Federal Practitioner

For patients undergoing major oncologic surgery, the best definition of malnutrition used to assess postoperative risk varies by cancer type, results of a retrospective study suggest.

The current, one-size-fits-all approach to nutritional status leads to both undertreatment and overtreatment of malnutrition, as well as inaccurate estimations of postoperative risk, reported lead study author Nicholas P. McKenna, MD, of the Mayo Clinic in Rochester, Minn., and colleagues.

“Assessing nutritional status is important because it impacts preoperative planning, particularly with respect to the use of prehabilitation,” the investigators wrote. Their report is in the Journal of the American College of Surgeons. They noted that while prehabilitation has been shown to reduce postoperative risk among those who need it, identification of these patients is an area that needs improvement.

With this in mind, Dr. McKenna and colleagues analyzed 205,840 major oncologic operations, with data drawn from the American College of Surgeons National Surgical Quality Improvement (NSQIP) database.

The researchers evaluated patients’ nutritional status using three techniques: the NSQIP method, the European Society for Clinical Nutrition and Metabolism (ESPEN) definitions, and the World Health Organization body mass index (BMI) classification system.

Combining these three assessments led to seven hierarchical nutritional status categories:

• Severe malnutrition – BMI less than 18.5 kg/m² and greater than 10% weight loss
• ESPEN 1 – BMI 18.5-20 kg/m² (if younger than 70 years) or less than 22 kg/m² (if 70 years or older) plus greater than 10% weight loss
• ESPEN 2 – BMI less than 18.5 kg/m²
• NSQIP – BMI greater than 20 kg/m² (if younger than 70 years) or 22 kg/m² (if 70 years or older) plus greater than 10% weight loss
• Mild malnutrition – BMI 18.5-20 kg/m² (if younger than 70 years) or less than 22 kg/m² (if 70 years or older)
• Obese – BMI at least 30 kg/m²
• No malnutrition.

The study’s primary outcomes were 30-day mortality and 30-day morbidity. The latter included a variety of complications, such as deep incisional surgical site infection, septic shock, and acute renal failure. Demographic and clinical factors were included in multivariate analyses.
 

Results

Most of the operations involved patients with colorectal cancer (74%), followed by pancreatic (10%), lung (9%), gastric (3%), esophageal (3%), and liver (2%) cancer.

Across all patients, 16% fell into one of five malnutrition categories: mild malnutrition (6%), NSQIP (6%), ESPEN 2 (2%), ESPEN 1 (1%), or severe malnutrition (0.6%). The remainder of patients were either obese (31%) or had normal nutritional status (54%).

Malnutrition was most common among patients with pancreatic cancer (28%) and least common among those with colorectal cancer (14%).

Aligning with previous research, this study showed that nutritional status was associated with postoperative risk. Mortality risk was highest among patients with severe malnutrition, and morbidity was most common in the severe and ESPEN 1 groups (P less than .0001 for both).

While the spectrum of classifications appeared accurate across the population, multivariable models for mortality and morbidity revealed an interaction between cancer type and malnutrition definition (P less than .0001 for both), which suggested the most accurate definition of malnutrition differed from one type of cancer to another.

Specifically, a classification of severe malnutrition was most predictive of mortality among patients with esophageal or colorectal cancer. ESPEN 1 was most predictive of mortality for patients with gastric or lung cancer, and NSQIP was most predictive for those with liver cancer.

For predicting morbidity, severe malnutrition was most accurate among patients with colorectal cancer, whereas ESPEN 1 was better suited for gastric and lung cancer.
 

Interpreting and applying the results

“The biggest takeaway is that the optimal definition of malnutrition varies by cancer type,” Dr. McKenna said in an interview.

He went on to explain that weight loss is a particularly important indicator of malnutrition for patients with esophageal or gastric cancer. “These are the cancers that more commonly undergo neoadjuvant chemotherapy,” he noted.

The other major finding, Dr. McKenna said, offers some perspective on short-term versus long-term risk.

“Most people consider obesity a negative prognostic factor,” he said. “But in terms of operative risk, it’s kind of a neutral effect. It doesn’t really affect the short-term outcomes of an operation.”

Still, Dr. McKenna warned that a visual assessment of patient body condition is not enough to predict postoperative risk. Instead, he recommended accurate height and weight measurements during annual and preoperative exams. He also noted that more patients are at risk than clinicians may suspect.

“Even definitions that didn’t previously exist, such as mild malnutrition, had a somewhat negative effect within colorectal cancer and esophageal cancer,” Dr. McKenna said. “So these are patients who previously probably would be considered pretty healthy, but there is probably some room to improve their nutritional status.”

While the study revealed that different types of cancer should have unique tools for measuring nutritional status, development of these systems will require more research concerning prehabilitation outcomes, according to Dr. McKenna. In the meantime, he highlighted a point of action in the clinic.

“We think, overall, especially with the rise of neoadjuvant chemotherapy upfront, before surgery, that identifying patients at risk before they start neoadjuvant chemotherapy is going to be important,” he said. “They are the ones who really need to be targeted.”

There was no external funding for this study, and the investigators reported no conflicts of interest.
 

SOURCE: McKenna NP et al. J Am Coll Surg. 2020 Feb 26. doi: 10.1016/j.jamcollsurg.2019.12.034.

For patients undergoing major oncologic surgery, the best definition of malnutrition used to assess postoperative risk varies by cancer type, results of a retrospective study suggest.

The current, one-size-fits-all approach to nutritional status leads to both undertreatment and overtreatment of malnutrition, as well as inaccurate estimations of postoperative risk, reported lead study author Nicholas P. McKenna, MD, of the Mayo Clinic in Rochester, Minn., and colleagues.

“Assessing nutritional status is important because it impacts preoperative planning, particularly with respect to the use of prehabilitation,” the investigators wrote. Their report is in the Journal of the American College of Surgeons. They noted that while prehabilitation has been shown to reduce postoperative risk among those who need it, identification of these patients is an area that needs improvement.

With this in mind, Dr. McKenna and colleagues analyzed 205,840 major oncologic operations, with data drawn from the American College of Surgeons National Surgical Quality Improvement (NSQIP) database.

The researchers evaluated patients’ nutritional status using three techniques: the NSQIP method, the European Society for Clinical Nutrition and Metabolism (ESPEN) definitions, and the World Health Organization body mass index (BMI) classification system.

Combining these three assessments led to seven hierarchical nutritional status categories:

• Severe malnutrition – BMI less than 18.5 kg/m² and greater than 10% weight loss
• ESPEN 1 – BMI 18.5-20 kg/m² (if younger than 70 years) or less than 22 kg/m² (if 70 years or older) plus greater than 10% weight loss
• ESPEN 2 – BMI less than 18.5 kg/m²
• NSQIP – BMI greater than 20 kg/m² (if younger than 70 years) or 22 kg/m² (if 70 years or older) plus greater than 10% weight loss
• Mild malnutrition – BMI 18.5-20 kg/m² (if younger than 70 years) or less than 22 kg/m² (if 70 years or older)
• Obese – BMI at least 30 kg/m²
• No malnutrition.

The study’s primary outcomes were 30-day mortality and 30-day morbidity. The latter included a variety of complications, such as deep incisional surgical site infection, septic shock, and acute renal failure. Demographic and clinical factors were included in multivariate analyses.
 

Results

Most of the operations involved patients with colorectal cancer (74%), followed by pancreatic (10%), lung (9%), gastric (3%), esophageal (3%), and liver (2%) cancer.

Across all patients, 16% fell into one of five malnutrition categories: mild malnutrition (6%), NSQIP (6%), ESPEN 2 (2%), ESPEN 1 (1%), or severe malnutrition (0.6%). The remainder of patients were either obese (31%) or had normal nutritional status (54%).

Malnutrition was most common among patients with pancreatic cancer (28%) and least common among those with colorectal cancer (14%).

Aligning with previous research, this study showed that nutritional status was associated with postoperative risk. Mortality risk was highest among patients with severe malnutrition, and morbidity was most common in the severe and ESPEN 1 groups (P less than .0001 for both).

While the spectrum of classifications appeared accurate across the population, multivariable models for mortality and morbidity revealed an interaction between cancer type and malnutrition definition (P less than .0001 for both), which suggested the most accurate definition of malnutrition differed from one type of cancer to another.

Specifically, a classification of severe malnutrition was most predictive of mortality among patients with esophageal or colorectal cancer. ESPEN 1 was most predictive of mortality for patients with gastric or lung cancer, and NSQIP was most predictive for those with liver cancer.

For predicting morbidity, severe malnutrition was most accurate among patients with colorectal cancer, whereas ESPEN 1 was better suited for gastric and lung cancer.
 

Interpreting and applying the results

“The biggest takeaway is that the optimal definition of malnutrition varies by cancer type,” Dr. McKenna said in an interview.

He went on to explain that weight loss is a particularly important indicator of malnutrition for patients with esophageal or gastric cancer. “These are the cancers that more commonly undergo neoadjuvant chemotherapy,” he noted.

The other major finding, Dr. McKenna said, offers some perspective on short-term versus long-term risk.

“Most people consider obesity a negative prognostic factor,” he said. “But in terms of operative risk, it’s kind of a neutral effect. It doesn’t really affect the short-term outcomes of an operation.”

Still, Dr. McKenna warned that a visual assessment of patient body condition is not enough to predict postoperative risk. Instead, he recommended accurate height and weight measurements during annual and preoperative exams. He also noted that more patients are at risk than clinicians may suspect.

“Even definitions that didn’t previously exist, such as mild malnutrition, had a somewhat negative effect within colorectal cancer and esophageal cancer,” Dr. McKenna said. “So these are patients who previously probably would be considered pretty healthy, but there is probably some room to improve their nutritional status.”

While the study revealed that different types of cancer should have unique tools for measuring nutritional status, development of these systems will require more research concerning prehabilitation outcomes, according to Dr. McKenna. In the meantime, he highlighted a point of action in the clinic.

“We think, overall, especially with the rise of neoadjuvant chemotherapy upfront, before surgery, that identifying patients at risk before they start neoadjuvant chemotherapy is going to be important,” he said. “They are the ones who really need to be targeted.”

There was no external funding for this study, and the investigators reported no conflicts of interest.
 

SOURCE: McKenna NP et al. J Am Coll Surg. 2020 Feb 26. doi: 10.1016/j.jamcollsurg.2019.12.034.

For patients undergoing major oncologic surgery, the best definition of malnutrition used to assess postoperative risk varies by cancer type, results of a retrospective study suggest.

The current, one-size-fits-all approach to nutritional status leads to both undertreatment and overtreatment of malnutrition, as well as inaccurate estimations of postoperative risk, reported lead study author Nicholas P. McKenna, MD, of the Mayo Clinic in Rochester, Minn., and colleagues.

“Assessing nutritional status is important because it impacts preoperative planning, particularly with respect to the use of prehabilitation,” the investigators wrote. Their report is in the Journal of the American College of Surgeons. They noted that while prehabilitation has been shown to reduce postoperative risk among those who need it, identification of these patients is an area that needs improvement.

With this in mind, Dr. McKenna and colleagues analyzed 205,840 major oncologic operations, with data drawn from the American College of Surgeons National Surgical Quality Improvement (NSQIP) database.

The researchers evaluated patients’ nutritional status using three techniques: the NSQIP method, the European Society for Clinical Nutrition and Metabolism (ESPEN) definitions, and the World Health Organization body mass index (BMI) classification system.

Combining these three assessments led to seven hierarchical nutritional status categories:

• Severe malnutrition – BMI less than 18.5 kg/m² and greater than 10% weight loss
• ESPEN 1 – BMI 18.5-20 kg/m² (if younger than 70 years) or less than 22 kg/m² (if 70 years or older) plus greater than 10% weight loss
• ESPEN 2 – BMI less than 18.5 kg/m²
• NSQIP – BMI greater than 20 kg/m² (if younger than 70 years) or 22 kg/m² (if 70 years or older) plus greater than 10% weight loss
• Mild malnutrition – BMI 18.5-20 kg/m² (if younger than 70 years) or less than 22 kg/m² (if 70 years or older)
• Obese – BMI at least 30 kg/m²
• No malnutrition.

The study’s primary outcomes were 30-day mortality and 30-day morbidity. The latter included a variety of complications, such as deep incisional surgical site infection, septic shock, and acute renal failure. Demographic and clinical factors were included in multivariate analyses.
 

Results

Most of the operations involved patients with colorectal cancer (74%), followed by pancreatic (10%), lung (9%), gastric (3%), esophageal (3%), and liver (2%) cancer.

Across all patients, 16% fell into one of five malnutrition categories: mild malnutrition (6%), NSQIP (6%), ESPEN 2 (2%), ESPEN 1 (1%), or severe malnutrition (0.6%). The remainder of patients were either obese (31%) or had normal nutritional status (54%).

Malnutrition was most common among patients with pancreatic cancer (28%) and least common among those with colorectal cancer (14%).

Aligning with previous research, this study showed that nutritional status was associated with postoperative risk. Mortality risk was highest among patients with severe malnutrition, and morbidity was most common in the severe and ESPEN 1 groups (P less than .0001 for both).

While the spectrum of classifications appeared accurate across the population, multivariable models for mortality and morbidity revealed an interaction between cancer type and malnutrition definition (P less than .0001 for both), which suggested the most accurate definition of malnutrition differed from one type of cancer to another.

Specifically, a classification of severe malnutrition was most predictive of mortality among patients with esophageal or colorectal cancer. ESPEN 1 was most predictive of mortality for patients with gastric or lung cancer, and NSQIP was most predictive for those with liver cancer.

For predicting morbidity, severe malnutrition was most accurate among patients with colorectal cancer, whereas ESPEN 1 was better suited for gastric and lung cancer.
 

Interpreting and applying the results

“The biggest takeaway is that the optimal definition of malnutrition varies by cancer type,” Dr. McKenna said in an interview.

He went on to explain that weight loss is a particularly important indicator of malnutrition for patients with esophageal or gastric cancer. “These are the cancers that more commonly undergo neoadjuvant chemotherapy,” he noted.

The other major finding, Dr. McKenna said, offers some perspective on short-term versus long-term risk.

“Most people consider obesity a negative prognostic factor,” he said. “But in terms of operative risk, it’s kind of a neutral effect. It doesn’t really affect the short-term outcomes of an operation.”

Still, Dr. McKenna warned that a visual assessment of patient body condition is not enough to predict postoperative risk. Instead, he recommended accurate height and weight measurements during annual and preoperative exams. He also noted that more patients are at risk than clinicians may suspect.

“Even definitions that didn’t previously exist, such as mild malnutrition, had a somewhat negative effect within colorectal cancer and esophageal cancer,” Dr. McKenna said. “So these are patients who previously probably would be considered pretty healthy, but there is probably some room to improve their nutritional status.”

While the study revealed that different types of cancer should have unique tools for measuring nutritional status, development of these systems will require more research concerning prehabilitation outcomes, according to Dr. McKenna. In the meantime, he highlighted a point of action in the clinic.

“We think, overall, especially with the rise of neoadjuvant chemotherapy upfront, before surgery, that identifying patients at risk before they start neoadjuvant chemotherapy is going to be important,” he said. “They are the ones who really need to be targeted.”

There was no external funding for this study, and the investigators reported no conflicts of interest.
 

SOURCE: McKenna NP et al. J Am Coll Surg. 2020 Feb 26. doi: 10.1016/j.jamcollsurg.2019.12.034.

Telehealth is increasingly being viewed as a key way to help fight the COVID-19 outbreak in the United States. Recognizing the potential of this technology to slow the spread of the disease, the House of Representatives included a provision in an $8.3 billion emergency response bill it approved today that would temporarily lift restrictions on Medicare telehealth coverage to assist in the efforts to contain the virus.

Nancy Messonnier, MD, director of the National Center for Immunization and Respiratory Diseases at the Centers for Disease Control and Prevention (CDC), said that hospitals should be prepared to use telehealth as one of their tools in fighting the outbreak, according to a recent news release from the American Hospital Association (AHA).

Congress is responding to that need by including the service in the new coronavirus legislation now headed to the Senate, after the funding bill was approved in a 415-2 vote by the House.

The bill empowers the Secretary of Health and Human Services (HHS) to “waive or modify application of certain Medicare requirements with respect to telehealth services furnished during certain emergency periods.”

While the measure adds telehealth to the waiver authority that the HHS secretary currently has during national emergencies, it’s only for the coronavirus crisis in this case, Krista Drobac, executive director of the Alliance for Connected Care, told Medscape Medical News.

The waiver would apply to originating sites of telehealth visits, she noted. Thus Medicare coverage of telemedicine would be expanded beyond rural areas.

In addition, the waiver would allow coverage of virtual visits conducted on smartphones with audio and video capabilities. A “qualified provider,” as defined by the legislation, would be a practitioner who has an established relationship with the patient or who is in the same practice as the provider who has that relationship.

An advantage of telehealth, proponents say, is that it can enable people who believe they have COVID-19 to be seen at home rather than visit offices or emergency departments (EDs) where they might spread the disease or be in proximity to others who have it.

In an editorial published March 2 in Modern Healthcare, medical directors from Stanford Medicine, MedStar Health, and Intermountain Healthcare also noted that telehealth can give patients 24/7 access to care, allow surveillance of patients at risk while keeping them at home, ensure that treatment in hospitals is reserved for high-need patients, and enable providers to triage and screen more patients than can be handled in brick-and-mortar care settings.

