The application deadline for the Community Awareness and Prevention Project Grant is April 15. This award is intended to help vascular surgeons conduct community-based projects that address emerging issues in vascular health, wellness and disease prevention. The SVS Foundation encourages applicants to establish collaborative community partnerships with organizations who share our goals for maximizing public health and can contribute to the success of the project. Read more about the grant here.

The application deadline for the Community Awareness and Prevention Project Grant is April 15. This award is intended to help vascular surgeons conduct community-based projects that address emerging issues in vascular health, wellness and disease prevention. The SVS Foundation encourages applicants to establish collaborative community partnerships with organizations who share our goals for maximizing public health and can contribute to the success of the project. Read more about the grant here.

The application deadline for the Community Awareness and Prevention Project Grant is April 15. This award is intended to help vascular surgeons conduct community-based projects that address emerging issues in vascular health, wellness and disease prevention. The SVS Foundation encourages applicants to establish collaborative community partnerships with organizations who share our goals for maximizing public health and can contribute to the success of the project. Read more about the grant here.

Begin planning your Vascular Annual Meeting experience with the SVS Online Planner. This includes the entire VAM schedule, plus the schedule for the Society for Vascular Nursing’s annual conference. The full schedule for the Vascular Quality Initiative's meeting, VQI@VAM, also will be available in the future. Users can easily find such information as presenters, certain topics, session types, intended audience and credit availability. Find the online planner on the VAM site here.

Begin planning your Vascular Annual Meeting experience with the SVS Online Planner. This includes the entire VAM schedule, plus the schedule for the Society for Vascular Nursing’s annual conference. The full schedule for the Vascular Quality Initiative's meeting, VQI@VAM, also will be available in the future. Users can easily find such information as presenters, certain topics, session types, intended audience and credit availability. Find the online planner on the VAM site here.

Begin planning your Vascular Annual Meeting experience with the SVS Online Planner. This includes the entire VAM schedule, plus the schedule for the Society for Vascular Nursing’s annual conference. The full schedule for the Vascular Quality Initiative's meeting, VQI@VAM, also will be available in the future. Users can easily find such information as presenters, certain topics, session types, intended audience and credit availability. Find the online planner on the VAM site here.

MAUI, HAWAII – Vigilance for the possibility of bronchiolitis is warranted in patients with rheumatoid arthritis, Sjögren’s syndrome, or systemic lupus erythematosus who develop shortness of breath and cough or a precipitous drop in their forced expiratory volume on pulmonary function testing, Aryeh Fischer, MD, said at the 2019 Rheumatology Winter Clinical Symposium.

Bruce Jancin/MDedge News

“This is an underappreciated – and I think among the most potentially devastating – of the lung diseases we as rheumatologists will encounter in our patients,” said Dr. Fischer, a rheumatologist at the University of Colorado at Denver, Aurora, with a special interest in autoimmune lung disease.

“If you’re seeing patients with rheumatoid arthritis, SLE, or Sjögren’s and they’ve got bad asthma they can’t get under control, you’ve got to think about bronchiolitis because I can tell you your lung doc quite often is not thinking about this,” he added.

Bronchiolitis involves inflammation, narrowing, or obliteration of the small airways. The diagnosis is often missed because of the false sense of reassurance provided by the normal chest x-ray and regular CT findings, which are a feature of the disease.

“This is really important: You have to get a high-resolution CT that includes expiratory images, because that’s the only way you’re going to be able to tell if your patient has small airways disease,” he explained. “You must, must, must do an expiratory CT.”

A normal expiratory CT image should be gray, since the lungs are empty. Air is black on CT, so large areas of black intermixed with gray on an expiratory CT – a finding known as mosaicism – indicate air trapping due to small airways disease, Dr. Fischer noted.

Surgical lung biopsy will yield a pathologic report documenting isolated constrictive, follicular, and/or lymphocytic bronchiolitis. However, the terminology can be confusing: What pathologists describe as constrictive bronchiolitis is called obliterative by pulmonologists and radiologists.

Pulmonary function testing shows an obstructive defect. The diffusing capacity of the lungs for carbon monoxide (DLCO) is fairly normal, the forced expiratory volume in 1 second (FEV₁) is sharply reduced, and the forced vital capacity (FVC) is near normal, with a resultant abnormally low FEV₁/FVC ratio. A patient with bronchiolitis may or may not have a response to bronchodilators.

“I tell you, I’ve seen a bunch of these patients. They typically have a precipitous drop in their FEV1 and then stay stable at a very low level of lung function without much opportunity for improvement,” Dr. Fischer said. “Stability equals success in these patients. It’s really unusual to see much improvement.”

In theory, patients with follicular or lymphocytic bronchiolitis have an ongoing inflammatory process that should be amenable to rheumatologic ministrations. But there is no convincing evidence of treatment efficacy to date. And in obliterative bronchiolitis, marked by airway scarring, there is no reason to think anti-inflammatory therapies should be helpful. Anecdotally, Dr. Fischer said, he has seen immunosuppression help patients with obliterative bronchiolitis.

“Actually, the only proven therapy is lung transplantation,” he said.

He recommended that his fellow rheumatologists periodically use office spirometry to check the FEV₁ in their patients with rheumatoid arthritis, Sjögren’s, or SLE, the forms of connective tissue disease most often associated with bronchiolitis. Compared with all the other testing rheumatologists routinely order in their patients, having them blow into a tube is a simple enough matter.

“We really don’t have anything to impact the natural history, but I like the notion of not being surprised. What are you going to do with that [abnormal] FEV₁ data? I have no idea. But maybe it’s better to know earlier,” he said.

Dr. Fischer reported having no financial conflicts of interest regarding his presentation.

[email protected]

MAUI, HAWAII – Vigilance for the possibility of bronchiolitis is warranted in patients with rheumatoid arthritis, Sjögren’s syndrome, or systemic lupus erythematosus who develop shortness of breath and cough or a precipitous drop in their forced expiratory volume on pulmonary function testing, Aryeh Fischer, MD, said at the 2019 Rheumatology Winter Clinical Symposium.

Bruce Jancin/MDedge News

“This is an underappreciated – and I think among the most potentially devastating – of the lung diseases we as rheumatologists will encounter in our patients,” said Dr. Fischer, a rheumatologist at the University of Colorado at Denver, Aurora, with a special interest in autoimmune lung disease.

“If you’re seeing patients with rheumatoid arthritis, SLE, or Sjögren’s and they’ve got bad asthma they can’t get under control, you’ve got to think about bronchiolitis because I can tell you your lung doc quite often is not thinking about this,” he added.

Bronchiolitis involves inflammation, narrowing, or obliteration of the small airways. The diagnosis is often missed because of the false sense of reassurance provided by the normal chest x-ray and regular CT findings, which are a feature of the disease.

“This is really important: You have to get a high-resolution CT that includes expiratory images, because that’s the only way you’re going to be able to tell if your patient has small airways disease,” he explained. “You must, must, must do an expiratory CT.”

A normal expiratory CT image should be gray, since the lungs are empty. Air is black on CT, so large areas of black intermixed with gray on an expiratory CT – a finding known as mosaicism – indicate air trapping due to small airways disease, Dr. Fischer noted.

Surgical lung biopsy will yield a pathologic report documenting isolated constrictive, follicular, and/or lymphocytic bronchiolitis. However, the terminology can be confusing: What pathologists describe as constrictive bronchiolitis is called obliterative by pulmonologists and radiologists.

Pulmonary function testing shows an obstructive defect. The diffusing capacity of the lungs for carbon monoxide (DLCO) is fairly normal, the forced expiratory volume in 1 second (FEV₁) is sharply reduced, and the forced vital capacity (FVC) is near normal, with a resultant abnormally low FEV₁/FVC ratio. A patient with bronchiolitis may or may not have a response to bronchodilators.

“I tell you, I’ve seen a bunch of these patients. They typically have a precipitous drop in their FEV1 and then stay stable at a very low level of lung function without much opportunity for improvement,” Dr. Fischer said. “Stability equals success in these patients. It’s really unusual to see much improvement.”

In theory, patients with follicular or lymphocytic bronchiolitis have an ongoing inflammatory process that should be amenable to rheumatologic ministrations. But there is no convincing evidence of treatment efficacy to date. And in obliterative bronchiolitis, marked by airway scarring, there is no reason to think anti-inflammatory therapies should be helpful. Anecdotally, Dr. Fischer said, he has seen immunosuppression help patients with obliterative bronchiolitis.

“Actually, the only proven therapy is lung transplantation,” he said.

He recommended that his fellow rheumatologists periodically use office spirometry to check the FEV₁ in their patients with rheumatoid arthritis, Sjögren’s, or SLE, the forms of connective tissue disease most often associated with bronchiolitis. Compared with all the other testing rheumatologists routinely order in their patients, having them blow into a tube is a simple enough matter.

“We really don’t have anything to impact the natural history, but I like the notion of not being surprised. What are you going to do with that [abnormal] FEV₁ data? I have no idea. But maybe it’s better to know earlier,” he said.

Dr. Fischer reported having no financial conflicts of interest regarding his presentation.

[email protected]

MAUI, HAWAII – Vigilance for the possibility of bronchiolitis is warranted in patients with rheumatoid arthritis, Sjögren’s syndrome, or systemic lupus erythematosus who develop shortness of breath and cough or a precipitous drop in their forced expiratory volume on pulmonary function testing, Aryeh Fischer, MD, said at the 2019 Rheumatology Winter Clinical Symposium.

Bruce Jancin/MDedge News

“This is an underappreciated – and I think among the most potentially devastating – of the lung diseases we as rheumatologists will encounter in our patients,” said Dr. Fischer, a rheumatologist at the University of Colorado at Denver, Aurora, with a special interest in autoimmune lung disease.

“If you’re seeing patients with rheumatoid arthritis, SLE, or Sjögren’s and they’ve got bad asthma they can’t get under control, you’ve got to think about bronchiolitis because I can tell you your lung doc quite often is not thinking about this,” he added.

Bronchiolitis involves inflammation, narrowing, or obliteration of the small airways. The diagnosis is often missed because of the false sense of reassurance provided by the normal chest x-ray and regular CT findings, which are a feature of the disease.

“This is really important: You have to get a high-resolution CT that includes expiratory images, because that’s the only way you’re going to be able to tell if your patient has small airways disease,” he explained. “You must, must, must do an expiratory CT.”

A normal expiratory CT image should be gray, since the lungs are empty. Air is black on CT, so large areas of black intermixed with gray on an expiratory CT – a finding known as mosaicism – indicate air trapping due to small airways disease, Dr. Fischer noted.

Surgical lung biopsy will yield a pathologic report documenting isolated constrictive, follicular, and/or lymphocytic bronchiolitis. However, the terminology can be confusing: What pathologists describe as constrictive bronchiolitis is called obliterative by pulmonologists and radiologists.

Pulmonary function testing shows an obstructive defect. The diffusing capacity of the lungs for carbon monoxide (DLCO) is fairly normal, the forced expiratory volume in 1 second (FEV₁) is sharply reduced, and the forced vital capacity (FVC) is near normal, with a resultant abnormally low FEV₁/FVC ratio. A patient with bronchiolitis may or may not have a response to bronchodilators.

“I tell you, I’ve seen a bunch of these patients. They typically have a precipitous drop in their FEV1 and then stay stable at a very low level of lung function without much opportunity for improvement,” Dr. Fischer said. “Stability equals success in these patients. It’s really unusual to see much improvement.”

In theory, patients with follicular or lymphocytic bronchiolitis have an ongoing inflammatory process that should be amenable to rheumatologic ministrations. But there is no convincing evidence of treatment efficacy to date. And in obliterative bronchiolitis, marked by airway scarring, there is no reason to think anti-inflammatory therapies should be helpful. Anecdotally, Dr. Fischer said, he has seen immunosuppression help patients with obliterative bronchiolitis.

“Actually, the only proven therapy is lung transplantation,” he said.

He recommended that his fellow rheumatologists periodically use office spirometry to check the FEV₁ in their patients with rheumatoid arthritis, Sjögren’s, or SLE, the forms of connective tissue disease most often associated with bronchiolitis. Compared with all the other testing rheumatologists routinely order in their patients, having them blow into a tube is a simple enough matter.

“We really don’t have anything to impact the natural history, but I like the notion of not being surprised. What are you going to do with that [abnormal] FEV₁ data? I have no idea. But maybe it’s better to know earlier,” he said.

Dr. Fischer reported having no financial conflicts of interest regarding his presentation.

[email protected]

ANSWER

The radiograph demonstrates bilateral hip dislocations. On the right, the femoral head appears to be posteriorly dislocated and slightly internally rotated. On the left, the femoral head appears to be anteriorly and superiorly dislocated (although evaluation is limited by a single view). Neither side appears to have any obvious fractures.

The patient’s dislocations were promptly reduced in the trauma bay by the orthopedic service before he was sent for CT.

ANSWER

The radiograph demonstrates bilateral hip dislocations. On the right, the femoral head appears to be posteriorly dislocated and slightly internally rotated. On the left, the femoral head appears to be anteriorly and superiorly dislocated (although evaluation is limited by a single view). Neither side appears to have any obvious fractures.

The patient’s dislocations were promptly reduced in the trauma bay by the orthopedic service before he was sent for CT.

ANSWER

The radiograph demonstrates bilateral hip dislocations. On the right, the femoral head appears to be posteriorly dislocated and slightly internally rotated. On the left, the femoral head appears to be anteriorly and superiorly dislocated (although evaluation is limited by a single view). Neither side appears to have any obvious fractures.

The patient’s dislocations were promptly reduced in the trauma bay by the orthopedic service before he was sent for CT.

ATLANTA – Lynch syndrome serves as an excellent platform for the development of immunoprevention cancer vaccines, and findings from a preclinical Lynch syndrome mouse model support ongoing research, according to Steven M. Lipkin, MD, PhD.

A novel vaccine, which included peptides encoding four intestinal cancer frameshift peptide (FSP) neoantigens derived from coding microsatellite (cMS) mutations in the genes Nacad, Maz, Xirp1, and Senp6 elicited strong antigen-specific cellular immune responses in the model, Dr. Lipkin, the Gladys and Roland Harriman Professor of Medicine and vice chair for research in the Sanford and Joan Weill Department of Medicine, Weill Cornell Medical College, New York, reported at the annual meeting of the American Association for Cancer Research.

CD4-specific T cell responses were detected for Maz, Nacad, and Senp6, and CD8-positive T cells were detected for Xirp1 and Nacad, he noted, explaining that the findings come in the wake of a recently completed clinical phase 1/2a trial that successfully demonstrated safety and immunogenicity of an FSP neoantigen-based vaccine in microsatellite unstable (MSI) colorectal cancer patients.

The current effort to further develop a cancer preventive vaccine against MSI cancers in Lynch syndrome using a preclinical mouse model involved a systematic database search to identify cMS sequences in the murine genome. Intestinal tumors obtained from Lynch syndrome mice were evaluated for mutations affecting these candidate cMS, and of 13 with a mutation frequency of 15% or higher, the 4 FSP neoantigens ultimately included in the vaccine elicited strong antigen-specific cellular immune responses.

Vaccination with peptides encoding these four intestinal cancer FSP neoantigens promoted antineoantigen immunity, reduced intestinal tumorigenicity, and prolonged overall survival, Dr. Lipkin said.

Further, based on preclinical data suggesting that naproxen in this setting might provide better risk-reducing effects, compared with aspirin (which has previously been shown to reduce colorectal cancer risk in Lynch syndrome patients), its addition to the vaccine did, indeed, improve response, he noted, explaining that naproxen worked as “sort of a super-aspirin,” that improved overall survival, compared with vaccine alone or nonsteroidal anti-inflammatory agents alone.

In a video interview, Dr. Lipkin describes his research and its potential implications for the immunoprevention of Lynch syndrome and other cancers.

Vaccination with as few as four mutations that occur across Lynch syndrome tumors induced complete cures in some mice and delays in disease onset in others, he said.

“[This is] a very simple approach, very effective,” he added, noting that the T cells are now being studied to better understand the biology of the effects. “The idea of immunoprevention ... is actually very exciting and ... can be expanded beyond this.”

Lynch syndrome is a “great place to start,” because of the high rate of mutations, which are the most immunogenic types of mutations, he said.

“If we can get this basic paradigm to work, I think we can expand it to other types of mutations – for example, KRAS or BRAF, which are seen frequently in lung cancers, colon cancers, stomach cancers, pancreatic cancers, and others,” he said, noting that a proposal for a phase 1 clinical trial has been submitted.
 

ATLANTA – Lynch syndrome serves as an excellent platform for the development of immunoprevention cancer vaccines, and findings from a preclinical Lynch syndrome mouse model support ongoing research, according to Steven M. Lipkin, MD, PhD.

A novel vaccine, which included peptides encoding four intestinal cancer frameshift peptide (FSP) neoantigens derived from coding microsatellite (cMS) mutations in the genes Nacad, Maz, Xirp1, and Senp6 elicited strong antigen-specific cellular immune responses in the model, Dr. Lipkin, the Gladys and Roland Harriman Professor of Medicine and vice chair for research in the Sanford and Joan Weill Department of Medicine, Weill Cornell Medical College, New York, reported at the annual meeting of the American Association for Cancer Research.

CD4-specific T cell responses were detected for Maz, Nacad, and Senp6, and CD8-positive T cells were detected for Xirp1 and Nacad, he noted, explaining that the findings come in the wake of a recently completed clinical phase 1/2a trial that successfully demonstrated safety and immunogenicity of an FSP neoantigen-based vaccine in microsatellite unstable (MSI) colorectal cancer patients.

The current effort to further develop a cancer preventive vaccine against MSI cancers in Lynch syndrome using a preclinical mouse model involved a systematic database search to identify cMS sequences in the murine genome. Intestinal tumors obtained from Lynch syndrome mice were evaluated for mutations affecting these candidate cMS, and of 13 with a mutation frequency of 15% or higher, the 4 FSP neoantigens ultimately included in the vaccine elicited strong antigen-specific cellular immune responses.

