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ITL: Physician Reviews of HM-Relevant Research
Clinical question: With the current use of warfarin for stroke prophylaxis in patients with nonvalvular atrial fibrillation, what do the most recent data show with regard to time spent in the therapeutic window, stroke risk, and bleeding risk?
Background: Historically, warfarin has been shown to decrease stroke risk in nonvalvular atrial fibrillation by 62% compared with placebo, balanced by a significant risk of bleeding. Despite the availability of multiple new antithrombotic agents, warfarin will likely continue to be widely used given its lower cost. As a result, physicians need an accurate estimate of warfarin’s efficacy and safety as currently used in practice.
Study design: Meta-analysis of randomized controlled trials (RCTs).
Setting: RCTs comparing warfarin to an alternative antithrombotic agent from 2001 to 2011.
Synopsis: Eight RCTs of nonvalvular atrial fibrillation were included, yielding data on 32,053 patients with a mean age range of 70 to 82 years and widely variable CHADS2 scores. The time spent at a therapeutic INR was found to be improved when compared to historical rates, ranging from 55% to 68%. The rate of stroke or non-central-nervous-system embolism ranged from 1.2% to 2.3% per year, with a pooled event rate of 1.66% per year, compared with 2.09% per year in earlier trials.
Major bleeding was defined differently across studies, with a reported incidence of 1.4% to 3.4% per year, a pooled event rate of intracranial hemorrhage of 0.61%, and a cumulative adverse event rate of 3.0% to 7.64%. Stroke rates were highest in patients older than 75 years, women, those with a history of transient ischemic attack or stroke, those new to warfarin, and those with higher CHADS2 scores.
Bottom line: Warfarin as currently used is associated with an annual rate of stroke or systemic embolism of 1.66% and an annual rate of major bleeding ranging from 1.4% to 3.4%.
Citation: Agarwal S, Hachamovitch R, Menon V. Current trial-associated outcomes with warfarin in prevention of stroke in patients with nonvalvular atrial fibrillation: a meta-analysis. Arch Intern Med. 2012;172:623-631.
Clinical question: With the current use of warfarin for stroke prophylaxis in patients with nonvalvular atrial fibrillation, what do the most recent data show with regard to time spent in the therapeutic window, stroke risk, and bleeding risk?
Background: Historically, warfarin has been shown to decrease stroke risk in nonvalvular atrial fibrillation by 62% compared with placebo, balanced by a significant risk of bleeding. Despite the availability of multiple new antithrombotic agents, warfarin will likely continue to be widely used given its lower cost. As a result, physicians need an accurate estimate of warfarin’s efficacy and safety as currently used in practice.
Study design: Meta-analysis of randomized controlled trials (RCTs).
Setting: RCTs comparing warfarin to an alternative antithrombotic agent from 2001 to 2011.
Synopsis: Eight RCTs of nonvalvular atrial fibrillation were included, yielding data on 32,053 patients with a mean age range of 70 to 82 years and widely variable CHADS2 scores. The time spent at a therapeutic INR was found to be improved when compared to historical rates, ranging from 55% to 68%. The rate of stroke or non-central-nervous-system embolism ranged from 1.2% to 2.3% per year, with a pooled event rate of 1.66% per year, compared with 2.09% per year in earlier trials.
Major bleeding was defined differently across studies, with a reported incidence of 1.4% to 3.4% per year, a pooled event rate of intracranial hemorrhage of 0.61%, and a cumulative adverse event rate of 3.0% to 7.64%. Stroke rates were highest in patients older than 75 years, women, those with a history of transient ischemic attack or stroke, those new to warfarin, and those with higher CHADS2 scores.
Bottom line: Warfarin as currently used is associated with an annual rate of stroke or systemic embolism of 1.66% and an annual rate of major bleeding ranging from 1.4% to 3.4%.
Citation: Agarwal S, Hachamovitch R, Menon V. Current trial-associated outcomes with warfarin in prevention of stroke in patients with nonvalvular atrial fibrillation: a meta-analysis. Arch Intern Med. 2012;172:623-631.
Clinical question: With the current use of warfarin for stroke prophylaxis in patients with nonvalvular atrial fibrillation, what do the most recent data show with regard to time spent in the therapeutic window, stroke risk, and bleeding risk?
Background: Historically, warfarin has been shown to decrease stroke risk in nonvalvular atrial fibrillation by 62% compared with placebo, balanced by a significant risk of bleeding. Despite the availability of multiple new antithrombotic agents, warfarin will likely continue to be widely used given its lower cost. As a result, physicians need an accurate estimate of warfarin’s efficacy and safety as currently used in practice.
Study design: Meta-analysis of randomized controlled trials (RCTs).
Setting: RCTs comparing warfarin to an alternative antithrombotic agent from 2001 to 2011.
Synopsis: Eight RCTs of nonvalvular atrial fibrillation were included, yielding data on 32,053 patients with a mean age range of 70 to 82 years and widely variable CHADS2 scores. The time spent at a therapeutic INR was found to be improved when compared to historical rates, ranging from 55% to 68%. The rate of stroke or non-central-nervous-system embolism ranged from 1.2% to 2.3% per year, with a pooled event rate of 1.66% per year, compared with 2.09% per year in earlier trials.
Major bleeding was defined differently across studies, with a reported incidence of 1.4% to 3.4% per year, a pooled event rate of intracranial hemorrhage of 0.61%, and a cumulative adverse event rate of 3.0% to 7.64%. Stroke rates were highest in patients older than 75 years, women, those with a history of transient ischemic attack or stroke, those new to warfarin, and those with higher CHADS2 scores.
Bottom line: Warfarin as currently used is associated with an annual rate of stroke or systemic embolism of 1.66% and an annual rate of major bleeding ranging from 1.4% to 3.4%.
Citation: Agarwal S, Hachamovitch R, Menon V. Current trial-associated outcomes with warfarin in prevention of stroke in patients with nonvalvular atrial fibrillation: a meta-analysis. Arch Intern Med. 2012;172:623-631.
Society of Hospital Medicine (SHM) Backs Anti-SGR Legislation
SHM has joined the growing number of professional medical societies calling for the repeal of the sustainable growth rate (SGR) formula, and they want you to join the fight.
In the past few weeks, SHM, the Medical Group Management Association (MGMA), and the American Medical Association (AMA) have decried the Medicare payment system and called for its end. All were responding to a U.S. House of Representatives request for comments on how to rebuild Medicare reimbursement for physicians.
Unless Congress repeals the formula or approves the latest in a series of extensions, Medicare physician payments will be reduced by 30.9% on Jan. 1, 2013. And while most observers doubt the deep cuts will ever be implemented, the specter of them is cause for concern.
"It's hugely disruptive to the planning process for any business, no matter what size," says Ron Greeno, MD, MHM, Cogent HMG's chief medical officer and the chair of SHM's Public Policy Committee.
SHM has thrown its support behind one potential solution, a bipartisan bill drafted by U.S. Reps. Allyson Schwartz (D-Pa.) and Joe Heck (R-Nev.). If passed, it would eliminate the SGR formula and push for new payment models.
Dr. Greeno, who is "hopeful but not optimistic" that the bill can pass, says hospitalists need to step up and support those who are supporting hospitalists. To that end, the society is urging members to contact their local representatives to support the legislation.
"You have to be vocal, you have to be consistently vocal," he says. "We have to be diligent, continue to advance this as an issue, continue to support the people that are seeking reasonable solutions. Despite everything that gets put in our way, we have to continue to be vocal and continue to support this. One of these times, it’s going to work."
For more information, check out SHM's Advocacy portal. Use this directory to find and email your elected officials.
SHM has joined the growing number of professional medical societies calling for the repeal of the sustainable growth rate (SGR) formula, and they want you to join the fight.
In the past few weeks, SHM, the Medical Group Management Association (MGMA), and the American Medical Association (AMA) have decried the Medicare payment system and called for its end. All were responding to a U.S. House of Representatives request for comments on how to rebuild Medicare reimbursement for physicians.
Unless Congress repeals the formula or approves the latest in a series of extensions, Medicare physician payments will be reduced by 30.9% on Jan. 1, 2013. And while most observers doubt the deep cuts will ever be implemented, the specter of them is cause for concern.
"It's hugely disruptive to the planning process for any business, no matter what size," says Ron Greeno, MD, MHM, Cogent HMG's chief medical officer and the chair of SHM's Public Policy Committee.
SHM has thrown its support behind one potential solution, a bipartisan bill drafted by U.S. Reps. Allyson Schwartz (D-Pa.) and Joe Heck (R-Nev.). If passed, it would eliminate the SGR formula and push for new payment models.
Dr. Greeno, who is "hopeful but not optimistic" that the bill can pass, says hospitalists need to step up and support those who are supporting hospitalists. To that end, the society is urging members to contact their local representatives to support the legislation.
"You have to be vocal, you have to be consistently vocal," he says. "We have to be diligent, continue to advance this as an issue, continue to support the people that are seeking reasonable solutions. Despite everything that gets put in our way, we have to continue to be vocal and continue to support this. One of these times, it’s going to work."
For more information, check out SHM's Advocacy portal. Use this directory to find and email your elected officials.
SHM has joined the growing number of professional medical societies calling for the repeal of the sustainable growth rate (SGR) formula, and they want you to join the fight.
In the past few weeks, SHM, the Medical Group Management Association (MGMA), and the American Medical Association (AMA) have decried the Medicare payment system and called for its end. All were responding to a U.S. House of Representatives request for comments on how to rebuild Medicare reimbursement for physicians.
Unless Congress repeals the formula or approves the latest in a series of extensions, Medicare physician payments will be reduced by 30.9% on Jan. 1, 2013. And while most observers doubt the deep cuts will ever be implemented, the specter of them is cause for concern.
"It's hugely disruptive to the planning process for any business, no matter what size," says Ron Greeno, MD, MHM, Cogent HMG's chief medical officer and the chair of SHM's Public Policy Committee.
SHM has thrown its support behind one potential solution, a bipartisan bill drafted by U.S. Reps. Allyson Schwartz (D-Pa.) and Joe Heck (R-Nev.). If passed, it would eliminate the SGR formula and push for new payment models.
Dr. Greeno, who is "hopeful but not optimistic" that the bill can pass, says hospitalists need to step up and support those who are supporting hospitalists. To that end, the society is urging members to contact their local representatives to support the legislation.
"You have to be vocal, you have to be consistently vocal," he says. "We have to be diligent, continue to advance this as an issue, continue to support the people that are seeking reasonable solutions. Despite everything that gets put in our way, we have to continue to be vocal and continue to support this. One of these times, it’s going to work."
For more information, check out SHM's Advocacy portal. Use this directory to find and email your elected officials.
Minnesota Readmissions Initiative Breaks Down Silos
In less than four months CMS' Hospital Readmissions Reduction Program will start penalizing hospitals with higher-than-projected readmissions rates. But as the Oct. 1 program launch looms for many hospitals, one readmission initiative is making significant progress to reduce unnecessary hospitalizations.
The Minnesota Reducing Avoidable Readmissions Effectively (RARE) campaign set a goal of preventing 4,000 avoidable readmissions among commercial health plan patients by the end of 2012, a 20% reduction from 2009 baseline data. The campaign was launched last September by three operating partners: the Minnesota Hospital Association (MHA); the Institute for Clinical Systems Improvement (ICSI), a nonprofit collaborative of 55 medical groups and hospitals; and Stratis Health, the state's QI organization. RARE's partners include more than 80 hospitals, which according to the MHA already have prevented 1,011 avoidable readmissions in 2011 and expect to surpass the target goal by the end of 2012.
"We had a specific process for each partner to follow, including a commitment by leadership to support and provide needed resources and development of a guidance team and a working team at each site," says Kathy Cummings, RN, MA, project manager at ICSI.
Each participating hospital was invited to join one of three quality collaboratives: one based on Project RED; one based on Dr. Eric Coleman's Care Transitions model; and one focused on safe transitions-of-care communication developed by the MHA.
"Everyone is rallying around the goals. They are all talking at the table, and starting to break down the silos between hospital, nursing home, clinic, and the chasms in between," says hospitalist Howard Epstein, MD, FHM, ICSI's chief health systems officer. "One of the key attributes of hospitalists is collaboration and systems improvement within their hospitals. Working with RARE is broadening their perspectives on the workings of the healthcare system as a whole."
In less than four months CMS' Hospital Readmissions Reduction Program will start penalizing hospitals with higher-than-projected readmissions rates. But as the Oct. 1 program launch looms for many hospitals, one readmission initiative is making significant progress to reduce unnecessary hospitalizations.
The Minnesota Reducing Avoidable Readmissions Effectively (RARE) campaign set a goal of preventing 4,000 avoidable readmissions among commercial health plan patients by the end of 2012, a 20% reduction from 2009 baseline data. The campaign was launched last September by three operating partners: the Minnesota Hospital Association (MHA); the Institute for Clinical Systems Improvement (ICSI), a nonprofit collaborative of 55 medical groups and hospitals; and Stratis Health, the state's QI organization. RARE's partners include more than 80 hospitals, which according to the MHA already have prevented 1,011 avoidable readmissions in 2011 and expect to surpass the target goal by the end of 2012.
"We had a specific process for each partner to follow, including a commitment by leadership to support and provide needed resources and development of a guidance team and a working team at each site," says Kathy Cummings, RN, MA, project manager at ICSI.
Each participating hospital was invited to join one of three quality collaboratives: one based on Project RED; one based on Dr. Eric Coleman's Care Transitions model; and one focused on safe transitions-of-care communication developed by the MHA.
"Everyone is rallying around the goals. They are all talking at the table, and starting to break down the silos between hospital, nursing home, clinic, and the chasms in between," says hospitalist Howard Epstein, MD, FHM, ICSI's chief health systems officer. "One of the key attributes of hospitalists is collaboration and systems improvement within their hospitals. Working with RARE is broadening their perspectives on the workings of the healthcare system as a whole."
In less than four months CMS' Hospital Readmissions Reduction Program will start penalizing hospitals with higher-than-projected readmissions rates. But as the Oct. 1 program launch looms for many hospitals, one readmission initiative is making significant progress to reduce unnecessary hospitalizations.
The Minnesota Reducing Avoidable Readmissions Effectively (RARE) campaign set a goal of preventing 4,000 avoidable readmissions among commercial health plan patients by the end of 2012, a 20% reduction from 2009 baseline data. The campaign was launched last September by three operating partners: the Minnesota Hospital Association (MHA); the Institute for Clinical Systems Improvement (ICSI), a nonprofit collaborative of 55 medical groups and hospitals; and Stratis Health, the state's QI organization. RARE's partners include more than 80 hospitals, which according to the MHA already have prevented 1,011 avoidable readmissions in 2011 and expect to surpass the target goal by the end of 2012.
"We had a specific process for each partner to follow, including a commitment by leadership to support and provide needed resources and development of a guidance team and a working team at each site," says Kathy Cummings, RN, MA, project manager at ICSI.
Each participating hospital was invited to join one of three quality collaboratives: one based on Project RED; one based on Dr. Eric Coleman's Care Transitions model; and one focused on safe transitions-of-care communication developed by the MHA.
"Everyone is rallying around the goals. They are all talking at the table, and starting to break down the silos between hospital, nursing home, clinic, and the chasms in between," says hospitalist Howard Epstein, MD, FHM, ICSI's chief health systems officer. "One of the key attributes of hospitalists is collaboration and systems improvement within their hospitals. Working with RARE is broadening their perspectives on the workings of the healthcare system as a whole."
Early Returns: ACOs Improve Management of Patient Populations, Offer Short-Term Savings
Several years ago, Presbyterian Medical Group in Albuquerque, N.M., decided to integrate three elements of its healthcare system: its health plan, the employed medical group, and the hospital delivery system. Knitting those parts into a cohesive whole helped the group realize that “lowering the cost of care by improving efficiency, by improving coordination, and by enhancing collaboration between payor and physicians made a lot of sense,” executive medical director David Arredondo, MD, says.
When the accountable care organization (ACO) concept came along, Dr. Arredondo says, “it really was just a natural extension of what we were doing.”
By year’s end, though, the final rules had assuaged many of the biggest concerns, and the April 10 announcement of 27 participants for the program’s first round—more than half of which are physician-led organizations—has rekindled much of the enthusiasm. According to CMS officials, the agency is reviewing more than 150 applications for the program’s next round, which will begin in July.
Keys to Success
In December, CMS selected 32 organizations to participate in an even more ambitious initiative called the Pioneer ACO Model. That separate but related experiment in shared accountability launched Jan. 1, and it may be months before enrolled organizations can say whether the rewards outweigh the risks. Interviews with Presbyterian’s Dr. Arredondo and two other Pioneer participants about why they took the plunge, however, have highlighted some potential keys to success.
—David Arredondo, MD, executive medical director, Presbyterian Medical Group, Albuquerque, N.M.
All three agree that the ACO model offers a better match for their long-term, patient-centered goals and that the fee-for-service model is gradually becoming a thing of the past.
“In some ways, it was actually kind of a relief that the system was going this way because we, probably like many systems, were beginning to be caught between the budgeted model and a fee-for-service model,” Dr. Arredondo says. “When you’re heavily one way or heavily the other way, then it makes things a little easier to manage and understand. When you’re right in the middle, it becomes a little uncomfortable.”
Penny Wheeler, MD, chief clinical officer for Minneapolis-based Allina Hospitals & Clinics, says organizations in that precarious position need to carefully examine their capabilities and consider how best to pace their transition. Otherwise, they might prematurely give up too much revenue that could be used to reinvest in care improvements.
“We can tolerate it if we shoot ourselves in one foot, but we can’t tolerate it if we shoot ourselves in both feet, in this new world,” Dr. Wheeler says.
If caution is warranted, she says, the ACO model still aligns well with a strategy of building toward outcome-based healthcare. Despite the likelihood of “lumps and bumps and warts along the way,” Dr. Wheeler says, “we really wanted to be part of the shaping of that outcome-based delivery, and receive market rewards for what we were creating for our community.”
Austin, Texas-based Seton Health Alliance, a third Pioneer participant, is a collaborative effort between a hospital delivery system known as Seton Health Care Family and a multispecialty physician group called Austin Regional Clinic. Greg Sheff, MD, president and chief medical officer of the ACO, says the partnering organizations were separately moving toward more population health initiatives and more proactive, coordinated, and accountable care.
“The Pioneer ACO, for us, really provided an opportunity to light the fire and motivate the organizations to put the entity together and start doing the work,” he says, adding PCPs and hospitalists will be critical to his organization’s ongoing integration efforts.
—Greg Sheff, MD, president, chief medical officer, Seton Health Alliance, Austin, Texas
“The areas where there are opportunities to be more efficient are largely under the care of the hospitalists,” he says, citing in-house utilization as well as care transitions, comprehensive post-acute placement, and readmission prevention efforts. To support those providers, Pioneer participants say well-designed electronic medical records are paramount, while separate efforts, such as patient-centered medical homes and unit-based rounding, might offer timely assists. (Click here to listen to more of The Hospitalist’s interview with Dr. Sheff.)
No one’s expecting the next few years to be seamless, but Dr. Sheff views his newly formed ACO as a long-term endeavor in which success isn’t necessarily defined by whether the group achieves shared cost savings.
“We define success by whether we are able to move our delivery system to a place where we’ll be much more adept at going forward, continuing to manage populations,” he says. “We really see this as a strategic organizational decision more than, ‘Boy, that contract looks like something that we can leverage in the short term.’”
Bryn Nelson is a freelance medical writer in Seattle.
Several years ago, Presbyterian Medical Group in Albuquerque, N.M., decided to integrate three elements of its healthcare system: its health plan, the employed medical group, and the hospital delivery system. Knitting those parts into a cohesive whole helped the group realize that “lowering the cost of care by improving efficiency, by improving coordination, and by enhancing collaboration between payor and physicians made a lot of sense,” executive medical director David Arredondo, MD, says.
When the accountable care organization (ACO) concept came along, Dr. Arredondo says, “it really was just a natural extension of what we were doing.”