However, telehealth screening would allow physicians only to judge whether a patient’s symptoms might be indicative of COVID-19, the Alliance for Connected Care, a telehealth advocacy group, noted in a letter to Congressional leaders. Patients would still have to be seen in person to be tested for the disease.

The group, which represents technology companies, health insurers, pharmacies, and other healthcare players, has been lobbying Congress to include telehealth in federal funds to combat the outbreak.

The American Telemedicine Association (ATA) also supports this goal, ATA President Joseph Kvedar, MD, told Medscape Medical News. And the authors of the Modern Healthcare editorial also advocated for this legislative solution. Because the fatality rate for COVID-19 is significantly higher for older people than for other age groups, they noted, telehealth should be an economically viable option for all seniors.

The Centers for Medicare and Medicaid Services (CMS) long covered telemedicine only in rural areas and only when initiated in healthcare settings. Recently, however, CMS loosened its approach to some extent. Virtual “check-in visits” can now be initiated from any location, including home, to determine whether a Medicare patient needs to be seen in the office. In addition, CMS allows Medicare Advantage plans to offer telemedicine as a core benefit.

Are healthcare systems prepared?

Some large healthcare systems such as Stanford, MedStar, and Intermountain are already using telehealth to diagnose and treat patients who have traditional influenza. Telehealth providers at Stanford estimate that almost 50% of these patients are being prescribed the antiviral drug Tamiflu.

It’s unclear whether other healthcare systems are this well prepared to offer telehealth on a large scale. But, according to an AHA survey, Kvedar noted, three quarters of AHA members are engaged in some form of telehealth.

Drobac said “it wouldn’t require too much effort” to ramp up a wide-scale telehealth program that could help reduce the impact of the outbreak. “The technology is there,” she noted. “You need a HIPAA-compliant telehealth platform, but there are so many out there.”

Kvedar agreed. To begin with, he said, hospitals might sequester patients who visit the ED with COVID-19 symptoms in a video-equipped “isolation room.” Staff members could then do the patient intake from a different location in the hospital.

He admitted that this approach would be infeasible if a lot of patients arrived in EDs with coronavirus symptoms. However, Kvedar noted, “All the tools are in place to go well beyond that. American Well, Teladoc, and others are all offering ways to get out in front of this. There are plenty of vendors out there, and most people have a connected cell phone that you can do a video call on.”

Hospital leaders would have to decide whether to embrace telehealth, which would mean less use of services in their institutions, he said. “But it would be for the greater good of the public.”

Kvedar recalled that there was some use of telehealth in the New York area after 9/11. Telehealth was also used in the aftermath of Hurricane Katrina in 2005. But the ATA president, who is also vice president of connected health at Partners HealthCare in Boston, noted that the COVID-19 outbreak is the first public health emergency to occur in the era of Skype and smartphones.

If Congress does ultimately authorize CMS to cover telehealth across the board during this emergency, might that lead to a permanent change in Medicare coverage policy? Kvedar wouldn’t venture an opinion. “However, the current CMS leadership has been incredibly telehealth friendly,” he said. “So it’s possible they would [embrace a lifting of restrictions]. As patients get a sense of this modality of care and how convenient it is for them, they’ll start asking for more.”

Meanwhile, he said, the telehealth opportunity goes beyond video visits with doctors to mitigate the outbreak. Telehealth data could also be used to track disease spread, similar to how researchers have studied Google searches to predict the spread of the flu, he noted.

Teladoc, a major telehealth vendor, recently told stock analysts it’s already working with the CDC on disease surveillance, according to a report in FierceHealthcare.

This article first appeared on Medscape.com.

Telehealth is increasingly being viewed as a key way to help fight the COVID-19 outbreak in the United States. Recognizing the potential of this technology to slow the spread of the disease, the House of Representatives included a provision in an $8.3 billion emergency response bill it approved today that would temporarily lift restrictions on Medicare telehealth coverage to assist in the efforts to contain the virus.

Nancy Messonnier, MD, director of the National Center for Immunization and Respiratory Diseases at the Centers for Disease Control and Prevention (CDC), said that hospitals should be prepared to use telehealth as one of their tools in fighting the outbreak, according to a recent news release from the American Hospital Association (AHA).

Congress is responding to that need by including the service in the new coronavirus legislation now headed to the Senate, after the funding bill was approved in a 415-2 vote by the House.

The bill empowers the Secretary of Health and Human Services (HHS) to “waive or modify application of certain Medicare requirements with respect to telehealth services furnished during certain emergency periods.”

While the measure adds telehealth to the waiver authority that the HHS secretary currently has during national emergencies, it’s only for the coronavirus crisis in this case, Krista Drobac, executive director of the Alliance for Connected Care, told Medscape Medical News.

The waiver would apply to originating sites of telehealth visits, she noted. Thus Medicare coverage of telemedicine would be expanded beyond rural areas.

In addition, the waiver would allow coverage of virtual visits conducted on smartphones with audio and video capabilities. A “qualified provider,” as defined by the legislation, would be a practitioner who has an established relationship with the patient or who is in the same practice as the provider who has that relationship.

An advantage of telehealth, proponents say, is that it can enable people who believe they have COVID-19 to be seen at home rather than visit offices or emergency departments (EDs) where they might spread the disease or be in proximity to others who have it.

In an editorial published March 2 in Modern Healthcare, medical directors from Stanford Medicine, MedStar Health, and Intermountain Healthcare also noted that telehealth can give patients 24/7 access to care, allow surveillance of patients at risk while keeping them at home, ensure that treatment in hospitals is reserved for high-need patients, and enable providers to triage and screen more patients than can be handled in brick-and-mortar care settings.

However, telehealth screening would allow physicians only to judge whether a patient’s symptoms might be indicative of COVID-19, the Alliance for Connected Care, a telehealth advocacy group, noted in a letter to Congressional leaders. Patients would still have to be seen in person to be tested for the disease.

The group, which represents technology companies, health insurers, pharmacies, and other healthcare players, has been lobbying Congress to include telehealth in federal funds to combat the outbreak.

The American Telemedicine Association (ATA) also supports this goal, ATA President Joseph Kvedar, MD, told Medscape Medical News. And the authors of the Modern Healthcare editorial also advocated for this legislative solution. Because the fatality rate for COVID-19 is significantly higher for older people than for other age groups, they noted, telehealth should be an economically viable option for all seniors.

The Centers for Medicare and Medicaid Services (CMS) long covered telemedicine only in rural areas and only when initiated in healthcare settings. Recently, however, CMS loosened its approach to some extent. Virtual “check-in visits” can now be initiated from any location, including home, to determine whether a Medicare patient needs to be seen in the office. In addition, CMS allows Medicare Advantage plans to offer telemedicine as a core benefit.

Are healthcare systems prepared?

Some large healthcare systems such as Stanford, MedStar, and Intermountain are already using telehealth to diagnose and treat patients who have traditional influenza. Telehealth providers at Stanford estimate that almost 50% of these patients are being prescribed the antiviral drug Tamiflu.

It’s unclear whether other healthcare systems are this well prepared to offer telehealth on a large scale. But, according to an AHA survey, Kvedar noted, three quarters of AHA members are engaged in some form of telehealth.

Drobac said “it wouldn’t require too much effort” to ramp up a wide-scale telehealth program that could help reduce the impact of the outbreak. “The technology is there,” she noted. “You need a HIPAA-compliant telehealth platform, but there are so many out there.”

Kvedar agreed. To begin with, he said, hospitals might sequester patients who visit the ED with COVID-19 symptoms in a video-equipped “isolation room.” Staff members could then do the patient intake from a different location in the hospital.

He admitted that this approach would be infeasible if a lot of patients arrived in EDs with coronavirus symptoms. However, Kvedar noted, “All the tools are in place to go well beyond that. American Well, Teladoc, and others are all offering ways to get out in front of this. There are plenty of vendors out there, and most people have a connected cell phone that you can do a video call on.”

Hospital leaders would have to decide whether to embrace telehealth, which would mean less use of services in their institutions, he said. “But it would be for the greater good of the public.”

Kvedar recalled that there was some use of telehealth in the New York area after 9/11. Telehealth was also used in the aftermath of Hurricane Katrina in 2005. But the ATA president, who is also vice president of connected health at Partners HealthCare in Boston, noted that the COVID-19 outbreak is the first public health emergency to occur in the era of Skype and smartphones.

If Congress does ultimately authorize CMS to cover telehealth across the board during this emergency, might that lead to a permanent change in Medicare coverage policy? Kvedar wouldn’t venture an opinion. “However, the current CMS leadership has been incredibly telehealth friendly,” he said. “So it’s possible they would [embrace a lifting of restrictions]. As patients get a sense of this modality of care and how convenient it is for them, they’ll start asking for more.”

Meanwhile, he said, the telehealth opportunity goes beyond video visits with doctors to mitigate the outbreak. Telehealth data could also be used to track disease spread, similar to how researchers have studied Google searches to predict the spread of the flu, he noted.

Teladoc, a major telehealth vendor, recently told stock analysts it’s already working with the CDC on disease surveillance, according to a report in FierceHealthcare.

This article first appeared on Medscape.com.

Telehealth is increasingly being viewed as a key way to help fight the COVID-19 outbreak in the United States. Recognizing the potential of this technology to slow the spread of the disease, the House of Representatives included a provision in an $8.3 billion emergency response bill it approved today that would temporarily lift restrictions on Medicare telehealth coverage to assist in the efforts to contain the virus.

Nancy Messonnier, MD, director of the National Center for Immunization and Respiratory Diseases at the Centers for Disease Control and Prevention (CDC), said that hospitals should be prepared to use telehealth as one of their tools in fighting the outbreak, according to a recent news release from the American Hospital Association (AHA).

Congress is responding to that need by including the service in the new coronavirus legislation now headed to the Senate, after the funding bill was approved in a 415-2 vote by the House.

The bill empowers the Secretary of Health and Human Services (HHS) to “waive or modify application of certain Medicare requirements with respect to telehealth services furnished during certain emergency periods.”

While the measure adds telehealth to the waiver authority that the HHS secretary currently has during national emergencies, it’s only for the coronavirus crisis in this case, Krista Drobac, executive director of the Alliance for Connected Care, told Medscape Medical News.

The waiver would apply to originating sites of telehealth visits, she noted. Thus Medicare coverage of telemedicine would be expanded beyond rural areas.

In addition, the waiver would allow coverage of virtual visits conducted on smartphones with audio and video capabilities. A “qualified provider,” as defined by the legislation, would be a practitioner who has an established relationship with the patient or who is in the same practice as the provider who has that relationship.

An advantage of telehealth, proponents say, is that it can enable people who believe they have COVID-19 to be seen at home rather than visit offices or emergency departments (EDs) where they might spread the disease or be in proximity to others who have it.

In an editorial published March 2 in Modern Healthcare, medical directors from Stanford Medicine, MedStar Health, and Intermountain Healthcare also noted that telehealth can give patients 24/7 access to care, allow surveillance of patients at risk while keeping them at home, ensure that treatment in hospitals is reserved for high-need patients, and enable providers to triage and screen more patients than can be handled in brick-and-mortar care settings.

However, telehealth screening would allow physicians only to judge whether a patient’s symptoms might be indicative of COVID-19, the Alliance for Connected Care, a telehealth advocacy group, noted in a letter to Congressional leaders. Patients would still have to be seen in person to be tested for the disease.

The group, which represents technology companies, health insurers, pharmacies, and other healthcare players, has been lobbying Congress to include telehealth in federal funds to combat the outbreak.

The American Telemedicine Association (ATA) also supports this goal, ATA President Joseph Kvedar, MD, told Medscape Medical News. And the authors of the Modern Healthcare editorial also advocated for this legislative solution. Because the fatality rate for COVID-19 is significantly higher for older people than for other age groups, they noted, telehealth should be an economically viable option for all seniors.

The Centers for Medicare and Medicaid Services (CMS) long covered telemedicine only in rural areas and only when initiated in healthcare settings. Recently, however, CMS loosened its approach to some extent. Virtual “check-in visits” can now be initiated from any location, including home, to determine whether a Medicare patient needs to be seen in the office. In addition, CMS allows Medicare Advantage plans to offer telemedicine as a core benefit.

Are healthcare systems prepared?

Some large healthcare systems such as Stanford, MedStar, and Intermountain are already using telehealth to diagnose and treat patients who have traditional influenza. Telehealth providers at Stanford estimate that almost 50% of these patients are being prescribed the antiviral drug Tamiflu.

It’s unclear whether other healthcare systems are this well prepared to offer telehealth on a large scale. But, according to an AHA survey, Kvedar noted, three quarters of AHA members are engaged in some form of telehealth.

Drobac said “it wouldn’t require too much effort” to ramp up a wide-scale telehealth program that could help reduce the impact of the outbreak. “The technology is there,” she noted. “You need a HIPAA-compliant telehealth platform, but there are so many out there.”

Kvedar agreed. To begin with, he said, hospitals might sequester patients who visit the ED with COVID-19 symptoms in a video-equipped “isolation room.” Staff members could then do the patient intake from a different location in the hospital.

He admitted that this approach would be infeasible if a lot of patients arrived in EDs with coronavirus symptoms. However, Kvedar noted, “All the tools are in place to go well beyond that. American Well, Teladoc, and others are all offering ways to get out in front of this. There are plenty of vendors out there, and most people have a connected cell phone that you can do a video call on.”

Hospital leaders would have to decide whether to embrace telehealth, which would mean less use of services in their institutions, he said. “But it would be for the greater good of the public.”

Kvedar recalled that there was some use of telehealth in the New York area after 9/11. Telehealth was also used in the aftermath of Hurricane Katrina in 2005. But the ATA president, who is also vice president of connected health at Partners HealthCare in Boston, noted that the COVID-19 outbreak is the first public health emergency to occur in the era of Skype and smartphones.

If Congress does ultimately authorize CMS to cover telehealth across the board during this emergency, might that lead to a permanent change in Medicare coverage policy? Kvedar wouldn’t venture an opinion. “However, the current CMS leadership has been incredibly telehealth friendly,” he said. “So it’s possible they would [embrace a lifting of restrictions]. As patients get a sense of this modality of care and how convenient it is for them, they’ll start asking for more.”

Meanwhile, he said, the telehealth opportunity goes beyond video visits with doctors to mitigate the outbreak. Telehealth data could also be used to track disease spread, similar to how researchers have studied Google searches to predict the spread of the flu, he noted.

Teladoc, a major telehealth vendor, recently told stock analysts it’s already working with the CDC on disease surveillance, according to a report in FierceHealthcare.

This article first appeared on Medscape.com.

Self-injurious behaviors are common and can be either volitional or unintentional. Often people who perform these behaviors receive “tongue lashings” from family, friends, and loved ones. We recently treated a patient whose lesion in the oral cavity was thought to be caused by some form of self-injury, though the prognosis clearly depended on the true culprit. It is important for clinicians to identify the cause of the injury when encountering patients with oral cavity lesions.

Case Presentation

A 40-year-old white male with a medical history of bipolar disorder, posttraumatic stress disorder, polysubstance abuse, and recently diagnosed temporomandibular joint (TMJ) syndrome was seen in outpatient primary care for a bleeding lesion in his mouth for the past 3 weeks. The lesion was under the surface of his right tongue. He first noted the lesion after he had burned himself tasting some homemade rice pudding while under the influence of marijuana. The next day, an impression was taken of his mouth by a dental assistant who was fitting him for an oral appliance for his TMJ syndrome; according to his history, she did not perform a visual inspection of his mouth nor could he recall his last dental examination. He had neither lost weight nor experienced dysphagia. He was not taking any prescribed medications, had an 8 pack-year history of smoking cigarettes, and had smoked crack cocaine intermittently for several years. The also patient had chewed one-half tin per day of chewing tobacco for 5 years, though he had quit 7 years before presentation. He was consuming 6 alcoholic drinks daily and had no history of chewing betel nuts.

On physical examination, the patient seemed extremely anxious, but his vital signs were unremarkable. The nasal dorsum was straight, and the nares were widely patent. There were no suspicious cutaneous lesions noted of the face, head, trunk, or extremities. The salivary glands were soft and showed no lesions or masses within the parotid or submandibular glands bilaterally. There was no obvious obstruction of Stenson or Wharton ducts bilaterally. He had normal lips and oral competence. The dentition was noted to be fair.

A nonfriable, 1.5 cm-wide lesion was found on the ventral surface of the right tongue (Figure 1). The tongue was mobile. The mouth floor was soft and without evidence of masses or lesions. The tonsils, tonsillar pillars, palate, and base of tongue did not show any concerning lesions or masses. The neck revealed a nonenlarged thyroid and no lymphadenopathy. The remainder of the examination was unremarkable.

Diagnosis

Given his risk factors of alcohol use disorder and a history of both inhaled and chewing tobacco, oral squamous cell carcinoma (SCC) was considered. The differential diagnosis also included pyogenic granuloma, mucocele, sublingual fibroma, and metastasis to the oral soft tissue. Due to its implications with respect to morbidity and mortality, we thought it necessary to rule out SCC of the oral cavity. SCC comprises more than 90% of oral malignancies, and tobacco-related products, alcohol, and human papilloma virus are well-established risk factors.¹

Pyogenic granuloma, also known as eruptive hemangioma and lobular capillary hemangioma, is a relatively common benign lesion of the skin and mucosal surfaces that often presents as a solitary, rapidly enlarging papule or nodule that is extremely friable.² Interestingly, pyogenic granuloma is a misnomer, since it is neither infectious in origin nor granulomatous when visualized under the microscope and is thought to arise from an exuberant tissue response to localized irritation or trauma. An individual lesion can range in size from a few millimeters to a few centimeters and generally reaches its maximum size within a matter of weeks; they often arise at sites of minor trauma.³ While the pathogenesis of pyogenic granuloma has not been clearly established, it seems to be related to an imbalance of angiogenesis secondary to overexpression of vascular endothelial growth factor and basic fibroblast growth factor.⁴ While they can occur at any age, pyogenic granulomas are frequently seen in pediatric patients and during pregnancy.