Vaccination with peptides encoding these four intestinal cancer FSP neoantigens promoted antineoantigen immunity, reduced intestinal tumorigenicity, and prolonged overall survival, Dr. Lipkin said.

Further, based on preclinical data suggesting that naproxen in this setting might provide better risk-reducing effects, compared with aspirin (which has previously been shown to reduce colorectal cancer risk in Lynch syndrome patients), its addition to the vaccine did, indeed, improve response, he noted, explaining that naproxen worked as “sort of a super-aspirin,” that improved overall survival, compared with vaccine alone or nonsteroidal anti-inflammatory agents alone.

In a video interview, Dr. Lipkin describes his research and its potential implications for the immunoprevention of Lynch syndrome and other cancers.

Vaccination with as few as four mutations that occur across Lynch syndrome tumors induced complete cures in some mice and delays in disease onset in others, he said.

“[This is] a very simple approach, very effective,” he added, noting that the T cells are now being studied to better understand the biology of the effects. “The idea of immunoprevention ... is actually very exciting and ... can be expanded beyond this.”

Lynch syndrome is a “great place to start,” because of the high rate of mutations, which are the most immunogenic types of mutations, he said.

“If we can get this basic paradigm to work, I think we can expand it to other types of mutations – for example, KRAS or BRAF, which are seen frequently in lung cancers, colon cancers, stomach cancers, pancreatic cancers, and others,” he said, noting that a proposal for a phase 1 clinical trial has been submitted.
 

ATLANTA – Lynch syndrome serves as an excellent platform for the development of immunoprevention cancer vaccines, and findings from a preclinical Lynch syndrome mouse model support ongoing research, according to Steven M. Lipkin, MD, PhD.

A novel vaccine, which included peptides encoding four intestinal cancer frameshift peptide (FSP) neoantigens derived from coding microsatellite (cMS) mutations in the genes Nacad, Maz, Xirp1, and Senp6 elicited strong antigen-specific cellular immune responses in the model, Dr. Lipkin, the Gladys and Roland Harriman Professor of Medicine and vice chair for research in the Sanford and Joan Weill Department of Medicine, Weill Cornell Medical College, New York, reported at the annual meeting of the American Association for Cancer Research.

CD4-specific T cell responses were detected for Maz, Nacad, and Senp6, and CD8-positive T cells were detected for Xirp1 and Nacad, he noted, explaining that the findings come in the wake of a recently completed clinical phase 1/2a trial that successfully demonstrated safety and immunogenicity of an FSP neoantigen-based vaccine in microsatellite unstable (MSI) colorectal cancer patients.

The current effort to further develop a cancer preventive vaccine against MSI cancers in Lynch syndrome using a preclinical mouse model involved a systematic database search to identify cMS sequences in the murine genome. Intestinal tumors obtained from Lynch syndrome mice were evaluated for mutations affecting these candidate cMS, and of 13 with a mutation frequency of 15% or higher, the 4 FSP neoantigens ultimately included in the vaccine elicited strong antigen-specific cellular immune responses.

Vaccination with peptides encoding these four intestinal cancer FSP neoantigens promoted antineoantigen immunity, reduced intestinal tumorigenicity, and prolonged overall survival, Dr. Lipkin said.

Further, based on preclinical data suggesting that naproxen in this setting might provide better risk-reducing effects, compared with aspirin (which has previously been shown to reduce colorectal cancer risk in Lynch syndrome patients), its addition to the vaccine did, indeed, improve response, he noted, explaining that naproxen worked as “sort of a super-aspirin,” that improved overall survival, compared with vaccine alone or nonsteroidal anti-inflammatory agents alone.

In a video interview, Dr. Lipkin describes his research and its potential implications for the immunoprevention of Lynch syndrome and other cancers.

Vaccination with as few as four mutations that occur across Lynch syndrome tumors induced complete cures in some mice and delays in disease onset in others, he said.

“[This is] a very simple approach, very effective,” he added, noting that the T cells are now being studied to better understand the biology of the effects. “The idea of immunoprevention ... is actually very exciting and ... can be expanded beyond this.”

Lynch syndrome is a “great place to start,” because of the high rate of mutations, which are the most immunogenic types of mutations, he said.

“If we can get this basic paradigm to work, I think we can expand it to other types of mutations – for example, KRAS or BRAF, which are seen frequently in lung cancers, colon cancers, stomach cancers, pancreatic cancers, and others,” he said, noting that a proposal for a phase 1 clinical trial has been submitted.
 

Individuals who are younger when diagnosed with type 2 diabetes are at greater risk of cardiovascular disease and death, compared with those diagnosed at an older age, according to a retrospective study involving almost 2 million people.

People diagnosed with type 2 diabetes at age 40 or younger were at greatest risk of most outcomes, reported lead author Naveed Sattar, MD, PhD, professor of metabolic medicine, University of Glasgow, Scotland, and his colleagues. “Treatment target recommendations in regards to the risk factor control may need to be more aggressive in people developing diabetes at younger ages,” they wrote in Circulation. 

In contrast, developing type 2 diabetes over the age of 80 years had little impact on risks.

“[R]eassessment of treatment goals in elderly might be useful,” the investigators wrote. “Diabetes screening needs for the elderly (above 80) should also be reevaluated.”

The study involved 318,083 patients with type 2 diabetes registered  in the Swedish National Diabetes Registry between 1998 and 2012. Each patient was matched with 5 individuals from the general population based on sex, age, and country of residence, providing a control population of 1,575,108. Outcomes assessed included non-cardiovascular mortality, cardiovascular mortality, all causemortality, hospitalization for heart failure, coronary heart disease, stroke, atrial fibrillation, and acute myocardial infarction. Patients were followed for cardiovascular outcomes from 1998 to December 2013, while mortality surveillance continued through 2014.

In comparison with controls, patients 40 years or less had the highest excess risk of the most outcomes. *Excess risk of heart failure was elevated almost 5-fold (hazard ratio (HR), R 4.77), and risk of coronary heart disease wasn’t far behind (HR, 4.33). Risks of acute MI (HR, 3.41), stroke (HR, 3.58), and atrial fibrillation (HR, 1.95) were also elevated. Cardiovascular-related mortality was increased almost 3-fold (HR, 2.72), while total mortality (HR, 2.05) and non-cardiovascular mortality (HR, 1.95) were raised to a lesser degree.

“Thereafter, incremental risks generally declined with each higher decade age at diagnosis” of type 2 diabetes,” the investigators wrote.

After 80 years of age, all relative mortality risk factors dropped to less than 1, indicating lower risk than controls. Although non-fatal outcomes were still greater than 1 in this age group, these risks were “substantially attenuated compared with relative incremental risks in those diagnosed with T2DM at younger ages,” the investigators wrote.

The study was funded by the Swedish Association of Local Authorities Regions, the Swedish Heart and Lung Foundation, and the Swedish Research Council.

The investigators disclosed financial relationships with Amgen, AstraZeneca, Eli Lilly, and other pharmaceutical companies.

SOURCE: Sattar et al. Circulation. 2019 Apr 8. doi:10.1161/CIRCULATIONAHA.118.037885.

Individuals who are younger when diagnosed with type 2 diabetes are at greater risk of cardiovascular disease and death, compared with those diagnosed at an older age, according to a retrospective study involving almost 2 million people.

People diagnosed with type 2 diabetes at age 40 or younger were at greatest risk of most outcomes, reported lead author Naveed Sattar, MD, PhD, professor of metabolic medicine, University of Glasgow, Scotland, and his colleagues. “Treatment target recommendations in regards to the risk factor control may need to be more aggressive in people developing diabetes at younger ages,” they wrote in Circulation. 

In contrast, developing type 2 diabetes over the age of 80 years had little impact on risks.

“[R]eassessment of treatment goals in elderly might be useful,” the investigators wrote. “Diabetes screening needs for the elderly (above 80) should also be reevaluated.”

The study involved 318,083 patients with type 2 diabetes registered  in the Swedish National Diabetes Registry between 1998 and 2012. Each patient was matched with 5 individuals from the general population based on sex, age, and country of residence, providing a control population of 1,575,108. Outcomes assessed included non-cardiovascular mortality, cardiovascular mortality, all causemortality, hospitalization for heart failure, coronary heart disease, stroke, atrial fibrillation, and acute myocardial infarction. Patients were followed for cardiovascular outcomes from 1998 to December 2013, while mortality surveillance continued through 2014.

In comparison with controls, patients 40 years or less had the highest excess risk of the most outcomes. *Excess risk of heart failure was elevated almost 5-fold (hazard ratio (HR), R 4.77), and risk of coronary heart disease wasn’t far behind (HR, 4.33). Risks of acute MI (HR, 3.41), stroke (HR, 3.58), and atrial fibrillation (HR, 1.95) were also elevated. Cardiovascular-related mortality was increased almost 3-fold (HR, 2.72), while total mortality (HR, 2.05) and non-cardiovascular mortality (HR, 1.95) were raised to a lesser degree.

“Thereafter, incremental risks generally declined with each higher decade age at diagnosis” of type 2 diabetes,” the investigators wrote.

After 80 years of age, all relative mortality risk factors dropped to less than 1, indicating lower risk than controls. Although non-fatal outcomes were still greater than 1 in this age group, these risks were “substantially attenuated compared with relative incremental risks in those diagnosed with T2DM at younger ages,” the investigators wrote.

The study was funded by the Swedish Association of Local Authorities Regions, the Swedish Heart and Lung Foundation, and the Swedish Research Council.

The investigators disclosed financial relationships with Amgen, AstraZeneca, Eli Lilly, and other pharmaceutical companies.

SOURCE: Sattar et al. Circulation. 2019 Apr 8. doi:10.1161/CIRCULATIONAHA.118.037885.

Individuals who are younger when diagnosed with type 2 diabetes are at greater risk of cardiovascular disease and death, compared with those diagnosed at an older age, according to a retrospective study involving almost 2 million people.

People diagnosed with type 2 diabetes at age 40 or younger were at greatest risk of most outcomes, reported lead author Naveed Sattar, MD, PhD, professor of metabolic medicine, University of Glasgow, Scotland, and his colleagues. “Treatment target recommendations in regards to the risk factor control may need to be more aggressive in people developing diabetes at younger ages,” they wrote in Circulation. 

In contrast, developing type 2 diabetes over the age of 80 years had little impact on risks.

“[R]eassessment of treatment goals in elderly might be useful,” the investigators wrote. “Diabetes screening needs for the elderly (above 80) should also be reevaluated.”

The study involved 318,083 patients with type 2 diabetes registered  in the Swedish National Diabetes Registry between 1998 and 2012. Each patient was matched with 5 individuals from the general population based on sex, age, and country of residence, providing a control population of 1,575,108. Outcomes assessed included non-cardiovascular mortality, cardiovascular mortality, all causemortality, hospitalization for heart failure, coronary heart disease, stroke, atrial fibrillation, and acute myocardial infarction. Patients were followed for cardiovascular outcomes from 1998 to December 2013, while mortality surveillance continued through 2014.

In comparison with controls, patients 40 years or less had the highest excess risk of the most outcomes. *Excess risk of heart failure was elevated almost 5-fold (hazard ratio (HR), R 4.77), and risk of coronary heart disease wasn’t far behind (HR, 4.33). Risks of acute MI (HR, 3.41), stroke (HR, 3.58), and atrial fibrillation (HR, 1.95) were also elevated. Cardiovascular-related mortality was increased almost 3-fold (HR, 2.72), while total mortality (HR, 2.05) and non-cardiovascular mortality (HR, 1.95) were raised to a lesser degree.

“Thereafter, incremental risks generally declined with each higher decade age at diagnosis” of type 2 diabetes,” the investigators wrote.

After 80 years of age, all relative mortality risk factors dropped to less than 1, indicating lower risk than controls. Although non-fatal outcomes were still greater than 1 in this age group, these risks were “substantially attenuated compared with relative incremental risks in those diagnosed with T2DM at younger ages,” the investigators wrote.

The study was funded by the Swedish Association of Local Authorities Regions, the Swedish Heart and Lung Foundation, and the Swedish Research Council.

The investigators disclosed financial relationships with Amgen, AstraZeneca, Eli Lilly, and other pharmaceutical companies.

SOURCE: Sattar et al. Circulation. 2019 Apr 8. doi:10.1161/CIRCULATIONAHA.118.037885.

Hospitalizations for nutritional rehabilitation of patients with restrictive eating disorders are increasing.¹ Among primary mental health admissions at free-standing children’s hospitals, eating disorders represent 5.5% of hospitalizations and are associated with the longest length of stay (LOS; mean 14.3 days) and costliest care (mean $46,130).² Admission is necessary to ensure initial weight restoration and monitoring for symptoms of refeeding syndrome, including electrolyte shifts and vital sign abnormalities.^3-5

Supervision is generally considered an essential element of caring for hospitalized patients with eating disorders, who may experience difficulty adhering to nutritional treatment, perform excessive movement or exercise, or demonstrate purging or self-harming behaviors. Supervision is presumed to prevent counterproductive behaviors, facilitating weight gain and earlier discharge to psychiatric treatment. Best practices for patient supervision to address these challenges have not been established but often include meal time or continuous one-to-one supervision by nursing assistants (NAs) or other staff.^6,7 While meal supervision has been shown to decrease medical LOS, it is costly, reduces staff availability for the care of other patient care, and can be a barrier to caring for patients with eating disorders in many institutions.⁸

Although not previously used in patients with eating disorders, centralized video monitoring (CVM) may provide an additional mode of supervision. CVM is an emerging technology consisting of real-time video streaming, without video recording, enabling tracking of patient movement, redirection of behaviors, and communication with unit nurses when necessary. CVM has been used in multiple patient safety initiatives to reduce falls, address staffing shortages, reduce costs,^9,10 supervise patients at risk for self-harm or elopement, and prevent controlled medication diversion.^10,11

We sought to pilot a novel use of CVM to replace our institution’s standard practice of continuous one-to-one nursing assistant (NA) supervision of patients admitted for medical stabilization of an eating disorder. Our objective was to evaluate the supervision cost and feasibility of CVM, using LOS and days to weight gain as balancing measures.

Setting and Participants

This retrospective cohort study included patients 12-18 years old admitted to the pediatric hospital medicine service on a general unit of an academic quaternary care children’s hospital for medical stabilization of an eating disorder between September 2013 and March 2017. Patients were identified using administrative data based on primary or secondary diagnosis of anorexia nervosa, eating disorder not other wise specified, or another specified eating disorder (ICD 9 3071, 20759, or ICD 10 f5000, 5001, f5089, f509).^12,13 This research study was considered exempt by the University of Wisconsin School of Medicine and Public Health’s Institutional Review Board.

Supervision Interventions

A standard medical stabilization protocol was used for patients admitted with an eating disorder throughout the study period (Appendix). All patients received continuous one-to-one NA supervision until they reached the target calorie intake and demonstrated the ability to follow the nutritional meal protocol. Beginning July 2015, patients received continuous CVM supervision unless they expressed suicidal ideation (SI), which triggered one-to-one NA supervision until they no longer endorsed suicidality.

Centralized Video Monitoring Implementation

Institutional CVM technology was AvaSys TeleSitter Solution (AvaSure, Inc). Our institution purchased CVM devices for use in adult settings, and one was assigned for pediatric CVM. Mobile CVM video carts were deployed to patient rooms and generated live video streams, without recorded capture, which were supervised by CVM technicians. These technicians were NAs hired and trained specifically for this role; worked four-, eight-, and 12-hour shifts; and observed up to eight camera feeds on a single monitor in a centralized room. Patients and family members could refuse CVM, which would trigger one-to-one NA supervision. Patients were not observed by CVM while in the restroom; staff were notified by either the patient or technician, and one-to-one supervision was provided. CVM had two-way audio communication, which allowed technicians to redirect patients verbally. Technicians could contact nursing staff directly by phone when additional intervention was needed.

Supervision Costs

NA supervision costs were estimated at $19/hour, based upon institutional human resources average NA salaries at that time. No additional mealtime supervision was included, as in-person supervision was already occurring.

CVM supervision costs were defined as the sum of the device cost plus CVM technician costs and two hours of one-to-one NA mealtime supervision per day. The CVM device cost was estimated at $2.10/hour, assuming a 10-year machine life expectancy (single unit cost $82,893 in 2015, 3,944 hours of use in fiscal year of 2018). CVM technician costs were $19/hour, based upon institutional human resources average CVM technician salaries at that time. Because technicians monitored an average of six patients simultaneously during this study, one-sixth of a CVM technician’s salary (ie, $3.17/hour) was used for each hour of CVM monitoring. Patients with mixed (NA and CVM) supervision were analyzed with those having CVM supervision. These patients’ costs were the sum of their NA supervision costs plus their CVM supervision costs.

Data Collection

Descriptive variables including age, gender, race/ethnicity, insurance, and LOS were collected from administrative data. The duration and type of supervision for all patients were collected from daily staffing logs. The eating disorder protocol standardized the process of obtaining daily weights (Appendix). Days to weight gain following admission were defined as the total number of days from admission to the first day of weight gain that was followed by another day of weight gain or maintaining the same weight. CVM acceptability and feasibility were assessed by family refusal of CVM, conversion from CVM to NA, technological failure, complaints, and unplanned discontinuation, which were prospectively documented by the unit nurse manager.

Data Analysis

Patient and hospitalization characteristics were summarized. A sample size of at least 14 in each group was estimated as necessary to detect a 50% reduction in supervision cost between the groups using alpha = 0.05, a power of 80%, a mean cost of $4,400 in the NA group, and a standard deviation of $1,600.Wilcoxon rank-sum tests were used to assess differences in median supervision cost between NA and CVM use. Differences in mean LOS and days to weight gain between NA and CVM use were assessed with t-tests because these data were normally distributed.