By year’s end, though, the final rules had assuaged many of the biggest concerns, and the April 10 announcement of 27 participants for the program’s first round—more than half of which are physician-led organizations—has rekindled much of the enthusiasm. According to CMS officials, the agency is reviewing more than 150 applications for the program’s next round, which will begin in July.
Keys to Success
In December, CMS selected 32 organizations to participate in an even more ambitious initiative called the Pioneer ACO Model. That separate but related experiment in shared accountability launched Jan. 1, and it may be months before enrolled organizations can say whether the rewards outweigh the risks. Interviews with Presbyterian’s Dr. Arredondo and two other Pioneer participants about why they took the plunge, however, have highlighted some potential keys to success.
—David Arredondo, MD, executive medical director, Presbyterian Medical Group, Albuquerque, N.M.
All three agree that the ACO model offers a better match for their long-term, patient-centered goals and that the fee-for-service model is gradually becoming a thing of the past.
“In some ways, it was actually kind of a relief that the system was going this way because we, probably like many systems, were beginning to be caught between the budgeted model and a fee-for-service model,” Dr. Arredondo says. “When you’re heavily one way or heavily the other way, then it makes things a little easier to manage and understand. When you’re right in the middle, it becomes a little uncomfortable.”
Penny Wheeler, MD, chief clinical officer for Minneapolis-based Allina Hospitals & Clinics, says organizations in that precarious position need to carefully examine their capabilities and consider how best to pace their transition. Otherwise, they might prematurely give up too much revenue that could be used to reinvest in care improvements.
“We can tolerate it if we shoot ourselves in one foot, but we can’t tolerate it if we shoot ourselves in both feet, in this new world,” Dr. Wheeler says.
If caution is warranted, she says, the ACO model still aligns well with a strategy of building toward outcome-based healthcare. Despite the likelihood of “lumps and bumps and warts along the way,” Dr. Wheeler says, “we really wanted to be part of the shaping of that outcome-based delivery, and receive market rewards for what we were creating for our community.”
Austin, Texas-based Seton Health Alliance, a third Pioneer participant, is a collaborative effort between a hospital delivery system known as Seton Health Care Family and a multispecialty physician group called Austin Regional Clinic. Greg Sheff, MD, president and chief medical officer of the ACO, says the partnering organizations were separately moving toward more population health initiatives and more proactive, coordinated, and accountable care.
“The Pioneer ACO, for us, really provided an opportunity to light the fire and motivate the organizations to put the entity together and start doing the work,” he says, adding PCPs and hospitalists will be critical to his organization’s ongoing integration efforts.
—Greg Sheff, MD, president, chief medical officer, Seton Health Alliance, Austin, Texas
“The areas where there are opportunities to be more efficient are largely under the care of the hospitalists,” he says, citing in-house utilization as well as care transitions, comprehensive post-acute placement, and readmission prevention efforts. To support those providers, Pioneer participants say well-designed electronic medical records are paramount, while separate efforts, such as patient-centered medical homes and unit-based rounding, might offer timely assists. (Click here to listen to more of The Hospitalist’s interview with Dr. Sheff.)
No one’s expecting the next few years to be seamless, but Dr. Sheff views his newly formed ACO as a long-term endeavor in which success isn’t necessarily defined by whether the group achieves shared cost savings.
“We define success by whether we are able to move our delivery system to a place where we’ll be much more adept at going forward, continuing to manage populations,” he says. “We really see this as a strategic organizational decision more than, ‘Boy, that contract looks like something that we can leverage in the short term.’”
Bryn Nelson is a freelance medical writer in Seattle.
Several years ago, Presbyterian Medical Group in Albuquerque, N.M., decided to integrate three elements of its healthcare system: its health plan, the employed medical group, and the hospital delivery system. Knitting those parts into a cohesive whole helped the group realize that “lowering the cost of care by improving efficiency, by improving coordination, and by enhancing collaboration between payor and physicians made a lot of sense,” executive medical director David Arredondo, MD, says.
When the accountable care organization (ACO) concept came along, Dr. Arredondo says, “it really was just a natural extension of what we were doing.”
By year’s end, though, the final rules had assuaged many of the biggest concerns, and the April 10 announcement of 27 participants for the program’s first round—more than half of which are physician-led organizations—has rekindled much of the enthusiasm. According to CMS officials, the agency is reviewing more than 150 applications for the program’s next round, which will begin in July.
Keys to Success
In December, CMS selected 32 organizations to participate in an even more ambitious initiative called the Pioneer ACO Model. That separate but related experiment in shared accountability launched Jan. 1, and it may be months before enrolled organizations can say whether the rewards outweigh the risks. Interviews with Presbyterian’s Dr. Arredondo and two other Pioneer participants about why they took the plunge, however, have highlighted some potential keys to success.
—David Arredondo, MD, executive medical director, Presbyterian Medical Group, Albuquerque, N.M.
All three agree that the ACO model offers a better match for their long-term, patient-centered goals and that the fee-for-service model is gradually becoming a thing of the past.
“In some ways, it was actually kind of a relief that the system was going this way because we, probably like many systems, were beginning to be caught between the budgeted model and a fee-for-service model,” Dr. Arredondo says. “When you’re heavily one way or heavily the other way, then it makes things a little easier to manage and understand. When you’re right in the middle, it becomes a little uncomfortable.”
Penny Wheeler, MD, chief clinical officer for Minneapolis-based Allina Hospitals & Clinics, says organizations in that precarious position need to carefully examine their capabilities and consider how best to pace their transition. Otherwise, they might prematurely give up too much revenue that could be used to reinvest in care improvements.
“We can tolerate it if we shoot ourselves in one foot, but we can’t tolerate it if we shoot ourselves in both feet, in this new world,” Dr. Wheeler says.
If caution is warranted, she says, the ACO model still aligns well with a strategy of building toward outcome-based healthcare. Despite the likelihood of “lumps and bumps and warts along the way,” Dr. Wheeler says, “we really wanted to be part of the shaping of that outcome-based delivery, and receive market rewards for what we were creating for our community.”
Austin, Texas-based Seton Health Alliance, a third Pioneer participant, is a collaborative effort between a hospital delivery system known as Seton Health Care Family and a multispecialty physician group called Austin Regional Clinic. Greg Sheff, MD, president and chief medical officer of the ACO, says the partnering organizations were separately moving toward more population health initiatives and more proactive, coordinated, and accountable care.
“The Pioneer ACO, for us, really provided an opportunity to light the fire and motivate the organizations to put the entity together and start doing the work,” he says, adding PCPs and hospitalists will be critical to his organization’s ongoing integration efforts.
—Greg Sheff, MD, president, chief medical officer, Seton Health Alliance, Austin, Texas
“The areas where there are opportunities to be more efficient are largely under the care of the hospitalists,” he says, citing in-house utilization as well as care transitions, comprehensive post-acute placement, and readmission prevention efforts. To support those providers, Pioneer participants say well-designed electronic medical records are paramount, while separate efforts, such as patient-centered medical homes and unit-based rounding, might offer timely assists. (Click here to listen to more of The Hospitalist’s interview with Dr. Sheff.)
No one’s expecting the next few years to be seamless, but Dr. Sheff views his newly formed ACO as a long-term endeavor in which success isn’t necessarily defined by whether the group achieves shared cost savings.
“We define success by whether we are able to move our delivery system to a place where we’ll be much more adept at going forward, continuing to manage populations,” he says. “We really see this as a strategic organizational decision more than, ‘Boy, that contract looks like something that we can leverage in the short term.’”
Bryn Nelson is a freelance medical writer in Seattle.
Ascites Care Suboptimal at Some Veterans Affairs Facilities
Quality of care for ascites, the most common complication of cirrhosis, was found to be suboptimal at several Veterans Affairs medical centers, reported Dr. Fasiha Kanwal and colleagues in the July issue of Gastroenterology.
"In general, care targeted at diagnosis and treatment was more likely to meet standards than preventive care," wrote Dr. Kanwal, of the Michael E. DeBakey Veterans Affairs Medical Center, Houston.
"We also found a trend towards improved outcomes in patients who met recommended quality indicators," added the investigators, although these findings "can only be regarded as preliminary."
The authors studied records from 774 patients (mean age 54.7 years, 99% male) in a database comprising 3 VA medical centers and 15 affiliated clinics in the Midwest (Gastroenterology 2012 [doi: 10.1053/j.gastro.2012.03.038]).
All patients had at least two ICD-9 codes for cirrhosis or at least one code for cirrhosis with either a code for complications of cirrhosis or an aspartate aminotransferase to platelet ratio greater than 2. The patients were seen between January 2000 and December 2007.
The authors compared data on these patients to a set of class 1 ascites care quality indicators (QIs). These indicators were derived by using the RAND/University of California, Los Angeles (UCLA), Appropriateness Method, which had been previously published elsewhere (Clin. Gastroenterol. Hepatol. 2010;8:709-17).
If a patient had been hospitalized more than once, only the first hospitalization was assessed. The rate of adherence to each QI was expressed as a percentage of subjects who received the recommended care, among those who were eligible for the QI.
The first QI assessed the percentage of new-onset ascites patients who underwent abdominal paracentesis within 30 days of diagnosis. On this measure, the VA scored 50.6%. The second indicator was whether known ascites patients admitted with either ascites or hepatic encephalopathy underwent abdominal paracentesis during the index hospitalization. Just over half (57.6%) of patients met this criterion.
The next QI was more likely to be met: 89.3% of patients who underwent abdominal paracentesis received ascitic fluid cell count and differential. Another indicator that was met for a high percentage of patients addressed whether ascites patients with normal renal function received diuretics within 30 days of diagnosis – 82.8% met this criterion.
Similarly, among hospitalized patients with spontaneous bacterial peritonitis (SBP), 72.0% received antibiotics within 24 hours before or after ascitic fluid analysis.
However, just 30% of patients with SBP who survived and were discharged from the facility received long-term outpatient antibiotics (for secondary prophylaxis) within 30 days. And just under half (49.2%) of patients admitted with a GI bleed received antibiotics during the index hospitalization.
The final QI was associated with the worst compliance rate: just 22.2% of patients with ascitic fluid total protein levels less than 1 g/dL and serum bilirubin of greater than 2.5 mg/dL received long-term outpatient antibiotics (for primary prophylaxis) within –3 to 30 days of that test result.
Next, the authors assessed which demographic or other independent factors were associated with higher QI compliance. In general, they reported that better care was inversely related to a worsening liver disease. More specifically, they found that patients who saw a gastroenterologist received higher-quality care than those who did not (odds ratio, 1.33), as did patients who were seen at a VA facility with academic affiliation, versus unaffiliated centers (OR, 1.73).
Finally, in two exploratory analyses, the authors examined how adherence to the ascites QIs affected patient outcomes.
Not surprisingly, "we found that after adjusting for age, liver disease severity, and comorbidity, patients receiving suboptimum care had 37% higher odds of death and 35% higher odds of readmission during the 12-month follow-up compared to patients who received optimum ascites care," although these figures did not reach statistical significance.
This study was supported by the 2008 American Society of Gastrointestinal Endoscopy Quality of Care Award and by the 2009 American College of Gastroenterology Clinical Research Award. The authors stated that they had no personal conflicts of interest.
Quality of care for ascites, the most common complication of cirrhosis, was found to be suboptimal at several Veterans Affairs medical centers, reported Dr. Fasiha Kanwal and colleagues in the July issue of Gastroenterology.
"In general, care targeted at diagnosis and treatment was more likely to meet standards than preventive care," wrote Dr. Kanwal, of the Michael E. DeBakey Veterans Affairs Medical Center, Houston.
"We also found a trend towards improved outcomes in patients who met recommended quality indicators," added the investigators, although these findings "can only be regarded as preliminary."
The authors studied records from 774 patients (mean age 54.7 years, 99% male) in a database comprising 3 VA medical centers and 15 affiliated clinics in the Midwest (Gastroenterology 2012 [doi: 10.1053/j.gastro.2012.03.038]).
All patients had at least two ICD-9 codes for cirrhosis or at least one code for cirrhosis with either a code for complications of cirrhosis or an aspartate aminotransferase to platelet ratio greater than 2. The patients were seen between January 2000 and December 2007.
The authors compared data on these patients to a set of class 1 ascites care quality indicators (QIs). These indicators were derived by using the RAND/University of California, Los Angeles (UCLA), Appropriateness Method, which had been previously published elsewhere (Clin. Gastroenterol. Hepatol. 2010;8:709-17).
If a patient had been hospitalized more than once, only the first hospitalization was assessed. The rate of adherence to each QI was expressed as a percentage of subjects who received the recommended care, among those who were eligible for the QI.
The first QI assessed the percentage of new-onset ascites patients who underwent abdominal paracentesis within 30 days of diagnosis. On this measure, the VA scored 50.6%. The second indicator was whether known ascites patients admitted with either ascites or hepatic encephalopathy underwent abdominal paracentesis during the index hospitalization. Just over half (57.6%) of patients met this criterion.
The next QI was more likely to be met: 89.3% of patients who underwent abdominal paracentesis received ascitic fluid cell count and differential. Another indicator that was met for a high percentage of patients addressed whether ascites patients with normal renal function received diuretics within 30 days of diagnosis – 82.8% met this criterion.
Similarly, among hospitalized patients with spontaneous bacterial peritonitis (SBP), 72.0% received antibiotics within 24 hours before or after ascitic fluid analysis.
However, just 30% of patients with SBP who survived and were discharged from the facility received long-term outpatient antibiotics (for secondary prophylaxis) within 30 days. And just under half (49.2%) of patients admitted with a GI bleed received antibiotics during the index hospitalization.
The final QI was associated with the worst compliance rate: just 22.2% of patients with ascitic fluid total protein levels less than 1 g/dL and serum bilirubin of greater than 2.5 mg/dL received long-term outpatient antibiotics (for primary prophylaxis) within –3 to 30 days of that test result.
Next, the authors assessed which demographic or other independent factors were associated with higher QI compliance. In general, they reported that better care was inversely related to a worsening liver disease. More specifically, they found that patients who saw a gastroenterologist received higher-quality care than those who did not (odds ratio, 1.33), as did patients who were seen at a VA facility with academic affiliation, versus unaffiliated centers (OR, 1.73).
Finally, in two exploratory analyses, the authors examined how adherence to the ascites QIs affected patient outcomes.
Not surprisingly, "we found that after adjusting for age, liver disease severity, and comorbidity, patients receiving suboptimum care had 37% higher odds of death and 35% higher odds of readmission during the 12-month follow-up compared to patients who received optimum ascites care," although these figures did not reach statistical significance.
This study was supported by the 2008 American Society of Gastrointestinal Endoscopy Quality of Care Award and by the 2009 American College of Gastroenterology Clinical Research Award. The authors stated that they had no personal conflicts of interest.
Quality of care for ascites, the most common complication of cirrhosis, was found to be suboptimal at several Veterans Affairs medical centers, reported Dr. Fasiha Kanwal and colleagues in the July issue of Gastroenterology.
"In general, care targeted at diagnosis and treatment was more likely to meet standards than preventive care," wrote Dr. Kanwal, of the Michael E. DeBakey Veterans Affairs Medical Center, Houston.
"We also found a trend towards improved outcomes in patients who met recommended quality indicators," added the investigators, although these findings "can only be regarded as preliminary."
The authors studied records from 774 patients (mean age 54.7 years, 99% male) in a database comprising 3 VA medical centers and 15 affiliated clinics in the Midwest (Gastroenterology 2012 [doi: 10.1053/j.gastro.2012.03.038]).
All patients had at least two ICD-9 codes for cirrhosis or at least one code for cirrhosis with either a code for complications of cirrhosis or an aspartate aminotransferase to platelet ratio greater than 2. The patients were seen between January 2000 and December 2007.
The authors compared data on these patients to a set of class 1 ascites care quality indicators (QIs). These indicators were derived by using the RAND/University of California, Los Angeles (UCLA), Appropriateness Method, which had been previously published elsewhere (Clin. Gastroenterol. Hepatol. 2010;8:709-17).
If a patient had been hospitalized more than once, only the first hospitalization was assessed. The rate of adherence to each QI was expressed as a percentage of subjects who received the recommended care, among those who were eligible for the QI.
The first QI assessed the percentage of new-onset ascites patients who underwent abdominal paracentesis within 30 days of diagnosis. On this measure, the VA scored 50.6%. The second indicator was whether known ascites patients admitted with either ascites or hepatic encephalopathy underwent abdominal paracentesis during the index hospitalization. Just over half (57.6%) of patients met this criterion.
The next QI was more likely to be met: 89.3% of patients who underwent abdominal paracentesis received ascitic fluid cell count and differential. Another indicator that was met for a high percentage of patients addressed whether ascites patients with normal renal function received diuretics within 30 days of diagnosis – 82.8% met this criterion.
Similarly, among hospitalized patients with spontaneous bacterial peritonitis (SBP), 72.0% received antibiotics within 24 hours before or after ascitic fluid analysis.
However, just 30% of patients with SBP who survived and were discharged from the facility received long-term outpatient antibiotics (for secondary prophylaxis) within 30 days. And just under half (49.2%) of patients admitted with a GI bleed received antibiotics during the index hospitalization.
The final QI was associated with the worst compliance rate: just 22.2% of patients with ascitic fluid total protein levels less than 1 g/dL and serum bilirubin of greater than 2.5 mg/dL received long-term outpatient antibiotics (for primary prophylaxis) within –3 to 30 days of that test result.
Next, the authors assessed which demographic or other independent factors were associated with higher QI compliance. In general, they reported that better care was inversely related to a worsening liver disease. More specifically, they found that patients who saw a gastroenterologist received higher-quality care than those who did not (odds ratio, 1.33), as did patients who were seen at a VA facility with academic affiliation, versus unaffiliated centers (OR, 1.73).
Finally, in two exploratory analyses, the authors examined how adherence to the ascites QIs affected patient outcomes.
Not surprisingly, "we found that after adjusting for age, liver disease severity, and comorbidity, patients receiving suboptimum care had 37% higher odds of death and 35% higher odds of readmission during the 12-month follow-up compared to patients who received optimum ascites care," although these figures did not reach statistical significance.
This study was supported by the 2008 American Society of Gastrointestinal Endoscopy Quality of Care Award and by the 2009 American College of Gastroenterology Clinical Research Award. The authors stated that they had no personal conflicts of interest.
FROM GASTROENTEROLOGY
Hiring the Right Employees
As I write this, the government’s "new jobs" figures are at last turning a bit optimistic. This is consistent with the growing number of questions I’m receiving on a subject that hasn’t come up for awhile: hiring new employees. So although we probably haven’t seen the end of the Great Recession just yet, now might be a good time to review the basic rules in preparation for getting your office back to full speed.
Many of the personnel questions I receive concern the dreaded "marginal employee": the person who has done neither anything heinous enough to merit firing, nor anything special to merit continued employment. I always advise getting rid of such people, and then changing the hiring criteria that all too often result in poor hires.
Most bad hires come about because the employer does not have a clear vision of the kind of employee he or she wants. Many office manuals do not contain detailed job descriptions. If you don’t know exactly what you are looking for, your entire selection process will be inadequate, from your initial screening of applicants through your assessments of their skills and personalities. Many physicians compound the problem with poor interview techniques and inadequate checking of references.
So now – before a job vacancy occurs – is the time to reevaluate your entire hiring process. Take a hard look at your job descriptions, or start compiling them if you don’t have any. A good description lists the major responsibilities of the position, with the relative importance of each duty and the critical knowledge, skills, and education levels necessary for each function. In other words, it describes (accurately and in detail) exactly what you expect from the employee you will hire to perform that job.
Once you have a clear job description in mind (and in print), take all the time you need to find the best possible match. This is not a place to cut corners. Screen your candidates carefully, and avoid lowering your expectations. This is the point at which it might be tempting to settle for a marginal candidate, just to get the process over with.
