Medical comorbidity burden is significantly associated with 6-month and overall mortality in individuals with suspected drug-induced liver injury (DILI). In addition, a model consisting of Charlson Comorbidity Index, model for end-stage liver disease score, and serum albumin strongly predicts 6-month mortality in patients with suspected DILI.

Those are key findings from a study which set out to investigate the association between comorbidity burden and outcomes of patients with DILI and to develop a model to calculate risk of death within 6 months.

“Drug-induced liver injury is an important cause of liver-related morbidity and mortality that is likely under-recognized,” investigators led by Marwan S. Ghabril, MD, of the division of gastroenterology and hepatology at Indiana University, Indianapolis, wrote in a study published in Gastroenterology. “Its diagnosis depends on high index of suspicion, compatible temporal relationship, and thorough exclusion of competing etiologies. DILI by an implicated drug commonly occurs in patients with one or several comorbid conditions such as hypertension, diabetes mellitus, cardiovascular disease, renal disease, and malignancy. However, the impact of comorbidity burden on mortality in patients with suspected DILI has not been previously investigated.”

For the current analysis and model development, the researchers drew from 306 patients enrolled in the multicenter Drug-Induced Liver Injury Network Prospective Study at Indiana University between 2003 and 2017 (discovery cohort; Drug Saf. 2009;32:55-68). To validate their model, they used data from 247 patients who were enrolled in the same study at the University of North Carolina (validation cohort). The primary outcome of interest was mortality within 6 months of onset of liver injury.

The mean ages of the discovery and validation cohorts were 49 years and 51 years, respectively. Dr. Ghabril and colleagues found that 6-month mortality was 8.5% in the discovery cohort and 4.5% in the validation cohort. “The most common class of implicated agent was antimicrobials with no significant differences between groups,” they wrote. “However, herbal and dietary supplements were predominantly implicated in patients with none to mild comorbidity, while cardiovascular agents were predominantly implicated in patients with significant comorbidity.”

Among patients in the discovery cohort, the presence of significant comorbidities, defined as a Charlson Comorbidity Index score greater than 2, was independently associated with 6-month mortality (odds ratio, 5.22), as was model for end-stage liver disease score (OR, 1.11) and serum level of albumin at presentation (OR, 0.39). When the researchers created a morbidity risk model based on those three clinical variables, it performed well, identifying patients who died within 6 months with a C statistic value of 0.89 in the discovery cohort and 0.91 in the validation cohort. This spurred the development of a web-based risk calculator, which clinicians can access at http://gihep.com/calculators/hepatology/dili-cam/.

“Since DILI is not a unique cause of liver injury, it is conceivable that models incorporating comorbidity burden and severity of liver injury could prove useful in improving the prediction of mortality in a variety of liver injuries and diseases, and as such warrants further studies,” the researchers wrote.

The study was funded by grants from the National Institute of Diabetes and Digestive and Kidney Diseases. Dr. Ghabril reported having no financial disclosures, but two coauthors reported having numerous financial ties to industry.

[email protected]

SOURCE: Ghabril M et al. Gastroenterology. 2019 Jul 11. doi: 10/1053/j.gastro.2019.07.006.

Medical comorbidity burden is significantly associated with 6-month and overall mortality in individuals with suspected drug-induced liver injury (DILI). In addition, a model consisting of Charlson Comorbidity Index, model for end-stage liver disease score, and serum albumin strongly predicts 6-month mortality in patients with suspected DILI.

Those are key findings from a study which set out to investigate the association between comorbidity burden and outcomes of patients with DILI and to develop a model to calculate risk of death within 6 months.

“Drug-induced liver injury is an important cause of liver-related morbidity and mortality that is likely under-recognized,” investigators led by Marwan S. Ghabril, MD, of the division of gastroenterology and hepatology at Indiana University, Indianapolis, wrote in a study published in Gastroenterology. “Its diagnosis depends on high index of suspicion, compatible temporal relationship, and thorough exclusion of competing etiologies. DILI by an implicated drug commonly occurs in patients with one or several comorbid conditions such as hypertension, diabetes mellitus, cardiovascular disease, renal disease, and malignancy. However, the impact of comorbidity burden on mortality in patients with suspected DILI has not been previously investigated.”

For the current analysis and model development, the researchers drew from 306 patients enrolled in the multicenter Drug-Induced Liver Injury Network Prospective Study at Indiana University between 2003 and 2017 (discovery cohort; Drug Saf. 2009;32:55-68). To validate their model, they used data from 247 patients who were enrolled in the same study at the University of North Carolina (validation cohort). The primary outcome of interest was mortality within 6 months of onset of liver injury.

The mean ages of the discovery and validation cohorts were 49 years and 51 years, respectively. Dr. Ghabril and colleagues found that 6-month mortality was 8.5% in the discovery cohort and 4.5% in the validation cohort. “The most common class of implicated agent was antimicrobials with no significant differences between groups,” they wrote. “However, herbal and dietary supplements were predominantly implicated in patients with none to mild comorbidity, while cardiovascular agents were predominantly implicated in patients with significant comorbidity.”

Among patients in the discovery cohort, the presence of significant comorbidities, defined as a Charlson Comorbidity Index score greater than 2, was independently associated with 6-month mortality (odds ratio, 5.22), as was model for end-stage liver disease score (OR, 1.11) and serum level of albumin at presentation (OR, 0.39). When the researchers created a morbidity risk model based on those three clinical variables, it performed well, identifying patients who died within 6 months with a C statistic value of 0.89 in the discovery cohort and 0.91 in the validation cohort. This spurred the development of a web-based risk calculator, which clinicians can access at http://gihep.com/calculators/hepatology/dili-cam/.

“Since DILI is not a unique cause of liver injury, it is conceivable that models incorporating comorbidity burden and severity of liver injury could prove useful in improving the prediction of mortality in a variety of liver injuries and diseases, and as such warrants further studies,” the researchers wrote.

The study was funded by grants from the National Institute of Diabetes and Digestive and Kidney Diseases. Dr. Ghabril reported having no financial disclosures, but two coauthors reported having numerous financial ties to industry.

[email protected]

SOURCE: Ghabril M et al. Gastroenterology. 2019 Jul 11. doi: 10/1053/j.gastro.2019.07.006.

Medical comorbidity burden is significantly associated with 6-month and overall mortality in individuals with suspected drug-induced liver injury (DILI). In addition, a model consisting of Charlson Comorbidity Index, model for end-stage liver disease score, and serum albumin strongly predicts 6-month mortality in patients with suspected DILI.

Those are key findings from a study which set out to investigate the association between comorbidity burden and outcomes of patients with DILI and to develop a model to calculate risk of death within 6 months.

“Drug-induced liver injury is an important cause of liver-related morbidity and mortality that is likely under-recognized,” investigators led by Marwan S. Ghabril, MD, of the division of gastroenterology and hepatology at Indiana University, Indianapolis, wrote in a study published in Gastroenterology. “Its diagnosis depends on high index of suspicion, compatible temporal relationship, and thorough exclusion of competing etiologies. DILI by an implicated drug commonly occurs in patients with one or several comorbid conditions such as hypertension, diabetes mellitus, cardiovascular disease, renal disease, and malignancy. However, the impact of comorbidity burden on mortality in patients with suspected DILI has not been previously investigated.”

For the current analysis and model development, the researchers drew from 306 patients enrolled in the multicenter Drug-Induced Liver Injury Network Prospective Study at Indiana University between 2003 and 2017 (discovery cohort; Drug Saf. 2009;32:55-68). To validate their model, they used data from 247 patients who were enrolled in the same study at the University of North Carolina (validation cohort). The primary outcome of interest was mortality within 6 months of onset of liver injury.

The mean ages of the discovery and validation cohorts were 49 years and 51 years, respectively. Dr. Ghabril and colleagues found that 6-month mortality was 8.5% in the discovery cohort and 4.5% in the validation cohort. “The most common class of implicated agent was antimicrobials with no significant differences between groups,” they wrote. “However, herbal and dietary supplements were predominantly implicated in patients with none to mild comorbidity, while cardiovascular agents were predominantly implicated in patients with significant comorbidity.”

Among patients in the discovery cohort, the presence of significant comorbidities, defined as a Charlson Comorbidity Index score greater than 2, was independently associated with 6-month mortality (odds ratio, 5.22), as was model for end-stage liver disease score (OR, 1.11) and serum level of albumin at presentation (OR, 0.39). When the researchers created a morbidity risk model based on those three clinical variables, it performed well, identifying patients who died within 6 months with a C statistic value of 0.89 in the discovery cohort and 0.91 in the validation cohort. This spurred the development of a web-based risk calculator, which clinicians can access at http://gihep.com/calculators/hepatology/dili-cam/.

“Since DILI is not a unique cause of liver injury, it is conceivable that models incorporating comorbidity burden and severity of liver injury could prove useful in improving the prediction of mortality in a variety of liver injuries and diseases, and as such warrants further studies,” the researchers wrote.

The study was funded by grants from the National Institute of Diabetes and Digestive and Kidney Diseases. Dr. Ghabril reported having no financial disclosures, but two coauthors reported having numerous financial ties to industry.

[email protected]

SOURCE: Ghabril M et al. Gastroenterology. 2019 Jul 11. doi: 10/1053/j.gastro.2019.07.006.

In 2017, the Australian state of New South Wales experienced an outbreak of rotavirus gastroenteritis in children despite a high level of rotavirus immunization. In a new study, researchers reported evidence that suggests a decline in vaccine effectiveness (VE) isn’t the cause, although they found that VE declines over time as children age.

CDC/Dr. Erskine Palmer

“More analysis is required to investigate how novel or unusual strains ... interact with rotavirus vaccines and whether antigenic changes affect VE and challenge vaccination programs,” the study authors wrote in Pediatrics.

Researchers led by Julia E. Maguire, BSc, MSci(Epi), of Australia’s National Center for Immunization Research and the Australian National University, Canberra, launched the analysis in the wake of a 2017 outbreak of 2,319 rotavirus cases in New South Wales, a 210% increase over the rate in 2016. (The state, the largest in Australia, has about 7.5 million residents.)

The study authors tracked VE from 2010 to 2017 by analyzing 9,517 rotavirus cases in the state (50% male; median age, 5 years). Half weren’t eligible for rotavirus immunization because of their age; of the rest, 31% weren’t vaccinated.

Ms. Maguire and associates found that VE did not decline in 2017 and doesn’t seem to be responsible for the Australian rotavirus outbreak. “In our study, two doses of RV1 [the Rotarix vaccine] was 73.7% effective in protecting children aged 6 months to 9 years against laboratory-confirmed rotavirus over our 8-year study period. Somewhat surprisingly in the 2017 outbreak year, a high two-dose VE of 88.4% in those aged 6-11 months was also observed.”

They added that “the median age of rotavirus cases has increased in Australia over the last 8 years from 3.9 years in 2010 to 7.1 years in 2017. Adults and older children born before the availability of vaccination in Australia are unimmunized and may have been less likely to have repeated subclinical infections because of reductions in virus circulation overall, resulting in less immune boosting.”

Going forward, the study authors wrote that “investigation of population-level VE in relation to rotavirus genotype data should continue in a range of settings to improve our understanding of rotavirus vaccines and the impact they have on disease across the age spectrum over time.”

In an accompanying commentary, Benjamin Lee, MD, and E. Ross Colgate, PhD, of the University of Vermont, Burlington, wrote that Australia’s adoption of rotavirus immunization in 2017 “with state-level implementation of either Rotarix or RotaTeq ... enabled a fascinating natural experiment of VE and strain selection.”

Pressure from vaccines “potentially enables the emergence of novel strains,” they wrote. “Despite this, large-scale strain replacement has not been demonstrated in rotaviruses, in contrast to the development of pneumococcal serotype replacement that was seen after pneumococcal conjugate vaccine introduction. Similarly, there has been no evidence of widespread vaccine escape due to antigenic drift or shift, as occurs with another important segmented RNA virus, influenza A.”

As Dr. Lee and Dr. Colgate noted, 100 million children worldwide remain unvaccinated against rotavirus, and more than 128,000 die because of rotavirus-associated gastroenteritis each year. “Improving vaccine access and coverage and solving the riddle of [oral rotavirus vaccine] underperformance in low-income countries are urgent priorities, which may ultimately require next-generation oral and/or parenteral vaccines, a number of which are under development and in clinical trials. In addition, because the emergence of novel strains of disease-causing pathogens is always a possibility, vigilance in rotavirus surveillance, including genotype assessment, should remain a priority for public health programs.”

The study was funded by Australia’s National Center for Immunization Research and Surveillance, which receives government funding. The Australian Rotavirus Surveillance Program is supported by government funding and the vaccine companies Commonwealth Serum Laboratories and GlaxoSmithKline. Ms. Maguire is supported by an Australian Government Research Training Program Scholarship. One author is director of the Australian Rotavirus Surveillance Program, which received funding as above. The other study authors and the commentary authors reported no relevant financial disclosures.

SOURCES: Maguire JE et al. Pediatrics. 2019 Sep 17. doi: 10.1542/peds.2019-1024; Lee B, Colgate ER. Pediatrics. 2019 Sep 17. doi: 10.1542/peds.2019-2426.

In 2017, the Australian state of New South Wales experienced an outbreak of rotavirus gastroenteritis in children despite a high level of rotavirus immunization. In a new study, researchers reported evidence that suggests a decline in vaccine effectiveness (VE) isn’t the cause, although they found that VE declines over time as children age.

CDC/Dr. Erskine Palmer

“More analysis is required to investigate how novel or unusual strains ... interact with rotavirus vaccines and whether antigenic changes affect VE and challenge vaccination programs,” the study authors wrote in Pediatrics.

Researchers led by Julia E. Maguire, BSc, MSci(Epi), of Australia’s National Center for Immunization Research and the Australian National University, Canberra, launched the analysis in the wake of a 2017 outbreak of 2,319 rotavirus cases in New South Wales, a 210% increase over the rate in 2016. (The state, the largest in Australia, has about 7.5 million residents.)

The study authors tracked VE from 2010 to 2017 by analyzing 9,517 rotavirus cases in the state (50% male; median age, 5 years). Half weren’t eligible for rotavirus immunization because of their age; of the rest, 31% weren’t vaccinated.

Ms. Maguire and associates found that VE did not decline in 2017 and doesn’t seem to be responsible for the Australian rotavirus outbreak. “In our study, two doses of RV1 [the Rotarix vaccine] was 73.7% effective in protecting children aged 6 months to 9 years against laboratory-confirmed rotavirus over our 8-year study period. Somewhat surprisingly in the 2017 outbreak year, a high two-dose VE of 88.4% in those aged 6-11 months was also observed.”

They added that “the median age of rotavirus cases has increased in Australia over the last 8 years from 3.9 years in 2010 to 7.1 years in 2017. Adults and older children born before the availability of vaccination in Australia are unimmunized and may have been less likely to have repeated subclinical infections because of reductions in virus circulation overall, resulting in less immune boosting.”

Going forward, the study authors wrote that “investigation of population-level VE in relation to rotavirus genotype data should continue in a range of settings to improve our understanding of rotavirus vaccines and the impact they have on disease across the age spectrum over time.”

In an accompanying commentary, Benjamin Lee, MD, and E. Ross Colgate, PhD, of the University of Vermont, Burlington, wrote that Australia’s adoption of rotavirus immunization in 2017 “with state-level implementation of either Rotarix or RotaTeq ... enabled a fascinating natural experiment of VE and strain selection.”

Pressure from vaccines “potentially enables the emergence of novel strains,” they wrote. “Despite this, large-scale strain replacement has not been demonstrated in rotaviruses, in contrast to the development of pneumococcal serotype replacement that was seen after pneumococcal conjugate vaccine introduction. Similarly, there has been no evidence of widespread vaccine escape due to antigenic drift or shift, as occurs with another important segmented RNA virus, influenza A.”

As Dr. Lee and Dr. Colgate noted, 100 million children worldwide remain unvaccinated against rotavirus, and more than 128,000 die because of rotavirus-associated gastroenteritis each year. “Improving vaccine access and coverage and solving the riddle of [oral rotavirus vaccine] underperformance in low-income countries are urgent priorities, which may ultimately require next-generation oral and/or parenteral vaccines, a number of which are under development and in clinical trials. In addition, because the emergence of novel strains of disease-causing pathogens is always a possibility, vigilance in rotavirus surveillance, including genotype assessment, should remain a priority for public health programs.”

The study was funded by Australia’s National Center for Immunization Research and Surveillance, which receives government funding. The Australian Rotavirus Surveillance Program is supported by government funding and the vaccine companies Commonwealth Serum Laboratories and GlaxoSmithKline. Ms. Maguire is supported by an Australian Government Research Training Program Scholarship. One author is director of the Australian Rotavirus Surveillance Program, which received funding as above. The other study authors and the commentary authors reported no relevant financial disclosures.

SOURCES: Maguire JE et al. Pediatrics. 2019 Sep 17. doi: 10.1542/peds.2019-1024; Lee B, Colgate ER. Pediatrics. 2019 Sep 17. doi: 10.1542/peds.2019-2426.

In 2017, the Australian state of New South Wales experienced an outbreak of rotavirus gastroenteritis in children despite a high level of rotavirus immunization. In a new study, researchers reported evidence that suggests a decline in vaccine effectiveness (VE) isn’t the cause, although they found that VE declines over time as children age.

CDC/Dr. Erskine Palmer

“More analysis is required to investigate how novel or unusual strains ... interact with rotavirus vaccines and whether antigenic changes affect VE and challenge vaccination programs,” the study authors wrote in Pediatrics.

Researchers led by Julia E. Maguire, BSc, MSci(Epi), of Australia’s National Center for Immunization Research and the Australian National University, Canberra, launched the analysis in the wake of a 2017 outbreak of 2,319 rotavirus cases in New South Wales, a 210% increase over the rate in 2016. (The state, the largest in Australia, has about 7.5 million residents.)

The study authors tracked VE from 2010 to 2017 by analyzing 9,517 rotavirus cases in the state (50% male; median age, 5 years). Half weren’t eligible for rotavirus immunization because of their age; of the rest, 31% weren’t vaccinated.

Ms. Maguire and associates found that VE did not decline in 2017 and doesn’t seem to be responsible for the Australian rotavirus outbreak. “In our study, two doses of RV1 [the Rotarix vaccine] was 73.7% effective in protecting children aged 6 months to 9 years against laboratory-confirmed rotavirus over our 8-year study period. Somewhat surprisingly in the 2017 outbreak year, a high two-dose VE of 88.4% in those aged 6-11 months was also observed.”

They added that “the median age of rotavirus cases has increased in Australia over the last 8 years from 3.9 years in 2010 to 7.1 years in 2017. Adults and older children born before the availability of vaccination in Australia are unimmunized and may have been less likely to have repeated subclinical infections because of reductions in virus circulation overall, resulting in less immune boosting.”

Going forward, the study authors wrote that “investigation of population-level VE in relation to rotavirus genotype data should continue in a range of settings to improve our understanding of rotavirus vaccines and the impact they have on disease across the age spectrum over time.”

In an accompanying commentary, Benjamin Lee, MD, and E. Ross Colgate, PhD, of the University of Vermont, Burlington, wrote that Australia’s adoption of rotavirus immunization in 2017 “with state-level implementation of either Rotarix or RotaTeq ... enabled a fascinating natural experiment of VE and strain selection.”

Pressure from vaccines “potentially enables the emergence of novel strains,” they wrote. “Despite this, large-scale strain replacement has not been demonstrated in rotaviruses, in contrast to the development of pneumococcal serotype replacement that was seen after pneumococcal conjugate vaccine introduction. Similarly, there has been no evidence of widespread vaccine escape due to antigenic drift or shift, as occurs with another important segmented RNA virus, influenza A.”

As Dr. Lee and Dr. Colgate noted, 100 million children worldwide remain unvaccinated against rotavirus, and more than 128,000 die because of rotavirus-associated gastroenteritis each year. “Improving vaccine access and coverage and solving the riddle of [oral rotavirus vaccine] underperformance in low-income countries are urgent priorities, which may ultimately require next-generation oral and/or parenteral vaccines, a number of which are under development and in clinical trials. In addition, because the emergence of novel strains of disease-causing pathogens is always a possibility, vigilance in rotavirus surveillance, including genotype assessment, should remain a priority for public health programs.”

The study was funded by Australia’s National Center for Immunization Research and Surveillance, which receives government funding. The Australian Rotavirus Surveillance Program is supported by government funding and the vaccine companies Commonwealth Serum Laboratories and GlaxoSmithKline. Ms. Maguire is supported by an Australian Government Research Training Program Scholarship. One author is director of the Australian Rotavirus Surveillance Program, which received funding as above. The other study authors and the commentary authors reported no relevant financial disclosures.

SOURCES: Maguire JE et al. Pediatrics. 2019 Sep 17. doi: 10.1542/peds.2019-1024; Lee B, Colgate ER. Pediatrics. 2019 Sep 17. doi: 10.1542/peds.2019-2426.

SAN DIEGO – Using laser and light sources to treat nonaggressive basal cell carcinoma (BCC) is emerging as a promising treatment option, especially for those with multiple tumors and those who are poor surgical candidates, Arisa E. Ortiz, MD, said at the annual Masters of Aesthetics Symposium.

“Topical therapies often result in recurrence, so there really is a need for an alternative [to surgery] that’s effective, efficient, and carries a low risk of side effects,” said Dr. Ortiz, who is director of laser and cosmetic dermatology at the University of California, San Diego,

“The prototypic feature of BCC is the presence of telangiectatic vessels,” she explained, and the postulated mechanism of action is selective photothermolysis of the tumor vasculature. “These vessels are slightly larger in caliber, compared with normal skin – 40 micrometers versus 15 micrometers – and more fragile. You can tailor your pulse duration to the size of the vessels. Theoretically, by targeting the vasculature then you get tumor regression with sparing of normal tissue.”

Initial studies of this approach have used the 595-nm pulsed-dye laser, which is well absorbed by oxyhemoglobin, but more recent studies have used the 1064-nm Nd:YAG to reach deep arterial vessels. In a prospective, open-label study, 10 patients with 13 BCCs less than 1.5 cm in diameter received one treatment with a 10-ms pulsed 1064-nm Nd:YAG laser delivered on the trunk or extremities at a fluence of 80-120 J/cm² (Lasers Surg Med. 2015;47[2]:106-10). Dr. Ortiz and her colleagues observed a 92% clearance rate overall.

She described other earlier studies of the approach as flawed, because they relied on confirmation of clearance rates with clinical exam or biopsy rather than with surgical excision. “Also, some of the protocols weren’t standardized, multiple treatments were required, and subjects with suboptimal response were currently on anticoagulation,” she said. “Intravascular coagulation is important for effective treatment with vascular lasers, so anticoagulation may interfere with efficacy.”

In a more recent multicenter study, Dr. Ortiz and her colleagues treated 33 BCCs once with the long-pulsed 1064-nm Nd:YAG laser delivered with a 5-6 mm spot size at a fluence of 125-140 J/cm² and a 7-10 ms pulse duration (Laser Surgery Med. Feb 13 2018. doi: 10.1002/lsm.22803). Standard surgical excision with 5-mm margins was performed 4 weeks after laser treatment. Among 31 subjects who completed the study, 28 of 31 BCC tumors (90%) cleared after one treatment.

“The treatments were performed without anesthesia, because we didn’t want the vasculature to be affected, but in clinical practice I am now using lidocaine with no epinephrine,” Dr. Ortiz said. She characterized the results as “at least comparable to, if not superior to” common modalities including methyl aminolevulinate–PDT (72.8%), imiquimod cream (83.4%), and fluorouracil cream (80.1%). “One criticism I hear is that with such high fluences, you’re probably getting some bulk heating,” she said. “Maybe so, but it seems to work and there’s no scarring, which suggests otherwise.”

Advantages of using a 1064-nm Nd:YAG for treating nonaggressive BCCs are that it requires just one treatment, it takes about 5 minutes, and there is no significant downtime, with no limitations in posttreatment activity. “Potentially there is a relatively decreased risk for complications, including infection and bleeding,” she added. “It’s a good alternative for treating patients with multiple tumors or those who are poor surgical candidates.”

She and her colleagues are currently performing a long-term follow-up study of 35 BCC lesions. Only one has potentially recurred, but that recurrence has not yet been confirmed.

Dr. Ortiz treats BCCs with a standard 5-mm margin and uses lidocaine without epinephrine to avoid vasoconstriction. She typically uses a 1064-nm Cutera excel V laser delivered at a pulse duration of 8 ms and a fluence of 140 J/cm^2, with no cooling. “Theoretically, any 1064-nm pulsed-dye laser could work, but the way the pulse is delivered is different, depending on which device” is used, she said.

“I always like waiting between passes to avoid any bulk heating. The immediate endpoint to strive for is slight graying and slight contraction,” she said. Billing codes for malignant destruction/electrodesiccation and curettage can be used (codes 17260-17266 for the trunk and 17280-17283 for the face).

In order to determine the mechanism of cell death and to optimize results, Dr. Ortiz said that further studies need to be conducted in vitro and in vivo. In order to determine treatment efficacy, clinical studies involving various heat sources and low concentrations of lidocaine are also required.

Dr. Ortiz disclosed having financial relationships with numerous pharmaceutical and device companies. She is also cochair of the MOAS.

[email protected]

SAN DIEGO – Using laser and light sources to treat nonaggressive basal cell carcinoma (BCC) is emerging as a promising treatment option, especially for those with multiple tumors and those who are poor surgical candidates, Arisa E. Ortiz, MD, said at the annual Masters of Aesthetics Symposium.

“Topical therapies often result in recurrence, so there really is a need for an alternative [to surgery] that’s effective, efficient, and carries a low risk of side effects,” said Dr. Ortiz, who is director of laser and cosmetic dermatology at the University of California, San Diego,

“The prototypic feature of BCC is the presence of telangiectatic vessels,” she explained, and the postulated mechanism of action is selective photothermolysis of the tumor vasculature. “These vessels are slightly larger in caliber, compared with normal skin – 40 micrometers versus 15 micrometers – and more fragile. You can tailor your pulse duration to the size of the vessels. Theoretically, by targeting the vasculature then you get tumor regression with sparing of normal tissue.”

Initial studies of this approach have used the 595-nm pulsed-dye laser, which is well absorbed by oxyhemoglobin, but more recent studies have used the 1064-nm Nd:YAG to reach deep arterial vessels. In a prospective, open-label study, 10 patients with 13 BCCs less than 1.5 cm in diameter received one treatment with a 10-ms pulsed 1064-nm Nd:YAG laser delivered on the trunk or extremities at a fluence of 80-120 J/cm² (Lasers Surg Med. 2015;47[2]:106-10). Dr. Ortiz and her colleagues observed a 92% clearance rate overall.

She described other earlier studies of the approach as flawed, because they relied on confirmation of clearance rates with clinical exam or biopsy rather than with surgical excision. “Also, some of the protocols weren’t standardized, multiple treatments were required, and subjects with suboptimal response were currently on anticoagulation,” she said. “Intravascular coagulation is important for effective treatment with vascular lasers, so anticoagulation may interfere with efficacy.”

In a more recent multicenter study, Dr. Ortiz and her colleagues treated 33 BCCs once with the long-pulsed 1064-nm Nd:YAG laser delivered with a 5-6 mm spot size at a fluence of 125-140 J/cm² and a 7-10 ms pulse duration (Laser Surgery Med. Feb 13 2018. doi: 10.1002/lsm.22803). Standard surgical excision with 5-mm margins was performed 4 weeks after laser treatment. Among 31 subjects who completed the study, 28 of 31 BCC tumors (90%) cleared after one treatment.