Patient Characteristics and Supervision Costs

The study included 37 consecutive admissions (NA = 23 and CVM = 14) with 35 unique patients. Patients were female, primarily non-Hispanic White, and privately insured (Table 1). Median supervision cost for the NA was statistically significantly more expensive at $4,104/admission versus $1,166/admission for CVM (P < .001, Table 2).

Balancing Measures, Acceptability, and Feasibility

Mean LOS was 11.7 days for NA and 9.8 days for CVM (P = .27; Table 2). The mean number of days to weight gain was 3.1 and 3.6 days, respectively (P = .28). No patients converted from CVM to NA supervision. One patient with SI converted to CVM after SI resolved and two patients required ongoing NA supervision due to continued SI. There were no reported refusals, technology failures, or unplanned discontinuations of CVM. One patient/family reported excessive CVM redirection of behavior.

This is the first description of CVM use in adolescent patients or patients with eating disorders. Our results suggest that CVM appears feasible and less costly in this population than one-to-one NA supervision, without statistically significant differences in LOS or time to weight gain. Patients with CVM with any NA supervision (except mealtime alone) were analyzed in the CVM group; therefore, this study may underestimate cost savings from CVM supervision. This innovative use of CVM may represent an opportunity for hospitals to repurpose monitoring technology for more efficient supervision of patients with eating disorders.

This pediatric pilot study adds to the growing body of literature in adult patients suggesting CVM supervision may be a feasible inpatient cost-reduction strategy.^9,10 One single-center study demonstrated that the use of CVM with adult inpatients led to fewer unsafe behaviors, eg, patient removal of intravenous catheters and oxygen therapy. Personnel savings exceeded the original investment cost of the monitor within one fiscal quarter.⁹ Results of another study suggest that CVM use with hospitalized adults who required supervision to prevent falls was associated with improved patient and family satisfaction.¹⁴ In the absence of a gold standard for supervision of patients hospitalized with eating disorders, CVM technology is a tool that may balance cost, care quality, and patient experience. Given the upfront investment in CVM units, this technology may be most appropriate for institutions already using CVM for other inpatient indications.

The results of this pilot study suggest that supervision costs for patients admitted for medical stabilization of eating disorders were statistically significantly lower with CVM when compared with one-to-one NA supervision, without a change in hospitalization LOS or time to weight gain. These findings are particularly important as hospitals seek opportunities to reduce costs while providing safe and effective care. Future efforts should focus on evaluating clinical outcomes and patient experiences with this technology and strategies to maximize efficiency to offset the initial device cost.

Disclosures

The authors have no financial relationships relevant to this article to disclose. The authors have no conflicts of interest relevant to this article to disclose.

Hospitalizations for nutritional rehabilitation of patients with restrictive eating disorders are increasing.¹ Among primary mental health admissions at free-standing children’s hospitals, eating disorders represent 5.5% of hospitalizations and are associated with the longest length of stay (LOS; mean 14.3 days) and costliest care (mean $46,130).² Admission is necessary to ensure initial weight restoration and monitoring for symptoms of refeeding syndrome, including electrolyte shifts and vital sign abnormalities.^3-5

Supervision is generally considered an essential element of caring for hospitalized patients with eating disorders, who may experience difficulty adhering to nutritional treatment, perform excessive movement or exercise, or demonstrate purging or self-harming behaviors. Supervision is presumed to prevent counterproductive behaviors, facilitating weight gain and earlier discharge to psychiatric treatment. Best practices for patient supervision to address these challenges have not been established but often include meal time or continuous one-to-one supervision by nursing assistants (NAs) or other staff.^6,7 While meal supervision has been shown to decrease medical LOS, it is costly, reduces staff availability for the care of other patient care, and can be a barrier to caring for patients with eating disorders in many institutions.⁸

Although not previously used in patients with eating disorders, centralized video monitoring (CVM) may provide an additional mode of supervision. CVM is an emerging technology consisting of real-time video streaming, without video recording, enabling tracking of patient movement, redirection of behaviors, and communication with unit nurses when necessary. CVM has been used in multiple patient safety initiatives to reduce falls, address staffing shortages, reduce costs,^9,10 supervise patients at risk for self-harm or elopement, and prevent controlled medication diversion.^10,11

We sought to pilot a novel use of CVM to replace our institution’s standard practice of continuous one-to-one nursing assistant (NA) supervision of patients admitted for medical stabilization of an eating disorder. Our objective was to evaluate the supervision cost and feasibility of CVM, using LOS and days to weight gain as balancing measures.

Setting and Participants

This retrospective cohort study included patients 12-18 years old admitted to the pediatric hospital medicine service on a general unit of an academic quaternary care children’s hospital for medical stabilization of an eating disorder between September 2013 and March 2017. Patients were identified using administrative data based on primary or secondary diagnosis of anorexia nervosa, eating disorder not other wise specified, or another specified eating disorder (ICD 9 3071, 20759, or ICD 10 f5000, 5001, f5089, f509).^12,13 This research study was considered exempt by the University of Wisconsin School of Medicine and Public Health’s Institutional Review Board.

Supervision Interventions

A standard medical stabilization protocol was used for patients admitted with an eating disorder throughout the study period (Appendix). All patients received continuous one-to-one NA supervision until they reached the target calorie intake and demonstrated the ability to follow the nutritional meal protocol. Beginning July 2015, patients received continuous CVM supervision unless they expressed suicidal ideation (SI), which triggered one-to-one NA supervision until they no longer endorsed suicidality.

Centralized Video Monitoring Implementation

Institutional CVM technology was AvaSys TeleSitter Solution (AvaSure, Inc). Our institution purchased CVM devices for use in adult settings, and one was assigned for pediatric CVM. Mobile CVM video carts were deployed to patient rooms and generated live video streams, without recorded capture, which were supervised by CVM technicians. These technicians were NAs hired and trained specifically for this role; worked four-, eight-, and 12-hour shifts; and observed up to eight camera feeds on a single monitor in a centralized room. Patients and family members could refuse CVM, which would trigger one-to-one NA supervision. Patients were not observed by CVM while in the restroom; staff were notified by either the patient or technician, and one-to-one supervision was provided. CVM had two-way audio communication, which allowed technicians to redirect patients verbally. Technicians could contact nursing staff directly by phone when additional intervention was needed.

Supervision Costs

NA supervision costs were estimated at $19/hour, based upon institutional human resources average NA salaries at that time. No additional mealtime supervision was included, as in-person supervision was already occurring.

CVM supervision costs were defined as the sum of the device cost plus CVM technician costs and two hours of one-to-one NA mealtime supervision per day. The CVM device cost was estimated at $2.10/hour, assuming a 10-year machine life expectancy (single unit cost $82,893 in 2015, 3,944 hours of use in fiscal year of 2018). CVM technician costs were $19/hour, based upon institutional human resources average CVM technician salaries at that time. Because technicians monitored an average of six patients simultaneously during this study, one-sixth of a CVM technician’s salary (ie, $3.17/hour) was used for each hour of CVM monitoring. Patients with mixed (NA and CVM) supervision were analyzed with those having CVM supervision. These patients’ costs were the sum of their NA supervision costs plus their CVM supervision costs.

Data Collection

Descriptive variables including age, gender, race/ethnicity, insurance, and LOS were collected from administrative data. The duration and type of supervision for all patients were collected from daily staffing logs. The eating disorder protocol standardized the process of obtaining daily weights (Appendix). Days to weight gain following admission were defined as the total number of days from admission to the first day of weight gain that was followed by another day of weight gain or maintaining the same weight. CVM acceptability and feasibility were assessed by family refusal of CVM, conversion from CVM to NA, technological failure, complaints, and unplanned discontinuation, which were prospectively documented by the unit nurse manager.

Data Analysis

Patient and hospitalization characteristics were summarized. A sample size of at least 14 in each group was estimated as necessary to detect a 50% reduction in supervision cost between the groups using alpha = 0.05, a power of 80%, a mean cost of $4,400 in the NA group, and a standard deviation of $1,600.Wilcoxon rank-sum tests were used to assess differences in median supervision cost between NA and CVM use. Differences in mean LOS and days to weight gain between NA and CVM use were assessed with t-tests because these data were normally distributed.

Patient Characteristics and Supervision Costs

The study included 37 consecutive admissions (NA = 23 and CVM = 14) with 35 unique patients. Patients were female, primarily non-Hispanic White, and privately insured (Table 1). Median supervision cost for the NA was statistically significantly more expensive at $4,104/admission versus $1,166/admission for CVM (P < .001, Table 2).

Balancing Measures, Acceptability, and Feasibility

Mean LOS was 11.7 days for NA and 9.8 days for CVM (P = .27; Table 2). The mean number of days to weight gain was 3.1 and 3.6 days, respectively (P = .28). No patients converted from CVM to NA supervision. One patient with SI converted to CVM after SI resolved and two patients required ongoing NA supervision due to continued SI. There were no reported refusals, technology failures, or unplanned discontinuations of CVM. One patient/family reported excessive CVM redirection of behavior.

This is the first description of CVM use in adolescent patients or patients with eating disorders. Our results suggest that CVM appears feasible and less costly in this population than one-to-one NA supervision, without statistically significant differences in LOS or time to weight gain. Patients with CVM with any NA supervision (except mealtime alone) were analyzed in the CVM group; therefore, this study may underestimate cost savings from CVM supervision. This innovative use of CVM may represent an opportunity for hospitals to repurpose monitoring technology for more efficient supervision of patients with eating disorders.

This pediatric pilot study adds to the growing body of literature in adult patients suggesting CVM supervision may be a feasible inpatient cost-reduction strategy.^9,10 One single-center study demonstrated that the use of CVM with adult inpatients led to fewer unsafe behaviors, eg, patient removal of intravenous catheters and oxygen therapy. Personnel savings exceeded the original investment cost of the monitor within one fiscal quarter.⁹ Results of another study suggest that CVM use with hospitalized adults who required supervision to prevent falls was associated with improved patient and family satisfaction.¹⁴ In the absence of a gold standard for supervision of patients hospitalized with eating disorders, CVM technology is a tool that may balance cost, care quality, and patient experience. Given the upfront investment in CVM units, this technology may be most appropriate for institutions already using CVM for other inpatient indications.

The results of this pilot study suggest that supervision costs for patients admitted for medical stabilization of eating disorders were statistically significantly lower with CVM when compared with one-to-one NA supervision, without a change in hospitalization LOS or time to weight gain. These findings are particularly important as hospitals seek opportunities to reduce costs while providing safe and effective care. Future efforts should focus on evaluating clinical outcomes and patient experiences with this technology and strategies to maximize efficiency to offset the initial device cost.

Disclosures

The authors have no financial relationships relevant to this article to disclose. The authors have no conflicts of interest relevant to this article to disclose.

Hospitalizations for nutritional rehabilitation of patients with restrictive eating disorders are increasing.¹ Among primary mental health admissions at free-standing children’s hospitals, eating disorders represent 5.5% of hospitalizations and are associated with the longest length of stay (LOS; mean 14.3 days) and costliest care (mean $46,130).² Admission is necessary to ensure initial weight restoration and monitoring for symptoms of refeeding syndrome, including electrolyte shifts and vital sign abnormalities.^3-5

Supervision is generally considered an essential element of caring for hospitalized patients with eating disorders, who may experience difficulty adhering to nutritional treatment, perform excessive movement or exercise, or demonstrate purging or self-harming behaviors. Supervision is presumed to prevent counterproductive behaviors, facilitating weight gain and earlier discharge to psychiatric treatment. Best practices for patient supervision to address these challenges have not been established but often include meal time or continuous one-to-one supervision by nursing assistants (NAs) or other staff.^6,7 While meal supervision has been shown to decrease medical LOS, it is costly, reduces staff availability for the care of other patient care, and can be a barrier to caring for patients with eating disorders in many institutions.⁸

Although not previously used in patients with eating disorders, centralized video monitoring (CVM) may provide an additional mode of supervision. CVM is an emerging technology consisting of real-time video streaming, without video recording, enabling tracking of patient movement, redirection of behaviors, and communication with unit nurses when necessary. CVM has been used in multiple patient safety initiatives to reduce falls, address staffing shortages, reduce costs,^9,10 supervise patients at risk for self-harm or elopement, and prevent controlled medication diversion.^10,11

We sought to pilot a novel use of CVM to replace our institution’s standard practice of continuous one-to-one nursing assistant (NA) supervision of patients admitted for medical stabilization of an eating disorder. Our objective was to evaluate the supervision cost and feasibility of CVM, using LOS and days to weight gain as balancing measures.

Setting and Participants

This retrospective cohort study included patients 12-18 years old admitted to the pediatric hospital medicine service on a general unit of an academic quaternary care children’s hospital for medical stabilization of an eating disorder between September 2013 and March 2017. Patients were identified using administrative data based on primary or secondary diagnosis of anorexia nervosa, eating disorder not other wise specified, or another specified eating disorder (ICD 9 3071, 20759, or ICD 10 f5000, 5001, f5089, f509).^12,13 This research study was considered exempt by the University of Wisconsin School of Medicine and Public Health’s Institutional Review Board.

Supervision Interventions

A standard medical stabilization protocol was used for patients admitted with an eating disorder throughout the study period (Appendix). All patients received continuous one-to-one NA supervision until they reached the target calorie intake and demonstrated the ability to follow the nutritional meal protocol. Beginning July 2015, patients received continuous CVM supervision unless they expressed suicidal ideation (SI), which triggered one-to-one NA supervision until they no longer endorsed suicidality.

Centralized Video Monitoring Implementation

Institutional CVM technology was AvaSys TeleSitter Solution (AvaSure, Inc). Our institution purchased CVM devices for use in adult settings, and one was assigned for pediatric CVM. Mobile CVM video carts were deployed to patient rooms and generated live video streams, without recorded capture, which were supervised by CVM technicians. These technicians were NAs hired and trained specifically for this role; worked four-, eight-, and 12-hour shifts; and observed up to eight camera feeds on a single monitor in a centralized room. Patients and family members could refuse CVM, which would trigger one-to-one NA supervision. Patients were not observed by CVM while in the restroom; staff were notified by either the patient or technician, and one-to-one supervision was provided. CVM had two-way audio communication, which allowed technicians to redirect patients verbally. Technicians could contact nursing staff directly by phone when additional intervention was needed.

Supervision Costs

NA supervision costs were estimated at $19/hour, based upon institutional human resources average NA salaries at that time. No additional mealtime supervision was included, as in-person supervision was already occurring.

CVM supervision costs were defined as the sum of the device cost plus CVM technician costs and two hours of one-to-one NA mealtime supervision per day. The CVM device cost was estimated at $2.10/hour, assuming a 10-year machine life expectancy (single unit cost $82,893 in 2015, 3,944 hours of use in fiscal year of 2018). CVM technician costs were $19/hour, based upon institutional human resources average CVM technician salaries at that time. Because technicians monitored an average of six patients simultaneously during this study, one-sixth of a CVM technician’s salary (ie, $3.17/hour) was used for each hour of CVM monitoring. Patients with mixed (NA and CVM) supervision were analyzed with those having CVM supervision. These patients’ costs were the sum of their NA supervision costs plus their CVM supervision costs.

Data Collection

Descriptive variables including age, gender, race/ethnicity, insurance, and LOS were collected from administrative data. The duration and type of supervision for all patients were collected from daily staffing logs. The eating disorder protocol standardized the process of obtaining daily weights (Appendix). Days to weight gain following admission were defined as the total number of days from admission to the first day of weight gain that was followed by another day of weight gain or maintaining the same weight. CVM acceptability and feasibility were assessed by family refusal of CVM, conversion from CVM to NA, technological failure, complaints, and unplanned discontinuation, which were prospectively documented by the unit nurse manager.

Data Analysis

Patient and hospitalization characteristics were summarized. A sample size of at least 14 in each group was estimated as necessary to detect a 50% reduction in supervision cost between the groups using alpha = 0.05, a power of 80%, a mean cost of $4,400 in the NA group, and a standard deviation of $1,600.Wilcoxon rank-sum tests were used to assess differences in median supervision cost between NA and CVM use. Differences in mean LOS and days to weight gain between NA and CVM use were assessed with t-tests because these data were normally distributed.

Patient Characteristics and Supervision Costs

The study included 37 consecutive admissions (NA = 23 and CVM = 14) with 35 unique patients. Patients were female, primarily non-Hispanic White, and privately insured (Table 1). Median supervision cost for the NA was statistically significantly more expensive at $4,104/admission versus $1,166/admission for CVM (P < .001, Table 2).

Balancing Measures, Acceptability, and Feasibility

Mean LOS was 11.7 days for NA and 9.8 days for CVM (P = .27; Table 2). The mean number of days to weight gain was 3.1 and 3.6 days, respectively (P = .28). No patients converted from CVM to NA supervision. One patient with SI converted to CVM after SI resolved and two patients required ongoing NA supervision due to continued SI. There were no reported refusals, technology failures, or unplanned discontinuations of CVM. One patient/family reported excessive CVM redirection of behavior.

This is the first description of CVM use in adolescent patients or patients with eating disorders. Our results suggest that CVM appears feasible and less costly in this population than one-to-one NA supervision, without statistically significant differences in LOS or time to weight gain. Patients with CVM with any NA supervision (except mealtime alone) were analyzed in the CVM group; therefore, this study may underestimate cost savings from CVM supervision. This innovative use of CVM may represent an opportunity for hospitals to repurpose monitoring technology for more efficient supervision of patients with eating disorders.

This pediatric pilot study adds to the growing body of literature in adult patients suggesting CVM supervision may be a feasible inpatient cost-reduction strategy.^9,10 One single-center study demonstrated that the use of CVM with adult inpatients led to fewer unsafe behaviors, eg, patient removal of intravenous catheters and oxygen therapy. Personnel savings exceeded the original investment cost of the monitor within one fiscal quarter.⁹ Results of another study suggest that CVM use with hospitalized adults who required supervision to prevent falls was associated with improved patient and family satisfaction.¹⁴ In the absence of a gold standard for supervision of patients hospitalized with eating disorders, CVM technology is a tool that may balance cost, care quality, and patient experience. Given the upfront investment in CVM units, this technology may be most appropriate for institutions already using CVM for other inpatient indications.

