WAIKOLOA, HAWAII – The work up of a case of onychomycosis doesn’t end with the detection of fungal hyphae.

Trichophyton rubrum remains the most common cause of toenail fungus in the United States, but nondermatophyte molds – Scopulariopsis, Fusarium, and others – are on the rise, so it’s important to speciate, especially when patients have atypical presentations or fail to respond to the T. rubrum go-to treatment, terbinafine, according to Nathaniel Jellinek, MD, of the department of dermatology, Brown University, Providence, R.I.

Standard in-office potassium hydroxide (KOH) testing can’t distinguish one species of fungus from another, nor can pathology with Gomori methenamine silver (GMS) or Periodic acid-Schiff (PAS) staining. Both culture and polymerase chain reaction (PCR), however, do.

Since few hospitals are equipped to run those tests, Dr. Jellinek uses the Case Western Center for Medical Mycology, in Cleveland, for testing.

In an interview at the Hawaii Dermatology Seminar provided by the Global Academy for Medical Education/Skin Disease Education Foundation, Dr. Jellinek explained how to speciate, and the importance of doing so.

He also shared his tips on getting good nail clippings and good scrapings for debris for testing, and explained when KOH testing is enough – and when to opt for more advanced diagnostic methods, including PCR, which he said trumps all previous methods.

Terbinafine is still the best option for T. rubrum, but new topicals are better for nondermatophyte molds. There’s also a clever new dosing regimen for terbinafine, one that should put patients at ease about liver toxicity and other concerns. “If you tell them they’re getting 1 month off in the middle, it seems to go over a little easier,” Dr. Jellinek said.

SDEF/Global Academy for Medical Education and this news organization are owned by the same parent company.
 

[email protected]

WAIKOLOA, HAWAII – The work up of a case of onychomycosis doesn’t end with the detection of fungal hyphae.

Trichophyton rubrum remains the most common cause of toenail fungus in the United States, but nondermatophyte molds – Scopulariopsis, Fusarium, and others – are on the rise, so it’s important to speciate, especially when patients have atypical presentations or fail to respond to the T. rubrum go-to treatment, terbinafine, according to Nathaniel Jellinek, MD, of the department of dermatology, Brown University, Providence, R.I.

Standard in-office potassium hydroxide (KOH) testing can’t distinguish one species of fungus from another, nor can pathology with Gomori methenamine silver (GMS) or Periodic acid-Schiff (PAS) staining. Both culture and polymerase chain reaction (PCR), however, do.

Since few hospitals are equipped to run those tests, Dr. Jellinek uses the Case Western Center for Medical Mycology, in Cleveland, for testing.

In an interview at the Hawaii Dermatology Seminar provided by the Global Academy for Medical Education/Skin Disease Education Foundation, Dr. Jellinek explained how to speciate, and the importance of doing so.

He also shared his tips on getting good nail clippings and good scrapings for debris for testing, and explained when KOH testing is enough – and when to opt for more advanced diagnostic methods, including PCR, which he said trumps all previous methods.

Terbinafine is still the best option for T. rubrum, but new topicals are better for nondermatophyte molds. There’s also a clever new dosing regimen for terbinafine, one that should put patients at ease about liver toxicity and other concerns. “If you tell them they’re getting 1 month off in the middle, it seems to go over a little easier,” Dr. Jellinek said.

SDEF/Global Academy for Medical Education and this news organization are owned by the same parent company.
 

[email protected]

WAIKOLOA, HAWAII – The work up of a case of onychomycosis doesn’t end with the detection of fungal hyphae.

Trichophyton rubrum remains the most common cause of toenail fungus in the United States, but nondermatophyte molds – Scopulariopsis, Fusarium, and others – are on the rise, so it’s important to speciate, especially when patients have atypical presentations or fail to respond to the T. rubrum go-to treatment, terbinafine, according to Nathaniel Jellinek, MD, of the department of dermatology, Brown University, Providence, R.I.

Standard in-office potassium hydroxide (KOH) testing can’t distinguish one species of fungus from another, nor can pathology with Gomori methenamine silver (GMS) or Periodic acid-Schiff (PAS) staining. Both culture and polymerase chain reaction (PCR), however, do.

Since few hospitals are equipped to run those tests, Dr. Jellinek uses the Case Western Center for Medical Mycology, in Cleveland, for testing.

In an interview at the Hawaii Dermatology Seminar provided by the Global Academy for Medical Education/Skin Disease Education Foundation, Dr. Jellinek explained how to speciate, and the importance of doing so.

He also shared his tips on getting good nail clippings and good scrapings for debris for testing, and explained when KOH testing is enough – and when to opt for more advanced diagnostic methods, including PCR, which he said trumps all previous methods.

Terbinafine is still the best option for T. rubrum, but new topicals are better for nondermatophyte molds. There’s also a clever new dosing regimen for terbinafine, one that should put patients at ease about liver toxicity and other concerns. “If you tell them they’re getting 1 month off in the middle, it seems to go over a little easier,” Dr. Jellinek said.

SDEF/Global Academy for Medical Education and this news organization are owned by the same parent company.
 

[email protected]

When a patient told Richard Balon, MD, that he could no longer afford to spend $300 a month on a tricyclic, Dr. Balon could not believe that the price was so high. “This is clearly unsustainable,” said Dr. Balon, professor of psychiatry and anesthesiology, and associate chair of education at Wayne State University in Detroit. In this Masterclass episode of the Psychcast from the 2018 AACP Encore meeting in Las Vegas, he lectures on the increase in the price of generic and brand name medications in the United States.

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When a patient told Richard Balon, MD, that he could no longer afford to spend $300 a month on a tricyclic, Dr. Balon could not believe that the price was so high. “This is clearly unsustainable,” said Dr. Balon, professor of psychiatry and anesthesiology, and associate chair of education at Wayne State University in Detroit. In this Masterclass episode of the Psychcast from the 2018 AACP Encore meeting in Las Vegas, he lectures on the increase in the price of generic and brand name medications in the United States.

Amazon

Apple Podcasts

Google Podcasts

Spotify


 

When a patient told Richard Balon, MD, that he could no longer afford to spend $300 a month on a tricyclic, Dr. Balon could not believe that the price was so high. “This is clearly unsustainable,” said Dr. Balon, professor of psychiatry and anesthesiology, and associate chair of education at Wayne State University in Detroit. In this Masterclass episode of the Psychcast from the 2018 AACP Encore meeting in Las Vegas, he lectures on the increase in the price of generic and brand name medications in the United States.

Amazon

Apple Podcasts

Google Podcasts

Spotify


 

SAN DIEGO – One year after implementation, a pulmonary embolism response team (PERT) is making a significant difference in care of patients with submassive pulmonary embolism and evidence of right ventricular (RV) dysfunction at the Christiana Medical Center in Delaware. An analysis of data collected showed reductions in ICU stays, early death, and overall hospital length of stay.

Andrei Malov/Thinkstock

Such patients pose a challenge to clinicians because some will go on to develop more serious pulmonary embolism (PE), yet aggressive treatment options carry their own risk. Existing guidelines, such as those by the European Society of Cardiology, recommend conservative treatment of these patients, with more aggressive measures if conditions don’t quickly improve.

However, about 12% of patients on conservative therapy die, or about 100,000 per year. Those patients who go on to have bad outcomes “are obviously intermediate high risk or high risk. This is the patient population that we’re interested in [addressing through PERT]. These aren’t really sick patients. The blood pressure is normal, but they have the risk based on comorbidities or the clot burden to do poorly over the next day or two,” said Michael Benninghoff, DO, section chief of medical critical care and director of respiratory therapy at Christiana Medical Center, Wilmington, Del. Dr. Benninghoff presented the study at the Critical Care Congress sponsored by the Society of Critical Care Medicine.

The PERT concept was developed by physicians at Massachusetts General Hospital. It establishes clinical criteria that, if met, prompt a PERT alert, which in turn triggers a meeting between the initiating provider, a pulmonary intensivist, and a vascular and interventional radiology physician within 15 minutes to review the case and make rapid clinical decisions.

A PERT alert requires either a CT diagnosis of PE or a VQ scan showing a high probability of PE, combined with one of three additional criteria: elevated B-type (brain) natriuretic peptide (BNP) and troponin; echocardiographic evidence of right ventricular dysfunction; or clinical instability as indicated by heart rate over 110 beats per minute, systolic blood pressure below 100 mm Hg, or oxygen saturation lower than 90%.

The PERT program caught Dr. Benninghoff’s attention because of institutional experience with patients deteriorating on conservative treatment, but also because the treatment of submassive PE with RV dysfunction is quite scattered. “We were seeing really conservative to really aggressive treatment. I don’t think we’ve had the data to support treating a patient whose blood pressure is normal but they have signs of right ventricular dysfunction, whether it’s echocardiographic, radiographic, or laboratory evidence of myocardial necrosis. I don’t think we have a group conscience as providers as far as how aggressive to be with those patients,” said Dr. Benninghoff.

To examine the efficacy of the PERT program after 1 year, Dr. Benninghoff’s team reviewed all PE cases from 2016 (pre-PERT, n = 717) and 2017 (post-PERT, n = 752). The mortality index declined 30%, from 1.13 to 0.79, while the percentage of early death declined 52%, from 2.51 to 1.20. The mean number of ICU days fell from 5.01 to 4.40.

When the team restricted the analysis to PE lysis patients (n = 27 in 2016; n = 33 in 2017), the mean length of ICU stay dropped from 66.1 hours to 58.8 hours, and fewer patients were transferred from a lower level of care to the ICU (6 vs. 3).

“We think we have shown that just by talking in real time, forcing physicians to communicate – it certainly doesn’t hurt, that it probably helps with ICU utilization and perhaps even mortality,” said Dr. Benninghoff. 

The results are far from definitive, and much more work needs to be done to determine how best to manage patients with submassive PEs and RV dysfunction. Dr. Benninghoff doesn’t have the answers, but he’s hopeful that the PERT program can eventually provide some. “Probably the most important thing is we’re giving back to the medical community by enrolling in the consortium, putting our data in the hands of the Boston research institute, and seeing what comes of it. Hopefully in 5 years we will have a standard of care based on the work we’re doing now,” he said.

The study was funded internally. Dr. Benninghoff declared no conflicts of interest.

SOURCE: Benninghoff M. Critical Care Congress 2019, Abstract 490.

SAN DIEGO – One year after implementation, a pulmonary embolism response team (PERT) is making a significant difference in care of patients with submassive pulmonary embolism and evidence of right ventricular (RV) dysfunction at the Christiana Medical Center in Delaware. An analysis of data collected showed reductions in ICU stays, early death, and overall hospital length of stay.

Andrei Malov/Thinkstock

Such patients pose a challenge to clinicians because some will go on to develop more serious pulmonary embolism (PE), yet aggressive treatment options carry their own risk. Existing guidelines, such as those by the European Society of Cardiology, recommend conservative treatment of these patients, with more aggressive measures if conditions don’t quickly improve.

However, about 12% of patients on conservative therapy die, or about 100,000 per year. Those patients who go on to have bad outcomes “are obviously intermediate high risk or high risk. This is the patient population that we’re interested in [addressing through PERT]. These aren’t really sick patients. The blood pressure is normal, but they have the risk based on comorbidities or the clot burden to do poorly over the next day or two,” said Michael Benninghoff, DO, section chief of medical critical care and director of respiratory therapy at Christiana Medical Center, Wilmington, Del. Dr. Benninghoff presented the study at the Critical Care Congress sponsored by the Society of Critical Care Medicine.

The PERT concept was developed by physicians at Massachusetts General Hospital. It establishes clinical criteria that, if met, prompt a PERT alert, which in turn triggers a meeting between the initiating provider, a pulmonary intensivist, and a vascular and interventional radiology physician within 15 minutes to review the case and make rapid clinical decisions.

A PERT alert requires either a CT diagnosis of PE or a VQ scan showing a high probability of PE, combined with one of three additional criteria: elevated B-type (brain) natriuretic peptide (BNP) and troponin; echocardiographic evidence of right ventricular dysfunction; or clinical instability as indicated by heart rate over 110 beats per minute, systolic blood pressure below 100 mm Hg, or oxygen saturation lower than 90%.

The PERT program caught Dr. Benninghoff’s attention because of institutional experience with patients deteriorating on conservative treatment, but also because the treatment of submassive PE with RV dysfunction is quite scattered. “We were seeing really conservative to really aggressive treatment. I don’t think we’ve had the data to support treating a patient whose blood pressure is normal but they have signs of right ventricular dysfunction, whether it’s echocardiographic, radiographic, or laboratory evidence of myocardial necrosis. I don’t think we have a group conscience as providers as far as how aggressive to be with those patients,” said Dr. Benninghoff.

To examine the efficacy of the PERT program after 1 year, Dr. Benninghoff’s team reviewed all PE cases from 2016 (pre-PERT, n = 717) and 2017 (post-PERT, n = 752). The mortality index declined 30%, from 1.13 to 0.79, while the percentage of early death declined 52%, from 2.51 to 1.20. The mean number of ICU days fell from 5.01 to 4.40.

When the team restricted the analysis to PE lysis patients (n = 27 in 2016; n = 33 in 2017), the mean length of ICU stay dropped from 66.1 hours to 58.8 hours, and fewer patients were transferred from a lower level of care to the ICU (6 vs. 3).

“We think we have shown that just by talking in real time, forcing physicians to communicate – it certainly doesn’t hurt, that it probably helps with ICU utilization and perhaps even mortality,” said Dr. Benninghoff. 

The results are far from definitive, and much more work needs to be done to determine how best to manage patients with submassive PEs and RV dysfunction. Dr. Benninghoff doesn’t have the answers, but he’s hopeful that the PERT program can eventually provide some. “Probably the most important thing is we’re giving back to the medical community by enrolling in the consortium, putting our data in the hands of the Boston research institute, and seeing what comes of it. Hopefully in 5 years we will have a standard of care based on the work we’re doing now,” he said.

The study was funded internally. Dr. Benninghoff declared no conflicts of interest.

SOURCE: Benninghoff M. Critical Care Congress 2019, Abstract 490.

SAN DIEGO – One year after implementation, a pulmonary embolism response team (PERT) is making a significant difference in care of patients with submassive pulmonary embolism and evidence of right ventricular (RV) dysfunction at the Christiana Medical Center in Delaware. An analysis of data collected showed reductions in ICU stays, early death, and overall hospital length of stay.

Andrei Malov/Thinkstock

Such patients pose a challenge to clinicians because some will go on to develop more serious pulmonary embolism (PE), yet aggressive treatment options carry their own risk. Existing guidelines, such as those by the European Society of Cardiology, recommend conservative treatment of these patients, with more aggressive measures if conditions don’t quickly improve.

However, about 12% of patients on conservative therapy die, or about 100,000 per year. Those patients who go on to have bad outcomes “are obviously intermediate high risk or high risk. This is the patient population that we’re interested in [addressing through PERT]. These aren’t really sick patients. The blood pressure is normal, but they have the risk based on comorbidities or the clot burden to do poorly over the next day or two,” said Michael Benninghoff, DO, section chief of medical critical care and director of respiratory therapy at Christiana Medical Center, Wilmington, Del. Dr. Benninghoff presented the study at the Critical Care Congress sponsored by the Society of Critical Care Medicine.

The PERT concept was developed by physicians at Massachusetts General Hospital. It establishes clinical criteria that, if met, prompt a PERT alert, which in turn triggers a meeting between the initiating provider, a pulmonary intensivist, and a vascular and interventional radiology physician within 15 minutes to review the case and make rapid clinical decisions.

A PERT alert requires either a CT diagnosis of PE or a VQ scan showing a high probability of PE, combined with one of three additional criteria: elevated B-type (brain) natriuretic peptide (BNP) and troponin; echocardiographic evidence of right ventricular dysfunction; or clinical instability as indicated by heart rate over 110 beats per minute, systolic blood pressure below 100 mm Hg, or oxygen saturation lower than 90%.

The PERT program caught Dr. Benninghoff’s attention because of institutional experience with patients deteriorating on conservative treatment, but also because the treatment of submassive PE with RV dysfunction is quite scattered. “We were seeing really conservative to really aggressive treatment. I don’t think we’ve had the data to support treating a patient whose blood pressure is normal but they have signs of right ventricular dysfunction, whether it’s echocardiographic, radiographic, or laboratory evidence of myocardial necrosis. I don’t think we have a group conscience as providers as far as how aggressive to be with those patients,” said Dr. Benninghoff.

To examine the efficacy of the PERT program after 1 year, Dr. Benninghoff’s team reviewed all PE cases from 2016 (pre-PERT, n = 717) and 2017 (post-PERT, n = 752). The mortality index declined 30%, from 1.13 to 0.79, while the percentage of early death declined 52%, from 2.51 to 1.20. The mean number of ICU days fell from 5.01 to 4.40.

When the team restricted the analysis to PE lysis patients (n = 27 in 2016; n = 33 in 2017), the mean length of ICU stay dropped from 66.1 hours to 58.8 hours, and fewer patients were transferred from a lower level of care to the ICU (6 vs. 3).

“We think we have shown that just by talking in real time, forcing physicians to communicate – it certainly doesn’t hurt, that it probably helps with ICU utilization and perhaps even mortality,” said Dr. Benninghoff. 

The results are far from definitive, and much more work needs to be done to determine how best to manage patients with submassive PEs and RV dysfunction. Dr. Benninghoff doesn’t have the answers, but he’s hopeful that the PERT program can eventually provide some. “Probably the most important thing is we’re giving back to the medical community by enrolling in the consortium, putting our data in the hands of the Boston research institute, and seeing what comes of it. Hopefully in 5 years we will have a standard of care based on the work we’re doing now,” he said.

The study was funded internally. Dr. Benninghoff declared no conflicts of interest.

SOURCE: Benninghoff M. Critical Care Congress 2019, Abstract 490.

Patients with diabetic foot ulcers have a high incidence of associated chronic vascular disease, including kidney disease, retinopathy, and peripheral artery disease (PAD). In addition, there was statistically significant association between both diabetic retinopathy and diabetic kidney disease and PAD, according to a study reported by Magdy H. Megallaa, MD, and colleagues at Alexandria (Egypt) University.

Rebecca L. Slebodnik/MDedge News

Their cross-sectional study, published in Diabetes & Metabolic Syndrome: Clinical Research and Reviews, comprised 180 type 2 diabetic patients (aged 30-70 years) with diabetic foot ulcers (DFUs) who attended an outpatient clinic between July and December 2017.

The prevalence of diabetic kidney disease and diabetic retinopathy was 86.1% and 90.0%, respectively, with 86.7% of patients having neuropathic DFUs, 11.1% having ischemic DFUs, and 2.2% having neuroischemic DFUs. The prevalence of peripheral neuropathy and PAD was 82% and 20%, respectively.

Using albuminuria as a measure of diabetic kidney disease, the researchers found that 86.1% of the patients had albuminuria and that there was a statistically significant association between albuminuria and the patient’s vibration reception threshold (VPT), a measure of diabetic neuropathy (P less than .001), and the ankle brachial index (ABI), a measure of PAD (P less than .031).

In addition, there was a statistically significant association between diabetic retinopathy and VPT (P less than .008) and between diabetic retinopathy and ABI (P less than .001).

“Albuminuria, diabetic retinopathy and peripheral neuropathy are very common among those patients and strongly associated with risk factors of diabetic foot ulceration,” the researchers concluded.

They reported that they had no conflicts.

[email protected]

SOURCE: Megallaa MH et al. Diabetes Metab Syndr. 2019 Mar-Apr;13(2):1287-92.

Patients with diabetic foot ulcers have a high incidence of associated chronic vascular disease, including kidney disease, retinopathy, and peripheral artery disease (PAD). In addition, there was statistically significant association between both diabetic retinopathy and diabetic kidney disease and PAD, according to a study reported by Magdy H. Megallaa, MD, and colleagues at Alexandria (Egypt) University.

