transplant, and while only a small number, this is an important limitation.”

Nevertheless, she added, “Artificial intelligence has great potential in predictive risk models and will likely be a tool that assists for risk stratification, clinical management, and hopefully improved clinical outcomes.”

This study was partly supported by a VA Merit review to Bajaj and the National Center for Advancing Translational Sciences, National Institutes of Health. No conflicts of interest were reported by any author.

A version of this article appeared on Medscape.com.