The results of this pilot study suggest that supervision costs for patients admitted for medical stabilization of eating disorders were statistically significantly lower with CVM when compared with one-to-one NA supervision, without a change in hospitalization LOS or time to weight gain. These findings are particularly important as hospitals seek opportunities to reduce costs while providing safe and effective care. Future efforts should focus on evaluating clinical outcomes and patient experiences with this technology and strategies to maximize efficiency to offset the initial device cost.

Disclosures

The authors have no financial relationships relevant to this article to disclose. The authors have no conflicts of interest relevant to this article to disclose.

The transfer of patients between acute care hospitals (interhospital transfer [IHT]) occurs regularly among patients with a variety of diagnoses, in theory, to gain access to unique specialty services and/or a higher level of care, among other reasons.^1,2

However, the practice of IHT is variable and nonstandardized,^3,4 and existing data largely suggests that transferred patients experience worse outcomes, including longer length of stay, higher hospitalization costs, longer ICU time, and greater mortality, even with rigorous adjustment for confounding by indication.^5,6 Though there are many possible reasons for these findings, existing literature suggests that there may be aspects of the transfer process itself which contribute to these outcomes.^2,6,7

Understanding which aspects of the transfer process contribute to poor patient outcomes is a key first step toward the development of targeted quality improvement initiatives to improve this process of care. In this study, we aim to examine the association between select characteristics of the transfer process, including the timing of transfer and workload of the admitting physician team, and clinical outcomes among patients undergoing IHT.

Data and Study Population

We performed a retrospective analysis of patients ≥age 18 years who transferred to Brigham and Women’s Hospital (BWH), a 777-bed tertiary care hospital, from another acute care hospital between January 2005, and September 2013. Dates of inclusion were purposefully chosen prior to BWH implementation of a new electronic health records system to avoid potential information bias. As at most academic medical centers, night coverage at BWH differs by service and includes a combination of long-call admitting teams and night float coverage. On weekends, many services are less well staffed, and some procedures may only be available if needed emergently. Some services have caps on the daily number of admissions or total patient census, but none have caps on the number of discharges per day. Patients were excluded from analysis if they left BWH against medical advice, were transferred from closely affiliated hospitals with shared personnel and electronic health records (Brigham and Women’s Faulkner Hospital, Dana Farber Cancer Institute), transferred from inpatient psychiatric or inpatient hospice facilities, or transferred to obstetrics or nursery services. Data were obtained from administrative sources and the research patient data repository (RPDR), a centralized clinical data repository that gathers data from various hospital legacy systems and stores them in one data warehouse.⁸ Our study was approved by the Partners Institutional Review Board (IRB) with a waiver of patient consent.

Transfer Process Characteristics

Predictors included select characteristics of the transfer process, including (1) Day of week of transfer, dichotomized into Friday through Sunday (“weekend”), versus Monday through Thursday (“weekday”);⁹ Friday was included with “weekend” given the suggestion of increased volume of transfers in advance of the weekend; (2) Time of arrival of the transferred patient, categorized into “daytime” (7 am-5 pm), “evening” (5 pm -10 pm), and “nighttime” (10 pm -7 am), with daytime as the reference group; (3) Admitting team “busyness” on day of patient transfer, defined as the total number of additional patient admissions and patient discharges performed by the admitting team on the calendar day of patient arrival, as has been used in prior research,¹⁰ and categorized into quartiles with lowest quartile as the reference group. Service-specific quartiles were calculated and used for stratified analyses (described below); and (4) “Time delay” between patient acceptance for transfer and patient arrival at BWH, categorized into 0-12 hours, 12-24 hours, 24-48 hours, and >48 hours, with 12-24 hours as the reference group (anticipating that time delay of 0-12 hours would be reflective of “sicker” patients in need of expedited transfer).

Outcomes

Outcomes included transfer to the intensive care unit (ICU) within 48 hours of arrival and 30-day mortality from date of index admission.^5,6

Patient Characteristics

Covariates for adjustment included: patient age, sex, race, Elixhauser comorbidity score,¹¹ Diagnosis-Related Group (DRG)-weight, insurance status, year of admission, number of preadmission medications, and service of admission.

Statistical Analyses

We used descriptive statistics to display baseline characteristics and performed a series of univariable and multivariable logistic regression models to obtain the adjusted odds of each transfer process characteristic on each outcome, adjusting for all covariates (proc logistic, SAS Statistical Software, Cary, North Carolina). For analyses of ICU transfer within 48 hours of arrival, all patients initially admitted to the ICU at time of transfer were excluded.

In the secondary analyses, we used a combined day-of-week and time-of-day variable (ie, Monday day, Monday evening, Monday night, Tuesday day, and so on, with Monday day as the reference group) to obtain a more detailed evaluation of timing of transfer on patient outcomes. We also performed stratified analyses to evaluate each transfer process characteristic on adjusted odds of 30-day mortality stratified by service of admission (ie, at the time of transfer to BWH), adjusting for all covariates. For all analyses, two-sided P values < .05 were considered significant.

Overall, 24,352 patients met our inclusion criteria and underwent IHT, of whom 2,174 (8.9%) died within 30 days. Of the 22,910 transferred patients originally admitted to a non-ICU service, 5,464 (23.8%) underwent ICU transfer within 48 hours of arrival. Cohort characteristics are shown in Table 1.

Multivariable regression analyses demonstrated no significant association between weekend (versus weekday) transfer or increased time delay between patient acceptance and arrival (>48 hours) and adjusted odds of ICU transfer within 48 hours or 30-day mortality. However, they did demonstrate that nighttime (versus daytime) transfer was associated with greater adjusted odds of both ICU transfer and 30-day mortality. Increased admitting team busyness was associated with lower adjusted odds of ICU transfer but was not significantly associated with adjusted odds of 30-day mortality (Table 2). As expected, decreased time delay between patient acceptance and arrival (0-12 hours) was associated with increased adjusted odds of both ICU transfer (adjusted OR 2.68; 95% CI 2.29, 3.15) and 30-day mortality (adjusted OR 1.25; 95% CI 1.03, 1.53) compared with 12-24 hours (results not shown). Time delay >48 hours was not associated with either outcome.

Regression analyses with the combined day/time variable demonstrated that compared with Monday daytime transfer, Sunday night transfer was significantly associated with increased adjusted odds of 30-day mortality, and Friday night transfer was associated with a trend toward increased 30-day mortality (adjusted OR [aOR] 1.88; 95% CI 1.25, 2.82, and aOR 1.43; 95% CI 0.99, 2.06, respectively). We also found that all nighttime transfers (ie, Monday through Sunday night) were associated with increased adjusted odds of ICU transfer within 48 hours (as compared with Monday daytime transfer). Other days/time analyses were not significant.

Univariable and multivariable analyses stratified by service were performed (Appendix). Multivariable stratified analyses demonstrated that weekend transfer, nighttime transfer, and increased admitting team busyness were associated with increased adjusted odds of 30-day mortality among cardiothoracic (CT) and gastrointestinal (GI) surgical service patients. Increased admitting team busyness was also associated with increased mortality among ICU service patients but was associated with decreased mortality among cardiology service patients. An increased time delay between patient acceptance and arrival was associated with decreased mortality among CT and GI surgical service patients (Figure; Appendix). Other adjusted stratified outcomes were not significant.

In this study of 24,352 patients undergoing IHT, we found no significant association between weekend transfer or increased time delay between transfer acceptance and arrival and patient outcomes in the cohort as a whole; but we found that nighttime transfer is associated with increased adjusted odds of both ICU transfer within 48 hours and 30-day mortality. Our analyses combining day-of-week and time-of-day demonstrate that Sunday night transfer is particularly associated with increased adjusted odds of 30-day mortality (as compared with Monday daytime transfer), and show a trend toward increased mortality with Friday night transfers. These detailed analyses otherwise reinforce that nighttime transfer across all nights of the week is associated with increased adjusted odds of ICU transfer within 48 hours. We also found that increased admitting team busyness on the day of patient transfer is associated with decreased odds of ICU transfer, though this may solely be reflective of higher turnover services (ie, cardiology) caring for lower acuity patients, as suggested by secondary analyses stratified by service. In addition, secondary analyses demonstrated differential associations between weekend transfers, nighttime transfers, and increased team busyness on the odds of 30-day mortality based on service of transfer. These analyses showed that patients transferred to higher acuity services requiring procedural care, including CT surgery, GI surgery, and Medical ICU, do worse under all three circumstances as compared with patients transferred to other services. Secondary analyses also demonstrated that increased time delay between patient acceptance and arrival is inversely associated with 30-day mortality among CT and GI surgery service patients, likely reflecting lower acuity patients (ie, less sick patients are less rapidly transferred).

There are several possible explanations for these findings. Patients transferred to surgical services at night may reflect a more urgent need for surgery and include a sicker cohort of patients, possibly explaining these findings. Alternatively, or in addition, both weekend and nighttime hospital admission expose patients to similar potential risks, ie, limited resources available during off-peak hours. Our findings could, therefore, reflect the possibility that patients transferred to higher acuity services in need of procedural care are most vulnerable to off-peak timing of transfer. Similar data looking at patients admitted through the emergency room (ER) find the strongest effect of off-peak admissions on patients in need of procedures, including GI hemorrhage,¹² atrial fibrillation¹³ and acute myocardial infarction (AMI),¹⁴ arguably because of the limited availability of necessary interventions. Patients undergoing IHT are a sicker cohort of patients than those admitted through the ER, and, therefore, may be even more vulnerable to these issues.^3,5 This is supported by our findings that Sunday night transfers (and trend toward Friday night transfers) are associated with greater mortality compared with Monday daytime transfers, when at-the-ready resources and/or specialty personnel may be less available (Sunday night), and delays until receipt of necessary procedures may be longer (Friday night). Though we did not observe similar results among cardiology service transfers, as may be expected based on existing literature,^13,14 this subset of patients includes more heterogeneous diagnoses, (ie, not solely those that require acute intervention) and exhibited a low level of acuity (low Elixhauser score and DRG-weight, data not shown).

Disclosures

Dr. Mueller and Dr. Schnipper have nothing to disclose. Ms. Fiskio has nothing to disclose. Dr. Schnipper is the recipient of grant funding from Mallinckrodt Pharmaceuticals to conduct an investigator-initiated study of predictors and impact of opioid-related adverse drug events.

The transfer of patients between acute care hospitals (interhospital transfer [IHT]) occurs regularly among patients with a variety of diagnoses, in theory, to gain access to unique specialty services and/or a higher level of care, among other reasons.^1,2

However, the practice of IHT is variable and nonstandardized,^3,4 and existing data largely suggests that transferred patients experience worse outcomes, including longer length of stay, higher hospitalization costs, longer ICU time, and greater mortality, even with rigorous adjustment for confounding by indication.^5,6 Though there are many possible reasons for these findings, existing literature suggests that there may be aspects of the transfer process itself which contribute to these outcomes.^2,6,7

Understanding which aspects of the transfer process contribute to poor patient outcomes is a key first step toward the development of targeted quality improvement initiatives to improve this process of care. In this study, we aim to examine the association between select characteristics of the transfer process, including the timing of transfer and workload of the admitting physician team, and clinical outcomes among patients undergoing IHT.

Data and Study Population

We performed a retrospective analysis of patients ≥age 18 years who transferred to Brigham and Women’s Hospital (BWH), a 777-bed tertiary care hospital, from another acute care hospital between January 2005, and September 2013. Dates of inclusion were purposefully chosen prior to BWH implementation of a new electronic health records system to avoid potential information bias. As at most academic medical centers, night coverage at BWH differs by service and includes a combination of long-call admitting teams and night float coverage. On weekends, many services are less well staffed, and some procedures may only be available if needed emergently. Some services have caps on the daily number of admissions or total patient census, but none have caps on the number of discharges per day. Patients were excluded from analysis if they left BWH against medical advice, were transferred from closely affiliated hospitals with shared personnel and electronic health records (Brigham and Women’s Faulkner Hospital, Dana Farber Cancer Institute), transferred from inpatient psychiatric or inpatient hospice facilities, or transferred to obstetrics or nursery services. Data were obtained from administrative sources and the research patient data repository (RPDR), a centralized clinical data repository that gathers data from various hospital legacy systems and stores them in one data warehouse.⁸ Our study was approved by the Partners Institutional Review Board (IRB) with a waiver of patient consent.

Transfer Process Characteristics

Predictors included select characteristics of the transfer process, including (1) Day of week of transfer, dichotomized into Friday through Sunday (“weekend”), versus Monday through Thursday (“weekday”);⁹ Friday was included with “weekend” given the suggestion of increased volume of transfers in advance of the weekend; (2) Time of arrival of the transferred patient, categorized into “daytime” (7 am-5 pm), “evening” (5 pm -10 pm), and “nighttime” (10 pm -7 am), with daytime as the reference group; (3) Admitting team “busyness” on day of patient transfer, defined as the total number of additional patient admissions and patient discharges performed by the admitting team on the calendar day of patient arrival, as has been used in prior research,¹⁰ and categorized into quartiles with lowest quartile as the reference group. Service-specific quartiles were calculated and used for stratified analyses (described below); and (4) “Time delay” between patient acceptance for transfer and patient arrival at BWH, categorized into 0-12 hours, 12-24 hours, 24-48 hours, and >48 hours, with 12-24 hours as the reference group (anticipating that time delay of 0-12 hours would be reflective of “sicker” patients in need of expedited transfer).

Outcomes

Outcomes included transfer to the intensive care unit (ICU) within 48 hours of arrival and 30-day mortality from date of index admission.^5,6

Patient Characteristics

Covariates for adjustment included: patient age, sex, race, Elixhauser comorbidity score,¹¹ Diagnosis-Related Group (DRG)-weight, insurance status, year of admission, number of preadmission medications, and service of admission.

Statistical Analyses

We used descriptive statistics to display baseline characteristics and performed a series of univariable and multivariable logistic regression models to obtain the adjusted odds of each transfer process characteristic on each outcome, adjusting for all covariates (proc logistic, SAS Statistical Software, Cary, North Carolina). For analyses of ICU transfer within 48 hours of arrival, all patients initially admitted to the ICU at time of transfer were excluded.

In the secondary analyses, we used a combined day-of-week and time-of-day variable (ie, Monday day, Monday evening, Monday night, Tuesday day, and so on, with Monday day as the reference group) to obtain a more detailed evaluation of timing of transfer on patient outcomes. We also performed stratified analyses to evaluate each transfer process characteristic on adjusted odds of 30-day mortality stratified by service of admission (ie, at the time of transfer to BWH), adjusting for all covariates. For all analyses, two-sided P values < .05 were considered significant.

Overall, 24,352 patients met our inclusion criteria and underwent IHT, of whom 2,174 (8.9%) died within 30 days. Of the 22,910 transferred patients originally admitted to a non-ICU service, 5,464 (23.8%) underwent ICU transfer within 48 hours of arrival. Cohort characteristics are shown in Table 1.

Multivariable regression analyses demonstrated no significant association between weekend (versus weekday) transfer or increased time delay between patient acceptance and arrival (>48 hours) and adjusted odds of ICU transfer within 48 hours or 30-day mortality. However, they did demonstrate that nighttime (versus daytime) transfer was associated with greater adjusted odds of both ICU transfer and 30-day mortality. Increased admitting team busyness was associated with lower adjusted odds of ICU transfer but was not significantly associated with adjusted odds of 30-day mortality (Table 2). As expected, decreased time delay between patient acceptance and arrival (0-12 hours) was associated with increased adjusted odds of both ICU transfer (adjusted OR 2.68; 95% CI 2.29, 3.15) and 30-day mortality (adjusted OR 1.25; 95% CI 1.03, 1.53) compared with 12-24 hours (results not shown). Time delay >48 hours was not associated with either outcome.

Regression analyses with the combined day/time variable demonstrated that compared with Monday daytime transfer, Sunday night transfer was significantly associated with increased adjusted odds of 30-day mortality, and Friday night transfer was associated with a trend toward increased 30-day mortality (adjusted OR [aOR] 1.88; 95% CI 1.25, 2.82, and aOR 1.43; 95% CI 0.99, 2.06, respectively). We also found that all nighttime transfers (ie, Monday through Sunday night) were associated with increased adjusted odds of ICU transfer within 48 hours (as compared with Monday daytime transfer). Other days/time analyses were not significant.

Univariable and multivariable analyses stratified by service were performed (Appendix). Multivariable stratified analyses demonstrated that weekend transfer, nighttime transfer, and increased admitting team busyness were associated with increased adjusted odds of 30-day mortality among cardiothoracic (CT) and gastrointestinal (GI) surgical service patients. Increased admitting team busyness was also associated with increased mortality among ICU service patients but was associated with decreased mortality among cardiology service patients. An increased time delay between patient acceptance and arrival was associated with decreased mortality among CT and GI surgical service patients (Figure; Appendix). Other adjusted stratified outcomes were not significant.

In this study of 24,352 patients undergoing IHT, we found no significant association between weekend transfer or increased time delay between transfer acceptance and arrival and patient outcomes in the cohort as a whole; but we found that nighttime transfer is associated with increased adjusted odds of both ICU transfer within 48 hours and 30-day mortality. Our analyses combining day-of-week and time-of-day demonstrate that Sunday night transfer is particularly associated with increased adjusted odds of 30-day mortality (as compared with Monday daytime transfer), and show a trend toward increased mortality with Friday night transfers. These detailed analyses otherwise reinforce that nighttime transfer across all nights of the week is associated with increased adjusted odds of ICU transfer within 48 hours. We also found that increased admitting team busyness on the day of patient transfer is associated with decreased odds of ICU transfer, though this may solely be reflective of higher turnover services (ie, cardiology) caring for lower acuity patients, as suggested by secondary analyses stratified by service. In addition, secondary analyses demonstrated differential associations between weekend transfers, nighttime transfers, and increased team busyness on the odds of 30-day mortality based on service of transfer. These analyses showed that patients transferred to higher acuity services requiring procedural care, including CT surgery, GI surgery, and Medical ICU, do worse under all three circumstances as compared with patients transferred to other services. Secondary analyses also demonstrated that increased time delay between patient acceptance and arrival is inversely associated with 30-day mortality among CT and GI surgery service patients, likely reflecting lower acuity patients (ie, less sick patients are less rapidly transferred).