A fibroma, also known as an irritation fibroma, is one of the more common fibrous tumorlike growths and is often caused by trauma or irritation. It usually presents as a smooth-surfaced, painless solid lesion, though it can be nodular and histopathologically shows collagen and connective tissue.⁵ While fibromas can occur anywhere in the oral cavity, they commonly arise on the buccal mucosa along the plane of occlusion between the maxillary and mandibular teeth.

Mucoceles are the most common benign lesions in the mouth and are commonly found on the lower lip and are mucus-filled cavities, arising from the accumulation of mucus from trauma or lip-biting and alteration of minor salivary glands.⁶ Our patient’s rapid evolution and history of trauma were consistent with a mucocele. Although the lower lip is the most common site of involvement, mucoceles also occur on the tongue, cheek, palate, and mouth floor.Metastases to the oral cavity are rare and comprise only 1% of all oral cavity malignancies.⁷ Although most commonly seen in the jaw, nearly one-third of oral cavity metastases are in the soft tissue.⁸ They generally occur late in the course of disease, and the time between appearance and death is usually short.⁸ Our patient’s lack of known primary malignancy and lack of weight loss rendered this diagnosis unlikely.

Other possibilities include peripheral giant cell granuloma, a reactive hyperplastic lesion of the oral cavity originating from the periosteum or periodontal membrane following local irritation or chronic trauma,⁹ and peripheral ossifying fibroma, a reactive soft tissue growth usually seen on the interdental papilla.¹⁰

Surgical excision was performed and revealed reactive epidermal hyperplasia, ulceration, granulation tissue formation, and marked inflammation with reactive changes. There was no evidence of malignancy and was interpreted as consistent with pyogenic granuloma (Figures 2 and 3) likely due to the trauma from the thermal burn or poor dentition.

Management

The patient was relieved to be informed of the diagnosis of an unusual presentation of pyogenic granuloma with no evidence of cancer. Current treatment strategies for pyogenic granuloma include surgical excision, shave excision with cautery, cryotherapy, sclerotherapy, carbon dioxide or pulsed dye laser, as well as expectant management. However, recurrence after initial treatment can occur, with lower recurrence rates occurring with surgical excision.¹¹

Although we wouldn’t state that we gave the patient a “tongue-lashing,” we strongly advised him that he return to his dentist and abstain from tobacco products, alcohol, illicit drugs, and taste-testing scalding food directly from the pot.

Self-injurious behaviors are common and can be either volitional or unintentional. Often people who perform these behaviors receive “tongue lashings” from family, friends, and loved ones. We recently treated a patient whose lesion in the oral cavity was thought to be caused by some form of self-injury, though the prognosis clearly depended on the true culprit. It is important for clinicians to identify the cause of the injury when encountering patients with oral cavity lesions.

Case Presentation

A 40-year-old white male with a medical history of bipolar disorder, posttraumatic stress disorder, polysubstance abuse, and recently diagnosed temporomandibular joint (TMJ) syndrome was seen in outpatient primary care for a bleeding lesion in his mouth for the past 3 weeks. The lesion was under the surface of his right tongue. He first noted the lesion after he had burned himself tasting some homemade rice pudding while under the influence of marijuana. The next day, an impression was taken of his mouth by a dental assistant who was fitting him for an oral appliance for his TMJ syndrome; according to his history, she did not perform a visual inspection of his mouth nor could he recall his last dental examination. He had neither lost weight nor experienced dysphagia. He was not taking any prescribed medications, had an 8 pack-year history of smoking cigarettes, and had smoked crack cocaine intermittently for several years. The also patient had chewed one-half tin per day of chewing tobacco for 5 years, though he had quit 7 years before presentation. He was consuming 6 alcoholic drinks daily and had no history of chewing betel nuts.

On physical examination, the patient seemed extremely anxious, but his vital signs were unremarkable. The nasal dorsum was straight, and the nares were widely patent. There were no suspicious cutaneous lesions noted of the face, head, trunk, or extremities. The salivary glands were soft and showed no lesions or masses within the parotid or submandibular glands bilaterally. There was no obvious obstruction of Stenson or Wharton ducts bilaterally. He had normal lips and oral competence. The dentition was noted to be fair.

A nonfriable, 1.5 cm-wide lesion was found on the ventral surface of the right tongue (Figure 1). The tongue was mobile. The mouth floor was soft and without evidence of masses or lesions. The tonsils, tonsillar pillars, palate, and base of tongue did not show any concerning lesions or masses. The neck revealed a nonenlarged thyroid and no lymphadenopathy. The remainder of the examination was unremarkable.

Diagnosis

Given his risk factors of alcohol use disorder and a history of both inhaled and chewing tobacco, oral squamous cell carcinoma (SCC) was considered. The differential diagnosis also included pyogenic granuloma, mucocele, sublingual fibroma, and metastasis to the oral soft tissue. Due to its implications with respect to morbidity and mortality, we thought it necessary to rule out SCC of the oral cavity. SCC comprises more than 90% of oral malignancies, and tobacco-related products, alcohol, and human papilloma virus are well-established risk factors.¹

Pyogenic granuloma, also known as eruptive hemangioma and lobular capillary hemangioma, is a relatively common benign lesion of the skin and mucosal surfaces that often presents as a solitary, rapidly enlarging papule or nodule that is extremely friable.² Interestingly, pyogenic granuloma is a misnomer, since it is neither infectious in origin nor granulomatous when visualized under the microscope and is thought to arise from an exuberant tissue response to localized irritation or trauma. An individual lesion can range in size from a few millimeters to a few centimeters and generally reaches its maximum size within a matter of weeks; they often arise at sites of minor trauma.³ While the pathogenesis of pyogenic granuloma has not been clearly established, it seems to be related to an imbalance of angiogenesis secondary to overexpression of vascular endothelial growth factor and basic fibroblast growth factor.⁴ While they can occur at any age, pyogenic granulomas are frequently seen in pediatric patients and during pregnancy.

A fibroma, also known as an irritation fibroma, is one of the more common fibrous tumorlike growths and is often caused by trauma or irritation. It usually presents as a smooth-surfaced, painless solid lesion, though it can be nodular and histopathologically shows collagen and connective tissue.⁵ While fibromas can occur anywhere in the oral cavity, they commonly arise on the buccal mucosa along the plane of occlusion between the maxillary and mandibular teeth.

Mucoceles are the most common benign lesions in the mouth and are commonly found on the lower lip and are mucus-filled cavities, arising from the accumulation of mucus from trauma or lip-biting and alteration of minor salivary glands.⁶ Our patient’s rapid evolution and history of trauma were consistent with a mucocele. Although the lower lip is the most common site of involvement, mucoceles also occur on the tongue, cheek, palate, and mouth floor.Metastases to the oral cavity are rare and comprise only 1% of all oral cavity malignancies.⁷ Although most commonly seen in the jaw, nearly one-third of oral cavity metastases are in the soft tissue.⁸ They generally occur late in the course of disease, and the time between appearance and death is usually short.⁸ Our patient’s lack of known primary malignancy and lack of weight loss rendered this diagnosis unlikely.

Other possibilities include peripheral giant cell granuloma, a reactive hyperplastic lesion of the oral cavity originating from the periosteum or periodontal membrane following local irritation or chronic trauma,⁹ and peripheral ossifying fibroma, a reactive soft tissue growth usually seen on the interdental papilla.¹⁰

Surgical excision was performed and revealed reactive epidermal hyperplasia, ulceration, granulation tissue formation, and marked inflammation with reactive changes. There was no evidence of malignancy and was interpreted as consistent with pyogenic granuloma (Figures 2 and 3) likely due to the trauma from the thermal burn or poor dentition.

Management

The patient was relieved to be informed of the diagnosis of an unusual presentation of pyogenic granuloma with no evidence of cancer. Current treatment strategies for pyogenic granuloma include surgical excision, shave excision with cautery, cryotherapy, sclerotherapy, carbon dioxide or pulsed dye laser, as well as expectant management. However, recurrence after initial treatment can occur, with lower recurrence rates occurring with surgical excision.¹¹

Although we wouldn’t state that we gave the patient a “tongue-lashing,” we strongly advised him that he return to his dentist and abstain from tobacco products, alcohol, illicit drugs, and taste-testing scalding food directly from the pot.

Self-injurious behaviors are common and can be either volitional or unintentional. Often people who perform these behaviors receive “tongue lashings” from family, friends, and loved ones. We recently treated a patient whose lesion in the oral cavity was thought to be caused by some form of self-injury, though the prognosis clearly depended on the true culprit. It is important for clinicians to identify the cause of the injury when encountering patients with oral cavity lesions.

Case Presentation

A 40-year-old white male with a medical history of bipolar disorder, posttraumatic stress disorder, polysubstance abuse, and recently diagnosed temporomandibular joint (TMJ) syndrome was seen in outpatient primary care for a bleeding lesion in his mouth for the past 3 weeks. The lesion was under the surface of his right tongue. He first noted the lesion after he had burned himself tasting some homemade rice pudding while under the influence of marijuana. The next day, an impression was taken of his mouth by a dental assistant who was fitting him for an oral appliance for his TMJ syndrome; according to his history, she did not perform a visual inspection of his mouth nor could he recall his last dental examination. He had neither lost weight nor experienced dysphagia. He was not taking any prescribed medications, had an 8 pack-year history of smoking cigarettes, and had smoked crack cocaine intermittently for several years. The also patient had chewed one-half tin per day of chewing tobacco for 5 years, though he had quit 7 years before presentation. He was consuming 6 alcoholic drinks daily and had no history of chewing betel nuts.

On physical examination, the patient seemed extremely anxious, but his vital signs were unremarkable. The nasal dorsum was straight, and the nares were widely patent. There were no suspicious cutaneous lesions noted of the face, head, trunk, or extremities. The salivary glands were soft and showed no lesions or masses within the parotid or submandibular glands bilaterally. There was no obvious obstruction of Stenson or Wharton ducts bilaterally. He had normal lips and oral competence. The dentition was noted to be fair.

A nonfriable, 1.5 cm-wide lesion was found on the ventral surface of the right tongue (Figure 1). The tongue was mobile. The mouth floor was soft and without evidence of masses or lesions. The tonsils, tonsillar pillars, palate, and base of tongue did not show any concerning lesions or masses. The neck revealed a nonenlarged thyroid and no lymphadenopathy. The remainder of the examination was unremarkable.

Diagnosis

Given his risk factors of alcohol use disorder and a history of both inhaled and chewing tobacco, oral squamous cell carcinoma (SCC) was considered. The differential diagnosis also included pyogenic granuloma, mucocele, sublingual fibroma, and metastasis to the oral soft tissue. Due to its implications with respect to morbidity and mortality, we thought it necessary to rule out SCC of the oral cavity. SCC comprises more than 90% of oral malignancies, and tobacco-related products, alcohol, and human papilloma virus are well-established risk factors.¹

Pyogenic granuloma, also known as eruptive hemangioma and lobular capillary hemangioma, is a relatively common benign lesion of the skin and mucosal surfaces that often presents as a solitary, rapidly enlarging papule or nodule that is extremely friable.² Interestingly, pyogenic granuloma is a misnomer, since it is neither infectious in origin nor granulomatous when visualized under the microscope and is thought to arise from an exuberant tissue response to localized irritation or trauma. An individual lesion can range in size from a few millimeters to a few centimeters and generally reaches its maximum size within a matter of weeks; they often arise at sites of minor trauma.³ While the pathogenesis of pyogenic granuloma has not been clearly established, it seems to be related to an imbalance of angiogenesis secondary to overexpression of vascular endothelial growth factor and basic fibroblast growth factor.⁴ While they can occur at any age, pyogenic granulomas are frequently seen in pediatric patients and during pregnancy.

A fibroma, also known as an irritation fibroma, is one of the more common fibrous tumorlike growths and is often caused by trauma or irritation. It usually presents as a smooth-surfaced, painless solid lesion, though it can be nodular and histopathologically shows collagen and connective tissue.⁵ While fibromas can occur anywhere in the oral cavity, they commonly arise on the buccal mucosa along the plane of occlusion between the maxillary and mandibular teeth.

Mucoceles are the most common benign lesions in the mouth and are commonly found on the lower lip and are mucus-filled cavities, arising from the accumulation of mucus from trauma or lip-biting and alteration of minor salivary glands.⁶ Our patient’s rapid evolution and history of trauma were consistent with a mucocele. Although the lower lip is the most common site of involvement, mucoceles also occur on the tongue, cheek, palate, and mouth floor.Metastases to the oral cavity are rare and comprise only 1% of all oral cavity malignancies.⁷ Although most commonly seen in the jaw, nearly one-third of oral cavity metastases are in the soft tissue.⁸ They generally occur late in the course of disease, and the time between appearance and death is usually short.⁸ Our patient’s lack of known primary malignancy and lack of weight loss rendered this diagnosis unlikely.

Other possibilities include peripheral giant cell granuloma, a reactive hyperplastic lesion of the oral cavity originating from the periosteum or periodontal membrane following local irritation or chronic trauma,⁹ and peripheral ossifying fibroma, a reactive soft tissue growth usually seen on the interdental papilla.¹⁰

Surgical excision was performed and revealed reactive epidermal hyperplasia, ulceration, granulation tissue formation, and marked inflammation with reactive changes. There was no evidence of malignancy and was interpreted as consistent with pyogenic granuloma (Figures 2 and 3) likely due to the trauma from the thermal burn or poor dentition.

Management

The patient was relieved to be informed of the diagnosis of an unusual presentation of pyogenic granuloma with no evidence of cancer. Current treatment strategies for pyogenic granuloma include surgical excision, shave excision with cautery, cryotherapy, sclerotherapy, carbon dioxide or pulsed dye laser, as well as expectant management. However, recurrence after initial treatment can occur, with lower recurrence rates occurring with surgical excision.¹¹

Although we wouldn’t state that we gave the patient a “tongue-lashing,” we strongly advised him that he return to his dentist and abstain from tobacco products, alcohol, illicit drugs, and taste-testing scalding food directly from the pot.

Cataract surgery is one of the most common ambulatory procedures performed in the US.^1-3 With the aging of the US population, the number of Americans with cataracts is projected to increase from 24.4 million in 2010 to 38.7 million in 2030.⁴

Approximately 20% of all cataract patients have preoperative astigmatism of > 1.5 diopters (D), underscoring the importance of training residents in the placement of toric intraocular lenses (IOLs).⁵ However, the implantation of toric IOLs is more challenging than monofocal IOLs, requiring precise surgical alignment of the IOL.⁶ Successful toric IOL implantation also requires accurate calculation of the IOL cylinder power and target axis of alignment. It is unclear which toric IOL calculation formula offers the most accurate refractive predictions, and practitioners have designed strategies to apply different formulae depending on the biometric dimensions of the target eye.^7-9

Previous studies of resident-performed cataract surgery using toric IOLs^6,10-13 and studies that compare the performance of the Barrett and Holladay toric formulae have been limited by their small sample sizes (< 107 eyes).^7,14-16 Moreover, none of the studies that evaluate the comparative effectiveness of these biometric formulae were conducted at a teaching hospital.^7,14-16

Given the added complexity of toric IOL placement and variable surgical experience of residents as ophthalmologists-in-training, it is important to assess outcomes in teaching hospitals.¹³ The primary aims of this study were to assess the visual and refractive outcomes of cataract surgery using toric IOLs in a US Department of Veterans Affairs (VA) teaching hospital and to compare the relative accuracy of the Holladay 2 or Barrett toric biometric formulae in predicting postoperative refraction outcomes.

Methods

The Providence VA Medical Center (PVAMC) Institutional Review Board approved this study. This retrospective chart review included patients with cataract and corneal astigmatism who underwent cataract surgery using Acrysof toric IOLs, model SN6AT (Alcon) at the PVAMC teaching hospital between November 2013 and May 2018.

Only 1 eye was included from each study subject to avoid compounding of data with the use of bilateral eyes.¹⁷ In addition, bilateral cataract surgery was only performed on some patients at the PVAMC, so including both eyes from eligible patients would disproportionately weigh those patients’ outcomes. If both eyes had cataract surgery and their postoperative visual acuities were unequal, we chose the eye with the better postoperative visual acuity since refraction accuracy decreases with worsening best-corrected visual acuity (BCVA). If both eyes had cataract surgery and the postoperative visual acuity was the same, the first operated eye was chosen.^17,18

Exclusion criteria included worse than 20/40 BCVA, posterior capsular rupture, sulcus IOL, history of corneal disease, history of refractive surgery (laser-assisted in situ keratomileusis [LASIK]/photorefractive keratectomy [PRK]), axial length not measurable by the Lenstar optical biometer (Haag-Streit USA), or no postoperative refraction within 3 weeks to 4 months.^19,20

Patient age, race/ethnicity, gender, preoperative refraction, preoperative BCVA, postoperative refraction, postoperative BCVA, and IOL power were recorded from patient charts (Table 1). Preoperative and postoperative refractive values were converted to spherical equivalents. The preoperative biometry and most of the postoperative refractions were performed by experienced technicians certified by the Joint Commission on Allied Health Personnel in Ophthalmology. The main outcomes for the assessment of surgeries included the postoperative BCVA, postoperative spherical equivalent refraction, and postoperative residual refractive astigmatism.