“The treatments were performed without anesthesia, because we didn’t want the vasculature to be affected, but in clinical practice I am now using lidocaine with no epinephrine,” Dr. Ortiz said. She characterized the results as “at least comparable to, if not superior to” common modalities including methyl aminolevulinate–PDT (72.8%), imiquimod cream (83.4%), and fluorouracil cream (80.1%). “One criticism I hear is that with such high fluences, you’re probably getting some bulk heating,” she said. “Maybe so, but it seems to work and there’s no scarring, which suggests otherwise.”

Advantages of using a 1064-nm Nd:YAG for treating nonaggressive BCCs are that it requires just one treatment, it takes about 5 minutes, and there is no significant downtime, with no limitations in posttreatment activity. “Potentially there is a relatively decreased risk for complications, including infection and bleeding,” she added. “It’s a good alternative for treating patients with multiple tumors or those who are poor surgical candidates.”

She and her colleagues are currently performing a long-term follow-up study of 35 BCC lesions. Only one has potentially recurred, but that recurrence has not yet been confirmed.

Dr. Ortiz treats BCCs with a standard 5-mm margin and uses lidocaine without epinephrine to avoid vasoconstriction. She typically uses a 1064-nm Cutera excel V laser delivered at a pulse duration of 8 ms and a fluence of 140 J/cm^2, with no cooling. “Theoretically, any 1064-nm pulsed-dye laser could work, but the way the pulse is delivered is different, depending on which device” is used, she said.

“I always like waiting between passes to avoid any bulk heating. The immediate endpoint to strive for is slight graying and slight contraction,” she said. Billing codes for malignant destruction/electrodesiccation and curettage can be used (codes 17260-17266 for the trunk and 17280-17283 for the face).

In order to determine the mechanism of cell death and to optimize results, Dr. Ortiz said that further studies need to be conducted in vitro and in vivo. In order to determine treatment efficacy, clinical studies involving various heat sources and low concentrations of lidocaine are also required.

Dr. Ortiz disclosed having financial relationships with numerous pharmaceutical and device companies. She is also cochair of the MOAS.

[email protected]

SAN DIEGO – Using laser and light sources to treat nonaggressive basal cell carcinoma (BCC) is emerging as a promising treatment option, especially for those with multiple tumors and those who are poor surgical candidates, Arisa E. Ortiz, MD, said at the annual Masters of Aesthetics Symposium.

“Topical therapies often result in recurrence, so there really is a need for an alternative [to surgery] that’s effective, efficient, and carries a low risk of side effects,” said Dr. Ortiz, who is director of laser and cosmetic dermatology at the University of California, San Diego,

“The prototypic feature of BCC is the presence of telangiectatic vessels,” she explained, and the postulated mechanism of action is selective photothermolysis of the tumor vasculature. “These vessels are slightly larger in caliber, compared with normal skin – 40 micrometers versus 15 micrometers – and more fragile. You can tailor your pulse duration to the size of the vessels. Theoretically, by targeting the vasculature then you get tumor regression with sparing of normal tissue.”

Initial studies of this approach have used the 595-nm pulsed-dye laser, which is well absorbed by oxyhemoglobin, but more recent studies have used the 1064-nm Nd:YAG to reach deep arterial vessels. In a prospective, open-label study, 10 patients with 13 BCCs less than 1.5 cm in diameter received one treatment with a 10-ms pulsed 1064-nm Nd:YAG laser delivered on the trunk or extremities at a fluence of 80-120 J/cm² (Lasers Surg Med. 2015;47[2]:106-10). Dr. Ortiz and her colleagues observed a 92% clearance rate overall.

She described other earlier studies of the approach as flawed, because they relied on confirmation of clearance rates with clinical exam or biopsy rather than with surgical excision. “Also, some of the protocols weren’t standardized, multiple treatments were required, and subjects with suboptimal response were currently on anticoagulation,” she said. “Intravascular coagulation is important for effective treatment with vascular lasers, so anticoagulation may interfere with efficacy.”

In a more recent multicenter study, Dr. Ortiz and her colleagues treated 33 BCCs once with the long-pulsed 1064-nm Nd:YAG laser delivered with a 5-6 mm spot size at a fluence of 125-140 J/cm² and a 7-10 ms pulse duration (Laser Surgery Med. Feb 13 2018. doi: 10.1002/lsm.22803). Standard surgical excision with 5-mm margins was performed 4 weeks after laser treatment. Among 31 subjects who completed the study, 28 of 31 BCC tumors (90%) cleared after one treatment.

“The treatments were performed without anesthesia, because we didn’t want the vasculature to be affected, but in clinical practice I am now using lidocaine with no epinephrine,” Dr. Ortiz said. She characterized the results as “at least comparable to, if not superior to” common modalities including methyl aminolevulinate–PDT (72.8%), imiquimod cream (83.4%), and fluorouracil cream (80.1%). “One criticism I hear is that with such high fluences, you’re probably getting some bulk heating,” she said. “Maybe so, but it seems to work and there’s no scarring, which suggests otherwise.”

Advantages of using a 1064-nm Nd:YAG for treating nonaggressive BCCs are that it requires just one treatment, it takes about 5 minutes, and there is no significant downtime, with no limitations in posttreatment activity. “Potentially there is a relatively decreased risk for complications, including infection and bleeding,” she added. “It’s a good alternative for treating patients with multiple tumors or those who are poor surgical candidates.”

She and her colleagues are currently performing a long-term follow-up study of 35 BCC lesions. Only one has potentially recurred, but that recurrence has not yet been confirmed.

Dr. Ortiz treats BCCs with a standard 5-mm margin and uses lidocaine without epinephrine to avoid vasoconstriction. She typically uses a 1064-nm Cutera excel V laser delivered at a pulse duration of 8 ms and a fluence of 140 J/cm^2, with no cooling. “Theoretically, any 1064-nm pulsed-dye laser could work, but the way the pulse is delivered is different, depending on which device” is used, she said.

“I always like waiting between passes to avoid any bulk heating. The immediate endpoint to strive for is slight graying and slight contraction,” she said. Billing codes for malignant destruction/electrodesiccation and curettage can be used (codes 17260-17266 for the trunk and 17280-17283 for the face).

In order to determine the mechanism of cell death and to optimize results, Dr. Ortiz said that further studies need to be conducted in vitro and in vivo. In order to determine treatment efficacy, clinical studies involving various heat sources and low concentrations of lidocaine are also required.

Dr. Ortiz disclosed having financial relationships with numerous pharmaceutical and device companies. She is also cochair of the MOAS.

[email protected]

The first Pediatric Hospital Medicine (PHM) fellowships in the United States were established in 2003;¹ and since then, the field has expanded and matured dramatically. This growth, accompanied by greater definition of the role and recommended competencies of pediatric hospitalists,² culminated in the submission of a petition to the American Board of Pediatrics (ABP) in August 2014 to consider recognition of PHM as a new pediatric subspecialty.³ After an 18-month iterative process requiring extensive input from the Joint Council of Pediatric Hospital Medicine, ABP subcommittees, the Association of Medical School Pediatric Department Chairs, the Association of Pediatric Program Directors, and other prominent pediatric professional societies, the ABP voted in December 2015 to recommend that the American Board of Medical Subspecialties (ABMS) recognize PHM as a new subspecialty.³

The ABP subsequently announced three pathways for board certification in PHM:

• Training pathway for those completing an Accreditation Council for Graduate Medical Education–accredited two-year PHM fellowship program;
• Practice pathway for those satisfying ABP criteria for clinical activity in PHM for four years prior to exam dates (in 2019, 2021, and 2023), initially described as “direct patient care of hospitalized children ≥25% full-time equivalent (FTE) defined as ≥450-500 hours per year every year for the preceding four years”;⁴
• Combined pathway for those completing less than two years of fellowship, who would be required to complete two years of practice experience that satisfy the same criteria as each year of the practice pathway.⁵

While the training pathway met near-uniform acceptance, concerns were raised through the American Academy of Pediatrics Section of Hospital Medicine (AAP SOHM) Listserv regarding the practice pathway, and by extension, the combined pathway. Specifically, language describing the necessary characteristics of acceptable PHM practice was felt to be vague and not transparent. Listserv posts also raised concerns regarding the potential exclusion of “niche” practices such as subspecialty hospitalists and newborn hospitalists. As applicants in the practice pathway began to receive denials, opinions voiced in listserv posts were increasingly critical of the ABP’s lack of transparency regarding the specific criteria adjudicating applications.

A group of hospitalists, led by Dr. David Skey, a pediatric hospitalist at Arnold Palmer Children’s Hospital in Orlando, Florida, created a petition which was submitted to the ABP on August 6, 2019, and raised the following issues:

• “A perception of unfairness/bias in the practice pathway criteria and the way these criteria have been applied.
• Denials based on gaps in employment without reasonable consideration of mitigating factors.
• Lack of transparency, accountability, and responsiveness from the ABP.”⁶

The petition, posted on the AAP SOHM listserv and signed by 1,479 individuals,⁷ raised concerns of anecdotal evidence that the practice pathway criteria disproportionately disadvantaged women, although intentional bias was not suspected by the signers of the letter. The petition’s signers submitted the following demands to the ABP:

• “Facilitate a timely analysis to determine if gender bias is present or perform this analysis internally and release the findings publicly.
• Revise the practice pathway criteria to be more inclusive of applicants with interrupted practice and varied clinical experience, to include clear-cut parameters rather than considering these applications on a closed-door ‘case-by-case basis...at the discretion of the ABP’.
• Clarify the appeals process and improve responsiveness to appeals and inquiries regarding denials.
• Provide a formal response to this petition letter through the PHM ListServ and/or the ABP website within one week of receiving the signed petition.”⁶

A formal response to the petition was released on the AAP SOHM Listserv on August 29, 2019, to address the concerns raised and is published in this issue of the Journal of Hospital Medicine.⁴ In response to the allegation of gender bias, the ABP maintained that the data did not support this, as the denial rate for females (4.0%) was not significantly different than that for males (3.7%). The response acknowledged that once clear-cut criteria were decided upon to augment the general practice pathway criteria published at the outset, these criteria should have been disseminated. The ABP maintained, however, that these criteria, once established, were used consistently in adjudicating all applications. To clarify and simplify the eligibility criteria, the percentage of the full-time equivalent and practice interruption criteria were removed, as the work-hours criteria (direct patient care of hospitalized children ≥450-500 hours per year every year for the preceding four years)⁸ were deemed sufficient to ensure adequate clinical participation.

The Society of Hospital Medicine (SHM), through pediatric hospitalists and pediatricians on its Board, committees, and the Executive Council of the Pediatric Special Interest Group, has followed with great interest the public debate surrounding the PHM certification process and the subsequent PHM petition to the ABP. The ABP responded swiftly and with full transparency to the petition, and SHM supports these efforts by the ABP to provide a timely, honest, data-driven response to the concerns raised by the PHM petition. SHM recognizes that the mission of the ABP is to provide the public with confidence that physicians with ABP board certifications meet appropriate “standards of excellence”. While the revisions implemented by the ABP in its response still may not satisfy the concerns of all members of the PHM community, SHM recognizes that the revised requirements remain true to the mission of the ABP.

SHM applauds the authors and signatories of the PHM petition for bravely raising their concerns of gender bias and lack of transparency. The response of the ABP to this petition by further improving transparency serves as an example of continuous improvement in collaborative practice to all medical specialty boards.

While SHM supports the ABP response to the PHM petition, it is clear that excellent physicians caring for hospitalized children will be unable to achieve PHM board certification for a variety of reasons. For these physicians who are not PHM board certified as pediatric hospitalists by the ABP, SHM supports providing these physicians with recognition as hospitalists. These include “niche” hospitalists, such as newborn hospitalists, subacute hospitalists, and subspecialty hospitalists. SHM will also continue to support and recognize community-based hospitalists, family medicine-trained hospitalists, and Med-Peds hospitalists whose practice may not comply with criteria laid out by the ABP. For these physicians, receiving Fellow designation through SHM, a merit-based distinction requiring demonstration of clinical excellence and commitment to hospital medicine, is another route whereby physicians can achieve designation as a hospitalist.

SHM supports future efforts by the ABP to be vigilant for bias of any sort in the certification process. Other future considerations for the PHM community include the possibility of a focused practice pathway in hospital medicine (FPHM) for pediatrics as is currently jointly offered by the American Board of Internal Medicine (ABIM) and the American Board of Family Medicine (ABFM). This maintenance of certification program is a variation of internal medicine or family medicine recertification, not a subspecialty, but allows physicians practicing primarily in inpatient settings to focus continuing education efforts on skills and attitudes needed for inpatient practice.⁹ While this possibility was discounted by the ABP in the past based on initially low numbers of physicians choosing this pathway, this pathway has grown from initially attracting 150 internal medicine applicants yearly to 265 in 2015.¹⁰ The ABMS approved the ABIM/ABFM FPHM as its first approved designation in March 2017 after more than 2,500 physicians earned this designation.¹¹ Of the >2,800 pediatric residency graduates (not including combined programs) each year, 10% report planning on becoming pediatric hospitalists,¹² and currently only 72-74 fellows graduate from PHM fellowships yearly.¹³ FPHM for pediatric hospital medicine would provide focused maintenance of certification and hospitalist designation for those who cannot match to fellowship programs.

Acknowledgments

The authors would like to acknowledge the input and support from the Executive Council of the Society of Hospital Medicine Pediatric Special Interest Group in writing this statement.

Disclosures

Dr. Chang served as an author of the Pediatric Hospital Medicine Petition to the American Board of Pediatrics for Subspecialty Certification. Drs. Hopkins, Rehm, Gage, and Shen have nothing to disclose.

The first Pediatric Hospital Medicine (PHM) fellowships in the United States were established in 2003;¹ and since then, the field has expanded and matured dramatically. This growth, accompanied by greater definition of the role and recommended competencies of pediatric hospitalists,² culminated in the submission of a petition to the American Board of Pediatrics (ABP) in August 2014 to consider recognition of PHM as a new pediatric subspecialty.³ After an 18-month iterative process requiring extensive input from the Joint Council of Pediatric Hospital Medicine, ABP subcommittees, the Association of Medical School Pediatric Department Chairs, the Association of Pediatric Program Directors, and other prominent pediatric professional societies, the ABP voted in December 2015 to recommend that the American Board of Medical Subspecialties (ABMS) recognize PHM as a new subspecialty.³

The ABP subsequently announced three pathways for board certification in PHM:

• Training pathway for those completing an Accreditation Council for Graduate Medical Education–accredited two-year PHM fellowship program;
• Practice pathway for those satisfying ABP criteria for clinical activity in PHM for four years prior to exam dates (in 2019, 2021, and 2023), initially described as “direct patient care of hospitalized children ≥25% full-time equivalent (FTE) defined as ≥450-500 hours per year every year for the preceding four years”;⁴
• Combined pathway for those completing less than two years of fellowship, who would be required to complete two years of practice experience that satisfy the same criteria as each year of the practice pathway.⁵

While the training pathway met near-uniform acceptance, concerns were raised through the American Academy of Pediatrics Section of Hospital Medicine (AAP SOHM) Listserv regarding the practice pathway, and by extension, the combined pathway. Specifically, language describing the necessary characteristics of acceptable PHM practice was felt to be vague and not transparent. Listserv posts also raised concerns regarding the potential exclusion of “niche” practices such as subspecialty hospitalists and newborn hospitalists. As applicants in the practice pathway began to receive denials, opinions voiced in listserv posts were increasingly critical of the ABP’s lack of transparency regarding the specific criteria adjudicating applications.

A group of hospitalists, led by Dr. David Skey, a pediatric hospitalist at Arnold Palmer Children’s Hospital in Orlando, Florida, created a petition which was submitted to the ABP on August 6, 2019, and raised the following issues:

• “A perception of unfairness/bias in the practice pathway criteria and the way these criteria have been applied.
• Denials based on gaps in employment without reasonable consideration of mitigating factors.
• Lack of transparency, accountability, and responsiveness from the ABP.”⁶

The petition, posted on the AAP SOHM listserv and signed by 1,479 individuals,⁷ raised concerns of anecdotal evidence that the practice pathway criteria disproportionately disadvantaged women, although intentional bias was not suspected by the signers of the letter. The petition’s signers submitted the following demands to the ABP:

• “Facilitate a timely analysis to determine if gender bias is present or perform this analysis internally and release the findings publicly.
• Revise the practice pathway criteria to be more inclusive of applicants with interrupted practice and varied clinical experience, to include clear-cut parameters rather than considering these applications on a closed-door ‘case-by-case basis...at the discretion of the ABP’.
• Clarify the appeals process and improve responsiveness to appeals and inquiries regarding denials.
• Provide a formal response to this petition letter through the PHM ListServ and/or the ABP website within one week of receiving the signed petition.”⁶

A formal response to the petition was released on the AAP SOHM Listserv on August 29, 2019, to address the concerns raised and is published in this issue of the Journal of Hospital Medicine.⁴ In response to the allegation of gender bias, the ABP maintained that the data did not support this, as the denial rate for females (4.0%) was not significantly different than that for males (3.7%). The response acknowledged that once clear-cut criteria were decided upon to augment the general practice pathway criteria published at the outset, these criteria should have been disseminated. The ABP maintained, however, that these criteria, once established, were used consistently in adjudicating all applications. To clarify and simplify the eligibility criteria, the percentage of the full-time equivalent and practice interruption criteria were removed, as the work-hours criteria (direct patient care of hospitalized children ≥450-500 hours per year every year for the preceding four years)⁸ were deemed sufficient to ensure adequate clinical participation.

The Society of Hospital Medicine (SHM), through pediatric hospitalists and pediatricians on its Board, committees, and the Executive Council of the Pediatric Special Interest Group, has followed with great interest the public debate surrounding the PHM certification process and the subsequent PHM petition to the ABP. The ABP responded swiftly and with full transparency to the petition, and SHM supports these efforts by the ABP to provide a timely, honest, data-driven response to the concerns raised by the PHM petition. SHM recognizes that the mission of the ABP is to provide the public with confidence that physicians with ABP board certifications meet appropriate “standards of excellence”. While the revisions implemented by the ABP in its response still may not satisfy the concerns of all members of the PHM community, SHM recognizes that the revised requirements remain true to the mission of the ABP.

SHM applauds the authors and signatories of the PHM petition for bravely raising their concerns of gender bias and lack of transparency. The response of the ABP to this petition by further improving transparency serves as an example of continuous improvement in collaborative practice to all medical specialty boards.

While SHM supports the ABP response to the PHM petition, it is clear that excellent physicians caring for hospitalized children will be unable to achieve PHM board certification for a variety of reasons. For these physicians who are not PHM board certified as pediatric hospitalists by the ABP, SHM supports providing these physicians with recognition as hospitalists. These include “niche” hospitalists, such as newborn hospitalists, subacute hospitalists, and subspecialty hospitalists. SHM will also continue to support and recognize community-based hospitalists, family medicine-trained hospitalists, and Med-Peds hospitalists whose practice may not comply with criteria laid out by the ABP. For these physicians, receiving Fellow designation through SHM, a merit-based distinction requiring demonstration of clinical excellence and commitment to hospital medicine, is another route whereby physicians can achieve designation as a hospitalist.

SHM supports future efforts by the ABP to be vigilant for bias of any sort in the certification process. Other future considerations for the PHM community include the possibility of a focused practice pathway in hospital medicine (FPHM) for pediatrics as is currently jointly offered by the American Board of Internal Medicine (ABIM) and the American Board of Family Medicine (ABFM). This maintenance of certification program is a variation of internal medicine or family medicine recertification, not a subspecialty, but allows physicians practicing primarily in inpatient settings to focus continuing education efforts on skills and attitudes needed for inpatient practice.⁹ While this possibility was discounted by the ABP in the past based on initially low numbers of physicians choosing this pathway, this pathway has grown from initially attracting 150 internal medicine applicants yearly to 265 in 2015.¹⁰ The ABMS approved the ABIM/ABFM FPHM as its first approved designation in March 2017 after more than 2,500 physicians earned this designation.¹¹ Of the >2,800 pediatric residency graduates (not including combined programs) each year, 10% report planning on becoming pediatric hospitalists,¹² and currently only 72-74 fellows graduate from PHM fellowships yearly.¹³ FPHM for pediatric hospital medicine would provide focused maintenance of certification and hospitalist designation for those who cannot match to fellowship programs.

Acknowledgments

The authors would like to acknowledge the input and support from the Executive Council of the Society of Hospital Medicine Pediatric Special Interest Group in writing this statement.

Disclosures

Dr. Chang served as an author of the Pediatric Hospital Medicine Petition to the American Board of Pediatrics for Subspecialty Certification. Drs. Hopkins, Rehm, Gage, and Shen have nothing to disclose.

The first Pediatric Hospital Medicine (PHM) fellowships in the United States were established in 2003;¹ and since then, the field has expanded and matured dramatically. This growth, accompanied by greater definition of the role and recommended competencies of pediatric hospitalists,² culminated in the submission of a petition to the American Board of Pediatrics (ABP) in August 2014 to consider recognition of PHM as a new pediatric subspecialty.³ After an 18-month iterative process requiring extensive input from the Joint Council of Pediatric Hospital Medicine, ABP subcommittees, the Association of Medical School Pediatric Department Chairs, the Association of Pediatric Program Directors, and other prominent pediatric professional societies, the ABP voted in December 2015 to recommend that the American Board of Medical Subspecialties (ABMS) recognize PHM as a new subspecialty.³

The ABP subsequently announced three pathways for board certification in PHM:

• Training pathway for those completing an Accreditation Council for Graduate Medical Education–accredited two-year PHM fellowship program;
• Practice pathway for those satisfying ABP criteria for clinical activity in PHM for four years prior to exam dates (in 2019, 2021, and 2023), initially described as “direct patient care of hospitalized children ≥25% full-time equivalent (FTE) defined as ≥450-500 hours per year every year for the preceding four years”;⁴
• Combined pathway for those completing less than two years of fellowship, who would be required to complete two years of practice experience that satisfy the same criteria as each year of the practice pathway.⁵

While the training pathway met near-uniform acceptance, concerns were raised through the American Academy of Pediatrics Section of Hospital Medicine (AAP SOHM) Listserv regarding the practice pathway, and by extension, the combined pathway. Specifically, language describing the necessary characteristics of acceptable PHM practice was felt to be vague and not transparent. Listserv posts also raised concerns regarding the potential exclusion of “niche” practices such as subspecialty hospitalists and newborn hospitalists. As applicants in the practice pathway began to receive denials, opinions voiced in listserv posts were increasingly critical of the ABP’s lack of transparency regarding the specific criteria adjudicating applications.

A group of hospitalists, led by Dr. David Skey, a pediatric hospitalist at Arnold Palmer Children’s Hospital in Orlando, Florida, created a petition which was submitted to the ABP on August 6, 2019, and raised the following issues:

• “A perception of unfairness/bias in the practice pathway criteria and the way these criteria have been applied.
• Denials based on gaps in employment without reasonable consideration of mitigating factors.
• Lack of transparency, accountability, and responsiveness from the ABP.”⁶

The petition, posted on the AAP SOHM listserv and signed by 1,479 individuals,⁷ raised concerns of anecdotal evidence that the practice pathway criteria disproportionately disadvantaged women, although intentional bias was not suspected by the signers of the letter. The petition’s signers submitted the following demands to the ABP:

• “Facilitate a timely analysis to determine if gender bias is present or perform this analysis internally and release the findings publicly.
• Revise the practice pathway criteria to be more inclusive of applicants with interrupted practice and varied clinical experience, to include clear-cut parameters rather than considering these applications on a closed-door ‘case-by-case basis...at the discretion of the ABP’.
• Clarify the appeals process and improve responsiveness to appeals and inquiries regarding denials.
• Provide a formal response to this petition letter through the PHM ListServ and/or the ABP website within one week of receiving the signed petition.”⁶

A formal response to the petition was released on the AAP SOHM Listserv on August 29, 2019, to address the concerns raised and is published in this issue of the Journal of Hospital Medicine.⁴ In response to the allegation of gender bias, the ABP maintained that the data did not support this, as the denial rate for females (4.0%) was not significantly different than that for males (3.7%). The response acknowledged that once clear-cut criteria were decided upon to augment the general practice pathway criteria published at the outset, these criteria should have been disseminated. The ABP maintained, however, that these criteria, once established, were used consistently in adjudicating all applications. To clarify and simplify the eligibility criteria, the percentage of the full-time equivalent and practice interruption criteria were removed, as the work-hours criteria (direct patient care of hospitalized children ≥450-500 hours per year every year for the preceding four years)⁸ were deemed sufficient to ensure adequate clinical participation.

The Society of Hospital Medicine (SHM), through pediatric hospitalists and pediatricians on its Board, committees, and the Executive Council of the Pediatric Special Interest Group, has followed with great interest the public debate surrounding the PHM certification process and the subsequent PHM petition to the ABP. The ABP responded swiftly and with full transparency to the petition, and SHM supports these efforts by the ABP to provide a timely, honest, data-driven response to the concerns raised by the PHM petition. SHM recognizes that the mission of the ABP is to provide the public with confidence that physicians with ABP board certifications meet appropriate “standards of excellence”. While the revisions implemented by the ABP in its response still may not satisfy the concerns of all members of the PHM community, SHM recognizes that the revised requirements remain true to the mission of the ABP.

SHM applauds the authors and signatories of the PHM petition for bravely raising their concerns of gender bias and lack of transparency. The response of the ABP to this petition by further improving transparency serves as an example of continuous improvement in collaborative practice to all medical specialty boards.

While SHM supports the ABP response to the PHM petition, it is clear that excellent physicians caring for hospitalized children will be unable to achieve PHM board certification for a variety of reasons. For these physicians who are not PHM board certified as pediatric hospitalists by the ABP, SHM supports providing these physicians with recognition as hospitalists. These include “niche” hospitalists, such as newborn hospitalists, subacute hospitalists, and subspecialty hospitalists. SHM will also continue to support and recognize community-based hospitalists, family medicine-trained hospitalists, and Med-Peds hospitalists whose practice may not comply with criteria laid out by the ABP. For these physicians, receiving Fellow designation through SHM, a merit-based distinction requiring demonstration of clinical excellence and commitment to hospital medicine, is another route whereby physicians can achieve designation as a hospitalist.

SHM supports future efforts by the ABP to be vigilant for bias of any sort in the certification process. Other future considerations for the PHM community include the possibility of a focused practice pathway in hospital medicine (FPHM) for pediatrics as is currently jointly offered by the American Board of Internal Medicine (ABIM) and the American Board of Family Medicine (ABFM). This maintenance of certification program is a variation of internal medicine or family medicine recertification, not a subspecialty, but allows physicians practicing primarily in inpatient settings to focus continuing education efforts on skills and attitudes needed for inpatient practice.⁹ While this possibility was discounted by the ABP in the past based on initially low numbers of physicians choosing this pathway, this pathway has grown from initially attracting 150 internal medicine applicants yearly to 265 in 2015.¹⁰ The ABMS approved the ABIM/ABFM FPHM as its first approved designation in March 2017 after more than 2,500 physicians earned this designation.¹¹ Of the >2,800 pediatric residency graduates (not including combined programs) each year, 10% report planning on becoming pediatric hospitalists,¹² and currently only 72-74 fellows graduate from PHM fellowships yearly.¹³ FPHM for pediatric hospital medicine would provide focused maintenance of certification and hospitalist designation for those who cannot match to fellowship programs.