There are several possible explanations for these findings. Patients transferred to surgical services at night may reflect a more urgent need for surgery and include a sicker cohort of patients, possibly explaining these findings. Alternatively, or in addition, both weekend and nighttime hospital admission expose patients to similar potential risks, ie, limited resources available during off-peak hours. Our findings could, therefore, reflect the possibility that patients transferred to higher acuity services in need of procedural care are most vulnerable to off-peak timing of transfer. Similar data looking at patients admitted through the emergency room (ER) find the strongest effect of off-peak admissions on patients in need of procedures, including GI hemorrhage,¹² atrial fibrillation¹³ and acute myocardial infarction (AMI),¹⁴ arguably because of the limited availability of necessary interventions. Patients undergoing IHT are a sicker cohort of patients than those admitted through the ER, and, therefore, may be even more vulnerable to these issues.^3,5 This is supported by our findings that Sunday night transfers (and trend toward Friday night transfers) are associated with greater mortality compared with Monday daytime transfers, when at-the-ready resources and/or specialty personnel may be less available (Sunday night), and delays until receipt of necessary procedures may be longer (Friday night). Though we did not observe similar results among cardiology service transfers, as may be expected based on existing literature,^13,14 this subset of patients includes more heterogeneous diagnoses, (ie, not solely those that require acute intervention) and exhibited a low level of acuity (low Elixhauser score and DRG-weight, data not shown).

Disclosures

Dr. Mueller and Dr. Schnipper have nothing to disclose. Ms. Fiskio has nothing to disclose. Dr. Schnipper is the recipient of grant funding from Mallinckrodt Pharmaceuticals to conduct an investigator-initiated study of predictors and impact of opioid-related adverse drug events.

The transfer of patients between acute care hospitals (interhospital transfer [IHT]) occurs regularly among patients with a variety of diagnoses, in theory, to gain access to unique specialty services and/or a higher level of care, among other reasons.^1,2

However, the practice of IHT is variable and nonstandardized,^3,4 and existing data largely suggests that transferred patients experience worse outcomes, including longer length of stay, higher hospitalization costs, longer ICU time, and greater mortality, even with rigorous adjustment for confounding by indication.^5,6 Though there are many possible reasons for these findings, existing literature suggests that there may be aspects of the transfer process itself which contribute to these outcomes.^2,6,7

Understanding which aspects of the transfer process contribute to poor patient outcomes is a key first step toward the development of targeted quality improvement initiatives to improve this process of care. In this study, we aim to examine the association between select characteristics of the transfer process, including the timing of transfer and workload of the admitting physician team, and clinical outcomes among patients undergoing IHT.

Data and Study Population

We performed a retrospective analysis of patients ≥age 18 years who transferred to Brigham and Women’s Hospital (BWH), a 777-bed tertiary care hospital, from another acute care hospital between January 2005, and September 2013. Dates of inclusion were purposefully chosen prior to BWH implementation of a new electronic health records system to avoid potential information bias. As at most academic medical centers, night coverage at BWH differs by service and includes a combination of long-call admitting teams and night float coverage. On weekends, many services are less well staffed, and some procedures may only be available if needed emergently. Some services have caps on the daily number of admissions or total patient census, but none have caps on the number of discharges per day. Patients were excluded from analysis if they left BWH against medical advice, were transferred from closely affiliated hospitals with shared personnel and electronic health records (Brigham and Women’s Faulkner Hospital, Dana Farber Cancer Institute), transferred from inpatient psychiatric or inpatient hospice facilities, or transferred to obstetrics or nursery services. Data were obtained from administrative sources and the research patient data repository (RPDR), a centralized clinical data repository that gathers data from various hospital legacy systems and stores them in one data warehouse.⁸ Our study was approved by the Partners Institutional Review Board (IRB) with a waiver of patient consent.

Transfer Process Characteristics

Predictors included select characteristics of the transfer process, including (1) Day of week of transfer, dichotomized into Friday through Sunday (“weekend”), versus Monday through Thursday (“weekday”);⁹ Friday was included with “weekend” given the suggestion of increased volume of transfers in advance of the weekend; (2) Time of arrival of the transferred patient, categorized into “daytime” (7 am-5 pm), “evening” (5 pm -10 pm), and “nighttime” (10 pm -7 am), with daytime as the reference group; (3) Admitting team “busyness” on day of patient transfer, defined as the total number of additional patient admissions and patient discharges performed by the admitting team on the calendar day of patient arrival, as has been used in prior research,¹⁰ and categorized into quartiles with lowest quartile as the reference group. Service-specific quartiles were calculated and used for stratified analyses (described below); and (4) “Time delay” between patient acceptance for transfer and patient arrival at BWH, categorized into 0-12 hours, 12-24 hours, 24-48 hours, and >48 hours, with 12-24 hours as the reference group (anticipating that time delay of 0-12 hours would be reflective of “sicker” patients in need of expedited transfer).

Outcomes

Outcomes included transfer to the intensive care unit (ICU) within 48 hours of arrival and 30-day mortality from date of index admission.^5,6

Patient Characteristics

Covariates for adjustment included: patient age, sex, race, Elixhauser comorbidity score,¹¹ Diagnosis-Related Group (DRG)-weight, insurance status, year of admission, number of preadmission medications, and service of admission.

Statistical Analyses

We used descriptive statistics to display baseline characteristics and performed a series of univariable and multivariable logistic regression models to obtain the adjusted odds of each transfer process characteristic on each outcome, adjusting for all covariates (proc logistic, SAS Statistical Software, Cary, North Carolina). For analyses of ICU transfer within 48 hours of arrival, all patients initially admitted to the ICU at time of transfer were excluded.

In the secondary analyses, we used a combined day-of-week and time-of-day variable (ie, Monday day, Monday evening, Monday night, Tuesday day, and so on, with Monday day as the reference group) to obtain a more detailed evaluation of timing of transfer on patient outcomes. We also performed stratified analyses to evaluate each transfer process characteristic on adjusted odds of 30-day mortality stratified by service of admission (ie, at the time of transfer to BWH), adjusting for all covariates. For all analyses, two-sided P values < .05 were considered significant.

Overall, 24,352 patients met our inclusion criteria and underwent IHT, of whom 2,174 (8.9%) died within 30 days. Of the 22,910 transferred patients originally admitted to a non-ICU service, 5,464 (23.8%) underwent ICU transfer within 48 hours of arrival. Cohort characteristics are shown in Table 1.

Multivariable regression analyses demonstrated no significant association between weekend (versus weekday) transfer or increased time delay between patient acceptance and arrival (>48 hours) and adjusted odds of ICU transfer within 48 hours or 30-day mortality. However, they did demonstrate that nighttime (versus daytime) transfer was associated with greater adjusted odds of both ICU transfer and 30-day mortality. Increased admitting team busyness was associated with lower adjusted odds of ICU transfer but was not significantly associated with adjusted odds of 30-day mortality (Table 2). As expected, decreased time delay between patient acceptance and arrival (0-12 hours) was associated with increased adjusted odds of both ICU transfer (adjusted OR 2.68; 95% CI 2.29, 3.15) and 30-day mortality (adjusted OR 1.25; 95% CI 1.03, 1.53) compared with 12-24 hours (results not shown). Time delay >48 hours was not associated with either outcome.

Regression analyses with the combined day/time variable demonstrated that compared with Monday daytime transfer, Sunday night transfer was significantly associated with increased adjusted odds of 30-day mortality, and Friday night transfer was associated with a trend toward increased 30-day mortality (adjusted OR [aOR] 1.88; 95% CI 1.25, 2.82, and aOR 1.43; 95% CI 0.99, 2.06, respectively). We also found that all nighttime transfers (ie, Monday through Sunday night) were associated with increased adjusted odds of ICU transfer within 48 hours (as compared with Monday daytime transfer). Other days/time analyses were not significant.

Univariable and multivariable analyses stratified by service were performed (Appendix). Multivariable stratified analyses demonstrated that weekend transfer, nighttime transfer, and increased admitting team busyness were associated with increased adjusted odds of 30-day mortality among cardiothoracic (CT) and gastrointestinal (GI) surgical service patients. Increased admitting team busyness was also associated with increased mortality among ICU service patients but was associated with decreased mortality among cardiology service patients. An increased time delay between patient acceptance and arrival was associated with decreased mortality among CT and GI surgical service patients (Figure; Appendix). Other adjusted stratified outcomes were not significant.

In this study of 24,352 patients undergoing IHT, we found no significant association between weekend transfer or increased time delay between transfer acceptance and arrival and patient outcomes in the cohort as a whole; but we found that nighttime transfer is associated with increased adjusted odds of both ICU transfer within 48 hours and 30-day mortality. Our analyses combining day-of-week and time-of-day demonstrate that Sunday night transfer is particularly associated with increased adjusted odds of 30-day mortality (as compared with Monday daytime transfer), and show a trend toward increased mortality with Friday night transfers. These detailed analyses otherwise reinforce that nighttime transfer across all nights of the week is associated with increased adjusted odds of ICU transfer within 48 hours. We also found that increased admitting team busyness on the day of patient transfer is associated with decreased odds of ICU transfer, though this may solely be reflective of higher turnover services (ie, cardiology) caring for lower acuity patients, as suggested by secondary analyses stratified by service. In addition, secondary analyses demonstrated differential associations between weekend transfers, nighttime transfers, and increased team busyness on the odds of 30-day mortality based on service of transfer. These analyses showed that patients transferred to higher acuity services requiring procedural care, including CT surgery, GI surgery, and Medical ICU, do worse under all three circumstances as compared with patients transferred to other services. Secondary analyses also demonstrated that increased time delay between patient acceptance and arrival is inversely associated with 30-day mortality among CT and GI surgery service patients, likely reflecting lower acuity patients (ie, less sick patients are less rapidly transferred).

There are several possible explanations for these findings. Patients transferred to surgical services at night may reflect a more urgent need for surgery and include a sicker cohort of patients, possibly explaining these findings. Alternatively, or in addition, both weekend and nighttime hospital admission expose patients to similar potential risks, ie, limited resources available during off-peak hours. Our findings could, therefore, reflect the possibility that patients transferred to higher acuity services in need of procedural care are most vulnerable to off-peak timing of transfer. Similar data looking at patients admitted through the emergency room (ER) find the strongest effect of off-peak admissions on patients in need of procedures, including GI hemorrhage,¹² atrial fibrillation¹³ and acute myocardial infarction (AMI),¹⁴ arguably because of the limited availability of necessary interventions. Patients undergoing IHT are a sicker cohort of patients than those admitted through the ER, and, therefore, may be even more vulnerable to these issues.^3,5 This is supported by our findings that Sunday night transfers (and trend toward Friday night transfers) are associated with greater mortality compared with Monday daytime transfers, when at-the-ready resources and/or specialty personnel may be less available (Sunday night), and delays until receipt of necessary procedures may be longer (Friday night). Though we did not observe similar results among cardiology service transfers, as may be expected based on existing literature,^13,14 this subset of patients includes more heterogeneous diagnoses, (ie, not solely those that require acute intervention) and exhibited a low level of acuity (low Elixhauser score and DRG-weight, data not shown).

Disclosures

Dr. Mueller and Dr. Schnipper have nothing to disclose. Ms. Fiskio has nothing to disclose. Dr. Schnipper is the recipient of grant funding from Mallinckrodt Pharmaceuticals to conduct an investigator-initiated study of predictors and impact of opioid-related adverse drug events.

Many studies have shown that improved control can be achieved for most children with asthma if inhaled medications are taken correctly and adequately.^1-3 Drug delivery studies have shown that bioavailability of medication with a pressurized metered-dose inhaler (MDI) improves from 34% to 83% with the addition of spacer devices. This difference is largely due to the decrease in oropharyngeal deposition,^1,4,5 and therefore, the use of a spacer with proper technique has been recommended in all pediatric patients.^1,6

Poor inhaler technique is common among children.^1,7 Previous studies of children with asthma have evaluated inhaler technique, primarily in the outpatient and community settings, and reported variable rates of error (from 45% to >90%).^8,9 No studies have evaluated children hospitalized with asthma. As these children represent a particularly high-risk group for morbidity and mortality,^10,11 the objectives of this study were to assess errors in inhaler technique in hospitalized asthmatic children and identify risk factors for improper use.

As part of a larger interventional study, we conducted a prospective cross-sectional study at a tertiary urban children’s hospital. We enrolled a convenience sample of children aged 2-16 years admitted to the inpatient ward with an asthma exacerbation Monday-Friday from 8 AM to 6 PM. Participants were required to have a diagnosis of asthma (an established diagnosis by their primary care provider or meets the National Heart, Lung, and Blood Institute [NHLBI] criteria¹), have a consenting adult available, and speak English. Patients were excluded if they had a codiagnosis of an additional respiratory disease (ie, pneumonia), cardiac disease, or sickle cell anemia. The Institutional Review Board approved this study.

We asked caregivers, or children >10 years old if they independently use their inhaler, to demonstrate their typical home inhaler technique using a spacer with mask (SM), spacer with mouthpiece (SMP), or no spacer (per their usual home practice). Inhaler technique was scored using a previously validated asthma checklist (Table 1).¹² Certain steps in the checklist were identified as critical: (Step 1) removing the cap, (Step 3) attaching to a spacer, (Step 7) taking six breaths (SM), and (Step 9) holding breath for five seconds (SMP). Caregivers only were also asked to complete questionnaires assessing their literacy (Brief Health Literacy Screen [BHLS]), confidence (Parent Asthma Management Self-Efficacy scale [PAMSE]), and any barriers to managing their child’s asthma (Barriers to Asthma Care). Demographic and medical history information was extracted from the medical chart.

Participants

From October 2016 to June 2017, 380 participants were assessed for participation; 215 were excluded for not having a parent available (59%), not speaking English (27%), not having an asthma diagnosis (ie, viral wheezing; 14%), and 52 (14%) declined to participate. Therefore, a total of 113 participants were enrolled, with demonstrations provided by 100 caregivers and 13 children. The mean age of the patients overall was 6.6 ± 3.4 years and over half (55%) of the participants had uncontrolled asthma (NHLBI criteria¹).

Errors in Inhaler Technique

The mean asthma checklist score was 6.7 (maximum score of 10 for SM and 12 for SMP). A third (35%) scored <7 on the asthma checklist and 42% of participants missed at least one critical step. Overall, children who missed a critical step were significantly older (7.8 [6.7-8.9] vs 5.8 [5.1-6.5] years; P = .002). More participants missed a critical step with the SMP than the SM (75% [51%-90%] vs 36% [27%-46%]; P = .003), and this was the most prominent factor for missing a critical step in the adjusted regression analysis (OR 6.95 [1.71-28.23], P = .007). The most commonly missed steps were breathing normally for 30 seconds for SM, and for SMP, it was breathing out fully and breathing away from the spacer (Table 1). Twenty participants (18%) did not use a spacer device; these patients were older than those who did use a spacer (mean age 8.5 [6.7-10.4] vs 6.2 [5.6-6.9] years; P = .005); however, no other significant differences were identified.

Demographic, Medical History, and Socioeconomic Characteristics

Overall, race, ethnicity, and insurance status did not vary significantly based on asthma checklist score ≥7 or missing a critical step. Patients in the SM group who had received inpatient asthma education during a previous admission, had a history of pediatric intensive care unit (PICU) admission, and had been prescribed a daily controller were less likely to miss a critical step (Table 2). Parental education level varied, with 33% having a high school degree or less, but was not associated with asthma checklist score or missing critical steps. Parental BHLS and parental confidence (PAMSE) were not significantly associated with inhaler proficiency. However, transportation-related barriers were more common in patients with checklist scores <7 and more missed critical steps (OR 1.62 [1.06-2.46]; P = .02).

Nearly half of the participants in this study missed at least one critical step in inhaler use. In addition, 18% did not use a spacer when demonstrating their inhaler technique. Despite robust studies demonstrating how asthma education can improve both asthma skills and clinical outcomes,¹³ our study demonstrates that a large gap remains in proper inhaler technique among asthmatic patients presenting for inpatient care. Specifically, in the mouthpiece group, steps related to breathing technique were the most commonly missed. Our results also show that inhaler technique errors were most prominent in the adolescent population, possibly coinciding with the process of transitioning to a mouthpiece and more independence in medication administration. Adolescents may be a high-impact population on which to focus inpatient asthma education. Additionally, we found that a previous PICU admission and previous inpatient asthma education were associated with missing fewer critical steps in inhaler technique. This finding is consistent with those of another study that evaluated inhaler technique in the emergency department and found that previous hospitalization for asthma was inversely related to improper inhaler use (RR 0.55, 95% CI 0.36-0.84).¹⁴ This supports that when provided, inpatient education can increase inhaler administration skills.

Previous studies conducted in the outpatient setting have demonstrated variable rates of inhaler skill, from 0% to approximately 89% of children performing all steps of inhalation correctly.⁸ This wide range may be related to variations in the number and definition of critical steps between the different studies. In our study, we highlighted removing the cap, attaching a spacer, and adequate breathing technique as critical steps, because failure to complete them would significantly reduce lung deposition of medication. While past studies did evaluate both MDIs and discuss the devices, our study is the first to report difference in problems with technique between SM and SMP. As asthma educational interventions are developed and/or implemented, it is important to stress that different steps in inhaler technique are being missed in those using a mask versus mouthpiece.