It is also sometimes tempting to hire the candidate that you have the "best feeling" about, even though he or she is a poor match for the job, and then try to mold the job to that person. Every doctor knows that hunches are no substitute for hard data.
Be alert for red flags in resumes: significant time gaps between jobs; positions at companies that are no longer in business, or are otherwise impossible to verify; job titles that don’t make sense, given the applicant’s qualifications.
Background checks are a dicey subject, but publicly available information can be found, cheaply or free, on multiple websites created for that purpose. Be sure to tell applicants that you will be verifying facts in their resumes; it’s usually wise to get their written consent to do so.
Many employers skip the essential step of calling references; many applicants know that. Some old bosses will be reluctant to tell you anything substantive; I always ask, "Would you hire this person again?" You can interpret a lot from the answer – or lack of.
Interviews often get short shrift as well. Many doctors tend to do all the talking; as I’ve observed numerous times, listening is not our strong suit, as a general rule. The purpose of an interview is to allow you to size up the prospective employee, not to deliver a lecture on the sterling attributes of your office. Important interview topics include educational background, skills, experience, and unrelated job history.
By law, you cannot ask an applicant’s age, date of birth, gender, creed, color, religion, or national origin. Other forbidden subjects include disabilities, marital status, military record, number of children (or who cares for them), addiction history, citizenship, criminal record, psychiatric history, absenteeism, or workers’ compensation.
But there are acceptable alternatives to some of those questions: You can ask if an applicant has ever gone by another name (for your background check), for example. You can ask if he or she is legally authorized to work in this country, and whether he or she will be physically able to perform the duties specified in the job description. Although past addictions are off limits, you do have a right to know about current addictions to illegal substances.
Once you have hired people whose skills and personalities best fit your needs, train them well, and then give them the opportunity to succeed. "The best executive," wrote Theodore Roosevelt, "is the one who has sense enough to pick good people to do what he [or she] wants done, and self-restraint enough to keep from meddling with them while they do it."
Dr. Eastern practices dermatology and dermatologic surgery in Belleville, N.J.
As I write this, the government’s "new jobs" figures are at last turning a bit optimistic. This is consistent with the growing number of questions I’m receiving on a subject that hasn’t come up for awhile: hiring new employees. So although we probably haven’t seen the end of the Great Recession just yet, now might be a good time to review the basic rules in preparation for getting your office back to full speed.
Many of the personnel questions I receive concern the dreaded "marginal employee": the person who has done neither anything heinous enough to merit firing, nor anything special to merit continued employment. I always advise getting rid of such people, and then changing the hiring criteria that all too often result in poor hires.
Most bad hires come about because the employer does not have a clear vision of the kind of employee he or she wants. Many office manuals do not contain detailed job descriptions. If you don’t know exactly what you are looking for, your entire selection process will be inadequate, from your initial screening of applicants through your assessments of their skills and personalities. Many physicians compound the problem with poor interview techniques and inadequate checking of references.
So now – before a job vacancy occurs – is the time to reevaluate your entire hiring process. Take a hard look at your job descriptions, or start compiling them if you don’t have any. A good description lists the major responsibilities of the position, with the relative importance of each duty and the critical knowledge, skills, and education levels necessary for each function. In other words, it describes (accurately and in detail) exactly what you expect from the employee you will hire to perform that job.
Once you have a clear job description in mind (and in print), take all the time you need to find the best possible match. This is not a place to cut corners. Screen your candidates carefully, and avoid lowering your expectations. This is the point at which it might be tempting to settle for a marginal candidate, just to get the process over with.
It is also sometimes tempting to hire the candidate that you have the "best feeling" about, even though he or she is a poor match for the job, and then try to mold the job to that person. Every doctor knows that hunches are no substitute for hard data.
Be alert for red flags in resumes: significant time gaps between jobs; positions at companies that are no longer in business, or are otherwise impossible to verify; job titles that don’t make sense, given the applicant’s qualifications.
Background checks are a dicey subject, but publicly available information can be found, cheaply or free, on multiple websites created for that purpose. Be sure to tell applicants that you will be verifying facts in their resumes; it’s usually wise to get their written consent to do so.
Many employers skip the essential step of calling references; many applicants know that. Some old bosses will be reluctant to tell you anything substantive; I always ask, "Would you hire this person again?" You can interpret a lot from the answer – or lack of.
Interviews often get short shrift as well. Many doctors tend to do all the talking; as I’ve observed numerous times, listening is not our strong suit, as a general rule. The purpose of an interview is to allow you to size up the prospective employee, not to deliver a lecture on the sterling attributes of your office. Important interview topics include educational background, skills, experience, and unrelated job history.
By law, you cannot ask an applicant’s age, date of birth, gender, creed, color, religion, or national origin. Other forbidden subjects include disabilities, marital status, military record, number of children (or who cares for them), addiction history, citizenship, criminal record, psychiatric history, absenteeism, or workers’ compensation.
But there are acceptable alternatives to some of those questions: You can ask if an applicant has ever gone by another name (for your background check), for example. You can ask if he or she is legally authorized to work in this country, and whether he or she will be physically able to perform the duties specified in the job description. Although past addictions are off limits, you do have a right to know about current addictions to illegal substances.
Once you have hired people whose skills and personalities best fit your needs, train them well, and then give them the opportunity to succeed. "The best executive," wrote Theodore Roosevelt, "is the one who has sense enough to pick good people to do what he [or she] wants done, and self-restraint enough to keep from meddling with them while they do it."
Dr. Eastern practices dermatology and dermatologic surgery in Belleville, N.J.
As I write this, the government’s "new jobs" figures are at last turning a bit optimistic. This is consistent with the growing number of questions I’m receiving on a subject that hasn’t come up for awhile: hiring new employees. So although we probably haven’t seen the end of the Great Recession just yet, now might be a good time to review the basic rules in preparation for getting your office back to full speed.
Many of the personnel questions I receive concern the dreaded "marginal employee": the person who has done neither anything heinous enough to merit firing, nor anything special to merit continued employment. I always advise getting rid of such people, and then changing the hiring criteria that all too often result in poor hires.
Most bad hires come about because the employer does not have a clear vision of the kind of employee he or she wants. Many office manuals do not contain detailed job descriptions. If you don’t know exactly what you are looking for, your entire selection process will be inadequate, from your initial screening of applicants through your assessments of their skills and personalities. Many physicians compound the problem with poor interview techniques and inadequate checking of references.
So now – before a job vacancy occurs – is the time to reevaluate your entire hiring process. Take a hard look at your job descriptions, or start compiling them if you don’t have any. A good description lists the major responsibilities of the position, with the relative importance of each duty and the critical knowledge, skills, and education levels necessary for each function. In other words, it describes (accurately and in detail) exactly what you expect from the employee you will hire to perform that job.
Once you have a clear job description in mind (and in print), take all the time you need to find the best possible match. This is not a place to cut corners. Screen your candidates carefully, and avoid lowering your expectations. This is the point at which it might be tempting to settle for a marginal candidate, just to get the process over with.
It is also sometimes tempting to hire the candidate that you have the "best feeling" about, even though he or she is a poor match for the job, and then try to mold the job to that person. Every doctor knows that hunches are no substitute for hard data.
Be alert for red flags in resumes: significant time gaps between jobs; positions at companies that are no longer in business, or are otherwise impossible to verify; job titles that don’t make sense, given the applicant’s qualifications.
Background checks are a dicey subject, but publicly available information can be found, cheaply or free, on multiple websites created for that purpose. Be sure to tell applicants that you will be verifying facts in their resumes; it’s usually wise to get their written consent to do so.
Many employers skip the essential step of calling references; many applicants know that. Some old bosses will be reluctant to tell you anything substantive; I always ask, "Would you hire this person again?" You can interpret a lot from the answer – or lack of.
Interviews often get short shrift as well. Many doctors tend to do all the talking; as I’ve observed numerous times, listening is not our strong suit, as a general rule. The purpose of an interview is to allow you to size up the prospective employee, not to deliver a lecture on the sterling attributes of your office. Important interview topics include educational background, skills, experience, and unrelated job history.
By law, you cannot ask an applicant’s age, date of birth, gender, creed, color, religion, or national origin. Other forbidden subjects include disabilities, marital status, military record, number of children (or who cares for them), addiction history, citizenship, criminal record, psychiatric history, absenteeism, or workers’ compensation.
But there are acceptable alternatives to some of those questions: You can ask if an applicant has ever gone by another name (for your background check), for example. You can ask if he or she is legally authorized to work in this country, and whether he or she will be physically able to perform the duties specified in the job description. Although past addictions are off limits, you do have a right to know about current addictions to illegal substances.
Once you have hired people whose skills and personalities best fit your needs, train them well, and then give them the opportunity to succeed. "The best executive," wrote Theodore Roosevelt, "is the one who has sense enough to pick good people to do what he [or she] wants done, and self-restraint enough to keep from meddling with them while they do it."
Dr. Eastern practices dermatology and dermatologic surgery in Belleville, N.J.
Hospitalist Utilization and Performance
The past several years have seen a dramatic increase in the percentage of patients cared for by hospitalists, yet an emerging body of literature examining the association between care given by hospitalists and performance on a number of process measures has shown mixed results. Hospitalists do not appear to provide higher quality of care for pneumonia,1, 2 while results in heart failure are mixed.35 Each of these studies was conducted at a single site, and examined patient‐level effects. More recently, Vasilevskis et al6 assessed the association between the intensity of hospitalist use (measured as the percentage of patients admitted by hospitalists) and performance on process measures. In a cohort of 208 California hospitals, they found a significant improvement in performance on process measures in patients with acute myocardial infarction, heart failure, and pneumonia with increasing percentages of patients admitted by hospitalists.6
To date, no study has examined the association between the use of hospitalists and the publicly reported 30‐day mortality and readmission measures. Specifically, the Centers for Medicare and Medicaid Services (CMS) have developed and now publicly report risk‐standardized 30‐day mortality (RSMR) and readmission rates (RSRR) for Medicare patients hospitalized for 3 common and costly conditionsacute myocardial infarction (AMI), heart failure (HF), and pneumonia.7 Performance on these hospital‐based quality measures varies widely, and vary by hospital volume, ownership status, teaching status, and nurse staffing levels.813 However, even accounting for these characteristics leaves much of the variation in outcomes unexplained. We hypothesized that the presence of hospitalists within a hospital would be associated with higher performance on 30‐day mortality and 30‐day readmission measures for AMI, HF, and pneumonia. We further hypothesized that for hospitals using hospitalists, there would be a positive correlation between increasing percentage of patients admitted by hospitalists and performance on outcome measures. To test these hypotheses, we conducted a national survey of hospitalist leaders, linking data from survey responses to data on publicly reported outcome measures for AMI, HF, and pneumonia.
MATERIALS AND METHODS
Study Sites
Of the 4289 hospitals in operation in 2008, 1945 had 25 or more AMI discharges. We identified hospitals using American Hospital Association (AHA) data, calling hospitals up to 6 times each until we reached our target sample size of 600. Using this methodology, we contacted 1558 hospitals of a possible 1920 with AHA data; of the 1558 called, 598 provided survey results.
Survey Data
Our survey was adapted from the survey developed by Vasilevskis et al.6 The entire survey can be found in the Appendix (see Supporting Information in the online version of this article). Our key questions were: 1) Does your hospital have at least 1 hospitalist program or group? 2) Approximately what percentage of all medical patients in your hospital are admitted by hospitalists? The latter question was intended as an approximation of the intensity of hospitalist use, and has been used in prior studies.6, 14 A more direct measure was not feasible given the complexity of obtaining admission data for such a large and diverse set of hospitals. Respondents were also asked about hospitalist care of AMI, HF, and pneumonia patients. Given the low likelihood of precise estimation of hospitalist participation in care for specific conditions, the response choices were divided into percentage quartiles: 025, 2650, 5175, and 76100. Finally, participants were asked a number of questions regarding hospitalist organizational and clinical characteristics.
Survey Process
We obtained data regarding presence or absence of hospitalists and characteristics of the hospitalist services via phone‐ and fax‐administered survey (see Supporting Information, Appendix, in the online version of this article). Telephone and faxed surveys were administered between February 2010 and January 2011. Hospital telephone numbers were obtained from the 2008 AHA survey database and from a review of each hospital's website. Up to 6 attempts were made to obtain a completed survey from nonrespondents unless participation was specifically refused. Potential respondents were contacted in the following order: hospital medicine department leaders, hospital medicine clinical managers, vice president for medical affairs, chief medical officers, and other hospital executives with knowledge of the hospital medicine services. All respondents agreed with a question asking whether they had direct working knowledge of their hospital medicine services; contacts who said they did not have working knowledge of their hospital medicine services were asked to refer our surveyor to the appropriate person at their site. Absence of a hospitalist program was confirmed by contacting the Medical Staff Office.
Hospital Organizational and Patient‐Mix Characteristics
Hospital‐level organizational characteristics (eg, bed size, teaching status) and patient‐mix characteristics (eg, Medicare and Medicaid inpatient days) were obtained from the 2008 AHA survey database.
Outcome Performance Measures
The 30‐day risk‐standardized mortality and readmission rates (RSMR and RSRR) for 2008 for AMI, HF, and pneumonia were calculated for all admissions for people age 65 and over with traditional fee‐for‐service Medicare. Beneficiaries had to be enrolled for 12 months prior to their hospitalization for any of the 3 conditions, and had to have complete claims data available for that 12‐month period.7 These 6 outcome measures were constructed using hierarchical generalized linear models.1520 Using the RSMR for AMI as an example, for each hospital, the measure is estimated by dividing the predicted number of deaths within 30 days of admission for AMI by the expected number of deaths within 30 days of admission for AMI. This ratio is then divided by the national unadjusted 30‐day mortality rate for AMI, which is obtained using data on deaths from the Medicare beneficiary denominator file. Each measure is adjusted for patient characteristics such as age, gender, and comorbidities. All 6 measures are endorsed by the National Quality Forum (NQF) and are reported publicly by CMS on the Hospital Compare web site.
Statistical Analysis
Comparison of hospital‐ and patient‐level characteristics between hospitals with and without hospitalists was performed using chi‐square tests and Student t tests.
The primary outcome variables are the RSMRs and RSRRs for AMI, HF, and pneumonia. Multivariable linear regression models were used to assess the relationship between hospitals with at least 1 hospitalist group and each dependent variable. Models were adjusted for variables previously reported to be associated with quality of care. Hospital‐level characteristics included core‐based statistical area, teaching status, number of beds, region, safety‐net status, nursing staff ratio (number of registered nurse FTEs/number of hospital FTEs), and presence or absence of cardiac catheterization and coronary bypass capability. Patient‐level characteristics included Medicare and Medicaid inpatient days as a percentage of total inpatient days and percentage of admissions by race (black vs non‐black). The presence of hospitalists was correlated with each of the hospital and patient‐level characteristics. Further analyses of the subset of hospitals that use hospitalists included construction of multivariable linear regression models to assess the relationship between the percentage of patients admitted by hospitalists and the dependent variables. Models were adjusted for the same patient‐ and hospital‐level characteristics.
The institutional review boards at Yale University and University of California, San Francisco approved the study. All analyses were performed using Statistical Analysis Software (SAS) version 9.1 (SAS Institute, Inc, Cary, NC).
RESULTS
Characteristics of Participating Hospitals
Telephone, fax, and e‐mail surveys were attempted with 1558 hospitals; we received 598 completed surveys for a response rate of 40%. There was no difference between responders and nonresponders on any of the 6 outcome variables, the number of Medicare or Medicaid inpatient days, and the percentage of admissions by race. Responders and nonresponders were also similar in size, ownership, safety‐net and teaching status, nursing staff ratio, presence of cardiac catheterization and coronary bypass capability, and core‐based statistical area. They differed only on region of the country, where hospitals in the northwest Central and Pacific regions of the country had larger overall proportions of respondents. All hospitals provided information about the presence or absence of hospitalist programs. The majority of respondents were hospitalist clinical or administrative managers (n = 220) followed by hospitalist leaders (n = 106), other executives (n = 58), vice presidents for medical affairs (n = 39), and chief medical officers (n = 15). Each respondent indicated a working knowledge of their site's hospitalist utilization and practice characteristics. Absence of hospitalist utilization was confirmed by contact with the Medical Staff Office.
Comparisons of Sites With Hospitalists and Those Without Hospitalists
Hospitals with and without hospitalists differed by a number of organizational characteristics (Table 1). Sites with hospitalists were more likely to be larger, nonprofit teaching hospitals, located in metropolitan regions, and have cardiac surgical services. There was no difference in the hospitals' safety‐net status or RN staffing ratio. Hospitals with hospitalists admitted lower percentages of black patients.
| Hospitalist Program | No Hospitalist Program | ||
|---|---|---|---|
| N = 429 | N = 169 | ||
| N (%) | N (%) | P Value | |
| |||
| Core‐based statistical area | <0.0001 | ||
| Division | 94 (21.9%) | 53 (31.4%) | |
| Metro | 275 (64.1%) | 72 (42.6%) | |
| Micro | 52 (12.1%) | 38 (22.5%) | |
| Rural | 8 (1.9%) | 6 (3.6%) | |
| Owner | 0.0003 | ||
| Public | 47 (11.0%) | 20 (11.8%) | |
| Nonprofit | 333 (77.6%) | 108 (63.9%) | |
| Private | 49 (11.4%) | 41 (24.3%) | |
| Teaching status | <0.0001 | ||
| COTH | 54 (12.6%) | 7 (4.1%) | |
| Teaching | 110 (25.6%) | 26 (15.4%) | |
| Other | 265 (61.8%) | 136 (80.5%) | |
| Cardiac type | 0.0003 | ||
| CABG | 286 (66.7%) | 86 (50.9%) | |
| CATH | 79 (18.4%) | 36 (21.3%) | |
| Other | 64 (14.9%) | 47 (27.8%) | |
| Region | 0.007 | ||
| New England | 35 (8.2%) | 3 (1.8%) | |
| Middle Atlantic | 60 (14.0%) | 29 (17.2%) | |
| South Atlantic | 78 (18.2%) | 23 (13.6%) | |
| NE Central | 60 (14.0%) | 35 (20.7%) | |
| SE Central | 31 (7.2%) | 10 (5.9%) | |
| NW Central | 38 (8.9%) | 23 (13.6%) | |
| SW Central | 41 (9.6%) | 21 (12.4%) | |
| Mountain | 22 (5.1%) | 3 (1.8%) | |
| Pacific | 64 (14.9%) | 22 (13.0%) | |
| Safety‐net | 0.53 | ||
| Yes | 72 (16.8%) | 32 (18.9%) | |
| No | 357 (83.2%) | 137 (81.1%) | |
| Mean (SD) | Mean (SD) | P value | |
| RN staffing ratio (n = 455) | 27.3 (17.0) | 26.1 (7.6) | 0.28 |
| Total beds | 315.0 (216.6) | 214.8 (136.0) | <0.0001 |
| % Medicare inpatient days | 47.2 (42) | 49.7 (41) | 0.19 |
| % Medicaid inpatient days | 18.5 (28) | 21.4 (46) | 0.16 |
| % Black | 7.6 (9.6) | 10.6 (17.4) | 0.03 |
Characteristics of Hospitalist Programs and Responsibilities
Of the 429 sites reporting use of hospitalists, the median percentage of patients admitted by hospitalists was 60%, with an interquartile range (IQR) of 35% to 80%. The median number of full‐time equivalent hospitalists per hospital was 8 with an IQR of 5 to 14. The IQR reflects the middle 50% of the distribution of responses, and is not affected by outliers or extreme values. Additional characteristics of hospitalist programs can be found in Table 2. The estimated percentage of patients with AMI, HF, and pneumonia cared for by hospitalists varied considerably, with fewer patients with AMI and more patients with pneumonia under hospitalist care. Overall, a majority of hospitalist groups provided the following services: care of critical care patients, emergency department admission screening, observation unit coverage, coverage for cardiac arrests and rapid response teams, quality improvement or utilization review activities, development of hospital practice guidelines, and participation in implementation of major hospital system projects (such as implementation of an electronic health record system).