Acknowledgments

The authors would like to acknowledge the input and support from the Executive Council of the Society of Hospital Medicine Pediatric Special Interest Group in writing this statement.

Disclosures

Dr. Chang served as an author of the Pediatric Hospital Medicine Petition to the American Board of Pediatrics for Subspecialty Certification. Drs. Hopkins, Rehm, Gage, and Shen have nothing to disclose.

Can receiving all posttransplant care at home benefit patients undergoing hematopoietic stem cell transplant (HSCT)? Researchers are conducting phase 2 trials to find out.

Nelson Chao, MD, and colleagues at Duke University in Durham, N.C., completed a phase 1 trial that suggested post-HSCT care at home was feasible and safe (Blood. 2017;130:745).

Now, the team is conducting phase 2 trials – NCT01725022 and NCT02218151 – comparing patients who receive all posttransplant care at home with patients treated in the hospital or in the outpatient setting with daily visits to the clinic.

The main goal is to determine if allogeneic HSCT recipients treated at home can maintain their normal microbiome and, as a result, have a lower risk of graft-versus-host disease (GVHD). The researchers are also looking at other outcomes such as quality of life, treatment-related morbidities and mortality, and the cost of care for both allogeneic and autologous transplant recipients.

To be eligible for home care after HSCT, a patient must live within a 90-minute driving distance of Duke and have a caregiver available at home. The patient’s home must pass an inspection, showing it to be free of sources for potential infection, such as mold or pets that sleep in the patient’s bed.

When the time comes for treatment, the patient receives conditioning at the hospital but can return home the day before or the day of transplant. After discharge, the patient is visited by a nurse practitioner or physician assistant each morning for a physical examination and blood draw.

In the afternoon, the patient is visited by a clinic nurse who brings any necessary supplies or treatments, such as blood products or intravenous antibiotics. The patient also has daily video calls with an attending physician and can be admitted to the hospital for any events that cannot be managed in the home setting.

Patients can have visitors and spend time away from home, but precautions are necessary. Friends or family who are sick should not be allowed to visit, and patients should avoid crowds when they go out.

Vidyard Video

Initial findings

The Duke team has treated 41 HSCT recipients at home so far. Dr. Chao said it’s still too early to draw any conclusions about differences in outcomes between home care and inpatient/outpatient HSCT.

However, a preliminary analysis of costs suggests home care is cheaper than inpatient HSCT. The researchers found that, for the first several transplants, at day 60, the cost of home care was roughly half that of inpatient HSCT.

In addition, patients seem to be happy with posttransplant care at home.

“The patients love being at home, in their own environment, with their families,” Dr. Chao said. “Almost every single patient [in the phase 1 trial] said that he or she liked it much better. There was one patient in the phase 1 that felt a little isolated, and I can see why because we say, ‘You can stay home, but don’t have a whole lot of people in.’ ”

 

 

One patient’s experience

Beth Vanderkin said it was “a blessing” to receive care at home after undergoing HSCT at Duke.

Ms. Vanderkin was diagnosed with diffuse large B-cell lymphoma in 2014. After two chemotherapy regimens failed to shrink the tumor in her chest, she underwent radiotherapy and responded well. When a PET scan revealed the tumor had gone completely, she proceeded to transplant.

She received a haploidentical HSCT using cells donated by her eldest daughter, Hannah Eichhorst. Ms. Vanderkin received the transplant in the hospital, and for 2 weeks after that, she made daily visits to the transplant clinic.

After those 2 weeks, Ms. Vanderkin continued her treatment at home. Like other patients eligible for home care, Ms. Vanderkin lived close to Duke, had a caregiver available, and had passed a home inspection. The Duke team shipped the needed medical supplies to her house and arranged twice-daily visits from nurses and daily video calls with a doctor.

Ms. Vanderkin said receiving care at home was “a game changer.” She derived comfort from recovering in her own environment, could spend more time with her family, and didn’t have to miss special events. While receiving care at home, Ms. Vanderkin attended the homecoming event where her son, Josiah, was part of the court. Wearing a face mask and carrying a portable pump in her purse, Ms. Vanderkin joined other mothers in escorting their children onto the football field.

“I got to escort my son out onto the field, and he was crowned king that night,” Ms. Vanderkin said. “I didn’t do a lot of things [while receiving care at home], but there were things I didn’t have to miss because I was at home and not in the hospital.”

Ms. Vanderkin said home care was also beneficial for her husband, who was her caregiver. Thomas Vanderkin was able to work from home while caring for his wife, and the daily nurses’ visits allowed him to run errands without having to leave Ms. Vanderkin alone.

Since her experience with home care, Ms. Vanderkin has spent many more days in the hospital and clinic. She experienced a relapse after the transplant and went on to receive more chemotherapy as well as ipilimumab. She responded to that treatment and has now been cancer-free for 3 years.

The ipilimumab did cause side effects, including intestinal problems that resulted in the need for parenteral nutrition. This side effect was made more bearable, Ms. Vanderkin said, because she was able to receive the parenteral nutrition at home. She and her husband were comfortable with additional home care because of their positive experience with posttransplant care.

“I think we’re conditioned to think that, to receive the best care, we have to be sitting in a hospital room or a clinic, but I think there’s a lot of things we can probably do at home,” Ms. Vanderkin said. “And we might fare a lot better as patients if we’re in an environment that we feel comfortable in.”

 

 

Experience at other centers

The team at Duke is not the first to study HSCT care at home. In fact, researchers in Sweden have been studying posttransplant home care since 1998.

A pilot trial the group published in 2000 suggested that home care was safe and, in some ways, superior to inpatient HSCT (Bone Marrow Transplant. 2000 Nov;26[10]:1057-60). Patients treated at home had a lower rate of bacteremia, fewer days of total parenteral nutrition, fewer erythrocyte transfusions, and fewer days on antibiotics and analgesics. Rates of fever, engraftment time, and acute GVHD were similar between the inpatient and home-care groups.

A study published by the same researchers in 2002 showed that patients who received home care had lower rates of grade 2-4 acute GVHD and transplant-related mortality compared to inpatients (Blood. 2002 Dec 15;100[13]:4317-24). Two-year overall survival was superior with home care as well.

On the other hand, a study the group published in 2013 showed no significant differences in 5-year survival, transplant-related mortality, relapse, or chronic GVHD between inpatients and those who received care at home (Biol Blood Marrow Transplant. 2013. doi: 10.1016/j.bbmt.2012.11.5189).

The phase 2 trials at Duke should provide more insight into patient outcomes, but results probably won’t be available for 2 more years, Dr. Chao said.

In the meantime, other U.S. researchers are studying home care as well. Memorial Sloan Kettering Cancer Center is conducting a pilot study to determine if HSCT care at home is feasible (NCT02671448).

Dr. Chao said home care should be possible for other centers, particularly those that already perform outpatient HSCT.

“Having the outpatient infrastructure to support these patients is a big step,” he said. “And I think we were able to do that mainly because we do most of our transplants in the outpatient setting already. So that jump to the home is a little less compared to a center that does no outpatient transplants.”

He added, “There’s a certain amount of inertia to overcome and a certain amount of apprehension from the caregivers initially because [patients aren’t] sitting in your unit all the time, but I don’t see this as a huge barrier.”

In fact, Dr. Chao said, if results with home care are favorable, it could potentially replace inpatient HSCT for certain patients.

Dr. Chao’s research is supported by Duke University, and he reported having no relevant financial disclosures.

[email protected]

Can receiving all posttransplant care at home benefit patients undergoing hematopoietic stem cell transplant (HSCT)? Researchers are conducting phase 2 trials to find out.

Nelson Chao, MD, and colleagues at Duke University in Durham, N.C., completed a phase 1 trial that suggested post-HSCT care at home was feasible and safe (Blood. 2017;130:745).

Now, the team is conducting phase 2 trials – NCT01725022 and NCT02218151 – comparing patients who receive all posttransplant care at home with patients treated in the hospital or in the outpatient setting with daily visits to the clinic.

The main goal is to determine if allogeneic HSCT recipients treated at home can maintain their normal microbiome and, as a result, have a lower risk of graft-versus-host disease (GVHD). The researchers are also looking at other outcomes such as quality of life, treatment-related morbidities and mortality, and the cost of care for both allogeneic and autologous transplant recipients.

To be eligible for home care after HSCT, a patient must live within a 90-minute driving distance of Duke and have a caregiver available at home. The patient’s home must pass an inspection, showing it to be free of sources for potential infection, such as mold or pets that sleep in the patient’s bed.

When the time comes for treatment, the patient receives conditioning at the hospital but can return home the day before or the day of transplant. After discharge, the patient is visited by a nurse practitioner or physician assistant each morning for a physical examination and blood draw.

In the afternoon, the patient is visited by a clinic nurse who brings any necessary supplies or treatments, such as blood products or intravenous antibiotics. The patient also has daily video calls with an attending physician and can be admitted to the hospital for any events that cannot be managed in the home setting.

Patients can have visitors and spend time away from home, but precautions are necessary. Friends or family who are sick should not be allowed to visit, and patients should avoid crowds when they go out.

Vidyard Video

Initial findings

The Duke team has treated 41 HSCT recipients at home so far. Dr. Chao said it’s still too early to draw any conclusions about differences in outcomes between home care and inpatient/outpatient HSCT.

However, a preliminary analysis of costs suggests home care is cheaper than inpatient HSCT. The researchers found that, for the first several transplants, at day 60, the cost of home care was roughly half that of inpatient HSCT.

In addition, patients seem to be happy with posttransplant care at home.

“The patients love being at home, in their own environment, with their families,” Dr. Chao said. “Almost every single patient [in the phase 1 trial] said that he or she liked it much better. There was one patient in the phase 1 that felt a little isolated, and I can see why because we say, ‘You can stay home, but don’t have a whole lot of people in.’ ”

 

 

One patient’s experience

Beth Vanderkin said it was “a blessing” to receive care at home after undergoing HSCT at Duke.

Ms. Vanderkin was diagnosed with diffuse large B-cell lymphoma in 2014. After two chemotherapy regimens failed to shrink the tumor in her chest, she underwent radiotherapy and responded well. When a PET scan revealed the tumor had gone completely, she proceeded to transplant.

She received a haploidentical HSCT using cells donated by her eldest daughter, Hannah Eichhorst. Ms. Vanderkin received the transplant in the hospital, and for 2 weeks after that, she made daily visits to the transplant clinic.

After those 2 weeks, Ms. Vanderkin continued her treatment at home. Like other patients eligible for home care, Ms. Vanderkin lived close to Duke, had a caregiver available, and had passed a home inspection. The Duke team shipped the needed medical supplies to her house and arranged twice-daily visits from nurses and daily video calls with a doctor.

Ms. Vanderkin said receiving care at home was “a game changer.” She derived comfort from recovering in her own environment, could spend more time with her family, and didn’t have to miss special events. While receiving care at home, Ms. Vanderkin attended the homecoming event where her son, Josiah, was part of the court. Wearing a face mask and carrying a portable pump in her purse, Ms. Vanderkin joined other mothers in escorting their children onto the football field.

“I got to escort my son out onto the field, and he was crowned king that night,” Ms. Vanderkin said. “I didn’t do a lot of things [while receiving care at home], but there were things I didn’t have to miss because I was at home and not in the hospital.”

Ms. Vanderkin said home care was also beneficial for her husband, who was her caregiver. Thomas Vanderkin was able to work from home while caring for his wife, and the daily nurses’ visits allowed him to run errands without having to leave Ms. Vanderkin alone.

Since her experience with home care, Ms. Vanderkin has spent many more days in the hospital and clinic. She experienced a relapse after the transplant and went on to receive more chemotherapy as well as ipilimumab. She responded to that treatment and has now been cancer-free for 3 years.

The ipilimumab did cause side effects, including intestinal problems that resulted in the need for parenteral nutrition. This side effect was made more bearable, Ms. Vanderkin said, because she was able to receive the parenteral nutrition at home. She and her husband were comfortable with additional home care because of their positive experience with posttransplant care.

“I think we’re conditioned to think that, to receive the best care, we have to be sitting in a hospital room or a clinic, but I think there’s a lot of things we can probably do at home,” Ms. Vanderkin said. “And we might fare a lot better as patients if we’re in an environment that we feel comfortable in.”

 

 

Experience at other centers

The team at Duke is not the first to study HSCT care at home. In fact, researchers in Sweden have been studying posttransplant home care since 1998.

A pilot trial the group published in 2000 suggested that home care was safe and, in some ways, superior to inpatient HSCT (Bone Marrow Transplant. 2000 Nov;26[10]:1057-60). Patients treated at home had a lower rate of bacteremia, fewer days of total parenteral nutrition, fewer erythrocyte transfusions, and fewer days on antibiotics and analgesics. Rates of fever, engraftment time, and acute GVHD were similar between the inpatient and home-care groups.

A study published by the same researchers in 2002 showed that patients who received home care had lower rates of grade 2-4 acute GVHD and transplant-related mortality compared to inpatients (Blood. 2002 Dec 15;100[13]:4317-24). Two-year overall survival was superior with home care as well.

On the other hand, a study the group published in 2013 showed no significant differences in 5-year survival, transplant-related mortality, relapse, or chronic GVHD between inpatients and those who received care at home (Biol Blood Marrow Transplant. 2013. doi: 10.1016/j.bbmt.2012.11.5189).

The phase 2 trials at Duke should provide more insight into patient outcomes, but results probably won’t be available for 2 more years, Dr. Chao said.

In the meantime, other U.S. researchers are studying home care as well. Memorial Sloan Kettering Cancer Center is conducting a pilot study to determine if HSCT care at home is feasible (NCT02671448).

Dr. Chao said home care should be possible for other centers, particularly those that already perform outpatient HSCT.

“Having the outpatient infrastructure to support these patients is a big step,” he said. “And I think we were able to do that mainly because we do most of our transplants in the outpatient setting already. So that jump to the home is a little less compared to a center that does no outpatient transplants.”

He added, “There’s a certain amount of inertia to overcome and a certain amount of apprehension from the caregivers initially because [patients aren’t] sitting in your unit all the time, but I don’t see this as a huge barrier.”

In fact, Dr. Chao said, if results with home care are favorable, it could potentially replace inpatient HSCT for certain patients.

Dr. Chao’s research is supported by Duke University, and he reported having no relevant financial disclosures.

[email protected]

Can receiving all posttransplant care at home benefit patients undergoing hematopoietic stem cell transplant (HSCT)? Researchers are conducting phase 2 trials to find out.

Nelson Chao, MD, and colleagues at Duke University in Durham, N.C., completed a phase 1 trial that suggested post-HSCT care at home was feasible and safe (Blood. 2017;130:745).

Now, the team is conducting phase 2 trials – NCT01725022 and NCT02218151 – comparing patients who receive all posttransplant care at home with patients treated in the hospital or in the outpatient setting with daily visits to the clinic.

The main goal is to determine if allogeneic HSCT recipients treated at home can maintain their normal microbiome and, as a result, have a lower risk of graft-versus-host disease (GVHD). The researchers are also looking at other outcomes such as quality of life, treatment-related morbidities and mortality, and the cost of care for both allogeneic and autologous transplant recipients.

To be eligible for home care after HSCT, a patient must live within a 90-minute driving distance of Duke and have a caregiver available at home. The patient’s home must pass an inspection, showing it to be free of sources for potential infection, such as mold or pets that sleep in the patient’s bed.

When the time comes for treatment, the patient receives conditioning at the hospital but can return home the day before or the day of transplant. After discharge, the patient is visited by a nurse practitioner or physician assistant each morning for a physical examination and blood draw.

In the afternoon, the patient is visited by a clinic nurse who brings any necessary supplies or treatments, such as blood products or intravenous antibiotics. The patient also has daily video calls with an attending physician and can be admitted to the hospital for any events that cannot be managed in the home setting.

Patients can have visitors and spend time away from home, but precautions are necessary. Friends or family who are sick should not be allowed to visit, and patients should avoid crowds when they go out.

Vidyard Video

Initial findings

The Duke team has treated 41 HSCT recipients at home so far. Dr. Chao said it’s still too early to draw any conclusions about differences in outcomes between home care and inpatient/outpatient HSCT.

However, a preliminary analysis of costs suggests home care is cheaper than inpatient HSCT. The researchers found that, for the first several transplants, at day 60, the cost of home care was roughly half that of inpatient HSCT.

In addition, patients seem to be happy with posttransplant care at home.

“The patients love being at home, in their own environment, with their families,” Dr. Chao said. “Almost every single patient [in the phase 1 trial] said that he or she liked it much better. There was one patient in the phase 1 that felt a little isolated, and I can see why because we say, ‘You can stay home, but don’t have a whole lot of people in.’ ”

 

 

One patient’s experience

Beth Vanderkin said it was “a blessing” to receive care at home after undergoing HSCT at Duke.

Ms. Vanderkin was diagnosed with diffuse large B-cell lymphoma in 2014. After two chemotherapy regimens failed to shrink the tumor in her chest, she underwent radiotherapy and responded well. When a PET scan revealed the tumor had gone completely, she proceeded to transplant.

She received a haploidentical HSCT using cells donated by her eldest daughter, Hannah Eichhorst. Ms. Vanderkin received the transplant in the hospital, and for 2 weeks after that, she made daily visits to the transplant clinic.

After those 2 weeks, Ms. Vanderkin continued her treatment at home. Like other patients eligible for home care, Ms. Vanderkin lived close to Duke, had a caregiver available, and had passed a home inspection. The Duke team shipped the needed medical supplies to her house and arranged twice-daily visits from nurses and daily video calls with a doctor.

Ms. Vanderkin said receiving care at home was “a game changer.” She derived comfort from recovering in her own environment, could spend more time with her family, and didn’t have to miss special events. While receiving care at home, Ms. Vanderkin attended the homecoming event where her son, Josiah, was part of the court. Wearing a face mask and carrying a portable pump in her purse, Ms. Vanderkin joined other mothers in escorting their children onto the football field.

“I got to escort my son out onto the field, and he was crowned king that night,” Ms. Vanderkin said. “I didn’t do a lot of things [while receiving care at home], but there were things I didn’t have to miss because I was at home and not in the hospital.”

Ms. Vanderkin said home care was also beneficial for her husband, who was her caregiver. Thomas Vanderkin was able to work from home while caring for his wife, and the daily nurses’ visits allowed him to run errands without having to leave Ms. Vanderkin alone.

Since her experience with home care, Ms. Vanderkin has spent many more days in the hospital and clinic. She experienced a relapse after the transplant and went on to receive more chemotherapy as well as ipilimumab. She responded to that treatment and has now been cancer-free for 3 years.

The ipilimumab did cause side effects, including intestinal problems that resulted in the need for parenteral nutrition. This side effect was made more bearable, Ms. Vanderkin said, because she was able to receive the parenteral nutrition at home. She and her husband were comfortable with additional home care because of their positive experience with posttransplant care.

“I think we’re conditioned to think that, to receive the best care, we have to be sitting in a hospital room or a clinic, but I think there’s a lot of things we can probably do at home,” Ms. Vanderkin said. “And we might fare a lot better as patients if we’re in an environment that we feel comfortable in.”

 

 

Experience at other centers

The team at Duke is not the first to study HSCT care at home. In fact, researchers in Sweden have been studying posttransplant home care since 1998.

A pilot trial the group published in 2000 suggested that home care was safe and, in some ways, superior to inpatient HSCT (Bone Marrow Transplant. 2000 Nov;26[10]:1057-60). Patients treated at home had a lower rate of bacteremia, fewer days of total parenteral nutrition, fewer erythrocyte transfusions, and fewer days on antibiotics and analgesics. Rates of fever, engraftment time, and acute GVHD were similar between the inpatient and home-care groups.

A study published by the same researchers in 2002 showed that patients who received home care had lower rates of grade 2-4 acute GVHD and transplant-related mortality compared to inpatients (Blood. 2002 Dec 15;100[13]:4317-24). Two-year overall survival was superior with home care as well.

On the other hand, a study the group published in 2013 showed no significant differences in 5-year survival, transplant-related mortality, relapse, or chronic GVHD between inpatients and those who received care at home (Biol Blood Marrow Transplant. 2013. doi: 10.1016/j.bbmt.2012.11.5189).

The phase 2 trials at Duke should provide more insight into patient outcomes, but results probably won’t be available for 2 more years, Dr. Chao said.

In the meantime, other U.S. researchers are studying home care as well. Memorial Sloan Kettering Cancer Center is conducting a pilot study to determine if HSCT care at home is feasible (NCT02671448).

Dr. Chao said home care should be possible for other centers, particularly those that already perform outpatient HSCT.

“Having the outpatient infrastructure to support these patients is a big step,” he said. “And I think we were able to do that mainly because we do most of our transplants in the outpatient setting already. So that jump to the home is a little less compared to a center that does no outpatient transplants.”

He added, “There’s a certain amount of inertia to overcome and a certain amount of apprehension from the caregivers initially because [patients aren’t] sitting in your unit all the time, but I don’t see this as a huge barrier.”

In fact, Dr. Chao said, if results with home care are favorable, it could potentially replace inpatient HSCT for certain patients.

Dr. Chao’s research is supported by Duke University, and he reported having no relevant financial disclosures.

[email protected]

Hospitalizations for infective endocarditis associated with drug abuse doubled in the United States from 2002 to 2016, in a trend investigators call “alarming,” and link to a concurrent rise in opioid abuse.

Patients tend to be younger, poorer white males, according to findings published online in the Journal of the American Heart Association.

For their research, Amer N. Kadri, MD, of the Cleveland Clinic and colleagues looked at records for nearly a million hospitalizations for infective endocarditis (IE) in the National Inpatient Sample registry. All U.S. regions saw increases in drug abuse–linked cases of IE as a share of IE hospitalizations. Incidence of drug abuse–associated IC rose from 48 cases/100,000 population in 2002 to 79/100,000 in 2016. The Midwest saw the highest rate of change, with an annual percent increase of 4.9%.

While most IE hospitalizations in the study cohort were of white men (including 68% for drug-linked cases), the drug abuse–related cases were younger (median age, 38 vs. 70 years for nondrug-related IE), and more likely male (55.5% vs. 50%). About 45% of the drug-related cases were in people receiving Medicaid, and 42% were in the lowest quartile of median household income.

The drug abuse cases had fewer renal and cardiovascular comorbidities, compared with the nondrug cases, but were significantly more likely to present with HIV, hepatitis C, alcohol abuse, and liver disease. Inpatient mortality was lower among the drug-linked cases – 6% vs. 9% – but the drug cases saw significantly more cardiac or valve surgeries, longer hospital stays, and higher costs.

“Hospitalizations for IE have been increasing side by side with the opioid epidemic,” the investigators wrote in their analysis. “The opioid crisis has reached epidemic levels, and now drug overdoses have been the leading cause of injury-related death in the U.S. Heroin deaths had remained relatively low from 1999 until 2010 whereas it then increased threefold from 2010-2015.” The analysis showed a rise in drug abuse–associated IE “that corresponds to this general period.” The findings argue, the investigators said, for better treatment for opioid addiction after hospitalization and greater efforts to make drug rehabilitation available after discharge. The researchers described as a limitation of their study the use of billing codes that changed late in the study period, increasing detection of drug abuse cases after 2015. They reported no outside funding or conflicts of interest.
 

SOURCE: Kadri AN et al. J Am Heart Assoc. 2019 Sep 18.

Hospitalizations for infective endocarditis associated with drug abuse doubled in the United States from 2002 to 2016, in a trend investigators call “alarming,” and link to a concurrent rise in opioid abuse.

Patients tend to be younger, poorer white males, according to findings published online in the Journal of the American Heart Association.

For their research, Amer N. Kadri, MD, of the Cleveland Clinic and colleagues looked at records for nearly a million hospitalizations for infective endocarditis (IE) in the National Inpatient Sample registry. All U.S. regions saw increases in drug abuse–linked cases of IE as a share of IE hospitalizations. Incidence of drug abuse–associated IC rose from 48 cases/100,000 population in 2002 to 79/100,000 in 2016. The Midwest saw the highest rate of change, with an annual percent increase of 4.9%.

While most IE hospitalizations in the study cohort were of white men (including 68% for drug-linked cases), the drug abuse–related cases were younger (median age, 38 vs. 70 years for nondrug-related IE), and more likely male (55.5% vs. 50%). About 45% of the drug-related cases were in people receiving Medicaid, and 42% were in the lowest quartile of median household income.

The drug abuse cases had fewer renal and cardiovascular comorbidities, compared with the nondrug cases, but were significantly more likely to present with HIV, hepatitis C, alcohol abuse, and liver disease. Inpatient mortality was lower among the drug-linked cases – 6% vs. 9% – but the drug cases saw significantly more cardiac or valve surgeries, longer hospital stays, and higher costs.

“Hospitalizations for IE have been increasing side by side with the opioid epidemic,” the investigators wrote in their analysis. “The opioid crisis has reached epidemic levels, and now drug overdoses have been the leading cause of injury-related death in the U.S. Heroin deaths had remained relatively low from 1999 until 2010 whereas it then increased threefold from 2010-2015.” The analysis showed a rise in drug abuse–associated IE “that corresponds to this general period.” The findings argue, the investigators said, for better treatment for opioid addiction after hospitalization and greater efforts to make drug rehabilitation available after discharge. The researchers described as a limitation of their study the use of billing codes that changed late in the study period, increasing detection of drug abuse cases after 2015. They reported no outside funding or conflicts of interest.
 

SOURCE: Kadri AN et al. J Am Heart Assoc. 2019 Sep 18.

Hospitalizations for infective endocarditis associated with drug abuse doubled in the United States from 2002 to 2016, in a trend investigators call “alarming,” and link to a concurrent rise in opioid abuse.

Patients tend to be younger, poorer white males, according to findings published online in the Journal of the American Heart Association.

For their research, Amer N. Kadri, MD, of the Cleveland Clinic and colleagues looked at records for nearly a million hospitalizations for infective endocarditis (IE) in the National Inpatient Sample registry. All U.S. regions saw increases in drug abuse–linked cases of IE as a share of IE hospitalizations. Incidence of drug abuse–associated IC rose from 48 cases/100,000 population in 2002 to 79/100,000 in 2016. The Midwest saw the highest rate of change, with an annual percent increase of 4.9%.

While most IE hospitalizations in the study cohort were of white men (including 68% for drug-linked cases), the drug abuse–related cases were younger (median age, 38 vs. 70 years for nondrug-related IE), and more likely male (55.5% vs. 50%). About 45% of the drug-related cases were in people receiving Medicaid, and 42% were in the lowest quartile of median household income.

The drug abuse cases had fewer renal and cardiovascular comorbidities, compared with the nondrug cases, but were significantly more likely to present with HIV, hepatitis C, alcohol abuse, and liver disease. Inpatient mortality was lower among the drug-linked cases – 6% vs. 9% – but the drug cases saw significantly more cardiac or valve surgeries, longer hospital stays, and higher costs.

“Hospitalizations for IE have been increasing side by side with the opioid epidemic,” the investigators wrote in their analysis. “The opioid crisis has reached epidemic levels, and now drug overdoses have been the leading cause of injury-related death in the U.S. Heroin deaths had remained relatively low from 1999 until 2010 whereas it then increased threefold from 2010-2015.” The analysis showed a rise in drug abuse–associated IE “that corresponds to this general period.” The findings argue, the investigators said, for better treatment for opioid addiction after hospitalization and greater efforts to make drug rehabilitation available after discharge. The researchers described as a limitation of their study the use of billing codes that changed late in the study period, increasing detection of drug abuse cases after 2015. They reported no outside funding or conflicts of interest.
 