Rebecca L. Slebodnik/MDedge News

Their cross-sectional study, published in Diabetes & Metabolic Syndrome: Clinical Research and Reviews, comprised 180 type 2 diabetic patients (aged 30-70 years) with diabetic foot ulcers (DFUs) who attended an outpatient clinic between July and December 2017.

The prevalence of diabetic kidney disease and diabetic retinopathy was 86.1% and 90.0%, respectively, with 86.7% of patients having neuropathic DFUs, 11.1% having ischemic DFUs, and 2.2% having neuroischemic DFUs. The prevalence of peripheral neuropathy and PAD was 82% and 20%, respectively.

Using albuminuria as a measure of diabetic kidney disease, the researchers found that 86.1% of the patients had albuminuria and that there was a statistically significant association between albuminuria and the patient’s vibration reception threshold (VPT), a measure of diabetic neuropathy (P less than .001), and the ankle brachial index (ABI), a measure of PAD (P less than .031).

In addition, there was a statistically significant association between diabetic retinopathy and VPT (P less than .008) and between diabetic retinopathy and ABI (P less than .001).

“Albuminuria, diabetic retinopathy and peripheral neuropathy are very common among those patients and strongly associated with risk factors of diabetic foot ulceration,” the researchers concluded.

They reported that they had no conflicts.

[email protected]

SOURCE: Megallaa MH et al. Diabetes Metab Syndr. 2019 Mar-Apr;13(2):1287-92.

Patients with diabetic foot ulcers have a high incidence of associated chronic vascular disease, including kidney disease, retinopathy, and peripheral artery disease (PAD). In addition, there was statistically significant association between both diabetic retinopathy and diabetic kidney disease and PAD, according to a study reported by Magdy H. Megallaa, MD, and colleagues at Alexandria (Egypt) University.

Rebecca L. Slebodnik/MDedge News

Their cross-sectional study, published in Diabetes & Metabolic Syndrome: Clinical Research and Reviews, comprised 180 type 2 diabetic patients (aged 30-70 years) with diabetic foot ulcers (DFUs) who attended an outpatient clinic between July and December 2017.

The prevalence of diabetic kidney disease and diabetic retinopathy was 86.1% and 90.0%, respectively, with 86.7% of patients having neuropathic DFUs, 11.1% having ischemic DFUs, and 2.2% having neuroischemic DFUs. The prevalence of peripheral neuropathy and PAD was 82% and 20%, respectively.

Using albuminuria as a measure of diabetic kidney disease, the researchers found that 86.1% of the patients had albuminuria and that there was a statistically significant association between albuminuria and the patient’s vibration reception threshold (VPT), a measure of diabetic neuropathy (P less than .001), and the ankle brachial index (ABI), a measure of PAD (P less than .031).

In addition, there was a statistically significant association between diabetic retinopathy and VPT (P less than .008) and between diabetic retinopathy and ABI (P less than .001).

“Albuminuria, diabetic retinopathy and peripheral neuropathy are very common among those patients and strongly associated with risk factors of diabetic foot ulceration,” the researchers concluded.

They reported that they had no conflicts.

[email protected]

SOURCE: Megallaa MH et al. Diabetes Metab Syndr. 2019 Mar-Apr;13(2):1287-92.

WASHINGTON – As the rising cost of prescription drugs continues to garner heightened scrutiny from the federal government, one thing is missing from the conversation that would make any solution more effective, according to American Medical Association President Barbara L. McAneny, MD.

“We would like to see transparency from end to end in this pipeline because that is the way we will have the ability to look for savings,” she said in an interview at a national advocacy conference sponsored by the American Medical Association.

“I think we don’t have the information we need to make rational decisions,” she said. “I think the first thing we need to do is to understand the entire pipeline from the basic research that results in a drug’s clinical trials that results in a new drug to the pharmacy benefit managers and all of the ways that they have increased the cost of the drugs all the way to when the patient actually gets it.”

In particular, Dr. McAneny targeted the need for transparency in the role and financial impact pharmacy benefit managers have on the cost of prescription drugs.

“We were told at our state advocacy conference, which we held in January, by an expert who studied pharmacy benefit managers, that 42% of the cost of any drug is attributable to the profits of pharmacy benefit managers,” she said. “To me, that makes me wonder what value do they add that is worth 42% of these exorbitant costs and if they are not adding value, why do we have them in this process?”

She also called on the pharmaceutical manufacturers to be more forthcoming with their financial information regarding marketing and advertising, but she stressed that it needs to be done in a way that does not hinder future development of life-saving therapies.

“We do not want to stifle innovation.” Dr. McAneny said. “As a cancer doctor, I have seen diseases that I used to treat with morphine and sympathy now be diseases that I can treat, where I can restore people to good quality of life and buy them additional years of life because of these drugs. They are amazing and I don’t want to do without them and I want more of them. ... We want to support that research. That is probably worth a lot of the price tag. But how much of that goes to direct-to-consumer advertising? How much of that is the advertising budget? Where can we cut some of this out of the system so that we get the innovation, but we get innovation that all of our patients can afford?”

Another issue that is looming for physicians is a payment rate freeze from 2020-2025 under the Merit-based Incentive Payment System (MIPS) track of the Quality Payment Program, which was created under the Medicare Access and CHIP Reauthorization Act (MACRA).

“A 5-year freeze when my expenses do not freeze is a terrifying thing and could be a practice-ending expense for a lot of practices,” Dr. McAneny warned. “I will point out that independent practices, if they sell to a hospital, the cost of care immediately doubles. The amount that is paid for it, not the cost of delivering it. So that is not a great solution. But we also have this increasing practice expense and a flat rate is not going to help practices survive. So I am very concerned.”

Related to the QPP, Dr. McAneny also wants to see the Centers for Medicare & Medicaid Services do more with physician-developed alternative payment models. The Physician-Focused Payment Model Technical Advisory Committee (PTAC) continues to review and evaluate submissions, but to date, the CMS has yet to implement any of the committee’s recommendations.

“I read all of the PTAC submissions that went to CMS. Some of them I thought were a little on the weak side, but there are a lot of them that I thought were really good ideas. I do not know why CMS did not approve them and fund them and let them be tried,” she said, although she did offer an opinion on why the agency has yet to act on any of them.

“I read their paper saying why they didn’t approve [the recommended physician-developed alternative payment models]. It seemed to me they are waiting for one silver bullet that will fix all of health care. I don’t think a silver bullet is going to fix health care. It’s not an issue. It’s a complicated set of issues. You will not have a quick fix.”

[email protected]

WASHINGTON – As the rising cost of prescription drugs continues to garner heightened scrutiny from the federal government, one thing is missing from the conversation that would make any solution more effective, according to American Medical Association President Barbara L. McAneny, MD.

“We would like to see transparency from end to end in this pipeline because that is the way we will have the ability to look for savings,” she said in an interview at a national advocacy conference sponsored by the American Medical Association.

“I think we don’t have the information we need to make rational decisions,” she said. “I think the first thing we need to do is to understand the entire pipeline from the basic research that results in a drug’s clinical trials that results in a new drug to the pharmacy benefit managers and all of the ways that they have increased the cost of the drugs all the way to when the patient actually gets it.”

In particular, Dr. McAneny targeted the need for transparency in the role and financial impact pharmacy benefit managers have on the cost of prescription drugs.

“We were told at our state advocacy conference, which we held in January, by an expert who studied pharmacy benefit managers, that 42% of the cost of any drug is attributable to the profits of pharmacy benefit managers,” she said. “To me, that makes me wonder what value do they add that is worth 42% of these exorbitant costs and if they are not adding value, why do we have them in this process?”

She also called on the pharmaceutical manufacturers to be more forthcoming with their financial information regarding marketing and advertising, but she stressed that it needs to be done in a way that does not hinder future development of life-saving therapies.

“We do not want to stifle innovation.” Dr. McAneny said. “As a cancer doctor, I have seen diseases that I used to treat with morphine and sympathy now be diseases that I can treat, where I can restore people to good quality of life and buy them additional years of life because of these drugs. They are amazing and I don’t want to do without them and I want more of them. ... We want to support that research. That is probably worth a lot of the price tag. But how much of that goes to direct-to-consumer advertising? How much of that is the advertising budget? Where can we cut some of this out of the system so that we get the innovation, but we get innovation that all of our patients can afford?”

Another issue that is looming for physicians is a payment rate freeze from 2020-2025 under the Merit-based Incentive Payment System (MIPS) track of the Quality Payment Program, which was created under the Medicare Access and CHIP Reauthorization Act (MACRA).

“A 5-year freeze when my expenses do not freeze is a terrifying thing and could be a practice-ending expense for a lot of practices,” Dr. McAneny warned. “I will point out that independent practices, if they sell to a hospital, the cost of care immediately doubles. The amount that is paid for it, not the cost of delivering it. So that is not a great solution. But we also have this increasing practice expense and a flat rate is not going to help practices survive. So I am very concerned.”

Related to the QPP, Dr. McAneny also wants to see the Centers for Medicare & Medicaid Services do more with physician-developed alternative payment models. The Physician-Focused Payment Model Technical Advisory Committee (PTAC) continues to review and evaluate submissions, but to date, the CMS has yet to implement any of the committee’s recommendations.

“I read all of the PTAC submissions that went to CMS. Some of them I thought were a little on the weak side, but there are a lot of them that I thought were really good ideas. I do not know why CMS did not approve them and fund them and let them be tried,” she said, although she did offer an opinion on why the agency has yet to act on any of them.

“I read their paper saying why they didn’t approve [the recommended physician-developed alternative payment models]. It seemed to me they are waiting for one silver bullet that will fix all of health care. I don’t think a silver bullet is going to fix health care. It’s not an issue. It’s a complicated set of issues. You will not have a quick fix.”

[email protected]

WASHINGTON – As the rising cost of prescription drugs continues to garner heightened scrutiny from the federal government, one thing is missing from the conversation that would make any solution more effective, according to American Medical Association President Barbara L. McAneny, MD.

“We would like to see transparency from end to end in this pipeline because that is the way we will have the ability to look for savings,” she said in an interview at a national advocacy conference sponsored by the American Medical Association.

“I think we don’t have the information we need to make rational decisions,” she said. “I think the first thing we need to do is to understand the entire pipeline from the basic research that results in a drug’s clinical trials that results in a new drug to the pharmacy benefit managers and all of the ways that they have increased the cost of the drugs all the way to when the patient actually gets it.”

In particular, Dr. McAneny targeted the need for transparency in the role and financial impact pharmacy benefit managers have on the cost of prescription drugs.

“We were told at our state advocacy conference, which we held in January, by an expert who studied pharmacy benefit managers, that 42% of the cost of any drug is attributable to the profits of pharmacy benefit managers,” she said. “To me, that makes me wonder what value do they add that is worth 42% of these exorbitant costs and if they are not adding value, why do we have them in this process?”

She also called on the pharmaceutical manufacturers to be more forthcoming with their financial information regarding marketing and advertising, but she stressed that it needs to be done in a way that does not hinder future development of life-saving therapies.

“We do not want to stifle innovation.” Dr. McAneny said. “As a cancer doctor, I have seen diseases that I used to treat with morphine and sympathy now be diseases that I can treat, where I can restore people to good quality of life and buy them additional years of life because of these drugs. They are amazing and I don’t want to do without them and I want more of them. ... We want to support that research. That is probably worth a lot of the price tag. But how much of that goes to direct-to-consumer advertising? How much of that is the advertising budget? Where can we cut some of this out of the system so that we get the innovation, but we get innovation that all of our patients can afford?”

Another issue that is looming for physicians is a payment rate freeze from 2020-2025 under the Merit-based Incentive Payment System (MIPS) track of the Quality Payment Program, which was created under the Medicare Access and CHIP Reauthorization Act (MACRA).

“A 5-year freeze when my expenses do not freeze is a terrifying thing and could be a practice-ending expense for a lot of practices,” Dr. McAneny warned. “I will point out that independent practices, if they sell to a hospital, the cost of care immediately doubles. The amount that is paid for it, not the cost of delivering it. So that is not a great solution. But we also have this increasing practice expense and a flat rate is not going to help practices survive. So I am very concerned.”

Related to the QPP, Dr. McAneny also wants to see the Centers for Medicare & Medicaid Services do more with physician-developed alternative payment models. The Physician-Focused Payment Model Technical Advisory Committee (PTAC) continues to review and evaluate submissions, but to date, the CMS has yet to implement any of the committee’s recommendations.

“I read all of the PTAC submissions that went to CMS. Some of them I thought were a little on the weak side, but there are a lot of them that I thought were really good ideas. I do not know why CMS did not approve them and fund them and let them be tried,” she said, although she did offer an opinion on why the agency has yet to act on any of them.

“I read their paper saying why they didn’t approve [the recommended physician-developed alternative payment models]. It seemed to me they are waiting for one silver bullet that will fix all of health care. I don’t think a silver bullet is going to fix health care. It’s not an issue. It’s a complicated set of issues. You will not have a quick fix.”

[email protected]

Patients with peripheral artery disease (PAD) were also more likely to have left ventricular diastolic dysfunction, according to a study published in the Journal of Cardiology.

The study enrolled 1,121 patients with preserved left ventricular (LV) systolic function. The mean age was 68 years and 56% of patients were men. A total of 200 patients (17.8%) had PAD; 33.0% of these had no symptoms, 54.5% had intermittent symptoms, and 12.5% had critical ischemia, according to Koji Yanaka, MD, and colleagues at the Hyogo College of Medicine, Nishinomiya, Japan.

Multivariate logistic regression analysis showed that PAD was an independent predictor of LV diastolic dysfunction (adjusted odds ratio, 1.77; P = .01).

“The prevalence of LV diastolic dysfunction was higher in patients with PAD than those without PAD. These findings suggest that patients with PAD should be evaluated not only for LV systolic but also diastolic function in echocardiography,” the researchers concluded.

The authors reported that they had no disclosures.

[email protected]

SOURCE: Yanaka K et al. J Cardiol. 2019 Feb 18. doi: 10.1016/j.jjcc.2019.01.011.

Patients with peripheral artery disease (PAD) were also more likely to have left ventricular diastolic dysfunction, according to a study published in the Journal of Cardiology.

The study enrolled 1,121 patients with preserved left ventricular (LV) systolic function. The mean age was 68 years and 56% of patients were men. A total of 200 patients (17.8%) had PAD; 33.0% of these had no symptoms, 54.5% had intermittent symptoms, and 12.5% had critical ischemia, according to Koji Yanaka, MD, and colleagues at the Hyogo College of Medicine, Nishinomiya, Japan.

Multivariate logistic regression analysis showed that PAD was an independent predictor of LV diastolic dysfunction (adjusted odds ratio, 1.77; P = .01).

“The prevalence of LV diastolic dysfunction was higher in patients with PAD than those without PAD. These findings suggest that patients with PAD should be evaluated not only for LV systolic but also diastolic function in echocardiography,” the researchers concluded.

The authors reported that they had no disclosures.

[email protected]

SOURCE: Yanaka K et al. J Cardiol. 2019 Feb 18. doi: 10.1016/j.jjcc.2019.01.011.

Patients with peripheral artery disease (PAD) were also more likely to have left ventricular diastolic dysfunction, according to a study published in the Journal of Cardiology.

The study enrolled 1,121 patients with preserved left ventricular (LV) systolic function. The mean age was 68 years and 56% of patients were men. A total of 200 patients (17.8%) had PAD; 33.0% of these had no symptoms, 54.5% had intermittent symptoms, and 12.5% had critical ischemia, according to Koji Yanaka, MD, and colleagues at the Hyogo College of Medicine, Nishinomiya, Japan.

Multivariate logistic regression analysis showed that PAD was an independent predictor of LV diastolic dysfunction (adjusted odds ratio, 1.77; P = .01).

“The prevalence of LV diastolic dysfunction was higher in patients with PAD than those without PAD. These findings suggest that patients with PAD should be evaluated not only for LV systolic but also diastolic function in echocardiography,” the researchers concluded.

The authors reported that they had no disclosures.

[email protected]

SOURCE: Yanaka K et al. J Cardiol. 2019 Feb 18. doi: 10.1016/j.jjcc.2019.01.011.

The Food and Drug Administration has approved turoctocog alfa pegol (N8-GP, Esperoct), a glycopegylated recombinant factor VIII product, to treat hemophilia A.

The agency approved turoctocog alfa pegol for use as routine prophylaxis to reduce the frequency of bleeding episodes, for on-demand treatment and control of bleeding episodes, and for perioperative management of bleeding in adults and children with hemophilia A.

Turoctocog alfa pegol will not be available in the United States before 2020, according to Novo Nordisk. The company cannot yet launch the product because of third-party intellectual property agreements.

The FDA’s approval of turoctocog alfa pegol was supported by results from the pathfinder 2 (NCT01480180), pathfinder 3 (NCT01489111), and pathfinder 5 (NCT01731600) trials.

The trials included children, adolescents, and adults with previously treated, severe hemophilia A and no history of inhibitors. Turoctocog alfa pegol was considered effective and well tolerated in these trials.

Pooled results from pathfinder 2 and pathfinder 5 were presented at the 2018 annual meeting of the American Society of Hematology (Blood. 2018:132:1177).

Results from pathfinder 3 were previously published in Haemophilia (2017. Sep;23[5]:689-96).

[email protected]

The Food and Drug Administration has approved turoctocog alfa pegol (N8-GP, Esperoct), a glycopegylated recombinant factor VIII product, to treat hemophilia A.

The agency approved turoctocog alfa pegol for use as routine prophylaxis to reduce the frequency of bleeding episodes, for on-demand treatment and control of bleeding episodes, and for perioperative management of bleeding in adults and children with hemophilia A.

Turoctocog alfa pegol will not be available in the United States before 2020, according to Novo Nordisk. The company cannot yet launch the product because of third-party intellectual property agreements.

The FDA’s approval of turoctocog alfa pegol was supported by results from the pathfinder 2 (NCT01480180), pathfinder 3 (NCT01489111), and pathfinder 5 (NCT01731600) trials.

The trials included children, adolescents, and adults with previously treated, severe hemophilia A and no history of inhibitors. Turoctocog alfa pegol was considered effective and well tolerated in these trials.

Pooled results from pathfinder 2 and pathfinder 5 were presented at the 2018 annual meeting of the American Society of Hematology (Blood. 2018:132:1177).

Results from pathfinder 3 were previously published in Haemophilia (2017. Sep;23[5]:689-96).

[email protected]

The Food and Drug Administration has approved turoctocog alfa pegol (N8-GP, Esperoct), a glycopegylated recombinant factor VIII product, to treat hemophilia A.

The agency approved turoctocog alfa pegol for use as routine prophylaxis to reduce the frequency of bleeding episodes, for on-demand treatment and control of bleeding episodes, and for perioperative management of bleeding in adults and children with hemophilia A.

Turoctocog alfa pegol will not be available in the United States before 2020, according to Novo Nordisk. The company cannot yet launch the product because of third-party intellectual property agreements.

The FDA’s approval of turoctocog alfa pegol was supported by results from the pathfinder 2 (NCT01480180), pathfinder 3 (NCT01489111), and pathfinder 5 (NCT01731600) trials.

The trials included children, adolescents, and adults with previously treated, severe hemophilia A and no history of inhibitors. Turoctocog alfa pegol was considered effective and well tolerated in these trials.

Pooled results from pathfinder 2 and pathfinder 5 were presented at the 2018 annual meeting of the American Society of Hematology (Blood. 2018:132:1177).

Results from pathfinder 3 were previously published in Haemophilia (2017. Sep;23[5]:689-96).