| N (%) | |
|---|---|
| |
| Date program established | |
| 19871994 | 9 (2.2%) |
| 19952002 | 130 (32.1%) |
| 20032011 | 266 (65.7%) |
| Missing date | 24 |
| No. of hospitalist FTEs | |
| Median (IQR) | 8 (5, 14) |
| Percent of medical patients admitted by hospitalists | |
| Median (IQR) | 60% (35, 80) |
| No. of hospitalists groups | |
| 1 | 333 (77.6%) |
| 2 | 54 (12.6%) |
| 3 | 36 (8.4%) |
| Don't know | 6 (1.4%) |
| Employment of hospitalists (not mutually exclusive) | |
| Hospital system | 98 (22.8%) |
| Hospital | 185 (43.1%) |
| Local physician practice group | 62 (14.5%) |
| Hospitalist physician practice group (local) | 83 (19.3%) |
| Hospitalist physician practice group (national/regional) | 36 (8.4%) |
| Other/unknown | 36 (8.4%) |
| Any 24‐hr in‐house coverage by hospitalists | |
| Yes | 329 (76.7%) |
| No | 98 (22.8%) |
| 3 | 1 (0.2%) |
| Unknown | 1 (0.2%) |
| No. of hospitalist international medical graduates | |
| Median (IQR) | 3 (1, 6) |
| No. of hospitalists that are <1 yr out of residency | |
| Median (IQR) | 1 (0, 2) |
| Percent of patients with AMI cared for by hospitalists | |
| 0%25% | 148 (34.5%) |
| 26%50% | 67 (15.6%) |
| 51%75% | 50 (11.7%) |
| 76%100% | 54 (12.6%) |
| Don't know | 110 (25.6%) |
| Percent of patients with heart failure cared for by hospitalists | |
| 0%25% | 79 (18.4%) |
| 26%50% | 78 (18.2%) |
| 51%75% | 75 (17.5%) |
| 76%100% | 84 (19.6%) |
| Don't know | 113 (26.3%) |
| Percent of patients with pneumonia cared for by hospitalists | |
| 0%25% | 47 (11.0%) |
| 26%50% | 61 (14.3%) |
| 51%75% | 74 (17.3%) |
| 76%100% | 141 (32.9%) |
| Don't know | 105 (24.5%) |
| Hospitalist provision of services | |
| Care of critical care patients | |
| Hospitalists provide service | 346 (80.7%) |
| Hospitalists do not provide service | 80 (18.7%) |
| Don't know | 3 (0.7%) |
| Emergency department admission screening | |
| Hospitalists provide service | 281 (65.5%) |
| Hospitalists do not provide service | 143 (33.3%) |
| Don't know | 5 (1.2%) |
| Observation unit coverage | |
| Hospitalists provide service | 359 (83.7%) |
| Hospitalists do not provide service | 64 (14.9%) |
| Don't know | 6 (1.4%) |
| Emergency department coverage | |
| Hospitalists provide service | 145 (33.8%) |
| Hospitalists do not provide service | 280 (65.3%) |
| Don't know | 4 (0.9%) |
| Coverage for cardiac arrests | |
| Hospitalists provide service | 283 (66.0%) |
| Hospitalists do not provide service | 135 (31.5%) |
| Don't know | 11 (2.6%) |
| Rapid response team coverage | |
| Hospitalists provide service | 240 (55.9%) |
| Hospitalists do not provide service | 168 (39.2%) |
| Don't know | 21 (4.9%) |
| Quality improvement or utilization review | |
| Hospitalists provide service | 376 (87.7%) |
| Hospitalists do not provide service | 37 (8.6%) |
| Don't know | 16 (3.7%) |
| Hospital practice guideline development | |
| Hospitalists provide service | 339 (79.0%) |
| Hospitalists do not provide service | 55 (12.8%) |
| Don't know | 35 (8.2%) |
| Implementation of major hospital system projects | |
| Hospitalists provide service | 309 (72.0%) |
| Hospitalists do not provide service | 96 (22.4%) |
| Don't know | 24 (5.6%) |
Relationship Between Hospitalist Utilization and Outcomes
Tables 3 and 4 show the comparisons between hospitals with and without hospitalists on each of the 6 outcome measures. In the bivariate analysis (Table 3), there was no statistically significant difference between groups on any of the outcome measures with the exception of the risk‐stratified readmission rate for heart failure. Sites with hospitalists had a lower RSRR for HF than sites without hospitalists (24.7% vs 25.4%, P < 0.0001). These results were similar in the multivariable models as seen in Table 4, in which the beta estimate (slope) was not significantly different for hospitals utilizing hospitalists compared to those that did not, on all measures except the RSRR for HF. For the subset of hospitals that used hospitalists, there was no statistically significant change in any of the 6 outcome measures, with increasing percentage of patients admitted by hospitalists. Table 5 demonstrates that for each RSMR and RSRR, the slope did not consistently increase or decrease with incrementally higher percentages of patients admitted by hospitalists, and the confidence intervals for all estimates crossed zero.
| Hospitalist Program | No Hospitalist Program | ||
|---|---|---|---|
| N = 429 | N = 169 | ||
| Outcome Measure | Mean % (SD) | Mean (SD) | P Value |
| |||
| MI RSMR | 16.0 (1.6) | 16.1 (1.5) | 0.56 |
| MI RSRR | 19.9 (0.88) | 20.0 (0.86) | 0.16 |
| HF RSMR | 11.3 (1.4) | 11.3 (1.4) | 0.77 |
| HF RSRR | 24.7 (1.6) | 25.4 (1.8) | <0.0001 |
| Pneumonia RSMR | 11.7 (1.7) | 12.0 (1.7) | 0.08 |
| Pneumonia RSRR | 18.2 (1.2) | 18.3 (1.1) | 0.28 |
| Adjusted beta estimate (95% CI) | |
|---|---|
| |
| MI RSMR | |
| Hospitalist | 0.001 (0.002, 004) |
| MI RSRR | |
| Hospitalist | 0.001 (0.002, 0.001) |
| HF RSMR | |
| Hospitalist | 0.0004 (0.002, 0.003) |
| HF RSRR | |
| Hospitalist | 0.006 (0.009, 0.003) |
| Pneumonia RSMR | |
| Hospitalist | 0.002 (0.005, 0.001) |
| Pneumonia RSRR | |
| Hospitalist | 0.00001 (0.002, 0.002) |
| Adjusted Beta Estimate (95% CI) | |
|---|---|
| |
| MI RSMR | |
| Percent admit | |
| 0%30% | 0.003 (0.007, 0.002) |
| 32%48% | 0.001 (0.005, 0.006) |
| 50%66% | Ref |
| 70%80% | 0.004 (0.001, 0.009) |
| 85% | 0.004 (0.009, 0.001) |
| MI RSRR | |
| Percent admit | |
| 0%30% | 0.001 (0.002, 0.004) |
| 32%48% | 0.001 (0.004, 0.004) |
| 50%66% | Ref |
| 70%80% | 0.001 (0.002, 0.004) |
| 85% | 0.001 (0.002, 0.004) |
| HF RSMR | |
| Percent admit | |
| 0%30% | 0.001 (0.005, 0.003) |
| 32%48% | 0.002 (0.007, 0.003) |
| 50%66% | Ref |
| 70%80% | 0.002 (0.006, 0.002) |
| 85% | 0.001 (0.004, 0.005) |
| HF RSRR | |
| Percent admit | |
| 0%30% | 0.002 (0.004, 0.007) |
| 32%48% | 0.0003 (0.005, 0.006) |
| 50%66% | Ref |
| 70%80% | 0.001 (0.005, 0.004) |
| 85% | 0.002 (0.007, 0.003) |
| Pneumonia RSMR | |
| Percent admit | |
| 0%30% | 0.001 (0.004, 0.006) |
| 32%48% | 0.00001 (0.006, 0.006) |
| 50%66% | Ref |
| 70%80% | 0.001 (0.004, 0.006) |
| 85% | 0.001 (0.006, 0.005) |
| Pneumonia RSRR | |
| Percent admit | |
| 0%30% | 0.0002 (0.004, 0.003) |
| 32%48% | 0.004 (0.0003, 0.008) |
| 50%66% | Ref |
| 70%80% | 0.001 (0.003, 0.004) |
| 85% | 0.002 (0.002, 0.006) |
DISCUSSION
In this national survey of hospitals, we did not find a significant association between the use of hospitalists and hospitals' performance on 30‐day mortality or readmissions measures for AMI, HF, or pneumonia. While there was a statistically lower 30‐day risk‐standardized readmission rate measure for the heart failure measure among hospitals that use hospitalists, the effect size was small. The survey response rate of 40% is comparable to other surveys of physicians and other healthcare personnel, however, there were no significant differences between responders and nonresponders, so the potential for response bias, while present, is small.
Contrary to the findings of a recent study,21 we did not find a higher readmission rate for any of the 3 conditions in hospitals with hospitalist programs. One advantage of our study is the use of more robust risk‐adjustment methods. Our study used NQF‐endorsed risk‐standardized measures of readmission, which capture readmissions to any hospital for common, high priority conditions where the impact of care coordination and discontinuity of care are paramount. The models use administrative claims data, but have been validated by medical record data. Another advantage is that our study focused on a time period when hospital readmissions were a standard quality benchmark and increasing priority for hospitals, hospitalists, and community‐based care delivery systems. While our study is not able to discern whether patients had primary care physicians or the reason for admission to a hospitalist's care, our data do suggest that hospitalists continue to care for a large percentage of hospitalized patients. Moreover, increasing the proportion of patients being admitted to hospitalists did not affect the risk for readmission, providing perhaps reassuring evidence (or lack of proof) for a direct association between use of hospitalist systems and higher risk for readmission.
While hospitals with hospitalists clearly did not have better mortality or readmission rates, an alternate viewpoint might hold that, despite concerns that hospitalists negatively impact care continuity, our data do not demonstrate an association between readmission rates and use of hospitalist services. It is possible that hospitals that have hospitalists may have more ability to invest in hospital‐based systems of care,22 an association which may incorporate any hospitalist effect, but our results were robust even after testing whether adjustment for hospital factors (such as profit status, size) affected our results.
It is also possible that secular trends in hospitals or hospitalist systems affected our results. A handful of single‐site studies carried out soon after the hospitalist model's earliest descriptions found a reduction in mortality and readmission rates with the implementation of a hospitalist program.2325 Alternatively, it may be that there has been a dilution of the effect of hospitalists as often occurs when any new innovation is spread from early adopter sites to routine practice. Consistent with other multicenter studies from recent eras,21, 26 our article's findings do not demonstrate an association between hospitalists and improved outcomes. Unlike other multicenter studies, we had access to disease‐specific risk‐adjustment methodologies, which may partially account for referral biases related to patient‐specific measures of acute or chronic illness severity.
Changes in the hospitalist effect over time have a number of explanations, some of which are relevant to our study. Recent evidence suggests that complex organizational characteristics, such as organizational values and goals, may contribute to performance on 30‐day mortality for AMI rather than specific processes and protocols27; intense focus on AMI as a quality improvement target is emblematic of a number of national initiatives that may have affected our results. Interestingly, hospitalist systems have changed over time as well. Early in the hospitalist movement, hospitalist systems were implemented largely at the behest of hospitals trying to reduce costs. In recent years, however, hospitalist systems are at least as frequently being implemented because outpatient‐based physicians or surgeons request hospitalists; hospitalists have been focused on care of uncoveredpatients, since the model's earliest description. In addition, some hospitals invest in hospitalist programs based on perceived ability of hospitalists to improve quality and achieve better patient outcomes in an era of payment increasingly being linked to quality of care metrics.
Our study has several limitations, six of which are noted here. First, while the hospitalist model has been widely embraced in the adult medicine field, in the absence of board certification, there is no gold standard definition of a hospitalist. It is therefore possible that some respondents may have represented groups that were identified incorrectly as hospitalists. Second, the data for the primary independent variable of interest was based upon self‐report and, therefore, subject to recall bias and potential misclassification of results. Respondents were not aware of our hypothesis, so the bias should not have been in one particular direction. Third, the data for the outcome variables are from 2008. They may, therefore, not reflect organizational enhancements related to use of hospitalists that are in process, and take years to yield downstream improvements on performance metrics. In addition, of the 429 hospitals that have hospitalist programs, 46 programs were initiated after 2008. While national performance on the 6 outcome variables has been relatively static over time,7 any significant change in hospital performance on these metrics since 2008 could suggest an overestimation or underestimation of the effect of hospitalist programs on patient outcomes. Fourth, we were not able to adjust for additional hospital or health system level characteristics that may be associated with hospitalist use or patient outcomes. Fifth, our regression models had significant collinearity, in that the presence of hospitalists was correlated with each of the covariates. However, this finding would indicate that our estimates may be overly conservative and could have contributed to our nonsignificant findings. Finally, outcomes for 2 of the 3 clinical conditions measured are ones for which hospitalists may less frequently provide care: acute myocardial infarction and heart failure. Outcome measures more relevant for hospitalists may be all‐condition, all‐cause, 30‐day mortality and readmission.
This work adds to the growing body of literature examining the impact of hospitalists on quality of care. To our knowledge, it is the first study to assess the association between hospitalist use and performance on outcome metrics at a national level. While our findings suggest that use of hospitalists alone may not lead to improved performance on outcome measures, a parallel body of research is emerging implicating broader system and organizational factors as key to high performance on outcome measures. It is likely that multiple factors contribute to performance on outcome measures, including type and mix of hospital personnel, patient care processes and workflow, and system level attributes. Comparative effectiveness and implementation research that assess the contextual factors and interventions that lead to successful system improvement and better performance is increasingly needed. It is unlikely that a single factor, such as hospitalist use, will significantly impact 30‐day mortality or readmission and, therefore, multifactorial interventions are likely required. In addition, hospitalist use is a complex intervention as the structure, processes, training, experience, role in the hospital system, and other factors (including quality of hospitalists or the hospitalist program) vary across programs. Rather than focusing on the volume of care delivered by hospitalists, hospitals will likely need to support hospital medicine programs that have the time and expertise to devote to improving the quality and value of care delivered across the hospital system. This study highlights that interventions leading to improvement on core outcome measures are more complex than simply having a hospital medicine program.
Acknowledgements
The authors acknowledge Judy Maselli, MPH, Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, for her assistance with statistical analyses and preparation of tables.
Disclosures: Work on this project was supported by the Robert Wood Johnson Clinical Scholars Program (K.G.); California Healthcare Foundation grant 15763 (A.D.A.); and a grant from the National Heart, Lung, and Blood Institute (NHLBI), study 1U01HL105270‐02 (H.M.K.). Dr Krumholz is the chair of the Cardiac Scientific Advisory Board for United Health and has a research grant with Medtronic through Yale University; Dr Auerbach has a grant through the National Heart, Lung, and Blood Institute (NHLBI). The authors have no other disclosures to report.
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The past several years have seen a dramatic increase in the percentage of patients cared for by hospitalists, yet an emerging body of literature examining the association between care given by hospitalists and performance on a number of process measures has shown mixed results. Hospitalists do not appear to provide higher quality of care for pneumonia,1, 2 while results in heart failure are mixed.35 Each of these studies was conducted at a single site, and examined patient‐level effects. More recently, Vasilevskis et al6 assessed the association between the intensity of hospitalist use (measured as the percentage of patients admitted by hospitalists) and performance on process measures. In a cohort of 208 California hospitals, they found a significant improvement in performance on process measures in patients with acute myocardial infarction, heart failure, and pneumonia with increasing percentages of patients admitted by hospitalists.6
To date, no study has examined the association between the use of hospitalists and the publicly reported 30‐day mortality and readmission measures. Specifically, the Centers for Medicare and Medicaid Services (CMS) have developed and now publicly report risk‐standardized 30‐day mortality (RSMR) and readmission rates (RSRR) for Medicare patients hospitalized for 3 common and costly conditionsacute myocardial infarction (AMI), heart failure (HF), and pneumonia.7 Performance on these hospital‐based quality measures varies widely, and vary by hospital volume, ownership status, teaching status, and nurse staffing levels.813 However, even accounting for these characteristics leaves much of the variation in outcomes unexplained. We hypothesized that the presence of hospitalists within a hospital would be associated with higher performance on 30‐day mortality and 30‐day readmission measures for AMI, HF, and pneumonia. We further hypothesized that for hospitals using hospitalists, there would be a positive correlation between increasing percentage of patients admitted by hospitalists and performance on outcome measures. To test these hypotheses, we conducted a national survey of hospitalist leaders, linking data from survey responses to data on publicly reported outcome measures for AMI, HF, and pneumonia.
MATERIALS AND METHODS
Study Sites
Of the 4289 hospitals in operation in 2008, 1945 had 25 or more AMI discharges. We identified hospitals using American Hospital Association (AHA) data, calling hospitals up to 6 times each until we reached our target sample size of 600. Using this methodology, we contacted 1558 hospitals of a possible 1920 with AHA data; of the 1558 called, 598 provided survey results.
Survey Data
Our survey was adapted from the survey developed by Vasilevskis et al.6 The entire survey can be found in the Appendix (see Supporting Information in the online version of this article). Our key questions were: 1) Does your hospital have at least 1 hospitalist program or group? 2) Approximately what percentage of all medical patients in your hospital are admitted by hospitalists? The latter question was intended as an approximation of the intensity of hospitalist use, and has been used in prior studies.6, 14 A more direct measure was not feasible given the complexity of obtaining admission data for such a large and diverse set of hospitals. Respondents were also asked about hospitalist care of AMI, HF, and pneumonia patients. Given the low likelihood of precise estimation of hospitalist participation in care for specific conditions, the response choices were divided into percentage quartiles: 025, 2650, 5175, and 76100. Finally, participants were asked a number of questions regarding hospitalist organizational and clinical characteristics.
Survey Process
We obtained data regarding presence or absence of hospitalists and characteristics of the hospitalist services via phone‐ and fax‐administered survey (see Supporting Information, Appendix, in the online version of this article). Telephone and faxed surveys were administered between February 2010 and January 2011. Hospital telephone numbers were obtained from the 2008 AHA survey database and from a review of each hospital's website. Up to 6 attempts were made to obtain a completed survey from nonrespondents unless participation was specifically refused. Potential respondents were contacted in the following order: hospital medicine department leaders, hospital medicine clinical managers, vice president for medical affairs, chief medical officers, and other hospital executives with knowledge of the hospital medicine services. All respondents agreed with a question asking whether they had direct working knowledge of their hospital medicine services; contacts who said they did not have working knowledge of their hospital medicine services were asked to refer our surveyor to the appropriate person at their site. Absence of a hospitalist program was confirmed by contacting the Medical Staff Office.
Hospital Organizational and Patient‐Mix Characteristics
Hospital‐level organizational characteristics (eg, bed size, teaching status) and patient‐mix characteristics (eg, Medicare and Medicaid inpatient days) were obtained from the 2008 AHA survey database.
Outcome Performance Measures
The 30‐day risk‐standardized mortality and readmission rates (RSMR and RSRR) for 2008 for AMI, HF, and pneumonia were calculated for all admissions for people age 65 and over with traditional fee‐for‐service Medicare. Beneficiaries had to be enrolled for 12 months prior to their hospitalization for any of the 3 conditions, and had to have complete claims data available for that 12‐month period.7 These 6 outcome measures were constructed using hierarchical generalized linear models.1520 Using the RSMR for AMI as an example, for each hospital, the measure is estimated by dividing the predicted number of deaths within 30 days of admission for AMI by the expected number of deaths within 30 days of admission for AMI. This ratio is then divided by the national unadjusted 30‐day mortality rate for AMI, which is obtained using data on deaths from the Medicare beneficiary denominator file. Each measure is adjusted for patient characteristics such as age, gender, and comorbidities. All 6 measures are endorsed by the National Quality Forum (NQF) and are reported publicly by CMS on the Hospital Compare web site.
Statistical Analysis
Comparison of hospital‐ and patient‐level characteristics between hospitals with and without hospitalists was performed using chi‐square tests and Student t tests.