Axial length (AL), preoperative anterior chamber depth (ACD), preoperative flat corneal front power (K1), preoperative steep corneal front power (K2), lens thickness, horizontal white-to-white (WTW) corneal diameter, and central corneal thickness (CCT) were recorded from the Lenstar biometric device. Predicted postoperative refractions for the Holladay 2 formula were calculated using Holladay IOL Consultant software (Holladay Consulting). Predicted postoperative refractions for the Barrett toric IOL formula were calculated using the online Barrett toric formula calculator.²¹ Since previous studies have shown that both the Holladay and Barrett formulae account for posterior corneal astigmatism, a comparison of refractive outcomes in eyes with against-the-rule astigmatism vs with-the-rule astigmatism was not performed.¹⁴ An estimated standardized value for surgically-induced astigmatism was entered into both formulae; 0.3 diopter (D) was chosen based on previously published averages.^22-24

A formula’s prediction error is defined as the predicted postoperative refraction minus the actual postoperative refraction. The mean absolute prediction error (MAE), defined as the mean of the absolute values of the prediction errors, and the median absolute prediction error (MedAE), defined as the median of the absolute values of the prediction errors, were used to assess the overall accuracy of each formula. Also, the percentages of eyes with postoperative refraction within ≥ 0.25 D, ≥ 0.50 D, and ≥ 1.0 D were calculated for both formulae. Two-tailed t tests were performed to compare the MAE between the formulae. Subgroup analyses were performed for short eyes (AL < 22 mm), medium length eyes (AL = 22-25 mm), and long eyes (AL > 25 mm). Statistical analysis was performed using STATA 11 (STATA Corp). The preoperative corneal astigmatism and postoperative refractive astigmatism of all the cases were compared in double-angle plots to assess how well the toric IOL neutralized the corneal astigmatism.

Results

Of 325 charts reviewed during the study period, 34 patients were excluded due to lack of postoperative refraction within the designated follow-up period, 5 for worse than 20/40 postoperative BCVA (4 had preexisting ocular disease), 2 for complications, and 1 for missing data. We included 283 eyes from 283 patients in the final study. Resident ophthalmologists were the primary surgeons in 87.6% (248/283) of the cases.

The median postoperative BCVA was 20/20, and 92% of patients had a postoperative BCVA of 20/25 or better. The prediction outcomes of the toric SN6AT IOLs are shown in Table 2. The Barrett toric formula had a lower MAE than the Holladay 2 formula, but this difference was not statistically significant. The Barrett toric formula also predicted a higher percentage of eyes with postoperative refraction within ≥ 0.25 D (53.2%), ≥ 0.5 D (77.3%), and ≥ 1.0 D (96.1%). For both formulae, > 95% of eyes had prediction errors that fell within 1.0 D.

While the Barrett formula demonstrated a lower MAE in all 3 AL groups, no statistically significant differences were found between the Barrett and Holladay formulae (P = .94, P = .49, and P = .08 for short, medium, and long eyes, respectively). Both formulae produced the lowest MAE in the long AL group: Barrett had a MAE of 0.221 D and Holladay 2 had one of 0.329 D. The Barrett formula produced its highest percentage of eyes with prediction errors falling within 0.25 D and 0.5 D in the long AL group. In comparison, both formulae had the highest MAEs in the short AL group (Barrett toric, 0.598 D; Holladay 2, 0.613 D) and produced the lowest percentage of eyes with prediction errors falling within ≥ 0.25 D and ≥ 0.5 D in the short AL group.

A cumulative histogram of the preoperative corneal and postoperative refractive astigmatism magnitude is shown in Figure 1. The same data are presented as double-angle plots in the Appendix, which shows that the centroid values for preoperative corneal astigmatism were greatlyreduced when compared with the postoperative refractive astigmatism (mean absolute value of 1.77 D ≥ 0.73 D to 0.5 D ≥ 0.50 D).

Preoperative corneal astigmatism and postoperative refractive astigmatism were compared since preoperative refractive astigmatism has noncorneal contributions, including lenticular astigmatism, and there is minimal expected change between preoperative and postoperative corneal astigmatism.¹⁴ For comparison, double-angle plots of postoperative refractive astigmatism prediction errors for the Holladay and Barrett formulae are shown in Figure 2.

Discussion

To our knowledge, this is the largest study of resident-performed cataract surgery using toric IOLs, the largest study that compared the performance of the Barrett toric and Holladay 2 formulae, and the first that compared these formulae in a teaching hospital setting. This study found no significant difference in the predictive accuracy of the Barrett and Holladay 2 biometric formulae for cataract surgery using toric IOLs. In addition, our refractive outcomes were consistent with the results of previous toric IOL outcome studies conducted in teaching and nonteaching hospital settings.^6,10-13

In 4 previous studies that compared the MAE of the Barrett and Holladay formulae for toric IOLs, the Barrett formula produced a lower MAE than the Holladay 2 formula.^7,14-16 However, this difference was significant in only 2 of the studies, which had sample sizes of only 68 and 107 eyes.^14,16 Furthermore, the Barrett toric formula produced the lower MAE for the entire AL range, though this was not statistically significant at our sample size. In addition, both formulae produced the lowest MAE in the long AL group and the highest MAE in the short AL group. The unique anatomy and high IOL power needed in short eyes may explain the challenges in attaining accurate IOL power predictions in this AL group.^19,25

Limitations

The sample size of this study may have prevented us from detecting statistically significant differences in the performance of the Barrett and Holladay formulae. However, our findings are consistent with studies that compare the accuracy of these formulae in teaching and nonteaching hospital settings. Second, the study was conducted at a VA hospital, and a high proportion of patients were male; thus, our findings may not be generalizable to patients who receive cataract surgery with toric IOLs in other settings.

Conclusions

In a single VA teaching hospital, the Barrett and Holladay 2 biometric formulae provide similar refractive predictions for cataract surgery using toric IOLs. Larger studies would be necessary to detect smaller differences in the relative performance of the biometric formulae.

Cataract surgery is one of the most common ambulatory procedures performed in the US.^1-3 With the aging of the US population, the number of Americans with cataracts is projected to increase from 24.4 million in 2010 to 38.7 million in 2030.⁴

Approximately 20% of all cataract patients have preoperative astigmatism of > 1.5 diopters (D), underscoring the importance of training residents in the placement of toric intraocular lenses (IOLs).⁵ However, the implantation of toric IOLs is more challenging than monofocal IOLs, requiring precise surgical alignment of the IOL.⁶ Successful toric IOL implantation also requires accurate calculation of the IOL cylinder power and target axis of alignment. It is unclear which toric IOL calculation formula offers the most accurate refractive predictions, and practitioners have designed strategies to apply different formulae depending on the biometric dimensions of the target eye.^7-9

Previous studies of resident-performed cataract surgery using toric IOLs^6,10-13 and studies that compare the performance of the Barrett and Holladay toric formulae have been limited by their small sample sizes (< 107 eyes).^7,14-16 Moreover, none of the studies that evaluate the comparative effectiveness of these biometric formulae were conducted at a teaching hospital.^7,14-16

Given the added complexity of toric IOL placement and variable surgical experience of residents as ophthalmologists-in-training, it is important to assess outcomes in teaching hospitals.¹³ The primary aims of this study were to assess the visual and refractive outcomes of cataract surgery using toric IOLs in a US Department of Veterans Affairs (VA) teaching hospital and to compare the relative accuracy of the Holladay 2 or Barrett toric biometric formulae in predicting postoperative refraction outcomes.

Methods

The Providence VA Medical Center (PVAMC) Institutional Review Board approved this study. This retrospective chart review included patients with cataract and corneal astigmatism who underwent cataract surgery using Acrysof toric IOLs, model SN6AT (Alcon) at the PVAMC teaching hospital between November 2013 and May 2018.

Only 1 eye was included from each study subject to avoid compounding of data with the use of bilateral eyes.¹⁷ In addition, bilateral cataract surgery was only performed on some patients at the PVAMC, so including both eyes from eligible patients would disproportionately weigh those patients’ outcomes. If both eyes had cataract surgery and their postoperative visual acuities were unequal, we chose the eye with the better postoperative visual acuity since refraction accuracy decreases with worsening best-corrected visual acuity (BCVA). If both eyes had cataract surgery and the postoperative visual acuity was the same, the first operated eye was chosen.^17,18

Exclusion criteria included worse than 20/40 BCVA, posterior capsular rupture, sulcus IOL, history of corneal disease, history of refractive surgery (laser-assisted in situ keratomileusis [LASIK]/photorefractive keratectomy [PRK]), axial length not measurable by the Lenstar optical biometer (Haag-Streit USA), or no postoperative refraction within 3 weeks to 4 months.^19,20

Patient age, race/ethnicity, gender, preoperative refraction, preoperative BCVA, postoperative refraction, postoperative BCVA, and IOL power were recorded from patient charts (Table 1). Preoperative and postoperative refractive values were converted to spherical equivalents. The preoperative biometry and most of the postoperative refractions were performed by experienced technicians certified by the Joint Commission on Allied Health Personnel in Ophthalmology. The main outcomes for the assessment of surgeries included the postoperative BCVA, postoperative spherical equivalent refraction, and postoperative residual refractive astigmatism.

Axial length (AL), preoperative anterior chamber depth (ACD), preoperative flat corneal front power (K1), preoperative steep corneal front power (K2), lens thickness, horizontal white-to-white (WTW) corneal diameter, and central corneal thickness (CCT) were recorded from the Lenstar biometric device. Predicted postoperative refractions for the Holladay 2 formula were calculated using Holladay IOL Consultant software (Holladay Consulting). Predicted postoperative refractions for the Barrett toric IOL formula were calculated using the online Barrett toric formula calculator.²¹ Since previous studies have shown that both the Holladay and Barrett formulae account for posterior corneal astigmatism, a comparison of refractive outcomes in eyes with against-the-rule astigmatism vs with-the-rule astigmatism was not performed.¹⁴ An estimated standardized value for surgically-induced astigmatism was entered into both formulae; 0.3 diopter (D) was chosen based on previously published averages.^22-24

A formula’s prediction error is defined as the predicted postoperative refraction minus the actual postoperative refraction. The mean absolute prediction error (MAE), defined as the mean of the absolute values of the prediction errors, and the median absolute prediction error (MedAE), defined as the median of the absolute values of the prediction errors, were used to assess the overall accuracy of each formula. Also, the percentages of eyes with postoperative refraction within ≥ 0.25 D, ≥ 0.50 D, and ≥ 1.0 D were calculated for both formulae. Two-tailed t tests were performed to compare the MAE between the formulae. Subgroup analyses were performed for short eyes (AL < 22 mm), medium length eyes (AL = 22-25 mm), and long eyes (AL > 25 mm). Statistical analysis was performed using STATA 11 (STATA Corp). The preoperative corneal astigmatism and postoperative refractive astigmatism of all the cases were compared in double-angle plots to assess how well the toric IOL neutralized the corneal astigmatism.

Results

Of 325 charts reviewed during the study period, 34 patients were excluded due to lack of postoperative refraction within the designated follow-up period, 5 for worse than 20/40 postoperative BCVA (4 had preexisting ocular disease), 2 for complications, and 1 for missing data. We included 283 eyes from 283 patients in the final study. Resident ophthalmologists were the primary surgeons in 87.6% (248/283) of the cases.

The median postoperative BCVA was 20/20, and 92% of patients had a postoperative BCVA of 20/25 or better. The prediction outcomes of the toric SN6AT IOLs are shown in Table 2. The Barrett toric formula had a lower MAE than the Holladay 2 formula, but this difference was not statistically significant. The Barrett toric formula also predicted a higher percentage of eyes with postoperative refraction within ≥ 0.25 D (53.2%), ≥ 0.5 D (77.3%), and ≥ 1.0 D (96.1%). For both formulae, > 95% of eyes had prediction errors that fell within 1.0 D.

While the Barrett formula demonstrated a lower MAE in all 3 AL groups, no statistically significant differences were found between the Barrett and Holladay formulae (P = .94, P = .49, and P = .08 for short, medium, and long eyes, respectively). Both formulae produced the lowest MAE in the long AL group: Barrett had a MAE of 0.221 D and Holladay 2 had one of 0.329 D. The Barrett formula produced its highest percentage of eyes with prediction errors falling within 0.25 D and 0.5 D in the long AL group. In comparison, both formulae had the highest MAEs in the short AL group (Barrett toric, 0.598 D; Holladay 2, 0.613 D) and produced the lowest percentage of eyes with prediction errors falling within ≥ 0.25 D and ≥ 0.5 D in the short AL group.

A cumulative histogram of the preoperative corneal and postoperative refractive astigmatism magnitude is shown in Figure 1. The same data are presented as double-angle plots in the Appendix, which shows that the centroid values for preoperative corneal astigmatism were greatlyreduced when compared with the postoperative refractive astigmatism (mean absolute value of 1.77 D ≥ 0.73 D to 0.5 D ≥ 0.50 D).

Preoperative corneal astigmatism and postoperative refractive astigmatism were compared since preoperative refractive astigmatism has noncorneal contributions, including lenticular astigmatism, and there is minimal expected change between preoperative and postoperative corneal astigmatism.¹⁴ For comparison, double-angle plots of postoperative refractive astigmatism prediction errors for the Holladay and Barrett formulae are shown in Figure 2.

Discussion

To our knowledge, this is the largest study of resident-performed cataract surgery using toric IOLs, the largest study that compared the performance of the Barrett toric and Holladay 2 formulae, and the first that compared these formulae in a teaching hospital setting. This study found no significant difference in the predictive accuracy of the Barrett and Holladay 2 biometric formulae for cataract surgery using toric IOLs. In addition, our refractive outcomes were consistent with the results of previous toric IOL outcome studies conducted in teaching and nonteaching hospital settings.^6,10-13

In 4 previous studies that compared the MAE of the Barrett and Holladay formulae for toric IOLs, the Barrett formula produced a lower MAE than the Holladay 2 formula.^7,14-16 However, this difference was significant in only 2 of the studies, which had sample sizes of only 68 and 107 eyes.^14,16 Furthermore, the Barrett toric formula produced the lower MAE for the entire AL range, though this was not statistically significant at our sample size. In addition, both formulae produced the lowest MAE in the long AL group and the highest MAE in the short AL group. The unique anatomy and high IOL power needed in short eyes may explain the challenges in attaining accurate IOL power predictions in this AL group.^19,25

Limitations

The sample size of this study may have prevented us from detecting statistically significant differences in the performance of the Barrett and Holladay formulae. However, our findings are consistent with studies that compare the accuracy of these formulae in teaching and nonteaching hospital settings. Second, the study was conducted at a VA hospital, and a high proportion of patients were male; thus, our findings may not be generalizable to patients who receive cataract surgery with toric IOLs in other settings.

Conclusions

In a single VA teaching hospital, the Barrett and Holladay 2 biometric formulae provide similar refractive predictions for cataract surgery using toric IOLs. Larger studies would be necessary to detect smaller differences in the relative performance of the biometric formulae.

Cataract surgery is one of the most common ambulatory procedures performed in the US.^1-3 With the aging of the US population, the number of Americans with cataracts is projected to increase from 24.4 million in 2010 to 38.7 million in 2030.⁴

Approximately 20% of all cataract patients have preoperative astigmatism of > 1.5 diopters (D), underscoring the importance of training residents in the placement of toric intraocular lenses (IOLs).⁵ However, the implantation of toric IOLs is more challenging than monofocal IOLs, requiring precise surgical alignment of the IOL.⁶ Successful toric IOL implantation also requires accurate calculation of the IOL cylinder power and target axis of alignment. It is unclear which toric IOL calculation formula offers the most accurate refractive predictions, and practitioners have designed strategies to apply different formulae depending on the biometric dimensions of the target eye.^7-9

Previous studies of resident-performed cataract surgery using toric IOLs^6,10-13 and studies that compare the performance of the Barrett and Holladay toric formulae have been limited by their small sample sizes (< 107 eyes).^7,14-16 Moreover, none of the studies that evaluate the comparative effectiveness of these biometric formulae were conducted at a teaching hospital.^7,14-16

Given the added complexity of toric IOL placement and variable surgical experience of residents as ophthalmologists-in-training, it is important to assess outcomes in teaching hospitals.¹³ The primary aims of this study were to assess the visual and refractive outcomes of cataract surgery using toric IOLs in a US Department of Veterans Affairs (VA) teaching hospital and to compare the relative accuracy of the Holladay 2 or Barrett toric biometric formulae in predicting postoperative refraction outcomes.

Methods

The Providence VA Medical Center (PVAMC) Institutional Review Board approved this study. This retrospective chart review included patients with cataract and corneal astigmatism who underwent cataract surgery using Acrysof toric IOLs, model SN6AT (Alcon) at the PVAMC teaching hospital between November 2013 and May 2018.