The limitations of this study include that it was at a single center with a primarily urban and English-speaking population; however, this study population reflects the racial diversity of pediatric asthma patients. Further studies may explore the reproducibility of these findings at multiple centers and with non-English-speaking families. This study included younger patients than in some previous publications investigating asthma; however, all patients met the criteria for asthma diagnosis and this age range is reflective of patients presenting for inpatient asthma care. Furthermore, because of our daytime research hours, 59% of patients were excluded because a primary caregiver was not available. It is possible that these families have decreased access to inpatient asthma educators as well and may be another target group for future studies. Finally, a large proportion of parents had a college education or greater in our sample. However, there was no association within our analysis between parental education level and inhaler proficiency.

The findings from this study indicate that continued efforts are needed to establish that inhaler technique is adequate for all families regardless of their educational status or socioeconomic background, especially for adolescents and in the setting of poor asthma control. Furthermore, our findings support that inhaler technique education may be beneficial in the inpatient setting and that acute care settings can provide a valuable “teachable moment.”^14,15

CONCLUSION

Errors in inhaler technique are prevalent in pediatric inpatients with asthma, primarily those using a mouthpiece device. Educational efforts in both inpatient and outpatient settings have the potential to improve drug delivery and therefore asthma control. Inpatient hospitalization may serve as a platform for further studies to investigate innovative educational interventions.

Acknowledgments

The authors thank Tina Carter for her assistance in the recruitment and data collection and Ashley Hull and Susannah Butters for training the study staff on the use of the asthma checklist.

Disclosures

Dr. Gupta receives research grant support from the National Institutes of Health and the United Healthcare Group. Dr. Gupta serves as a consultant for DBV Technology, Aimmune Therapeutics, Kaleo & BEFORE Brands. Dr. Gupta has received lecture fees/honorariums from the Allergy Asthma Network & the American College of Asthma, Allergy & Immunology. Dr. Press reports research support from the Chicago Center for Diabetes Translation Research Pilot and Feasibility Grant, the Bucksbaum Institute for Clinical Excellence Pilot Grant Program, the Academy of Distinguished Medical Educators, the Development of Novel Hospital-initiated Care Bundle in Adults Hospitalized for Acute Asthma: the 41st Multicenter Airway Research Collaboration (MARC-41) Study, UCM’s Innovation Grant Program, the University of Chicago-Chapin Hall Join Research Fund, the NIH/NHLBI Loan Repayment Program, 1 K23 HL118151 01, NIH NLBHI R03 (RFA-HL-18-025), the George and Carol Abramson Pilot Awards, the COPD Foundation Green Shoots Grant, the University of Chicago Women’s Board Grant, NIH NHLBI UG1 (RFA-HL-17-009), and the CTSA Pilot Award, outside the submitted work. These disclosures have been reported to Dr. Press’ institutional IRB board. Additionally, a management plan is on file that details how to address conflicts such as these which are sources of research support but do not directly support the work at hand. The remaining authors have no conflicts of interest relevant to the article to disclose.

Funding

This study was funded by internal grants from Ann and Robert H. Lurie Children’s Hospital of Chicago. Dr. Press was funded by a K23HL118151.

Many studies have shown that improved control can be achieved for most children with asthma if inhaled medications are taken correctly and adequately.^1-3 Drug delivery studies have shown that bioavailability of medication with a pressurized metered-dose inhaler (MDI) improves from 34% to 83% with the addition of spacer devices. This difference is largely due to the decrease in oropharyngeal deposition,^1,4,5 and therefore, the use of a spacer with proper technique has been recommended in all pediatric patients.^1,6

Poor inhaler technique is common among children.^1,7 Previous studies of children with asthma have evaluated inhaler technique, primarily in the outpatient and community settings, and reported variable rates of error (from 45% to >90%).^8,9 No studies have evaluated children hospitalized with asthma. As these children represent a particularly high-risk group for morbidity and mortality,^10,11 the objectives of this study were to assess errors in inhaler technique in hospitalized asthmatic children and identify risk factors for improper use.

As part of a larger interventional study, we conducted a prospective cross-sectional study at a tertiary urban children’s hospital. We enrolled a convenience sample of children aged 2-16 years admitted to the inpatient ward with an asthma exacerbation Monday-Friday from 8 AM to 6 PM. Participants were required to have a diagnosis of asthma (an established diagnosis by their primary care provider or meets the National Heart, Lung, and Blood Institute [NHLBI] criteria¹), have a consenting adult available, and speak English. Patients were excluded if they had a codiagnosis of an additional respiratory disease (ie, pneumonia), cardiac disease, or sickle cell anemia. The Institutional Review Board approved this study.

We asked caregivers, or children >10 years old if they independently use their inhaler, to demonstrate their typical home inhaler technique using a spacer with mask (SM), spacer with mouthpiece (SMP), or no spacer (per their usual home practice). Inhaler technique was scored using a previously validated asthma checklist (Table 1).¹² Certain steps in the checklist were identified as critical: (Step 1) removing the cap, (Step 3) attaching to a spacer, (Step 7) taking six breaths (SM), and (Step 9) holding breath for five seconds (SMP). Caregivers only were also asked to complete questionnaires assessing their literacy (Brief Health Literacy Screen [BHLS]), confidence (Parent Asthma Management Self-Efficacy scale [PAMSE]), and any barriers to managing their child’s asthma (Barriers to Asthma Care). Demographic and medical history information was extracted from the medical chart.

Participants

From October 2016 to June 2017, 380 participants were assessed for participation; 215 were excluded for not having a parent available (59%), not speaking English (27%), not having an asthma diagnosis (ie, viral wheezing; 14%), and 52 (14%) declined to participate. Therefore, a total of 113 participants were enrolled, with demonstrations provided by 100 caregivers and 13 children. The mean age of the patients overall was 6.6 ± 3.4 years and over half (55%) of the participants had uncontrolled asthma (NHLBI criteria¹).

Errors in Inhaler Technique

The mean asthma checklist score was 6.7 (maximum score of 10 for SM and 12 for SMP). A third (35%) scored <7 on the asthma checklist and 42% of participants missed at least one critical step. Overall, children who missed a critical step were significantly older (7.8 [6.7-8.9] vs 5.8 [5.1-6.5] years; P = .002). More participants missed a critical step with the SMP than the SM (75% [51%-90%] vs 36% [27%-46%]; P = .003), and this was the most prominent factor for missing a critical step in the adjusted regression analysis (OR 6.95 [1.71-28.23], P = .007). The most commonly missed steps were breathing normally for 30 seconds for SM, and for SMP, it was breathing out fully and breathing away from the spacer (Table 1). Twenty participants (18%) did not use a spacer device; these patients were older than those who did use a spacer (mean age 8.5 [6.7-10.4] vs 6.2 [5.6-6.9] years; P = .005); however, no other significant differences were identified.

Demographic, Medical History, and Socioeconomic Characteristics

Overall, race, ethnicity, and insurance status did not vary significantly based on asthma checklist score ≥7 or missing a critical step. Patients in the SM group who had received inpatient asthma education during a previous admission, had a history of pediatric intensive care unit (PICU) admission, and had been prescribed a daily controller were less likely to miss a critical step (Table 2). Parental education level varied, with 33% having a high school degree or less, but was not associated with asthma checklist score or missing critical steps. Parental BHLS and parental confidence (PAMSE) were not significantly associated with inhaler proficiency. However, transportation-related barriers were more common in patients with checklist scores <7 and more missed critical steps (OR 1.62 [1.06-2.46]; P = .02).

Nearly half of the participants in this study missed at least one critical step in inhaler use. In addition, 18% did not use a spacer when demonstrating their inhaler technique. Despite robust studies demonstrating how asthma education can improve both asthma skills and clinical outcomes,¹³ our study demonstrates that a large gap remains in proper inhaler technique among asthmatic patients presenting for inpatient care. Specifically, in the mouthpiece group, steps related to breathing technique were the most commonly missed. Our results also show that inhaler technique errors were most prominent in the adolescent population, possibly coinciding with the process of transitioning to a mouthpiece and more independence in medication administration. Adolescents may be a high-impact population on which to focus inpatient asthma education. Additionally, we found that a previous PICU admission and previous inpatient asthma education were associated with missing fewer critical steps in inhaler technique. This finding is consistent with those of another study that evaluated inhaler technique in the emergency department and found that previous hospitalization for asthma was inversely related to improper inhaler use (RR 0.55, 95% CI 0.36-0.84).¹⁴ This supports that when provided, inpatient education can increase inhaler administration skills.

Previous studies conducted in the outpatient setting have demonstrated variable rates of inhaler skill, from 0% to approximately 89% of children performing all steps of inhalation correctly.⁸ This wide range may be related to variations in the number and definition of critical steps between the different studies. In our study, we highlighted removing the cap, attaching a spacer, and adequate breathing technique as critical steps, because failure to complete them would significantly reduce lung deposition of medication. While past studies did evaluate both MDIs and discuss the devices, our study is the first to report difference in problems with technique between SM and SMP. As asthma educational interventions are developed and/or implemented, it is important to stress that different steps in inhaler technique are being missed in those using a mask versus mouthpiece.

The limitations of this study include that it was at a single center with a primarily urban and English-speaking population; however, this study population reflects the racial diversity of pediatric asthma patients. Further studies may explore the reproducibility of these findings at multiple centers and with non-English-speaking families. This study included younger patients than in some previous publications investigating asthma; however, all patients met the criteria for asthma diagnosis and this age range is reflective of patients presenting for inpatient asthma care. Furthermore, because of our daytime research hours, 59% of patients were excluded because a primary caregiver was not available. It is possible that these families have decreased access to inpatient asthma educators as well and may be another target group for future studies. Finally, a large proportion of parents had a college education or greater in our sample. However, there was no association within our analysis between parental education level and inhaler proficiency.

The findings from this study indicate that continued efforts are needed to establish that inhaler technique is adequate for all families regardless of their educational status or socioeconomic background, especially for adolescents and in the setting of poor asthma control. Furthermore, our findings support that inhaler technique education may be beneficial in the inpatient setting and that acute care settings can provide a valuable “teachable moment.”^14,15

CONCLUSION

Errors in inhaler technique are prevalent in pediatric inpatients with asthma, primarily those using a mouthpiece device. Educational efforts in both inpatient and outpatient settings have the potential to improve drug delivery and therefore asthma control. Inpatient hospitalization may serve as a platform for further studies to investigate innovative educational interventions.

Acknowledgments

The authors thank Tina Carter for her assistance in the recruitment and data collection and Ashley Hull and Susannah Butters for training the study staff on the use of the asthma checklist.

Disclosures

Dr. Gupta receives research grant support from the National Institutes of Health and the United Healthcare Group. Dr. Gupta serves as a consultant for DBV Technology, Aimmune Therapeutics, Kaleo & BEFORE Brands. Dr. Gupta has received lecture fees/honorariums from the Allergy Asthma Network & the American College of Asthma, Allergy & Immunology. Dr. Press reports research support from the Chicago Center for Diabetes Translation Research Pilot and Feasibility Grant, the Bucksbaum Institute for Clinical Excellence Pilot Grant Program, the Academy of Distinguished Medical Educators, the Development of Novel Hospital-initiated Care Bundle in Adults Hospitalized for Acute Asthma: the 41st Multicenter Airway Research Collaboration (MARC-41) Study, UCM’s Innovation Grant Program, the University of Chicago-Chapin Hall Join Research Fund, the NIH/NHLBI Loan Repayment Program, 1 K23 HL118151 01, NIH NLBHI R03 (RFA-HL-18-025), the George and Carol Abramson Pilot Awards, the COPD Foundation Green Shoots Grant, the University of Chicago Women’s Board Grant, NIH NHLBI UG1 (RFA-HL-17-009), and the CTSA Pilot Award, outside the submitted work. These disclosures have been reported to Dr. Press’ institutional IRB board. Additionally, a management plan is on file that details how to address conflicts such as these which are sources of research support but do not directly support the work at hand. The remaining authors have no conflicts of interest relevant to the article to disclose.

Funding

This study was funded by internal grants from Ann and Robert H. Lurie Children’s Hospital of Chicago. Dr. Press was funded by a K23HL118151.

Many studies have shown that improved control can be achieved for most children with asthma if inhaled medications are taken correctly and adequately.^1-3 Drug delivery studies have shown that bioavailability of medication with a pressurized metered-dose inhaler (MDI) improves from 34% to 83% with the addition of spacer devices. This difference is largely due to the decrease in oropharyngeal deposition,^1,4,5 and therefore, the use of a spacer with proper technique has been recommended in all pediatric patients.^1,6

Poor inhaler technique is common among children.^1,7 Previous studies of children with asthma have evaluated inhaler technique, primarily in the outpatient and community settings, and reported variable rates of error (from 45% to >90%).^8,9 No studies have evaluated children hospitalized with asthma. As these children represent a particularly high-risk group for morbidity and mortality,^10,11 the objectives of this study were to assess errors in inhaler technique in hospitalized asthmatic children and identify risk factors for improper use.

As part of a larger interventional study, we conducted a prospective cross-sectional study at a tertiary urban children’s hospital. We enrolled a convenience sample of children aged 2-16 years admitted to the inpatient ward with an asthma exacerbation Monday-Friday from 8 AM to 6 PM. Participants were required to have a diagnosis of asthma (an established diagnosis by their primary care provider or meets the National Heart, Lung, and Blood Institute [NHLBI] criteria¹), have a consenting adult available, and speak English. Patients were excluded if they had a codiagnosis of an additional respiratory disease (ie, pneumonia), cardiac disease, or sickle cell anemia. The Institutional Review Board approved this study.

We asked caregivers, or children >10 years old if they independently use their inhaler, to demonstrate their typical home inhaler technique using a spacer with mask (SM), spacer with mouthpiece (SMP), or no spacer (per their usual home practice). Inhaler technique was scored using a previously validated asthma checklist (Table 1).¹² Certain steps in the checklist were identified as critical: (Step 1) removing the cap, (Step 3) attaching to a spacer, (Step 7) taking six breaths (SM), and (Step 9) holding breath for five seconds (SMP). Caregivers only were also asked to complete questionnaires assessing their literacy (Brief Health Literacy Screen [BHLS]), confidence (Parent Asthma Management Self-Efficacy scale [PAMSE]), and any barriers to managing their child’s asthma (Barriers to Asthma Care). Demographic and medical history information was extracted from the medical chart.

Participants

From October 2016 to June 2017, 380 participants were assessed for participation; 215 were excluded for not having a parent available (59%), not speaking English (27%), not having an asthma diagnosis (ie, viral wheezing; 14%), and 52 (14%) declined to participate. Therefore, a total of 113 participants were enrolled, with demonstrations provided by 100 caregivers and 13 children. The mean age of the patients overall was 6.6 ± 3.4 years and over half (55%) of the participants had uncontrolled asthma (NHLBI criteria¹).

Errors in Inhaler Technique

The mean asthma checklist score was 6.7 (maximum score of 10 for SM and 12 for SMP). A third (35%) scored <7 on the asthma checklist and 42% of participants missed at least one critical step. Overall, children who missed a critical step were significantly older (7.8 [6.7-8.9] vs 5.8 [5.1-6.5] years; P = .002). More participants missed a critical step with the SMP than the SM (75% [51%-90%] vs 36% [27%-46%]; P = .003), and this was the most prominent factor for missing a critical step in the adjusted regression analysis (OR 6.95 [1.71-28.23], P = .007). The most commonly missed steps were breathing normally for 30 seconds for SM, and for SMP, it was breathing out fully and breathing away from the spacer (Table 1). Twenty participants (18%) did not use a spacer device; these patients were older than those who did use a spacer (mean age 8.5 [6.7-10.4] vs 6.2 [5.6-6.9] years; P = .005); however, no other significant differences were identified.

Demographic, Medical History, and Socioeconomic Characteristics

Overall, race, ethnicity, and insurance status did not vary significantly based on asthma checklist score ≥7 or missing a critical step. Patients in the SM group who had received inpatient asthma education during a previous admission, had a history of pediatric intensive care unit (PICU) admission, and had been prescribed a daily controller were less likely to miss a critical step (Table 2). Parental education level varied, with 33% having a high school degree or less, but was not associated with asthma checklist score or missing critical steps. Parental BHLS and parental confidence (PAMSE) were not significantly associated with inhaler proficiency. However, transportation-related barriers were more common in patients with checklist scores <7 and more missed critical steps (OR 1.62 [1.06-2.46]; P = .02).

Nearly half of the participants in this study missed at least one critical step in inhaler use. In addition, 18% did not use a spacer when demonstrating their inhaler technique. Despite robust studies demonstrating how asthma education can improve both asthma skills and clinical outcomes,¹³ our study demonstrates that a large gap remains in proper inhaler technique among asthmatic patients presenting for inpatient care. Specifically, in the mouthpiece group, steps related to breathing technique were the most commonly missed. Our results also show that inhaler technique errors were most prominent in the adolescent population, possibly coinciding with the process of transitioning to a mouthpiece and more independence in medication administration. Adolescents may be a high-impact population on which to focus inpatient asthma education. Additionally, we found that a previous PICU admission and previous inpatient asthma education were associated with missing fewer critical steps in inhaler technique. This finding is consistent with those of another study that evaluated inhaler technique in the emergency department and found that previous hospitalization for asthma was inversely related to improper inhaler use (RR 0.55, 95% CI 0.36-0.84).¹⁴ This supports that when provided, inpatient education can increase inhaler administration skills.

Previous studies conducted in the outpatient setting have demonstrated variable rates of inhaler skill, from 0% to approximately 89% of children performing all steps of inhalation correctly.⁸ This wide range may be related to variations in the number and definition of critical steps between the different studies. In our study, we highlighted removing the cap, attaching a spacer, and adequate breathing technique as critical steps, because failure to complete them would significantly reduce lung deposition of medication. While past studies did evaluate both MDIs and discuss the devices, our study is the first to report difference in problems with technique between SM and SMP. As asthma educational interventions are developed and/or implemented, it is important to stress that different steps in inhaler technique are being missed in those using a mask versus mouthpiece.