The primary outcome variables are the RSMRs and RSRRs for AMI, HF, and pneumonia. Multivariable linear regression models were used to assess the relationship between hospitals with at least 1 hospitalist group and each dependent variable. Models were adjusted for variables previously reported to be associated with quality of care. Hospital‐level characteristics included core‐based statistical area, teaching status, number of beds, region, safety‐net status, nursing staff ratio (number of registered nurse FTEs/number of hospital FTEs), and presence or absence of cardiac catheterization and coronary bypass capability. Patient‐level characteristics included Medicare and Medicaid inpatient days as a percentage of total inpatient days and percentage of admissions by race (black vs non‐black). The presence of hospitalists was correlated with each of the hospital and patient‐level characteristics. Further analyses of the subset of hospitals that use hospitalists included construction of multivariable linear regression models to assess the relationship between the percentage of patients admitted by hospitalists and the dependent variables. Models were adjusted for the same patient‐ and hospital‐level characteristics.
The institutional review boards at Yale University and University of California, San Francisco approved the study. All analyses were performed using Statistical Analysis Software (SAS) version 9.1 (SAS Institute, Inc, Cary, NC).
RESULTS
Characteristics of Participating Hospitals
Telephone, fax, and e‐mail surveys were attempted with 1558 hospitals; we received 598 completed surveys for a response rate of 40%. There was no difference between responders and nonresponders on any of the 6 outcome variables, the number of Medicare or Medicaid inpatient days, and the percentage of admissions by race. Responders and nonresponders were also similar in size, ownership, safety‐net and teaching status, nursing staff ratio, presence of cardiac catheterization and coronary bypass capability, and core‐based statistical area. They differed only on region of the country, where hospitals in the northwest Central and Pacific regions of the country had larger overall proportions of respondents. All hospitals provided information about the presence or absence of hospitalist programs. The majority of respondents were hospitalist clinical or administrative managers (n = 220) followed by hospitalist leaders (n = 106), other executives (n = 58), vice presidents for medical affairs (n = 39), and chief medical officers (n = 15). Each respondent indicated a working knowledge of their site's hospitalist utilization and practice characteristics. Absence of hospitalist utilization was confirmed by contact with the Medical Staff Office.
Comparisons of Sites With Hospitalists and Those Without Hospitalists
Hospitals with and without hospitalists differed by a number of organizational characteristics (Table 1). Sites with hospitalists were more likely to be larger, nonprofit teaching hospitals, located in metropolitan regions, and have cardiac surgical services. There was no difference in the hospitals' safety‐net status or RN staffing ratio. Hospitals with hospitalists admitted lower percentages of black patients.
| Hospitalist Program | No Hospitalist Program | ||
|---|---|---|---|
| N = 429 | N = 169 | ||
| N (%) | N (%) | P Value | |
| |||
| Core‐based statistical area | <0.0001 | ||
| Division | 94 (21.9%) | 53 (31.4%) | |
| Metro | 275 (64.1%) | 72 (42.6%) | |
| Micro | 52 (12.1%) | 38 (22.5%) | |
| Rural | 8 (1.9%) | 6 (3.6%) | |
| Owner | 0.0003 | ||
| Public | 47 (11.0%) | 20 (11.8%) | |
| Nonprofit | 333 (77.6%) | 108 (63.9%) | |
| Private | 49 (11.4%) | 41 (24.3%) | |
| Teaching status | <0.0001 | ||
| COTH | 54 (12.6%) | 7 (4.1%) | |
| Teaching | 110 (25.6%) | 26 (15.4%) | |
| Other | 265 (61.8%) | 136 (80.5%) | |
| Cardiac type | 0.0003 | ||
| CABG | 286 (66.7%) | 86 (50.9%) | |
| CATH | 79 (18.4%) | 36 (21.3%) | |
| Other | 64 (14.9%) | 47 (27.8%) | |
| Region | 0.007 | ||
| New England | 35 (8.2%) | 3 (1.8%) | |
| Middle Atlantic | 60 (14.0%) | 29 (17.2%) | |
| South Atlantic | 78 (18.2%) | 23 (13.6%) | |
| NE Central | 60 (14.0%) | 35 (20.7%) | |
| SE Central | 31 (7.2%) | 10 (5.9%) | |
| NW Central | 38 (8.9%) | 23 (13.6%) | |
| SW Central | 41 (9.6%) | 21 (12.4%) | |
| Mountain | 22 (5.1%) | 3 (1.8%) | |
| Pacific | 64 (14.9%) | 22 (13.0%) | |
| Safety‐net | 0.53 | ||
| Yes | 72 (16.8%) | 32 (18.9%) | |
| No | 357 (83.2%) | 137 (81.1%) | |
| Mean (SD) | Mean (SD) | P value | |
| RN staffing ratio (n = 455) | 27.3 (17.0) | 26.1 (7.6) | 0.28 |
| Total beds | 315.0 (216.6) | 214.8 (136.0) | <0.0001 |
| % Medicare inpatient days | 47.2 (42) | 49.7 (41) | 0.19 |
| % Medicaid inpatient days | 18.5 (28) | 21.4 (46) | 0.16 |
| % Black | 7.6 (9.6) | 10.6 (17.4) | 0.03 |
Characteristics of Hospitalist Programs and Responsibilities
Of the 429 sites reporting use of hospitalists, the median percentage of patients admitted by hospitalists was 60%, with an interquartile range (IQR) of 35% to 80%. The median number of full‐time equivalent hospitalists per hospital was 8 with an IQR of 5 to 14. The IQR reflects the middle 50% of the distribution of responses, and is not affected by outliers or extreme values. Additional characteristics of hospitalist programs can be found in Table 2. The estimated percentage of patients with AMI, HF, and pneumonia cared for by hospitalists varied considerably, with fewer patients with AMI and more patients with pneumonia under hospitalist care. Overall, a majority of hospitalist groups provided the following services: care of critical care patients, emergency department admission screening, observation unit coverage, coverage for cardiac arrests and rapid response teams, quality improvement or utilization review activities, development of hospital practice guidelines, and participation in implementation of major hospital system projects (such as implementation of an electronic health record system).
| N (%) | |
|---|---|
| |
| Date program established | |
| 19871994 | 9 (2.2%) |
| 19952002 | 130 (32.1%) |
| 20032011 | 266 (65.7%) |
| Missing date | 24 |
| No. of hospitalist FTEs | |
| Median (IQR) | 8 (5, 14) |
| Percent of medical patients admitted by hospitalists | |
| Median (IQR) | 60% (35, 80) |
| No. of hospitalists groups | |
| 1 | 333 (77.6%) |
| 2 | 54 (12.6%) |
| 3 | 36 (8.4%) |
| Don't know | 6 (1.4%) |
| Employment of hospitalists (not mutually exclusive) | |
| Hospital system | 98 (22.8%) |
| Hospital | 185 (43.1%) |
| Local physician practice group | 62 (14.5%) |
| Hospitalist physician practice group (local) | 83 (19.3%) |
| Hospitalist physician practice group (national/regional) | 36 (8.4%) |
| Other/unknown | 36 (8.4%) |
| Any 24‐hr in‐house coverage by hospitalists | |
| Yes | 329 (76.7%) |
| No | 98 (22.8%) |
| 3 | 1 (0.2%) |
| Unknown | 1 (0.2%) |
| No. of hospitalist international medical graduates | |
| Median (IQR) | 3 (1, 6) |
| No. of hospitalists that are <1 yr out of residency | |
| Median (IQR) | 1 (0, 2) |
| Percent of patients with AMI cared for by hospitalists | |
| 0%25% | 148 (34.5%) |
| 26%50% | 67 (15.6%) |
| 51%75% | 50 (11.7%) |
| 76%100% | 54 (12.6%) |
| Don't know | 110 (25.6%) |
| Percent of patients with heart failure cared for by hospitalists | |
| 0%25% | 79 (18.4%) |
| 26%50% | 78 (18.2%) |
| 51%75% | 75 (17.5%) |
| 76%100% | 84 (19.6%) |
| Don't know | 113 (26.3%) |
| Percent of patients with pneumonia cared for by hospitalists | |
| 0%25% | 47 (11.0%) |
| 26%50% | 61 (14.3%) |
| 51%75% | 74 (17.3%) |
| 76%100% | 141 (32.9%) |
| Don't know | 105 (24.5%) |
| Hospitalist provision of services | |
| Care of critical care patients | |
| Hospitalists provide service | 346 (80.7%) |
| Hospitalists do not provide service | 80 (18.7%) |
| Don't know | 3 (0.7%) |
| Emergency department admission screening | |
| Hospitalists provide service | 281 (65.5%) |
| Hospitalists do not provide service | 143 (33.3%) |
| Don't know | 5 (1.2%) |
| Observation unit coverage | |
| Hospitalists provide service | 359 (83.7%) |
| Hospitalists do not provide service | 64 (14.9%) |
| Don't know | 6 (1.4%) |
| Emergency department coverage | |
| Hospitalists provide service | 145 (33.8%) |
| Hospitalists do not provide service | 280 (65.3%) |
| Don't know | 4 (0.9%) |
| Coverage for cardiac arrests | |
| Hospitalists provide service | 283 (66.0%) |
| Hospitalists do not provide service | 135 (31.5%) |
| Don't know | 11 (2.6%) |
| Rapid response team coverage | |
| Hospitalists provide service | 240 (55.9%) |
| Hospitalists do not provide service | 168 (39.2%) |
| Don't know | 21 (4.9%) |
| Quality improvement or utilization review | |
| Hospitalists provide service | 376 (87.7%) |
| Hospitalists do not provide service | 37 (8.6%) |
| Don't know | 16 (3.7%) |
| Hospital practice guideline development | |
| Hospitalists provide service | 339 (79.0%) |
| Hospitalists do not provide service | 55 (12.8%) |
| Don't know | 35 (8.2%) |
| Implementation of major hospital system projects | |
| Hospitalists provide service | 309 (72.0%) |
| Hospitalists do not provide service | 96 (22.4%) |
| Don't know | 24 (5.6%) |
Relationship Between Hospitalist Utilization and Outcomes
Tables 3 and 4 show the comparisons between hospitals with and without hospitalists on each of the 6 outcome measures. In the bivariate analysis (Table 3), there was no statistically significant difference between groups on any of the outcome measures with the exception of the risk‐stratified readmission rate for heart failure. Sites with hospitalists had a lower RSRR for HF than sites without hospitalists (24.7% vs 25.4%, P < 0.0001). These results were similar in the multivariable models as seen in Table 4, in which the beta estimate (slope) was not significantly different for hospitals utilizing hospitalists compared to those that did not, on all measures except the RSRR for HF. For the subset of hospitals that used hospitalists, there was no statistically significant change in any of the 6 outcome measures, with increasing percentage of patients admitted by hospitalists. Table 5 demonstrates that for each RSMR and RSRR, the slope did not consistently increase or decrease with incrementally higher percentages of patients admitted by hospitalists, and the confidence intervals for all estimates crossed zero.
| Hospitalist Program | No Hospitalist Program | ||
|---|---|---|---|
| N = 429 | N = 169 | ||
| Outcome Measure | Mean % (SD) | Mean (SD) | P Value |
| |||
| MI RSMR | 16.0 (1.6) | 16.1 (1.5) | 0.56 |
| MI RSRR | 19.9 (0.88) | 20.0 (0.86) | 0.16 |
| HF RSMR | 11.3 (1.4) | 11.3 (1.4) | 0.77 |
| HF RSRR | 24.7 (1.6) | 25.4 (1.8) | <0.0001 |
| Pneumonia RSMR | 11.7 (1.7) | 12.0 (1.7) | 0.08 |
| Pneumonia RSRR | 18.2 (1.2) | 18.3 (1.1) | 0.28 |
| Adjusted beta estimate (95% CI) | |
|---|---|
| |
| MI RSMR | |
| Hospitalist | 0.001 (0.002, 004) |
| MI RSRR | |
| Hospitalist | 0.001 (0.002, 0.001) |
| HF RSMR | |
| Hospitalist | 0.0004 (0.002, 0.003) |
| HF RSRR | |
| Hospitalist | 0.006 (0.009, 0.003) |
| Pneumonia RSMR | |
| Hospitalist | 0.002 (0.005, 0.001) |
| Pneumonia RSRR | |
| Hospitalist | 0.00001 (0.002, 0.002) |
| Adjusted Beta Estimate (95% CI) | |
|---|---|
| |
| MI RSMR | |
| Percent admit | |
| 0%30% | 0.003 (0.007, 0.002) |
| 32%48% | 0.001 (0.005, 0.006) |
| 50%66% | Ref |
| 70%80% | 0.004 (0.001, 0.009) |
| 85% | 0.004 (0.009, 0.001) |
| MI RSRR | |
| Percent admit | |
| 0%30% | 0.001 (0.002, 0.004) |
| 32%48% | 0.001 (0.004, 0.004) |
| 50%66% | Ref |
| 70%80% | 0.001 (0.002, 0.004) |
| 85% | 0.001 (0.002, 0.004) |
| HF RSMR | |
| Percent admit | |
| 0%30% | 0.001 (0.005, 0.003) |
| 32%48% | 0.002 (0.007, 0.003) |
| 50%66% | Ref |
| 70%80% | 0.002 (0.006, 0.002) |
| 85% | 0.001 (0.004, 0.005) |
| HF RSRR | |
| Percent admit | |
| 0%30% | 0.002 (0.004, 0.007) |
| 32%48% | 0.0003 (0.005, 0.006) |
| 50%66% | Ref |
| 70%80% | 0.001 (0.005, 0.004) |
| 85% | 0.002 (0.007, 0.003) |
| Pneumonia RSMR | |
| Percent admit | |
| 0%30% | 0.001 (0.004, 0.006) |
| 32%48% | 0.00001 (0.006, 0.006) |
| 50%66% | Ref |
| 70%80% | 0.001 (0.004, 0.006) |
| 85% | 0.001 (0.006, 0.005) |
| Pneumonia RSRR | |
| Percent admit | |
| 0%30% | 0.0002 (0.004, 0.003) |
| 32%48% | 0.004 (0.0003, 0.008) |
| 50%66% | Ref |
| 70%80% | 0.001 (0.003, 0.004) |
| 85% | 0.002 (0.002, 0.006) |
DISCUSSION
In this national survey of hospitals, we did not find a significant association between the use of hospitalists and hospitals' performance on 30‐day mortality or readmissions measures for AMI, HF, or pneumonia. While there was a statistically lower 30‐day risk‐standardized readmission rate measure for the heart failure measure among hospitals that use hospitalists, the effect size was small. The survey response rate of 40% is comparable to other surveys of physicians and other healthcare personnel, however, there were no significant differences between responders and nonresponders, so the potential for response bias, while present, is small.
Contrary to the findings of a recent study,21 we did not find a higher readmission rate for any of the 3 conditions in hospitals with hospitalist programs. One advantage of our study is the use of more robust risk‐adjustment methods. Our study used NQF‐endorsed risk‐standardized measures of readmission, which capture readmissions to any hospital for common, high priority conditions where the impact of care coordination and discontinuity of care are paramount. The models use administrative claims data, but have been validated by medical record data. Another advantage is that our study focused on a time period when hospital readmissions were a standard quality benchmark and increasing priority for hospitals, hospitalists, and community‐based care delivery systems. While our study is not able to discern whether patients had primary care physicians or the reason for admission to a hospitalist's care, our data do suggest that hospitalists continue to care for a large percentage of hospitalized patients. Moreover, increasing the proportion of patients being admitted to hospitalists did not affect the risk for readmission, providing perhaps reassuring evidence (or lack of proof) for a direct association between use of hospitalist systems and higher risk for readmission.
While hospitals with hospitalists clearly did not have better mortality or readmission rates, an alternate viewpoint might hold that, despite concerns that hospitalists negatively impact care continuity, our data do not demonstrate an association between readmission rates and use of hospitalist services. It is possible that hospitals that have hospitalists may have more ability to invest in hospital‐based systems of care,22 an association which may incorporate any hospitalist effect, but our results were robust even after testing whether adjustment for hospital factors (such as profit status, size) affected our results.
It is also possible that secular trends in hospitals or hospitalist systems affected our results. A handful of single‐site studies carried out soon after the hospitalist model's earliest descriptions found a reduction in mortality and readmission rates with the implementation of a hospitalist program.2325 Alternatively, it may be that there has been a dilution of the effect of hospitalists as often occurs when any new innovation is spread from early adopter sites to routine practice. Consistent with other multicenter studies from recent eras,21, 26 our article's findings do not demonstrate an association between hospitalists and improved outcomes. Unlike other multicenter studies, we had access to disease‐specific risk‐adjustment methodologies, which may partially account for referral biases related to patient‐specific measures of acute or chronic illness severity.
Changes in the hospitalist effect over time have a number of explanations, some of which are relevant to our study. Recent evidence suggests that complex organizational characteristics, such as organizational values and goals, may contribute to performance on 30‐day mortality for AMI rather than specific processes and protocols27; intense focus on AMI as a quality improvement target is emblematic of a number of national initiatives that may have affected our results. Interestingly, hospitalist systems have changed over time as well. Early in the hospitalist movement, hospitalist systems were implemented largely at the behest of hospitals trying to reduce costs. In recent years, however, hospitalist systems are at least as frequently being implemented because outpatient‐based physicians or surgeons request hospitalists; hospitalists have been focused on care of uncoveredpatients, since the model's earliest description. In addition, some hospitals invest in hospitalist programs based on perceived ability of hospitalists to improve quality and achieve better patient outcomes in an era of payment increasingly being linked to quality of care metrics.
Our study has several limitations, six of which are noted here. First, while the hospitalist model has been widely embraced in the adult medicine field, in the absence of board certification, there is no gold standard definition of a hospitalist. It is therefore possible that some respondents may have represented groups that were identified incorrectly as hospitalists. Second, the data for the primary independent variable of interest was based upon self‐report and, therefore, subject to recall bias and potential misclassification of results. Respondents were not aware of our hypothesis, so the bias should not have been in one particular direction. Third, the data for the outcome variables are from 2008. They may, therefore, not reflect organizational enhancements related to use of hospitalists that are in process, and take years to yield downstream improvements on performance metrics. In addition, of the 429 hospitals that have hospitalist programs, 46 programs were initiated after 2008. While national performance on the 6 outcome variables has been relatively static over time,7 any significant change in hospital performance on these metrics since 2008 could suggest an overestimation or underestimation of the effect of hospitalist programs on patient outcomes. Fourth, we were not able to adjust for additional hospital or health system level characteristics that may be associated with hospitalist use or patient outcomes. Fifth, our regression models had significant collinearity, in that the presence of hospitalists was correlated with each of the covariates. However, this finding would indicate that our estimates may be overly conservative and could have contributed to our nonsignificant findings. Finally, outcomes for 2 of the 3 clinical conditions measured are ones for which hospitalists may less frequently provide care: acute myocardial infarction and heart failure. Outcome measures more relevant for hospitalists may be all‐condition, all‐cause, 30‐day mortality and readmission.
This work adds to the growing body of literature examining the impact of hospitalists on quality of care. To our knowledge, it is the first study to assess the association between hospitalist use and performance on outcome metrics at a national level. While our findings suggest that use of hospitalists alone may not lead to improved performance on outcome measures, a parallel body of research is emerging implicating broader system and organizational factors as key to high performance on outcome measures. It is likely that multiple factors contribute to performance on outcome measures, including type and mix of hospital personnel, patient care processes and workflow, and system level attributes. Comparative effectiveness and implementation research that assess the contextual factors and interventions that lead to successful system improvement and better performance is increasingly needed. It is unlikely that a single factor, such as hospitalist use, will significantly impact 30‐day mortality or readmission and, therefore, multifactorial interventions are likely required. In addition, hospitalist use is a complex intervention as the structure, processes, training, experience, role in the hospital system, and other factors (including quality of hospitalists or the hospitalist program) vary across programs. Rather than focusing on the volume of care delivered by hospitalists, hospitals will likely need to support hospital medicine programs that have the time and expertise to devote to improving the quality and value of care delivered across the hospital system. This study highlights that interventions leading to improvement on core outcome measures are more complex than simply having a hospital medicine program.