SOURCE: Kadri AN et al. J Am Heart Assoc. 2019 Sep 18.

Bronchiolitis is the most common cause of nonbirth hospitalization in children in the United States less than one year of age.¹ For children with severe bronchiolitis, high-flow nasal cannula (HFNC) is increasingly used^2-4 to reduce work of breathing and prevent the need for further escalation of ventilatory support.^5,6 Although previous studies suggest that enteral feeding is recommended in the management of patients hospitalized with bronchiolitis,^7-9 limited evidence exists to guide feeding practices for patients receiving HFNC support.^5,10,11

Respiratory support with HFNC has been associated with prolonged periods without enteral hydration/nutrition (ie, nil per os [NPO])¹² primarily due to anticipation of further escalation of respiratory support or concern for increased risk of aspiration. The majority of patients with bronchiolitis managed with HFNC, however, do not require escalation of care.^5,13 When feeding is attempted during HFNC support, it is frequently interrupted.⁵ Moreover, keeping all children NPO when receiving HFNC may be associated with weight loss and longer length of stay (LOS).^12,14 Two small studies found that children admitted to the intensive care unit who received HFNC support for bronchiolitis did not have increased rates of emesis, worsening respiratory distress or aspiration pneumonia when enterally fed.^10,11 However, no comparison of adverse events or LOS has been made between patients who were fed and those who were not fed during HFNC therapy, and previous studies have included only patients who have received HFNC in the intensive care setting.

Supporting safe feeding early in hospitalizations for bronchiolitis may facilitate expedited clinical improvement and discharge. As part of an ongoing bronchiolitis quality improvement initiative at our hospital, we sought to characterize feeding practices during HFNC therapy and assess whether feeding exposure was associated with (1) time to discharge after HFNC or (2) feeding-related adverse events. We hypothesized that feeding during HFNC therapy would be associated with a shorter time to discharge after HFNC cessation.

Study Design, Setting, Participants

This was a retrospective cohort study of patients aged 1-24 months receiving HFNC support for respiratory failure due to bronchiolitis at an academic children’s hospital between January 1, 2015 and March 1, 2017. Our institution has had a clinical practice guideline, associated order set, and respiratory therapy protocol for general care patients with bronchiolitis since 2009. Patients with bronchiolitis who were weaning HFNC have been cared for in both the intensive and general care settings since 2013. A formal process for initiation of HFNC on general care units was instituted in January of 2017. During the study period, no patients with HFNC support for bronchiolitis had all their care entirely outside the intensive care unit at our institution. However, initiation and subsequent use of HFNC may have occurred in either the intensive care or general care setting. No specific guidance for feeding during HFNC existed during this period.

Patients were identified using the Virtual PICU Systems database, (VPS LLC, myvps.org, Los Angeles, California) and, by definition, all patients received at least some of their care in the intensive care unit. Patients with comorbid conditions of prematurity (<35 weeks) and those with cardiopulmonary, neuromuscular, and genetic diseases were included. Patients with preexisting dysphagia, defined as ongoing outpatient speech therapy for swallowing concerns, an admission diagnosis of aspiration pneumonia or on home respiratory support, were excluded. Children (n = 7) were excluded if they had more than one period of HFNC during admission. This study was determined to be exempt by the University of Wisconsin School of Medicine and Public Health’s Institutional Review Board.

Data Collection and Study Variables

The following variables were collected from VPS administrative data: patient gender, age, admission and discharge date and time, type and total hours of respiratory support, intensive care admission, and LOS (in hours). Additional demographic, clinical, and feeding exposure variables were abstracted manually from the electronic medical record (Epic, Verona, Wisconsin) using a structured data collection tool and stored in REDCap (Research Electronic Data Capture)¹⁵ including prematurity, race/ethnicity, insurance status, primary language, and passive tobacco smoke exposure. Clinical variables included duration of illness (days) at the time of admission, unit of HFNC initiation (emergency department, general care, intensive care, respiratory rate and oxygen saturation at HFNC initiation (<90%, 91%-92%, or >92%), acquisition of blood gas at HFNC admission, duration of time on HFNC (hours) and need for intubation or noninvasive ventilation prior to HFNC. The Pediatric Index of Mortality 2 Risk of Mortality (PIM 2 ROM)¹⁶ was used to estimate the severity of illness. The PIM2ROM uses clinical variables (systolic blood pressure, fixed pupils, measure of hypoxia using PaO₂/FiO₂ ratio, base excess, mechanical ventilation, elective admission, recovery from surgery, cardiac bypass, high-risk diagnosis, low-risk diagnosis) collected at the time of intensive care admission to generate a score that predicts the risk of mortality for an individual patient.¹⁷

Feeding exposures were documented in three-hour intervals from HFNC initiation to completion using a structured protocol. At each interval the following feeding information was abstracted from a review of nursing and physician documentation and relevant clinical flowsheets: presence or absence of feeding during the interval, route of feeding (oral, nasogastric [NG] or nasojejunal [NJ]). Feeding exposure was categorized a priori as fed at any point during HFNC (vs not fed at any point). Fed children were further characterized as (1) mixed feeding consisting of oral and tube feeds (NG or NJ) or (2) exclusive oral feeding throughout HFNC support (Appendix 1).

The primary outcome was the number of hours to discharge from HFNC cessation. Secondary outcomes were time to discharge from HFNC initiation, all-cause readmissions within seven days of discharge, and potential feeding-related adverse events. Potential adverse events included: (1) aspiration, defined as initiation of antibiotic AND either chest radiograph official interpreted as evidence for aspiration and/or documented concern for aspiration from the treating physician, or (2) intubation after feeding during HFNC.

Statistical Analysis

Descriptive statistics evaluated differences in demographics and clinical variables for feeding exposure groups. We used chi-squared tests for differences in proportions and t-tests or Wilcoxon Rank-Sum tests for differences in means or medians for continuous variables, respectively. Associations between feeding exposure during HFNC and time to discharge (measured in hours) after HFNC completion were modeled with Cox proportional hazards regression. Using this approach, hazard ratios (HR)>1 indicate a higher hazard (rate) of discharge for children with a feeding exposure than for children without the exposure. For example, a hazard ratio equal to two indicates that the exposed population is discharged at twice the rate per unit time as the nonexposed population. Death or censoring events did not occur. Feeding exposure was first modeled dichotomously as not fed or fed. To further explore associations between feeding modality and our outcome, we then modeled feeding exposure categorically as not fed (reference), mixed (oral and tube) feeding, or exclusive oral feeding throughout HFNC.

After constructing a set of unadjusted models, we then adjusted the models for variables having independent (bivariate P < .10) associations with time to discharge: age, unit of HFNC initiation, highest respiratory support required before HFNC, and HFNC duration. Finally, to attempt to account for residual confounding from latent constructs, we also created a set of propensity-weighted Cox proportional hazards models. Propensity weights¹⁸ reflecting the probability of being fed or never being fed during HFNC were created using logistic regression with predictors we hypothesized a priori that may have influenced the clinical decision to feed during HFNC: age, day of illness on admission, prematurity, PIM2 ROM score, respiratory rate, oxygen saturation and blood gas acquisition at HFNC initiation, and highest respiratory support required before HFNC. All analyses were conducted using STATA 14.0 (StataCorp, College Station, Texas), and adjusted hazard ratios (aHR) with 95% confidence intervals (95% CIs) were reported.

Patients (n = 123) had a mean age of 7.3 months (standard deviation [SD] 7.1) and presented on day of illness 4.8 (SD 2.3). Prior to HFNC, 10% required higher respiratory support (3% mechanical ventilation). Former preterm children were 12% of the overall sample.

During HFNC, 37% of patients were never fed, 41% were exclusively orally fed, and 23% had tube or mixed oral and tube feedings (Table 1 and Appendix 2). Children who were not fed were older, but groups were otherwise similar in terms of gender, race/ethnicity, passive smoke exposure, day of illness, unit of HFNC initiation, respiratory support required prior to HFNC, and respiratory rate at HFNC initiation.

This observational study found that being fed during HFNC was associated with shorter time to discharge after HFNC support was completed. Exclusive oral feeding was associated with the shortest time to discharge, and these results were consistent across a variety of analytical approaches. Adverse events were rare and occurred in both fed and unfed children.

These findings advance research on relationships between nutrition and bronchiolitis outcomes. Studies of general care patients with bronchiolitis without HFNC have observed associations between poor nutrition and prolonged LOS.^19,20 Two previous studies of patients receiving HFNC therapy for bronchiolitis concluded that frequent interruption¹¹ and later initiation of enteral nutrition¹⁰ during ICU stay was associated with longer time to discharge.¹¹ To our knowledge, this is the first study of patients with bronchiolitis treated with HFNC in both general care and ICU settings that compared outcomes according to whether children were fed during HFNC therapy. Our results extend previous work demonstrating that delays in feeding may be associated with longer LOS.

Children fed while receiving HFNC for bronchiolitis may have shorter time to discharge than those who are not fed. Feeding-related adverse events were rare regardless of the feeding method. Controlled prospective studies addressing residual confounding are needed to justify a change in the current practice.

Acknowledgments

The authors would like to acknowledge the valuable feedback on earlier drafts from members of the University of Wisconsin Division of Pediatric Hospital Medicine CREATE writing group.

Bronchiolitis is the most common cause of nonbirth hospitalization in children in the United States less than one year of age.¹ For children with severe bronchiolitis, high-flow nasal cannula (HFNC) is increasingly used^2-4 to reduce work of breathing and prevent the need for further escalation of ventilatory support.^5,6 Although previous studies suggest that enteral feeding is recommended in the management of patients hospitalized with bronchiolitis,^7-9 limited evidence exists to guide feeding practices for patients receiving HFNC support.^5,10,11

Respiratory support with HFNC has been associated with prolonged periods without enteral hydration/nutrition (ie, nil per os [NPO])¹² primarily due to anticipation of further escalation of respiratory support or concern for increased risk of aspiration. The majority of patients with bronchiolitis managed with HFNC, however, do not require escalation of care.^5,13 When feeding is attempted during HFNC support, it is frequently interrupted.⁵ Moreover, keeping all children NPO when receiving HFNC may be associated with weight loss and longer length of stay (LOS).^12,14 Two small studies found that children admitted to the intensive care unit who received HFNC support for bronchiolitis did not have increased rates of emesis, worsening respiratory distress or aspiration pneumonia when enterally fed.^10,11 However, no comparison of adverse events or LOS has been made between patients who were fed and those who were not fed during HFNC therapy, and previous studies have included only patients who have received HFNC in the intensive care setting.

Supporting safe feeding early in hospitalizations for bronchiolitis may facilitate expedited clinical improvement and discharge. As part of an ongoing bronchiolitis quality improvement initiative at our hospital, we sought to characterize feeding practices during HFNC therapy and assess whether feeding exposure was associated with (1) time to discharge after HFNC or (2) feeding-related adverse events. We hypothesized that feeding during HFNC therapy would be associated with a shorter time to discharge after HFNC cessation.

Study Design, Setting, Participants

This was a retrospective cohort study of patients aged 1-24 months receiving HFNC support for respiratory failure due to bronchiolitis at an academic children’s hospital between January 1, 2015 and March 1, 2017. Our institution has had a clinical practice guideline, associated order set, and respiratory therapy protocol for general care patients with bronchiolitis since 2009. Patients with bronchiolitis who were weaning HFNC have been cared for in both the intensive and general care settings since 2013. A formal process for initiation of HFNC on general care units was instituted in January of 2017. During the study period, no patients with HFNC support for bronchiolitis had all their care entirely outside the intensive care unit at our institution. However, initiation and subsequent use of HFNC may have occurred in either the intensive care or general care setting. No specific guidance for feeding during HFNC existed during this period.

Patients were identified using the Virtual PICU Systems database, (VPS LLC, myvps.org, Los Angeles, California) and, by definition, all patients received at least some of their care in the intensive care unit. Patients with comorbid conditions of prematurity (<35 weeks) and those with cardiopulmonary, neuromuscular, and genetic diseases were included. Patients with preexisting dysphagia, defined as ongoing outpatient speech therapy for swallowing concerns, an admission diagnosis of aspiration pneumonia or on home respiratory support, were excluded. Children (n = 7) were excluded if they had more than one period of HFNC during admission. This study was determined to be exempt by the University of Wisconsin School of Medicine and Public Health’s Institutional Review Board.

Data Collection and Study Variables

The following variables were collected from VPS administrative data: patient gender, age, admission and discharge date and time, type and total hours of respiratory support, intensive care admission, and LOS (in hours). Additional demographic, clinical, and feeding exposure variables were abstracted manually from the electronic medical record (Epic, Verona, Wisconsin) using a structured data collection tool and stored in REDCap (Research Electronic Data Capture)¹⁵ including prematurity, race/ethnicity, insurance status, primary language, and passive tobacco smoke exposure. Clinical variables included duration of illness (days) at the time of admission, unit of HFNC initiation (emergency department, general care, intensive care, respiratory rate and oxygen saturation at HFNC initiation (<90%, 91%-92%, or >92%), acquisition of blood gas at HFNC admission, duration of time on HFNC (hours) and need for intubation or noninvasive ventilation prior to HFNC. The Pediatric Index of Mortality 2 Risk of Mortality (PIM 2 ROM)¹⁶ was used to estimate the severity of illness. The PIM2ROM uses clinical variables (systolic blood pressure, fixed pupils, measure of hypoxia using PaO₂/FiO₂ ratio, base excess, mechanical ventilation, elective admission, recovery from surgery, cardiac bypass, high-risk diagnosis, low-risk diagnosis) collected at the time of intensive care admission to generate a score that predicts the risk of mortality for an individual patient.¹⁷

Feeding exposures were documented in three-hour intervals from HFNC initiation to completion using a structured protocol. At each interval the following feeding information was abstracted from a review of nursing and physician documentation and relevant clinical flowsheets: presence or absence of feeding during the interval, route of feeding (oral, nasogastric [NG] or nasojejunal [NJ]). Feeding exposure was categorized a priori as fed at any point during HFNC (vs not fed at any point). Fed children were further characterized as (1) mixed feeding consisting of oral and tube feeds (NG or NJ) or (2) exclusive oral feeding throughout HFNC support (Appendix 1).

The primary outcome was the number of hours to discharge from HFNC cessation. Secondary outcomes were time to discharge from HFNC initiation, all-cause readmissions within seven days of discharge, and potential feeding-related adverse events. Potential adverse events included: (1) aspiration, defined as initiation of antibiotic AND either chest radiograph official interpreted as evidence for aspiration and/or documented concern for aspiration from the treating physician, or (2) intubation after feeding during HFNC.

Statistical Analysis

Descriptive statistics evaluated differences in demographics and clinical variables for feeding exposure groups. We used chi-squared tests for differences in proportions and t-tests or Wilcoxon Rank-Sum tests for differences in means or medians for continuous variables, respectively. Associations between feeding exposure during HFNC and time to discharge (measured in hours) after HFNC completion were modeled with Cox proportional hazards regression. Using this approach, hazard ratios (HR)>1 indicate a higher hazard (rate) of discharge for children with a feeding exposure than for children without the exposure. For example, a hazard ratio equal to two indicates that the exposed population is discharged at twice the rate per unit time as the nonexposed population. Death or censoring events did not occur. Feeding exposure was first modeled dichotomously as not fed or fed. To further explore associations between feeding modality and our outcome, we then modeled feeding exposure categorically as not fed (reference), mixed (oral and tube) feeding, or exclusive oral feeding throughout HFNC.

After constructing a set of unadjusted models, we then adjusted the models for variables having independent (bivariate P < .10) associations with time to discharge: age, unit of HFNC initiation, highest respiratory support required before HFNC, and HFNC duration. Finally, to attempt to account for residual confounding from latent constructs, we also created a set of propensity-weighted Cox proportional hazards models. Propensity weights¹⁸ reflecting the probability of being fed or never being fed during HFNC were created using logistic regression with predictors we hypothesized a priori that may have influenced the clinical decision to feed during HFNC: age, day of illness on admission, prematurity, PIM2 ROM score, respiratory rate, oxygen saturation and blood gas acquisition at HFNC initiation, and highest respiratory support required before HFNC. All analyses were conducted using STATA 14.0 (StataCorp, College Station, Texas), and adjusted hazard ratios (aHR) with 95% confidence intervals (95% CIs) were reported.

Patients (n = 123) had a mean age of 7.3 months (standard deviation [SD] 7.1) and presented on day of illness 4.8 (SD 2.3). Prior to HFNC, 10% required higher respiratory support (3% mechanical ventilation). Former preterm children were 12% of the overall sample.

During HFNC, 37% of patients were never fed, 41% were exclusively orally fed, and 23% had tube or mixed oral and tube feedings (Table 1 and Appendix 2). Children who were not fed were older, but groups were otherwise similar in terms of gender, race/ethnicity, passive smoke exposure, day of illness, unit of HFNC initiation, respiratory support required prior to HFNC, and respiratory rate at HFNC initiation.

This observational study found that being fed during HFNC was associated with shorter time to discharge after HFNC support was completed. Exclusive oral feeding was associated with the shortest time to discharge, and these results were consistent across a variety of analytical approaches. Adverse events were rare and occurred in both fed and unfed children.

These findings advance research on relationships between nutrition and bronchiolitis outcomes. Studies of general care patients with bronchiolitis without HFNC have observed associations between poor nutrition and prolonged LOS.^19,20 Two previous studies of patients receiving HFNC therapy for bronchiolitis concluded that frequent interruption¹¹ and later initiation of enteral nutrition¹⁰ during ICU stay was associated with longer time to discharge.¹¹ To our knowledge, this is the first study of patients with bronchiolitis treated with HFNC in both general care and ICU settings that compared outcomes according to whether children were fed during HFNC therapy. Our results extend previous work demonstrating that delays in feeding may be associated with longer LOS.

Children fed while receiving HFNC for bronchiolitis may have shorter time to discharge than those who are not fed. Feeding-related adverse events were rare regardless of the feeding method. Controlled prospective studies addressing residual confounding are needed to justify a change in the current practice.

Acknowledgments

The authors would like to acknowledge the valuable feedback on earlier drafts from members of the University of Wisconsin Division of Pediatric Hospital Medicine CREATE writing group.

Bronchiolitis is the most common cause of nonbirth hospitalization in children in the United States less than one year of age.¹ For children with severe bronchiolitis, high-flow nasal cannula (HFNC) is increasingly used^2-4 to reduce work of breathing and prevent the need for further escalation of ventilatory support.^5,6 Although previous studies suggest that enteral feeding is recommended in the management of patients hospitalized with bronchiolitis,^7-9 limited evidence exists to guide feeding practices for patients receiving HFNC support.^5,10,11

Respiratory support with HFNC has been associated with prolonged periods without enteral hydration/nutrition (ie, nil per os [NPO])¹² primarily due to anticipation of further escalation of respiratory support or concern for increased risk of aspiration. The majority of patients with bronchiolitis managed with HFNC, however, do not require escalation of care.^5,13 When feeding is attempted during HFNC support, it is frequently interrupted.⁵ Moreover, keeping all children NPO when receiving HFNC may be associated with weight loss and longer length of stay (LOS).^12,14 Two small studies found that children admitted to the intensive care unit who received HFNC support for bronchiolitis did not have increased rates of emesis, worsening respiratory distress or aspiration pneumonia when enterally fed.^10,11 However, no comparison of adverse events or LOS has been made between patients who were fed and those who were not fed during HFNC therapy, and previous studies have included only patients who have received HFNC in the intensive care setting.

Supporting safe feeding early in hospitalizations for bronchiolitis may facilitate expedited clinical improvement and discharge. As part of an ongoing bronchiolitis quality improvement initiative at our hospital, we sought to characterize feeding practices during HFNC therapy and assess whether feeding exposure was associated with (1) time to discharge after HFNC or (2) feeding-related adverse events. We hypothesized that feeding during HFNC therapy would be associated with a shorter time to discharge after HFNC cessation.

Study Design, Setting, Participants

This was a retrospective cohort study of patients aged 1-24 months receiving HFNC support for respiratory failure due to bronchiolitis at an academic children’s hospital between January 1, 2015 and March 1, 2017. Our institution has had a clinical practice guideline, associated order set, and respiratory therapy protocol for general care patients with bronchiolitis since 2009. Patients with bronchiolitis who were weaning HFNC have been cared for in both the intensive and general care settings since 2013. A formal process for initiation of HFNC on general care units was instituted in January of 2017. During the study period, no patients with HFNC support for bronchiolitis had all their care entirely outside the intensive care unit at our institution. However, initiation and subsequent use of HFNC may have occurred in either the intensive care or general care setting. No specific guidance for feeding during HFNC existed during this period.

Patients were identified using the Virtual PICU Systems database, (VPS LLC, myvps.org, Los Angeles, California) and, by definition, all patients received at least some of their care in the intensive care unit. Patients with comorbid conditions of prematurity (<35 weeks) and those with cardiopulmonary, neuromuscular, and genetic diseases were included. Patients with preexisting dysphagia, defined as ongoing outpatient speech therapy for swallowing concerns, an admission diagnosis of aspiration pneumonia or on home respiratory support, were excluded. Children (n = 7) were excluded if they had more than one period of HFNC during admission. This study was determined to be exempt by the University of Wisconsin School of Medicine and Public Health’s Institutional Review Board.

Data Collection and Study Variables

The following variables were collected from VPS administrative data: patient gender, age, admission and discharge date and time, type and total hours of respiratory support, intensive care admission, and LOS (in hours). Additional demographic, clinical, and feeding exposure variables were abstracted manually from the electronic medical record (Epic, Verona, Wisconsin) using a structured data collection tool and stored in REDCap (Research Electronic Data Capture)¹⁵ including prematurity, race/ethnicity, insurance status, primary language, and passive tobacco smoke exposure. Clinical variables included duration of illness (days) at the time of admission, unit of HFNC initiation (emergency department, general care, intensive care, respiratory rate and oxygen saturation at HFNC initiation (<90%, 91%-92%, or >92%), acquisition of blood gas at HFNC admission, duration of time on HFNC (hours) and need for intubation or noninvasive ventilation prior to HFNC. The Pediatric Index of Mortality 2 Risk of Mortality (PIM 2 ROM)¹⁶ was used to estimate the severity of illness. The PIM2ROM uses clinical variables (systolic blood pressure, fixed pupils, measure of hypoxia using PaO₂/FiO₂ ratio, base excess, mechanical ventilation, elective admission, recovery from surgery, cardiac bypass, high-risk diagnosis, low-risk diagnosis) collected at the time of intensive care admission to generate a score that predicts the risk of mortality for an individual patient.¹⁷

Feeding exposures were documented in three-hour intervals from HFNC initiation to completion using a structured protocol. At each interval the following feeding information was abstracted from a review of nursing and physician documentation and relevant clinical flowsheets: presence or absence of feeding during the interval, route of feeding (oral, nasogastric [NG] or nasojejunal [NJ]). Feeding exposure was categorized a priori as fed at any point during HFNC (vs not fed at any point). Fed children were further characterized as (1) mixed feeding consisting of oral and tube feeds (NG or NJ) or (2) exclusive oral feeding throughout HFNC support (Appendix 1).

The primary outcome was the number of hours to discharge from HFNC cessation. Secondary outcomes were time to discharge from HFNC initiation, all-cause readmissions within seven days of discharge, and potential feeding-related adverse events. Potential adverse events included: (1) aspiration, defined as initiation of antibiotic AND either chest radiograph official interpreted as evidence for aspiration and/or documented concern for aspiration from the treating physician, or (2) intubation after feeding during HFNC.

Statistical Analysis

Descriptive statistics evaluated differences in demographics and clinical variables for feeding exposure groups. We used chi-squared tests for differences in proportions and t-tests or Wilcoxon Rank-Sum tests for differences in means or medians for continuous variables, respectively. Associations between feeding exposure during HFNC and time to discharge (measured in hours) after HFNC completion were modeled with Cox proportional hazards regression. Using this approach, hazard ratios (HR)>1 indicate a higher hazard (rate) of discharge for children with a feeding exposure than for children without the exposure. For example, a hazard ratio equal to two indicates that the exposed population is discharged at twice the rate per unit time as the nonexposed population. Death or censoring events did not occur. Feeding exposure was first modeled dichotomously as not fed or fed. To further explore associations between feeding modality and our outcome, we then modeled feeding exposure categorically as not fed (reference), mixed (oral and tube) feeding, or exclusive oral feeding throughout HFNC.

After constructing a set of unadjusted models, we then adjusted the models for variables having independent (bivariate P < .10) associations with time to discharge: age, unit of HFNC initiation, highest respiratory support required before HFNC, and HFNC duration. Finally, to attempt to account for residual confounding from latent constructs, we also created a set of propensity-weighted Cox proportional hazards models. Propensity weights¹⁸ reflecting the probability of being fed or never being fed during HFNC were created using logistic regression with predictors we hypothesized a priori that may have influenced the clinical decision to feed during HFNC: age, day of illness on admission, prematurity, PIM2 ROM score, respiratory rate, oxygen saturation and blood gas acquisition at HFNC initiation, and highest respiratory support required before HFNC. All analyses were conducted using STATA 14.0 (StataCorp, College Station, Texas), and adjusted hazard ratios (aHR) with 95% confidence intervals (95% CIs) were reported.

Patients (n = 123) had a mean age of 7.3 months (standard deviation [SD] 7.1) and presented on day of illness 4.8 (SD 2.3). Prior to HFNC, 10% required higher respiratory support (3% mechanical ventilation). Former preterm children were 12% of the overall sample.

During HFNC, 37% of patients were never fed, 41% were exclusively orally fed, and 23% had tube or mixed oral and tube feedings (Table 1 and Appendix 2). Children who were not fed were older, but groups were otherwise similar in terms of gender, race/ethnicity, passive smoke exposure, day of illness, unit of HFNC initiation, respiratory support required prior to HFNC, and respiratory rate at HFNC initiation.

This observational study found that being fed during HFNC was associated with shorter time to discharge after HFNC support was completed. Exclusive oral feeding was associated with the shortest time to discharge, and these results were consistent across a variety of analytical approaches. Adverse events were rare and occurred in both fed and unfed children.

These findings advance research on relationships between nutrition and bronchiolitis outcomes. Studies of general care patients with bronchiolitis without HFNC have observed associations between poor nutrition and prolonged LOS.^19,20 Two previous studies of patients receiving HFNC therapy for bronchiolitis concluded that frequent interruption¹¹ and later initiation of enteral nutrition¹⁰ during ICU stay was associated with longer time to discharge.¹¹ To our knowledge, this is the first study of patients with bronchiolitis treated with HFNC in both general care and ICU settings that compared outcomes according to whether children were fed during HFNC therapy. Our results extend previous work demonstrating that delays in feeding may be associated with longer LOS.

Children fed while receiving HFNC for bronchiolitis may have shorter time to discharge than those who are not fed. Feeding-related adverse events were rare regardless of the feeding method. Controlled prospective studies addressing residual confounding are needed to justify a change in the current practice.

Acknowledgments

The authors would like to acknowledge the valuable feedback on earlier drafts from members of the University of Wisconsin Division of Pediatric Hospital Medicine CREATE writing group.

Under the Hospital Readmission Reduction Program (HRRP), hospitals with higher than expected readmissions for select conditions receive a financial penalty. In 2017, hospitals were penalized a total of $528 million.^1,2 In an effort to deter readmissions, hospitals have focused on the transition from inpatient to outpatient care with particular emphasis on timely follow-up with a primary care physician (PCP).^3-7 Medicare has also introduced transitional care codes, which reimburse physicians for follow-up care after a hospitalization.