Only 1 eye was included from each study subject to avoid compounding of data with the use of bilateral eyes.¹⁷ In addition, bilateral cataract surgery was only performed on some patients at the PVAMC, so including both eyes from eligible patients would disproportionately weigh those patients’ outcomes. If both eyes had cataract surgery and their postoperative visual acuities were unequal, we chose the eye with the better postoperative visual acuity since refraction accuracy decreases with worsening best-corrected visual acuity (BCVA). If both eyes had cataract surgery and the postoperative visual acuity was the same, the first operated eye was chosen.^17,18

Exclusion criteria included worse than 20/40 BCVA, posterior capsular rupture, sulcus IOL, history of corneal disease, history of refractive surgery (laser-assisted in situ keratomileusis [LASIK]/photorefractive keratectomy [PRK]), axial length not measurable by the Lenstar optical biometer (Haag-Streit USA), or no postoperative refraction within 3 weeks to 4 months.^19,20

Patient age, race/ethnicity, gender, preoperative refraction, preoperative BCVA, postoperative refraction, postoperative BCVA, and IOL power were recorded from patient charts (Table 1). Preoperative and postoperative refractive values were converted to spherical equivalents. The preoperative biometry and most of the postoperative refractions were performed by experienced technicians certified by the Joint Commission on Allied Health Personnel in Ophthalmology. The main outcomes for the assessment of surgeries included the postoperative BCVA, postoperative spherical equivalent refraction, and postoperative residual refractive astigmatism.

Axial length (AL), preoperative anterior chamber depth (ACD), preoperative flat corneal front power (K1), preoperative steep corneal front power (K2), lens thickness, horizontal white-to-white (WTW) corneal diameter, and central corneal thickness (CCT) were recorded from the Lenstar biometric device. Predicted postoperative refractions for the Holladay 2 formula were calculated using Holladay IOL Consultant software (Holladay Consulting). Predicted postoperative refractions for the Barrett toric IOL formula were calculated using the online Barrett toric formula calculator.²¹ Since previous studies have shown that both the Holladay and Barrett formulae account for posterior corneal astigmatism, a comparison of refractive outcomes in eyes with against-the-rule astigmatism vs with-the-rule astigmatism was not performed.¹⁴ An estimated standardized value for surgically-induced astigmatism was entered into both formulae; 0.3 diopter (D) was chosen based on previously published averages.^22-24

A formula’s prediction error is defined as the predicted postoperative refraction minus the actual postoperative refraction. The mean absolute prediction error (MAE), defined as the mean of the absolute values of the prediction errors, and the median absolute prediction error (MedAE), defined as the median of the absolute values of the prediction errors, were used to assess the overall accuracy of each formula. Also, the percentages of eyes with postoperative refraction within ≥ 0.25 D, ≥ 0.50 D, and ≥ 1.0 D were calculated for both formulae. Two-tailed t tests were performed to compare the MAE between the formulae. Subgroup analyses were performed for short eyes (AL < 22 mm), medium length eyes (AL = 22-25 mm), and long eyes (AL > 25 mm). Statistical analysis was performed using STATA 11 (STATA Corp). The preoperative corneal astigmatism and postoperative refractive astigmatism of all the cases were compared in double-angle plots to assess how well the toric IOL neutralized the corneal astigmatism.

Results

Of 325 charts reviewed during the study period, 34 patients were excluded due to lack of postoperative refraction within the designated follow-up period, 5 for worse than 20/40 postoperative BCVA (4 had preexisting ocular disease), 2 for complications, and 1 for missing data. We included 283 eyes from 283 patients in the final study. Resident ophthalmologists were the primary surgeons in 87.6% (248/283) of the cases.

The median postoperative BCVA was 20/20, and 92% of patients had a postoperative BCVA of 20/25 or better. The prediction outcomes of the toric SN6AT IOLs are shown in Table 2. The Barrett toric formula had a lower MAE than the Holladay 2 formula, but this difference was not statistically significant. The Barrett toric formula also predicted a higher percentage of eyes with postoperative refraction within ≥ 0.25 D (53.2%), ≥ 0.5 D (77.3%), and ≥ 1.0 D (96.1%). For both formulae, > 95% of eyes had prediction errors that fell within 1.0 D.

While the Barrett formula demonstrated a lower MAE in all 3 AL groups, no statistically significant differences were found between the Barrett and Holladay formulae (P = .94, P = .49, and P = .08 for short, medium, and long eyes, respectively). Both formulae produced the lowest MAE in the long AL group: Barrett had a MAE of 0.221 D and Holladay 2 had one of 0.329 D. The Barrett formula produced its highest percentage of eyes with prediction errors falling within 0.25 D and 0.5 D in the long AL group. In comparison, both formulae had the highest MAEs in the short AL group (Barrett toric, 0.598 D; Holladay 2, 0.613 D) and produced the lowest percentage of eyes with prediction errors falling within ≥ 0.25 D and ≥ 0.5 D in the short AL group.

A cumulative histogram of the preoperative corneal and postoperative refractive astigmatism magnitude is shown in Figure 1. The same data are presented as double-angle plots in the Appendix, which shows that the centroid values for preoperative corneal astigmatism were greatlyreduced when compared with the postoperative refractive astigmatism (mean absolute value of 1.77 D ≥ 0.73 D to 0.5 D ≥ 0.50 D).

Preoperative corneal astigmatism and postoperative refractive astigmatism were compared since preoperative refractive astigmatism has noncorneal contributions, including lenticular astigmatism, and there is minimal expected change between preoperative and postoperative corneal astigmatism.¹⁴ For comparison, double-angle plots of postoperative refractive astigmatism prediction errors for the Holladay and Barrett formulae are shown in Figure 2.

Discussion

To our knowledge, this is the largest study of resident-performed cataract surgery using toric IOLs, the largest study that compared the performance of the Barrett toric and Holladay 2 formulae, and the first that compared these formulae in a teaching hospital setting. This study found no significant difference in the predictive accuracy of the Barrett and Holladay 2 biometric formulae for cataract surgery using toric IOLs. In addition, our refractive outcomes were consistent with the results of previous toric IOL outcome studies conducted in teaching and nonteaching hospital settings.^6,10-13

In 4 previous studies that compared the MAE of the Barrett and Holladay formulae for toric IOLs, the Barrett formula produced a lower MAE than the Holladay 2 formula.^7,14-16 However, this difference was significant in only 2 of the studies, which had sample sizes of only 68 and 107 eyes.^14,16 Furthermore, the Barrett toric formula produced the lower MAE for the entire AL range, though this was not statistically significant at our sample size. In addition, both formulae produced the lowest MAE in the long AL group and the highest MAE in the short AL group. The unique anatomy and high IOL power needed in short eyes may explain the challenges in attaining accurate IOL power predictions in this AL group.^19,25

Limitations

The sample size of this study may have prevented us from detecting statistically significant differences in the performance of the Barrett and Holladay formulae. However, our findings are consistent with studies that compare the accuracy of these formulae in teaching and nonteaching hospital settings. Second, the study was conducted at a VA hospital, and a high proportion of patients were male; thus, our findings may not be generalizable to patients who receive cataract surgery with toric IOLs in other settings.

Conclusions

In a single VA teaching hospital, the Barrett and Holladay 2 biometric formulae provide similar refractive predictions for cataract surgery using toric IOLs. Larger studies would be necessary to detect smaller differences in the relative performance of the biometric formulae.

The nature of combat and associated injuries in Operation Iraqi Freedom (OIF), Operation Enduring Freedom (OEF), Operation New Dawn (OND), and Afghanistan War is different from previous conflicts. Multiple protracted deployments with infrequent breaks after September 11, 2001 (9/11) have further compounded the problem.

Posttraumatic stress disorder (PTSD) and traumatic brain injury (TBI) are the signature wounds of recent wars, with a higher incidence among the veterans of OEF and OIF compared with those from previous conflicts.^1,2 More than 2.7 million who served in Iraq and Afghanistan suffer from PTSD.^3,4 Symptoms of PTSD may appear within the first 3 months after exposure to a traumatic event or after many months and, in some cases, after a delay of many years and continue for life.⁵ Although delayed onset of PTSD in the absence of prior symptoms is rare,^6,7 its incidence rises with increasing frequency of exposure to traumatic events^8,9 and over time.¹⁰

According to the Brain Injury Association of America, TBI is “an alteration in brain function, or other evidence of brain pathology, caused by an external force.”⁸ TBI is often associated with increased risk of PTSD, depression, and posttraumatic headache,^11-13 which may lead to broader cognitive, somatic, neurobiological, and psychosocial dysfunctions.^14-17 According to Veterans Health Administration (VHA) data, 201,435 veterans from all eras enrolled with the US Department of Veterans Affairs (VA) have a diagnosis associated with TBI and 56,695 OEF/OIF veterans have been evaluated for a TBI-related condition.² According to the Defense and Veterans Brain Injury Center (DVBIC), > 361,000 veterans have been diagnosed with TBI, with a peak of 32,000 cases in 2011.^1,18 Moreover, the reported incidence and prevalence of PTSD and TBI among US veterans are not consistent. The incidence of PTSD has been estimated at 15% to 20% in recent wars^3,19 compared with 10% to 30% in previous wars.^3,19,20

When PTSD or TBI is deemed “related” to military service, the veteran may receive a service-connected disability rating ranging from 0% (no life-interfering symptoms due to injury) to 100% (totally disabling injury). The percentage of service connection associated with an injury is a quantifiable measure of the debilitating effect of injury on the individual. A significant majority (94%) of those who seek mental health services and treatment at VHA clinics apply for PTSD-related disability benefits.²¹ The estimated cost related to PTSD/TBI service-connected pensions is $20.28 billion per year and approximately $514 billion over 50 years.²² The cost of VA and Social Security disability payments combined with health care costs and treatment of PTSD is estimated to exceed $1 trillion over the next 30 years.²²

The National Vietnam Veterans Readjustment Study (NVVRS) provided valuable information on prevalence rates of PTSD and other postwar psychological problems.²³ Meanwhile, there have been no recent large-scale studies to compare the demographics of veterans diagnosed with PTSD and TBI who served prior to and after 9/11. A better understanding of demographic changes is considered essential for designing and tailoring therapeutic interventions to manage the rising cost.²²

The present study focused on identifying changing trends in the demographics of veterans who served prior to and after 9/11 and who received a VA inpatient or outpatient diagnosis of PTSD and/or TBI. Specifically, this study addressed the changes in demographics of veterans with PTSD, TBI, or PTSD+TBI seen at the VHA clinics between December 1,1998 and May 31, 2014 (before and after September 11, 2001) for diagnosis, treatment and health care policy issues.

Methods

This study was approved by the Kansas City VA Medical Center Institutional Review Board. VHA data from the Corporate Data Warehouse (CDW) and the National Patient Care Database were extracted using the VA Informatics and Computing Infrastructure (VINCI) workspace. CDW uses a unique identifier to identify veterans across treatment episodes at more than 1,400 VHA centers organized under 21 Veterans Integrated Service Networks (VISNs). These sources of VA data are widely used for retrospective longitudinal studies.

Study Population

The study population consisted of 1,339,937 veterans with a VA inpatient/outpatient diagnosis of PTSD or TBI using International Statistical Classification of Diseases and Related Health Problems, Ninth Revision (ICD-9) codes between December 1, 1998 and May 31, 2014. Demographic (gender classification, race, ethnicity, marital status, age at date of data extraction, and date of death if indicated), service-connection disability rating, and geographic distribution within VISN data on each veteran were then extracted.

Veterans in the cohort were assigned to 1 of 4 US military services period groups. The pre-9/11 group included veterans who entered and left the military prior to September 11, 2001. This group mostly included veterans from World War II, Korean War, Vietnam War, and the first Gulf War (1990-1991). The post-9/11 group included veterans who first entered military services after September 11, 2001. The overlap group included veterans who entered military services prior to 9/11, remained in service and left after September 11, 2001. The reentered group included veterans who entered and left service prior to September 11, 2001, and then reentered military service after September 11, 2001 (Figure 1). Using ICD-9 codes, veterans also were placed into the following categories: PTSD alone (ICD-9 309.81 only), TBI alone (ICD-9 850.0-859.9, V15.52), and PTSD+TBI (any combination of ICD-9 codes from the other categories).

Statistical Analysis 

Descriptive statistics were applied using proportions and means. Relationships between variables were examined using χ² tests, t tests, analysis of variance, and nonparametric tests. All hypotheses were 2-sided at 95% CI. Results are presented as absolute numbers.

Results

PTSD only (n = 1,132,356, 85%) was the predominant diagnosis category followed by PTSD+TBI (n = 106,792, 8%) and TBI only (n = 100,789, 7%) (Figure 2). Most of the veterans in the study served pre-9/11 (77%), followed by post-9/11 (15%); 7% were in the overlap group, and 1% in the reentered group (Table 1). It is notable that the proportion of veterans diagnosed with PTSD decreased from pre-9/11 (88%) to post-9/11 (71%), overlap (77%), and reentered (74%) service periods. Increases were noted in those with PTSD+TBI diagnosis category from pre-9/11 (4%) to post-9/11 (23%), overlap (17%), and reentered (22%) service periods (Figure 3). In general, the relative distribution of diagnostic categories in all the service periods showed a similar trend, with the majority of veterans diagnosed with PTSD only. Across all service periods, significantly smaller proportions of veterans were diagnosed with TBI only (P < .001).

Distribution by Gender and Age

The cohort was 92% male (n = 1,239,295), but there was a marked increase in the percentage of nonmale veterans in post-9/11 groups. Study population ages ranged from 18 to 99 years based on date of birth to the date data were obtained; or date of birth to date of death, for those who were reported deceased at the time the data were obtained. The average (SD) ages for veterans in the pre-9/11 group were significantly older (66.3 [11.2] years) compared with the ages of veterans in the post-9/11 group (36.1 [8.7] years), the overlap group (41.4 [8.2] years), and the reentered group (46.9 [9.2] years), respectively.

Distribution by Race and Marital Status

The cohort identified as 65.7% white and 18.2% African American with much smaller percentages of Asians, American Indian/Alaska Natives (AI/AN) and Native Hawaiian/Pacific Islanders (Table 2). The relative proportion of AI/AN and Native Hawaiian/Pacific Islanders remained constant across all groups, whereas the number of Asians diagnosed with PTSD, TBI, or PTSD+TBI increased in the post-9/11 group. The number of African Americans diagnosed with PTSD, TBI, or both markedly increased in the overlap and reentered groups when compared with the pre-9/11 group, yet it went down in the post-9/11/group.

Half the cohort identified themselves as married (n = 675,145) (Table 3). A slightly larger proportion of those diagnosed with PTSD alone were married (51.7%), compared with those diagnosed with TBI only (40.3%), or PTSD+TBI (45.8%). Veterans in the post-9/11 group were less likely to identify as married (45.2%) compared with the pre-9/11 (51.2%), overlap (52.6%), or reentered (53.2%) groups. Divorce rates among pre-9/11 group, overlap group, and reentered group were higher compared with that of the post-9/11 group in all diagnosis categories.

Geographic Distribution

Veterans diagnosed with PTSD, TBI, or both were not evenly distributed across the VISNs VISNs 7, 8, 10, and 22 treated the most veterans, whereas VISN 9 and 15 treated the fewest. Taken together, the top 3 VISNs accounted for 27% to 28% of the total while lowest 3 accounted for 8% to 9% of the total cohort.

Service-Connected Disability

Of 1,339,937 veterans in the cohort, 1,067,691 had a service-connected disability rating for PTSD and/or TBI. Most were diagnosed with PTSD (n = 923,523, 86.5%) followed by both PTSD+TBI (n = 94,051, 8.8%). Three-quarters of the veterans with a service-connected disability were in the pre-9/11 group. Nearly 80% of veterans with a service-connected disability rating had a rating of > 50%. The average (SD) age of veterans with PTSD+TBI and a > 50% service-connected disability was 66.3 (11.2) years in the pre-9/11 group compared with 36.1 (8.7) years in the post-9/11 group.

Discussion

The demographic profile of veterans diagnosed with PTSD+TBI has changed across the service periods covered in this study. Compared with pre-9/11 veterans, the post-9/11 cohort: (1) higher percentage were diagnosed with PTSD+TBI; (2) higher proportion were nonmale veterans; (3) included more young veterans with > 50% service-connected disability; (4) were more racially diverse; and (5) were less likely to be married and divorced and more likely to be self-identified as single. Additionally, data revealed that veterans tended to locate more to some geographic regions than to others.

The nature of the warfare has changed remarkably over the past few decades. Gunshot wounds accounted for 65% of all injuries in World War I, 35% during Vietnam War, and 16% to 23% in the First Gulf War.²⁴ In post-9/11 military conflicts, 81% of injuries were explosion related.^24,25 Although improvements in personal protective gear and battlefield trauma care led to increased survival, several factors may have contributed to increased reporting of TBI, which peaked in 2011 at 32,000 cases.^24-26

Increases in PTSD Diagnosis

Increasing media awareness, mandatory battlefield concussion screening programs instituted by the US Department of Defense (DoD), and stressful conditions that exacerbate mild TBI (mTBI) may have all contributed to the increase in numbers of veterans seeking evaluations and being diagnosed with PTSD and/or TBI in the post-9/11 groups. Additionally, the 2007 National Defense Authorization Act requested the Secretary of Defense to develop a comprehensive, systematic approach for the identification, treatment, disposition, and documentation of TBI in combat and peacetime. By a conservative estimate, significant numbers of veterans will continue to be seen for mTBI at about 20,000 new cases per year.^25-27

More frequent diagnosis of mTBI may have contributed to the increase in veterans diagnosed with PTSD+TBI in the post-9/11 groups. A recent study found that almost 44% of US Army infantry soldiers in Iraq did not lose consciousness but reported symptoms consistent with TBI.¹⁴ Compared with veterans of previous wars, veterans of the post-9/11 conflicts (OIF, OED, and OND) have experienced multiple, protracted deployments with infrequent breaks that can have a cumulative effect on the development of PTSD.^8-10

The findings from the NVVRS study led to creation of specialized PTSD programs in the late 1980s. Since then, there has been an explosion of knowledge and awareness about PTSD, TBI, and the associated service-connected disability ratings and benefits, leading to an increased number of veterans seeking care for PTSD. For example, media coverage of the 50th anniversary of the D-day celebrations resulted in a surge of World War II veterans seeking treatment for PTSD and a surge of Vietnam veterans sought treatment for PTSD during the wars in Iraq and Afghanistan.²⁸ An increased number of veterans reporting PTSD symptoms prompted the DoD to increase screening for PTSD, and to encourage service members to seek treatment when appropriate.