Acknowledgements
The authors acknowledge Judy Maselli, MPH, Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, for her assistance with statistical analyses and preparation of tables.
Disclosures: Work on this project was supported by the Robert Wood Johnson Clinical Scholars Program (K.G.); California Healthcare Foundation grant 15763 (A.D.A.); and a grant from the National Heart, Lung, and Blood Institute (NHLBI), study 1U01HL105270‐02 (H.M.K.). Dr Krumholz is the chair of the Cardiac Scientific Advisory Board for United Health and has a research grant with Medtronic through Yale University; Dr Auerbach has a grant through the National Heart, Lung, and Blood Institute (NHLBI). The authors have no other disclosures to report.
The past several years have seen a dramatic increase in the percentage of patients cared for by hospitalists, yet an emerging body of literature examining the association between care given by hospitalists and performance on a number of process measures has shown mixed results. Hospitalists do not appear to provide higher quality of care for pneumonia,1, 2 while results in heart failure are mixed.35 Each of these studies was conducted at a single site, and examined patient‐level effects. More recently, Vasilevskis et al6 assessed the association between the intensity of hospitalist use (measured as the percentage of patients admitted by hospitalists) and performance on process measures. In a cohort of 208 California hospitals, they found a significant improvement in performance on process measures in patients with acute myocardial infarction, heart failure, and pneumonia with increasing percentages of patients admitted by hospitalists.6
To date, no study has examined the association between the use of hospitalists and the publicly reported 30‐day mortality and readmission measures. Specifically, the Centers for Medicare and Medicaid Services (CMS) have developed and now publicly report risk‐standardized 30‐day mortality (RSMR) and readmission rates (RSRR) for Medicare patients hospitalized for 3 common and costly conditionsacute myocardial infarction (AMI), heart failure (HF), and pneumonia.7 Performance on these hospital‐based quality measures varies widely, and vary by hospital volume, ownership status, teaching status, and nurse staffing levels.813 However, even accounting for these characteristics leaves much of the variation in outcomes unexplained. We hypothesized that the presence of hospitalists within a hospital would be associated with higher performance on 30‐day mortality and 30‐day readmission measures for AMI, HF, and pneumonia. We further hypothesized that for hospitals using hospitalists, there would be a positive correlation between increasing percentage of patients admitted by hospitalists and performance on outcome measures. To test these hypotheses, we conducted a national survey of hospitalist leaders, linking data from survey responses to data on publicly reported outcome measures for AMI, HF, and pneumonia.
MATERIALS AND METHODS
Study Sites
Of the 4289 hospitals in operation in 2008, 1945 had 25 or more AMI discharges. We identified hospitals using American Hospital Association (AHA) data, calling hospitals up to 6 times each until we reached our target sample size of 600. Using this methodology, we contacted 1558 hospitals of a possible 1920 with AHA data; of the 1558 called, 598 provided survey results.
Survey Data
Our survey was adapted from the survey developed by Vasilevskis et al.6 The entire survey can be found in the Appendix (see Supporting Information in the online version of this article). Our key questions were: 1) Does your hospital have at least 1 hospitalist program or group? 2) Approximately what percentage of all medical patients in your hospital are admitted by hospitalists? The latter question was intended as an approximation of the intensity of hospitalist use, and has been used in prior studies.6, 14 A more direct measure was not feasible given the complexity of obtaining admission data for such a large and diverse set of hospitals. Respondents were also asked about hospitalist care of AMI, HF, and pneumonia patients. Given the low likelihood of precise estimation of hospitalist participation in care for specific conditions, the response choices were divided into percentage quartiles: 025, 2650, 5175, and 76100. Finally, participants were asked a number of questions regarding hospitalist organizational and clinical characteristics.
Survey Process
We obtained data regarding presence or absence of hospitalists and characteristics of the hospitalist services via phone‐ and fax‐administered survey (see Supporting Information, Appendix, in the online version of this article). Telephone and faxed surveys were administered between February 2010 and January 2011. Hospital telephone numbers were obtained from the 2008 AHA survey database and from a review of each hospital's website. Up to 6 attempts were made to obtain a completed survey from nonrespondents unless participation was specifically refused. Potential respondents were contacted in the following order: hospital medicine department leaders, hospital medicine clinical managers, vice president for medical affairs, chief medical officers, and other hospital executives with knowledge of the hospital medicine services. All respondents agreed with a question asking whether they had direct working knowledge of their hospital medicine services; contacts who said they did not have working knowledge of their hospital medicine services were asked to refer our surveyor to the appropriate person at their site. Absence of a hospitalist program was confirmed by contacting the Medical Staff Office.
Hospital Organizational and Patient‐Mix Characteristics
Hospital‐level organizational characteristics (eg, bed size, teaching status) and patient‐mix characteristics (eg, Medicare and Medicaid inpatient days) were obtained from the 2008 AHA survey database.
Outcome Performance Measures
The 30‐day risk‐standardized mortality and readmission rates (RSMR and RSRR) for 2008 for AMI, HF, and pneumonia were calculated for all admissions for people age 65 and over with traditional fee‐for‐service Medicare. Beneficiaries had to be enrolled for 12 months prior to their hospitalization for any of the 3 conditions, and had to have complete claims data available for that 12‐month period.7 These 6 outcome measures were constructed using hierarchical generalized linear models.1520 Using the RSMR for AMI as an example, for each hospital, the measure is estimated by dividing the predicted number of deaths within 30 days of admission for AMI by the expected number of deaths within 30 days of admission for AMI. This ratio is then divided by the national unadjusted 30‐day mortality rate for AMI, which is obtained using data on deaths from the Medicare beneficiary denominator file. Each measure is adjusted for patient characteristics such as age, gender, and comorbidities. All 6 measures are endorsed by the National Quality Forum (NQF) and are reported publicly by CMS on the Hospital Compare web site.
Statistical Analysis
Comparison of hospital‐ and patient‐level characteristics between hospitals with and without hospitalists was performed using chi‐square tests and Student t tests.
The primary outcome variables are the RSMRs and RSRRs for AMI, HF, and pneumonia. Multivariable linear regression models were used to assess the relationship between hospitals with at least 1 hospitalist group and each dependent variable. Models were adjusted for variables previously reported to be associated with quality of care. Hospital‐level characteristics included core‐based statistical area, teaching status, number of beds, region, safety‐net status, nursing staff ratio (number of registered nurse FTEs/number of hospital FTEs), and presence or absence of cardiac catheterization and coronary bypass capability. Patient‐level characteristics included Medicare and Medicaid inpatient days as a percentage of total inpatient days and percentage of admissions by race (black vs non‐black). The presence of hospitalists was correlated with each of the hospital and patient‐level characteristics. Further analyses of the subset of hospitals that use hospitalists included construction of multivariable linear regression models to assess the relationship between the percentage of patients admitted by hospitalists and the dependent variables. Models were adjusted for the same patient‐ and hospital‐level characteristics.
The institutional review boards at Yale University and University of California, San Francisco approved the study. All analyses were performed using Statistical Analysis Software (SAS) version 9.1 (SAS Institute, Inc, Cary, NC).
RESULTS
Characteristics of Participating Hospitals
Telephone, fax, and e‐mail surveys were attempted with 1558 hospitals; we received 598 completed surveys for a response rate of 40%. There was no difference between responders and nonresponders on any of the 6 outcome variables, the number of Medicare or Medicaid inpatient days, and the percentage of admissions by race. Responders and nonresponders were also similar in size, ownership, safety‐net and teaching status, nursing staff ratio, presence of cardiac catheterization and coronary bypass capability, and core‐based statistical area. They differed only on region of the country, where hospitals in the northwest Central and Pacific regions of the country had larger overall proportions of respondents. All hospitals provided information about the presence or absence of hospitalist programs. The majority of respondents were hospitalist clinical or administrative managers (n = 220) followed by hospitalist leaders (n = 106), other executives (n = 58), vice presidents for medical affairs (n = 39), and chief medical officers (n = 15). Each respondent indicated a working knowledge of their site's hospitalist utilization and practice characteristics. Absence of hospitalist utilization was confirmed by contact with the Medical Staff Office.
Comparisons of Sites With Hospitalists and Those Without Hospitalists
Hospitals with and without hospitalists differed by a number of organizational characteristics (Table 1). Sites with hospitalists were more likely to be larger, nonprofit teaching hospitals, located in metropolitan regions, and have cardiac surgical services. There was no difference in the hospitals' safety‐net status or RN staffing ratio. Hospitals with hospitalists admitted lower percentages of black patients.
| Hospitalist Program | No Hospitalist Program | ||
|---|---|---|---|
| N = 429 | N = 169 | ||
| N (%) | N (%) | P Value | |
| |||
| Core‐based statistical area | <0.0001 | ||
| Division | 94 (21.9%) | 53 (31.4%) | |
| Metro | 275 (64.1%) | 72 (42.6%) | |
| Micro | 52 (12.1%) | 38 (22.5%) | |
| Rural | 8 (1.9%) | 6 (3.6%) | |
| Owner | 0.0003 | ||
| Public | 47 (11.0%) | 20 (11.8%) | |
| Nonprofit | 333 (77.6%) | 108 (63.9%) | |
| Private | 49 (11.4%) | 41 (24.3%) | |
| Teaching status | <0.0001 | ||
| COTH | 54 (12.6%) | 7 (4.1%) | |
| Teaching | 110 (25.6%) | 26 (15.4%) | |
| Other | 265 (61.8%) | 136 (80.5%) | |
| Cardiac type | 0.0003 | ||
| CABG | 286 (66.7%) | 86 (50.9%) | |
| CATH | 79 (18.4%) | 36 (21.3%) | |
| Other | 64 (14.9%) | 47 (27.8%) | |
| Region | 0.007 | ||
| New England | 35 (8.2%) | 3 (1.8%) | |
| Middle Atlantic | 60 (14.0%) | 29 (17.2%) | |
| South Atlantic | 78 (18.2%) | 23 (13.6%) | |
| NE Central | 60 (14.0%) | 35 (20.7%) | |
| SE Central | 31 (7.2%) | 10 (5.9%) | |
| NW Central | 38 (8.9%) | 23 (13.6%) | |
| SW Central | 41 (9.6%) | 21 (12.4%) | |
| Mountain | 22 (5.1%) | 3 (1.8%) | |
| Pacific | 64 (14.9%) | 22 (13.0%) | |
| Safety‐net | 0.53 | ||
| Yes | 72 (16.8%) | 32 (18.9%) | |
| No | 357 (83.2%) | 137 (81.1%) | |
| Mean (SD) | Mean (SD) | P value | |
| RN staffing ratio (n = 455) | 27.3 (17.0) | 26.1 (7.6) | 0.28 |
| Total beds | 315.0 (216.6) | 214.8 (136.0) | <0.0001 |
| % Medicare inpatient days | 47.2 (42) | 49.7 (41) | 0.19 |
| % Medicaid inpatient days | 18.5 (28) | 21.4 (46) | 0.16 |
| % Black | 7.6 (9.6) | 10.6 (17.4) | 0.03 |
Characteristics of Hospitalist Programs and Responsibilities
Of the 429 sites reporting use of hospitalists, the median percentage of patients admitted by hospitalists was 60%, with an interquartile range (IQR) of 35% to 80%. The median number of full‐time equivalent hospitalists per hospital was 8 with an IQR of 5 to 14. The IQR reflects the middle 50% of the distribution of responses, and is not affected by outliers or extreme values. Additional characteristics of hospitalist programs can be found in Table 2. The estimated percentage of patients with AMI, HF, and pneumonia cared for by hospitalists varied considerably, with fewer patients with AMI and more patients with pneumonia under hospitalist care. Overall, a majority of hospitalist groups provided the following services: care of critical care patients, emergency department admission screening, observation unit coverage, coverage for cardiac arrests and rapid response teams, quality improvement or utilization review activities, development of hospital practice guidelines, and participation in implementation of major hospital system projects (such as implementation of an electronic health record system).
| N (%) | |
|---|---|
| |
| Date program established | |
| 19871994 | 9 (2.2%) |
| 19952002 | 130 (32.1%) |
| 20032011 | 266 (65.7%) |
| Missing date | 24 |
| No. of hospitalist FTEs | |
| Median (IQR) | 8 (5, 14) |
| Percent of medical patients admitted by hospitalists | |
| Median (IQR) | 60% (35, 80) |
| No. of hospitalists groups | |
| 1 | 333 (77.6%) |
| 2 | 54 (12.6%) |
| 3 | 36 (8.4%) |
| Don't know | 6 (1.4%) |
| Employment of hospitalists (not mutually exclusive) | |
| Hospital system | 98 (22.8%) |
| Hospital | 185 (43.1%) |
| Local physician practice group | 62 (14.5%) |
| Hospitalist physician practice group (local) | 83 (19.3%) |
| Hospitalist physician practice group (national/regional) | 36 (8.4%) |
| Other/unknown | 36 (8.4%) |
| Any 24‐hr in‐house coverage by hospitalists | |
| Yes | 329 (76.7%) |
| No | 98 (22.8%) |
| 3 | 1 (0.2%) |
| Unknown | 1 (0.2%) |
| No. of hospitalist international medical graduates | |
| Median (IQR) | 3 (1, 6) |
| No. of hospitalists that are <1 yr out of residency | |
| Median (IQR) | 1 (0, 2) |
| Percent of patients with AMI cared for by hospitalists | |
| 0%25% | 148 (34.5%) |
| 26%50% | 67 (15.6%) |
| 51%75% | 50 (11.7%) |
| 76%100% | 54 (12.6%) |
| Don't know | 110 (25.6%) |
| Percent of patients with heart failure cared for by hospitalists | |
| 0%25% | 79 (18.4%) |
| 26%50% | 78 (18.2%) |
| 51%75% | 75 (17.5%) |
| 76%100% | 84 (19.6%) |
| Don't know | 113 (26.3%) |
| Percent of patients with pneumonia cared for by hospitalists | |
| 0%25% | 47 (11.0%) |
| 26%50% | 61 (14.3%) |
| 51%75% | 74 (17.3%) |
| 76%100% | 141 (32.9%) |
| Don't know | 105 (24.5%) |
| Hospitalist provision of services | |
| Care of critical care patients | |
| Hospitalists provide service | 346 (80.7%) |
| Hospitalists do not provide service | 80 (18.7%) |
| Don't know | 3 (0.7%) |
| Emergency department admission screening | |
| Hospitalists provide service | 281 (65.5%) |
| Hospitalists do not provide service | 143 (33.3%) |
| Don't know | 5 (1.2%) |
| Observation unit coverage | |
| Hospitalists provide service | 359 (83.7%) |
| Hospitalists do not provide service | 64 (14.9%) |
| Don't know | 6 (1.4%) |
| Emergency department coverage | |
| Hospitalists provide service | 145 (33.8%) |
| Hospitalists do not provide service | 280 (65.3%) |
| Don't know | 4 (0.9%) |
| Coverage for cardiac arrests | |
| Hospitalists provide service | 283 (66.0%) |
| Hospitalists do not provide service | 135 (31.5%) |
| Don't know | 11 (2.6%) |
| Rapid response team coverage | |
| Hospitalists provide service | 240 (55.9%) |
| Hospitalists do not provide service | 168 (39.2%) |
| Don't know | 21 (4.9%) |
| Quality improvement or utilization review | |
| Hospitalists provide service | 376 (87.7%) |
| Hospitalists do not provide service | 37 (8.6%) |
| Don't know | 16 (3.7%) |
| Hospital practice guideline development | |
| Hospitalists provide service | 339 (79.0%) |
| Hospitalists do not provide service | 55 (12.8%) |
| Don't know | 35 (8.2%) |
| Implementation of major hospital system projects | |
| Hospitalists provide service | 309 (72.0%) |
| Hospitalists do not provide service | 96 (22.4%) |
| Don't know | 24 (5.6%) |
Relationship Between Hospitalist Utilization and Outcomes
Tables 3 and 4 show the comparisons between hospitals with and without hospitalists on each of the 6 outcome measures. In the bivariate analysis (Table 3), there was no statistically significant difference between groups on any of the outcome measures with the exception of the risk‐stratified readmission rate for heart failure. Sites with hospitalists had a lower RSRR for HF than sites without hospitalists (24.7% vs 25.4%, P < 0.0001). These results were similar in the multivariable models as seen in Table 4, in which the beta estimate (slope) was not significantly different for hospitals utilizing hospitalists compared to those that did not, on all measures except the RSRR for HF. For the subset of hospitals that used hospitalists, there was no statistically significant change in any of the 6 outcome measures, with increasing percentage of patients admitted by hospitalists. Table 5 demonstrates that for each RSMR and RSRR, the slope did not consistently increase or decrease with incrementally higher percentages of patients admitted by hospitalists, and the confidence intervals for all estimates crossed zero.
| Hospitalist Program | No Hospitalist Program | ||
|---|---|---|---|
| N = 429 | N = 169 | ||
| Outcome Measure | Mean % (SD) | Mean (SD) | P Value |
| |||
| MI RSMR | 16.0 (1.6) | 16.1 (1.5) | 0.56 |
| MI RSRR | 19.9 (0.88) | 20.0 (0.86) | 0.16 |
| HF RSMR | 11.3 (1.4) | 11.3 (1.4) | 0.77 |
| HF RSRR | 24.7 (1.6) | 25.4 (1.8) | <0.0001 |
| Pneumonia RSMR | 11.7 (1.7) | 12.0 (1.7) | 0.08 |
| Pneumonia RSRR | 18.2 (1.2) | 18.3 (1.1) | 0.28 |
| Adjusted beta estimate (95% CI) | |
|---|---|
| |
| MI RSMR | |
| Hospitalist | 0.001 (0.002, 004) |
| MI RSRR | |
| Hospitalist | 0.001 (0.002, 0.001) |
| HF RSMR | |
| Hospitalist | 0.0004 (0.002, 0.003) |
| HF RSRR | |
| Hospitalist | 0.006 (0.009, 0.003) |
| Pneumonia RSMR | |
| Hospitalist | 0.002 (0.005, 0.001) |
| Pneumonia RSRR | |
| Hospitalist | 0.00001 (0.002, 0.002) |
| Adjusted Beta Estimate (95% CI) | |
|---|---|
| |
| MI RSMR | |
| Percent admit | |
| 0%30% | 0.003 (0.007, 0.002) |
| 32%48% | 0.001 (0.005, 0.006) |
| 50%66% | Ref |
| 70%80% | 0.004 (0.001, 0.009) |
| 85% | 0.004 (0.009, 0.001) |
| MI RSRR | |
| Percent admit | |
| 0%30% | 0.001 (0.002, 0.004) |
| 32%48% | 0.001 (0.004, 0.004) |
| 50%66% | Ref |
| 70%80% | 0.001 (0.002, 0.004) |
| 85% | 0.001 (0.002, 0.004) |
| HF RSMR | |
| Percent admit | |
| 0%30% | 0.001 (0.005, 0.003) |
| 32%48% | 0.002 (0.007, 0.003) |
| 50%66% | Ref |
| 70%80% | 0.002 (0.006, 0.002) |
| 85% | 0.001 (0.004, 0.005) |
| HF RSRR | |
| Percent admit | |
| 0%30% | 0.002 (0.004, 0.007) |
| 32%48% | 0.0003 (0.005, 0.006) |
| 50%66% | Ref |
| 70%80% | 0.001 (0.005, 0.004) |
| 85% | 0.002 (0.007, 0.003) |
| Pneumonia RSMR | |
| Percent admit | |
| 0%30% | 0.001 (0.004, 0.006) |
| 32%48% | 0.00001 (0.006, 0.006) |
| 50%66% | Ref |
| 70%80% | 0.001 (0.004, 0.006) |
| 85% | 0.001 (0.006, 0.005) |
| Pneumonia RSRR | |
| Percent admit | |
| 0%30% | 0.0002 (0.004, 0.003) |
| 32%48% | 0.004 (0.0003, 0.008) |
| 50%66% | Ref |
| 70%80% | 0.001 (0.003, 0.004) |
| 85% | 0.002 (0.002, 0.006) |
DISCUSSION
In this national survey of hospitals, we did not find a significant association between the use of hospitalists and hospitals' performance on 30‐day mortality or readmissions measures for AMI, HF, or pneumonia. While there was a statistically lower 30‐day risk‐standardized readmission rate measure for the heart failure measure among hospitals that use hospitalists, the effect size was small. The survey response rate of 40% is comparable to other surveys of physicians and other healthcare personnel, however, there were no significant differences between responders and nonresponders, so the potential for response bias, while present, is small.