[email protected]

Individuals aged 65 years and over represent 13% of the United States population and account for nearly 40% of hospital discharges.¹ Bedrest hastens the functional decline of older patients^2-5 and is associated with risk of serious complications, such as falls, delirium, venous thrombosis, and skin breakdown.^6,7 Ambulation is widely recognized as important for improving hospital outcomes.^8-10 Observational studies suggest that increases of 600 steps per day are associated with shortened length of hospital stay.⁹ However, randomized trials of assisted ambulation have not demonstrated consistent benefit.^11-14 As a result, usual care at most hospitals in the United States does not include assisted ambulation. Even when ambulation is ordered, execution of the orders is inconsistent.^15-17

Studies have demonstrated the benefits of various exercise protocols for older patients in rehabilitation facilities,^18,19 medical intensive care units,²⁰ and medical and surgical wards.^13,18,21 These interventions are usually nursing centered; however, assisting patients with ambulation multiple times per day may be a burdensome addition to the myriad responsibilities of nurses.^19,22,23 In fact, ambulation orders are the most frequently overlooked nursing task.²⁴

We designed a graded protocol of assisted ambulation implemented by a dedicated patient care nursing assistant (PCNA) multiple times daily to increase patient mobility. The objective of this study was to assess the feasibility and effectiveness of such an intervention for older inpatients. We hypothesized that the intervention would prove feasible and improve hospital outcomes, including less need for inpatient rehabilitation and shorter length of stay.

We conducted a single-blind randomized controlled trial of patients aged ≥60 years and admitted as medical inpatients to the Cleveland Clinic Main Campus, a tertiary care center with over 1,440 inpatient beds. The consent form and study protocol were approved by the Cleveland Clinic Institutional Review Board, and the study was registered with ClinicalTrials.gov (NCT02757131).

Patients

All patients who were admitted to study wards for a medical illness and evaluated by Physical Therapy (PT) were eligible for the study. PT evaluations were ordered by the medical team if deemed necessary on the basis of factors, such as age, estimated mobility, and concerns raised by the ancillary staff. All patients who were expected to be discharged to a skilled nursing facility placement or who required home PT received a PT evaluation. Assessment of mobility was documented via Activity Measure for Postacute Care Inpatient Basic Mobility “six-clicks” short form, hereafter abbreviated as “six-clicks.” Based on past experience, patients with scores <16 rarely go home (<20% of the time), and those with scores >20 usually go home regardless of ambulation. Therefore, only patients with scores of 16-20 were invited to participate in the study. Although patients who were not evaluated by PT might also benefit from the intervention, we required a six-clicks score to assess eligibility. The exclusion criteria included anticipated remaining length of stay less than three days, admission under observation status, admission to the intensive care unit (ICU,) patients receiving comfort care measures, and patients with medical conditions precluding ambulation, such as decompensated heart failure or unstable angina.

Randomization

Patients were randomized to “usual care” or “mobility technician” after baseline evaluation using a computerized system. A block randomization scheme with a size of four was used to ensure an approximately equal number of patients per group.

Intervention

Patients randomized to the intervention group were asked to participate in the ambulation protocol outlined by the PT three times daily under the supervision of the mobility technician. The protocol involved four exercise levels (sitting, standing, walking, and stairs), which were implemented depending on the patient’s physical capacity. The mobility technicians, who were PCNAs, were trained by the PT team. PCNAs have no specific degrees or certification. They are taught safe handling techniques during their job orientation, so they already had an understanding of how to transfer and assist a patient with ambulation. The mobility technician training consisted of one four-hour session run by the PT team in the physical therapy department and the nursing unit. The training included safe handling practices and basic mobility, such as transfers from bed to chair, bed to standing, walking with assistance, and walking independently with equipment such as cane, rolling walker, and walking belt. All instruction was demonstrated by the trainer, and the mobility technician was then able to practice. The mobility technician then shadowed the trainer and practiced the techniques under supervision. Competency was assessed by the trainer.

The cohort of patients randomized to “usual care” was not seen by the mobility technicians but was not otherwise restricted in nursing’s baseline ability to execute recommendations placed by the PT team. Compliance with the recommendations is highly variable and dependent on patient acuity during the shift, staffing issues, and competing duties. Cleveland Clinic promotes a “culture of mobility,” and nurses are encouraged to get patients out of bed and assist with ambulation.

Study Instruments—Measures of Mobility

The six-clicks instrument is a tool for measuring basic mobility. It was adapted from the Activity Measures for Post-Acute Care (AM-PAC) instrument.²⁵ Although initially created for self-report in the post-acute care setting, six-clicks has been validated for use by PTs in the acute care setting²⁶ and is currently in use at more than 1,000 US hospitals. Cleveland Clinic PTs have used this measure for routine evaluation since 2011. The instrument has high interrater reliability and can predict discharge disposition.^27-29

Each patient was provided with a tracking device (Fitbit) attached at the wrist to record daily steps for measuring mobility. The use of Fitbit has been validated in ambulatory and inpatient settings.³⁰ The device produces step counts within 3% of the observed step count for most patients but may undercount steps in patients with very slow gait.³¹ The device was provided to each enrollee and collected at discharge.

Variables

Demographic information, comorbid diagnoses, and discharge destination were extracted from the electronic medical record. Information on prehospitalization physical activity level was obtained from the initial PT assessment. Falls were tracked through the safety event reporting system.

Outcomes

The primary outcomes were discharge disposition and hospital length of stay. The secondary outcomes included average steps per day, change in six-clicks score from admission to discharge, inpatient mortality, admission to ICU, falls, deep vein thrombosis, pulmonary embolism, or pneumonia, and readmission within 30 days.

Statistical Analysis

Patient characteristics were summarized as means and standard deviations or medians and interquartile ranges for continuous variables and as frequencies and percentages for categorical variables. The t-test or Wilcoxon rank sum test was applied to compare continuous characteristics between the intervention and control groups. Chi-squared test or Fisher’s exact test was applied to compare categorical characteristics. Given its skewed distribution, the length of stay was log-transformed and compared between the two groups using Student’s t-test. Chi-squared test was used to compare categorical outcomes. The analysis of final six-clicks scores was adjusted for baseline scores, and the least-square estimates are provided. A linear mixed effects model was used to compare the number of daily steps taken because each participant had multiple steps measured. Results were adjusted for prehospital activity. In addition to comparing the total steps taken by each group, we determined the proportion of patients who exceeded a particular threshold by taking the average number of steps per day for all subjects and relating it to home discharge using the Receiver Operating Characteristics (ROC) curve. An optimal cut-off was determined to maximize the Youden index. We also compared the proportion of patients who exceeded 900 steps because this value was previously reported as an important threshold.³² All analyses were conducted using intention-to-treat principles. We also conducted a per-protocol analysis in which we limited the intervention group to those who received at least one assisted ambulation session. A dose-response analysis was also performed, in which patients were categorized as not receiving the therapy, receiving sessions on one or two days, or receiving them on more than two days.

All analyses were conducted using R-studio (Boston, MA). Statistical significance was defined as a P-value < .05. Given that this is a pilot study, the results were not adjusted for multiple comparisons.

Characteristics of patients in the intervention and control groups are shown in Table 1. The patients were mostly white and female, with an average age in the mid-70s (range 61-98). All measures evaluated were not significantly different between the intervention and control groups. However, more patients in the intervention group had a prehospital activity level classified as independent.

Table 2 demonstrates the feasibility of the intervention. Of patients randomized to the intervention group, 74% were ambulated at least once. Once enrolled, the patients successfully participated in assisted ambulation for about two-thirds of their hospital stay. However, the intervention was delivered for only one-third of the total length of stay because most patients were not enrolled on admission. On average, the mobility technicians made 11 attempts to ambulate each patient and 56% of these attempts were successful. The proportion of unsuccessful attempts did not change over the course of the study. The reasons for unsuccessful attempts included patient refusal (n = 102) or unavailability (n = 68), mobility technicians running out of time (n = 2), and other (n = 12).

In this pilot study conducted with older medical inpatients, we found that assisted ambulation provided by a dedicated mobility technician was feasible and increased the number of steps taken by patients. Not all patients in the treatment group received the intervention partly due to the fact that the program initially did not include weekends and the mobility technicians were sometimes redirected to other nursing duties. Both issues were addressed during the course of the study. In the per protocol analysis, the intervention increased the average six-clicks score and there was a nonsignificant reduction in the percentage of patients discharged to a rehabilitation facility.

A range of hospital-based mobility interventions have been described. Several of which were complex multidisciplinary interventions that included a mobility component. The compliance rates have ranged from 82% to 93.7%,12,13 although a systematic review noted that many studies do not provide this level of information.¹¹ Interventions were carried out by nursing staff and PT with support from family members and social workers.^33-35 Ambulation-specific programs have also relied on nurses and PT13,14,36 and, occasionally, on research assistants to implement assisted ambulation protocols.^12,37 A recent study that employed research assistants to deliver inhospital ambulation reported achieving 51.3% of intended walks.³⁷

In contradistinction to previous studies, we created a new role, employing PCNAs as dedicated mobility technicians. We did this for two reasons. First, the approach is less expensive than deploying registered nurses or PTs to ambulate patients and therefore more likely to be adopted by hospitals, especially if it can decrease the cost of an episode of care by avoiding subsequent inpatient rehabilitation. Mobility technicians have no degree or certification requirements and are therefore paid less than nurses or physical therapists. Second, by having a single responsibility, mobility technicians were more likely to engage in their task than nurses, who have competing responsibilities. However, when nurse staffing was short, nurse managers were tempted to recall the PCNAs for other nursing duties. It took time before PCNAs and supervisors prioritized this new responsibility. When they did, the number of attempted walks increased substantially, but the percentage of successful attempts remained constant at 56%, highlighting the difficulty of getting hospitalized patients to walk.

On average, patients who received the intervention engaged in 72 minutes of additional physical activity and averaged 990 steps per day. Observational data suggest patients accrue about 1,100 steps in the day before discharge, with older patients accruing closer to 900.21 One study found that older patients with fewer than 900 steps per day were likely to experience a functional decline.32 We also found that patients who achieved at least 900 steps were more likely to go home. However, we found that a lower threshold, namely, 400 steps, offered better discrimination between patients who go home and those who do not. Future prospective studies are needed to establish the appropriate goal for exercise interventions. A lower step goal could dramatically enhance the efficiency of the intervention.

A Cochrane review found that pooled analysis of multidisciplinary interventions that included exercise, often in the form of walking, achieved a small but significant increase in the proportion of patients discharged to home (RR 1.08, 95%CI 1.03 to 1.14).¹¹ We found no significant change in the discharge disposition, but our study was underpowered for this endpoint. The six-clicks score showed a small but significant change in the per protocol analysis. The six-clicks score has been shown to correlate with discharge disposition,^28,29 and an improvement in the score suggests that discharge disposition may be influenced.

The intervention may also not have been implemented for long enough. On average, visits were achieved for one-third of the hospital stay partly because of the delay in PT evaluation, which we required for eligibility. In practice, PT evaluation can occur just a few days before the anticipated discharge. We observed a dose dependent response among patients in the intervention group, suggesting that earlier intervention could be more effective. Earlier intervention might be achieved if the MT performed the six-clicks on potentially eligible patients.

The effects of hospitalization on mobility may be the most pronounced in the long term; one study found that 40% of hospitalized older patients manifested new ADL or IADL disability three months after discharge compared with 31% at discharge.⁷ Hospital-based mobility interventions may continue to affect subjects’ independence for weeks or months. In one RCT, an inpatient ambulation intervention improved mobility in the community one month after discharge.³⁷ A hospital-based exercise program that included ambulation achieved better functional outcomes one month later.¹³ One RCT that combined inpatient exercise with outpatient care coordination also decreased readmission rates.³⁴ We found that the intervention did not affect readmission.

This pilot study has several limitations. The sample size was small, and the findings need to be replicated in a larger randomized controlled trial. This is particularly important because the two study arms were not balanced in terms of their prehospital activity. After adjustment for prehospital activity, the differences in the step count in the intention-to-treat analysis were no longer significant. As we adjusted the intervention to hospital workflow, the intervention changed over time. The intention-to-treat analysis may therefore underestimate the effect of the intervention. This work provides a basis for future trial. Finally, discharge disposition depends on a complex interplay of factors, including social factors and preferences, which may not be affected by a mobility intervention.

In summary, an inhospital mobility protocol of attempting ambulation delivered by dedicated mobility technicians three times daily successfully increased the daily step counts and mobility scores of the patients. Studies with a larger sample size are needed to determine whether the proposed approach can affect length of hospital stay, discharge disposition, and overall cost of an episode of care.

Disclosures

Mary Stilphen reports consulting for CreCare and Adeo, which license and distribute AM-PAC short forms, including 6 clicks. All other authors report no conflicts of interest.

Funding

This study was supported by a Research Program Committee grant from the Cleveland Clinic.

Individuals aged 65 years and over represent 13% of the United States population and account for nearly 40% of hospital discharges.¹ Bedrest hastens the functional decline of older patients^2-5 and is associated with risk of serious complications, such as falls, delirium, venous thrombosis, and skin breakdown.^6,7 Ambulation is widely recognized as important for improving hospital outcomes.^8-10 Observational studies suggest that increases of 600 steps per day are associated with shortened length of hospital stay.⁹ However, randomized trials of assisted ambulation have not demonstrated consistent benefit.^11-14 As a result, usual care at most hospitals in the United States does not include assisted ambulation. Even when ambulation is ordered, execution of the orders is inconsistent.^15-17

Studies have demonstrated the benefits of various exercise protocols for older patients in rehabilitation facilities,^18,19 medical intensive care units,²⁰ and medical and surgical wards.^13,18,21 These interventions are usually nursing centered; however, assisting patients with ambulation multiple times per day may be a burdensome addition to the myriad responsibilities of nurses.^19,22,23 In fact, ambulation orders are the most frequently overlooked nursing task.²⁴

We designed a graded protocol of assisted ambulation implemented by a dedicated patient care nursing assistant (PCNA) multiple times daily to increase patient mobility. The objective of this study was to assess the feasibility and effectiveness of such an intervention for older inpatients. We hypothesized that the intervention would prove feasible and improve hospital outcomes, including less need for inpatient rehabilitation and shorter length of stay.

We conducted a single-blind randomized controlled trial of patients aged ≥60 years and admitted as medical inpatients to the Cleveland Clinic Main Campus, a tertiary care center with over 1,440 inpatient beds. The consent form and study protocol were approved by the Cleveland Clinic Institutional Review Board, and the study was registered with ClinicalTrials.gov (NCT02757131).

Patients

All patients who were admitted to study wards for a medical illness and evaluated by Physical Therapy (PT) were eligible for the study. PT evaluations were ordered by the medical team if deemed necessary on the basis of factors, such as age, estimated mobility, and concerns raised by the ancillary staff. All patients who were expected to be discharged to a skilled nursing facility placement or who required home PT received a PT evaluation. Assessment of mobility was documented via Activity Measure for Postacute Care Inpatient Basic Mobility “six-clicks” short form, hereafter abbreviated as “six-clicks.” Based on past experience, patients with scores <16 rarely go home (<20% of the time), and those with scores >20 usually go home regardless of ambulation. Therefore, only patients with scores of 16-20 were invited to participate in the study. Although patients who were not evaluated by PT might also benefit from the intervention, we required a six-clicks score to assess eligibility. The exclusion criteria included anticipated remaining length of stay less than three days, admission under observation status, admission to the intensive care unit (ICU,) patients receiving comfort care measures, and patients with medical conditions precluding ambulation, such as decompensated heart failure or unstable angina.

Randomization

Patients were randomized to “usual care” or “mobility technician” after baseline evaluation using a computerized system. A block randomization scheme with a size of four was used to ensure an approximately equal number of patients per group.

Intervention

Patients randomized to the intervention group were asked to participate in the ambulation protocol outlined by the PT three times daily under the supervision of the mobility technician. The protocol involved four exercise levels (sitting, standing, walking, and stairs), which were implemented depending on the patient’s physical capacity. The mobility technicians, who were PCNAs, were trained by the PT team. PCNAs have no specific degrees or certification. They are taught safe handling techniques during their job orientation, so they already had an understanding of how to transfer and assist a patient with ambulation. The mobility technician training consisted of one four-hour session run by the PT team in the physical therapy department and the nursing unit. The training included safe handling practices and basic mobility, such as transfers from bed to chair, bed to standing, walking with assistance, and walking independently with equipment such as cane, rolling walker, and walking belt. All instruction was demonstrated by the trainer, and the mobility technician was then able to practice. The mobility technician then shadowed the trainer and practiced the techniques under supervision. Competency was assessed by the trainer.

The cohort of patients randomized to “usual care” was not seen by the mobility technicians but was not otherwise restricted in nursing’s baseline ability to execute recommendations placed by the PT team. Compliance with the recommendations is highly variable and dependent on patient acuity during the shift, staffing issues, and competing duties. Cleveland Clinic promotes a “culture of mobility,” and nurses are encouraged to get patients out of bed and assist with ambulation.

Study Instruments—Measures of Mobility

The six-clicks instrument is a tool for measuring basic mobility. It was adapted from the Activity Measures for Post-Acute Care (AM-PAC) instrument.²⁵ Although initially created for self-report in the post-acute care setting, six-clicks has been validated for use by PTs in the acute care setting²⁶ and is currently in use at more than 1,000 US hospitals. Cleveland Clinic PTs have used this measure for routine evaluation since 2011. The instrument has high interrater reliability and can predict discharge disposition.^27-29

Each patient was provided with a tracking device (Fitbit) attached at the wrist to record daily steps for measuring mobility. The use of Fitbit has been validated in ambulatory and inpatient settings.³⁰ The device produces step counts within 3% of the observed step count for most patients but may undercount steps in patients with very slow gait.³¹ The device was provided to each enrollee and collected at discharge.

Variables

Demographic information, comorbid diagnoses, and discharge destination were extracted from the electronic medical record. Information on prehospitalization physical activity level was obtained from the initial PT assessment. Falls were tracked through the safety event reporting system.

Outcomes

The primary outcomes were discharge disposition and hospital length of stay. The secondary outcomes included average steps per day, change in six-clicks score from admission to discharge, inpatient mortality, admission to ICU, falls, deep vein thrombosis, pulmonary embolism, or pneumonia, and readmission within 30 days.

Statistical Analysis

Patient characteristics were summarized as means and standard deviations or medians and interquartile ranges for continuous variables and as frequencies and percentages for categorical variables. The t-test or Wilcoxon rank sum test was applied to compare continuous characteristics between the intervention and control groups. Chi-squared test or Fisher’s exact test was applied to compare categorical characteristics. Given its skewed distribution, the length of stay was log-transformed and compared between the two groups using Student’s t-test. Chi-squared test was used to compare categorical outcomes. The analysis of final six-clicks scores was adjusted for baseline scores, and the least-square estimates are provided. A linear mixed effects model was used to compare the number of daily steps taken because each participant had multiple steps measured. Results were adjusted for prehospital activity. In addition to comparing the total steps taken by each group, we determined the proportion of patients who exceeded a particular threshold by taking the average number of steps per day for all subjects and relating it to home discharge using the Receiver Operating Characteristics (ROC) curve. An optimal cut-off was determined to maximize the Youden index. We also compared the proportion of patients who exceeded 900 steps because this value was previously reported as an important threshold.³² All analyses were conducted using intention-to-treat principles. We also conducted a per-protocol analysis in which we limited the intervention group to those who received at least one assisted ambulation session. A dose-response analysis was also performed, in which patients were categorized as not receiving the therapy, receiving sessions on one or two days, or receiving them on more than two days.

All analyses were conducted using R-studio (Boston, MA). Statistical significance was defined as a P-value < .05. Given that this is a pilot study, the results were not adjusted for multiple comparisons.