The limitations of this study include that it was at a single center with a primarily urban and English-speaking population; however, this study population reflects the racial diversity of pediatric asthma patients. Further studies may explore the reproducibility of these findings at multiple centers and with non-English-speaking families. This study included younger patients than in some previous publications investigating asthma; however, all patients met the criteria for asthma diagnosis and this age range is reflective of patients presenting for inpatient asthma care. Furthermore, because of our daytime research hours, 59% of patients were excluded because a primary caregiver was not available. It is possible that these families have decreased access to inpatient asthma educators as well and may be another target group for future studies. Finally, a large proportion of parents had a college education or greater in our sample. However, there was no association within our analysis between parental education level and inhaler proficiency.

The findings from this study indicate that continued efforts are needed to establish that inhaler technique is adequate for all families regardless of their educational status or socioeconomic background, especially for adolescents and in the setting of poor asthma control. Furthermore, our findings support that inhaler technique education may be beneficial in the inpatient setting and that acute care settings can provide a valuable “teachable moment.”^14,15

CONCLUSION

Errors in inhaler technique are prevalent in pediatric inpatients with asthma, primarily those using a mouthpiece device. Educational efforts in both inpatient and outpatient settings have the potential to improve drug delivery and therefore asthma control. Inpatient hospitalization may serve as a platform for further studies to investigate innovative educational interventions.

Acknowledgments

The authors thank Tina Carter for her assistance in the recruitment and data collection and Ashley Hull and Susannah Butters for training the study staff on the use of the asthma checklist.

Disclosures

Dr. Gupta receives research grant support from the National Institutes of Health and the United Healthcare Group. Dr. Gupta serves as a consultant for DBV Technology, Aimmune Therapeutics, Kaleo & BEFORE Brands. Dr. Gupta has received lecture fees/honorariums from the Allergy Asthma Network & the American College of Asthma, Allergy & Immunology. Dr. Press reports research support from the Chicago Center for Diabetes Translation Research Pilot and Feasibility Grant, the Bucksbaum Institute for Clinical Excellence Pilot Grant Program, the Academy of Distinguished Medical Educators, the Development of Novel Hospital-initiated Care Bundle in Adults Hospitalized for Acute Asthma: the 41st Multicenter Airway Research Collaboration (MARC-41) Study, UCM’s Innovation Grant Program, the University of Chicago-Chapin Hall Join Research Fund, the NIH/NHLBI Loan Repayment Program, 1 K23 HL118151 01, NIH NLBHI R03 (RFA-HL-18-025), the George and Carol Abramson Pilot Awards, the COPD Foundation Green Shoots Grant, the University of Chicago Women’s Board Grant, NIH NHLBI UG1 (RFA-HL-17-009), and the CTSA Pilot Award, outside the submitted work. These disclosures have been reported to Dr. Press’ institutional IRB board. Additionally, a management plan is on file that details how to address conflicts such as these which are sources of research support but do not directly support the work at hand. The remaining authors have no conflicts of interest relevant to the article to disclose.

Funding

This study was funded by internal grants from Ann and Robert H. Lurie Children’s Hospital of Chicago. Dr. Press was funded by a K23HL118151.

Postgraduate training for physician assistants (PAs) and nurse practitioners (NPs) is a rapidly evolving field. It has been estimated that the number of these advanced practice providers (APPs) almost doubled between 2000 and 2016 (from 15.3 to 28.2 per 100 physicians) and is expected to double again by 2030.¹ As APPs continue to become a progressively larger part of the healthcare workforce, medical organizations are seeking more comprehensive strategies to train and mentor them.² This has led to the development of formal postgraduate programs, often called APP fellowships.

Historically, postgraduate APP fellowships have functioned to help bridge the gap in clinical practice experience between physicians and APPs.³ This gap is evident in hours of clinical training. Whereas NPs are generally expected to complete 500-1,500 hours of clinical practice before graduating,⁴ and PAs are expected to complete 2,000 hours,⁵ most physicians will complete over 15,000 hours of clinical training by the end of residency.⁶ As increasing patient complexity continues to challenge the healthcare workforce,⁷ both the NP and the PA leadership have recommended increased training of graduates and outcome studies of formal postgraduate fellowships.^8,9 In 2007, there were over 60 of these programs in the United States,¹⁰ most of them offering training in surgical specialties.

First described in 2010 by the Mayo Clinic,¹¹ APP fellowships in hospital medicine are also being developed. These programs are built to improve the training of nonphysician hospitalists, who often work independently¹² and manage medically complex patients.¹³ However, little is known about the number or structure of these fellowships. The limited understanding of the current APP fellowship environment is partly due to the lack of an administrative body overseeing these programs.¹⁴ The Accreditation Review Commission on Education for the Physician Assistant (ARC-PA) pioneered a model in 2007 for postgraduate PA programs, but it has been held in abeyance since 2014.¹⁵ Both the American Nurses Credentialing Center and the National Nurse Practitioner Residency and Fellowship Training Consortium have fellowship accreditation review processes, but they are not specific to hospital medicine.¹⁶ The Society of Hospital Medicine (SHM) has several resources for the training of APPs;¹⁷ however, it neither reviews nor accredits fellowship programs. Without standards, guidelines, or active accrediting bodies, APP fellowships in hospital medicine are poorly understood and are of unknown efficacy. The purpose of this study was to identify and describe the active APP fellowships in hospital medicine.

This was a cross-sectional study of all APP adult and pediatric fellowships in hospital medicine, in the United States, that were identifiable through May 2018. Multiple methods were used to identify all active fellowships. First, all training programs offering a Hospital Medicine Fellowship in the ARC-PA and Association of Postgraduate PA Programs databases were noted. Second, questionnaires were given out at the NP/PA forum at the national SHM conference in 2018 to gather information on existing APP fellowships. Third, similar online requests to identify known programs were posted to the SHM web forum Hospital Medicine Exchange (HMX). Fourth, Internet searches were used to discover additional programs. Once those fellowships were identified, surveys were sent to their program directors (PDs). These surveys not only asked the PDs about their fellowship but also asked them to identify additional APP fellowships beyond those that we had captured. Once additional programs were identified, a second round of surveys was sent to their PDs. This was performed in an iterative fashion until no additional fellowships were discovered.

The survey tool was developed and validated internally in the AAMC Survey Development style¹⁸ and was influenced by prior validated surveys of postgraduate medical fellowships.^10,19-21 Each question was developed by a team that had expertise in survey design (Wright and Tackett), and two survey design team members were themselves PDs of APP fellowships in hospital medicine (Kisuule and Franco). The survey was revised iteratively by the team on the basis of meetings and pilot testing with PDs of other programs. All qualitative or descriptive questions had a free response option available to allow PDs to answer the survey accurately and exhaustively. The final version of the survey was approved by consensus of all authors. It consisted of 25 multiple choice questions which were created to gather information about the following key areas of APP hospital medicine fellowships: fellowship and learner characteristics, program rationales, curricula, and methods of fellow assessment.

A web-based survey format (Qualtrics) was used to distribute the questionnaire e-mail to the PDs. Follow up e-mail reminders were sent to all nonresponders to encourage full participation. Survey completion was voluntary; no financial incentives or gifts were offered. IRB approval was obtained at Johns Hopkins Bayview (IRB number 00181629). Descriptive statistics (proportions, means, and ranges as appropriate) were calculated for all variables. Stata 13 (StataCorp. 2013. Stata Statistical Software: Release 13. College Station, Texas. StataCorp LP) was used for data analysis.

In total, 11 fellowships were identified using our multimethod approach. We found four (36%) programs by utilizing existing online databases, two (18%) through the SHM questionnaire and HMX forum, three (27%) through internet searches, and the remaining two (18%) were referred to us by the other PDs who were surveyed. Of the programs surveyed, 10 were adult programs and one was a pediatric program. Surveys were sent to the PDs of the 11 fellowships, and all but one of them (10/11, 91%) responded. Respondent programs were given alphabetical designations A through J (Table). 

Fellowship and Individual Characteristics

Most programs have been in existence for five years or fewer. Eighty percent of the programs are about one year in duration; two outlier programs have fellowship lengths of six months and 18 months. The main hospital where training occurs has a mean of 496 beds (range 213 to 900). Ninety percent of the hospitals also have physician residency training programs. Sixty percent of programs enroll two to four fellows per year while 40% enroll five or more. The salary range paid by the programs is $55,000 to >$70,000, and half the programs pay more than $65,000.

The majority of fellows accepted into APP fellowships in hospital medicine are women. Eighty percent of fellows are 26-30 years old, and 90% of fellows have been out of NP or PA school for one year or less. Both NP and PA applicants are accepted in 80% of fellowships.

Program Rationales

All programs reported that training and retaining applicants is the main driver for developing their fellowship, and 50% of them offer financial incentives for retention upon successful completion of the program. Forty percent of PDs stated that there is an implicit or explicit understanding that successful completion of the fellowship would result in further employment. Over the last five years, 89% (range: 71%-100%) of graduates were asked to remain for a full-time position after program completion.

In addition to training and retention, building an interprofessional team (50%), managing patient volume (30%), and reducing overhead (20%) were also reported as rationales for program development. The majority of programs (80%) have fellows bill for clinical services, and five of those eight programs do so after their fellows become more clinically competent.

Curricula

Of the nine adult programs, 67% teach explicitly to SHM core competencies and 33% send their fellows to the SHM NP/PA Boot Camp. Thirty percent of fellowships partner formally with either a physician residency or a local PA program to develop educational content. Six of the nine programs with active physician residencies, including the pediatric fellowship, offer shared educational experiences for the residents and APPs.

There are notable differences in clinical rotations between the programs (Figure 1). No single rotation is universally required, although general hospital internal medicine is required in all adult fellowships. The majority (80%) of programs offer at least one elective. Six programs reported mandatory rotations outside the department of medicine, most commonly neurology or the stroke service (four programs). Only one program reported only general medicine rotations, with no subspecialty electives.

Methods of Fellow Assessment

Each program surveyed has a unique method of fellow assessment. Ninety percent of the programs use more than one method to assess their fellows. Faculty reviews are most commonly used and are conducted in all rotations in 80% of fellowships. Both self-assessment exercises and written examinations are used in some rotations by the majority of programs. Capstone projects are required infrequently (30%).

We found several commonalities between the fellowships surveyed. Many of the program characteristics, such as years in operation, salary, duration, and lack of accreditation, are quite similar. Most fellowships also have a similar rationale for building their programs and use resources from the SHM to inform their curricula. Fellows, on average, share several demographic characteristics, such as age, gender, and time out of schooling. Conversely, we found wide variability in clinical rotations, the general teaching structure, and methods of fellow evaluation.

There have been several publications detailing successful individual APP fellowships in medical subspecialties,²² psychiatry,²³ and surgical specialties,²⁴ all of which describe the benefits to the institution. One study found that physician hospitalists have a poor understanding of the training PAs undergo and would favor a standardized curriculum for PA hospitalists.²⁵ Another study compared all PA postgraduate training programs in emergency medicine;¹⁹ it also described a small number of relatively young programs with variable curricula and a need for standardization. Yet another paper¹⁰ surveyed postgraduate PA programs across all specialties; however, that study only captured two hospital medicine programs, and it was not focused on several key areas studied in this paper—such as the program rationale, curricular elements, and assessment.

It is noteworthy that every program surveyed was created with training and retention in mind, rather than other factors like decreasing overhead or managing patient volume. Training one’s own APPs so that they can learn on the job, come to understand expectations within a group, and witness the culture is extremely valuable. From a patient safety standpoint, it has been documented that physician hospitalists straight out of residency have a higher patient mortality compared with more experienced providers.²⁶ Given the findings that on a national level, the majority of hospitalist NPs and PAs practice autonomously or somewhat autonomously,¹² it is reasonable to assume that similar trends of more experienced providers delivering safer care would be expected for APPs, but this remains speculative. From a retention standpoint, it has been well described that high APP turnover is often due to decreased feelings of competence and confidence during their transition from trainees to medical providers.²⁷ APPs who have completed fellowships feel more confident and able to succeed in their field.²⁸ To this point, in one survey of hospitalist PAs, almost all reported that they would have been interested in completing a fellowship, even it meant a lower initial salary.²⁹Despite having the same general goals and using similar national resources, our study reveals that APP fellows are trained and assessed very differently between programs. This might represent an area of future growth in the field of hospitalist APP education. For physician learning, competency-based medical education (CBME) has emerged as a learner centric, outcomes-based model of teaching and assessment that emphasizes mastery of skills and progression through milestones.³⁰ Both the ACGME³¹ and the SHM³² have described core competencies that provide a framework within CBME for determining readiness for independent practice. While we were not surprised to find that each fellowship has its own unique method of determining readiness for practice, these findings suggest that graduates from different programs likely have very different skill sets and aptitude levels. In the future, an active accrediting body could offer guidance in defining hospitalist APP core competencies and help standardize education.

Several limitations to this study should be considered. While we used multiple strategies to locate as many fellowships as possible, it is unlikely that we successfully captured all existing programs, and new programs are being developed annually. We also relied on self-reported data from PDs. While we would expect PDs to provide accurate data, we could not externally validate their answers. Additionally, although our survey tool was reviewed extensively and validated internally, it was developed de novo for this study.

APP fellowships in hospital medicine have experienced marked growth since the first program was described in 2010. The majority of programs are 12 months long, operate in existing teaching centers, and are intended to further enhance the training and retention of newly graduated PAs and NPs. Despite their similarities, fellowships have striking variability in their methods of teaching and assessing their learners. Best practices have yet to be identified, and further study is required to determine how to standardize curricula across the board.

Acknowledgments

Disclosures

The authors report no conflicts of interest.

Funding

This project was supported by the Johns Hopkins School of Medicine Biostatistics, Epidemiology and Data Management (BEAD) Core. Dr. Wright is the Anne Gaines and G. Thomas Miller Professor of Medicine, which is supported through the Johns Hopkins’ Center for Innovative Medicine.

Postgraduate training for physician assistants (PAs) and nurse practitioners (NPs) is a rapidly evolving field. It has been estimated that the number of these advanced practice providers (APPs) almost doubled between 2000 and 2016 (from 15.3 to 28.2 per 100 physicians) and is expected to double again by 2030.¹ As APPs continue to become a progressively larger part of the healthcare workforce, medical organizations are seeking more comprehensive strategies to train and mentor them.² This has led to the development of formal postgraduate programs, often called APP fellowships.

Historically, postgraduate APP fellowships have functioned to help bridge the gap in clinical practice experience between physicians and APPs.³ This gap is evident in hours of clinical training. Whereas NPs are generally expected to complete 500-1,500 hours of clinical practice before graduating,⁴ and PAs are expected to complete 2,000 hours,⁵ most physicians will complete over 15,000 hours of clinical training by the end of residency.⁶ As increasing patient complexity continues to challenge the healthcare workforce,⁷ both the NP and the PA leadership have recommended increased training of graduates and outcome studies of formal postgraduate fellowships.^8,9 In 2007, there were over 60 of these programs in the United States,¹⁰ most of them offering training in surgical specialties.

First described in 2010 by the Mayo Clinic,¹¹ APP fellowships in hospital medicine are also being developed. These programs are built to improve the training of nonphysician hospitalists, who often work independently¹² and manage medically complex patients.¹³ However, little is known about the number or structure of these fellowships. The limited understanding of the current APP fellowship environment is partly due to the lack of an administrative body overseeing these programs.¹⁴ The Accreditation Review Commission on Education for the Physician Assistant (ARC-PA) pioneered a model in 2007 for postgraduate PA programs, but it has been held in abeyance since 2014.¹⁵ Both the American Nurses Credentialing Center and the National Nurse Practitioner Residency and Fellowship Training Consortium have fellowship accreditation review processes, but they are not specific to hospital medicine.¹⁶ The Society of Hospital Medicine (SHM) has several resources for the training of APPs;¹⁷ however, it neither reviews nor accredits fellowship programs. Without standards, guidelines, or active accrediting bodies, APP fellowships in hospital medicine are poorly understood and are of unknown efficacy. The purpose of this study was to identify and describe the active APP fellowships in hospital medicine.

This was a cross-sectional study of all APP adult and pediatric fellowships in hospital medicine, in the United States, that were identifiable through May 2018. Multiple methods were used to identify all active fellowships. First, all training programs offering a Hospital Medicine Fellowship in the ARC-PA and Association of Postgraduate PA Programs databases were noted. Second, questionnaires were given out at the NP/PA forum at the national SHM conference in 2018 to gather information on existing APP fellowships. Third, similar online requests to identify known programs were posted to the SHM web forum Hospital Medicine Exchange (HMX). Fourth, Internet searches were used to discover additional programs. Once those fellowships were identified, surveys were sent to their program directors (PDs). These surveys not only asked the PDs about their fellowship but also asked them to identify additional APP fellowships beyond those that we had captured. Once additional programs were identified, a second round of surveys was sent to their PDs. This was performed in an iterative fashion until no additional fellowships were discovered.

The survey tool was developed and validated internally in the AAMC Survey Development style¹⁸ and was influenced by prior validated surveys of postgraduate medical fellowships.^10,19-21 Each question was developed by a team that had expertise in survey design (Wright and Tackett), and two survey design team members were themselves PDs of APP fellowships in hospital medicine (Kisuule and Franco). The survey was revised iteratively by the team on the basis of meetings and pilot testing with PDs of other programs. All qualitative or descriptive questions had a free response option available to allow PDs to answer the survey accurately and exhaustively. The final version of the survey was approved by consensus of all authors. It consisted of 25 multiple choice questions which were created to gather information about the following key areas of APP hospital medicine fellowships: fellowship and learner characteristics, program rationales, curricula, and methods of fellow assessment.