Contrary to the findings of a recent study,21 we did not find a higher readmission rate for any of the 3 conditions in hospitals with hospitalist programs. One advantage of our study is the use of more robust risk‐adjustment methods. Our study used NQF‐endorsed risk‐standardized measures of readmission, which capture readmissions to any hospital for common, high priority conditions where the impact of care coordination and discontinuity of care are paramount. The models use administrative claims data, but have been validated by medical record data. Another advantage is that our study focused on a time period when hospital readmissions were a standard quality benchmark and increasing priority for hospitals, hospitalists, and community‐based care delivery systems. While our study is not able to discern whether patients had primary care physicians or the reason for admission to a hospitalist's care, our data do suggest that hospitalists continue to care for a large percentage of hospitalized patients. Moreover, increasing the proportion of patients being admitted to hospitalists did not affect the risk for readmission, providing perhaps reassuring evidence (or lack of proof) for a direct association between use of hospitalist systems and higher risk for readmission.
While hospitals with hospitalists clearly did not have better mortality or readmission rates, an alternate viewpoint might hold that, despite concerns that hospitalists negatively impact care continuity, our data do not demonstrate an association between readmission rates and use of hospitalist services. It is possible that hospitals that have hospitalists may have more ability to invest in hospital‐based systems of care,22 an association which may incorporate any hospitalist effect, but our results were robust even after testing whether adjustment for hospital factors (such as profit status, size) affected our results.
It is also possible that secular trends in hospitals or hospitalist systems affected our results. A handful of single‐site studies carried out soon after the hospitalist model's earliest descriptions found a reduction in mortality and readmission rates with the implementation of a hospitalist program.2325 Alternatively, it may be that there has been a dilution of the effect of hospitalists as often occurs when any new innovation is spread from early adopter sites to routine practice. Consistent with other multicenter studies from recent eras,21, 26 our article's findings do not demonstrate an association between hospitalists and improved outcomes. Unlike other multicenter studies, we had access to disease‐specific risk‐adjustment methodologies, which may partially account for referral biases related to patient‐specific measures of acute or chronic illness severity.
Changes in the hospitalist effect over time have a number of explanations, some of which are relevant to our study. Recent evidence suggests that complex organizational characteristics, such as organizational values and goals, may contribute to performance on 30‐day mortality for AMI rather than specific processes and protocols27; intense focus on AMI as a quality improvement target is emblematic of a number of national initiatives that may have affected our results. Interestingly, hospitalist systems have changed over time as well. Early in the hospitalist movement, hospitalist systems were implemented largely at the behest of hospitals trying to reduce costs. In recent years, however, hospitalist systems are at least as frequently being implemented because outpatient‐based physicians or surgeons request hospitalists; hospitalists have been focused on care of uncoveredpatients, since the model's earliest description. In addition, some hospitals invest in hospitalist programs based on perceived ability of hospitalists to improve quality and achieve better patient outcomes in an era of payment increasingly being linked to quality of care metrics.
Our study has several limitations, six of which are noted here. First, while the hospitalist model has been widely embraced in the adult medicine field, in the absence of board certification, there is no gold standard definition of a hospitalist. It is therefore possible that some respondents may have represented groups that were identified incorrectly as hospitalists. Second, the data for the primary independent variable of interest was based upon self‐report and, therefore, subject to recall bias and potential misclassification of results. Respondents were not aware of our hypothesis, so the bias should not have been in one particular direction. Third, the data for the outcome variables are from 2008. They may, therefore, not reflect organizational enhancements related to use of hospitalists that are in process, and take years to yield downstream improvements on performance metrics. In addition, of the 429 hospitals that have hospitalist programs, 46 programs were initiated after 2008. While national performance on the 6 outcome variables has been relatively static over time,7 any significant change in hospital performance on these metrics since 2008 could suggest an overestimation or underestimation of the effect of hospitalist programs on patient outcomes. Fourth, we were not able to adjust for additional hospital or health system level characteristics that may be associated with hospitalist use or patient outcomes. Fifth, our regression models had significant collinearity, in that the presence of hospitalists was correlated with each of the covariates. However, this finding would indicate that our estimates may be overly conservative and could have contributed to our nonsignificant findings. Finally, outcomes for 2 of the 3 clinical conditions measured are ones for which hospitalists may less frequently provide care: acute myocardial infarction and heart failure. Outcome measures more relevant for hospitalists may be all‐condition, all‐cause, 30‐day mortality and readmission.
This work adds to the growing body of literature examining the impact of hospitalists on quality of care. To our knowledge, it is the first study to assess the association between hospitalist use and performance on outcome metrics at a national level. While our findings suggest that use of hospitalists alone may not lead to improved performance on outcome measures, a parallel body of research is emerging implicating broader system and organizational factors as key to high performance on outcome measures. It is likely that multiple factors contribute to performance on outcome measures, including type and mix of hospital personnel, patient care processes and workflow, and system level attributes. Comparative effectiveness and implementation research that assess the contextual factors and interventions that lead to successful system improvement and better performance is increasingly needed. It is unlikely that a single factor, such as hospitalist use, will significantly impact 30‐day mortality or readmission and, therefore, multifactorial interventions are likely required. In addition, hospitalist use is a complex intervention as the structure, processes, training, experience, role in the hospital system, and other factors (including quality of hospitalists or the hospitalist program) vary across programs. Rather than focusing on the volume of care delivered by hospitalists, hospitals will likely need to support hospital medicine programs that have the time and expertise to devote to improving the quality and value of care delivered across the hospital system. This study highlights that interventions leading to improvement on core outcome measures are more complex than simply having a hospital medicine program.
Acknowledgements
The authors acknowledge Judy Maselli, MPH, Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, for her assistance with statistical analyses and preparation of tables.
Disclosures: Work on this project was supported by the Robert Wood Johnson Clinical Scholars Program (K.G.); California Healthcare Foundation grant 15763 (A.D.A.); and a grant from the National Heart, Lung, and Blood Institute (NHLBI), study 1U01HL105270‐02 (H.M.K.). Dr Krumholz is the chair of the Cardiac Scientific Advisory Board for United Health and has a research grant with Medtronic through Yale University; Dr Auerbach has a grant through the National Heart, Lung, and Blood Institute (NHLBI). The authors have no other disclosures to report.
- ,,,.Comparison of hospitalists and nonhospitalists regarding core measures of pneumonia care.Am J Manag Care.2007;13:129–132.
- ,,,.Comparison of processes and outcomes of pneumonia care between hospitalists and community‐based primary care physicians.Mayo Clin Proc.2002;77(10):1053–1058.
- ,,,,.Quality of care for patients hospitalized with heart failure: assessing the impact of hospitalists.Arch Intern Med.2002;162(11):1251–1256.
- ,,, et al.Quality of care for decompensated heart failure: comparable performance between academic hospitalists and non‐hospitalists.J Gen Intern Med.2008;23(9):1399–1406.
- ,,.Comparison of practice patterns of hospitalists and community physicians in the care of patients with congestive heart failure.J Hosp Med.2008;3(1):35–41.
- ,,,,.Cross‐sectional analysis of hospitalist prevalence and quality of care in California.J Hosp Med.2010;5(4):200–207.
- Hospital Compare. Department of Health and Human Services. Available at: http://www.hospitalcompare.hhs.gov. Accessed September 3,2011.
- ,.Teaching hospitals and quality of care: a review of the literature.Milbank Q.2002;80(3):569–593.
- ,,, et al.A systematic review and meta‐analysis of studies comparing mortality rates of private for‐profit and private not‐for‐profit hospitals.Can Med Assoc J.2002;166(11):1399–1406.
- ,,,,.Patient and hospital characteristics associated with recommended processes of care for elderly patients hospitalized with pneumonia: results from the Medicare Quality Indicator System Pneumonia Module.Arch Intern Med.2002;162(7):827–833.
- ,,,.Care in U.S. hospitals—The Hospital Quality Alliance Program.N Engl J Med.2005;353(3):265–274.
- ,,, et al.Hospital characteristics and quality of care.JAMA.1992;268(13):1709–1714.
- ,,,,.Nurse‐staffing levels and the quality of care in hospitals.N Engl J Med.2002;346(22):1715–1722.
- ,,,.Health care market trends and the evolution of hospitalist use and roles.J Gen Intern Med.2005;20:101–107.
- ,,, et al.An administrative claims model suitable for profiling hospital performance based on 30‐day mortality rates among patients with an acute myocardial infarction.Circulation.2006;113:1683–1692.
- ,,, et al.An administrative claims measure suitable for profiling hospital performance based on 30‐day all‐cause readmission rates among patients with acute myocardial infarction.Circulation.2011;4:243–252.
- ,,, et al.An administrative claims measure suitable for profiling hospital performance on the basis of 30‐day all‐cause readmission rates among patients with heart failure.Circ Cardiovasc Qual Outcomes.2008;1:29–37.
- ,,, et al.An administrative claims model suitable for profiling hospital performance based on 30‐day mortality rates among patients with heart failure.Circulation.2006;113:1693–1701.
- ,,, et al.An administrative claims model for profiling hospital 30‐day mortality rates for pneumonia patients.PLoS ONE.2011;6(4):e17401.
- ,,, et al.Development, validation and results of a measure of 30‐day readmission following hospitalization for pneumonia.J Hosp Med.2011;6:142–150.
- ,.Association of hospitalist care with medical utilization after discharge: evidence of cost shift from a cohort study.Ann Intern Med.2011;155:152–159.
- ,,,.California hospital leaders' views of hospitalists: meeting needs of the present and future.J Hosp Med.2009;4:528–534.
- ,,, et al.Effects of physician experience on costs and outcomes on an academic general medicine service: results of a trial of hospitalists.Ann Intern Med.2002;137:866–874.
- ,,,,,.Implementation of a voluntary hospitalist service at a community teaching hospital: improved clinical efficiency and patients outcomes.Ann Intern Med.2002;137:859–865.
- ,,,.A comparative study of unscheduled hospital readmissions in a resident‐staffed teaching service and a hospitalist‐based service.South Med J.2009;102:145–149.
- ,,,,,.Outcomes of care by hospitalists, general internists, and family physicians.N Engl J Med.2007;357:2589–2600.
- ,,, et al.What distinguishes top‐performing hospitals in acute myocardial infarction mortality rates?Ann Intern Med.2011;154:384–390.
- ,,,.Comparison of hospitalists and nonhospitalists regarding core measures of pneumonia care.Am J Manag Care.2007;13:129–132.
- ,,,.Comparison of processes and outcomes of pneumonia care between hospitalists and community‐based primary care physicians.Mayo Clin Proc.2002;77(10):1053–1058.
- ,,,,.Quality of care for patients hospitalized with heart failure: assessing the impact of hospitalists.Arch Intern Med.2002;162(11):1251–1256.
- ,,, et al.Quality of care for decompensated heart failure: comparable performance between academic hospitalists and non‐hospitalists.J Gen Intern Med.2008;23(9):1399–1406.
- ,,.Comparison of practice patterns of hospitalists and community physicians in the care of patients with congestive heart failure.J Hosp Med.2008;3(1):35–41.
- ,,,,.Cross‐sectional analysis of hospitalist prevalence and quality of care in California.J Hosp Med.2010;5(4):200–207.
- Hospital Compare. Department of Health and Human Services. Available at: http://www.hospitalcompare.hhs.gov. Accessed September 3,2011.
- ,.Teaching hospitals and quality of care: a review of the literature.Milbank Q.2002;80(3):569–593.
- ,,, et al.A systematic review and meta‐analysis of studies comparing mortality rates of private for‐profit and private not‐for‐profit hospitals.Can Med Assoc J.2002;166(11):1399–1406.
- ,,,,.Patient and hospital characteristics associated with recommended processes of care for elderly patients hospitalized with pneumonia: results from the Medicare Quality Indicator System Pneumonia Module.Arch Intern Med.2002;162(7):827–833.
- ,,,.Care in U.S. hospitals—The Hospital Quality Alliance Program.N Engl J Med.2005;353(3):265–274.
- ,,, et al.Hospital characteristics and quality of care.JAMA.1992;268(13):1709–1714.
- ,,,,.Nurse‐staffing levels and the quality of care in hospitals.N Engl J Med.2002;346(22):1715–1722.
- ,,,.Health care market trends and the evolution of hospitalist use and roles.J Gen Intern Med.2005;20:101–107.
- ,,, et al.An administrative claims model suitable for profiling hospital performance based on 30‐day mortality rates among patients with an acute myocardial infarction.Circulation.2006;113:1683–1692.
- ,,, et al.An administrative claims measure suitable for profiling hospital performance based on 30‐day all‐cause readmission rates among patients with acute myocardial infarction.Circulation.2011;4:243–252.
- ,,, et al.An administrative claims measure suitable for profiling hospital performance on the basis of 30‐day all‐cause readmission rates among patients with heart failure.Circ Cardiovasc Qual Outcomes.2008;1:29–37.
- ,,, et al.An administrative claims model suitable for profiling hospital performance based on 30‐day mortality rates among patients with heart failure.Circulation.2006;113:1693–1701.
- ,,, et al.An administrative claims model for profiling hospital 30‐day mortality rates for pneumonia patients.PLoS ONE.2011;6(4):e17401.
- ,,, et al.Development, validation and results of a measure of 30‐day readmission following hospitalization for pneumonia.J Hosp Med.2011;6:142–150.
- ,.Association of hospitalist care with medical utilization after discharge: evidence of cost shift from a cohort study.Ann Intern Med.2011;155:152–159.
- ,,,.California hospital leaders' views of hospitalists: meeting needs of the present and future.J Hosp Med.2009;4:528–534.
- ,,, et al.Effects of physician experience on costs and outcomes on an academic general medicine service: results of a trial of hospitalists.Ann Intern Med.2002;137:866–874.
- ,,,,,.Implementation of a voluntary hospitalist service at a community teaching hospital: improved clinical efficiency and patients outcomes.Ann Intern Med.2002;137:859–865.
- ,,,.A comparative study of unscheduled hospital readmissions in a resident‐staffed teaching service and a hospitalist‐based service.South Med J.2009;102:145–149.
- ,,,,,.Outcomes of care by hospitalists, general internists, and family physicians.N Engl J Med.2007;357:2589–2600.
- ,,, et al.What distinguishes top‐performing hospitals in acute myocardial infarction mortality rates?Ann Intern Med.2011;154:384–390.
Copyright © 2012 Society of Hospital Medicine
Delirium in Hospitalized Patients
Delirium is a syndrome of disturbance of consciousness, with reduced ability to focus, sustain, or shift attention, that occurs over a short period of time and fluctuates over the course of the day.1 It encompasses a variety of cognitive, behavioral, and psychological symptoms including inattention, short‐term memory loss, sleep disturbances, agitated behaviors, delusions, and visual hallucinations.2 Delirium complicates the care of 70% to 80% of mechanically ventilated patients in intensive care units (ICUs).3 Of 13 million patients aged 65 and older hospitalized in 2002, 10% to 52% had delirium at some point during their admission.4, 5
Patients experiencing delirium have a higher probability of death during their hospital stay, adjusted for age, gender, race, and comorbidities.3, 6, 7 They are more vulnerable to hospital‐acquired complications leading to prolonged ICU and hospital stay, new institutionalization, and higher healthcare costs.3, 6, 7 Even with such a range of poor outcomes, the rates of delirium recognition are low,8 resulting in inadequate management.9 There has been considerable growth in the number of articles published on delirium in recent years. Therefore, it is of value to provide a state‐of‐the‐art summary of robust evidence in the field to healthcare personnel and delirium investigators.
We systematically reviewed the literature to identify published systematic evidence reviews (SERs), which evaluated the evidence on delirium risk factors, diagnosis, pathogenesis, prevention, treatment, and outcomes. We then summarized the data from the methodologically sound SERs to provide the reader with a clinically oriented summary of delirium literature for patient care. We also identify current gaps in delirium literature, and present future directions for delirium investigators to design studies that will enhance delirium care.
DATA SOURCES AND REVIEW METHODS
The domains of risk factors, diagnosis, pathophysiology, prevention, treatment, and outcomes were selected a priori to capture all relevant SERs regarding delirium based on the framework suggested by the American Delirium Society task force.10 To maximize article retrieval, a 3‐step search strategy was applied. First, we searched the electronic database utilizing OVID Medline, PubMed, the Cochrane Library, and Cumulative Index of Nursing and Allied Health Literature (CINAHL) using the following delirium‐specific search terms: delirium, confusion, agitation, mental status change, inattention, encephalopathy, organic mental disorders, and disorientation. We combined the above terms with the following study design terms: technical report, systematic evidence review, systematic review, meta‐analysis, editorial, and clinical reviews. We limited our search to human subjects. We excluded studies that: a) enrolled patients aged <18; b) enrolled patients with current or past Diagnostic and Statistical Manual of Mental Disorders (DSM) Axis I psychotic disorders; c) did not have standardized delirium evaluation; d) evaluated alcohol or substance abuse‐related delirium; e) did not use a systematic search method for identifying delirium‐related articles; and f) evaluated delirium sub‐types. We searched articles published from January 1966 through April 2011. Second, a manual search of references of the retrieved papers plus an Internet search using Google Scholar was conducted to find additional SERs. Titles and abstracts were screened by 2 reviewers (B.A.K., M.Z.). Authors of the included studies were contacted as necessary. Third, a library professional at the Indiana University School of Medicine independently performed a literature search, and those results were compared with our search to retrieve any missing SERs.