Characteristics of patients in the intervention and control groups are shown in Table 1. The patients were mostly white and female, with an average age in the mid-70s (range 61-98). All measures evaluated were not significantly different between the intervention and control groups. However, more patients in the intervention group had a prehospital activity level classified as independent.

Table 2 demonstrates the feasibility of the intervention. Of patients randomized to the intervention group, 74% were ambulated at least once. Once enrolled, the patients successfully participated in assisted ambulation for about two-thirds of their hospital stay. However, the intervention was delivered for only one-third of the total length of stay because most patients were not enrolled on admission. On average, the mobility technicians made 11 attempts to ambulate each patient and 56% of these attempts were successful. The proportion of unsuccessful attempts did not change over the course of the study. The reasons for unsuccessful attempts included patient refusal (n = 102) or unavailability (n = 68), mobility technicians running out of time (n = 2), and other (n = 12).

In this pilot study conducted with older medical inpatients, we found that assisted ambulation provided by a dedicated mobility technician was feasible and increased the number of steps taken by patients. Not all patients in the treatment group received the intervention partly due to the fact that the program initially did not include weekends and the mobility technicians were sometimes redirected to other nursing duties. Both issues were addressed during the course of the study. In the per protocol analysis, the intervention increased the average six-clicks score and there was a nonsignificant reduction in the percentage of patients discharged to a rehabilitation facility.

A range of hospital-based mobility interventions have been described. Several of which were complex multidisciplinary interventions that included a mobility component. The compliance rates have ranged from 82% to 93.7%,12,13 although a systematic review noted that many studies do not provide this level of information.¹¹ Interventions were carried out by nursing staff and PT with support from family members and social workers.^33-35 Ambulation-specific programs have also relied on nurses and PT13,14,36 and, occasionally, on research assistants to implement assisted ambulation protocols.^12,37 A recent study that employed research assistants to deliver inhospital ambulation reported achieving 51.3% of intended walks.³⁷

In contradistinction to previous studies, we created a new role, employing PCNAs as dedicated mobility technicians. We did this for two reasons. First, the approach is less expensive than deploying registered nurses or PTs to ambulate patients and therefore more likely to be adopted by hospitals, especially if it can decrease the cost of an episode of care by avoiding subsequent inpatient rehabilitation. Mobility technicians have no degree or certification requirements and are therefore paid less than nurses or physical therapists. Second, by having a single responsibility, mobility technicians were more likely to engage in their task than nurses, who have competing responsibilities. However, when nurse staffing was short, nurse managers were tempted to recall the PCNAs for other nursing duties. It took time before PCNAs and supervisors prioritized this new responsibility. When they did, the number of attempted walks increased substantially, but the percentage of successful attempts remained constant at 56%, highlighting the difficulty of getting hospitalized patients to walk.

On average, patients who received the intervention engaged in 72 minutes of additional physical activity and averaged 990 steps per day. Observational data suggest patients accrue about 1,100 steps in the day before discharge, with older patients accruing closer to 900.21 One study found that older patients with fewer than 900 steps per day were likely to experience a functional decline.32 We also found that patients who achieved at least 900 steps were more likely to go home. However, we found that a lower threshold, namely, 400 steps, offered better discrimination between patients who go home and those who do not. Future prospective studies are needed to establish the appropriate goal for exercise interventions. A lower step goal could dramatically enhance the efficiency of the intervention.

A Cochrane review found that pooled analysis of multidisciplinary interventions that included exercise, often in the form of walking, achieved a small but significant increase in the proportion of patients discharged to home (RR 1.08, 95%CI 1.03 to 1.14).¹¹ We found no significant change in the discharge disposition, but our study was underpowered for this endpoint. The six-clicks score showed a small but significant change in the per protocol analysis. The six-clicks score has been shown to correlate with discharge disposition,^28,29 and an improvement in the score suggests that discharge disposition may be influenced.

The intervention may also not have been implemented for long enough. On average, visits were achieved for one-third of the hospital stay partly because of the delay in PT evaluation, which we required for eligibility. In practice, PT evaluation can occur just a few days before the anticipated discharge. We observed a dose dependent response among patients in the intervention group, suggesting that earlier intervention could be more effective. Earlier intervention might be achieved if the MT performed the six-clicks on potentially eligible patients.

The effects of hospitalization on mobility may be the most pronounced in the long term; one study found that 40% of hospitalized older patients manifested new ADL or IADL disability three months after discharge compared with 31% at discharge.⁷ Hospital-based mobility interventions may continue to affect subjects’ independence for weeks or months. In one RCT, an inpatient ambulation intervention improved mobility in the community one month after discharge.³⁷ A hospital-based exercise program that included ambulation achieved better functional outcomes one month later.¹³ One RCT that combined inpatient exercise with outpatient care coordination also decreased readmission rates.³⁴ We found that the intervention did not affect readmission.

This pilot study has several limitations. The sample size was small, and the findings need to be replicated in a larger randomized controlled trial. This is particularly important because the two study arms were not balanced in terms of their prehospital activity. After adjustment for prehospital activity, the differences in the step count in the intention-to-treat analysis were no longer significant. As we adjusted the intervention to hospital workflow, the intervention changed over time. The intention-to-treat analysis may therefore underestimate the effect of the intervention. This work provides a basis for future trial. Finally, discharge disposition depends on a complex interplay of factors, including social factors and preferences, which may not be affected by a mobility intervention.

In summary, an inhospital mobility protocol of attempting ambulation delivered by dedicated mobility technicians three times daily successfully increased the daily step counts and mobility scores of the patients. Studies with a larger sample size are needed to determine whether the proposed approach can affect length of hospital stay, discharge disposition, and overall cost of an episode of care.

Disclosures

Mary Stilphen reports consulting for CreCare and Adeo, which license and distribute AM-PAC short forms, including 6 clicks. All other authors report no conflicts of interest.

Funding

This study was supported by a Research Program Committee grant from the Cleveland Clinic.

Individuals aged 65 years and over represent 13% of the United States population and account for nearly 40% of hospital discharges.¹ Bedrest hastens the functional decline of older patients^2-5 and is associated with risk of serious complications, such as falls, delirium, venous thrombosis, and skin breakdown.^6,7 Ambulation is widely recognized as important for improving hospital outcomes.^8-10 Observational studies suggest that increases of 600 steps per day are associated with shortened length of hospital stay.⁹ However, randomized trials of assisted ambulation have not demonstrated consistent benefit.^11-14 As a result, usual care at most hospitals in the United States does not include assisted ambulation. Even when ambulation is ordered, execution of the orders is inconsistent.^15-17

Studies have demonstrated the benefits of various exercise protocols for older patients in rehabilitation facilities,^18,19 medical intensive care units,²⁰ and medical and surgical wards.^13,18,21 These interventions are usually nursing centered; however, assisting patients with ambulation multiple times per day may be a burdensome addition to the myriad responsibilities of nurses.^19,22,23 In fact, ambulation orders are the most frequently overlooked nursing task.²⁴

We designed a graded protocol of assisted ambulation implemented by a dedicated patient care nursing assistant (PCNA) multiple times daily to increase patient mobility. The objective of this study was to assess the feasibility and effectiveness of such an intervention for older inpatients. We hypothesized that the intervention would prove feasible and improve hospital outcomes, including less need for inpatient rehabilitation and shorter length of stay.

We conducted a single-blind randomized controlled trial of patients aged ≥60 years and admitted as medical inpatients to the Cleveland Clinic Main Campus, a tertiary care center with over 1,440 inpatient beds. The consent form and study protocol were approved by the Cleveland Clinic Institutional Review Board, and the study was registered with ClinicalTrials.gov (NCT02757131).

Patients

All patients who were admitted to study wards for a medical illness and evaluated by Physical Therapy (PT) were eligible for the study. PT evaluations were ordered by the medical team if deemed necessary on the basis of factors, such as age, estimated mobility, and concerns raised by the ancillary staff. All patients who were expected to be discharged to a skilled nursing facility placement or who required home PT received a PT evaluation. Assessment of mobility was documented via Activity Measure for Postacute Care Inpatient Basic Mobility “six-clicks” short form, hereafter abbreviated as “six-clicks.” Based on past experience, patients with scores <16 rarely go home (<20% of the time), and those with scores >20 usually go home regardless of ambulation. Therefore, only patients with scores of 16-20 were invited to participate in the study. Although patients who were not evaluated by PT might also benefit from the intervention, we required a six-clicks score to assess eligibility. The exclusion criteria included anticipated remaining length of stay less than three days, admission under observation status, admission to the intensive care unit (ICU,) patients receiving comfort care measures, and patients with medical conditions precluding ambulation, such as decompensated heart failure or unstable angina.

Randomization

Patients were randomized to “usual care” or “mobility technician” after baseline evaluation using a computerized system. A block randomization scheme with a size of four was used to ensure an approximately equal number of patients per group.

Intervention

Patients randomized to the intervention group were asked to participate in the ambulation protocol outlined by the PT three times daily under the supervision of the mobility technician. The protocol involved four exercise levels (sitting, standing, walking, and stairs), which were implemented depending on the patient’s physical capacity. The mobility technicians, who were PCNAs, were trained by the PT team. PCNAs have no specific degrees or certification. They are taught safe handling techniques during their job orientation, so they already had an understanding of how to transfer and assist a patient with ambulation. The mobility technician training consisted of one four-hour session run by the PT team in the physical therapy department and the nursing unit. The training included safe handling practices and basic mobility, such as transfers from bed to chair, bed to standing, walking with assistance, and walking independently with equipment such as cane, rolling walker, and walking belt. All instruction was demonstrated by the trainer, and the mobility technician was then able to practice. The mobility technician then shadowed the trainer and practiced the techniques under supervision. Competency was assessed by the trainer.

The cohort of patients randomized to “usual care” was not seen by the mobility technicians but was not otherwise restricted in nursing’s baseline ability to execute recommendations placed by the PT team. Compliance with the recommendations is highly variable and dependent on patient acuity during the shift, staffing issues, and competing duties. Cleveland Clinic promotes a “culture of mobility,” and nurses are encouraged to get patients out of bed and assist with ambulation.

Study Instruments—Measures of Mobility

The six-clicks instrument is a tool for measuring basic mobility. It was adapted from the Activity Measures for Post-Acute Care (AM-PAC) instrument.²⁵ Although initially created for self-report in the post-acute care setting, six-clicks has been validated for use by PTs in the acute care setting²⁶ and is currently in use at more than 1,000 US hospitals. Cleveland Clinic PTs have used this measure for routine evaluation since 2011. The instrument has high interrater reliability and can predict discharge disposition.^27-29

Each patient was provided with a tracking device (Fitbit) attached at the wrist to record daily steps for measuring mobility. The use of Fitbit has been validated in ambulatory and inpatient settings.³⁰ The device produces step counts within 3% of the observed step count for most patients but may undercount steps in patients with very slow gait.³¹ The device was provided to each enrollee and collected at discharge.

Variables

Demographic information, comorbid diagnoses, and discharge destination were extracted from the electronic medical record. Information on prehospitalization physical activity level was obtained from the initial PT assessment. Falls were tracked through the safety event reporting system.

Outcomes

The primary outcomes were discharge disposition and hospital length of stay. The secondary outcomes included average steps per day, change in six-clicks score from admission to discharge, inpatient mortality, admission to ICU, falls, deep vein thrombosis, pulmonary embolism, or pneumonia, and readmission within 30 days.

Statistical Analysis

Patient characteristics were summarized as means and standard deviations or medians and interquartile ranges for continuous variables and as frequencies and percentages for categorical variables. The t-test or Wilcoxon rank sum test was applied to compare continuous characteristics between the intervention and control groups. Chi-squared test or Fisher’s exact test was applied to compare categorical characteristics. Given its skewed distribution, the length of stay was log-transformed and compared between the two groups using Student’s t-test. Chi-squared test was used to compare categorical outcomes. The analysis of final six-clicks scores was adjusted for baseline scores, and the least-square estimates are provided. A linear mixed effects model was used to compare the number of daily steps taken because each participant had multiple steps measured. Results were adjusted for prehospital activity. In addition to comparing the total steps taken by each group, we determined the proportion of patients who exceeded a particular threshold by taking the average number of steps per day for all subjects and relating it to home discharge using the Receiver Operating Characteristics (ROC) curve. An optimal cut-off was determined to maximize the Youden index. We also compared the proportion of patients who exceeded 900 steps because this value was previously reported as an important threshold.³² All analyses were conducted using intention-to-treat principles. We also conducted a per-protocol analysis in which we limited the intervention group to those who received at least one assisted ambulation session. A dose-response analysis was also performed, in which patients were categorized as not receiving the therapy, receiving sessions on one or two days, or receiving them on more than two days.

All analyses were conducted using R-studio (Boston, MA). Statistical significance was defined as a P-value < .05. Given that this is a pilot study, the results were not adjusted for multiple comparisons.

Characteristics of patients in the intervention and control groups are shown in Table 1. The patients were mostly white and female, with an average age in the mid-70s (range 61-98). All measures evaluated were not significantly different between the intervention and control groups. However, more patients in the intervention group had a prehospital activity level classified as independent.

Table 2 demonstrates the feasibility of the intervention. Of patients randomized to the intervention group, 74% were ambulated at least once. Once enrolled, the patients successfully participated in assisted ambulation for about two-thirds of their hospital stay. However, the intervention was delivered for only one-third of the total length of stay because most patients were not enrolled on admission. On average, the mobility technicians made 11 attempts to ambulate each patient and 56% of these attempts were successful. The proportion of unsuccessful attempts did not change over the course of the study. The reasons for unsuccessful attempts included patient refusal (n = 102) or unavailability (n = 68), mobility technicians running out of time (n = 2), and other (n = 12).

In this pilot study conducted with older medical inpatients, we found that assisted ambulation provided by a dedicated mobility technician was feasible and increased the number of steps taken by patients. Not all patients in the treatment group received the intervention partly due to the fact that the program initially did not include weekends and the mobility technicians were sometimes redirected to other nursing duties. Both issues were addressed during the course of the study. In the per protocol analysis, the intervention increased the average six-clicks score and there was a nonsignificant reduction in the percentage of patients discharged to a rehabilitation facility.

A range of hospital-based mobility interventions have been described. Several of which were complex multidisciplinary interventions that included a mobility component. The compliance rates have ranged from 82% to 93.7%,12,13 although a systematic review noted that many studies do not provide this level of information.¹¹ Interventions were carried out by nursing staff and PT with support from family members and social workers.^33-35 Ambulation-specific programs have also relied on nurses and PT13,14,36 and, occasionally, on research assistants to implement assisted ambulation protocols.^12,37 A recent study that employed research assistants to deliver inhospital ambulation reported achieving 51.3% of intended walks.³⁷

In contradistinction to previous studies, we created a new role, employing PCNAs as dedicated mobility technicians. We did this for two reasons. First, the approach is less expensive than deploying registered nurses or PTs to ambulate patients and therefore more likely to be adopted by hospitals, especially if it can decrease the cost of an episode of care by avoiding subsequent inpatient rehabilitation. Mobility technicians have no degree or certification requirements and are therefore paid less than nurses or physical therapists. Second, by having a single responsibility, mobility technicians were more likely to engage in their task than nurses, who have competing responsibilities. However, when nurse staffing was short, nurse managers were tempted to recall the PCNAs for other nursing duties. It took time before PCNAs and supervisors prioritized this new responsibility. When they did, the number of attempted walks increased substantially, but the percentage of successful attempts remained constant at 56%, highlighting the difficulty of getting hospitalized patients to walk.

On average, patients who received the intervention engaged in 72 minutes of additional physical activity and averaged 990 steps per day. Observational data suggest patients accrue about 1,100 steps in the day before discharge, with older patients accruing closer to 900.21 One study found that older patients with fewer than 900 steps per day were likely to experience a functional decline.32 We also found that patients who achieved at least 900 steps were more likely to go home. However, we found that a lower threshold, namely, 400 steps, offered better discrimination between patients who go home and those who do not. Future prospective studies are needed to establish the appropriate goal for exercise interventions. A lower step goal could dramatically enhance the efficiency of the intervention.

A Cochrane review found that pooled analysis of multidisciplinary interventions that included exercise, often in the form of walking, achieved a small but significant increase in the proportion of patients discharged to home (RR 1.08, 95%CI 1.03 to 1.14).¹¹ We found no significant change in the discharge disposition, but our study was underpowered for this endpoint. The six-clicks score showed a small but significant change in the per protocol analysis. The six-clicks score has been shown to correlate with discharge disposition,^28,29 and an improvement in the score suggests that discharge disposition may be influenced.

The intervention may also not have been implemented for long enough. On average, visits were achieved for one-third of the hospital stay partly because of the delay in PT evaluation, which we required for eligibility. In practice, PT evaluation can occur just a few days before the anticipated discharge. We observed a dose dependent response among patients in the intervention group, suggesting that earlier intervention could be more effective. Earlier intervention might be achieved if the MT performed the six-clicks on potentially eligible patients.

The effects of hospitalization on mobility may be the most pronounced in the long term; one study found that 40% of hospitalized older patients manifested new ADL or IADL disability three months after discharge compared with 31% at discharge.⁷ Hospital-based mobility interventions may continue to affect subjects’ independence for weeks or months. In one RCT, an inpatient ambulation intervention improved mobility in the community one month after discharge.³⁷ A hospital-based exercise program that included ambulation achieved better functional outcomes one month later.¹³ One RCT that combined inpatient exercise with outpatient care coordination also decreased readmission rates.³⁴ We found that the intervention did not affect readmission.

This pilot study has several limitations. The sample size was small, and the findings need to be replicated in a larger randomized controlled trial. This is particularly important because the two study arms were not balanced in terms of their prehospital activity. After adjustment for prehospital activity, the differences in the step count in the intention-to-treat analysis were no longer significant. As we adjusted the intervention to hospital workflow, the intervention changed over time. The intention-to-treat analysis may therefore underestimate the effect of the intervention. This work provides a basis for future trial. Finally, discharge disposition depends on a complex interplay of factors, including social factors and preferences, which may not be affected by a mobility intervention.

In summary, an inhospital mobility protocol of attempting ambulation delivered by dedicated mobility technicians three times daily successfully increased the daily step counts and mobility scores of the patients. Studies with a larger sample size are needed to determine whether the proposed approach can affect length of hospital stay, discharge disposition, and overall cost of an episode of care.

Disclosures

Mary Stilphen reports consulting for CreCare and Adeo, which license and distribute AM-PAC short forms, including 6 clicks. All other authors report no conflicts of interest.

Funding

This study was supported by a Research Program Committee grant from the Cleveland Clinic.

Using methodology created by the Centers for Medicare & Medicaid Services (CMS), the Department of Veterans Affairs (VA) calculates and reports hospital performance measures for several key conditions, including acute myocardial infarction (AMI), heart failure (HF), and pneumonia.¹ These measures are designed to benchmark individual hospitals against how average hospitals perform when caring for a similar case-mix index. Because readmissions to the hospital within 30-days of discharge are common and costly, this metric has garnered extensive attention in recent years.

To summarize the 30-day readmission metric, the VA utilizes the Strategic Analytics for Improvement and Learning (SAIL) system to present internally its findings to VA practitioners and leadership.² The VA provides these data as a means to drive quality improvement and allow for comparison of individual hospitals’ performance across measures throughout the VA healthcare system. Since 2010, the VA began using and publicly reporting the CMS-derived 30-day Risk-Stratified Readmission Rate (RSRR) on the Hospital Compare website.³ Similar to CMS, the VA uses three years of combined data so that patients, providers, and other stakeholders can compare individual hospitals’ performance across these measures.¹ In response to this, hospitals and healthcare organizations have implemented quality improvement and large-scale programmatic interventions in an attempt to improve quality around readmissions.^4-6 A recent assessment on how hospitals within the Medicare fee-for-service program have responded to such reporting found large degrees of variability, with more than half of the participating institutions facing penalties due to greater-than-expected readmission rates.⁵ Although the VA utilizes the same CMS-derived model in its assessments and reporting, the variability and distribution around this metric are not publicly reported—thus making it difficult to ascertain how individual VA hospitals compare with one another. Without such information, individual facilities may not know how to benchmark the quality of their care to others, nor would the VA recognize which interventions addressing readmissions are working, and which are not. Although previous assessments of interinstitutional variance have been performed in Medicare populations,⁷ a focused analysis of such variance within the VA has yet to be performed.