The VA has instituted training programs for clinicians and psychologists to screen and provide care for PTSD. Beginning in 2007, the VA implemented mandatory TBI screening for all veterans who served in combat operations and separated from active-duty service after September 11, 2001. The 4-question screen identifies veterans who are at increased risk of TBI and who experience symptoms that may be related to specific event(s).²⁹ A positive screen does not diagnose TBI but rather indicates a need for further evaluation, which may or may not be responsible for inflated reporting of TBI. Renewed research also has led providers to recognize and study PTSD resulting from noncombat trauma and moral injury. The possibility of delayed onset also drives up the number of veterans diagnosed with PTSD.^5-7

Prevalence

A wide variability exists in the reported prevalence of PTSD among US war veterans with estimates ranging from 15% to 20% of veterans from recent conflicts^3,20 and 10% to 30% of veterans from previous wars.^3,19 These rates are higher than estimates from allied forces from other countries.¹⁹ Meta-analyses suggest that the prevalence of PTSD is 2% to 15% among Vietnam War veterans, 1% to 13% among first (pre-9/11) Gulf War veterans, 4% to 17% among OEF/OIF/OND veterans; these veterans have a lifetime prevalence of 6% to 31%.^{3,11,19,30-38} The prevalence of PTSD is 2 to 4 times higher among the US veterans^19,39 when compared with that of civilians.^40,41 According to one study, concomitant PTSD and TBI appears to be much higher in US war veterans (4%-17%) compared with United Kingdom Iraq War veterans (3%-6%).¹⁹

This study’s finding of an increase in nonmale soldiers with PTSD and/or TBI was not surprising. There is a paucity of data on the effect of war zone exposure on women veterans. Recently, women have been more actively involved in combat roles with 41,000 women deployed to a combat zone. Results of this study indicate a 2- to 3-fold increase in veterans identifying themselves as nonmale in post-9/11 groups with a higher proportion diagnosed with either PTSD alone or PTSD and TBI. Women are at a higher risk for PTSD than are men due in part to exposure to abuse/trauma prior to deployment, experience of higher rates of discrimination, and/or sexual assault.^31-33 One study involving First Gulf War female veterans reported higher precombat psychiatric histories as well as higher rates of physical and sexual abuse when compared with that of men.³¹

In this study, the average age of veterans adjudicated and compensated for PTSD and/or TBI pre-9/11, was 66 years compared with 36 years for post-9/11 veterans. Sixty-six percent of veterans from the post-9/11 group had ≥ 50% service-connected disability at age 36 years; 75% of veterans from the overlap group had ≥ 50% service-connected disability at age 41 years; and 76% veterans from the reentered group had ≥ 50% service-connected disability at age 46 years. Younger age at diagnosis and higher rates of disability not only pose unique challenges for veterans and family members, but also suggest implications for career prospects, family earnings, loss of productivity, and disease-adjusted life years. Also noted in the results, this younger cohort has a higher percentage of single/unmarried veterans, suggesting familial support systems may be more parental than spousal. Treatment for this younger cohort will likely need to focus on early and sustained rehabilitation that can be integrated with career plans.

For treatment to be effective, there must be evidence for veterans enrolling, remaining, and reporting benefits from the treatment. Limited research has shown currently advocated evidence-based therapies to have low enrollment rates, high drop-out rates, and mixed outcomes.⁴²

Results showing a gradual increase in the proportion of nonwhite, non-African American veterans diagnosed with PTSD alone, TBI alone, or both, likely reflect the changing demographic profile of the US as well as the Army. However, the reason that more African Americans were diagnosed with PTSD and/or TBI in the overlap and reentered groups when compared with the pre-9/11 group could not be ascertained. It is possible that more veterans identified themselves as African Americans as evident from a decrease in the number of veterans in the unknown category post-9/11 when compared with the pre-9/11 group. In 2016, the American Community Survey showed that Hispanic and African American veterans were more likely to use VA health care and other benefits than were any other racial group.⁴⁰ Improved screening for PTSD and TBI diagnoses, increased awareness, and education about the availability of VA services and benefits may have contributed to the increased use of VA benefits in these groups.

Data from this study are concordant with data from the National Center for Veterans Analysis and Statistics reporting on the younger age of diagnosis and higher rates of initial service-connected disability in veterans with PTSD and PTSD+TBI.⁴³ One study analyzing records from 1999 to 2004 showed that the number of PTSD cases grew by 79.5%, resulting in 148.7% increase in benefits payment from $1.7 billion to $4.3 billion per year.⁴⁴ In contrast, the compensation cost for all other disability categories increased by only 41.7% over this period. This study also revealed that while veterans with PTSD represented only 8.7% of compensation recipients, they received 20.5% of all compensation payments, driven in large part by an increase in > 50% service-connected disability ratings.⁴⁴

Thus, from financial as well as treatment points of view, the change in the demographic profile of the veteran must be considered when developing PTSD treatment strategies. While treatment in the past focused solely on addressing trauma-associated psychiatric issues, TBI and PTSD association will likely shift the focus to concurrent psychiatric and physical symptomology. Similarly, PTSD/TBI treatment modalities must consider that the profile of post-9/11 service members includes more women, younger age, and a greater racial diversity. For instance, younger age for a disabled veteran brings additional challenges, including reliance on parental or buddy support systems vs a spousal support system, integrating career with treatment, selecting geographic locations that can support both career and treatment, sustaining rehabilitation over time. The treatment needs of a 35-year-old soldier with PTSD and/or TBI, whether male or female, Asian or African American are likely to be very different from the treatment needs of a 65-year-old white male. Newer treatment approaches will have to address the needs of all soldiers.

Limitations

Our study may underestimate the actual PTSD and/or TBI disease burden because of the social stigma associated with diagnosis, military culture, limitations in data collection.^45-50 In addition, in this retrospective database cohort study, we considered and tried to minimize the impact of any of the usual potential limitations, including (1) accuracy of data quality and linkage; (2) identifying cohort appropriately (study groups); (3) defining endpoints clearly to avoid misclassifications; and (4) incorporating all important confounders. We identified veterans utilizing medical services at VA hospitals during a defined period and diagnosed with PTSD and TBI using ICD-9 codes and divided in 4 well-defined groups. In addition, another limitation of our study is to not accurately capture the veterans who have alternative health coverage and may choose not to enroll and/or participate in VA health care. In addition, some service members leaving war zones may not disclose or downplay the mental health symptoms to avoid any delay in their return home.

Conclusions

This study highlights the changing profile of the soldier diagnosed with PTSD and/or TBI who served pre-9/11 compared with that of those who served post-9/11. Treatment modalities must address the changes in warfare and demographics of US service members. Future treatment will need to focus more on concurrent PTSD/TBI therapies, the needs of younger soldiers, the needs of women injured in combat, and the needs of a more racially and ethnically diverse population. Severe injuries at a younger age will require early detection and rehabilitation for return to optimum functioning over a lifetime. The current study underscores a need for identifying the gaps in ongoing programs and services, developing alternatives, and implementing improved systems of care. More studies are needed to identify the cost implications and the effectiveness of current therapies for PTSD and/or TBI.

Acknowledgments

This study was supported by VA Medical Center and Midwest BioMedical Research Foundation (MBRF), Kansas City, Missouri. The manuscript received support, in part, from NIH-RO1 DK107490. These agencies did not participate in the design/conduct of the study or, in the interpretation of the data.

The nature of combat and associated injuries in Operation Iraqi Freedom (OIF), Operation Enduring Freedom (OEF), Operation New Dawn (OND), and Afghanistan War is different from previous conflicts. Multiple protracted deployments with infrequent breaks after September 11, 2001 (9/11) have further compounded the problem.

Posttraumatic stress disorder (PTSD) and traumatic brain injury (TBI) are the signature wounds of recent wars, with a higher incidence among the veterans of OEF and OIF compared with those from previous conflicts.^1,2 More than 2.7 million who served in Iraq and Afghanistan suffer from PTSD.^3,4 Symptoms of PTSD may appear within the first 3 months after exposure to a traumatic event or after many months and, in some cases, after a delay of many years and continue for life.⁵ Although delayed onset of PTSD in the absence of prior symptoms is rare,^6,7 its incidence rises with increasing frequency of exposure to traumatic events^8,9 and over time.¹⁰

According to the Brain Injury Association of America, TBI is “an alteration in brain function, or other evidence of brain pathology, caused by an external force.”⁸ TBI is often associated with increased risk of PTSD, depression, and posttraumatic headache,^11-13 which may lead to broader cognitive, somatic, neurobiological, and psychosocial dysfunctions.^14-17 According to Veterans Health Administration (VHA) data, 201,435 veterans from all eras enrolled with the US Department of Veterans Affairs (VA) have a diagnosis associated with TBI and 56,695 OEF/OIF veterans have been evaluated for a TBI-related condition.² According to the Defense and Veterans Brain Injury Center (DVBIC), > 361,000 veterans have been diagnosed with TBI, with a peak of 32,000 cases in 2011.^1,18 Moreover, the reported incidence and prevalence of PTSD and TBI among US veterans are not consistent. The incidence of PTSD has been estimated at 15% to 20% in recent wars^3,19 compared with 10% to 30% in previous wars.^3,19,20

When PTSD or TBI is deemed “related” to military service, the veteran may receive a service-connected disability rating ranging from 0% (no life-interfering symptoms due to injury) to 100% (totally disabling injury). The percentage of service connection associated with an injury is a quantifiable measure of the debilitating effect of injury on the individual. A significant majority (94%) of those who seek mental health services and treatment at VHA clinics apply for PTSD-related disability benefits.²¹ The estimated cost related to PTSD/TBI service-connected pensions is $20.28 billion per year and approximately $514 billion over 50 years.²² The cost of VA and Social Security disability payments combined with health care costs and treatment of PTSD is estimated to exceed $1 trillion over the next 30 years.²²

The National Vietnam Veterans Readjustment Study (NVVRS) provided valuable information on prevalence rates of PTSD and other postwar psychological problems.²³ Meanwhile, there have been no recent large-scale studies to compare the demographics of veterans diagnosed with PTSD and TBI who served prior to and after 9/11. A better understanding of demographic changes is considered essential for designing and tailoring therapeutic interventions to manage the rising cost.²²

The present study focused on identifying changing trends in the demographics of veterans who served prior to and after 9/11 and who received a VA inpatient or outpatient diagnosis of PTSD and/or TBI. Specifically, this study addressed the changes in demographics of veterans with PTSD, TBI, or PTSD+TBI seen at the VHA clinics between December 1,1998 and May 31, 2014 (before and after September 11, 2001) for diagnosis, treatment and health care policy issues.

Methods

This study was approved by the Kansas City VA Medical Center Institutional Review Board. VHA data from the Corporate Data Warehouse (CDW) and the National Patient Care Database were extracted using the VA Informatics and Computing Infrastructure (VINCI) workspace. CDW uses a unique identifier to identify veterans across treatment episodes at more than 1,400 VHA centers organized under 21 Veterans Integrated Service Networks (VISNs). These sources of VA data are widely used for retrospective longitudinal studies.

Study Population

The study population consisted of 1,339,937 veterans with a VA inpatient/outpatient diagnosis of PTSD or TBI using International Statistical Classification of Diseases and Related Health Problems, Ninth Revision (ICD-9) codes between December 1, 1998 and May 31, 2014. Demographic (gender classification, race, ethnicity, marital status, age at date of data extraction, and date of death if indicated), service-connection disability rating, and geographic distribution within VISN data on each veteran were then extracted.

Veterans in the cohort were assigned to 1 of 4 US military services period groups. The pre-9/11 group included veterans who entered and left the military prior to September 11, 2001. This group mostly included veterans from World War II, Korean War, Vietnam War, and the first Gulf War (1990-1991). The post-9/11 group included veterans who first entered military services after September 11, 2001. The overlap group included veterans who entered military services prior to 9/11, remained in service and left after September 11, 2001. The reentered group included veterans who entered and left service prior to September 11, 2001, and then reentered military service after September 11, 2001 (Figure 1). Using ICD-9 codes, veterans also were placed into the following categories: PTSD alone (ICD-9 309.81 only), TBI alone (ICD-9 850.0-859.9, V15.52), and PTSD+TBI (any combination of ICD-9 codes from the other categories).

Statistical Analysis 

Descriptive statistics were applied using proportions and means. Relationships between variables were examined using χ² tests, t tests, analysis of variance, and nonparametric tests. All hypotheses were 2-sided at 95% CI. Results are presented as absolute numbers.

Results

PTSD only (n = 1,132,356, 85%) was the predominant diagnosis category followed by PTSD+TBI (n = 106,792, 8%) and TBI only (n = 100,789, 7%) (Figure 2). Most of the veterans in the study served pre-9/11 (77%), followed by post-9/11 (15%); 7% were in the overlap group, and 1% in the reentered group (Table 1). It is notable that the proportion of veterans diagnosed with PTSD decreased from pre-9/11 (88%) to post-9/11 (71%), overlap (77%), and reentered (74%) service periods. Increases were noted in those with PTSD+TBI diagnosis category from pre-9/11 (4%) to post-9/11 (23%), overlap (17%), and reentered (22%) service periods (Figure 3). In general, the relative distribution of diagnostic categories in all the service periods showed a similar trend, with the majority of veterans diagnosed with PTSD only. Across all service periods, significantly smaller proportions of veterans were diagnosed with TBI only (P < .001).

Distribution by Gender and Age

The cohort was 92% male (n = 1,239,295), but there was a marked increase in the percentage of nonmale veterans in post-9/11 groups. Study population ages ranged from 18 to 99 years based on date of birth to the date data were obtained; or date of birth to date of death, for those who were reported deceased at the time the data were obtained. The average (SD) ages for veterans in the pre-9/11 group were significantly older (66.3 [11.2] years) compared with the ages of veterans in the post-9/11 group (36.1 [8.7] years), the overlap group (41.4 [8.2] years), and the reentered group (46.9 [9.2] years), respectively.

Distribution by Race and Marital Status

The cohort identified as 65.7% white and 18.2% African American with much smaller percentages of Asians, American Indian/Alaska Natives (AI/AN) and Native Hawaiian/Pacific Islanders (Table 2). The relative proportion of AI/AN and Native Hawaiian/Pacific Islanders remained constant across all groups, whereas the number of Asians diagnosed with PTSD, TBI, or PTSD+TBI increased in the post-9/11 group. The number of African Americans diagnosed with PTSD, TBI, or both markedly increased in the overlap and reentered groups when compared with the pre-9/11 group, yet it went down in the post-9/11/group.

Half the cohort identified themselves as married (n = 675,145) (Table 3). A slightly larger proportion of those diagnosed with PTSD alone were married (51.7%), compared with those diagnosed with TBI only (40.3%), or PTSD+TBI (45.8%). Veterans in the post-9/11 group were less likely to identify as married (45.2%) compared with the pre-9/11 (51.2%), overlap (52.6%), or reentered (53.2%) groups. Divorce rates among pre-9/11 group, overlap group, and reentered group were higher compared with that of the post-9/11 group in all diagnosis categories.

Geographic Distribution

Veterans diagnosed with PTSD, TBI, or both were not evenly distributed across the VISNs VISNs 7, 8, 10, and 22 treated the most veterans, whereas VISN 9 and 15 treated the fewest. Taken together, the top 3 VISNs accounted for 27% to 28% of the total while lowest 3 accounted for 8% to 9% of the total cohort.

Service-Connected Disability

Of 1,339,937 veterans in the cohort, 1,067,691 had a service-connected disability rating for PTSD and/or TBI. Most were diagnosed with PTSD (n = 923,523, 86.5%) followed by both PTSD+TBI (n = 94,051, 8.8%). Three-quarters of the veterans with a service-connected disability were in the pre-9/11 group. Nearly 80% of veterans with a service-connected disability rating had a rating of > 50%. The average (SD) age of veterans with PTSD+TBI and a > 50% service-connected disability was 66.3 (11.2) years in the pre-9/11 group compared with 36.1 (8.7) years in the post-9/11 group.

Discussion

The demographic profile of veterans diagnosed with PTSD+TBI has changed across the service periods covered in this study. Compared with pre-9/11 veterans, the post-9/11 cohort: (1) higher percentage were diagnosed with PTSD+TBI; (2) higher proportion were nonmale veterans; (3) included more young veterans with > 50% service-connected disability; (4) were more racially diverse; and (5) were less likely to be married and divorced and more likely to be self-identified as single. Additionally, data revealed that veterans tended to locate more to some geographic regions than to others.