A web-based survey format (Qualtrics) was used to distribute the questionnaire e-mail to the PDs. Follow up e-mail reminders were sent to all nonresponders to encourage full participation. Survey completion was voluntary; no financial incentives or gifts were offered. IRB approval was obtained at Johns Hopkins Bayview (IRB number 00181629). Descriptive statistics (proportions, means, and ranges as appropriate) were calculated for all variables. Stata 13 (StataCorp. 2013. Stata Statistical Software: Release 13. College Station, Texas. StataCorp LP) was used for data analysis.

In total, 11 fellowships were identified using our multimethod approach. We found four (36%) programs by utilizing existing online databases, two (18%) through the SHM questionnaire and HMX forum, three (27%) through internet searches, and the remaining two (18%) were referred to us by the other PDs who were surveyed. Of the programs surveyed, 10 were adult programs and one was a pediatric program. Surveys were sent to the PDs of the 11 fellowships, and all but one of them (10/11, 91%) responded. Respondent programs were given alphabetical designations A through J (Table). 

Fellowship and Individual Characteristics

Most programs have been in existence for five years or fewer. Eighty percent of the programs are about one year in duration; two outlier programs have fellowship lengths of six months and 18 months. The main hospital where training occurs has a mean of 496 beds (range 213 to 900). Ninety percent of the hospitals also have physician residency training programs. Sixty percent of programs enroll two to four fellows per year while 40% enroll five or more. The salary range paid by the programs is $55,000 to >$70,000, and half the programs pay more than $65,000.

The majority of fellows accepted into APP fellowships in hospital medicine are women. Eighty percent of fellows are 26-30 years old, and 90% of fellows have been out of NP or PA school for one year or less. Both NP and PA applicants are accepted in 80% of fellowships.

Program Rationales

All programs reported that training and retaining applicants is the main driver for developing their fellowship, and 50% of them offer financial incentives for retention upon successful completion of the program. Forty percent of PDs stated that there is an implicit or explicit understanding that successful completion of the fellowship would result in further employment. Over the last five years, 89% (range: 71%-100%) of graduates were asked to remain for a full-time position after program completion.

In addition to training and retention, building an interprofessional team (50%), managing patient volume (30%), and reducing overhead (20%) were also reported as rationales for program development. The majority of programs (80%) have fellows bill for clinical services, and five of those eight programs do so after their fellows become more clinically competent.

Curricula

Of the nine adult programs, 67% teach explicitly to SHM core competencies and 33% send their fellows to the SHM NP/PA Boot Camp. Thirty percent of fellowships partner formally with either a physician residency or a local PA program to develop educational content. Six of the nine programs with active physician residencies, including the pediatric fellowship, offer shared educational experiences for the residents and APPs.

There are notable differences in clinical rotations between the programs (Figure 1). No single rotation is universally required, although general hospital internal medicine is required in all adult fellowships. The majority (80%) of programs offer at least one elective. Six programs reported mandatory rotations outside the department of medicine, most commonly neurology or the stroke service (four programs). Only one program reported only general medicine rotations, with no subspecialty electives.

Methods of Fellow Assessment

Each program surveyed has a unique method of fellow assessment. Ninety percent of the programs use more than one method to assess their fellows. Faculty reviews are most commonly used and are conducted in all rotations in 80% of fellowships. Both self-assessment exercises and written examinations are used in some rotations by the majority of programs. Capstone projects are required infrequently (30%).

We found several commonalities between the fellowships surveyed. Many of the program characteristics, such as years in operation, salary, duration, and lack of accreditation, are quite similar. Most fellowships also have a similar rationale for building their programs and use resources from the SHM to inform their curricula. Fellows, on average, share several demographic characteristics, such as age, gender, and time out of schooling. Conversely, we found wide variability in clinical rotations, the general teaching structure, and methods of fellow evaluation.

There have been several publications detailing successful individual APP fellowships in medical subspecialties,²² psychiatry,²³ and surgical specialties,²⁴ all of which describe the benefits to the institution. One study found that physician hospitalists have a poor understanding of the training PAs undergo and would favor a standardized curriculum for PA hospitalists.²⁵ Another study compared all PA postgraduate training programs in emergency medicine;¹⁹ it also described a small number of relatively young programs with variable curricula and a need for standardization. Yet another paper¹⁰ surveyed postgraduate PA programs across all specialties; however, that study only captured two hospital medicine programs, and it was not focused on several key areas studied in this paper—such as the program rationale, curricular elements, and assessment.

It is noteworthy that every program surveyed was created with training and retention in mind, rather than other factors like decreasing overhead or managing patient volume. Training one’s own APPs so that they can learn on the job, come to understand expectations within a group, and witness the culture is extremely valuable. From a patient safety standpoint, it has been documented that physician hospitalists straight out of residency have a higher patient mortality compared with more experienced providers.²⁶ Given the findings that on a national level, the majority of hospitalist NPs and PAs practice autonomously or somewhat autonomously,¹² it is reasonable to assume that similar trends of more experienced providers delivering safer care would be expected for APPs, but this remains speculative. From a retention standpoint, it has been well described that high APP turnover is often due to decreased feelings of competence and confidence during their transition from trainees to medical providers.²⁷ APPs who have completed fellowships feel more confident and able to succeed in their field.²⁸ To this point, in one survey of hospitalist PAs, almost all reported that they would have been interested in completing a fellowship, even it meant a lower initial salary.²⁹Despite having the same general goals and using similar national resources, our study reveals that APP fellows are trained and assessed very differently between programs. This might represent an area of future growth in the field of hospitalist APP education. For physician learning, competency-based medical education (CBME) has emerged as a learner centric, outcomes-based model of teaching and assessment that emphasizes mastery of skills and progression through milestones.³⁰ Both the ACGME³¹ and the SHM³² have described core competencies that provide a framework within CBME for determining readiness for independent practice. While we were not surprised to find that each fellowship has its own unique method of determining readiness for practice, these findings suggest that graduates from different programs likely have very different skill sets and aptitude levels. In the future, an active accrediting body could offer guidance in defining hospitalist APP core competencies and help standardize education.

Several limitations to this study should be considered. While we used multiple strategies to locate as many fellowships as possible, it is unlikely that we successfully captured all existing programs, and new programs are being developed annually. We also relied on self-reported data from PDs. While we would expect PDs to provide accurate data, we could not externally validate their answers. Additionally, although our survey tool was reviewed extensively and validated internally, it was developed de novo for this study.

APP fellowships in hospital medicine have experienced marked growth since the first program was described in 2010. The majority of programs are 12 months long, operate in existing teaching centers, and are intended to further enhance the training and retention of newly graduated PAs and NPs. Despite their similarities, fellowships have striking variability in their methods of teaching and assessing their learners. Best practices have yet to be identified, and further study is required to determine how to standardize curricula across the board.

Acknowledgments

Disclosures

The authors report no conflicts of interest.

Funding

This project was supported by the Johns Hopkins School of Medicine Biostatistics, Epidemiology and Data Management (BEAD) Core. Dr. Wright is the Anne Gaines and G. Thomas Miller Professor of Medicine, which is supported through the Johns Hopkins’ Center for Innovative Medicine.

Postgraduate training for physician assistants (PAs) and nurse practitioners (NPs) is a rapidly evolving field. It has been estimated that the number of these advanced practice providers (APPs) almost doubled between 2000 and 2016 (from 15.3 to 28.2 per 100 physicians) and is expected to double again by 2030.¹ As APPs continue to become a progressively larger part of the healthcare workforce, medical organizations are seeking more comprehensive strategies to train and mentor them.² This has led to the development of formal postgraduate programs, often called APP fellowships.

Historically, postgraduate APP fellowships have functioned to help bridge the gap in clinical practice experience between physicians and APPs.³ This gap is evident in hours of clinical training. Whereas NPs are generally expected to complete 500-1,500 hours of clinical practice before graduating,⁴ and PAs are expected to complete 2,000 hours,⁵ most physicians will complete over 15,000 hours of clinical training by the end of residency.⁶ As increasing patient complexity continues to challenge the healthcare workforce,⁷ both the NP and the PA leadership have recommended increased training of graduates and outcome studies of formal postgraduate fellowships.^8,9 In 2007, there were over 60 of these programs in the United States,¹⁰ most of them offering training in surgical specialties.

First described in 2010 by the Mayo Clinic,¹¹ APP fellowships in hospital medicine are also being developed. These programs are built to improve the training of nonphysician hospitalists, who often work independently¹² and manage medically complex patients.¹³ However, little is known about the number or structure of these fellowships. The limited understanding of the current APP fellowship environment is partly due to the lack of an administrative body overseeing these programs.¹⁴ The Accreditation Review Commission on Education for the Physician Assistant (ARC-PA) pioneered a model in 2007 for postgraduate PA programs, but it has been held in abeyance since 2014.¹⁵ Both the American Nurses Credentialing Center and the National Nurse Practitioner Residency and Fellowship Training Consortium have fellowship accreditation review processes, but they are not specific to hospital medicine.¹⁶ The Society of Hospital Medicine (SHM) has several resources for the training of APPs;¹⁷ however, it neither reviews nor accredits fellowship programs. Without standards, guidelines, or active accrediting bodies, APP fellowships in hospital medicine are poorly understood and are of unknown efficacy. The purpose of this study was to identify and describe the active APP fellowships in hospital medicine.

This was a cross-sectional study of all APP adult and pediatric fellowships in hospital medicine, in the United States, that were identifiable through May 2018. Multiple methods were used to identify all active fellowships. First, all training programs offering a Hospital Medicine Fellowship in the ARC-PA and Association of Postgraduate PA Programs databases were noted. Second, questionnaires were given out at the NP/PA forum at the national SHM conference in 2018 to gather information on existing APP fellowships. Third, similar online requests to identify known programs were posted to the SHM web forum Hospital Medicine Exchange (HMX). Fourth, Internet searches were used to discover additional programs. Once those fellowships were identified, surveys were sent to their program directors (PDs). These surveys not only asked the PDs about their fellowship but also asked them to identify additional APP fellowships beyond those that we had captured. Once additional programs were identified, a second round of surveys was sent to their PDs. This was performed in an iterative fashion until no additional fellowships were discovered.

The survey tool was developed and validated internally in the AAMC Survey Development style¹⁸ and was influenced by prior validated surveys of postgraduate medical fellowships.^10,19-21 Each question was developed by a team that had expertise in survey design (Wright and Tackett), and two survey design team members were themselves PDs of APP fellowships in hospital medicine (Kisuule and Franco). The survey was revised iteratively by the team on the basis of meetings and pilot testing with PDs of other programs. All qualitative or descriptive questions had a free response option available to allow PDs to answer the survey accurately and exhaustively. The final version of the survey was approved by consensus of all authors. It consisted of 25 multiple choice questions which were created to gather information about the following key areas of APP hospital medicine fellowships: fellowship and learner characteristics, program rationales, curricula, and methods of fellow assessment.

A web-based survey format (Qualtrics) was used to distribute the questionnaire e-mail to the PDs. Follow up e-mail reminders were sent to all nonresponders to encourage full participation. Survey completion was voluntary; no financial incentives or gifts were offered. IRB approval was obtained at Johns Hopkins Bayview (IRB number 00181629). Descriptive statistics (proportions, means, and ranges as appropriate) were calculated for all variables. Stata 13 (StataCorp. 2013. Stata Statistical Software: Release 13. College Station, Texas. StataCorp LP) was used for data analysis.

In total, 11 fellowships were identified using our multimethod approach. We found four (36%) programs by utilizing existing online databases, two (18%) through the SHM questionnaire and HMX forum, three (27%) through internet searches, and the remaining two (18%) were referred to us by the other PDs who were surveyed. Of the programs surveyed, 10 were adult programs and one was a pediatric program. Surveys were sent to the PDs of the 11 fellowships, and all but one of them (10/11, 91%) responded. Respondent programs were given alphabetical designations A through J (Table). 

Fellowship and Individual Characteristics

Most programs have been in existence for five years or fewer. Eighty percent of the programs are about one year in duration; two outlier programs have fellowship lengths of six months and 18 months. The main hospital where training occurs has a mean of 496 beds (range 213 to 900). Ninety percent of the hospitals also have physician residency training programs. Sixty percent of programs enroll two to four fellows per year while 40% enroll five or more. The salary range paid by the programs is $55,000 to >$70,000, and half the programs pay more than $65,000.

The majority of fellows accepted into APP fellowships in hospital medicine are women. Eighty percent of fellows are 26-30 years old, and 90% of fellows have been out of NP or PA school for one year or less. Both NP and PA applicants are accepted in 80% of fellowships.

Program Rationales

All programs reported that training and retaining applicants is the main driver for developing their fellowship, and 50% of them offer financial incentives for retention upon successful completion of the program. Forty percent of PDs stated that there is an implicit or explicit understanding that successful completion of the fellowship would result in further employment. Over the last five years, 89% (range: 71%-100%) of graduates were asked to remain for a full-time position after program completion.

In addition to training and retention, building an interprofessional team (50%), managing patient volume (30%), and reducing overhead (20%) were also reported as rationales for program development. The majority of programs (80%) have fellows bill for clinical services, and five of those eight programs do so after their fellows become more clinically competent.

Curricula

Of the nine adult programs, 67% teach explicitly to SHM core competencies and 33% send their fellows to the SHM NP/PA Boot Camp. Thirty percent of fellowships partner formally with either a physician residency or a local PA program to develop educational content. Six of the nine programs with active physician residencies, including the pediatric fellowship, offer shared educational experiences for the residents and APPs.

There are notable differences in clinical rotations between the programs (Figure 1). No single rotation is universally required, although general hospital internal medicine is required in all adult fellowships. The majority (80%) of programs offer at least one elective. Six programs reported mandatory rotations outside the department of medicine, most commonly neurology or the stroke service (four programs). Only one program reported only general medicine rotations, with no subspecialty electives.

Methods of Fellow Assessment

Each program surveyed has a unique method of fellow assessment. Ninety percent of the programs use more than one method to assess their fellows. Faculty reviews are most commonly used and are conducted in all rotations in 80% of fellowships. Both self-assessment exercises and written examinations are used in some rotations by the majority of programs. Capstone projects are required infrequently (30%).

We found several commonalities between the fellowships surveyed. Many of the program characteristics, such as years in operation, salary, duration, and lack of accreditation, are quite similar. Most fellowships also have a similar rationale for building their programs and use resources from the SHM to inform their curricula. Fellows, on average, share several demographic characteristics, such as age, gender, and time out of schooling. Conversely, we found wide variability in clinical rotations, the general teaching structure, and methods of fellow evaluation.

There have been several publications detailing successful individual APP fellowships in medical subspecialties,²² psychiatry,²³ and surgical specialties,²⁴ all of which describe the benefits to the institution. One study found that physician hospitalists have a poor understanding of the training PAs undergo and would favor a standardized curriculum for PA hospitalists.²⁵ Another study compared all PA postgraduate training programs in emergency medicine;¹⁹ it also described a small number of relatively young programs with variable curricula and a need for standardization. Yet another paper¹⁰ surveyed postgraduate PA programs across all specialties; however, that study only captured two hospital medicine programs, and it was not focused on several key areas studied in this paper—such as the program rationale, curricular elements, and assessment.

It is noteworthy that every program surveyed was created with training and retention in mind, rather than other factors like decreasing overhead or managing patient volume. Training one’s own APPs so that they can learn on the job, come to understand expectations within a group, and witness the culture is extremely valuable. From a patient safety standpoint, it has been documented that physician hospitalists straight out of residency have a higher patient mortality compared with more experienced providers.²⁶ Given the findings that on a national level, the majority of hospitalist NPs and PAs practice autonomously or somewhat autonomously,¹² it is reasonable to assume that similar trends of more experienced providers delivering safer care would be expected for APPs, but this remains speculative. From a retention standpoint, it has been well described that high APP turnover is often due to decreased feelings of competence and confidence during their transition from trainees to medical providers.²⁷ APPs who have completed fellowships feel more confident and able to succeed in their field.²⁸ To this point, in one survey of hospitalist PAs, almost all reported that they would have been interested in completing a fellowship, even it meant a lower initial salary.²⁹Despite having the same general goals and using similar national resources, our study reveals that APP fellows are trained and assessed very differently between programs. This might represent an area of future growth in the field of hospitalist APP education. For physician learning, competency-based medical education (CBME) has emerged as a learner centric, outcomes-based model of teaching and assessment that emphasizes mastery of skills and progression through milestones.³⁰ Both the ACGME³¹ and the SHM³² have described core competencies that provide a framework within CBME for determining readiness for independent practice. While we were not surprised to find that each fellowship has its own unique method of determining readiness for practice, these findings suggest that graduates from different programs likely have very different skill sets and aptitude levels. In the future, an active accrediting body could offer guidance in defining hospitalist APP core competencies and help standardize education.

Several limitations to this study should be considered. While we used multiple strategies to locate as many fellowships as possible, it is unlikely that we successfully captured all existing programs, and new programs are being developed annually. We also relied on self-reported data from PDs. While we would expect PDs to provide accurate data, we could not externally validate their answers. Additionally, although our survey tool was reviewed extensively and validated internally, it was developed de novo for this study.

APP fellowships in hospital medicine have experienced marked growth since the first program was described in 2010. The majority of programs are 12 months long, operate in existing teaching centers, and are intended to further enhance the training and retention of newly graduated PAs and NPs. Despite their similarities, fellowships have striking variability in their methods of teaching and assessing their learners. Best practices have yet to be identified, and further study is required to determine how to standardize curricula across the board.

Acknowledgments

Disclosures

The authors report no conflicts of interest.

Funding

This project was supported by the Johns Hopkins School of Medicine Biostatistics, Epidemiology and Data Management (BEAD) Core. Dr. Wright is the Anne Gaines and G. Thomas Miller Professor of Medicine, which is supported through the Johns Hopkins’ Center for Innovative Medicine.