In this study, we performed a multiyear assessment of the CMS-derived 30-day RSRR metric for AMI, HF, and pneumonia as a useful measure to drive VA quality improvement or distinguish VA facility performance based on its ability to detect interfacility variability.

Data Source

We used VA administrative and Medicare claims data from 2010 to 2012. After identifying index hospitalizations to VA hospitals, we obtained patients’ respective inpatient Medicare claims data from the Medicare Provider Analysis and Review (MedPAR) and Outpatient files. All Medicare records were linked to VA records via scrambled Social Security numbers and were provided by the VA Information Resource Center. This study was approved by the San Francisco VA Medical Center Institutional Review Board.

Study Sample

Our cohort consisted of hospitalized VA beneficiary and Medicare fee-for-service patients who were aged ≥65 years and admitted to and discharged from a VA acute care center with a primary discharge diagnosis of AMI, HF, or pneumonia. These comorbidities were chosen as they are publicly reported and frequently used for interfacility comparisons. Because studies have found that inclusion of secondary payer data (ie, CMS data) may affect hospital-profiling outcomes, we included Medicare data on all available patients.⁸ We excluded hospitalizations that resulted in a transfer to another acute care facility and those admitted to observation status at their index admission. To ensure a full year of data for risk adjustment, beneficiaries were included only if they were enrolled in Medicare for 12 months prior to and including the date of the index admission.

Index hospitalizations were first identified using VA-only inpatient data similar to methods outlined by the CMS and endorsed by the National Quality Forum for Hospital Profiling.⁹ An index hospitalization was defined as an acute inpatient discharge between 2010 and 2012 in which the principal diagnosis was AMI, HF, or pneumonia. We excluded in-hospital deaths, discharges against medical advice, and--for the AMI cohort only--discharges on the same day as admission. Patients may have multiple admissions per year, but only admissions after 30 days of discharge from an index admission were eligible to be included as an additional index admission.

Outcomes

A readmission was defined as any unplanned rehospitalization to either non-VA or VA acute care facilities for any cause within 30 days of discharge from the index hospitalization. Readmissions to observation status or nonacute or rehabilitation units, such as skilled nursing facilities, were not included. Planned readmissions for elective procedures, such as elective chemotherapy and revascularization following an AMI index admission, were not considered as an outcome event.

Risk Standardization for 30-day Readmission

Using approaches developed by CMS,^10-12 we calculated hospital-specific 30-day RSRRs for each VA. Briefly, the RSRR is a ratio of the number of predicted readmissions within 30 days of discharge to the expected number of readmissions within 30 days of hospital discharge, multiplied by the national unadjusted 30-day readmission rate. This measure calculates hospital-specific RSRRs using hierarchical logistic regression models, which account for clustering of patients within hospitals and risk-adjusting for differences in case-mix, during the assessed time periods.¹³ This approach simultaneously models two levels (patient and hospital) to account for the variance in patient outcomes within and between hospitals.¹⁴ At the patient level, the model uses the log odds of readmissions as the dependent variable and age and selected comorbidities as the independent variables. The second level models the hospital-specific intercepts. According to CMS guidelines, the analysis was limited to facilities with at least 25 patient admissions annually for each condition. All readmissions were attributed to the hospital that initially discharged the patient to a nonacute setting.

Analysis

We examined and reported the distribution of patient and clinical characteristics at the hospital level. For each condition, we determined the number of hospitals that had a sufficient number of admissions (n ≥ 25) to be included in the analyses. We calculated the mean, median, and interquartile range for the observed unadjusted readmission rates across all included hospitals.

Similar to methods used by CMS, we used one year of data in the VA to assess hospital quality and variation in facility performance. First, we calculated the 30-day RSRRs using one year (2012) of data. To assess how variability changed with higher facility volume (ie, more years included in the analysis), we also calculated the 30-day RSRRs using two and three years of data. For this, we identified and quantified the number of hospitals whose RSRRs were calculated as being above or below the national VA average (mean ± 95% CI). Specifically, we calculated the number and percentage of hospitals that were classified as either above (+95% CI) or below the national average (−95% CI) using data from all three time periods. All analyses were conducted using SAS Enterprise Guide, Version 7.1. The SAS statistical packages made available by the CMS Measure Team were used to calculate RSRRs.

Patient Characteristics

Patients were predominantly older males (98.3%). Among those hospitalized for AMI, most of them had a history of previous coronary artery bypass graft (CABG) (69.1%), acute coronary syndrome (ACS; 66.2%), or documented coronary atherosclerosis (89.8%). Similarly, patients admitted for HF had high rates of CABG (71.3%) and HF (94.6%), in addition to cardiac arrhythmias (69.3%) and diabetes (60.8%). Patients admitted with a diagnosis of pneumonia had high rates of CABG (61.9%), chronic obstructive pulmonary disease (COPD; 58.1%), and previous diagnosis of pneumonia (78.8%; Table 1). Patient characteristics for two and three years of data are presented in Supplementary Table 1.

VA Hospitals with Sufficient Volume to Be Included in Profiling Assessments

There were 146 acute-care hospitals in the VA. In 2012, 56 (38%) VA hospitals had at least 25 admissions for AMI, 102 (70%) hospitals had at least 25 admissions for CHF, and 106 (73%) hospitals had at least 25 admissions for pneumonia (Table 1) and therefore qualified for analysis based on CMS criteria for 30-day RSRR calculation. The study sample included 3,571 patients with AMI, 10,609 patients with CHF, and 10,191 patients with pneumonia.

30-Day Readmission Rates

The mean observed readmission rates in 2012 were 20% (95% CI 19%-21%) among patients admitted for AMI, 20% (95% CI 19%-20%) for patients admitted with CHF, and 15% (95% CI 15%-16%) for patients admitted with pneumonia. No significant variation from these rates was noted following risk standardization across hospitals (Table 2). Observed and risk-standardized rates were also calculated for two and three years of data (Supplementary Table 2) but were not found to be grossly different when utilizing a single year of data.

In 2012, two hospitals (2%) exhibited HF RSRRs worse than the national average (+95% CI), whereas no hospital demonstrated worse-than-average rates (+95% CI) for AMI or pneumonia (Table 3, Figure 1). Similarly, in 2012, only three hospitals had RSRRs better than the national average (−95% CI) for HF and pneumonia.

We found that the CMS-derived 30-day risk-stratified readmission metric for AMI, HF, and pneumonia showed little variation among VA hospitals. The lack of institutional 30-day readmission volume appears to be a fundamental limitation that subsequently requires multiple years of data to make this metric clinically meaningful. As the largest integrated healthcare system in the United States, the VA relies upon and makes large-scale programmatic decisions based on such performance data. The inability to detect meaningful interhospital variation in a timely manner suggests that the CMS-derived 30-day RSRR may not be a sensitive metric to distinguish facility performance or drive quality improvement initiatives within the VA.

First, we found it notable that among the 146 VA medical centers available for analysis,¹⁵ between 38% and 77% of hospitals qualified for evaluation when using CMS-based participation criteria—which excludes institutions with fewer than 25 episodes per year. Although this low degree of qualification for profiling was most dramatic when using one year of data (range: 38%-72%), we noted that it did not dramatically improve when we combined three years of data (range: 52%-77%). These findings act to highlight the population and systems differences between CMS and VA populations¹⁶ and further support the idea that CMS-derived models may not be optimized for use in the VA healthcare system.

Our findings are particularly relevant within the VA given the quarterly rate with which these data are reported within the VA SAIL scorecard.² The VA designed SAIL for internal benchmarking to spotlight successful strategies of top performing institutions and promote high-quality, value-based care. Using one year of data, the minimum required to utilize CMS models, showed that quarterly feedback (ie, three months of data) may not be informative or useful given that few hospitals are able to differentiate themselves from the mean (±95% CI). Although the capacity to distinguish between high and low performers does improve by combining hospital admissions over three years, this is not a reasonable timeline for institutions to wait for quality comparisons. Furthermore, although the VA does present its data on CMS’s Hospital Compare website using three years of combined data, the variability and distribution of such results are not supplied.³

This lack of discriminability raises concerns about the ability to compare hospital performance between low- and high-volume institutions. Although these models function well in CMS settings with large patient volumes in which greater variability exists,⁵ they lose their capacity to discriminate when applied to low-volume settings such as the VA. Given that several hospitals in the US are small community hospitals with low patient volumes,¹⁷ this issue probably occurs in other non-VA settings. Although our study focuses on the VA, others have been able to compare VA and non-VA settings’ variation and distribution. For example, Nuti et al. explored the differences in 30-day RSRRs among hospitalized patients with AMI, HF, and pneumonia and similarly showed little variation, narrow distributions, and few outliers in the VA setting compared to those in the non-VA setting. For small patient volume institutions, including the VA, a focus on high-volume services, outcomes, and measures (ie, blood pressure control, medication reconciliation, etc.) may offer more discriminability between high- and low-performing facilities. For example, Patel et al. found that VA process measures in patients with HF (ie, beta-blocker and ACE-inhibitor use) can be used as valid quality measures as they exhibited consistent reliability over time and validity with adjusted mortality rates, whereas the 30-day RSRR did not.¹⁸

Our findings may have substantial financial, resource, and policy implications. Automatically developing and reporting measures created for the Medicare program in the VA may not be a good use of VA resources. In addition, facilities may react to these reported outcomes and expend local resources and finances to implement interventions to improve on a performance outcome whose measure is statistically no different than the vast majority of its comparators. Such events have been highlighted in the public media and have pointed to the fact that small changes in quality, or statistical errors themselves, can have large ramifications within the VA’s hospital rating system.¹⁹

These findings may also add to the discussion on whether public reporting of health and quality outcomes improves patient care. Since the CMS began public reporting on RSRRs in 2009, these rates have fallen for all three examined conditions (AMI, HF, and pneumonia),^7,20,21 in addition to several other health outcomes.¹⁷ Although recent studies have suggested that these decreased rates have been driven by the CMS-sponsored Hospital Readmissions Reduction Program (HRRP),²² others have suggested that these findings are consistent with ongoing secular trends toward decreased readmissions and may not be completely explained by public reporting alone.²³ Moreover, prior work has also found that readmissions may be strongly impacted by factors external to the hospital setting, such as patients’ social demographics (ie, household income, social isolation), that are not currently captured in risk-prediction models.²⁴ Given the small variability we see in our data, public reporting within the VA is probably not beneficial, as only a small number of facilities are outliers based on RSRR.

Our study has several limitations. First, although we adapted the CMS model to the VA, we did not include gender in the model because >99% of all patient admissions were male. Second, we assessed only three medical conditions that were being tracked by both CMS and VA during this time period, and these outcomes may not be representative of other aspects of care and cannot be generalized to other medical conditions. Finally, more contemporary data could lead to differing results – though we note that no large-scale structural or policy changes addressing readmission rates have been implemented within the VA since our study period.

The results of this study suggest that the CMS-derived 30-day risk-stratified readmission metric for AMI, HF, and pneumonia may not have the capacity to properly detect interfacility variance and thus may not be an optimal quality indicator within the VA. As the VA and other healthcare systems continually strive to improve the quality of care they provide, they will require more accurate and timely metrics for which to index their performance.

Disclosures

The authors have nothing to disclose

Using methodology created by the Centers for Medicare & Medicaid Services (CMS), the Department of Veterans Affairs (VA) calculates and reports hospital performance measures for several key conditions, including acute myocardial infarction (AMI), heart failure (HF), and pneumonia.¹ These measures are designed to benchmark individual hospitals against how average hospitals perform when caring for a similar case-mix index. Because readmissions to the hospital within 30-days of discharge are common and costly, this metric has garnered extensive attention in recent years.

To summarize the 30-day readmission metric, the VA utilizes the Strategic Analytics for Improvement and Learning (SAIL) system to present internally its findings to VA practitioners and leadership.² The VA provides these data as a means to drive quality improvement and allow for comparison of individual hospitals’ performance across measures throughout the VA healthcare system. Since 2010, the VA began using and publicly reporting the CMS-derived 30-day Risk-Stratified Readmission Rate (RSRR) on the Hospital Compare website.³ Similar to CMS, the VA uses three years of combined data so that patients, providers, and other stakeholders can compare individual hospitals’ performance across these measures.¹ In response to this, hospitals and healthcare organizations have implemented quality improvement and large-scale programmatic interventions in an attempt to improve quality around readmissions.^4-6 A recent assessment on how hospitals within the Medicare fee-for-service program have responded to such reporting found large degrees of variability, with more than half of the participating institutions facing penalties due to greater-than-expected readmission rates.⁵ Although the VA utilizes the same CMS-derived model in its assessments and reporting, the variability and distribution around this metric are not publicly reported—thus making it difficult to ascertain how individual VA hospitals compare with one another. Without such information, individual facilities may not know how to benchmark the quality of their care to others, nor would the VA recognize which interventions addressing readmissions are working, and which are not. Although previous assessments of interinstitutional variance have been performed in Medicare populations,⁷ a focused analysis of such variance within the VA has yet to be performed.

In this study, we performed a multiyear assessment of the CMS-derived 30-day RSRR metric for AMI, HF, and pneumonia as a useful measure to drive VA quality improvement or distinguish VA facility performance based on its ability to detect interfacility variability.

Data Source

We used VA administrative and Medicare claims data from 2010 to 2012. After identifying index hospitalizations to VA hospitals, we obtained patients’ respective inpatient Medicare claims data from the Medicare Provider Analysis and Review (MedPAR) and Outpatient files. All Medicare records were linked to VA records via scrambled Social Security numbers and were provided by the VA Information Resource Center. This study was approved by the San Francisco VA Medical Center Institutional Review Board.

Study Sample

Our cohort consisted of hospitalized VA beneficiary and Medicare fee-for-service patients who were aged ≥65 years and admitted to and discharged from a VA acute care center with a primary discharge diagnosis of AMI, HF, or pneumonia. These comorbidities were chosen as they are publicly reported and frequently used for interfacility comparisons. Because studies have found that inclusion of secondary payer data (ie, CMS data) may affect hospital-profiling outcomes, we included Medicare data on all available patients.⁸ We excluded hospitalizations that resulted in a transfer to another acute care facility and those admitted to observation status at their index admission. To ensure a full year of data for risk adjustment, beneficiaries were included only if they were enrolled in Medicare for 12 months prior to and including the date of the index admission.

Index hospitalizations were first identified using VA-only inpatient data similar to methods outlined by the CMS and endorsed by the National Quality Forum for Hospital Profiling.⁹ An index hospitalization was defined as an acute inpatient discharge between 2010 and 2012 in which the principal diagnosis was AMI, HF, or pneumonia. We excluded in-hospital deaths, discharges against medical advice, and--for the AMI cohort only--discharges on the same day as admission. Patients may have multiple admissions per year, but only admissions after 30 days of discharge from an index admission were eligible to be included as an additional index admission.

Outcomes

A readmission was defined as any unplanned rehospitalization to either non-VA or VA acute care facilities for any cause within 30 days of discharge from the index hospitalization. Readmissions to observation status or nonacute or rehabilitation units, such as skilled nursing facilities, were not included. Planned readmissions for elective procedures, such as elective chemotherapy and revascularization following an AMI index admission, were not considered as an outcome event.

Risk Standardization for 30-day Readmission

Using approaches developed by CMS,^10-12 we calculated hospital-specific 30-day RSRRs for each VA. Briefly, the RSRR is a ratio of the number of predicted readmissions within 30 days of discharge to the expected number of readmissions within 30 days of hospital discharge, multiplied by the national unadjusted 30-day readmission rate. This measure calculates hospital-specific RSRRs using hierarchical logistic regression models, which account for clustering of patients within hospitals and risk-adjusting for differences in case-mix, during the assessed time periods.¹³ This approach simultaneously models two levels (patient and hospital) to account for the variance in patient outcomes within and between hospitals.¹⁴ At the patient level, the model uses the log odds of readmissions as the dependent variable and age and selected comorbidities as the independent variables. The second level models the hospital-specific intercepts. According to CMS guidelines, the analysis was limited to facilities with at least 25 patient admissions annually for each condition. All readmissions were attributed to the hospital that initially discharged the patient to a nonacute setting.

Analysis

We examined and reported the distribution of patient and clinical characteristics at the hospital level. For each condition, we determined the number of hospitals that had a sufficient number of admissions (n ≥ 25) to be included in the analyses. We calculated the mean, median, and interquartile range for the observed unadjusted readmission rates across all included hospitals.

Similar to methods used by CMS, we used one year of data in the VA to assess hospital quality and variation in facility performance. First, we calculated the 30-day RSRRs using one year (2012) of data. To assess how variability changed with higher facility volume (ie, more years included in the analysis), we also calculated the 30-day RSRRs using two and three years of data. For this, we identified and quantified the number of hospitals whose RSRRs were calculated as being above or below the national VA average (mean ± 95% CI). Specifically, we calculated the number and percentage of hospitals that were classified as either above (+95% CI) or below the national average (−95% CI) using data from all three time periods. All analyses were conducted using SAS Enterprise Guide, Version 7.1. The SAS statistical packages made available by the CMS Measure Team were used to calculate RSRRs.

Patient Characteristics

Patients were predominantly older males (98.3%). Among those hospitalized for AMI, most of them had a history of previous coronary artery bypass graft (CABG) (69.1%), acute coronary syndrome (ACS; 66.2%), or documented coronary atherosclerosis (89.8%). Similarly, patients admitted for HF had high rates of CABG (71.3%) and HF (94.6%), in addition to cardiac arrhythmias (69.3%) and diabetes (60.8%). Patients admitted with a diagnosis of pneumonia had high rates of CABG (61.9%), chronic obstructive pulmonary disease (COPD; 58.1%), and previous diagnosis of pneumonia (78.8%; Table 1). Patient characteristics for two and three years of data are presented in Supplementary Table 1.

VA Hospitals with Sufficient Volume to Be Included in Profiling Assessments

There were 146 acute-care hospitals in the VA. In 2012, 56 (38%) VA hospitals had at least 25 admissions for AMI, 102 (70%) hospitals had at least 25 admissions for CHF, and 106 (73%) hospitals had at least 25 admissions for pneumonia (Table 1) and therefore qualified for analysis based on CMS criteria for 30-day RSRR calculation. The study sample included 3,571 patients with AMI, 10,609 patients with CHF, and 10,191 patients with pneumonia.

30-Day Readmission Rates

The mean observed readmission rates in 2012 were 20% (95% CI 19%-21%) among patients admitted for AMI, 20% (95% CI 19%-20%) for patients admitted with CHF, and 15% (95% CI 15%-16%) for patients admitted with pneumonia. No significant variation from these rates was noted following risk standardization across hospitals (Table 2). Observed and risk-standardized rates were also calculated for two and three years of data (Supplementary Table 2) but were not found to be grossly different when utilizing a single year of data.

In 2012, two hospitals (2%) exhibited HF RSRRs worse than the national average (+95% CI), whereas no hospital demonstrated worse-than-average rates (+95% CI) for AMI or pneumonia (Table 3, Figure 1). Similarly, in 2012, only three hospitals had RSRRs better than the national average (−95% CI) for HF and pneumonia.