Most observational studies have found an association among patients discharged from the hospital between early follow-up with a PCP and fewer readmissions. One study found that patients without timely PCP follow-up after hospitalization on medical wards had a 10-fold increase in the likelihood of readmission.⁵ This association between early PCP follow-up and readmissions has been echoed in studies of all general admissions,⁵ as well as hospitalizations specific to heart failure,^7,8 chronic obstructive pulmonary disease,³ high-risk surgery,⁹ and sickle cell disease.¹⁰ One potential concern with this prior literature is that unmeasured patient characteristics might be confounders; for example, patients with more social support may be both more likely to have follow-up visits and less likely to have readmissions. Also, there are several studies showing no association between early PCP appointments and readmission rates.^6,9,11-13

Several prior interventional studies to improve the care transition from hospital to outpatient care have successfully deterred readmissions.¹⁴ In these trials, facilitating early PCP follow-up is just one component of a larger intervention,¹⁵ and a systemic review noted that the interventions were heterogeneous and often consisted of multiple complex steps.⁶ It is less clear whether interventions to facilitate early PCP follow-up alone are successful.

In this study, we evaluated the impact of an intervention that focused on facilitating early follow-up of PCPs. We assessed the impact of this intervention on the likelihood of having a PCP appointment within seven days of discharge and being readmitted within 30 days of discharge.

Postdischarge Appointment Service

In the fall of 2009, Beth Israel Deaconess introduced a postdischarge appointment intervention to facilitate follow-up with PCPs and specialty physicians after discharge from the hospital. Within the provider order entry system, attending and resident physicians enter a discharge appointment request for specified providers within and outside of the medical center and a specified time period. For example, a physician may enter a request to schedule a PCP appointment within 2-3, 4-8, 9-15, 16-30, or >30 days of discharge. Physicians are asked to submit this request on the day of discharge. The request is transmitted to dedicated staff (four full-time administrative staff and four part-time registered nurses) who verify the PCP, process the orders, and call the relevant practices to book the appointments. The date and time of the follow-up appointments are set without input from the patient. The details of the appointment, location, phone number of the clinic, and any other relevant instructions are automatically entered into the discharge instructions and discharge summary. The service is available Monday through Friday, and the turnaround on appointment creation is typically within one to three hours of the request. For patients who do not have a PCP or want to switch their PCP, the discharging physician can request a new PCP within the health system, and the service will schedule an appointment in this new PCP’s practice. Anecdotally, physicians are more likely to order the postdischarge appointment service for patients with more complex illnesses and longer lengths of stay and for those who come from underserved populations, as they perceive that it is more important for the patient to have this follow-up appointment, and/or the patient may have a harder time navigating the system and scheduling an appointment. Because of funding limitations, the hospital limited the intervention to hospitalizations on the general medicine and cardiology services. It was expanded in late 2011 to include the trauma surgery service.

Study Population

We conducted a retrospective, cohort study at Beth Israel Deaconess Medical Center, a tertiary care hospital, using data derived from electronic health records for all hospitalizations from September 2008 to October 2015. At this medical center, the vast majority of patients on the general medicine service are cared for by hospitalists and not their PCPs. We focused on patients 18 years of age or older discharged home and excluded hospitalizations where the patient died, was transferred to another hospital, or was discharged to a skilled nursing facility or inpatient rehabilitation hospital. We excluded patients who were kept under observation in the emergency department (ED), but our data did include patients cared for on a hospital ward under observation. To measure whether patients attended a follow-up visit, we used internal scheduling data and therefore only included hospitalizations for patients with a PCP affiliated with the Beth Israel Deaconess medical system. This includes patients previously without a PCP whose first PCP appointment was after discharge. Finally, we limited our sample to hospitalizations on the general medicine and cardiology services because, as previously discussed, these are the services where the intervention was available. To address transfers within the hospital from one service to another, we categorized hospitalizations by the service on the date of discharge.

Outcomes

The primary outcomes of this study were kept PCP follow-up visits within seven days and readmission within 30 days of discharge. We focused on PCP visits within seven days, as this has been the measure used in prior research,^5,7 but conducted a sensitivity analysis of PCP follow-up within 14 days. No-shows for the scheduled follow-up PCP appointments were not included. We focused on readmissions within 30 days of discharge, given this is the measure used in the HRRP,¹⁶ but conducted a sensitivity analysis of 14 days. Secondary outcomes included ED revisit within the 30 days. Given the data available, we only observed physician visits and hospitalizations that occurred within the Beth Israel Deaconess system.

Analyses

We conducted two analyses to assess whether the implementation of the postdischarge appointment service was associated with an increase in PCP follow-up and a decrease in the readmission rate.

In the first analysis, we focused only on hospitalizations from the medical and cardiology services during the postintervention period between January 2011 and September 2015 (n = 17,582). We compared the PCP follow-up rate and the readmission rate among hospitalizations where the postdischarge appointment service was used versus those where it was not used. We used a multivariable logistic regression, and the covariates included in the model were age, gender, hospital length of stay, and diagnosis-related group (DRG) cost weight. The DRG cost weight captures the average resources used to treat Medicare patients’ hospitalizations within a given DRG category and was used as a surrogate marker for the complexity of hospitalization.¹⁷ Instead of presenting odds ratios, we used predictive margins to generate adjusted percentage point estimates of the differences in our outcomes associated with the use of the postdischarge appointment service.¹⁸

In our second analysis, we examined the period before and after the introduction of the postdischarge appointment service (September 2008 through October 2015, n = 20,918). Among these hospitalizations, we conducted an instrumental variable analysis to address the concern that there are unmeasured differences between those patients who receive the discharge appointment service and those who do not. Instrumental variable analyses are used to estimate causal relationships in observational studies.¹⁹ A valid instrument is associated with the explanatory variable (use of the postdischarge appointment service) but has no independent effect on the outcomes (follow-up visits, readmissions). In this analysis, our set of instruments was the day of the week of admission (indicator variable for each day) interacted with the time period (pre- vs postintervention period).

This instrumental variable exploits the fact that the postdischarge appointment service was only available on weekdays and that physicians are asked to only submit the order for follow-up appointments on the day of discharge. We focused on the day of the week of admission (versus discharge) because of concerns that patients with more complicated hospital courses might be kept in the hospital over the weekend (eg, to facilitate testing available only on weekdays or to consult with regular physicians only available on weekdays). This would create a relationship between the day of discharge and the outcomes (follow-up visits, readmissions). The day of admission is less likely to be impacted by this bias. Given concerns that admissions on different days of the week might be different, our instrument is the day of the week interacted with the time period. Therefore, to create bias, there must be a systematic change in the nature of admissions on a given day of the week during this time period. We provide more details on this analysis, testing of the instrument, and results in the Appendix.

Analyses were conducted in Stata, version 14.2 (StataCorp LP, College Station, Texas). Statistical testing was two-sided, with a significance level of 0.05, and the project was judged exempt by the Committee on Clinical Investigations for Beth Israel Deaconess Medical Center.

Overall, there were 17,582 hospitalizations on the medicine and cardiology services following implementation of the postdischarge appointment service. The use of the postdischarge appointment service rose rapidly after it was introduced (Figure) and then plateaued at roughly 50%. Of the hospitalizations where the postdischarge appointment service was used, the physician requested a new PCP for 1.2% of the patients. Among hospitalizations where the intervention was used, the average age was 65.5 years, 55.7% were female, the length of stay was 3.52 days, the DRG cost weight was 1.26 and 20.4% were patients on the cardiology service. Characteristics were similar in hospitalizations where the services were not used (Table 1).

Multivariable Logistic Regression

In this analysis, we focused on the 17,582 hospitalizations from January 2011 to September 2015 on the general medicine and cardiology services that occurred after the postdischarge appointment service was introduced. Among these hospitalizations, the postdischarge appointment service was used in 51.8% of discharges.

In an unadjusted analysis, patients discharged using the tool had higher rates of seven-day PCP follow-up (60.2% vs 29.2%, P < .001) and lower 30-day readmission rates (14.7% vs 16.7%; P < .001) than those who were not (Table 2). There was no significant difference in 30-day ED revisit between hospitalizations with and without use of the postdischarge appointment service (22.3% vs 23.1%; P = .23).

Instrumental Variable Analysis

In our instrumental variable analysis, we used all hospitalizations both before and after the introduction of the intervention. In this analysis, we estimate that use of the postdischarge appointment service increases the probability of visiting a PCP within seven days by 33.4 percentage points (95% CI: 7.9%, 58.9%; P = .01) (Table 3). The use of the postdischarge appointment was associated with a 2.5 percentage point (95% CI: −22.0%, 17.1%; P = .80) reduction in readmissions and a 4.8 percentage point (95% CI; −27.5%, 17.9%; P = .68) reduction in an ED visit within 30 days (Table 3). Neither of these differences were statistically significant with wide confidence intervals.

In sensitivity analyses, we obtained similar results when we considered PCP visits and readmissions within 14 days.

The hospital introduced the postdischarge appointment service to facilitate postdischarge appointments and to deter readmissions. In our analyses the use of the postdischarge appointment service was associated with a substantial 30 percentage point increase in the likelihood of a PCP follow-up visit within seven days after hospital discharge. There was a roughly 2% reduction in 30-day readmissions, but this difference was not consistently statistically significant across our analyses. Together, our evaluation implies that this type of intervention may make it much easier for patients to attend a PCP appointment, but scheduling an appointment alone may have a modest impact on deterring a readmission.

Our findings are inconsistent with prior studies that described a strong association between early PCP follow-up and readmissions. However, our results were consistent with research where follow-up visits were not clearly protective against readmissions.²⁰ One potential explanation of the discrepant findings is that there are unmeasured socioeconomic differences between patients who have a PCP follow-up appointment and those who do not.

We advance the literature by studying an intervention focused only on increasing early PCP follow-up. Most successful readmission programs that have been studied in randomized, controlled trials take a multipronged approach, including transitional care management with dedicated staff and medication reconciliation.^{3-7,9,15,21-23} For example, Coleman and colleagues randomized 750 hospitalized patients to a care-transitions intervention, which led to a substantial decrease in readmissions.¹⁵ Their care-transitions intervention included four components: (1) timely PCP or specialist follow-up, (2) educating patients on how best to take their medications, (3) a patient-centered record that allowed them to track their own disease and care, and (4) disease-specific patient education. The relative importance of each of these components in deterring readmissions is unclear. Instead of this multipronged strategy, we focused on a single component—timely follow-up. Together, our study and these prior studies are broadly consistent with a meta-analysis that suggests that transitional care programs with a narrow focus are less successful at reducing readmissions.²⁴ Facilitating early PCP follow-up alone is not a panacea and can be undermined by the incomplete or inexistent transmission of the discharge paperwork.^{25, 26} Moreover, the impact of interventions may only be seen among the highest-risk populations, and ongoing work by others seeks to identify these patients.²⁷

Regardless of the impact on readmissions, it is important to acknowledge that early PCP follow-up offers many potential benefits. Continuing to evaluate and treat new diagnoses, adjusting and reconciling medications, reconnecting with outpatient providers, capturing new incidental findings, and ensuring stability through regular follow-up are just a few of the potential benefits. We believe the dramatic increase observed in PCP follow-up reflects the administrative complexity required for a patient to call their PCP’s office and to schedule a follow-up appointment soon after they are discharged from the hospital. Our study implies that simply requesting that a patient call their PCP to schedule a timely appointment is often impossible, and this may be particularly true for those who need to obtain a new PCP.

Our study has many limitations. The study was limited to a single academic center, and the intervention was limited to patients cared for by the general medicine and cardiology services. Our multivariable regression analysis comparing outcomes among patients where the postdischarge appointment service was used and not used may be biased by unmeasured differences in these patients. We attempted to address this limitation by exploiting the fact that the intervention was only available on weekdays through an instrumental variable analysis, but the instrument we used itself is subject to bias. Also, in the instrumental variable analysis, our estimates were imprecise and therefore not powered to identify smaller but still clinically important reductions in readmissions. Given the data limitations, we could not compare the no-show rates among appointments made by the discharge appointment service versus those made by patients. Finally, we were only able to observe follow-up visits and hospitalizations within the health system, and it is possible that our results were biased by patients preferentially going to other hospitals for readmission.

In summary, we found that the introduction of a postdischarge appointment service resulted in substantially increased rates of early PCP follow-up but less clear benefits in preventing readmissions.

Under the Hospital Readmission Reduction Program (HRRP), hospitals with higher than expected readmissions for select conditions receive a financial penalty. In 2017, hospitals were penalized a total of $528 million.^1,2 In an effort to deter readmissions, hospitals have focused on the transition from inpatient to outpatient care with particular emphasis on timely follow-up with a primary care physician (PCP).^3-7 Medicare has also introduced transitional care codes, which reimburse physicians for follow-up care after a hospitalization.

Most observational studies have found an association among patients discharged from the hospital between early follow-up with a PCP and fewer readmissions. One study found that patients without timely PCP follow-up after hospitalization on medical wards had a 10-fold increase in the likelihood of readmission.⁵ This association between early PCP follow-up and readmissions has been echoed in studies of all general admissions,⁵ as well as hospitalizations specific to heart failure,^7,8 chronic obstructive pulmonary disease,³ high-risk surgery,⁹ and sickle cell disease.¹⁰ One potential concern with this prior literature is that unmeasured patient characteristics might be confounders; for example, patients with more social support may be both more likely to have follow-up visits and less likely to have readmissions. Also, there are several studies showing no association between early PCP appointments and readmission rates.^6,9,11-13

Several prior interventional studies to improve the care transition from hospital to outpatient care have successfully deterred readmissions.¹⁴ In these trials, facilitating early PCP follow-up is just one component of a larger intervention,¹⁵ and a systemic review noted that the interventions were heterogeneous and often consisted of multiple complex steps.⁶ It is less clear whether interventions to facilitate early PCP follow-up alone are successful.

In this study, we evaluated the impact of an intervention that focused on facilitating early follow-up of PCPs. We assessed the impact of this intervention on the likelihood of having a PCP appointment within seven days of discharge and being readmitted within 30 days of discharge.

Postdischarge Appointment Service

In the fall of 2009, Beth Israel Deaconess introduced a postdischarge appointment intervention to facilitate follow-up with PCPs and specialty physicians after discharge from the hospital. Within the provider order entry system, attending and resident physicians enter a discharge appointment request for specified providers within and outside of the medical center and a specified time period. For example, a physician may enter a request to schedule a PCP appointment within 2-3, 4-8, 9-15, 16-30, or >30 days of discharge. Physicians are asked to submit this request on the day of discharge. The request is transmitted to dedicated staff (four full-time administrative staff and four part-time registered nurses) who verify the PCP, process the orders, and call the relevant practices to book the appointments. The date and time of the follow-up appointments are set without input from the patient. The details of the appointment, location, phone number of the clinic, and any other relevant instructions are automatically entered into the discharge instructions and discharge summary. The service is available Monday through Friday, and the turnaround on appointment creation is typically within one to three hours of the request. For patients who do not have a PCP or want to switch their PCP, the discharging physician can request a new PCP within the health system, and the service will schedule an appointment in this new PCP’s practice. Anecdotally, physicians are more likely to order the postdischarge appointment service for patients with more complex illnesses and longer lengths of stay and for those who come from underserved populations, as they perceive that it is more important for the patient to have this follow-up appointment, and/or the patient may have a harder time navigating the system and scheduling an appointment. Because of funding limitations, the hospital limited the intervention to hospitalizations on the general medicine and cardiology services. It was expanded in late 2011 to include the trauma surgery service.

Study Population

We conducted a retrospective, cohort study at Beth Israel Deaconess Medical Center, a tertiary care hospital, using data derived from electronic health records for all hospitalizations from September 2008 to October 2015. At this medical center, the vast majority of patients on the general medicine service are cared for by hospitalists and not their PCPs. We focused on patients 18 years of age or older discharged home and excluded hospitalizations where the patient died, was transferred to another hospital, or was discharged to a skilled nursing facility or inpatient rehabilitation hospital. We excluded patients who were kept under observation in the emergency department (ED), but our data did include patients cared for on a hospital ward under observation. To measure whether patients attended a follow-up visit, we used internal scheduling data and therefore only included hospitalizations for patients with a PCP affiliated with the Beth Israel Deaconess medical system. This includes patients previously without a PCP whose first PCP appointment was after discharge. Finally, we limited our sample to hospitalizations on the general medicine and cardiology services because, as previously discussed, these are the services where the intervention was available. To address transfers within the hospital from one service to another, we categorized hospitalizations by the service on the date of discharge.

Outcomes

The primary outcomes of this study were kept PCP follow-up visits within seven days and readmission within 30 days of discharge. We focused on PCP visits within seven days, as this has been the measure used in prior research,^5,7 but conducted a sensitivity analysis of PCP follow-up within 14 days. No-shows for the scheduled follow-up PCP appointments were not included. We focused on readmissions within 30 days of discharge, given this is the measure used in the HRRP,¹⁶ but conducted a sensitivity analysis of 14 days. Secondary outcomes included ED revisit within the 30 days. Given the data available, we only observed physician visits and hospitalizations that occurred within the Beth Israel Deaconess system.

Analyses

We conducted two analyses to assess whether the implementation of the postdischarge appointment service was associated with an increase in PCP follow-up and a decrease in the readmission rate.

In the first analysis, we focused only on hospitalizations from the medical and cardiology services during the postintervention period between January 2011 and September 2015 (n = 17,582). We compared the PCP follow-up rate and the readmission rate among hospitalizations where the postdischarge appointment service was used versus those where it was not used. We used a multivariable logistic regression, and the covariates included in the model were age, gender, hospital length of stay, and diagnosis-related group (DRG) cost weight. The DRG cost weight captures the average resources used to treat Medicare patients’ hospitalizations within a given DRG category and was used as a surrogate marker for the complexity of hospitalization.¹⁷ Instead of presenting odds ratios, we used predictive margins to generate adjusted percentage point estimates of the differences in our outcomes associated with the use of the postdischarge appointment service.¹⁸

In our second analysis, we examined the period before and after the introduction of the postdischarge appointment service (September 2008 through October 2015, n = 20,918). Among these hospitalizations, we conducted an instrumental variable analysis to address the concern that there are unmeasured differences between those patients who receive the discharge appointment service and those who do not. Instrumental variable analyses are used to estimate causal relationships in observational studies.¹⁹ A valid instrument is associated with the explanatory variable (use of the postdischarge appointment service) but has no independent effect on the outcomes (follow-up visits, readmissions). In this analysis, our set of instruments was the day of the week of admission (indicator variable for each day) interacted with the time period (pre- vs postintervention period).

This instrumental variable exploits the fact that the postdischarge appointment service was only available on weekdays and that physicians are asked to only submit the order for follow-up appointments on the day of discharge. We focused on the day of the week of admission (versus discharge) because of concerns that patients with more complicated hospital courses might be kept in the hospital over the weekend (eg, to facilitate testing available only on weekdays or to consult with regular physicians only available on weekdays). This would create a relationship between the day of discharge and the outcomes (follow-up visits, readmissions). The day of admission is less likely to be impacted by this bias. Given concerns that admissions on different days of the week might be different, our instrument is the day of the week interacted with the time period. Therefore, to create bias, there must be a systematic change in the nature of admissions on a given day of the week during this time period. We provide more details on this analysis, testing of the instrument, and results in the Appendix.

Analyses were conducted in Stata, version 14.2 (StataCorp LP, College Station, Texas). Statistical testing was two-sided, with a significance level of 0.05, and the project was judged exempt by the Committee on Clinical Investigations for Beth Israel Deaconess Medical Center.

Overall, there were 17,582 hospitalizations on the medicine and cardiology services following implementation of the postdischarge appointment service. The use of the postdischarge appointment service rose rapidly after it was introduced (Figure) and then plateaued at roughly 50%. Of the hospitalizations where the postdischarge appointment service was used, the physician requested a new PCP for 1.2% of the patients. Among hospitalizations where the intervention was used, the average age was 65.5 years, 55.7% were female, the length of stay was 3.52 days, the DRG cost weight was 1.26 and 20.4% were patients on the cardiology service. Characteristics were similar in hospitalizations where the services were not used (Table 1).

Multivariable Logistic Regression

In this analysis, we focused on the 17,582 hospitalizations from January 2011 to September 2015 on the general medicine and cardiology services that occurred after the postdischarge appointment service was introduced. Among these hospitalizations, the postdischarge appointment service was used in 51.8% of discharges.

In an unadjusted analysis, patients discharged using the tool had higher rates of seven-day PCP follow-up (60.2% vs 29.2%, P < .001) and lower 30-day readmission rates (14.7% vs 16.7%; P < .001) than those who were not (Table 2). There was no significant difference in 30-day ED revisit between hospitalizations with and without use of the postdischarge appointment service (22.3% vs 23.1%; P = .23).

Instrumental Variable Analysis

In our instrumental variable analysis, we used all hospitalizations both before and after the introduction of the intervention. In this analysis, we estimate that use of the postdischarge appointment service increases the probability of visiting a PCP within seven days by 33.4 percentage points (95% CI: 7.9%, 58.9%; P = .01) (Table 3). The use of the postdischarge appointment was associated with a 2.5 percentage point (95% CI: −22.0%, 17.1%; P = .80) reduction in readmissions and a 4.8 percentage point (95% CI; −27.5%, 17.9%; P = .68) reduction in an ED visit within 30 days (Table 3). Neither of these differences were statistically significant with wide confidence intervals.

In sensitivity analyses, we obtained similar results when we considered PCP visits and readmissions within 14 days.

The hospital introduced the postdischarge appointment service to facilitate postdischarge appointments and to deter readmissions. In our analyses the use of the postdischarge appointment service was associated with a substantial 30 percentage point increase in the likelihood of a PCP follow-up visit within seven days after hospital discharge. There was a roughly 2% reduction in 30-day readmissions, but this difference was not consistently statistically significant across our analyses. Together, our evaluation implies that this type of intervention may make it much easier for patients to attend a PCP appointment, but scheduling an appointment alone may have a modest impact on deterring a readmission.

Our findings are inconsistent with prior studies that described a strong association between early PCP follow-up and readmissions. However, our results were consistent with research where follow-up visits were not clearly protective against readmissions.²⁰ One potential explanation of the discrepant findings is that there are unmeasured socioeconomic differences between patients who have a PCP follow-up appointment and those who do not.

We advance the literature by studying an intervention focused only on increasing early PCP follow-up. Most successful readmission programs that have been studied in randomized, controlled trials take a multipronged approach, including transitional care management with dedicated staff and medication reconciliation.^{3-7,9,15,21-23} For example, Coleman and colleagues randomized 750 hospitalized patients to a care-transitions intervention, which led to a substantial decrease in readmissions.¹⁵ Their care-transitions intervention included four components: (1) timely PCP or specialist follow-up, (2) educating patients on how best to take their medications, (3) a patient-centered record that allowed them to track their own disease and care, and (4) disease-specific patient education. The relative importance of each of these components in deterring readmissions is unclear. Instead of this multipronged strategy, we focused on a single component—timely follow-up. Together, our study and these prior studies are broadly consistent with a meta-analysis that suggests that transitional care programs with a narrow focus are less successful at reducing readmissions.²⁴ Facilitating early PCP follow-up alone is not a panacea and can be undermined by the incomplete or inexistent transmission of the discharge paperwork.^{25, 26} Moreover, the impact of interventions may only be seen among the highest-risk populations, and ongoing work by others seeks to identify these patients.²⁷

Regardless of the impact on readmissions, it is important to acknowledge that early PCP follow-up offers many potential benefits. Continuing to evaluate and treat new diagnoses, adjusting and reconciling medications, reconnecting with outpatient providers, capturing new incidental findings, and ensuring stability through regular follow-up are just a few of the potential benefits. We believe the dramatic increase observed in PCP follow-up reflects the administrative complexity required for a patient to call their PCP’s office and to schedule a follow-up appointment soon after they are discharged from the hospital. Our study implies that simply requesting that a patient call their PCP to schedule a timely appointment is often impossible, and this may be particularly true for those who need to obtain a new PCP.

Our study has many limitations. The study was limited to a single academic center, and the intervention was limited to patients cared for by the general medicine and cardiology services. Our multivariable regression analysis comparing outcomes among patients where the postdischarge appointment service was used and not used may be biased by unmeasured differences in these patients. We attempted to address this limitation by exploiting the fact that the intervention was only available on weekdays through an instrumental variable analysis, but the instrument we used itself is subject to bias. Also, in the instrumental variable analysis, our estimates were imprecise and therefore not powered to identify smaller but still clinically important reductions in readmissions. Given the data limitations, we could not compare the no-show rates among appointments made by the discharge appointment service versus those made by patients. Finally, we were only able to observe follow-up visits and hospitalizations within the health system, and it is possible that our results were biased by patients preferentially going to other hospitals for readmission.

In summary, we found that the introduction of a postdischarge appointment service resulted in substantially increased rates of early PCP follow-up but less clear benefits in preventing readmissions.

Under the Hospital Readmission Reduction Program (HRRP), hospitals with higher than expected readmissions for select conditions receive a financial penalty. In 2017, hospitals were penalized a total of $528 million.^1,2 In an effort to deter readmissions, hospitals have focused on the transition from inpatient to outpatient care with particular emphasis on timely follow-up with a primary care physician (PCP).^3-7 Medicare has also introduced transitional care codes, which reimburse physicians for follow-up care after a hospitalization.

Most observational studies have found an association among patients discharged from the hospital between early follow-up with a PCP and fewer readmissions. One study found that patients without timely PCP follow-up after hospitalization on medical wards had a 10-fold increase in the likelihood of readmission.⁵ This association between early PCP follow-up and readmissions has been echoed in studies of all general admissions,⁵ as well as hospitalizations specific to heart failure,^7,8 chronic obstructive pulmonary disease,³ high-risk surgery,⁹ and sickle cell disease.¹⁰ One potential concern with this prior literature is that unmeasured patient characteristics might be confounders; for example, patients with more social support may be both more likely to have follow-up visits and less likely to have readmissions. Also, there are several studies showing no association between early PCP appointments and readmission rates.^6,9,11-13

Several prior interventional studies to improve the care transition from hospital to outpatient care have successfully deterred readmissions.¹⁴ In these trials, facilitating early PCP follow-up is just one component of a larger intervention,¹⁵ and a systemic review noted that the interventions were heterogeneous and often consisted of multiple complex steps.⁶ It is less clear whether interventions to facilitate early PCP follow-up alone are successful.

In this study, we evaluated the impact of an intervention that focused on facilitating early follow-up of PCPs. We assessed the impact of this intervention on the likelihood of having a PCP appointment within seven days of discharge and being readmitted within 30 days of discharge.