The nature of the warfare has changed remarkably over the past few decades. Gunshot wounds accounted for 65% of all injuries in World War I, 35% during Vietnam War, and 16% to 23% in the First Gulf War.²⁴ In post-9/11 military conflicts, 81% of injuries were explosion related.^24,25 Although improvements in personal protective gear and battlefield trauma care led to increased survival, several factors may have contributed to increased reporting of TBI, which peaked in 2011 at 32,000 cases.^24-26

Increases in PTSD Diagnosis

Increasing media awareness, mandatory battlefield concussion screening programs instituted by the US Department of Defense (DoD), and stressful conditions that exacerbate mild TBI (mTBI) may have all contributed to the increase in numbers of veterans seeking evaluations and being diagnosed with PTSD and/or TBI in the post-9/11 groups. Additionally, the 2007 National Defense Authorization Act requested the Secretary of Defense to develop a comprehensive, systematic approach for the identification, treatment, disposition, and documentation of TBI in combat and peacetime. By a conservative estimate, significant numbers of veterans will continue to be seen for mTBI at about 20,000 new cases per year.^25-27

More frequent diagnosis of mTBI may have contributed to the increase in veterans diagnosed with PTSD+TBI in the post-9/11 groups. A recent study found that almost 44% of US Army infantry soldiers in Iraq did not lose consciousness but reported symptoms consistent with TBI.¹⁴ Compared with veterans of previous wars, veterans of the post-9/11 conflicts (OIF, OED, and OND) have experienced multiple, protracted deployments with infrequent breaks that can have a cumulative effect on the development of PTSD.^8-10

The findings from the NVVRS study led to creation of specialized PTSD programs in the late 1980s. Since then, there has been an explosion of knowledge and awareness about PTSD, TBI, and the associated service-connected disability ratings and benefits, leading to an increased number of veterans seeking care for PTSD. For example, media coverage of the 50th anniversary of the D-day celebrations resulted in a surge of World War II veterans seeking treatment for PTSD and a surge of Vietnam veterans sought treatment for PTSD during the wars in Iraq and Afghanistan.²⁸ An increased number of veterans reporting PTSD symptoms prompted the DoD to increase screening for PTSD, and to encourage service members to seek treatment when appropriate.

The VA has instituted training programs for clinicians and psychologists to screen and provide care for PTSD. Beginning in 2007, the VA implemented mandatory TBI screening for all veterans who served in combat operations and separated from active-duty service after September 11, 2001. The 4-question screen identifies veterans who are at increased risk of TBI and who experience symptoms that may be related to specific event(s).²⁹ A positive screen does not diagnose TBI but rather indicates a need for further evaluation, which may or may not be responsible for inflated reporting of TBI. Renewed research also has led providers to recognize and study PTSD resulting from noncombat trauma and moral injury. The possibility of delayed onset also drives up the number of veterans diagnosed with PTSD.^5-7

Prevalence

A wide variability exists in the reported prevalence of PTSD among US war veterans with estimates ranging from 15% to 20% of veterans from recent conflicts^3,20 and 10% to 30% of veterans from previous wars.^3,19 These rates are higher than estimates from allied forces from other countries.¹⁹ Meta-analyses suggest that the prevalence of PTSD is 2% to 15% among Vietnam War veterans, 1% to 13% among first (pre-9/11) Gulf War veterans, 4% to 17% among OEF/OIF/OND veterans; these veterans have a lifetime prevalence of 6% to 31%.^{3,11,19,30-38} The prevalence of PTSD is 2 to 4 times higher among the US veterans^19,39 when compared with that of civilians.^40,41 According to one study, concomitant PTSD and TBI appears to be much higher in US war veterans (4%-17%) compared with United Kingdom Iraq War veterans (3%-6%).¹⁹

This study’s finding of an increase in nonmale soldiers with PTSD and/or TBI was not surprising. There is a paucity of data on the effect of war zone exposure on women veterans. Recently, women have been more actively involved in combat roles with 41,000 women deployed to a combat zone. Results of this study indicate a 2- to 3-fold increase in veterans identifying themselves as nonmale in post-9/11 groups with a higher proportion diagnosed with either PTSD alone or PTSD and TBI. Women are at a higher risk for PTSD than are men due in part to exposure to abuse/trauma prior to deployment, experience of higher rates of discrimination, and/or sexual assault.^31-33 One study involving First Gulf War female veterans reported higher precombat psychiatric histories as well as higher rates of physical and sexual abuse when compared with that of men.³¹

In this study, the average age of veterans adjudicated and compensated for PTSD and/or TBI pre-9/11, was 66 years compared with 36 years for post-9/11 veterans. Sixty-six percent of veterans from the post-9/11 group had ≥ 50% service-connected disability at age 36 years; 75% of veterans from the overlap group had ≥ 50% service-connected disability at age 41 years; and 76% veterans from the reentered group had ≥ 50% service-connected disability at age 46 years. Younger age at diagnosis and higher rates of disability not only pose unique challenges for veterans and family members, but also suggest implications for career prospects, family earnings, loss of productivity, and disease-adjusted life years. Also noted in the results, this younger cohort has a higher percentage of single/unmarried veterans, suggesting familial support systems may be more parental than spousal. Treatment for this younger cohort will likely need to focus on early and sustained rehabilitation that can be integrated with career plans.

For treatment to be effective, there must be evidence for veterans enrolling, remaining, and reporting benefits from the treatment. Limited research has shown currently advocated evidence-based therapies to have low enrollment rates, high drop-out rates, and mixed outcomes.⁴²

Results showing a gradual increase in the proportion of nonwhite, non-African American veterans diagnosed with PTSD alone, TBI alone, or both, likely reflect the changing demographic profile of the US as well as the Army. However, the reason that more African Americans were diagnosed with PTSD and/or TBI in the overlap and reentered groups when compared with the pre-9/11 group could not be ascertained. It is possible that more veterans identified themselves as African Americans as evident from a decrease in the number of veterans in the unknown category post-9/11 when compared with the pre-9/11 group. In 2016, the American Community Survey showed that Hispanic and African American veterans were more likely to use VA health care and other benefits than were any other racial group.⁴⁰ Improved screening for PTSD and TBI diagnoses, increased awareness, and education about the availability of VA services and benefits may have contributed to the increased use of VA benefits in these groups.

Data from this study are concordant with data from the National Center for Veterans Analysis and Statistics reporting on the younger age of diagnosis and higher rates of initial service-connected disability in veterans with PTSD and PTSD+TBI.⁴³ One study analyzing records from 1999 to 2004 showed that the number of PTSD cases grew by 79.5%, resulting in 148.7% increase in benefits payment from $1.7 billion to $4.3 billion per year.⁴⁴ In contrast, the compensation cost for all other disability categories increased by only 41.7% over this period. This study also revealed that while veterans with PTSD represented only 8.7% of compensation recipients, they received 20.5% of all compensation payments, driven in large part by an increase in > 50% service-connected disability ratings.⁴⁴

Thus, from financial as well as treatment points of view, the change in the demographic profile of the veteran must be considered when developing PTSD treatment strategies. While treatment in the past focused solely on addressing trauma-associated psychiatric issues, TBI and PTSD association will likely shift the focus to concurrent psychiatric and physical symptomology. Similarly, PTSD/TBI treatment modalities must consider that the profile of post-9/11 service members includes more women, younger age, and a greater racial diversity. For instance, younger age for a disabled veteran brings additional challenges, including reliance on parental or buddy support systems vs a spousal support system, integrating career with treatment, selecting geographic locations that can support both career and treatment, sustaining rehabilitation over time. The treatment needs of a 35-year-old soldier with PTSD and/or TBI, whether male or female, Asian or African American are likely to be very different from the treatment needs of a 65-year-old white male. Newer treatment approaches will have to address the needs of all soldiers.

Limitations

Our study may underestimate the actual PTSD and/or TBI disease burden because of the social stigma associated with diagnosis, military culture, limitations in data collection.^45-50 In addition, in this retrospective database cohort study, we considered and tried to minimize the impact of any of the usual potential limitations, including (1) accuracy of data quality and linkage; (2) identifying cohort appropriately (study groups); (3) defining endpoints clearly to avoid misclassifications; and (4) incorporating all important confounders. We identified veterans utilizing medical services at VA hospitals during a defined period and diagnosed with PTSD and TBI using ICD-9 codes and divided in 4 well-defined groups. In addition, another limitation of our study is to not accurately capture the veterans who have alternative health coverage and may choose not to enroll and/or participate in VA health care. In addition, some service members leaving war zones may not disclose or downplay the mental health symptoms to avoid any delay in their return home.

Conclusions

This study highlights the changing profile of the soldier diagnosed with PTSD and/or TBI who served pre-9/11 compared with that of those who served post-9/11. Treatment modalities must address the changes in warfare and demographics of US service members. Future treatment will need to focus more on concurrent PTSD/TBI therapies, the needs of younger soldiers, the needs of women injured in combat, and the needs of a more racially and ethnically diverse population. Severe injuries at a younger age will require early detection and rehabilitation for return to optimum functioning over a lifetime. The current study underscores a need for identifying the gaps in ongoing programs and services, developing alternatives, and implementing improved systems of care. More studies are needed to identify the cost implications and the effectiveness of current therapies for PTSD and/or TBI.

Acknowledgments

This study was supported by VA Medical Center and Midwest BioMedical Research Foundation (MBRF), Kansas City, Missouri. The manuscript received support, in part, from NIH-RO1 DK107490. These agencies did not participate in the design/conduct of the study or, in the interpretation of the data.

The nature of combat and associated injuries in Operation Iraqi Freedom (OIF), Operation Enduring Freedom (OEF), Operation New Dawn (OND), and Afghanistan War is different from previous conflicts. Multiple protracted deployments with infrequent breaks after September 11, 2001 (9/11) have further compounded the problem.

Posttraumatic stress disorder (PTSD) and traumatic brain injury (TBI) are the signature wounds of recent wars, with a higher incidence among the veterans of OEF and OIF compared with those from previous conflicts.^1,2 More than 2.7 million who served in Iraq and Afghanistan suffer from PTSD.^3,4 Symptoms of PTSD may appear within the first 3 months after exposure to a traumatic event or after many months and, in some cases, after a delay of many years and continue for life.⁵ Although delayed onset of PTSD in the absence of prior symptoms is rare,^6,7 its incidence rises with increasing frequency of exposure to traumatic events^8,9 and over time.¹⁰

According to the Brain Injury Association of America, TBI is “an alteration in brain function, or other evidence of brain pathology, caused by an external force.”⁸ TBI is often associated with increased risk of PTSD, depression, and posttraumatic headache,^11-13 which may lead to broader cognitive, somatic, neurobiological, and psychosocial dysfunctions.^14-17 According to Veterans Health Administration (VHA) data, 201,435 veterans from all eras enrolled with the US Department of Veterans Affairs (VA) have a diagnosis associated with TBI and 56,695 OEF/OIF veterans have been evaluated for a TBI-related condition.² According to the Defense and Veterans Brain Injury Center (DVBIC), > 361,000 veterans have been diagnosed with TBI, with a peak of 32,000 cases in 2011.^1,18 Moreover, the reported incidence and prevalence of PTSD and TBI among US veterans are not consistent. The incidence of PTSD has been estimated at 15% to 20% in recent wars^3,19 compared with 10% to 30% in previous wars.^3,19,20

When PTSD or TBI is deemed “related” to military service, the veteran may receive a service-connected disability rating ranging from 0% (no life-interfering symptoms due to injury) to 100% (totally disabling injury). The percentage of service connection associated with an injury is a quantifiable measure of the debilitating effect of injury on the individual. A significant majority (94%) of those who seek mental health services and treatment at VHA clinics apply for PTSD-related disability benefits.²¹ The estimated cost related to PTSD/TBI service-connected pensions is $20.28 billion per year and approximately $514 billion over 50 years.²² The cost of VA and Social Security disability payments combined with health care costs and treatment of PTSD is estimated to exceed $1 trillion over the next 30 years.²²

The National Vietnam Veterans Readjustment Study (NVVRS) provided valuable information on prevalence rates of PTSD and other postwar psychological problems.²³ Meanwhile, there have been no recent large-scale studies to compare the demographics of veterans diagnosed with PTSD and TBI who served prior to and after 9/11. A better understanding of demographic changes is considered essential for designing and tailoring therapeutic interventions to manage the rising cost.²²

The present study focused on identifying changing trends in the demographics of veterans who served prior to and after 9/11 and who received a VA inpatient or outpatient diagnosis of PTSD and/or TBI. Specifically, this study addressed the changes in demographics of veterans with PTSD, TBI, or PTSD+TBI seen at the VHA clinics between December 1,1998 and May 31, 2014 (before and after September 11, 2001) for diagnosis, treatment and health care policy issues.

Methods

This study was approved by the Kansas City VA Medical Center Institutional Review Board. VHA data from the Corporate Data Warehouse (CDW) and the National Patient Care Database were extracted using the VA Informatics and Computing Infrastructure (VINCI) workspace. CDW uses a unique identifier to identify veterans across treatment episodes at more than 1,400 VHA centers organized under 21 Veterans Integrated Service Networks (VISNs). These sources of VA data are widely used for retrospective longitudinal studies.

Study Population

The study population consisted of 1,339,937 veterans with a VA inpatient/outpatient diagnosis of PTSD or TBI using International Statistical Classification of Diseases and Related Health Problems, Ninth Revision (ICD-9) codes between December 1, 1998 and May 31, 2014. Demographic (gender classification, race, ethnicity, marital status, age at date of data extraction, and date of death if indicated), service-connection disability rating, and geographic distribution within VISN data on each veteran were then extracted.

Veterans in the cohort were assigned to 1 of 4 US military services period groups. The pre-9/11 group included veterans who entered and left the military prior to September 11, 2001. This group mostly included veterans from World War II, Korean War, Vietnam War, and the first Gulf War (1990-1991). The post-9/11 group included veterans who first entered military services after September 11, 2001. The overlap group included veterans who entered military services prior to 9/11, remained in service and left after September 11, 2001. The reentered group included veterans who entered and left service prior to September 11, 2001, and then reentered military service after September 11, 2001 (Figure 1). Using ICD-9 codes, veterans also were placed into the following categories: PTSD alone (ICD-9 309.81 only), TBI alone (ICD-9 850.0-859.9, V15.52), and PTSD+TBI (any combination of ICD-9 codes from the other categories).

Statistical Analysis 

Descriptive statistics were applied using proportions and means. Relationships between variables were examined using χ² tests, t tests, analysis of variance, and nonparametric tests. All hypotheses were 2-sided at 95% CI. Results are presented as absolute numbers.

Results

PTSD only (n = 1,132,356, 85%) was the predominant diagnosis category followed by PTSD+TBI (n = 106,792, 8%) and TBI only (n = 100,789, 7%) (Figure 2). Most of the veterans in the study served pre-9/11 (77%), followed by post-9/11 (15%); 7% were in the overlap group, and 1% in the reentered group (Table 1). It is notable that the proportion of veterans diagnosed with PTSD decreased from pre-9/11 (88%) to post-9/11 (71%), overlap (77%), and reentered (74%) service periods. Increases were noted in those with PTSD+TBI diagnosis category from pre-9/11 (4%) to post-9/11 (23%), overlap (17%), and reentered (22%) service periods (Figure 3). In general, the relative distribution of diagnostic categories in all the service periods showed a similar trend, with the majority of veterans diagnosed with PTSD only. Across all service periods, significantly smaller proportions of veterans were diagnosed with TBI only (P < .001).

Distribution by Gender and Age

The cohort was 92% male (n = 1,239,295), but there was a marked increase in the percentage of nonmale veterans in post-9/11 groups. Study population ages ranged from 18 to 99 years based on date of birth to the date data were obtained; or date of birth to date of death, for those who were reported deceased at the time the data were obtained. The average (SD) ages for veterans in the pre-9/11 group were significantly older (66.3 [11.2] years) compared with the ages of veterans in the post-9/11 group (36.1 [8.7] years), the overlap group (41.4 [8.2] years), and the reentered group (46.9 [9.2] years), respectively.

Distribution by Race and Marital Status

The cohort identified as 65.7% white and 18.2% African American with much smaller percentages of Asians, American Indian/Alaska Natives (AI/AN) and Native Hawaiian/Pacific Islanders (Table 2). The relative proportion of AI/AN and Native Hawaiian/Pacific Islanders remained constant across all groups, whereas the number of Asians diagnosed with PTSD, TBI, or PTSD+TBI increased in the post-9/11 group. The number of African Americans diagnosed with PTSD, TBI, or both markedly increased in the overlap and reentered groups when compared with the pre-9/11 group, yet it went down in the post-9/11/group.

Half the cohort identified themselves as married (n = 675,145) (Table 3). A slightly larger proportion of those diagnosed with PTSD alone were married (51.7%), compared with those diagnosed with TBI only (40.3%), or PTSD+TBI (45.8%). Veterans in the post-9/11 group were less likely to identify as married (45.2%) compared with the pre-9/11 (51.2%), overlap (52.6%), or reentered (53.2%) groups. Divorce rates among pre-9/11 group, overlap group, and reentered group were higher compared with that of the post-9/11 group in all diagnosis categories.

Geographic Distribution

Veterans diagnosed with PTSD, TBI, or both were not evenly distributed across the VISNs VISNs 7, 8, 10, and 22 treated the most veterans, whereas VISN 9 and 15 treated the fewest. Taken together, the top 3 VISNs accounted for 27% to 28% of the total while lowest 3 accounted for 8% to 9% of the total cohort.