We found that the CMS-derived 30-day risk-stratified readmission metric for AMI, HF, and pneumonia showed little variation among VA hospitals. The lack of institutional 30-day readmission volume appears to be a fundamental limitation that subsequently requires multiple years of data to make this metric clinically meaningful. As the largest integrated healthcare system in the United States, the VA relies upon and makes large-scale programmatic decisions based on such performance data. The inability to detect meaningful interhospital variation in a timely manner suggests that the CMS-derived 30-day RSRR may not be a sensitive metric to distinguish facility performance or drive quality improvement initiatives within the VA.

First, we found it notable that among the 146 VA medical centers available for analysis,¹⁵ between 38% and 77% of hospitals qualified for evaluation when using CMS-based participation criteria—which excludes institutions with fewer than 25 episodes per year. Although this low degree of qualification for profiling was most dramatic when using one year of data (range: 38%-72%), we noted that it did not dramatically improve when we combined three years of data (range: 52%-77%). These findings act to highlight the population and systems differences between CMS and VA populations¹⁶ and further support the idea that CMS-derived models may not be optimized for use in the VA healthcare system.

Our findings are particularly relevant within the VA given the quarterly rate with which these data are reported within the VA SAIL scorecard.² The VA designed SAIL for internal benchmarking to spotlight successful strategies of top performing institutions and promote high-quality, value-based care. Using one year of data, the minimum required to utilize CMS models, showed that quarterly feedback (ie, three months of data) may not be informative or useful given that few hospitals are able to differentiate themselves from the mean (±95% CI). Although the capacity to distinguish between high and low performers does improve by combining hospital admissions over three years, this is not a reasonable timeline for institutions to wait for quality comparisons. Furthermore, although the VA does present its data on CMS’s Hospital Compare website using three years of combined data, the variability and distribution of such results are not supplied.³

This lack of discriminability raises concerns about the ability to compare hospital performance between low- and high-volume institutions. Although these models function well in CMS settings with large patient volumes in which greater variability exists,⁵ they lose their capacity to discriminate when applied to low-volume settings such as the VA. Given that several hospitals in the US are small community hospitals with low patient volumes,¹⁷ this issue probably occurs in other non-VA settings. Although our study focuses on the VA, others have been able to compare VA and non-VA settings’ variation and distribution. For example, Nuti et al. explored the differences in 30-day RSRRs among hospitalized patients with AMI, HF, and pneumonia and similarly showed little variation, narrow distributions, and few outliers in the VA setting compared to those in the non-VA setting. For small patient volume institutions, including the VA, a focus on high-volume services, outcomes, and measures (ie, blood pressure control, medication reconciliation, etc.) may offer more discriminability between high- and low-performing facilities. For example, Patel et al. found that VA process measures in patients with HF (ie, beta-blocker and ACE-inhibitor use) can be used as valid quality measures as they exhibited consistent reliability over time and validity with adjusted mortality rates, whereas the 30-day RSRR did not.¹⁸

Our findings may have substantial financial, resource, and policy implications. Automatically developing and reporting measures created for the Medicare program in the VA may not be a good use of VA resources. In addition, facilities may react to these reported outcomes and expend local resources and finances to implement interventions to improve on a performance outcome whose measure is statistically no different than the vast majority of its comparators. Such events have been highlighted in the public media and have pointed to the fact that small changes in quality, or statistical errors themselves, can have large ramifications within the VA’s hospital rating system.¹⁹

These findings may also add to the discussion on whether public reporting of health and quality outcomes improves patient care. Since the CMS began public reporting on RSRRs in 2009, these rates have fallen for all three examined conditions (AMI, HF, and pneumonia),^7,20,21 in addition to several other health outcomes.¹⁷ Although recent studies have suggested that these decreased rates have been driven by the CMS-sponsored Hospital Readmissions Reduction Program (HRRP),²² others have suggested that these findings are consistent with ongoing secular trends toward decreased readmissions and may not be completely explained by public reporting alone.²³ Moreover, prior work has also found that readmissions may be strongly impacted by factors external to the hospital setting, such as patients’ social demographics (ie, household income, social isolation), that are not currently captured in risk-prediction models.²⁴ Given the small variability we see in our data, public reporting within the VA is probably not beneficial, as only a small number of facilities are outliers based on RSRR.

Our study has several limitations. First, although we adapted the CMS model to the VA, we did not include gender in the model because >99% of all patient admissions were male. Second, we assessed only three medical conditions that were being tracked by both CMS and VA during this time period, and these outcomes may not be representative of other aspects of care and cannot be generalized to other medical conditions. Finally, more contemporary data could lead to differing results – though we note that no large-scale structural or policy changes addressing readmission rates have been implemented within the VA since our study period.

The results of this study suggest that the CMS-derived 30-day risk-stratified readmission metric for AMI, HF, and pneumonia may not have the capacity to properly detect interfacility variance and thus may not be an optimal quality indicator within the VA. As the VA and other healthcare systems continually strive to improve the quality of care they provide, they will require more accurate and timely metrics for which to index their performance.

Disclosures

The authors have nothing to disclose

Using methodology created by the Centers for Medicare & Medicaid Services (CMS), the Department of Veterans Affairs (VA) calculates and reports hospital performance measures for several key conditions, including acute myocardial infarction (AMI), heart failure (HF), and pneumonia.¹ These measures are designed to benchmark individual hospitals against how average hospitals perform when caring for a similar case-mix index. Because readmissions to the hospital within 30-days of discharge are common and costly, this metric has garnered extensive attention in recent years.

To summarize the 30-day readmission metric, the VA utilizes the Strategic Analytics for Improvement and Learning (SAIL) system to present internally its findings to VA practitioners and leadership.² The VA provides these data as a means to drive quality improvement and allow for comparison of individual hospitals’ performance across measures throughout the VA healthcare system. Since 2010, the VA began using and publicly reporting the CMS-derived 30-day Risk-Stratified Readmission Rate (RSRR) on the Hospital Compare website.³ Similar to CMS, the VA uses three years of combined data so that patients, providers, and other stakeholders can compare individual hospitals’ performance across these measures.¹ In response to this, hospitals and healthcare organizations have implemented quality improvement and large-scale programmatic interventions in an attempt to improve quality around readmissions.^4-6 A recent assessment on how hospitals within the Medicare fee-for-service program have responded to such reporting found large degrees of variability, with more than half of the participating institutions facing penalties due to greater-than-expected readmission rates.⁵ Although the VA utilizes the same CMS-derived model in its assessments and reporting, the variability and distribution around this metric are not publicly reported—thus making it difficult to ascertain how individual VA hospitals compare with one another. Without such information, individual facilities may not know how to benchmark the quality of their care to others, nor would the VA recognize which interventions addressing readmissions are working, and which are not. Although previous assessments of interinstitutional variance have been performed in Medicare populations,⁷ a focused analysis of such variance within the VA has yet to be performed.

In this study, we performed a multiyear assessment of the CMS-derived 30-day RSRR metric for AMI, HF, and pneumonia as a useful measure to drive VA quality improvement or distinguish VA facility performance based on its ability to detect interfacility variability.

Data Source

We used VA administrative and Medicare claims data from 2010 to 2012. After identifying index hospitalizations to VA hospitals, we obtained patients’ respective inpatient Medicare claims data from the Medicare Provider Analysis and Review (MedPAR) and Outpatient files. All Medicare records were linked to VA records via scrambled Social Security numbers and were provided by the VA Information Resource Center. This study was approved by the San Francisco VA Medical Center Institutional Review Board.

Study Sample

Our cohort consisted of hospitalized VA beneficiary and Medicare fee-for-service patients who were aged ≥65 years and admitted to and discharged from a VA acute care center with a primary discharge diagnosis of AMI, HF, or pneumonia. These comorbidities were chosen as they are publicly reported and frequently used for interfacility comparisons. Because studies have found that inclusion of secondary payer data (ie, CMS data) may affect hospital-profiling outcomes, we included Medicare data on all available patients.⁸ We excluded hospitalizations that resulted in a transfer to another acute care facility and those admitted to observation status at their index admission. To ensure a full year of data for risk adjustment, beneficiaries were included only if they were enrolled in Medicare for 12 months prior to and including the date of the index admission.

Index hospitalizations were first identified using VA-only inpatient data similar to methods outlined by the CMS and endorsed by the National Quality Forum for Hospital Profiling.⁹ An index hospitalization was defined as an acute inpatient discharge between 2010 and 2012 in which the principal diagnosis was AMI, HF, or pneumonia. We excluded in-hospital deaths, discharges against medical advice, and--for the AMI cohort only--discharges on the same day as admission. Patients may have multiple admissions per year, but only admissions after 30 days of discharge from an index admission were eligible to be included as an additional index admission.

Outcomes

A readmission was defined as any unplanned rehospitalization to either non-VA or VA acute care facilities for any cause within 30 days of discharge from the index hospitalization. Readmissions to observation status or nonacute or rehabilitation units, such as skilled nursing facilities, were not included. Planned readmissions for elective procedures, such as elective chemotherapy and revascularization following an AMI index admission, were not considered as an outcome event.

Risk Standardization for 30-day Readmission

Using approaches developed by CMS,^10-12 we calculated hospital-specific 30-day RSRRs for each VA. Briefly, the RSRR is a ratio of the number of predicted readmissions within 30 days of discharge to the expected number of readmissions within 30 days of hospital discharge, multiplied by the national unadjusted 30-day readmission rate. This measure calculates hospital-specific RSRRs using hierarchical logistic regression models, which account for clustering of patients within hospitals and risk-adjusting for differences in case-mix, during the assessed time periods.¹³ This approach simultaneously models two levels (patient and hospital) to account for the variance in patient outcomes within and between hospitals.¹⁴ At the patient level, the model uses the log odds of readmissions as the dependent variable and age and selected comorbidities as the independent variables. The second level models the hospital-specific intercepts. According to CMS guidelines, the analysis was limited to facilities with at least 25 patient admissions annually for each condition. All readmissions were attributed to the hospital that initially discharged the patient to a nonacute setting.

Analysis

We examined and reported the distribution of patient and clinical characteristics at the hospital level. For each condition, we determined the number of hospitals that had a sufficient number of admissions (n ≥ 25) to be included in the analyses. We calculated the mean, median, and interquartile range for the observed unadjusted readmission rates across all included hospitals.

Similar to methods used by CMS, we used one year of data in the VA to assess hospital quality and variation in facility performance. First, we calculated the 30-day RSRRs using one year (2012) of data. To assess how variability changed with higher facility volume (ie, more years included in the analysis), we also calculated the 30-day RSRRs using two and three years of data. For this, we identified and quantified the number of hospitals whose RSRRs were calculated as being above or below the national VA average (mean ± 95% CI). Specifically, we calculated the number and percentage of hospitals that were classified as either above (+95% CI) or below the national average (−95% CI) using data from all three time periods. All analyses were conducted using SAS Enterprise Guide, Version 7.1. The SAS statistical packages made available by the CMS Measure Team were used to calculate RSRRs.

Patient Characteristics

Patients were predominantly older males (98.3%). Among those hospitalized for AMI, most of them had a history of previous coronary artery bypass graft (CABG) (69.1%), acute coronary syndrome (ACS; 66.2%), or documented coronary atherosclerosis (89.8%). Similarly, patients admitted for HF had high rates of CABG (71.3%) and HF (94.6%), in addition to cardiac arrhythmias (69.3%) and diabetes (60.8%). Patients admitted with a diagnosis of pneumonia had high rates of CABG (61.9%), chronic obstructive pulmonary disease (COPD; 58.1%), and previous diagnosis of pneumonia (78.8%; Table 1). Patient characteristics for two and three years of data are presented in Supplementary Table 1.

VA Hospitals with Sufficient Volume to Be Included in Profiling Assessments

There were 146 acute-care hospitals in the VA. In 2012, 56 (38%) VA hospitals had at least 25 admissions for AMI, 102 (70%) hospitals had at least 25 admissions for CHF, and 106 (73%) hospitals had at least 25 admissions for pneumonia (Table 1) and therefore qualified for analysis based on CMS criteria for 30-day RSRR calculation. The study sample included 3,571 patients with AMI, 10,609 patients with CHF, and 10,191 patients with pneumonia.

30-Day Readmission Rates

The mean observed readmission rates in 2012 were 20% (95% CI 19%-21%) among patients admitted for AMI, 20% (95% CI 19%-20%) for patients admitted with CHF, and 15% (95% CI 15%-16%) for patients admitted with pneumonia. No significant variation from these rates was noted following risk standardization across hospitals (Table 2). Observed and risk-standardized rates were also calculated for two and three years of data (Supplementary Table 2) but were not found to be grossly different when utilizing a single year of data.

In 2012, two hospitals (2%) exhibited HF RSRRs worse than the national average (+95% CI), whereas no hospital demonstrated worse-than-average rates (+95% CI) for AMI or pneumonia (Table 3, Figure 1). Similarly, in 2012, only three hospitals had RSRRs better than the national average (−95% CI) for HF and pneumonia.

We found that the CMS-derived 30-day risk-stratified readmission metric for AMI, HF, and pneumonia showed little variation among VA hospitals. The lack of institutional 30-day readmission volume appears to be a fundamental limitation that subsequently requires multiple years of data to make this metric clinically meaningful. As the largest integrated healthcare system in the United States, the VA relies upon and makes large-scale programmatic decisions based on such performance data. The inability to detect meaningful interhospital variation in a timely manner suggests that the CMS-derived 30-day RSRR may not be a sensitive metric to distinguish facility performance or drive quality improvement initiatives within the VA.

First, we found it notable that among the 146 VA medical centers available for analysis,¹⁵ between 38% and 77% of hospitals qualified for evaluation when using CMS-based participation criteria—which excludes institutions with fewer than 25 episodes per year. Although this low degree of qualification for profiling was most dramatic when using one year of data (range: 38%-72%), we noted that it did not dramatically improve when we combined three years of data (range: 52%-77%). These findings act to highlight the population and systems differences between CMS and VA populations¹⁶ and further support the idea that CMS-derived models may not be optimized for use in the VA healthcare system.

Our findings are particularly relevant within the VA given the quarterly rate with which these data are reported within the VA SAIL scorecard.² The VA designed SAIL for internal benchmarking to spotlight successful strategies of top performing institutions and promote high-quality, value-based care. Using one year of data, the minimum required to utilize CMS models, showed that quarterly feedback (ie, three months of data) may not be informative or useful given that few hospitals are able to differentiate themselves from the mean (±95% CI). Although the capacity to distinguish between high and low performers does improve by combining hospital admissions over three years, this is not a reasonable timeline for institutions to wait for quality comparisons. Furthermore, although the VA does present its data on CMS’s Hospital Compare website using three years of combined data, the variability and distribution of such results are not supplied.³

This lack of discriminability raises concerns about the ability to compare hospital performance between low- and high-volume institutions. Although these models function well in CMS settings with large patient volumes in which greater variability exists,⁵ they lose their capacity to discriminate when applied to low-volume settings such as the VA. Given that several hospitals in the US are small community hospitals with low patient volumes,¹⁷ this issue probably occurs in other non-VA settings. Although our study focuses on the VA, others have been able to compare VA and non-VA settings’ variation and distribution. For example, Nuti et al. explored the differences in 30-day RSRRs among hospitalized patients with AMI, HF, and pneumonia and similarly showed little variation, narrow distributions, and few outliers in the VA setting compared to those in the non-VA setting. For small patient volume institutions, including the VA, a focus on high-volume services, outcomes, and measures (ie, blood pressure control, medication reconciliation, etc.) may offer more discriminability between high- and low-performing facilities. For example, Patel et al. found that VA process measures in patients with HF (ie, beta-blocker and ACE-inhibitor use) can be used as valid quality measures as they exhibited consistent reliability over time and validity with adjusted mortality rates, whereas the 30-day RSRR did not.¹⁸

Our findings may have substantial financial, resource, and policy implications. Automatically developing and reporting measures created for the Medicare program in the VA may not be a good use of VA resources. In addition, facilities may react to these reported outcomes and expend local resources and finances to implement interventions to improve on a performance outcome whose measure is statistically no different than the vast majority of its comparators. Such events have been highlighted in the public media and have pointed to the fact that small changes in quality, or statistical errors themselves, can have large ramifications within the VA’s hospital rating system.¹⁹

These findings may also add to the discussion on whether public reporting of health and quality outcomes improves patient care. Since the CMS began public reporting on RSRRs in 2009, these rates have fallen for all three examined conditions (AMI, HF, and pneumonia),^7,20,21 in addition to several other health outcomes.¹⁷ Although recent studies have suggested that these decreased rates have been driven by the CMS-sponsored Hospital Readmissions Reduction Program (HRRP),²² others have suggested that these findings are consistent with ongoing secular trends toward decreased readmissions and may not be completely explained by public reporting alone.²³ Moreover, prior work has also found that readmissions may be strongly impacted by factors external to the hospital setting, such as patients’ social demographics (ie, household income, social isolation), that are not currently captured in risk-prediction models.²⁴ Given the small variability we see in our data, public reporting within the VA is probably not beneficial, as only a small number of facilities are outliers based on RSRR.

Our study has several limitations. First, although we adapted the CMS model to the VA, we did not include gender in the model because >99% of all patient admissions were male. Second, we assessed only three medical conditions that were being tracked by both CMS and VA during this time period, and these outcomes may not be representative of other aspects of care and cannot be generalized to other medical conditions. Finally, more contemporary data could lead to differing results – though we note that no large-scale structural or policy changes addressing readmission rates have been implemented within the VA since our study period.

The results of this study suggest that the CMS-derived 30-day risk-stratified readmission metric for AMI, HF, and pneumonia may not have the capacity to properly detect interfacility variance and thus may not be an optimal quality indicator within the VA. As the VA and other healthcare systems continually strive to improve the quality of care they provide, they will require more accurate and timely metrics for which to index their performance.

Disclosures

The authors have nothing to disclose

Access to life-sustaining treatment (LST) became a mainstay in hospitals across the United States in the 1970s. This has raised complex ethical questions surrounding the use of these therapies, particularly in the face of a poor prognosis or significant morbidity. The Society for Critical Care Medicine formed a consensus panel in 1989 to construct ethical guidelines regarding the initiation, continuation, and withdrawal of intensive care.¹ These guidelines emphasized that withdrawing and withholding are not only permissible but may be necessary to preserve the balance between quantity and quality of life. Nevertheless, an increasing number of Americans are dying after aggressive LST in the hospital, and greater than one in five deaths occur after admission to the ICU.² Understanding the factors associated with decisions to withhold or withdraw LST are important to policy makers, ethicists, and healthcare leaders because they affect resources used at the end of life and the need for palliative care and hospice in the ICU setting.

Several studies have characterized the patient characteristics, incidence, and variability associated with limitation of LST in various populations of critically ill patients in the US. We are unaware of another systematic review of the literature that has examined data from these studies in order to understand the process and outcomes of LST limitations. We defined limitations of LST as decisions to withdraw or withhold cardiopulmonary resuscitation through Do Not Resuscitate (DNR) orders, mechanical ventilation, renal replacement therapy, intravenous blood pressure support, or artificial nutrition (enteric or intravenous).

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement was used for reporting. A comprehensive literature search was performed by a medical librarian (TWE) in Ovid MEDLINE, PubMed, Embase, the full Cochrane Library, CINAHL, PsycINFO, the Philosopher’s Index, Scopus, Web of Science, and Google Scholar. PubMed was limited to non-MEDLINE records in order to complement the Ovid results. The Georgetown Bioethics Research Library at the Kennedy Institute (https://bioethics.georgetown.edu/) was also searched for any unpublished literature. Initial searches were conducted in December 2014, and an update was performed in April 2017. All databases were searched from inception, and bibliographies of relevant studies were reviewed for additional references (Appendix 1).