Postdischarge Appointment Service

In the fall of 2009, Beth Israel Deaconess introduced a postdischarge appointment intervention to facilitate follow-up with PCPs and specialty physicians after discharge from the hospital. Within the provider order entry system, attending and resident physicians enter a discharge appointment request for specified providers within and outside of the medical center and a specified time period. For example, a physician may enter a request to schedule a PCP appointment within 2-3, 4-8, 9-15, 16-30, or >30 days of discharge. Physicians are asked to submit this request on the day of discharge. The request is transmitted to dedicated staff (four full-time administrative staff and four part-time registered nurses) who verify the PCP, process the orders, and call the relevant practices to book the appointments. The date and time of the follow-up appointments are set without input from the patient. The details of the appointment, location, phone number of the clinic, and any other relevant instructions are automatically entered into the discharge instructions and discharge summary. The service is available Monday through Friday, and the turnaround on appointment creation is typically within one to three hours of the request. For patients who do not have a PCP or want to switch their PCP, the discharging physician can request a new PCP within the health system, and the service will schedule an appointment in this new PCP’s practice. Anecdotally, physicians are more likely to order the postdischarge appointment service for patients with more complex illnesses and longer lengths of stay and for those who come from underserved populations, as they perceive that it is more important for the patient to have this follow-up appointment, and/or the patient may have a harder time navigating the system and scheduling an appointment. Because of funding limitations, the hospital limited the intervention to hospitalizations on the general medicine and cardiology services. It was expanded in late 2011 to include the trauma surgery service.

Study Population

We conducted a retrospective, cohort study at Beth Israel Deaconess Medical Center, a tertiary care hospital, using data derived from electronic health records for all hospitalizations from September 2008 to October 2015. At this medical center, the vast majority of patients on the general medicine service are cared for by hospitalists and not their PCPs. We focused on patients 18 years of age or older discharged home and excluded hospitalizations where the patient died, was transferred to another hospital, or was discharged to a skilled nursing facility or inpatient rehabilitation hospital. We excluded patients who were kept under observation in the emergency department (ED), but our data did include patients cared for on a hospital ward under observation. To measure whether patients attended a follow-up visit, we used internal scheduling data and therefore only included hospitalizations for patients with a PCP affiliated with the Beth Israel Deaconess medical system. This includes patients previously without a PCP whose first PCP appointment was after discharge. Finally, we limited our sample to hospitalizations on the general medicine and cardiology services because, as previously discussed, these are the services where the intervention was available. To address transfers within the hospital from one service to another, we categorized hospitalizations by the service on the date of discharge.

Outcomes

The primary outcomes of this study were kept PCP follow-up visits within seven days and readmission within 30 days of discharge. We focused on PCP visits within seven days, as this has been the measure used in prior research,^5,7 but conducted a sensitivity analysis of PCP follow-up within 14 days. No-shows for the scheduled follow-up PCP appointments were not included. We focused on readmissions within 30 days of discharge, given this is the measure used in the HRRP,¹⁶ but conducted a sensitivity analysis of 14 days. Secondary outcomes included ED revisit within the 30 days. Given the data available, we only observed physician visits and hospitalizations that occurred within the Beth Israel Deaconess system.

Analyses

We conducted two analyses to assess whether the implementation of the postdischarge appointment service was associated with an increase in PCP follow-up and a decrease in the readmission rate.

In the first analysis, we focused only on hospitalizations from the medical and cardiology services during the postintervention period between January 2011 and September 2015 (n = 17,582). We compared the PCP follow-up rate and the readmission rate among hospitalizations where the postdischarge appointment service was used versus those where it was not used. We used a multivariable logistic regression, and the covariates included in the model were age, gender, hospital length of stay, and diagnosis-related group (DRG) cost weight. The DRG cost weight captures the average resources used to treat Medicare patients’ hospitalizations within a given DRG category and was used as a surrogate marker for the complexity of hospitalization.¹⁷ Instead of presenting odds ratios, we used predictive margins to generate adjusted percentage point estimates of the differences in our outcomes associated with the use of the postdischarge appointment service.¹⁸

In our second analysis, we examined the period before and after the introduction of the postdischarge appointment service (September 2008 through October 2015, n = 20,918). Among these hospitalizations, we conducted an instrumental variable analysis to address the concern that there are unmeasured differences between those patients who receive the discharge appointment service and those who do not. Instrumental variable analyses are used to estimate causal relationships in observational studies.¹⁹ A valid instrument is associated with the explanatory variable (use of the postdischarge appointment service) but has no independent effect on the outcomes (follow-up visits, readmissions). In this analysis, our set of instruments was the day of the week of admission (indicator variable for each day) interacted with the time period (pre- vs postintervention period).

This instrumental variable exploits the fact that the postdischarge appointment service was only available on weekdays and that physicians are asked to only submit the order for follow-up appointments on the day of discharge. We focused on the day of the week of admission (versus discharge) because of concerns that patients with more complicated hospital courses might be kept in the hospital over the weekend (eg, to facilitate testing available only on weekdays or to consult with regular physicians only available on weekdays). This would create a relationship between the day of discharge and the outcomes (follow-up visits, readmissions). The day of admission is less likely to be impacted by this bias. Given concerns that admissions on different days of the week might be different, our instrument is the day of the week interacted with the time period. Therefore, to create bias, there must be a systematic change in the nature of admissions on a given day of the week during this time period. We provide more details on this analysis, testing of the instrument, and results in the Appendix.

Analyses were conducted in Stata, version 14.2 (StataCorp LP, College Station, Texas). Statistical testing was two-sided, with a significance level of 0.05, and the project was judged exempt by the Committee on Clinical Investigations for Beth Israel Deaconess Medical Center.

Overall, there were 17,582 hospitalizations on the medicine and cardiology services following implementation of the postdischarge appointment service. The use of the postdischarge appointment service rose rapidly after it was introduced (Figure) and then plateaued at roughly 50%. Of the hospitalizations where the postdischarge appointment service was used, the physician requested a new PCP for 1.2% of the patients. Among hospitalizations where the intervention was used, the average age was 65.5 years, 55.7% were female, the length of stay was 3.52 days, the DRG cost weight was 1.26 and 20.4% were patients on the cardiology service. Characteristics were similar in hospitalizations where the services were not used (Table 1).

Multivariable Logistic Regression

In this analysis, we focused on the 17,582 hospitalizations from January 2011 to September 2015 on the general medicine and cardiology services that occurred after the postdischarge appointment service was introduced. Among these hospitalizations, the postdischarge appointment service was used in 51.8% of discharges.

In an unadjusted analysis, patients discharged using the tool had higher rates of seven-day PCP follow-up (60.2% vs 29.2%, P < .001) and lower 30-day readmission rates (14.7% vs 16.7%; P < .001) than those who were not (Table 2). There was no significant difference in 30-day ED revisit between hospitalizations with and without use of the postdischarge appointment service (22.3% vs 23.1%; P = .23).

Instrumental Variable Analysis

In our instrumental variable analysis, we used all hospitalizations both before and after the introduction of the intervention. In this analysis, we estimate that use of the postdischarge appointment service increases the probability of visiting a PCP within seven days by 33.4 percentage points (95% CI: 7.9%, 58.9%; P = .01) (Table 3). The use of the postdischarge appointment was associated with a 2.5 percentage point (95% CI: −22.0%, 17.1%; P = .80) reduction in readmissions and a 4.8 percentage point (95% CI; −27.5%, 17.9%; P = .68) reduction in an ED visit within 30 days (Table 3). Neither of these differences were statistically significant with wide confidence intervals.

In sensitivity analyses, we obtained similar results when we considered PCP visits and readmissions within 14 days.

The hospital introduced the postdischarge appointment service to facilitate postdischarge appointments and to deter readmissions. In our analyses the use of the postdischarge appointment service was associated with a substantial 30 percentage point increase in the likelihood of a PCP follow-up visit within seven days after hospital discharge. There was a roughly 2% reduction in 30-day readmissions, but this difference was not consistently statistically significant across our analyses. Together, our evaluation implies that this type of intervention may make it much easier for patients to attend a PCP appointment, but scheduling an appointment alone may have a modest impact on deterring a readmission.

Our findings are inconsistent with prior studies that described a strong association between early PCP follow-up and readmissions. However, our results were consistent with research where follow-up visits were not clearly protective against readmissions.²⁰ One potential explanation of the discrepant findings is that there are unmeasured socioeconomic differences between patients who have a PCP follow-up appointment and those who do not.

We advance the literature by studying an intervention focused only on increasing early PCP follow-up. Most successful readmission programs that have been studied in randomized, controlled trials take a multipronged approach, including transitional care management with dedicated staff and medication reconciliation.^{3-7,9,15,21-23} For example, Coleman and colleagues randomized 750 hospitalized patients to a care-transitions intervention, which led to a substantial decrease in readmissions.¹⁵ Their care-transitions intervention included four components: (1) timely PCP or specialist follow-up, (2) educating patients on how best to take their medications, (3) a patient-centered record that allowed them to track their own disease and care, and (4) disease-specific patient education. The relative importance of each of these components in deterring readmissions is unclear. Instead of this multipronged strategy, we focused on a single component—timely follow-up. Together, our study and these prior studies are broadly consistent with a meta-analysis that suggests that transitional care programs with a narrow focus are less successful at reducing readmissions.²⁴ Facilitating early PCP follow-up alone is not a panacea and can be undermined by the incomplete or inexistent transmission of the discharge paperwork.^{25, 26} Moreover, the impact of interventions may only be seen among the highest-risk populations, and ongoing work by others seeks to identify these patients.²⁷

Regardless of the impact on readmissions, it is important to acknowledge that early PCP follow-up offers many potential benefits. Continuing to evaluate and treat new diagnoses, adjusting and reconciling medications, reconnecting with outpatient providers, capturing new incidental findings, and ensuring stability through regular follow-up are just a few of the potential benefits. We believe the dramatic increase observed in PCP follow-up reflects the administrative complexity required for a patient to call their PCP’s office and to schedule a follow-up appointment soon after they are discharged from the hospital. Our study implies that simply requesting that a patient call their PCP to schedule a timely appointment is often impossible, and this may be particularly true for those who need to obtain a new PCP.

Our study has many limitations. The study was limited to a single academic center, and the intervention was limited to patients cared for by the general medicine and cardiology services. Our multivariable regression analysis comparing outcomes among patients where the postdischarge appointment service was used and not used may be biased by unmeasured differences in these patients. We attempted to address this limitation by exploiting the fact that the intervention was only available on weekdays through an instrumental variable analysis, but the instrument we used itself is subject to bias. Also, in the instrumental variable analysis, our estimates were imprecise and therefore not powered to identify smaller but still clinically important reductions in readmissions. Given the data limitations, we could not compare the no-show rates among appointments made by the discharge appointment service versus those made by patients. Finally, we were only able to observe follow-up visits and hospitalizations within the health system, and it is possible that our results were biased by patients preferentially going to other hospitals for readmission.

In summary, we found that the introduction of a postdischarge appointment service resulted in substantially increased rates of early PCP follow-up but less clear benefits in preventing readmissions.

Accidental falls are among the most common incidents reported in hospitals, complicating approximately 2% of hospital stays.^1-3 Approximately 25% of falls in hospitalized patients result in injury, and 2% involve fractures.⁴ Substantial costs are associated with falls, including patient care costs associated with increased length of stay and liability.^5-7

Beginning October 1, 2008, the Centers for Medicare & Medicaid Services (CMS) stopped reimbursing hospitals for the additional care associated with eight hospital-acquired conditions (HACs), including serious fall-related injury, which were believed to be “reasonably preventable.”^8,9 Before this change, hospitals recovered the costs of these “never events” by assigning a higher level MS-DRG (Medicare Severity Diagnosis-Related Group) code for patients experiencing such an event. This is no longer allowed under the revised CMS Prospective Payment System rules.

Although the financial penalty for iatrogenic injury was modest, the payment change placed pressure on hospital staff to decrease falls, and some nurses reported changing practice to be more restrictive of patient mobility.¹⁰ Increased use of physical restraints is a potential unintended consequence of this rule change.¹¹ Restraints are known to cause agitation, delirium, decubiti, deconditioning, strangulation, and death.¹² Not surprisingly, use of restraints is discouraged in hospitals and is a CMS quality of care indicator.^13,14 Although there is no evidence that restraint use prevents patients from falling,^15,16 there is a perception among both health professionals and patients that restraints reduce the risk of falling, and they are often used as a “last resort” method of fall prevention.^17-19

The aim of this longitudinal study was to determine whether this payment change was associated with changes in short-, intermediate-, and long-term rates of falls, injurious falls, and physical restraint use in acute care hospitals. The CMS has included fall-related hip fracture in newer value-based purchasing programs by adding Patient Safety Indictor (PSI) 90 to both the HACs Reduction Program (HACRP)²⁰ and the Hospital Value-Based Purchasing (VBP)²¹ in FY2015. However, the HACs Initiative remains the only Medicare value program that directly penalizes all injurious inpatient falls.

Study Units

As previously described,²² the National Database of Nursing Quality Indicators (NDNQI) is a data collection project initiated by the American Nurses Association (ANA). The NDNQI provides national comparative data at the unit and facility levels on nursing-sensitive indicators endorsed by the National Quality Forum. More than 2,000 hospitals voluntarily participate in the NDNQI, including virtually all ANA Magnet-recognized hospitals, and more than 90% of nursing units participate in the fall measures (NDNQI, personal communication). At the start of study data collection, the project was administered by the School of Nursing at the University of Kansas Medical Center. In 2014, the ownership of the NDNQI was transferred from the ANA to Press Ganey Associates, Inc. In addition to standardized data on unit, facility, and staffing characteristics, the NDNQI member hospitals can elect to submit monthly data on falls and quarterly data on physical restraint use prevalence.

We examined the data collected from adult medical, medical-surgical, and surgical units in United States acute care hospitals that elected to participate in the fall and physical restraint use data collection within the NDNQI for the 27 months before and the 87 months after the implementation of the CMS rule change. Eligible units contributed at least one fall and physical restraint use data point during both the 27 months preceding October 1, 2008, and the 87 months immediately after. The Institutional Review Board at the University of Kansas Medical Center reviewed and approved the study before its implementation.

Endpoints

Fall Events

The NDNQI defines a patient fall as an unplanned descent to the floor, regardless of whether the fall results in injury and regardless of whether the patient was assisted to the floor by a member of the hospital staff. Events in which a patient lands on a surface where one would not expect to find a patient (eg, on a mat next to a low bed) are also counted as falls.

Using internal data sources (eg, medical records, incident reports), participating hospitals report the number of inpatient falls each month to the NDNQI. We analyzed the falls data for the period July 1, 2006, through December 31, 2015. Thus, each unit could contribute 114 months (27 months before the rule change and 87 months after the rule change) of falls data.

Hospitals classify the injury level of each fall as none, minor (resulting in bruise, pain, abrasion, wound cleaning, or limb elevation, or in the use of ice, dressing, or topical medication), moderate (resulting in suturing, splinting, muscle or joint strain, or application of steri-strips or skin glue), major (resulting in surgery, casting, traction, any type of fracture, consultation for neurological or internal injury, or receipt of blood products for patients with coagulopathy), or death (resulting from injuries sustained from falling). For this study, a fall resulting in any injury (including minor) was considered as an injurious fall. The NDNQI data have been validated for falls and fall injury.^23,24

Based on patient counts from unit censuses and/or internal data on actual patient hours on the unit, hospitals also report to the NDNQI the monthly number of patient days for each unit for which falls data are reported. The NDNQI uses these data to calculate each unit’s total and injurious fall rate per 1,000 patient days.

Physical Restraint Use

The NDNQI follows the CMS definition of restraint, which is “any manual method, physical or mechanical device, material, or equipment that immobilizes or reduces the ability of a patient to move his or her arms, legs, body, or head freely”.¹³ The NDNQI restraint use data are collected quarterly. Participating hospitals choose one day each quarter to conduct a restraint use prevalence survey on participating units. On the selected day, designated RNs within these hospitals visually assess each patient on the unit for restraint use. Based on this survey, hospitals report to the NDNQI the total count of patients surveyed and whether each was restrained. For restrained patients, hospitals also report the type of restraint as limb, vest, or other (eg, four side rails, net beds, mitts not attached to the bed).

We analyzed the restraint use data for the period October 1, 2006, through December 31, 2015. Thus, 37 quarters of data (eight pre- and 29 postrule change) were available. For this study, we computed for each unit the quarterly proportion of surveyed patients who were physically restrained by dividing the total count of restrained patients (regardless of the type of restraint) on the day of the survey by the total count of surveyed patients.

Covariates

Unit- and facility-level covariates were included in several model specifications to determine whether patient or facility characteristics affected the results. The unit-level covariates included the type of nursing unit (medical, medical and surgical, or surgical), monthly rates of total nursing hours per patient day, and nursing skill mix (percent registered nurses/total nursing personnel). The three facility-level variables included urban–rural location (defined as metropolitan [located in an area containing an urban core with a population of at least 50,000], micropolitan [located in an area containing an urban core with a population of 10,000-49,999], or neither), bed size (<300 beds or ≥300 beds), and teaching status (academic health center, major teaching hospital, or nonteaching hospital).

Because larger, academically affiliated hospitals are overrepresented in the NDNQI, we conducted stratified analyses of these variables to explore how change in the rates of falls and restraint use in the entire sample might differ between hospitals according to bed size (<300 beds, ≥300 beds) and teaching status (nonteaching versus teaching and academic health center).

Statistical Methods

We compared the mean annual rates of change in falls, injurious falls, and physical restraint use prevalence during the two years before the HACs Initiative went into effect (October 2006-September 2008) with the mean annual rates of change following the implementation of the payment rule. Short-term (one-year) change was the slope from October 2008 to September 2009, intermediate-term (four-year) change was the slope from October 2008 to September 2012, and long-term (seven-year) change was the slope from October 2008 to September 2015.

Monthly rates of falls and injurious falls over the 114-month period were modeled using negative binomial models with a random intercept to account for heterogeneity between units. Each base mean model included the preimplementation intercept and slope (over time), the postimplementation intercept, and slope (both linear and quadratic). We also fit the models that included the terms in the base model and facility-level covariates, unit-level covariates, both individually and combined. All models included terms for seasonality.

Quarterly prevalence rates of restraint use over the 37 quarters were modeled using beta-binomial models with a random intercept to account for heterogeneity between units. Each base mean model included the preimplementation intercept and slope (over time), the postimplementation intercept, and slope (both linear and quadratic). Similar to the one specified for falls, models were also fitted that included facility- and unit-level covariates as described above.

To adjust for multiple comparisons of the three postimplementation slopes, all confidence intervals were Bonferroni corrected.

Nursing Units

We included nursing units with one or more months of falls data and one or more quarters of restraint use data before and after the rule change. Of the 11,117 nursing units that submitted data to the NDNQI, 2,862 units (983 medical, 1,219 medical-surgical, and 660 surgical) with the requisite demographic, falls, and restraint use data were considered for inclusion in the study. The characteristics of the nursing units (ie, the type of unit, total nursing hours per patient day, and nursing skill mix) and hospitals (ie, location, bed size, teaching status) included in the study were similar to those of the overall NDNQI member units.

Baseline Characteristics

In the first study month (July 2006), 1,941 sample nursing units reported 5,101 falls during 1,401,652 patient-days of observation. Of these, 1,502 (29%) resulted in injury (1,281 minor, 144 moderate, 75 major, and two deaths). Across falls, the median (interquartile range [IQR]) patient age was 70 (55-80) years, with males accounting for 51% of falls. Most of the falls, 4,328 (85%), were documented as unassisted. A total of 209 (4%) falls occurred while physical restraints were in use.

In the first quarterly restraint use prevalence survey (October 2006), the 829 participating nursing units surveyed 19,979 patients (median [IQR] = 23 [20-23] patients per nursing unit). The median (IQR) age was 66 (51-78) years, and 54% of them were females. At the time of the survey, restraints were in use for 326 (1.6%) patients. Restrained patients were older than unrestrained patients (median age: 78 vs 65 years) and more likely to be male (56% vs 46%). Limb restraints were used for 139 patients, vest restraints for 66, both limb and vest restraints for 24, and other restraint types were used for 113 patients (including 11 in limb restraints and 5 in limb and vest restraints).

Change in Endpoints after Implementation of the HACs Initiative

Stratified Analysis

At baseline, fall rates and restraint use prevalence were slightly higher, whereas the rate of injurious falls was slightly lower, among teaching and academic medical centers compared to those in nonteaching hospitals. Declines in falls rate and restraint use prevalence were higher in teaching hospitals than in nonteaching hospitals (data not included).

We examined the rates of falls and fall injuries among 2,862 hospital units before and after the implementation of the HACs Initiative. Implementation of the CMS payment change was associated with a modest improvement in the rate of decrease for falls; a statistically significant effect on the rate of decrease for injurious falls was detectable only at seven years postchange. Fall reductions were the greatest among teaching and larger hospitals. These findings are consistent with our previous analysis of NDNQI data that found no short-term effect of the rule change on the rate of injurious falls.²⁵

We found no evidence indicating that restraint use prevalence increased because of this payment change. Physical -restraint use prevalence showed a rapidly decreasing trend before 2008, and although the rate of decline was attenuated seven years after the rule change, the overall physical restraint use prevalence in these units in 2015 was less than half of that in 2006. Unlike falls, the steepest declines in restraint use prevalence occurred in smaller hospitals.

The CMS decision to include falls with injury among the “reasonably preventable” HACs was controversial.¹¹ Inpatient falls are largely attributable to individual patient risk factors and are unusual among HACs in the extent to which patient behavior plays a role in their occurrence. Although hospital fall prevention guidelines have been published, only a few controlled trials have been conducted, with little evidence supporting the recommendations.^1,26 A quantitative review found no evidence of benefit in published hospital fall prevention studies using concurrent controls (internal rate of return = 0.92; 95% CI: 0.65-1.30),²⁶ and a recent, well-executed, cluster randomized trial of multifactorial fall prevention interventions found no change in fall rates compared with controls.²⁷ Current hospital fall prevention guidelines are limited to unproven and time-consuming nursing-level (eg, toileting schedules and use of alarms) or organizational-level strategies (eg, changing staff attitudes regarding the inevitability of falls or “leadership support”).^1,28

Despite the large sample size and the use of nurse-reported data that include patient falls from all age groups and not subject to bias due to the regulation itself (eg, ICD coding changes), our findings should be interpreted taking into account several limitations.

First, hospitals participating in the NDNQI self-select to participate and are larger and disproportionately urban compared with nonparticipating hospitals.²⁹ Although our findings were unchanged when hospital-level covariates were included in modeling, analyses stratified by teaching status and bed size demonstrated important differences. Larger teaching hospitals experienced greater fall reductions, whereas restraint use prevalence decreased more rapidly in smaller hospitals.

Second, the absence of a control group prevents us from conclusively attributing changes in falls rate and restraint use prevalence to the 2008 CMS payment change.³⁰ Our findings may have been influenced by other policy changes. For example, in October 2014, the CMS implemented the Hospital-Acquired Condition Reduction Program (HACRP)²⁰ and the Hospital Value-Based Purchasing (VBP)²¹ Program. Under these programs, falls with hip fractures were an indicator that could alter hospital payment.

Third, we did not ascertain the use of all available fall prevention measures such as companions, bed rails, very low beds, bed alarms, and restricted activity.³¹ Nor could the study address changes in patient functional status or discharge location. In a before- and after-study of four hospitals in a single hospital system, we found that bed alarm use increased, restraint orders decreased, and the use of room change or sitters remained stable after the implementation of the CMS payment.³²

Nevertheless, we believe that these findings are consistent with the hypothesis that the HACs Initiative increased the cost of patient falls to hospitals, and, in response, some hospitals were able to modestly reduce the rate of falls. We found no evidence that physical restraint use prevalence increased.

In summary, our findings suggest modest impact of the HACs Initiative on falls and injurious falls, but no unintended impact on restraint use. These results highlight the importance of ensuring that pay-for-performance initiatives target outcomes where there are evidence-based approaches to prevention. The creation or identification of prevention tools and guidelines does not make an outcome preventable. Despite interval improvement in these self-selected hospital units in fall rates and physical restraint use prevalence, falls remain a difficult patient safety problem for hospitals, and further research is required to develop cost-effective, generalizable strategies for their prevention.

Accidental falls are among the most common incidents reported in hospitals, complicating approximately 2% of hospital stays.^1-3 Approximately 25% of falls in hospitalized patients result in injury, and 2% involve fractures.⁴ Substantial costs are associated with falls, including patient care costs associated with increased length of stay and liability.^5-7

Beginning October 1, 2008, the Centers for Medicare & Medicaid Services (CMS) stopped reimbursing hospitals for the additional care associated with eight hospital-acquired conditions (HACs), including serious fall-related injury, which were believed to be “reasonably preventable.”^8,9 Before this change, hospitals recovered the costs of these “never events” by assigning a higher level MS-DRG (Medicare Severity Diagnosis-Related Group) code for patients experiencing such an event. This is no longer allowed under the revised CMS Prospective Payment System rules.

Although the financial penalty for iatrogenic injury was modest, the payment change placed pressure on hospital staff to decrease falls, and some nurses reported changing practice to be more restrictive of patient mobility.¹⁰ Increased use of physical restraints is a potential unintended consequence of this rule change.¹¹ Restraints are known to cause agitation, delirium, decubiti, deconditioning, strangulation, and death.¹² Not surprisingly, use of restraints is discouraged in hospitals and is a CMS quality of care indicator.^13,14 Although there is no evidence that restraint use prevents patients from falling,^15,16 there is a perception among both health professionals and patients that restraints reduce the risk of falling, and they are often used as a “last resort” method of fall prevention.^17-19

The aim of this longitudinal study was to determine whether this payment change was associated with changes in short-, intermediate-, and long-term rates of falls, injurious falls, and physical restraint use in acute care hospitals. The CMS has included fall-related hip fracture in newer value-based purchasing programs by adding Patient Safety Indictor (PSI) 90 to both the HACs Reduction Program (HACRP)²⁰ and the Hospital Value-Based Purchasing (VBP)²¹ in FY2015. However, the HACs Initiative remains the only Medicare value program that directly penalizes all injurious inpatient falls.

Study Units

As previously described,²² the National Database of Nursing Quality Indicators (NDNQI) is a data collection project initiated by the American Nurses Association (ANA). The NDNQI provides national comparative data at the unit and facility levels on nursing-sensitive indicators endorsed by the National Quality Forum. More than 2,000 hospitals voluntarily participate in the NDNQI, including virtually all ANA Magnet-recognized hospitals, and more than 90% of nursing units participate in the fall measures (NDNQI, personal communication). At the start of study data collection, the project was administered by the School of Nursing at the University of Kansas Medical Center. In 2014, the ownership of the NDNQI was transferred from the ANA to Press Ganey Associates, Inc. In addition to standardized data on unit, facility, and staffing characteristics, the NDNQI member hospitals can elect to submit monthly data on falls and quarterly data on physical restraint use prevalence.

We examined the data collected from adult medical, medical-surgical, and surgical units in United States acute care hospitals that elected to participate in the fall and physical restraint use data collection within the NDNQI for the 27 months before and the 87 months after the implementation of the CMS rule change. Eligible units contributed at least one fall and physical restraint use data point during both the 27 months preceding October 1, 2008, and the 87 months immediately after. The Institutional Review Board at the University of Kansas Medical Center reviewed and approved the study before its implementation.

Endpoints

Fall Events

The NDNQI defines a patient fall as an unplanned descent to the floor, regardless of whether the fall results in injury and regardless of whether the patient was assisted to the floor by a member of the hospital staff. Events in which a patient lands on a surface where one would not expect to find a patient (eg, on a mat next to a low bed) are also counted as falls.

Using internal data sources (eg, medical records, incident reports), participating hospitals report the number of inpatient falls each month to the NDNQI. We analyzed the falls data for the period July 1, 2006, through December 31, 2015. Thus, each unit could contribute 114 months (27 months before the rule change and 87 months after the rule change) of falls data.