Service-Connected Disability

Of 1,339,937 veterans in the cohort, 1,067,691 had a service-connected disability rating for PTSD and/or TBI. Most were diagnosed with PTSD (n = 923,523, 86.5%) followed by both PTSD+TBI (n = 94,051, 8.8%). Three-quarters of the veterans with a service-connected disability were in the pre-9/11 group. Nearly 80% of veterans with a service-connected disability rating had a rating of > 50%. The average (SD) age of veterans with PTSD+TBI and a > 50% service-connected disability was 66.3 (11.2) years in the pre-9/11 group compared with 36.1 (8.7) years in the post-9/11 group.

Discussion

The demographic profile of veterans diagnosed with PTSD+TBI has changed across the service periods covered in this study. Compared with pre-9/11 veterans, the post-9/11 cohort: (1) higher percentage were diagnosed with PTSD+TBI; (2) higher proportion were nonmale veterans; (3) included more young veterans with > 50% service-connected disability; (4) were more racially diverse; and (5) were less likely to be married and divorced and more likely to be self-identified as single. Additionally, data revealed that veterans tended to locate more to some geographic regions than to others.

The nature of the warfare has changed remarkably over the past few decades. Gunshot wounds accounted for 65% of all injuries in World War I, 35% during Vietnam War, and 16% to 23% in the First Gulf War.²⁴ In post-9/11 military conflicts, 81% of injuries were explosion related.^24,25 Although improvements in personal protective gear and battlefield trauma care led to increased survival, several factors may have contributed to increased reporting of TBI, which peaked in 2011 at 32,000 cases.^24-26

Increases in PTSD Diagnosis

Increasing media awareness, mandatory battlefield concussion screening programs instituted by the US Department of Defense (DoD), and stressful conditions that exacerbate mild TBI (mTBI) may have all contributed to the increase in numbers of veterans seeking evaluations and being diagnosed with PTSD and/or TBI in the post-9/11 groups. Additionally, the 2007 National Defense Authorization Act requested the Secretary of Defense to develop a comprehensive, systematic approach for the identification, treatment, disposition, and documentation of TBI in combat and peacetime. By a conservative estimate, significant numbers of veterans will continue to be seen for mTBI at about 20,000 new cases per year.^25-27

More frequent diagnosis of mTBI may have contributed to the increase in veterans diagnosed with PTSD+TBI in the post-9/11 groups. A recent study found that almost 44% of US Army infantry soldiers in Iraq did not lose consciousness but reported symptoms consistent with TBI.¹⁴ Compared with veterans of previous wars, veterans of the post-9/11 conflicts (OIF, OED, and OND) have experienced multiple, protracted deployments with infrequent breaks that can have a cumulative effect on the development of PTSD.^8-10

The findings from the NVVRS study led to creation of specialized PTSD programs in the late 1980s. Since then, there has been an explosion of knowledge and awareness about PTSD, TBI, and the associated service-connected disability ratings and benefits, leading to an increased number of veterans seeking care for PTSD. For example, media coverage of the 50th anniversary of the D-day celebrations resulted in a surge of World War II veterans seeking treatment for PTSD and a surge of Vietnam veterans sought treatment for PTSD during the wars in Iraq and Afghanistan.²⁸ An increased number of veterans reporting PTSD symptoms prompted the DoD to increase screening for PTSD, and to encourage service members to seek treatment when appropriate.

The VA has instituted training programs for clinicians and psychologists to screen and provide care for PTSD. Beginning in 2007, the VA implemented mandatory TBI screening for all veterans who served in combat operations and separated from active-duty service after September 11, 2001. The 4-question screen identifies veterans who are at increased risk of TBI and who experience symptoms that may be related to specific event(s).²⁹ A positive screen does not diagnose TBI but rather indicates a need for further evaluation, which may or may not be responsible for inflated reporting of TBI. Renewed research also has led providers to recognize and study PTSD resulting from noncombat trauma and moral injury. The possibility of delayed onset also drives up the number of veterans diagnosed with PTSD.^5-7

Prevalence

A wide variability exists in the reported prevalence of PTSD among US war veterans with estimates ranging from 15% to 20% of veterans from recent conflicts^3,20 and 10% to 30% of veterans from previous wars.^3,19 These rates are higher than estimates from allied forces from other countries.¹⁹ Meta-analyses suggest that the prevalence of PTSD is 2% to 15% among Vietnam War veterans, 1% to 13% among first (pre-9/11) Gulf War veterans, 4% to 17% among OEF/OIF/OND veterans; these veterans have a lifetime prevalence of 6% to 31%.^{3,11,19,30-38} The prevalence of PTSD is 2 to 4 times higher among the US veterans^19,39 when compared with that of civilians.^40,41 According to one study, concomitant PTSD and TBI appears to be much higher in US war veterans (4%-17%) compared with United Kingdom Iraq War veterans (3%-6%).¹⁹

This study’s finding of an increase in nonmale soldiers with PTSD and/or TBI was not surprising. There is a paucity of data on the effect of war zone exposure on women veterans. Recently, women have been more actively involved in combat roles with 41,000 women deployed to a combat zone. Results of this study indicate a 2- to 3-fold increase in veterans identifying themselves as nonmale in post-9/11 groups with a higher proportion diagnosed with either PTSD alone or PTSD and TBI. Women are at a higher risk for PTSD than are men due in part to exposure to abuse/trauma prior to deployment, experience of higher rates of discrimination, and/or sexual assault.^31-33 One study involving First Gulf War female veterans reported higher precombat psychiatric histories as well as higher rates of physical and sexual abuse when compared with that of men.³¹

In this study, the average age of veterans adjudicated and compensated for PTSD and/or TBI pre-9/11, was 66 years compared with 36 years for post-9/11 veterans. Sixty-six percent of veterans from the post-9/11 group had ≥ 50% service-connected disability at age 36 years; 75% of veterans from the overlap group had ≥ 50% service-connected disability at age 41 years; and 76% veterans from the reentered group had ≥ 50% service-connected disability at age 46 years. Younger age at diagnosis and higher rates of disability not only pose unique challenges for veterans and family members, but also suggest implications for career prospects, family earnings, loss of productivity, and disease-adjusted life years. Also noted in the results, this younger cohort has a higher percentage of single/unmarried veterans, suggesting familial support systems may be more parental than spousal. Treatment for this younger cohort will likely need to focus on early and sustained rehabilitation that can be integrated with career plans.

For treatment to be effective, there must be evidence for veterans enrolling, remaining, and reporting benefits from the treatment. Limited research has shown currently advocated evidence-based therapies to have low enrollment rates, high drop-out rates, and mixed outcomes.⁴²

Results showing a gradual increase in the proportion of nonwhite, non-African American veterans diagnosed with PTSD alone, TBI alone, or both, likely reflect the changing demographic profile of the US as well as the Army. However, the reason that more African Americans were diagnosed with PTSD and/or TBI in the overlap and reentered groups when compared with the pre-9/11 group could not be ascertained. It is possible that more veterans identified themselves as African Americans as evident from a decrease in the number of veterans in the unknown category post-9/11 when compared with the pre-9/11 group. In 2016, the American Community Survey showed that Hispanic and African American veterans were more likely to use VA health care and other benefits than were any other racial group.⁴⁰ Improved screening for PTSD and TBI diagnoses, increased awareness, and education about the availability of VA services and benefits may have contributed to the increased use of VA benefits in these groups.

Data from this study are concordant with data from the National Center for Veterans Analysis and Statistics reporting on the younger age of diagnosis and higher rates of initial service-connected disability in veterans with PTSD and PTSD+TBI.⁴³ One study analyzing records from 1999 to 2004 showed that the number of PTSD cases grew by 79.5%, resulting in 148.7% increase in benefits payment from $1.7 billion to $4.3 billion per year.⁴⁴ In contrast, the compensation cost for all other disability categories increased by only 41.7% over this period. This study also revealed that while veterans with PTSD represented only 8.7% of compensation recipients, they received 20.5% of all compensation payments, driven in large part by an increase in > 50% service-connected disability ratings.⁴⁴

Thus, from financial as well as treatment points of view, the change in the demographic profile of the veteran must be considered when developing PTSD treatment strategies. While treatment in the past focused solely on addressing trauma-associated psychiatric issues, TBI and PTSD association will likely shift the focus to concurrent psychiatric and physical symptomology. Similarly, PTSD/TBI treatment modalities must consider that the profile of post-9/11 service members includes more women, younger age, and a greater racial diversity. For instance, younger age for a disabled veteran brings additional challenges, including reliance on parental or buddy support systems vs a spousal support system, integrating career with treatment, selecting geographic locations that can support both career and treatment, sustaining rehabilitation over time. The treatment needs of a 35-year-old soldier with PTSD and/or TBI, whether male or female, Asian or African American are likely to be very different from the treatment needs of a 65-year-old white male. Newer treatment approaches will have to address the needs of all soldiers.

Limitations

Our study may underestimate the actual PTSD and/or TBI disease burden because of the social stigma associated with diagnosis, military culture, limitations in data collection.^45-50 In addition, in this retrospective database cohort study, we considered and tried to minimize the impact of any of the usual potential limitations, including (1) accuracy of data quality and linkage; (2) identifying cohort appropriately (study groups); (3) defining endpoints clearly to avoid misclassifications; and (4) incorporating all important confounders. We identified veterans utilizing medical services at VA hospitals during a defined period and diagnosed with PTSD and TBI using ICD-9 codes and divided in 4 well-defined groups. In addition, another limitation of our study is to not accurately capture the veterans who have alternative health coverage and may choose not to enroll and/or participate in VA health care. In addition, some service members leaving war zones may not disclose or downplay the mental health symptoms to avoid any delay in their return home.

Conclusions

This study highlights the changing profile of the soldier diagnosed with PTSD and/or TBI who served pre-9/11 compared with that of those who served post-9/11. Treatment modalities must address the changes in warfare and demographics of US service members. Future treatment will need to focus more on concurrent PTSD/TBI therapies, the needs of younger soldiers, the needs of women injured in combat, and the needs of a more racially and ethnically diverse population. Severe injuries at a younger age will require early detection and rehabilitation for return to optimum functioning over a lifetime. The current study underscores a need for identifying the gaps in ongoing programs and services, developing alternatives, and implementing improved systems of care. More studies are needed to identify the cost implications and the effectiveness of current therapies for PTSD and/or TBI.

Acknowledgments

This study was supported by VA Medical Center and Midwest BioMedical Research Foundation (MBRF), Kansas City, Missouri. The manuscript received support, in part, from NIH-RO1 DK107490. These agencies did not participate in the design/conduct of the study or, in the interpretation of the data.

A 53-year-old male veteran with a history of heavy tobacco and alcohol use presented with abdominal pain, emesis, and no bowel movements for 2 days. He had no history of surgical procedures, malignancies, diverticulitis, inflammatory bowel disease, traveling abroad, parasitic infections, tuberculosis exposure, or hospital admissions for abdominal pain. He reported experiencing no flushing, diarrhea, or cardiac symptoms. His medical history included hypertension, depression, and osteoarthritis. His vital signs were within normal limits.

A physical examination revealed a distended abdomen with mild tenderness. He had no inguinal or ventral hernias. He also had no abnormal skin lesions. A rectal examination did not reveal any masses or blood. His laboratory values were normal. X-ray and computed tomography (CT) scan revealed dilated loops of proximal small bowel, mild wall thickening in a segment of the midileum, and narrowing of the distal small bowel suggestive of a partial small bowel obstruction (Figure 1). A 1-cm nonspecific omental nodule also was seen on the CT scan, but no enlarged lymph nodes or mesenteric calcifications were seen. There was no thickening of the terminal ileum.

The patient underwent an exploratory laparotomy, which revealed no adhesions. In the midileum there was an area of thickened bowel with some nodularity associated with the thickness, but no discrete mass. In the mesentery there were multiple hard, white, calcified nodules, with the majority clustered near the thickened ileal segment. There also was a 1-cm hard, peritoneal mass on the anterior abdominal wall. The segment of thickened ileum, the adjacent mesentery, and the peritoneal nodule were resected.

Pathologic examination of the resected tissue showed immunohistochemical stains that were positive for CD79a, CD10, and BCL-2 and negative for CD23, CD5, and CD3. Nineteen mesenteric lymph nodes were negative for malignancy. The postoperative staging positron emission tomography (PET) scan did not reveal any fluorodeoxyglucose avid masses anywhere else, and bone marrow biopsy showed no infiltration.

• What is your diagnosis?
• How would you treat this patient?
   

   

Diagnosis

Based on the pathologic examination of the resected tissue and immunohistochemical stains, this patient was diagnosed with malignant non-Hodgkin B-cell lymphoma, follicular type, grade 1. PET scan and bone marrow biopsy revealed no other lesions, making this a primary lymphoma of the small intestine. The resected tissue showed negative margins and negative lymph nodes, indicating the full extent of the patient’s tumor was removed. He then underwent nasogastric tube decompression and IV fluid resuscitation. Two days later, he had a large bowel movement, and his abdominal pain resolved. He was provided the treatment options of observation only, radiation therapy, or rituximab treatment. Based on the high risk of enteritis following radiation therapy, the patient elected for observation only, with a repeat scan in 6 months. He also was counseled on alcohol and tobacco cessation. At the 6-month oncology follow-up, the patient showed no evidence of disease recurrence.

Discussion

Small bowel obstruction accounts for about 350,000 hospitalizations annually in the US.¹ The incidence is equal in men and women and can present at any age.^2,3 Patients typically present nonspecifically, with intermittent, colicky abdominal pain, nausea, vomiting, and constipation.² A physical examination may reveal abdominal distention, rigidity, and hypoactive or absent bowel sounds.¹ The 2 most common etiologies of small bowel obstruction are adhesions from prior abdominal surgery (65%) and incarcerated inguinal hernias (10%).¹ However, in a patient presenting with a small bowel obstruction in a surgically naïve abdomen with no hernias, a more detailed history covering current malignancies, past hospital admissions for abdominal pains, pelvic inflammatory disease, diverticulitis, inflammatory bowel disease, and risks for parasite infection must be taken. The differential should include intraluminal causes, including small bowel malignancy, which accounts for 5% of small bowel obstructions,¹ as well as extraluminal causes, including adhesions from diverticulitis, Meckel diverticulum, Ladd bands, and undiagnosed prior appendicitis.

To provide a tissue diagnosis and definitive treatment, surgical exploration was needed for this patient. Exploratory laparotomy revealed an area of thickened ileum and calcified nodules in its mesentery. Pathologic examination of the resected tissue revealed large lymphoid nodules in a follicular pattern with coarse chromatin (Figure 2). Taken together with the immunohistochemical stains, this was consistent with malignant B-cell non-Hodgkin lymphoma, follicular type, grade 1.

Small bowel malignancy accounts for > 5% of all gastrointestinal tumors.⁴ Of these, small bowel neuroendocrine tumors are the most common, followed by adenocarcinomas, lymphomas, and stromal tumors.⁴ Primary follicular lymphoma (PFL) is a B-cell non-Hodgkin lymphoma, and comprises between 3.8% and 11% of gastrointestinal lymphomas, commonly in the duodenum and terminal ileum.⁵

PFL typically occurs in middle-aged females and can be difficult to diagnose, as most patients are asymptomatic or present with unspecified abdominal pain. Many are diagnosed incidentally when endoscopy biopsies are performed for other reasons.^4,5 Histologically, PFL is composed of a mixed population of small (centrocytes) and large (centroblasts) lymphoid cells, with higher proportions of centroblasts corresponding to a higher grade lymphoma.⁶ The classic immunophenotype of PFL shows coexpression of CD79a (or CD20), CD10, and BCL-2; however, in rare cases, low-grade PFL may stain negative for BCL-2 and have diminished staining for CD10 in interfollicular areas.⁷

PFL generally carries a favorable prognosis. Most patients achieving complete disease regression or stable disease following treatment and a low recurrence rate. Treatment can include surgical resection, radiation, rituximab therapy, chemotherapy, or observation.⁸ Patient also should be counseled in alcohol and tobacco cessation to reduce recurrence risk.

Other small bowel malignancies may present as small bowel obstructions as well. Neuroendocrine tumors and adenocarcinomas are both more common than small bowel lymphomas and can present as small bowel obstruction. However, neuroendocrine tumors are derived from serotonin-expressing enterochromaffin cells of the midgut and often present with classic carcinoid syndrome symptoms, including diarrhea, flushing, and right heart fibrosis, which the patient lacked.⁹ Immunohistology of small bowel adenocarcinoma often shows expression of MUC1 or MUC5AC with tumor markers CEA and CA 19-9.¹⁰

Primary intestinal melanoma, another small bowel malignancy, is extremely rare. More commonly, the etiology of intestinal melanoma is cutaneous melanoma that metastasizes to the gastrointestinal tract.¹¹ This patient had no skin lesions to suggest metastatic melanoma. With intestinal melanoma, immunohistochemical evaluation may show S-100, the most sensitive marker for melanoma, or HMB-45, MART-1/Melan-A, tyrosinase, and MITF.¹²

Conclusion

This case is notable because it highlights the importance of examining the cause of small bowel obstruction in a surgically naïve abdomen, as exploration led to the discovery and curative treatment of a primary intestinal malignancy. It also underscores the nonspecific presentation that PFLs of the small intestine can have and the importance of understanding the different histopathology and immunohistochemical profiles of small bowel malignancies.