Database-specific subject headings and keyword variants for each of the five main concepts—intensive care, end-of-life, decision making, limitation of treatment, and death—were identified and combined. Results were limited to the adult population and to the English language.

Two authors independently reviewed article titles and abstracts (KM, AMT). The full text of potentially eligible studies was then reviewed for inclusion. All disputes were discussed and resolved by consensus. The criteria for inclusion were reporting of patient-level data, critical care patients only (or reported separately from other unit types), US setting, and reporting of data on limitations of LST. The exclusion criteria were studies published only as research abstracts, surveys of physicians or families, organ donors, studies of brain death, surveys, patients less than 18 years old, and long-term intensive care settings (ie, long-term acute care hospitals, long-term respiratory units). Also excluded were studies in which an intervention was performed; as a result, all included studies were observational. Research abstracts were excluded because they lacked sufficient detail from which to abstract study quality or results. Studies of organ donation, brain death, and pediatrics were excluded due to differences in the decision-making context that would make it difficult to draw conclusions about adult ICU care. Studies which included an intervention were excluded to avoid affecting the rate of limitation of LST as a result of the intervention, since our goal was to quantify the number of limitations of LST in usual medical practice.

For each article, we abstracted the number of patients who experienced a limitation of LST out of the total population and factors associated with the limitation. If a multivariable analysis was performed, we reported only variables that remained significant in this analysis. We also reported the number of patients who died, and of those, the number of decedents who underwent a limitation of LST before death. In some cases, this proportion was not reported in the manuscript but could be calculated based on the data presented. This number was calculated based on the number of deaths that were preceded by a limitation in life-sustaining care divided by the total number of deaths. Patients with brain death were not counted as having had a “limitation” if support was withdrawn after the declaration of brain death. We were unable to conduct a meta-analysis of the findings because of the wide variation in study populations and criteria used to define limitations of care.

To assess risk of bias in individual studies, the two raters independently made a yes/no determination regarding several quality metrics established at the outset of the review: clarity of the eligibility criteria for participant inclusion, whether a power or sample size calculation was done, adequacy of the description of the sampling approach and recruitment, and generalizability. Disagreements were resolved by consensus.

Study Selection

A total of 2,460 references were identified, and after removal of 578 duplicates, 1,882 unique titles and abstracts were reviewed. One hundred thirteen titles met the inclusion criteria. After review of complete texts, 83 were excluded based on the above criteria (Appendix). This led to a final number of 36 studies included for analysis.

Fifteen articles were prospective, observational studies. The rest were retrospective analyses of patient-level data. Seven were large, multicenter studies with greater than 20 centers involved (including Project IMPACT); six such studies included medical and surgical patients. The remaining large, multicenter study examined a surgical trauma cohort.

Historical Trends

Examination of the three studies that looked at >20 regionally diverse ICUs revealed a trend over time toward increased limitation prior to death (Figure). Jayes looked at the number of DNR orders preceding death from 1979 to 1980 then compared that to a cohort from 1988 to 1990; Prendergast included withholding/withdrawing of LST prior to death from 1994 to 1995;and Quill used the IMPACT database to examine limitations prior to death from 2001 to 2009.^3-5

Effect of Unit Specialty

Twelve studies were mixed (surgical/medical or medical/neuro) ICUs, 11 were medical/cardiac units, five were neurologic units, and six were surgical/trauma units only. Two studies did not report unit specialty. Four studies that compared surgical and medical ICUs found that surgical patients were more likely to die with full intervention.^4-7 In all of these studies, medical patients were more likely to have limitations of LST preceding death. Quill, et al. further detailed that emergency surgery was more likely to be associated with limitation than elective surgery.⁵

Patient Factors

In 15 studies, older age was associated with an increased likelihood of limitations on LST.^3,5-18 In one study, advanced age was associated with early versus late withdrawal.¹⁹ Poor performance status and multiple medical comorbidities were also associated with limitations of LST. The largest population-based study by Quill et al. found that being fully dependent on others upon admission to the ICU was associated with an increased likelihood of limiting LST.⁵ Sise et al. found, in an analysis performed over 10 years in one trauma center, that increased age, comorbidities, and a fall as the reason for trauma admission were associated with limitation of LST.⁹ Salottolo et al. found that if the reason for trauma admission was a fall, there was an increased odds ratio of DNR status.¹⁸ Many studies found that having medical comorbidities prior to admission was associated with increased likelihood of limiting LST in both medical and surgical patients.^{3,7,9,13,15,18}

Five studies found a statistically significant difference between women and men in the likelihood of limitation of LST,^3,5,9,14,16 and another study reported that women who were trauma patients had an increased odds ratio of changing to DNR code status.¹⁸ Only one study found that males were associated with an increased likelihood of limiting aggressive treatment.²⁰

White race was associated with increased limitation of LST in nine studies.^{4,5,10,11,14-16,21,22} One study in neurocritical care patients found that both white and Hispanic races were correlated with a higher likelihood of limitations.²³ Muni et al. found that nonwhite patients had a statistically significantly lower likelihood of having comfort measures and DNR orders written prior to death, but discussion of prognosis was more likely to be documented in nonwhite patients.²¹

In summary, white race, female gender, and older age were the most frequent factors associated with a higher likelihood of limiting LST.

Factors Related to Critical Illness

There were several illness severity indicators that were associated with limitations. The Acute Physiology and Chronic Health Evaluation (APACHE) scores were the most common for medical patients and Glasgow Coma Scale (GCS) was the most common for patients with neurologic injury. Eight studies reported that a higher APACHE score was associated with an increased likelihood of limitations.^{3,7,10,15,17,20,22,24} Similar associations were found based on the Sepsis Related Organ Failure Assessment score in one study and a scoring system developed by the author in a second study.^25,26

Seven studies, consisting of three neurologic, two medical-surgical, and two trauma cohorts, reported that a lower GCS score increased the likelihood that the patient would have limited LST.^{5,10,11,13,14,18,22} Additionally, Geocadin and colleagues discussed the difficulty with neurological prognostication in clinical practice; they reported that the cortical evoked potential (CEP) was correlated with the time to withdrawLST if the CEP was malignant, and the time to withdraw LST was less in malignant than in benign CEP.²⁷

Mortality and End Effects of Limiting LST

Chen and colleagues used propensity scores to control for mortality differences between patients who had full interventions versus those with limitations and found that higher mortality correlated with the decision to withhold or withdraw LST.¹⁰ Weimer and colleagues used modeling to predict the probable outcome of patients who experienced an intracranial hemorrhage who had limitation of LST. Based on this model, nearly all the patients in their study would have died or had severe disability at 12 months despite having maximal therapy; they concluded that withdrawal of LST may not have been a self-fulfilling prophecy as others have proposed.²⁸ Mulder and colleagues reported that in a small cohort of out-of-hospital cardiac arrest survivors admitted to the hospital, over one-third had good neurological outcomes after coding after 72 hours.²⁹ The study highlighted the importance of timing in neurological prognostication.

Variation in Limitation Rates among Centers

In the 36 studies, we found an overall range of DNR orders from 5.4%⁷ to 82.0%.³⁰ For other limitations, the rates ranged from 6.3%¹³ to 80.4%.³¹ Hart reported a low rate of limitations (4.8%) at the time of ICU admission.¹⁶ Four large, multicenter studies drew attention to the large variability between critical care centers and the limitation of end-of-life care.^3-5,14 Jayes first described this phenomenon when examining the frequency of DNR orders from 1979 to 1980 and 1988 to 1990.³ This study found a range from 1.5% to 22%. Later, in another large, multicenter study, Prendergast et al. looked at 131 ICUs at 110 different institutions in 38 states that participated in postgraduate training and found variability in CPR attempts prior to death between 4% and 79%.⁴ In 2008, Nathens et al. reported significant variation in DNR rates across trauma centers; they found a higher incidence of DNR orders when there was an open ICU structure.¹⁴

Overall, there was wide variation in the proportion of deaths preceded by limitation of LST, ranging from 29.5% in one study of trauma patients⁸ to 92% in another study of trauma patients whose death occurred after 24 hours of care.⁹ In the largest study to date by Quill and colleagues utilizing the IMPACT database, they found large variability in the number of deaths preceded by full intervention based on differences in practice patterns of critical care centers.⁵

Bias

All studies indicated clear eligibility criteria for inclusion and described their sampling approach in adequate detail. All but one stated their method of participant recruitment, and the one remaining study was a secondary analysis and referenced the earlier manuscript.³⁰ No study provided a power or sample size calculation, and sample sizes varied widely. Generalizability was most affected by the fact that many studies were conducted in a single ICU.

This systematic review of LST in US ICUs found several patient and illness factors that were associated with limitation of LST. The association of preadmission functional status and comorbidities with limitation of LST suggest that prior health is a factor in decision making. Further, ICU severity of illness, as measured by several commonly used indices, was associated with limitations.

Although variations in study design precluded meta-analysis, examination of the largest studies suggests that limitations are becoming more frequent over time. Also, early studies generally addressed DNR status, while later studies included withdrawal or withholding of LST, most commonly artificial ventilation. These findings reflect the current consensus in US medicine that it is ethically acceptable to limit LSTs in cases when they no longer benefit the patient or the patient would no longer want them.^32,33

Some studies found variability by unit type, suggesting that decision making may differ among surgical, medical, and neurologic illness. Mayerand Kossoff concluded, in study of a cohort of neurocritical care ICU patients, that medical patients often have issues of physiologic futility and imminent death, whereas neurologic patients more often confront issues of quality of life. They also note that there is a difference in how patients with differing illnesses die; medical patients will have limitation of hemodialysis or vasopressors, whereas neurologic surrogate decision makers often confront decisions around terminal extubation.²³

Some patient-level factors, such as race or ethnicity, may point to cultural differences in preferences for LST at the end of life. Other authors have documented that African American patients are more likely to choose end-of-life care for themselves or their family members, which may be due to cultural or religious factors as well as to a history of unequal access to medical care.³⁴ Reasons for the finding that women are more likely to have limitations has not been as well described. Further research could explore whether this is due to differences in patient preferences by gender or to other factors.

Even when examining patient-level factors, illness severity and type of ICU, the wide variability in end-of-life care in critical care units across the country is still large. A worldwide review also found a high degree of variability, even within geographical regions.³⁵ More research is needed to understand the factors associated with this wide variability, as this seems to indicate that approaches to end-of-life care may vary based on the ICU as much as individual patient preferences or clinical factors.

These findings can inform clinicians about variables that are important in the decision-making process. Patient age and race are factors to consider in the likelihood of reaching a decision to set limitations. Information about patients’ health status prior to critical illness, as well as ICU illness severity, are also important considerations.

The limitations of this review include the wide variety of LSTs assessed, including code status change, ventilator withdrawal, removal of pressors, and cessation of renal replacement therapy. Also, there was variation in sample size and the number of included units. There was also significant heterogeneity in the outcomes addressed and the variety of methods used in the included studies. We attempted to address this with an analysis of the quality of the studies, but given the wide variability, we were unable to account for all of the differences; unfortunately, this is a standard issue within studies that utilize systematic reviews, as well as similar concepts such as meta-analyses.

In conclusion, the increase in the frequency of limitations of LST in critically ill patients and a change in the nature of limitations from DNR order to withdrawal or withholding of LST suggests a trend toward growing acceptance of limiting treatments in critical illness. The wide variation in withdrawal of care in US ICUs does not seem fully explained by patient variables including preferences, illness type, or changes over time. Factors such as poor prefunctional status, a higher number of comorbid conditions prior to critical illness, and the severity of critical illness are likely important for surrogates and clinicians to consider during goals of care discussions. Further research is needed to explore why patients may receive very different types of care at the end of life depending the institution and ICU in which they receive their care.

Disclosures

The authors have no conflicts of interest to disclose. This work was performed at the Indiana University School of Medicine.

Funding

Financial support for Dr. Torke was provided by a Midcareer Investigator Award in Patient Oriented Research from the National Institute on Aging (K24AG053794). Dr. McPherson was supported by the Indiana University Department of Medicine.

Access to life-sustaining treatment (LST) became a mainstay in hospitals across the United States in the 1970s. This has raised complex ethical questions surrounding the use of these therapies, particularly in the face of a poor prognosis or significant morbidity. The Society for Critical Care Medicine formed a consensus panel in 1989 to construct ethical guidelines regarding the initiation, continuation, and withdrawal of intensive care.¹ These guidelines emphasized that withdrawing and withholding are not only permissible but may be necessary to preserve the balance between quantity and quality of life. Nevertheless, an increasing number of Americans are dying after aggressive LST in the hospital, and greater than one in five deaths occur after admission to the ICU.² Understanding the factors associated with decisions to withhold or withdraw LST are important to policy makers, ethicists, and healthcare leaders because they affect resources used at the end of life and the need for palliative care and hospice in the ICU setting.

Several studies have characterized the patient characteristics, incidence, and variability associated with limitation of LST in various populations of critically ill patients in the US. We are unaware of another systematic review of the literature that has examined data from these studies in order to understand the process and outcomes of LST limitations. We defined limitations of LST as decisions to withdraw or withhold cardiopulmonary resuscitation through Do Not Resuscitate (DNR) orders, mechanical ventilation, renal replacement therapy, intravenous blood pressure support, or artificial nutrition (enteric or intravenous).

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement was used for reporting. A comprehensive literature search was performed by a medical librarian (TWE) in Ovid MEDLINE, PubMed, Embase, the full Cochrane Library, CINAHL, PsycINFO, the Philosopher’s Index, Scopus, Web of Science, and Google Scholar. PubMed was limited to non-MEDLINE records in order to complement the Ovid results. The Georgetown Bioethics Research Library at the Kennedy Institute (https://bioethics.georgetown.edu/) was also searched for any unpublished literature. Initial searches were conducted in December 2014, and an update was performed in April 2017. All databases were searched from inception, and bibliographies of relevant studies were reviewed for additional references (Appendix 1).

Database-specific subject headings and keyword variants for each of the five main concepts—intensive care, end-of-life, decision making, limitation of treatment, and death—were identified and combined. Results were limited to the adult population and to the English language.

Two authors independently reviewed article titles and abstracts (KM, AMT). The full text of potentially eligible studies was then reviewed for inclusion. All disputes were discussed and resolved by consensus. The criteria for inclusion were reporting of patient-level data, critical care patients only (or reported separately from other unit types), US setting, and reporting of data on limitations of LST. The exclusion criteria were studies published only as research abstracts, surveys of physicians or families, organ donors, studies of brain death, surveys, patients less than 18 years old, and long-term intensive care settings (ie, long-term acute care hospitals, long-term respiratory units). Also excluded were studies in which an intervention was performed; as a result, all included studies were observational. Research abstracts were excluded because they lacked sufficient detail from which to abstract study quality or results. Studies of organ donation, brain death, and pediatrics were excluded due to differences in the decision-making context that would make it difficult to draw conclusions about adult ICU care. Studies which included an intervention were excluded to avoid affecting the rate of limitation of LST as a result of the intervention, since our goal was to quantify the number of limitations of LST in usual medical practice.

For each article, we abstracted the number of patients who experienced a limitation of LST out of the total population and factors associated with the limitation. If a multivariable analysis was performed, we reported only variables that remained significant in this analysis. We also reported the number of patients who died, and of those, the number of decedents who underwent a limitation of LST before death. In some cases, this proportion was not reported in the manuscript but could be calculated based on the data presented. This number was calculated based on the number of deaths that were preceded by a limitation in life-sustaining care divided by the total number of deaths. Patients with brain death were not counted as having had a “limitation” if support was withdrawn after the declaration of brain death. We were unable to conduct a meta-analysis of the findings because of the wide variation in study populations and criteria used to define limitations of care.

To assess risk of bias in individual studies, the two raters independently made a yes/no determination regarding several quality metrics established at the outset of the review: clarity of the eligibility criteria for participant inclusion, whether a power or sample size calculation was done, adequacy of the description of the sampling approach and recruitment, and generalizability. Disagreements were resolved by consensus.

Study Selection

A total of 2,460 references were identified, and after removal of 578 duplicates, 1,882 unique titles and abstracts were reviewed. One hundred thirteen titles met the inclusion criteria. After review of complete texts, 83 were excluded based on the above criteria (Appendix). This led to a final number of 36 studies included for analysis.

Fifteen articles were prospective, observational studies. The rest were retrospective analyses of patient-level data. Seven were large, multicenter studies with greater than 20 centers involved (including Project IMPACT); six such studies included medical and surgical patients. The remaining large, multicenter study examined a surgical trauma cohort.

Historical Trends

Examination of the three studies that looked at >20 regionally diverse ICUs revealed a trend over time toward increased limitation prior to death (Figure). Jayes looked at the number of DNR orders preceding death from 1979 to 1980 then compared that to a cohort from 1988 to 1990; Prendergast included withholding/withdrawing of LST prior to death from 1994 to 1995;and Quill used the IMPACT database to examine limitations prior to death from 2001 to 2009.^3-5

Effect of Unit Specialty

Twelve studies were mixed (surgical/medical or medical/neuro) ICUs, 11 were medical/cardiac units, five were neurologic units, and six were surgical/trauma units only. Two studies did not report unit specialty. Four studies that compared surgical and medical ICUs found that surgical patients were more likely to die with full intervention.^4-7 In all of these studies, medical patients were more likely to have limitations of LST preceding death. Quill, et al. further detailed that emergency surgery was more likely to be associated with limitation than elective surgery.⁵

Patient Factors

In 15 studies, older age was associated with an increased likelihood of limitations on LST.^3,5-18 In one study, advanced age was associated with early versus late withdrawal.¹⁹ Poor performance status and multiple medical comorbidities were also associated with limitations of LST. The largest population-based study by Quill et al. found that being fully dependent on others upon admission to the ICU was associated with an increased likelihood of limiting LST.⁵ Sise et al. found, in an analysis performed over 10 years in one trauma center, that increased age, comorbidities, and a fall as the reason for trauma admission were associated with limitation of LST.⁹ Salottolo et al. found that if the reason for trauma admission was a fall, there was an increased odds ratio of DNR status.¹⁸ Many studies found that having medical comorbidities prior to admission was associated with increased likelihood of limiting LST in both medical and surgical patients.^{3,7,9,13,15,18}

Five studies found a statistically significant difference between women and men in the likelihood of limitation of LST,^3,5,9,14,16 and another study reported that women who were trauma patients had an increased odds ratio of changing to DNR code status.¹⁸ Only one study found that males were associated with an increased likelihood of limiting aggressive treatment.²⁰

White race was associated with increased limitation of LST in nine studies.^{4,5,10,11,14-16,21,22} One study in neurocritical care patients found that both white and Hispanic races were correlated with a higher likelihood of limitations.²³ Muni et al. found that nonwhite patients had a statistically significantly lower likelihood of having comfort measures and DNR orders written prior to death, but discussion of prognosis was more likely to be documented in nonwhite patients.²¹

In summary, white race, female gender, and older age were the most frequent factors associated with a higher likelihood of limiting LST.

Factors Related to Critical Illness

There were several illness severity indicators that were associated with limitations. The Acute Physiology and Chronic Health Evaluation (APACHE) scores were the most common for medical patients and Glasgow Coma Scale (GCS) was the most common for patients with neurologic injury. Eight studies reported that a higher APACHE score was associated with an increased likelihood of limitations.^{3,7,10,15,17,20,22,24} Similar associations were found based on the Sepsis Related Organ Failure Assessment score in one study and a scoring system developed by the author in a second study.^25,26

Seven studies, consisting of three neurologic, two medical-surgical, and two trauma cohorts, reported that a lower GCS score increased the likelihood that the patient would have limited LST.^{5,10,11,13,14,18,22} Additionally, Geocadin and colleagues discussed the difficulty with neurological prognostication in clinical practice; they reported that the cortical evoked potential (CEP) was correlated with the time to withdrawLST if the CEP was malignant, and the time to withdraw LST was less in malignant than in benign CEP.²⁷

Mortality and End Effects of Limiting LST

Chen and colleagues used propensity scores to control for mortality differences between patients who had full interventions versus those with limitations and found that higher mortality correlated with the decision to withhold or withdraw LST.¹⁰ Weimer and colleagues used modeling to predict the probable outcome of patients who experienced an intracranial hemorrhage who had limitation of LST. Based on this model, nearly all the patients in their study would have died or had severe disability at 12 months despite having maximal therapy; they concluded that withdrawal of LST may not have been a self-fulfilling prophecy as others have proposed.²⁸ Mulder and colleagues reported that in a small cohort of out-of-hospital cardiac arrest survivors admitted to the hospital, over one-third had good neurological outcomes after coding after 72 hours.²⁹ The study highlighted the importance of timing in neurological prognostication.