Hospitals classify the injury level of each fall as none, minor (resulting in bruise, pain, abrasion, wound cleaning, or limb elevation, or in the use of ice, dressing, or topical medication), moderate (resulting in suturing, splinting, muscle or joint strain, or application of steri-strips or skin glue), major (resulting in surgery, casting, traction, any type of fracture, consultation for neurological or internal injury, or receipt of blood products for patients with coagulopathy), or death (resulting from injuries sustained from falling). For this study, a fall resulting in any injury (including minor) was considered as an injurious fall. The NDNQI data have been validated for falls and fall injury.^23,24

Based on patient counts from unit censuses and/or internal data on actual patient hours on the unit, hospitals also report to the NDNQI the monthly number of patient days for each unit for which falls data are reported. The NDNQI uses these data to calculate each unit’s total and injurious fall rate per 1,000 patient days.

Physical Restraint Use

The NDNQI follows the CMS definition of restraint, which is “any manual method, physical or mechanical device, material, or equipment that immobilizes or reduces the ability of a patient to move his or her arms, legs, body, or head freely”.¹³ The NDNQI restraint use data are collected quarterly. Participating hospitals choose one day each quarter to conduct a restraint use prevalence survey on participating units. On the selected day, designated RNs within these hospitals visually assess each patient on the unit for restraint use. Based on this survey, hospitals report to the NDNQI the total count of patients surveyed and whether each was restrained. For restrained patients, hospitals also report the type of restraint as limb, vest, or other (eg, four side rails, net beds, mitts not attached to the bed).

We analyzed the restraint use data for the period October 1, 2006, through December 31, 2015. Thus, 37 quarters of data (eight pre- and 29 postrule change) were available. For this study, we computed for each unit the quarterly proportion of surveyed patients who were physically restrained by dividing the total count of restrained patients (regardless of the type of restraint) on the day of the survey by the total count of surveyed patients.

Covariates

Unit- and facility-level covariates were included in several model specifications to determine whether patient or facility characteristics affected the results. The unit-level covariates included the type of nursing unit (medical, medical and surgical, or surgical), monthly rates of total nursing hours per patient day, and nursing skill mix (percent registered nurses/total nursing personnel). The three facility-level variables included urban–rural location (defined as metropolitan [located in an area containing an urban core with a population of at least 50,000], micropolitan [located in an area containing an urban core with a population of 10,000-49,999], or neither), bed size (<300 beds or ≥300 beds), and teaching status (academic health center, major teaching hospital, or nonteaching hospital).

Because larger, academically affiliated hospitals are overrepresented in the NDNQI, we conducted stratified analyses of these variables to explore how change in the rates of falls and restraint use in the entire sample might differ between hospitals according to bed size (<300 beds, ≥300 beds) and teaching status (nonteaching versus teaching and academic health center).

Statistical Methods

We compared the mean annual rates of change in falls, injurious falls, and physical restraint use prevalence during the two years before the HACs Initiative went into effect (October 2006-September 2008) with the mean annual rates of change following the implementation of the payment rule. Short-term (one-year) change was the slope from October 2008 to September 2009, intermediate-term (four-year) change was the slope from October 2008 to September 2012, and long-term (seven-year) change was the slope from October 2008 to September 2015.

Monthly rates of falls and injurious falls over the 114-month period were modeled using negative binomial models with a random intercept to account for heterogeneity between units. Each base mean model included the preimplementation intercept and slope (over time), the postimplementation intercept, and slope (both linear and quadratic). We also fit the models that included the terms in the base model and facility-level covariates, unit-level covariates, both individually and combined. All models included terms for seasonality.

Quarterly prevalence rates of restraint use over the 37 quarters were modeled using beta-binomial models with a random intercept to account for heterogeneity between units. Each base mean model included the preimplementation intercept and slope (over time), the postimplementation intercept, and slope (both linear and quadratic). Similar to the one specified for falls, models were also fitted that included facility- and unit-level covariates as described above.

To adjust for multiple comparisons of the three postimplementation slopes, all confidence intervals were Bonferroni corrected.

Nursing Units

We included nursing units with one or more months of falls data and one or more quarters of restraint use data before and after the rule change. Of the 11,117 nursing units that submitted data to the NDNQI, 2,862 units (983 medical, 1,219 medical-surgical, and 660 surgical) with the requisite demographic, falls, and restraint use data were considered for inclusion in the study. The characteristics of the nursing units (ie, the type of unit, total nursing hours per patient day, and nursing skill mix) and hospitals (ie, location, bed size, teaching status) included in the study were similar to those of the overall NDNQI member units.

Baseline Characteristics

In the first study month (July 2006), 1,941 sample nursing units reported 5,101 falls during 1,401,652 patient-days of observation. Of these, 1,502 (29%) resulted in injury (1,281 minor, 144 moderate, 75 major, and two deaths). Across falls, the median (interquartile range [IQR]) patient age was 70 (55-80) years, with males accounting for 51% of falls. Most of the falls, 4,328 (85%), were documented as unassisted. A total of 209 (4%) falls occurred while physical restraints were in use.

In the first quarterly restraint use prevalence survey (October 2006), the 829 participating nursing units surveyed 19,979 patients (median [IQR] = 23 [20-23] patients per nursing unit). The median (IQR) age was 66 (51-78) years, and 54% of them were females. At the time of the survey, restraints were in use for 326 (1.6%) patients. Restrained patients were older than unrestrained patients (median age: 78 vs 65 years) and more likely to be male (56% vs 46%). Limb restraints were used for 139 patients, vest restraints for 66, both limb and vest restraints for 24, and other restraint types were used for 113 patients (including 11 in limb restraints and 5 in limb and vest restraints).

Change in Endpoints after Implementation of the HACs Initiative

Stratified Analysis

At baseline, fall rates and restraint use prevalence were slightly higher, whereas the rate of injurious falls was slightly lower, among teaching and academic medical centers compared to those in nonteaching hospitals. Declines in falls rate and restraint use prevalence were higher in teaching hospitals than in nonteaching hospitals (data not included).

We examined the rates of falls and fall injuries among 2,862 hospital units before and after the implementation of the HACs Initiative. Implementation of the CMS payment change was associated with a modest improvement in the rate of decrease for falls; a statistically significant effect on the rate of decrease for injurious falls was detectable only at seven years postchange. Fall reductions were the greatest among teaching and larger hospitals. These findings are consistent with our previous analysis of NDNQI data that found no short-term effect of the rule change on the rate of injurious falls.²⁵

We found no evidence indicating that restraint use prevalence increased because of this payment change. Physical -restraint use prevalence showed a rapidly decreasing trend before 2008, and although the rate of decline was attenuated seven years after the rule change, the overall physical restraint use prevalence in these units in 2015 was less than half of that in 2006. Unlike falls, the steepest declines in restraint use prevalence occurred in smaller hospitals.

The CMS decision to include falls with injury among the “reasonably preventable” HACs was controversial.¹¹ Inpatient falls are largely attributable to individual patient risk factors and are unusual among HACs in the extent to which patient behavior plays a role in their occurrence. Although hospital fall prevention guidelines have been published, only a few controlled trials have been conducted, with little evidence supporting the recommendations.^1,26 A quantitative review found no evidence of benefit in published hospital fall prevention studies using concurrent controls (internal rate of return = 0.92; 95% CI: 0.65-1.30),²⁶ and a recent, well-executed, cluster randomized trial of multifactorial fall prevention interventions found no change in fall rates compared with controls.²⁷ Current hospital fall prevention guidelines are limited to unproven and time-consuming nursing-level (eg, toileting schedules and use of alarms) or organizational-level strategies (eg, changing staff attitudes regarding the inevitability of falls or “leadership support”).^1,28

Despite the large sample size and the use of nurse-reported data that include patient falls from all age groups and not subject to bias due to the regulation itself (eg, ICD coding changes), our findings should be interpreted taking into account several limitations.

First, hospitals participating in the NDNQI self-select to participate and are larger and disproportionately urban compared with nonparticipating hospitals.²⁹ Although our findings were unchanged when hospital-level covariates were included in modeling, analyses stratified by teaching status and bed size demonstrated important differences. Larger teaching hospitals experienced greater fall reductions, whereas restraint use prevalence decreased more rapidly in smaller hospitals.

Second, the absence of a control group prevents us from conclusively attributing changes in falls rate and restraint use prevalence to the 2008 CMS payment change.³⁰ Our findings may have been influenced by other policy changes. For example, in October 2014, the CMS implemented the Hospital-Acquired Condition Reduction Program (HACRP)²⁰ and the Hospital Value-Based Purchasing (VBP)²¹ Program. Under these programs, falls with hip fractures were an indicator that could alter hospital payment.

Third, we did not ascertain the use of all available fall prevention measures such as companions, bed rails, very low beds, bed alarms, and restricted activity.³¹ Nor could the study address changes in patient functional status or discharge location. In a before- and after-study of four hospitals in a single hospital system, we found that bed alarm use increased, restraint orders decreased, and the use of room change or sitters remained stable after the implementation of the CMS payment.³²

Nevertheless, we believe that these findings are consistent with the hypothesis that the HACs Initiative increased the cost of patient falls to hospitals, and, in response, some hospitals were able to modestly reduce the rate of falls. We found no evidence that physical restraint use prevalence increased.

In summary, our findings suggest modest impact of the HACs Initiative on falls and injurious falls, but no unintended impact on restraint use. These results highlight the importance of ensuring that pay-for-performance initiatives target outcomes where there are evidence-based approaches to prevention. The creation or identification of prevention tools and guidelines does not make an outcome preventable. Despite interval improvement in these self-selected hospital units in fall rates and physical restraint use prevalence, falls remain a difficult patient safety problem for hospitals, and further research is required to develop cost-effective, generalizable strategies for their prevention.

Accidental falls are among the most common incidents reported in hospitals, complicating approximately 2% of hospital stays.^1-3 Approximately 25% of falls in hospitalized patients result in injury, and 2% involve fractures.⁴ Substantial costs are associated with falls, including patient care costs associated with increased length of stay and liability.^5-7

Beginning October 1, 2008, the Centers for Medicare & Medicaid Services (CMS) stopped reimbursing hospitals for the additional care associated with eight hospital-acquired conditions (HACs), including serious fall-related injury, which were believed to be “reasonably preventable.”^8,9 Before this change, hospitals recovered the costs of these “never events” by assigning a higher level MS-DRG (Medicare Severity Diagnosis-Related Group) code for patients experiencing such an event. This is no longer allowed under the revised CMS Prospective Payment System rules.

Although the financial penalty for iatrogenic injury was modest, the payment change placed pressure on hospital staff to decrease falls, and some nurses reported changing practice to be more restrictive of patient mobility.¹⁰ Increased use of physical restraints is a potential unintended consequence of this rule change.¹¹ Restraints are known to cause agitation, delirium, decubiti, deconditioning, strangulation, and death.¹² Not surprisingly, use of restraints is discouraged in hospitals and is a CMS quality of care indicator.^13,14 Although there is no evidence that restraint use prevents patients from falling,^15,16 there is a perception among both health professionals and patients that restraints reduce the risk of falling, and they are often used as a “last resort” method of fall prevention.^17-19

The aim of this longitudinal study was to determine whether this payment change was associated with changes in short-, intermediate-, and long-term rates of falls, injurious falls, and physical restraint use in acute care hospitals. The CMS has included fall-related hip fracture in newer value-based purchasing programs by adding Patient Safety Indictor (PSI) 90 to both the HACs Reduction Program (HACRP)²⁰ and the Hospital Value-Based Purchasing (VBP)²¹ in FY2015. However, the HACs Initiative remains the only Medicare value program that directly penalizes all injurious inpatient falls.

Study Units

As previously described,²² the National Database of Nursing Quality Indicators (NDNQI) is a data collection project initiated by the American Nurses Association (ANA). The NDNQI provides national comparative data at the unit and facility levels on nursing-sensitive indicators endorsed by the National Quality Forum. More than 2,000 hospitals voluntarily participate in the NDNQI, including virtually all ANA Magnet-recognized hospitals, and more than 90% of nursing units participate in the fall measures (NDNQI, personal communication). At the start of study data collection, the project was administered by the School of Nursing at the University of Kansas Medical Center. In 2014, the ownership of the NDNQI was transferred from the ANA to Press Ganey Associates, Inc. In addition to standardized data on unit, facility, and staffing characteristics, the NDNQI member hospitals can elect to submit monthly data on falls and quarterly data on physical restraint use prevalence.

We examined the data collected from adult medical, medical-surgical, and surgical units in United States acute care hospitals that elected to participate in the fall and physical restraint use data collection within the NDNQI for the 27 months before and the 87 months after the implementation of the CMS rule change. Eligible units contributed at least one fall and physical restraint use data point during both the 27 months preceding October 1, 2008, and the 87 months immediately after. The Institutional Review Board at the University of Kansas Medical Center reviewed and approved the study before its implementation.

Endpoints

Fall Events

The NDNQI defines a patient fall as an unplanned descent to the floor, regardless of whether the fall results in injury and regardless of whether the patient was assisted to the floor by a member of the hospital staff. Events in which a patient lands on a surface where one would not expect to find a patient (eg, on a mat next to a low bed) are also counted as falls.

Using internal data sources (eg, medical records, incident reports), participating hospitals report the number of inpatient falls each month to the NDNQI. We analyzed the falls data for the period July 1, 2006, through December 31, 2015. Thus, each unit could contribute 114 months (27 months before the rule change and 87 months after the rule change) of falls data.

Hospitals classify the injury level of each fall as none, minor (resulting in bruise, pain, abrasion, wound cleaning, or limb elevation, or in the use of ice, dressing, or topical medication), moderate (resulting in suturing, splinting, muscle or joint strain, or application of steri-strips or skin glue), major (resulting in surgery, casting, traction, any type of fracture, consultation for neurological or internal injury, or receipt of blood products for patients with coagulopathy), or death (resulting from injuries sustained from falling). For this study, a fall resulting in any injury (including minor) was considered as an injurious fall. The NDNQI data have been validated for falls and fall injury.^23,24

Based on patient counts from unit censuses and/or internal data on actual patient hours on the unit, hospitals also report to the NDNQI the monthly number of patient days for each unit for which falls data are reported. The NDNQI uses these data to calculate each unit’s total and injurious fall rate per 1,000 patient days.

Physical Restraint Use

The NDNQI follows the CMS definition of restraint, which is “any manual method, physical or mechanical device, material, or equipment that immobilizes or reduces the ability of a patient to move his or her arms, legs, body, or head freely”.¹³ The NDNQI restraint use data are collected quarterly. Participating hospitals choose one day each quarter to conduct a restraint use prevalence survey on participating units. On the selected day, designated RNs within these hospitals visually assess each patient on the unit for restraint use. Based on this survey, hospitals report to the NDNQI the total count of patients surveyed and whether each was restrained. For restrained patients, hospitals also report the type of restraint as limb, vest, or other (eg, four side rails, net beds, mitts not attached to the bed).

We analyzed the restraint use data for the period October 1, 2006, through December 31, 2015. Thus, 37 quarters of data (eight pre- and 29 postrule change) were available. For this study, we computed for each unit the quarterly proportion of surveyed patients who were physically restrained by dividing the total count of restrained patients (regardless of the type of restraint) on the day of the survey by the total count of surveyed patients.

Covariates

Unit- and facility-level covariates were included in several model specifications to determine whether patient or facility characteristics affected the results. The unit-level covariates included the type of nursing unit (medical, medical and surgical, or surgical), monthly rates of total nursing hours per patient day, and nursing skill mix (percent registered nurses/total nursing personnel). The three facility-level variables included urban–rural location (defined as metropolitan [located in an area containing an urban core with a population of at least 50,000], micropolitan [located in an area containing an urban core with a population of 10,000-49,999], or neither), bed size (<300 beds or ≥300 beds), and teaching status (academic health center, major teaching hospital, or nonteaching hospital).

Because larger, academically affiliated hospitals are overrepresented in the NDNQI, we conducted stratified analyses of these variables to explore how change in the rates of falls and restraint use in the entire sample might differ between hospitals according to bed size (<300 beds, ≥300 beds) and teaching status (nonteaching versus teaching and academic health center).

Statistical Methods

We compared the mean annual rates of change in falls, injurious falls, and physical restraint use prevalence during the two years before the HACs Initiative went into effect (October 2006-September 2008) with the mean annual rates of change following the implementation of the payment rule. Short-term (one-year) change was the slope from October 2008 to September 2009, intermediate-term (four-year) change was the slope from October 2008 to September 2012, and long-term (seven-year) change was the slope from October 2008 to September 2015.

Monthly rates of falls and injurious falls over the 114-month period were modeled using negative binomial models with a random intercept to account for heterogeneity between units. Each base mean model included the preimplementation intercept and slope (over time), the postimplementation intercept, and slope (both linear and quadratic). We also fit the models that included the terms in the base model and facility-level covariates, unit-level covariates, both individually and combined. All models included terms for seasonality.

Quarterly prevalence rates of restraint use over the 37 quarters were modeled using beta-binomial models with a random intercept to account for heterogeneity between units. Each base mean model included the preimplementation intercept and slope (over time), the postimplementation intercept, and slope (both linear and quadratic). Similar to the one specified for falls, models were also fitted that included facility- and unit-level covariates as described above.

To adjust for multiple comparisons of the three postimplementation slopes, all confidence intervals were Bonferroni corrected.

Nursing Units

We included nursing units with one or more months of falls data and one or more quarters of restraint use data before and after the rule change. Of the 11,117 nursing units that submitted data to the NDNQI, 2,862 units (983 medical, 1,219 medical-surgical, and 660 surgical) with the requisite demographic, falls, and restraint use data were considered for inclusion in the study. The characteristics of the nursing units (ie, the type of unit, total nursing hours per patient day, and nursing skill mix) and hospitals (ie, location, bed size, teaching status) included in the study were similar to those of the overall NDNQI member units.

Baseline Characteristics

In the first study month (July 2006), 1,941 sample nursing units reported 5,101 falls during 1,401,652 patient-days of observation. Of these, 1,502 (29%) resulted in injury (1,281 minor, 144 moderate, 75 major, and two deaths). Across falls, the median (interquartile range [IQR]) patient age was 70 (55-80) years, with males accounting for 51% of falls. Most of the falls, 4,328 (85%), were documented as unassisted. A total of 209 (4%) falls occurred while physical restraints were in use.

In the first quarterly restraint use prevalence survey (October 2006), the 829 participating nursing units surveyed 19,979 patients (median [IQR] = 23 [20-23] patients per nursing unit). The median (IQR) age was 66 (51-78) years, and 54% of them were females. At the time of the survey, restraints were in use for 326 (1.6%) patients. Restrained patients were older than unrestrained patients (median age: 78 vs 65 years) and more likely to be male (56% vs 46%). Limb restraints were used for 139 patients, vest restraints for 66, both limb and vest restraints for 24, and other restraint types were used for 113 patients (including 11 in limb restraints and 5 in limb and vest restraints).

Change in Endpoints after Implementation of the HACs Initiative

Stratified Analysis

At baseline, fall rates and restraint use prevalence were slightly higher, whereas the rate of injurious falls was slightly lower, among teaching and academic medical centers compared to those in nonteaching hospitals. Declines in falls rate and restraint use prevalence were higher in teaching hospitals than in nonteaching hospitals (data not included).

We examined the rates of falls and fall injuries among 2,862 hospital units before and after the implementation of the HACs Initiative. Implementation of the CMS payment change was associated with a modest improvement in the rate of decrease for falls; a statistically significant effect on the rate of decrease for injurious falls was detectable only at seven years postchange. Fall reductions were the greatest among teaching and larger hospitals. These findings are consistent with our previous analysis of NDNQI data that found no short-term effect of the rule change on the rate of injurious falls.²⁵

We found no evidence indicating that restraint use prevalence increased because of this payment change. Physical -restraint use prevalence showed a rapidly decreasing trend before 2008, and although the rate of decline was attenuated seven years after the rule change, the overall physical restraint use prevalence in these units in 2015 was less than half of that in 2006. Unlike falls, the steepest declines in restraint use prevalence occurred in smaller hospitals.

The CMS decision to include falls with injury among the “reasonably preventable” HACs was controversial.¹¹ Inpatient falls are largely attributable to individual patient risk factors and are unusual among HACs in the extent to which patient behavior plays a role in their occurrence. Although hospital fall prevention guidelines have been published, only a few controlled trials have been conducted, with little evidence supporting the recommendations.^1,26 A quantitative review found no evidence of benefit in published hospital fall prevention studies using concurrent controls (internal rate of return = 0.92; 95% CI: 0.65-1.30),²⁶ and a recent, well-executed, cluster randomized trial of multifactorial fall prevention interventions found no change in fall rates compared with controls.²⁷ Current hospital fall prevention guidelines are limited to unproven and time-consuming nursing-level (eg, toileting schedules and use of alarms) or organizational-level strategies (eg, changing staff attitudes regarding the inevitability of falls or “leadership support”).^1,28

Despite the large sample size and the use of nurse-reported data that include patient falls from all age groups and not subject to bias due to the regulation itself (eg, ICD coding changes), our findings should be interpreted taking into account several limitations.

First, hospitals participating in the NDNQI self-select to participate and are larger and disproportionately urban compared with nonparticipating hospitals.²⁹ Although our findings were unchanged when hospital-level covariates were included in modeling, analyses stratified by teaching status and bed size demonstrated important differences. Larger teaching hospitals experienced greater fall reductions, whereas restraint use prevalence decreased more rapidly in smaller hospitals.

Second, the absence of a control group prevents us from conclusively attributing changes in falls rate and restraint use prevalence to the 2008 CMS payment change.³⁰ Our findings may have been influenced by other policy changes. For example, in October 2014, the CMS implemented the Hospital-Acquired Condition Reduction Program (HACRP)²⁰ and the Hospital Value-Based Purchasing (VBP)²¹ Program. Under these programs, falls with hip fractures were an indicator that could alter hospital payment.

Third, we did not ascertain the use of all available fall prevention measures such as companions, bed rails, very low beds, bed alarms, and restricted activity.³¹ Nor could the study address changes in patient functional status or discharge location. In a before- and after-study of four hospitals in a single hospital system, we found that bed alarm use increased, restraint orders decreased, and the use of room change or sitters remained stable after the implementation of the CMS payment.³²

Nevertheless, we believe that these findings are consistent with the hypothesis that the HACs Initiative increased the cost of patient falls to hospitals, and, in response, some hospitals were able to modestly reduce the rate of falls. We found no evidence that physical restraint use prevalence increased.

In summary, our findings suggest modest impact of the HACs Initiative on falls and injurious falls, but no unintended impact on restraint use. These results highlight the importance of ensuring that pay-for-performance initiatives target outcomes where there are evidence-based approaches to prevention. The creation or identification of prevention tools and guidelines does not make an outcome preventable. Despite interval improvement in these self-selected hospital units in fall rates and physical restraint use prevalence, falls remain a difficult patient safety problem for hospitals, and further research is required to develop cost-effective, generalizable strategies for their prevention.

Chronic obstructive pulmonary disease (COPD) is recognized as the third leading cause of death nationally. Globally, it has been estimated that 10% of the population has COPD; in the United States, approximately 15 million people are affected.^1,2 The annual estimated cost of COPD management in the United States is approximately $50 billion, one-third of which is directly related to inpatient hospitalization for COPD exacerbation.^3,4,5 The 30-day readmission rate after hospitalization for acute exacerbation of COPD (AECOPD) is approximately 21% with an approximate cost of $13 billion per year.^6,7 To reduce the cost and to improve patient outcomes, the Centers for Medicare and Medicaid Services (CMS) has designed several interventions with little effect.⁸

In October 2012, the Affordable Care Act added section 1886(q) to the Social Security Act and established the Hospital Readmission Reduction Program (HRRP), an initiative to decrease hospitalization costs by penalizing hospitals with high 30-day readmission rates. Under this program, hospitals received up to 3% penalty for excess readmissions after the index hospitalization with acute myocardial infarction (AMI), heart failure (HF), and pneumonia.^9-11 Hospitals are penalized if their annual readmission rates are significantly above the average national readmission rate. In 2014, the HRRP was extended to include AECOPD for the FY 2015.

Since the implementation of readmission penalties, data have shown a significant decrease in the 30-day readmission rates for all conditions.^12,13 On the other hand, studies have suggested that, at least for some conditions, the decrease in the 30-day readmission rate is associated with higher adverse patients outcomes, including higher mortality.^14,15 However, whether a decrease in readmission rates after an AECOPD hospitalization is associated with a concomitant increase in mortality has not been examined. Therefore, our objective was to examine the association of the 30-day risk-adjusted hospital readmission rate with the 30-day risk-adjusted hospital mortality rate for patients discharged with a diagnosis of AECOPD.

Data Sources

Publicly available data from three sources were used. The all-cause 30-day risk-standardized readmission rate (RSRR) and the 30-day risk-standardized mortality rate (RSMR) of each hospital for patients with AECOPD were obtained from the Hospital Compare database; a database maintained by the CMS.^16,17 In 2014, the CMS started reporting three-year running average of 30-day mortality and readmission rate data on hospitals for AECOPD hospitalizations; the data start date was July 2010.^18-22 We examined data from the FY 2010-2013 to 2014-2017 cycles on readmission and mortality reported by the CMS; this included data before and after the implementation of penalties.

Hospital characteristics were also obtained from the CMS website. Hospital ownership was defined as government (owned by Federal or state), for-profit (owned by physicians or another proprietary), or nonprofit (owned by a nonprofit organization such as a church). A hospital was considered as a teaching hospital if it obtained graduate medical education funding from the CMS.

Data on local population characteristics according to ZIP codes were obtained from the 2010 decennial census and the American Community Survey five-year (2009-2013) data files available at the United States Census Bureau website.²³ For each ZIP code, we obtained data on the total population, percentage of African Americans in the population, median income, poverty level, and insurance status.

We used Hospital service area (HSA) information obtained from the Dartmouth Atlas of Health Care crosswalk files to link local population characteristics to hospitals. The Dartmouth Atlas defined 3,436 HSAs by assigning the ZIP codes to the hospital area where the greatest proportion of their Medicare residents was hospitalized.^24,25

Hospital Compare data and Census Bureau population data were matched to the HSAs from the Dartmouth Atlas of Healthcare data at the ZIP code level. First, the ZIP code-level data from the Census Bureau were pooled by the HSAs obtained from the Dartmouth Atlas of Healthcare, followed by matching these data by the HSAs to the Hospital Compare data. Merging data from these three sources generated a dataset that contained information about readmission and mortality rates from a particular hospital and the population characteristics of the local healthcare market or neighborhood. Our final dataset included hospitals that had readmission and mortality information available at the Hospital Compare website and were included in the crosswalk files of the Dartmouth Atlas of Healthcare.

Statistical Analysis

Data are summarized as mean and standard deviation (SD), median with interquartile range, or frequencies as appropriate. To model the dependence of observations from the same hospital over time, we used mixed linear models with random intercept and slope. A strength of this modeling approach is that it incorporates information from all hospitals even when some hospitals are missing data for some time periods. We reached our final model through stages with increasing model complexity at each stage. In the first stage, we developed an empty model without any covariates to determine the unconditional variance components so that we can partition mortality variance into between- and within-hospital components. In the second stage, we developed an unconditional growth curve model to determine the shape of time trend in mortality over time using linear and quadratic (by including squared time in the model) growth curves. In the third stage, we added baseline readmission rates (from 2010 to 2013) to the model to determine the effect of baseline readmission rate on mortality trends and also examined its interaction with time and squared time. We generated a change in the readmission rate variable by subtracting the last readmission rate from the baseline readmission rate (readmission rate in 2010-2013 − readmission rate in 2014-2017). In the fourth stage, we included this change in readmission rate into the third-stage model to examine how changes in the readmission rate affected the time trends of mortality and also examined its interaction with time and squared time. In the final model, we included the following potential confounding variables to the fourth stage model: African American percentage in the HSA, HSA median income, percentage of people living in poverty in the HSA, median age, ownership of hospital (government, for profit), teaching status (teaching vs nonteaching), and acute care hospital beds in the HSA. Within each stage, the models were compared using the Akaike information criterion (AIC) and the Bayesian information criterion (BIC), and the model with the lowest value of each was moved to the next stage of model development. All analyses were performed in Stata 14.1 for Windows (College Station, Texas).

Of the 3,685 acute care hospitals analyzed in the 2010-2013 data cycle for COPD, the 30-day RSRR was 20.7% (1.28), which decreased to 19.6% (1.11) in 2014-2017 (Table 1). During the same period, the 30-day all-cause RSMR increased from 7.8% (1.03) in 2010-2013 to 8.4% (1.11) in 2014-2017. The partitioning of variance showed that 57% of variation in the mortality rate over the study period was due to between-hospital differences.

The unconditional growth model examining the linear time trend revealed a 0.13% per year (95% CI = 0.12 to 0.14; P < .0001) increase in mortality rate over the five data cycles. When the squared time variable was added to the model to examine a quadratic trend, both time and squared trend were statistically significant (Table 2) and the AIC and BIC were lower for the quadratic model. Thus, the unconditional growth curve model suggested that the mortality trend was nonlinear and the coefficients demonstrated that not only the mortality rate increased, but the rate of change in the mortality rate was also increasing during the study period.

When we added the baseline readmission rate to the abovementioned quadratic growth model, we found an inverse association; each 1% increase in baseline readmission rate was associated with 0.03% (95% CI = −0.05 to −0.005; P = .02) decrease in mortality rate. These findings suggest that hospitals with higher baseline readmission rates also had lower mortality rates. To examine whether the effect of baseline readmission rate on mortality varied over time, we included the interaction term with time in the model and then added the interaction term with squared time. As the AIC and BIC were the lowest for the model with interaction between time and baseline readmission (and not when interaction between squared time and baseline readmission were included), we accepted this model. In this model, although there was no difference in mortality according to readmissions at baseline, each 1% increase in baseline readmission rate was associated with a smaller increase in mortality rate by 0.015% (95% CI = −0.02 to −0.01; P < .0001; Table 2 and Figure 1). These findings suggest that hospitals with higher readmission rates at baseline had a smaller increase in mortality rate during the study period than those with lower readmission rates.

As efforts to decrease readmission rates continue as a part of the HRRP implementation by the CMS, our study shows that among hospitals that discharged patients with AECOPD during 2010-2017, the all-cause 30-day RSRR was decreased, whereas the all-cause 30-day RSMR was increased. Of particular concern is that the rate of increase in mortality also increased. We also found that hospitals with higher readmission rates in 2010-2013 had a lower rate of increase in mortality than hospitals with lower readmission rates. In addition, hospitals that had a larger decrease in readmission rates during the study period had a larger rate of increase in mortality than hospitals with a smaller decrease in readmission rates. Our findings were robust to potential confounders such as hospital characteristics and local population characteristics in which hospitals operate.

Our study findings raise the question whether the implementation of the HRRP resulted in unintentional patient harm by forcing hospitals to make changes that may affect overall patient care. This question is particularly important as other studies on hospitalized patients with HF have found similar results.^13,14 On the other hand, a similar association between readmission and mortality rates has not been observed in patients with pneumonia or AMI.¹⁴ Several possible explanations can be given for the observed discrepancy between the diseases and their effect on the relationship between readmission rate and mortality rate. Both COPD and HF are chronic diseases and characterized by exacerbations, whereas AMI and pneumonia are episodic diseases that are treatable. As the number of patients hospitalized with AECOPD and HF is much larger, hospitals may have a greater focus on reducing the 30-day readmission rates and may attempt to game the process, such as by delaying admissions through the emergency department within the 30-day period or by admitting patients for observation. In fact, a study found a 3% reduction in the within-hospital readmission rate with a concurrent 0.8% increase in observation unit use since the implementation of the HRRP.²⁶ Such approaches to patient care may lead to adverse outcomes.

It is possible that readmissions and mortality act as competing risks and hence hospitals with higher mortality rates are left with fewer patients and thus have fewer readmissions, whereas those with lower mortality rates have more patients and a higher readmission rate.²⁷ Such studies are not possible with hospital-level data, and patient-level studies will be required to examine this competing risk hypothesis. Our study results provide some support to the competing risk hypothesis (hospitals with lower baseline readmission rates had a steeper increase in mortality); however, it is not possible to draw any conclusions due to the high risk of ecological fallacy bias.

This study has important potential implications for healthcare policy, public health, and research. We found that an important national intervention aimed at decreasing readmission rates and improving the quality of care for patients with AECOPD may be associated with higher mortality rates in these patients. There may be a need to redefine measures for determining the performance of an institution. Our study supports research into the underlying mechanisms resulting in an inverse association between readmissions and mortality. In particular, health policy researchers may need to examine how incentives and penalties affect the allocation of resources within hospitals.

This study has several strengths and some potential weaknesses. We used a national dataset to examine readmission and mortality rates that include the majority of hospitals in the United States. We also included data from the local population for each hospital, thus allowing us to examine hospital performance within the context of its target population. One potential limitation is that we used hospital-level data and not patient-level data; however, the readmission penalties are designed for hospitals, which justifies our use of hospital-level data. Furthermore, data were not available for shorter time intervals; data from shorter time intervals may be associated with greater variability. Being an observational study, it is difficult to establish a causal relationship; the longitudinal nature of the study does establish temporality, an important factor in establishing causality.

In conclusion, we found that although the readmission rates decreased, there was an increase in the mortality rate within the 30 days of discharge from the hospital in patients with AECOPD. The rate of increase in mortality was higher in hospitals with lower readmission rates than in hospitals with higher readmission rates. Further research for determining the mechanism responsible for this association is needed. Future health policy interventions may need to consider the potential for adverse outcomes.

Chronic obstructive pulmonary disease (COPD) is recognized as the third leading cause of death nationally. Globally, it has been estimated that 10% of the population has COPD; in the United States, approximately 15 million people are affected.^1,2 The annual estimated cost of COPD management in the United States is approximately $50 billion, one-third of which is directly related to inpatient hospitalization for COPD exacerbation.^3,4,5 The 30-day readmission rate after hospitalization for acute exacerbation of COPD (AECOPD) is approximately 21% with an approximate cost of $13 billion per year.^6,7 To reduce the cost and to improve patient outcomes, the Centers for Medicare and Medicaid Services (CMS) has designed several interventions with little effect.⁸

In October 2012, the Affordable Care Act added section 1886(q) to the Social Security Act and established the Hospital Readmission Reduction Program (HRRP), an initiative to decrease hospitalization costs by penalizing hospitals with high 30-day readmission rates. Under this program, hospitals received up to 3% penalty for excess readmissions after the index hospitalization with acute myocardial infarction (AMI), heart failure (HF), and pneumonia.^9-11 Hospitals are penalized if their annual readmission rates are significantly above the average national readmission rate. In 2014, the HRRP was extended to include AECOPD for the FY 2015.

Since the implementation of readmission penalties, data have shown a significant decrease in the 30-day readmission rates for all conditions.^12,13 On the other hand, studies have suggested that, at least for some conditions, the decrease in the 30-day readmission rate is associated with higher adverse patients outcomes, including higher mortality.^14,15 However, whether a decrease in readmission rates after an AECOPD hospitalization is associated with a concomitant increase in mortality has not been examined. Therefore, our objective was to examine the association of the 30-day risk-adjusted hospital readmission rate with the 30-day risk-adjusted hospital mortality rate for patients discharged with a diagnosis of AECOPD.

Data Sources

Publicly available data from three sources were used. The all-cause 30-day risk-standardized readmission rate (RSRR) and the 30-day risk-standardized mortality rate (RSMR) of each hospital for patients with AECOPD were obtained from the Hospital Compare database; a database maintained by the CMS.^16,17 In 2014, the CMS started reporting three-year running average of 30-day mortality and readmission rate data on hospitals for AECOPD hospitalizations; the data start date was July 2010.^18-22 We examined data from the FY 2010-2013 to 2014-2017 cycles on readmission and mortality reported by the CMS; this included data before and after the implementation of penalties.

Hospital characteristics were also obtained from the CMS website. Hospital ownership was defined as government (owned by Federal or state), for-profit (owned by physicians or another proprietary), or nonprofit (owned by a nonprofit organization such as a church). A hospital was considered as a teaching hospital if it obtained graduate medical education funding from the CMS.

Data on local population characteristics according to ZIP codes were obtained from the 2010 decennial census and the American Community Survey five-year (2009-2013) data files available at the United States Census Bureau website.²³ For each ZIP code, we obtained data on the total population, percentage of African Americans in the population, median income, poverty level, and insurance status.

We used Hospital service area (HSA) information obtained from the Dartmouth Atlas of Health Care crosswalk files to link local population characteristics to hospitals. The Dartmouth Atlas defined 3,436 HSAs by assigning the ZIP codes to the hospital area where the greatest proportion of their Medicare residents was hospitalized.^24,25

Hospital Compare data and Census Bureau population data were matched to the HSAs from the Dartmouth Atlas of Healthcare data at the ZIP code level. First, the ZIP code-level data from the Census Bureau were pooled by the HSAs obtained from the Dartmouth Atlas of Healthcare, followed by matching these data by the HSAs to the Hospital Compare data. Merging data from these three sources generated a dataset that contained information about readmission and mortality rates from a particular hospital and the population characteristics of the local healthcare market or neighborhood. Our final dataset included hospitals that had readmission and mortality information available at the Hospital Compare website and were included in the crosswalk files of the Dartmouth Atlas of Healthcare.

Statistical Analysis

Data are summarized as mean and standard deviation (SD), median with interquartile range, or frequencies as appropriate. To model the dependence of observations from the same hospital over time, we used mixed linear models with random intercept and slope. A strength of this modeling approach is that it incorporates information from all hospitals even when some hospitals are missing data for some time periods. We reached our final model through stages with increasing model complexity at each stage. In the first stage, we developed an empty model without any covariates to determine the unconditional variance components so that we can partition mortality variance into between- and within-hospital components. In the second stage, we developed an unconditional growth curve model to determine the shape of time trend in mortality over time using linear and quadratic (by including squared time in the model) growth curves. In the third stage, we added baseline readmission rates (from 2010 to 2013) to the model to determine the effect of baseline readmission rate on mortality trends and also examined its interaction with time and squared time. We generated a change in the readmission rate variable by subtracting the last readmission rate from the baseline readmission rate (readmission rate in 2010-2013 − readmission rate in 2014-2017). In the fourth stage, we included this change in readmission rate into the third-stage model to examine how changes in the readmission rate affected the time trends of mortality and also examined its interaction with time and squared time. In the final model, we included the following potential confounding variables to the fourth stage model: African American percentage in the HSA, HSA median income, percentage of people living in poverty in the HSA, median age, ownership of hospital (government, for profit), teaching status (teaching vs nonteaching), and acute care hospital beds in the HSA. Within each stage, the models were compared using the Akaike information criterion (AIC) and the Bayesian information criterion (BIC), and the model with the lowest value of each was moved to the next stage of model development. All analyses were performed in Stata 14.1 for Windows (College Station, Texas).

Of the 3,685 acute care hospitals analyzed in the 2010-2013 data cycle for COPD, the 30-day RSRR was 20.7% (1.28), which decreased to 19.6% (1.11) in 2014-2017 (Table 1). During the same period, the 30-day all-cause RSMR increased from 7.8% (1.03) in 2010-2013 to 8.4% (1.11) in 2014-2017. The partitioning of variance showed that 57% of variation in the mortality rate over the study period was due to between-hospital differences.

The unconditional growth model examining the linear time trend revealed a 0.13% per year (95% CI = 0.12 to 0.14; P < .0001) increase in mortality rate over the five data cycles. When the squared time variable was added to the model to examine a quadratic trend, both time and squared trend were statistically significant (Table 2) and the AIC and BIC were lower for the quadratic model. Thus, the unconditional growth curve model suggested that the mortality trend was nonlinear and the coefficients demonstrated that not only the mortality rate increased, but the rate of change in the mortality rate was also increasing during the study period.

When we added the baseline readmission rate to the abovementioned quadratic growth model, we found an inverse association; each 1% increase in baseline readmission rate was associated with 0.03% (95% CI = −0.05 to −0.005; P = .02) decrease in mortality rate. These findings suggest that hospitals with higher baseline readmission rates also had lower mortality rates. To examine whether the effect of baseline readmission rate on mortality varied over time, we included the interaction term with time in the model and then added the interaction term with squared time. As the AIC and BIC were the lowest for the model with interaction between time and baseline readmission (and not when interaction between squared time and baseline readmission were included), we accepted this model. In this model, although there was no difference in mortality according to readmissions at baseline, each 1% increase in baseline readmission rate was associated with a smaller increase in mortality rate by 0.015% (95% CI = −0.02 to −0.01; P < .0001; Table 2 and Figure 1). These findings suggest that hospitals with higher readmission rates at baseline had a smaller increase in mortality rate during the study period than those with lower readmission rates.

As efforts to decrease readmission rates continue as a part of the HRRP implementation by the CMS, our study shows that among hospitals that discharged patients with AECOPD during 2010-2017, the all-cause 30-day RSRR was decreased, whereas the all-cause 30-day RSMR was increased. Of particular concern is that the rate of increase in mortality also increased. We also found that hospitals with higher readmission rates in 2010-2013 had a lower rate of increase in mortality than hospitals with lower readmission rates. In addition, hospitals that had a larger decrease in readmission rates during the study period had a larger rate of increase in mortality than hospitals with a smaller decrease in readmission rates. Our findings were robust to potential confounders such as hospital characteristics and local population characteristics in which hospitals operate.

Our study findings raise the question whether the implementation of the HRRP resulted in unintentional patient harm by forcing hospitals to make changes that may affect overall patient care. This question is particularly important as other studies on hospitalized patients with HF have found similar results.^13,14 On the other hand, a similar association between readmission and mortality rates has not been observed in patients with pneumonia or AMI.¹⁴ Several possible explanations can be given for the observed discrepancy between the diseases and their effect on the relationship between readmission rate and mortality rate. Both COPD and HF are chronic diseases and characterized by exacerbations, whereas AMI and pneumonia are episodic diseases that are treatable. As the number of patients hospitalized with AECOPD and HF is much larger, hospitals may have a greater focus on reducing the 30-day readmission rates and may attempt to game the process, such as by delaying admissions through the emergency department within the 30-day period or by admitting patients for observation. In fact, a study found a 3% reduction in the within-hospital readmission rate with a concurrent 0.8% increase in observation unit use since the implementation of the HRRP.²⁶ Such approaches to patient care may lead to adverse outcomes.

It is possible that readmissions and mortality act as competing risks and hence hospitals with higher mortality rates are left with fewer patients and thus have fewer readmissions, whereas those with lower mortality rates have more patients and a higher readmission rate.²⁷ Such studies are not possible with hospital-level data, and patient-level studies will be required to examine this competing risk hypothesis. Our study results provide some support to the competing risk hypothesis (hospitals with lower baseline readmission rates had a steeper increase in mortality); however, it is not possible to draw any conclusions due to the high risk of ecological fallacy bias.

This study has important potential implications for healthcare policy, public health, and research. We found that an important national intervention aimed at decreasing readmission rates and improving the quality of care for patients with AECOPD may be associated with higher mortality rates in these patients. There may be a need to redefine measures for determining the performance of an institution. Our study supports research into the underlying mechanisms resulting in an inverse association between readmissions and mortality. In particular, health policy researchers may need to examine how incentives and penalties affect the allocation of resources within hospitals.

This study has several strengths and some potential weaknesses. We used a national dataset to examine readmission and mortality rates that include the majority of hospitals in the United States. We also included data from the local population for each hospital, thus allowing us to examine hospital performance within the context of its target population. One potential limitation is that we used hospital-level data and not patient-level data; however, the readmission penalties are designed for hospitals, which justifies our use of hospital-level data. Furthermore, data were not available for shorter time intervals; data from shorter time intervals may be associated with greater variability. Being an observational study, it is difficult to establish a causal relationship; the longitudinal nature of the study does establish temporality, an important factor in establishing causality.

In conclusion, we found that although the readmission rates decreased, there was an increase in the mortality rate within the 30 days of discharge from the hospital in patients with AECOPD. The rate of increase in mortality was higher in hospitals with lower readmission rates than in hospitals with higher readmission rates. Further research for determining the mechanism responsible for this association is needed. Future health policy interventions may need to consider the potential for adverse outcomes.

Chronic obstructive pulmonary disease (COPD) is recognized as the third leading cause of death nationally. Globally, it has been estimated that 10% of the population has COPD; in the United States, approximately 15 million people are affected.^1,2 The annual estimated cost of COPD management in the United States is approximately $50 billion, one-third of which is directly related to inpatient hospitalization for COPD exacerbation.^3,4,5 The 30-day readmission rate after hospitalization for acute exacerbation of COPD (AECOPD) is approximately 21% with an approximate cost of $13 billion per year.^6,7 To reduce the cost and to improve patient outcomes, the Centers for Medicare and Medicaid Services (CMS) has designed several interventions with little effect.⁸

In October 2012, the Affordable Care Act added section 1886(q) to the Social Security Act and established the Hospital Readmission Reduction Program (HRRP), an initiative to decrease hospitalization costs by penalizing hospitals with high 30-day readmission rates. Under this program, hospitals received up to 3% penalty for excess readmissions after the index hospitalization with acute myocardial infarction (AMI), heart failure (HF), and pneumonia.^9-11 Hospitals are penalized if their annual readmission rates are significantly above the average national readmission rate. In 2014, the HRRP was extended to include AECOPD for the FY 2015.

Since the implementation of readmission penalties, data have shown a significant decrease in the 30-day readmission rates for all conditions.^12,13 On the other hand, studies have suggested that, at least for some conditions, the decrease in the 30-day readmission rate is associated with higher adverse patients outcomes, including higher mortality.^14,15 However, whether a decrease in readmission rates after an AECOPD hospitalization is associated with a concomitant increase in mortality has not been examined. Therefore, our objective was to examine the association of the 30-day risk-adjusted hospital readmission rate with the 30-day risk-adjusted hospital mortality rate for patients discharged with a diagnosis of AECOPD.

Data Sources

Publicly available data from three sources were used. The all-cause 30-day risk-standardized readmission rate (RSRR) and the 30-day risk-standardized mortality rate (RSMR) of each hospital for patients with AECOPD were obtained from the Hospital Compare database; a database maintained by the CMS.^16,17 In 2014, the CMS started reporting three-year running average of 30-day mortality and readmission rate data on hospitals for AECOPD hospitalizations; the data start date was July 2010.^18-22 We examined data from the FY 2010-2013 to 2014-2017 cycles on readmission and mortality reported by the CMS; this included data before and after the implementation of penalties.

Hospital characteristics were also obtained from the CMS website. Hospital ownership was defined as government (owned by Federal or state), for-profit (owned by physicians or another proprietary), or nonprofit (owned by a nonprofit organization such as a church). A hospital was considered as a teaching hospital if it obtained graduate medical education funding from the CMS.

Data on local population characteristics according to ZIP codes were obtained from the 2010 decennial census and the American Community Survey five-year (2009-2013) data files available at the United States Census Bureau website.²³ For each ZIP code, we obtained data on the total population, percentage of African Americans in the population, median income, poverty level, and insurance status.

We used Hospital service area (HSA) information obtained from the Dartmouth Atlas of Health Care crosswalk files to link local population characteristics to hospitals. The Dartmouth Atlas defined 3,436 HSAs by assigning the ZIP codes to the hospital area where the greatest proportion of their Medicare residents was hospitalized.^24,25

Hospital Compare data and Census Bureau population data were matched to the HSAs from the Dartmouth Atlas of Healthcare data at the ZIP code level. First, the ZIP code-level data from the Census Bureau were pooled by the HSAs obtained from the Dartmouth Atlas of Healthcare, followed by matching these data by the HSAs to the Hospital Compare data. Merging data from these three sources generated a dataset that contained information about readmission and mortality rates from a particular hospital and the population characteristics of the local healthcare market or neighborhood. Our final dataset included hospitals that had readmission and mortality information available at the Hospital Compare website and were included in the crosswalk files of the Dartmouth Atlas of Healthcare.

Statistical Analysis

Data are summarized as mean and standard deviation (SD), median with interquartile range, or frequencies as appropriate. To model the dependence of observations from the same hospital over time, we used mixed linear models with random intercept and slope. A strength of this modeling approach is that it incorporates information from all hospitals even when some hospitals are missing data for some time periods. We reached our final model through stages with increasing model complexity at each stage. In the first stage, we developed an empty model without any covariates to determine the unconditional variance components so that we can partition mortality variance into between- and within-hospital components. In the second stage, we developed an unconditional growth curve model to determine the shape of time trend in mortality over time using linear and quadratic (by including squared time in the model) growth curves. In the third stage, we added baseline readmission rates (from 2010 to 2013) to the model to determine the effect of baseline readmission rate on mortality trends and also examined its interaction with time and squared time. We generated a change in the readmission rate variable by subtracting the last readmission rate from the baseline readmission rate (readmission rate in 2010-2013 − readmission rate in 2014-2017). In the fourth stage, we included this change in readmission rate into the third-stage model to examine how changes in the readmission rate affected the time trends of mortality and also examined its interaction with time and squared time. In the final model, we included the following potential confounding variables to the fourth stage model: African American percentage in the HSA, HSA median income, percentage of people living in poverty in the HSA, median age, ownership of hospital (government, for profit), teaching status (teaching vs nonteaching), and acute care hospital beds in the HSA. Within each stage, the models were compared using the Akaike information criterion (AIC) and the Bayesian information criterion (BIC), and the model with the lowest value of each was moved to the next stage of model development. All analyses were performed in Stata 14.1 for Windows (College Station, Texas).

Of the 3,685 acute care hospitals analyzed in the 2010-2013 data cycle for COPD, the 30-day RSRR was 20.7% (1.28), which decreased to 19.6% (1.11) in 2014-2017 (Table 1). During the same period, the 30-day all-cause RSMR increased from 7.8% (1.03) in 2010-2013 to 8.4% (1.11) in 2014-2017. The partitioning of variance showed that 57% of variation in the mortality rate over the study period was due to between-hospital differences.

The unconditional growth model examining the linear time trend revealed a 0.13% per year (95% CI = 0.12 to 0.14; P < .0001) increase in mortality rate over the five data cycles. When the squared time variable was added to the model to examine a quadratic trend, both time and squared trend were statistically significant (Table 2) and the AIC and BIC were lower for the quadratic model. Thus, the unconditional growth curve model suggested that the mortality trend was nonlinear and the coefficients demonstrated that not only the mortality rate increased, but the rate of change in the mortality rate was also increasing during the study period.

When we added the baseline readmission rate to the abovementioned quadratic growth model, we found an inverse association; each 1% increase in baseline readmission rate was associated with 0.03% (95% CI = −0.05 to −0.005; P = .02) decrease in mortality rate. These findings suggest that hospitals with higher baseline readmission rates also had lower mortality rates. To examine whether the effect of baseline readmission rate on mortality varied over time, we included the interaction term with time in the model and then added the interaction term with squared time. As the AIC and BIC were the lowest for the model with interaction between time and baseline readmission (and not when interaction between squared time and baseline readmission were included), we accepted this model. In this model, although there was no difference in mortality according to readmissions at baseline, each 1% increase in baseline readmission rate was associated with a smaller increase in mortality rate by 0.015% (95% CI = −0.02 to −0.01; P < .0001; Table 2 and Figure 1). These findings suggest that hospitals with higher readmission rates at baseline had a smaller increase in mortality rate during the study period than those with lower readmission rates.

As efforts to decrease readmission rates continue as a part of the HRRP implementation by the CMS, our study shows that among hospitals that discharged patients with AECOPD during 2010-2017, the all-cause 30-day RSRR was decreased, whereas the all-cause 30-day RSMR was increased. Of particular concern is that the rate of increase in mortality also increased. We also found that hospitals with higher readmission rates in 2010-2013 had a lower rate of increase in mortality than hospitals with lower readmission rates. In addition, hospitals that had a larger decrease in readmission rates during the study period had a larger rate of increase in mortality than hospitals with a smaller decrease in readmission rates. Our findings were robust to potential confounders such as hospital characteristics and local population characteristics in which hospitals operate.

Our study findings raise the question whether the implementation of the HRRP resulted in unintentional patient harm by forcing hospitals to make changes that may affect overall patient care. This question is particularly important as other studies on hospitalized patients with HF have found similar results.^13,14 On the other hand, a similar association between readmission and mortality rates has not been observed in patients with pneumonia or AMI.¹⁴ Several possible explanations can be given for the observed discrepancy between the diseases and their effect on the relationship between readmission rate and mortality rate. Both COPD and HF are chronic diseases and characterized by exacerbations, whereas AMI and pneumonia are episodic diseases that are treatable. As the number of patients hospitalized with AECOPD and HF is much larger, hospitals may have a greater focus on reducing the 30-day readmission rates and may attempt to game the process, such as by delaying admissions through the emergency department within the 30-day period or by admitting patients for observation. In fact, a study found a 3% reduction in the within-hospital readmission rate with a concurrent 0.8% increase in observation unit use since the implementation of the HRRP.²⁶ Such approaches to patient care may lead to adverse outcomes.

It is possible that readmissions and mortality act as competing risks and hence hospitals with higher mortality rates are left with fewer patients and thus have fewer readmissions, whereas those with lower mortality rates have more patients and a higher readmission rate.²⁷ Such studies are not possible with hospital-level data, and patient-level studies will be required to examine this competing risk hypothesis. Our study results provide some support to the competing risk hypothesis (hospitals with lower baseline readmission rates had a steeper increase in mortality); however, it is not possible to draw any conclusions due to the high risk of ecological fallacy bias.

This study has important potential implications for healthcare policy, public health, and research. We found that an important national intervention aimed at decreasing readmission rates and improving the quality of care for patients with AECOPD may be associated with higher mortality rates in these patients. There may be a need to redefine measures for determining the performance of an institution. Our study supports research into the underlying mechanisms resulting in an inverse association between readmissions and mortality. In particular, health policy researchers may need to examine how incentives and penalties affect the allocation of resources within hospitals.

This study has several strengths and some potential weaknesses. We used a national dataset to examine readmission and mortality rates that include the majority of hospitals in the United States. We also included data from the local population for each hospital, thus allowing us to examine hospital performance within the context of its target population. One potential limitation is that we used hospital-level data and not patient-level data; however, the readmission penalties are designed for hospitals, which justifies our use of hospital-level data. Furthermore, data were not available for shorter time intervals; data from shorter time intervals may be associated with greater variability. Being an observational study, it is difficult to establish a causal relationship; the longitudinal nature of the study does establish temporality, an important factor in establishing causality.

In conclusion, we found that although the readmission rates decreased, there was an increase in the mortality rate within the 30 days of discharge from the hospital in patients with AECOPD. The rate of increase in mortality was higher in hospitals with lower readmission rates than in hospitals with higher readmission rates. Further research for determining the mechanism responsible for this association is needed. Future health policy interventions may need to consider the potential for adverse outcomes.