Variation in Limitation Rates among Centers

In the 36 studies, we found an overall range of DNR orders from 5.4%⁷ to 82.0%.³⁰ For other limitations, the rates ranged from 6.3%¹³ to 80.4%.³¹ Hart reported a low rate of limitations (4.8%) at the time of ICU admission.¹⁶ Four large, multicenter studies drew attention to the large variability between critical care centers and the limitation of end-of-life care.^3-5,14 Jayes first described this phenomenon when examining the frequency of DNR orders from 1979 to 1980 and 1988 to 1990.³ This study found a range from 1.5% to 22%. Later, in another large, multicenter study, Prendergast et al. looked at 131 ICUs at 110 different institutions in 38 states that participated in postgraduate training and found variability in CPR attempts prior to death between 4% and 79%.⁴ In 2008, Nathens et al. reported significant variation in DNR rates across trauma centers; they found a higher incidence of DNR orders when there was an open ICU structure.¹⁴

Overall, there was wide variation in the proportion of deaths preceded by limitation of LST, ranging from 29.5% in one study of trauma patients⁸ to 92% in another study of trauma patients whose death occurred after 24 hours of care.⁹ In the largest study to date by Quill and colleagues utilizing the IMPACT database, they found large variability in the number of deaths preceded by full intervention based on differences in practice patterns of critical care centers.⁵

Bias

All studies indicated clear eligibility criteria for inclusion and described their sampling approach in adequate detail. All but one stated their method of participant recruitment, and the one remaining study was a secondary analysis and referenced the earlier manuscript.³⁰ No study provided a power or sample size calculation, and sample sizes varied widely. Generalizability was most affected by the fact that many studies were conducted in a single ICU.

This systematic review of LST in US ICUs found several patient and illness factors that were associated with limitation of LST. The association of preadmission functional status and comorbidities with limitation of LST suggest that prior health is a factor in decision making. Further, ICU severity of illness, as measured by several commonly used indices, was associated with limitations.

Although variations in study design precluded meta-analysis, examination of the largest studies suggests that limitations are becoming more frequent over time. Also, early studies generally addressed DNR status, while later studies included withdrawal or withholding of LST, most commonly artificial ventilation. These findings reflect the current consensus in US medicine that it is ethically acceptable to limit LSTs in cases when they no longer benefit the patient or the patient would no longer want them.^32,33

Some studies found variability by unit type, suggesting that decision making may differ among surgical, medical, and neurologic illness. Mayerand Kossoff concluded, in study of a cohort of neurocritical care ICU patients, that medical patients often have issues of physiologic futility and imminent death, whereas neurologic patients more often confront issues of quality of life. They also note that there is a difference in how patients with differing illnesses die; medical patients will have limitation of hemodialysis or vasopressors, whereas neurologic surrogate decision makers often confront decisions around terminal extubation.²³

Some patient-level factors, such as race or ethnicity, may point to cultural differences in preferences for LST at the end of life. Other authors have documented that African American patients are more likely to choose end-of-life care for themselves or their family members, which may be due to cultural or religious factors as well as to a history of unequal access to medical care.³⁴ Reasons for the finding that women are more likely to have limitations has not been as well described. Further research could explore whether this is due to differences in patient preferences by gender or to other factors.

Even when examining patient-level factors, illness severity and type of ICU, the wide variability in end-of-life care in critical care units across the country is still large. A worldwide review also found a high degree of variability, even within geographical regions.³⁵ More research is needed to understand the factors associated with this wide variability, as this seems to indicate that approaches to end-of-life care may vary based on the ICU as much as individual patient preferences or clinical factors.

These findings can inform clinicians about variables that are important in the decision-making process. Patient age and race are factors to consider in the likelihood of reaching a decision to set limitations. Information about patients’ health status prior to critical illness, as well as ICU illness severity, are also important considerations.

The limitations of this review include the wide variety of LSTs assessed, including code status change, ventilator withdrawal, removal of pressors, and cessation of renal replacement therapy. Also, there was variation in sample size and the number of included units. There was also significant heterogeneity in the outcomes addressed and the variety of methods used in the included studies. We attempted to address this with an analysis of the quality of the studies, but given the wide variability, we were unable to account for all of the differences; unfortunately, this is a standard issue within studies that utilize systematic reviews, as well as similar concepts such as meta-analyses.

In conclusion, the increase in the frequency of limitations of LST in critically ill patients and a change in the nature of limitations from DNR order to withdrawal or withholding of LST suggests a trend toward growing acceptance of limiting treatments in critical illness. The wide variation in withdrawal of care in US ICUs does not seem fully explained by patient variables including preferences, illness type, or changes over time. Factors such as poor prefunctional status, a higher number of comorbid conditions prior to critical illness, and the severity of critical illness are likely important for surrogates and clinicians to consider during goals of care discussions. Further research is needed to explore why patients may receive very different types of care at the end of life depending the institution and ICU in which they receive their care.

Disclosures

The authors have no conflicts of interest to disclose. This work was performed at the Indiana University School of Medicine.

Funding

Financial support for Dr. Torke was provided by a Midcareer Investigator Award in Patient Oriented Research from the National Institute on Aging (K24AG053794). Dr. McPherson was supported by the Indiana University Department of Medicine.

Access to life-sustaining treatment (LST) became a mainstay in hospitals across the United States in the 1970s. This has raised complex ethical questions surrounding the use of these therapies, particularly in the face of a poor prognosis or significant morbidity. The Society for Critical Care Medicine formed a consensus panel in 1989 to construct ethical guidelines regarding the initiation, continuation, and withdrawal of intensive care.¹ These guidelines emphasized that withdrawing and withholding are not only permissible but may be necessary to preserve the balance between quantity and quality of life. Nevertheless, an increasing number of Americans are dying after aggressive LST in the hospital, and greater than one in five deaths occur after admission to the ICU.² Understanding the factors associated with decisions to withhold or withdraw LST are important to policy makers, ethicists, and healthcare leaders because they affect resources used at the end of life and the need for palliative care and hospice in the ICU setting.

Several studies have characterized the patient characteristics, incidence, and variability associated with limitation of LST in various populations of critically ill patients in the US. We are unaware of another systematic review of the literature that has examined data from these studies in order to understand the process and outcomes of LST limitations. We defined limitations of LST as decisions to withdraw or withhold cardiopulmonary resuscitation through Do Not Resuscitate (DNR) orders, mechanical ventilation, renal replacement therapy, intravenous blood pressure support, or artificial nutrition (enteric or intravenous).

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement was used for reporting. A comprehensive literature search was performed by a medical librarian (TWE) in Ovid MEDLINE, PubMed, Embase, the full Cochrane Library, CINAHL, PsycINFO, the Philosopher’s Index, Scopus, Web of Science, and Google Scholar. PubMed was limited to non-MEDLINE records in order to complement the Ovid results. The Georgetown Bioethics Research Library at the Kennedy Institute (https://bioethics.georgetown.edu/) was also searched for any unpublished literature. Initial searches were conducted in December 2014, and an update was performed in April 2017. All databases were searched from inception, and bibliographies of relevant studies were reviewed for additional references (Appendix 1).

Database-specific subject headings and keyword variants for each of the five main concepts—intensive care, end-of-life, decision making, limitation of treatment, and death—were identified and combined. Results were limited to the adult population and to the English language.

Two authors independently reviewed article titles and abstracts (KM, AMT). The full text of potentially eligible studies was then reviewed for inclusion. All disputes were discussed and resolved by consensus. The criteria for inclusion were reporting of patient-level data, critical care patients only (or reported separately from other unit types), US setting, and reporting of data on limitations of LST. The exclusion criteria were studies published only as research abstracts, surveys of physicians or families, organ donors, studies of brain death, surveys, patients less than 18 years old, and long-term intensive care settings (ie, long-term acute care hospitals, long-term respiratory units). Also excluded were studies in which an intervention was performed; as a result, all included studies were observational. Research abstracts were excluded because they lacked sufficient detail from which to abstract study quality or results. Studies of organ donation, brain death, and pediatrics were excluded due to differences in the decision-making context that would make it difficult to draw conclusions about adult ICU care. Studies which included an intervention were excluded to avoid affecting the rate of limitation of LST as a result of the intervention, since our goal was to quantify the number of limitations of LST in usual medical practice.

For each article, we abstracted the number of patients who experienced a limitation of LST out of the total population and factors associated with the limitation. If a multivariable analysis was performed, we reported only variables that remained significant in this analysis. We also reported the number of patients who died, and of those, the number of decedents who underwent a limitation of LST before death. In some cases, this proportion was not reported in the manuscript but could be calculated based on the data presented. This number was calculated based on the number of deaths that were preceded by a limitation in life-sustaining care divided by the total number of deaths. Patients with brain death were not counted as having had a “limitation” if support was withdrawn after the declaration of brain death. We were unable to conduct a meta-analysis of the findings because of the wide variation in study populations and criteria used to define limitations of care.

To assess risk of bias in individual studies, the two raters independently made a yes/no determination regarding several quality metrics established at the outset of the review: clarity of the eligibility criteria for participant inclusion, whether a power or sample size calculation was done, adequacy of the description of the sampling approach and recruitment, and generalizability. Disagreements were resolved by consensus.

Study Selection

A total of 2,460 references were identified, and after removal of 578 duplicates, 1,882 unique titles and abstracts were reviewed. One hundred thirteen titles met the inclusion criteria. After review of complete texts, 83 were excluded based on the above criteria (Appendix). This led to a final number of 36 studies included for analysis.

Fifteen articles were prospective, observational studies. The rest were retrospective analyses of patient-level data. Seven were large, multicenter studies with greater than 20 centers involved (including Project IMPACT); six such studies included medical and surgical patients. The remaining large, multicenter study examined a surgical trauma cohort.

Historical Trends

Examination of the three studies that looked at >20 regionally diverse ICUs revealed a trend over time toward increased limitation prior to death (Figure). Jayes looked at the number of DNR orders preceding death from 1979 to 1980 then compared that to a cohort from 1988 to 1990; Prendergast included withholding/withdrawing of LST prior to death from 1994 to 1995;and Quill used the IMPACT database to examine limitations prior to death from 2001 to 2009.^3-5

Effect of Unit Specialty

Twelve studies were mixed (surgical/medical or medical/neuro) ICUs, 11 were medical/cardiac units, five were neurologic units, and six were surgical/trauma units only. Two studies did not report unit specialty. Four studies that compared surgical and medical ICUs found that surgical patients were more likely to die with full intervention.^4-7 In all of these studies, medical patients were more likely to have limitations of LST preceding death. Quill, et al. further detailed that emergency surgery was more likely to be associated with limitation than elective surgery.⁵

Patient Factors

In 15 studies, older age was associated with an increased likelihood of limitations on LST.^3,5-18 In one study, advanced age was associated with early versus late withdrawal.¹⁹ Poor performance status and multiple medical comorbidities were also associated with limitations of LST. The largest population-based study by Quill et al. found that being fully dependent on others upon admission to the ICU was associated with an increased likelihood of limiting LST.⁵ Sise et al. found, in an analysis performed over 10 years in one trauma center, that increased age, comorbidities, and a fall as the reason for trauma admission were associated with limitation of LST.⁹ Salottolo et al. found that if the reason for trauma admission was a fall, there was an increased odds ratio of DNR status.¹⁸ Many studies found that having medical comorbidities prior to admission was associated with increased likelihood of limiting LST in both medical and surgical patients.^{3,7,9,13,15,18}

Five studies found a statistically significant difference between women and men in the likelihood of limitation of LST,^3,5,9,14,16 and another study reported that women who were trauma patients had an increased odds ratio of changing to DNR code status.¹⁸ Only one study found that males were associated with an increased likelihood of limiting aggressive treatment.²⁰

White race was associated with increased limitation of LST in nine studies.^{4,5,10,11,14-16,21,22} One study in neurocritical care patients found that both white and Hispanic races were correlated with a higher likelihood of limitations.²³ Muni et al. found that nonwhite patients had a statistically significantly lower likelihood of having comfort measures and DNR orders written prior to death, but discussion of prognosis was more likely to be documented in nonwhite patients.²¹

In summary, white race, female gender, and older age were the most frequent factors associated with a higher likelihood of limiting LST.

Factors Related to Critical Illness

There were several illness severity indicators that were associated with limitations. The Acute Physiology and Chronic Health Evaluation (APACHE) scores were the most common for medical patients and Glasgow Coma Scale (GCS) was the most common for patients with neurologic injury. Eight studies reported that a higher APACHE score was associated with an increased likelihood of limitations.^{3,7,10,15,17,20,22,24} Similar associations were found based on the Sepsis Related Organ Failure Assessment score in one study and a scoring system developed by the author in a second study.^25,26

Seven studies, consisting of three neurologic, two medical-surgical, and two trauma cohorts, reported that a lower GCS score increased the likelihood that the patient would have limited LST.^{5,10,11,13,14,18,22} Additionally, Geocadin and colleagues discussed the difficulty with neurological prognostication in clinical practice; they reported that the cortical evoked potential (CEP) was correlated with the time to withdrawLST if the CEP was malignant, and the time to withdraw LST was less in malignant than in benign CEP.²⁷

Mortality and End Effects of Limiting LST

Chen and colleagues used propensity scores to control for mortality differences between patients who had full interventions versus those with limitations and found that higher mortality correlated with the decision to withhold or withdraw LST.¹⁰ Weimer and colleagues used modeling to predict the probable outcome of patients who experienced an intracranial hemorrhage who had limitation of LST. Based on this model, nearly all the patients in their study would have died or had severe disability at 12 months despite having maximal therapy; they concluded that withdrawal of LST may not have been a self-fulfilling prophecy as others have proposed.²⁸ Mulder and colleagues reported that in a small cohort of out-of-hospital cardiac arrest survivors admitted to the hospital, over one-third had good neurological outcomes after coding after 72 hours.²⁹ The study highlighted the importance of timing in neurological prognostication.

Variation in Limitation Rates among Centers

In the 36 studies, we found an overall range of DNR orders from 5.4%⁷ to 82.0%.³⁰ For other limitations, the rates ranged from 6.3%¹³ to 80.4%.³¹ Hart reported a low rate of limitations (4.8%) at the time of ICU admission.¹⁶ Four large, multicenter studies drew attention to the large variability between critical care centers and the limitation of end-of-life care.^3-5,14 Jayes first described this phenomenon when examining the frequency of DNR orders from 1979 to 1980 and 1988 to 1990.³ This study found a range from 1.5% to 22%. Later, in another large, multicenter study, Prendergast et al. looked at 131 ICUs at 110 different institutions in 38 states that participated in postgraduate training and found variability in CPR attempts prior to death between 4% and 79%.⁴ In 2008, Nathens et al. reported significant variation in DNR rates across trauma centers; they found a higher incidence of DNR orders when there was an open ICU structure.¹⁴

Overall, there was wide variation in the proportion of deaths preceded by limitation of LST, ranging from 29.5% in one study of trauma patients⁸ to 92% in another study of trauma patients whose death occurred after 24 hours of care.⁹ In the largest study to date by Quill and colleagues utilizing the IMPACT database, they found large variability in the number of deaths preceded by full intervention based on differences in practice patterns of critical care centers.⁵

Bias

All studies indicated clear eligibility criteria for inclusion and described their sampling approach in adequate detail. All but one stated their method of participant recruitment, and the one remaining study was a secondary analysis and referenced the earlier manuscript.³⁰ No study provided a power or sample size calculation, and sample sizes varied widely. Generalizability was most affected by the fact that many studies were conducted in a single ICU.

This systematic review of LST in US ICUs found several patient and illness factors that were associated with limitation of LST. The association of preadmission functional status and comorbidities with limitation of LST suggest that prior health is a factor in decision making. Further, ICU severity of illness, as measured by several commonly used indices, was associated with limitations.

Although variations in study design precluded meta-analysis, examination of the largest studies suggests that limitations are becoming more frequent over time. Also, early studies generally addressed DNR status, while later studies included withdrawal or withholding of LST, most commonly artificial ventilation. These findings reflect the current consensus in US medicine that it is ethically acceptable to limit LSTs in cases when they no longer benefit the patient or the patient would no longer want them.^32,33

Some studies found variability by unit type, suggesting that decision making may differ among surgical, medical, and neurologic illness. Mayerand Kossoff concluded, in study of a cohort of neurocritical care ICU patients, that medical patients often have issues of physiologic futility and imminent death, whereas neurologic patients more often confront issues of quality of life. They also note that there is a difference in how patients with differing illnesses die; medical patients will have limitation of hemodialysis or vasopressors, whereas neurologic surrogate decision makers often confront decisions around terminal extubation.²³

Some patient-level factors, such as race or ethnicity, may point to cultural differences in preferences for LST at the end of life. Other authors have documented that African American patients are more likely to choose end-of-life care for themselves or their family members, which may be due to cultural or religious factors as well as to a history of unequal access to medical care.³⁴ Reasons for the finding that women are more likely to have limitations has not been as well described. Further research could explore whether this is due to differences in patient preferences by gender or to other factors.

Even when examining patient-level factors, illness severity and type of ICU, the wide variability in end-of-life care in critical care units across the country is still large. A worldwide review also found a high degree of variability, even within geographical regions.³⁵ More research is needed to understand the factors associated with this wide variability, as this seems to indicate that approaches to end-of-life care may vary based on the ICU as much as individual patient preferences or clinical factors.

These findings can inform clinicians about variables that are important in the decision-making process. Patient age and race are factors to consider in the likelihood of reaching a decision to set limitations. Information about patients’ health status prior to critical illness, as well as ICU illness severity, are also important considerations.

The limitations of this review include the wide variety of LSTs assessed, including code status change, ventilator withdrawal, removal of pressors, and cessation of renal replacement therapy. Also, there was variation in sample size and the number of included units. There was also significant heterogeneity in the outcomes addressed and the variety of methods used in the included studies. We attempted to address this with an analysis of the quality of the studies, but given the wide variability, we were unable to account for all of the differences; unfortunately, this is a standard issue within studies that utilize systematic reviews, as well as similar concepts such as meta-analyses.

In conclusion, the increase in the frequency of limitations of LST in critically ill patients and a change in the nature of limitations from DNR order to withdrawal or withholding of LST suggests a trend toward growing acceptance of limiting treatments in critical illness. The wide variation in withdrawal of care in US ICUs does not seem fully explained by patient variables including preferences, illness type, or changes over time. Factors such as poor prefunctional status, a higher number of comorbid conditions prior to critical illness, and the severity of critical illness are likely important for surrogates and clinicians to consider during goals of care discussions. Further research is needed to explore why patients may receive very different types of care at the end of life depending the institution and ICU in which they receive their care.

Disclosures

The authors have no conflicts of interest to disclose. This work was performed at the Indiana University School of Medicine.

Funding

Financial support for Dr. Torke was provided by a Midcareer Investigator Award in Patient Oriented Research from the National Institute on Aging (K24AG053794). Dr. McPherson was supported by the Indiana University Department of Medicine.