The methodological quality of each SER was independently assessed by 2 reviewers (B.A.K., M.Z.) using the United States Preventive Services Task Force (USPSTF) Critical Appraisal for SER.11 This scale assesses parameters that are critical to the scientific credibility of an SER and categorizes the SER as poor, fair, or good (Table 1). The 2 reviewers (B.A.K., M.Z.) used a data extraction form to record the following information from each SER: primary author, publication year, number and type of studies, number of participants and their mean age, study population, method for delirium diagnosis, risk factors, preventive and therapeutic interventions, and outcomes. Any disagreement between reviewers in SER selection, data extraction, or SER appraisal was resolved through discussion with a third reviewer (M.A.B.). The conflicting findings among SERs were resolved by consensus and by including the findings from a good SER over a fair SER.
| Criteria | Rating Definition |
|---|---|
| Recent, relevant review with comprehensive sources and search strategies | Good: If all the criteria are met |
| Explicit and relevant selection criteria | |
| Standard appraisal of included studies | |
| Valid conclusion | |
| Recent, relevant review that is not clearly biased but lacks comprehensive sources and search strategies | Fair: If this criterion is met |
| Outdated, irrelevant, or biased review | Poor: If one or more of the criteria are met |
| There is no systematic search for studies | |
| There are no explicit selection criteria | |
| There is no standard appraisal of studies |
RESULTS
Our search yielded 76,060 potential citations, out of which we identified 38 SERs meeting our inclusion criteria (Table 2). Figure 1 outlines our search strategy. Based on the USPSTF criteria, 22 SERs graded as good or fair provided the data to establish our review.
| Author (Year) | Studies (n)/ Participants (n) | Mean Age (Years) | Study Type | Service | Delirium/Cognition Assessment Scales | Review Objectives* | Rating |
|---|---|---|---|---|---|---|---|
| |||||||
| Van Rompaey et al15 (2008) | 6/7,114 | 61.2 | Prospective cohort, retrospective analysis | ICU (medical, surgical, coronary, mixed) | CAM‐ICU, psychiatric interview, ICU delirium screening checklist | 1/Risk factors | Fair |
| Bryson and Wyand13 (2006) | 18/3,473 | 71.93 | RCT | Surgery | MattisKovner Verbal Recall and Recognition, GDS, DST, DSM‐III, AMT, PRT, FOMTL, DCT, FPU, GEMS, WAIS‐R, Meta Memory Questionnaire, National Adult Reading Test | 1/Risk factors | Good |
| Fong et al14 (2006) | 9/1,078 | 63.1 | RCT, case control, prospective cohort, retrospective cohort | Surgery | CAM, DSM‐III, MMSE, SPMSQ, Digit Symbol Substitution Test, Trailmaking B Test | 1/Risk factors | Fair |
| Adamis et al53 (2009) | 6/882 | 54.59 | Case control | Medicine, ICU, surgery | CAM, DRS, DSM‐III‐R, DSM‐IV, ICD‐10 | 1/Risk factors | Poor |
| Balasundaram and Holmes12 (2007) | 4/364 | 66.8 | Prospective cohort | Surgery | CAM, DRS, HDS‐R, DSM‐IV | 1/Risk factors | Good |
| Dasgupta and Dumbrell49 (2006) | 25/5,175 | 72.5 | Prospective observational | Surgery | CAM, DSM‐III/IV | 1/Risk factors | Poor |
| Elie et al50 (1998) | 27/1,365 | 75.7 | Prospective | Medicine, surgery, psychiatry | CAM, NFRD, MMSE, MSQ, SPMSQ | 1/Risk factors | Poor |
| Van Munster et al52 (2009) | 5/1,099 | 77.86 | Cohort | Medicine, surgery | CAM, DRS | 1/Risk factors | Poor |
| Van der Mast and Roest51 (1996) | 57/6,129 | 48.2 | Prospective control, retrospective | Surgery | Psychiatric interview, chart review for signs of delirium, DSM‐III, MMSE | 1/Risk factors | Poor |
| Campbell et al16 (2009) | 27/8,492 | 71.35 | Longitudinal cohort, cross‐sectional, case control | Medicine, surgery, ICU, psychiatry | CAM, CAM‐ICU, DSI, DSM‐III/III‐R/IV, SDC, MMSE, Verbal N‐Back Test, BCRS, WMS | 1/Risk factors | Fair |
| Soiza et al17 (2008) | 12/764 | 72.4 | Cohort, case control, case series | Medicine, ICU, psychiatry | CAM, DSM‐III/III‐R/IV | 1/Risk factors | Good |
| Michaud et al9 (2007) | 29/NA | 76.7 | RCT, cohort | Medicine, surgery | CAM, BOMC, DRS, MDAS, ICD‐10, DSM‐IV, MMSE | 1/Risk factors, 2/Diagnosis, 4/Prevention, 5/Treatment | Fair |
| Steis and Fick54 (2008) | 10/3,059 | 72.5 | Prospective clinical trials, retrospective, observational, case study | Medicine, surgery, ICU | DSM‐III/IV | 2/Diagnosis | Poor |
| Wei et al20 (2008) | 7/1,071 | 70.17 | Validation, adaptation, translation, application | ICU, ED, medicine, surgery | CAM, CAM‐ICU, DSM‐IV, NH‐CAM, DI | 2/Diagnosis | Good |
| Wong et al18 (2010) | 25/3,027 | 72.76 | Prospective clinical studies | Medicine, surgery | CAC, CAM, DOSS, DRS, DRS‐R‐98, Digit Span Test, GAR, MDAS, MMSE, Nu‐DESC, Vigilance A Test | 2/Diagnosis | Fair |
| Devlin et al55 (2007) | 12/2,106 | 61.8 | Validation studies | ICU | CAM, ICDSC, CTD, ROC, DSM‐III/IV, DDS, MMSE | 2/Diagnosis | Poor |
| Fick et al47 (2002) | 14/7,701 | 79.51 | Prospective cohort, retrospective cohort, cross‐sectional, clinical trials | Medicine, surgery, ED | CAM, DRS, DSM‐III/III‐R/IV, CERAD, NINCDS‐ADRDA, IQCODE, MMSE | 2/Diagnosis, 4/Prevention, 6/Prognosis | Fair |
| Siddiqi et al46 (2006) | 40/12,220 | 78.8 | Prospective cohort, cross‐sectional, case‐controlled trials | Medicine | CAM, DRS, MDAS, SPMSQ, DSM‐III/III‐R/IV, MSQ, MMSE,BPRS, IQCODE, GHQ BAS | 2/Diagnosis, 6/Prognosis | Fair |
| 28/4,915 | |||||||
| Hall et al21 (2011) | 5/315 | 71.13 | Prospective cohort | Medicine, surgery, psychogeriatric | DSM‐III/III‐R/IV, MMSE, DRS, CAM, IQCODE, GDS | 3/Pathophysiology | Good |
| Cole et al56 (1996) | 10/999 | 71.6 | Randomized and nonrandomized trials | Medicine, surgery | DSM‐III, SPMSQ | 4/Prevention | Poor |
| Siddiqi et al25 (2007) | 6/833 | 76.67 | RCT | Surgery | CAM, DRS‐R‐98, DSM‐III/IV, DSI, MDAS, AMT, MMSE, OBS | 4/Prevention | Good |
| Campbell et al27 (2009) | 13/1,305 | 65.8 | RCT | Medicine, surgery, ICU | MDAS, DRS‐R‐98 | 4/Prevention, 5/Treatment | Good |
| Weber et al41 (2004) | 13/1,650 | 73.99 | RCT, non‐RCT, clinical trials, meta‐analysis, case report | Medicine, surgery | CAM, MDAS, DSI, DRS, DSM‐III‐R/IV, MMSE | 4/Prevention, 5/Treatment | Fair |
| Milisen et al22 (2005) | 7/1,683 | 80.73 | RCT, controlled trials, beforeafter study | Medicine, surgery | CAM, DSM‐III, SPMSQ, MMSE | 4/Prevention, 5/Treatment | Good |
| Lonergan et al39 (2009) | 3/629 | 74.5 | RCT | Medicine, surgery | CAM, DRS, DRS‐R‐98, MDAS, CGI, DSM‐IV | 5/Treatment | Good |
| Jackson and Lipman40 (2004) | 1/30 | 39.2 | RCT | Medicine | DRS, DSM‐III‐R | 5/Treatment | Good |
| Lonergan et al42 (2009) | 1/106 | 54.5 | RCT | ICU | CAM‐ICU | 5/Treatment | Good |
| Bourne et al57 (2008) | 33/1,880 | 60.99 | RCT, prospective trials, comparative trials | Medicine, surgery | DRS | 4/Prevention, 5/Treatment | Poor |
| Bitsch et al58 (2004) | 12/1,823 | 79.02 | Prospective, descriptive | Surgery | CAM, MDAS, DSI, OBS, MMSE | 4/Prevention, 5/Treatment | Poor |
| Overshott et al43 (2008) | 1/80 | 67 | RCT | Surgery | CAM, DSI, DSM‐IV, MMSE | 5/Treatment | Good |
| Lacasse et al59 (2006) | 4/158 | 60.8 | RCT | Medicine, surgery | CAM, DRS‐R‐98, MDAS, DI, DSM‐III‐R/IV, MMSE | 5/Treatment | Poor |
| Peritogiannis et al60 (2009) | 23/538 | 62.84 | RCT, retrospective, open label | Medicine, surgery | DRS, DRS‐R‐98, DRS‐R‐98‐J, MDAS, DI, 10‐Point Visual Analog Scale | 5/Treatment | Poor |
| Seitz et al38 (2007) | 14/448 | 63.09 | Prospective | Medicine, surgery, ICU | DSM‐III/III‐R/IV/IV‐TR, CAM, DRS‐R‐98, MDAS, DI | 5/Treatment | Good |
| Britton and Russell37 (2001/2004) | 1/227 | 82.35 | RCT | Medicine | CAM, SPMSQ, DSM‐III‐R, MMSE | 5/Treatment | Good |
| Jackson et al6 (2004) | 9/1,885 | 77.68 | Prospective, descriptive | Medicine, surgery, ICU, psychiatry | CAM, CAM‐ICU, DRS, MMSE, DSM | 6/Prognosis | Poor |
| Cole et al44 (2009) | 18/1,322 | 81.3 | Prospective cohort | Medicine, surgery | CAM, DSM‐III/III‐R/IV, ICD‐10, OBS | 6/Prognosis | Good |
| Witlox et al45 (2010) | 42/5,777 | 79.96 | Observational | Medicine, surgery | DSM, patient interview | 6/Prognosis | Good |
| Cole and Primeau61 (1993) | 8/573 | 77.25 | Prospective trials | Medicine, surgery, psychiatry | DSM‐I/III | 6/Prognosis | Poor |
1: What Are the Risk Factors for Development of Delirium in Hospitalized Patients?
We found 6 SERs1217 that evaluated risk factors for the development of delirium. Three reviews included only surgical patients,1214 1 focused on the intensive care unit (ICU),15 and the remaining 2 had both medical and surgical patients.16, 17 Risk factors identified in an elective vascular surgery population were age >64, preoperative cognitive impairment, depression, intraoperative blood transfusions, and previous amputation.12 The risk of incident delirium conferred by general anesthesia compared to regional anesthesia in non‐cardiac surgery patients was not significantly different among both groups.13 One SER14 focused on the effects of different opioid analgesics on postoperative delirium, and whether route of administration of medicines (intravenous vs epidural) had any impact on delirium. Mepiridine was consistently associated with an increased risk of delirium in elderly surgical patients, but there were no significant differences in postoperative delirium rates among those receiving morphine, fentanyl, or hydromorphone. The rates of delirium did not differ significantly between intravenous and epidural routes of analgesic administration, except in one study where epidural route had more delirium cases, but in 85% of those cases, mepiridine was used as an epidural agent. Risk factors explored in an ICU setting found multiple predisposing and precipitating risk factors, with the surprising finding that age was not a strong predictor of delirium.15 An association between delirium and drugs with anticholinergic properties was found in 1 SER.16 There was no causal relationship between structural or functional neuroimaging findings and delirium development.17
2: What Is the Clinical Utility of Bedside Tools in Delirium Diagnosis?
The accuracy of bedside instruments in diagnosing delirium was assessed in an SER of 25 prospective studies.18 Among the 11 scales reviewed, the Confusion Assessment Method (CAM) had the most evidence supporting its use as a bedside tool (+likelihood ratio [LR], 9.6; 95% CI [confidence interval], 5.816.0; LR, 0.16; 95% CI, 0.090.29). The Folstein mini‐mental status examination (MMSE)19 (score <24) was the least useful test for identifying delirium (LR, 1.6; 95% CI, 1.22.0). Another SER evaluating the psychometric properties of CAM demonstrated a sensitivity of 94% (CI, 91%97%) and specificity of 89% (CI, 85%94%).20 CAM also showed prognostic value with worsening of delirium outcomes depending on the number of CAM items present.20
3: What Is the Underlying Pathophysiology of Delirium and Is There a Role of Measuring Biomarkers for Delirium?
We found only 1 SER which examined the associations between cerebrospinal fluid biomarkers and delirium.21 Delirium was associated with raised levels of serotonin metabolites, interleukin‐8, cortisol, lactate, and protein. Additionally, higher acetylcholinesterase predicted poor outcome after delirium, and higher dopamine metabolites were associated with psychotic features. Delirium was also associated with reduced levels of somatostatin, ‐endorphin, and neuron‐specific enolase.
4: Can Delirium Be Prevented?
Nonpharmacologic Interventions
An SER22 reviewing multicomponent interventions to prevent delirium identified 2 studies23, 24 showing statistically significant results. In the Yale Delirium Prevention Trial,23 the intervention was targeted toward minimizing 6 risk factors in elderly patients (70 years of age) admitted to a general medicine service, who did not have delirium at the time of admission, but were at risk for delirium development. The interventions included: orientation activities for the cognitively impaired, early mobilization, preventing sleep deprivation, minimizing the use of psychoactive drugs, use of eyeglasses and hearing aids, and treating volume depletion. The incidence of delirium was 9.9% with this intervention compared with 15% in the usual care group (OR [odds ratio], 0.60; 95% CI, 0.390.92).23 The other studied patients with hip fractures, randomized to either standard care versus the addition of a geriatrics consultation preoperatively or immediately after hip repair, providing recommendations based on a structured protocol.24 The incidence of delirium during hospitalization was 32% in the geriatrics consultation group versus 50% in the standard care group (OR, 0.48; 95% CI, 0.230.98; relative risk [RR], 0.64; 95% CI, 0.370.98), but there was no difference in duration of delirium.24
Pharmacologic Interventions
A Cochrane review found 6 randomized controlled trials for preventing delirium in hospitalized surgical patients.25 Low‐dose haloperidol prophylaxis was found to be effective in reducing the severity (mean difference in delirium rating scale score of 4.0 (95% CI, 2.05.8) and duration of delirium (RR, 6.44; 95% CI, 7.64 to 5.24), along with shortening the length of hospital stay (mean difference in hospital days, 5.5; 95% CI, 1.42.3) in hip surgery patients, but it did not prevent delirium occurrence.26 A review by Campbell et al evaluated 9 studies testing pharmacological interventions in preventing delirium in surgical patients.27 Use of a single‐dose risperidone after cardiac surgery decreased delirium incidence compared to placebo.28 Donepezil and citicoline showed no benefit in preventing delirium.2931 Early restoration of sleep cycles with the use of a benzodiazepine/opiate combination and pain control with gabapentin postoperatively reduced delirium incidence.32, 33 Interventions started on day of surgery and continued for up to 3 days postoperatively were found to be effective in reducing delirium incidence.27
5: How Should Delirium Be Treated?
Nonpharmacologic Interventions
The multicomponent intervention SER22 mentioned above evaluated the efficacy of interventions ranging from a geriatric psychiatric consultation and a nursing liaison to assess patients' daily pain management, to treating hypoxemia and other metabolic derangements along with a standardized screening tool for early detection of delirium. Delirious patients randomized to a geriatrician or a geriatric psychiatrist's consultation making treatment decisions, along with daily visits by a nursing liaison, resulted in improvement in short portable mental status questionnaire scores (SPMSQ) from 8.2 to 7.9, two weeks after admission, whereas the usual care group showed a deterioration in scores (8.4 to 9.1).34 Though by week 8, the difference between both groups disappeared. While the severity and recurrence rates of delirium were unchanged, the trial by Inouye et al23 evaluating 6 standardized intervention protocols showed a significant reduction in the total number of hospital days with delirium (105 vs 161 days, P = 0.02). Training of nurses to use a delirium screening instrument to identify delirium in hip fracture patients, along with prompt implementation of interventions based on a nursing guide for evaluation of causes of delirium, resulted in a shorter duration of delirium (median = 1 day vs 4 days, P = 0.03) and severity, compared to the usual care group.35 Daily assessment by a gerontological nurse resulted in greater improvement in functional status (21% vs 10%).36 No difference in patients' length of stay or mortality was demonstrated in any of the studies included in the review.22 A Cochrane review assessing efficacy of multidisciplinary interventions for reducing delirium in cognitively impaired patients did not identify any studies.37
Pharmacologic Interventions
We identified 7 SERs,27, 3843 addressing the efficacy and safety of various pharmacological interventions to treat delirium. Campbell et al suggested that blocking the dopaminergic system with neuroleptics, and reducing the exposure to lorazepam, might reduce delirium severity and duration among hospitalized elders, including those in the ICU.27 There was no advantage of using atypical neuroleptics over haloperidol. Low‐dose haloperidol use was associated with reduced delirium severity and duration in hip surgery patients.26 Seitz et al38 evaluated the efficacy and safety of antipsychotics (haloperidol, olanzapine, quetiapine, risperidone, mianserin, and lorazepam) in treating delirium symptoms. They evaluated prospective single‐agent and comparison trials. None of the studies included a placebo group. An improvement in delirium severity was observed in the majority of studies, but there was no advantage of one agent over the other in comparison trials. Most trials were underpowered to detect a clinically significant difference and are of short duration (<7 days) to adequately assess for delirium resolution.
A Cochrane review39 comparing the efficacy of haloperidol over risperidone and olanzapine for treating delirium showed similar findings as Campbell and colleagues' SER.27 The decrease in delirium severity scores was not significantly different using low‐dose haloperidol (<3.0 mg per day) compared with olanzapine and risperidone (OR, 0.63; 95% CI, 0.291.38; P = 0.25). High‐dose haloperidol (>4.5 mg per day) was associated with an increased incidence of extrapyramidal adverse effects. The role of drug therapy for delirium in terminally ill adult patients was evaluated in a Cochrane review40 and by Weber et al.41 They suggested the use of haloperidol or chlorpromazine in reducing delirium in acquired immune deficiency syndrome (AIDS) patients. Benzodiazepines were ineffective for treatment of non‐alcohol withdrawal delirium.42 In mechanically ventilated ICU patients, dexmedetomidine treatment increased number of delirium/coma‐free days compared with lorazepam (7 vs 3 days, P = 0.01).42 Cholinesterase inhibitor donepezil did not decrease duration of delirium compared to placebo in postoperative orthopedic patients.43
6: What Is the Impact of Delirium on Patient Outcomes?
We found 4 SERs.4447 Persistent delirium defined as delirium present on admission and at the time of discharge or beyond, and its impact on outcomes in older hospitalized patients, was evaluated in 1 SER. The combined proportions of patients with persistent delirium at discharge, 1, 3, and 6 months were 44.7%, 32.8%, 25.6%, and 21%, respectively.44 Evaluation of prognosis was complicated by small number of subjects and differences in length of follow up.
Delirium in elderly (>65 years) patients was associated with an increased risk of death45, 46 compared with controls, with a mortality rate of 38% in delirious patients compared to 27.5% in controls (hazard ratio[HR], 1.95; 95% CI, 1.512.52).45 This association persisted independent of preexisting dementia. Patients with delirium compared to controls were also at increased risk of institutionalization (33.4% vs 10.7%) (OR, 2.41; 95% CI, 1.773.29) and dementia (62.5% vs 8.1%) (OR, 12.52; 95% CI, 1.8684.21).45 In patients with dementia, delirium increased the risk of 30‐day rehospitalization and admission to long‐term care, compared to patients with dementia or delirium alone.47
DISCUSSION AND CLINICAL IMPLICATIONS
Our study identified age, cognitive impairment, depression, and mepiridine use for analgesia as risk factors for delirium in surgical patients. Drugs with anticholinergic properties were implicated in delirium development in both medical and surgical patients. The CAM has the best available data to be used as a diagnostic tool for delirium. Multicomponent interventions to prevent delirium occurrence are effective in a non‐cognitively impaired population, and low‐dose haloperidol prophylaxis decreases delirium duration and severity without affecting delirium incidence in hip surgery patients. There is no advantage of using atypical antipsychotics over haloperidol in treating delirium, and low‐dose haloperidol is as effective as a higher dose without unwarranted extrapyramidal side effects. Delirium carries a poor prognosis with an increased risk of death, institutionalization, and dementia.
Hospitals may benefit from implementing multicomponent strategies, focusing on at‐risk elderly medical and surgical patients, administered by a multidisciplinary team to reduce delirium incidence. For ICU physicians and administrators, development of sedation guidelines minimizing the use of benzodiazepines will decrease the risk of delirium development.
A structured approach in diagnosing delirium is required to maximize identification. Use of the CAM, based on best available data is recommended. However, the length of time in doing the CAM (more than 10 minutes with the requisite mental status examination) and insensitivity in nonexpert hands suggest a need for alternative screening tools. Haloperidol should be the preferred first‐line pharmacological therapy for delirium, with atypical antipsychotics reserved for patients with contraindications to haloperidol or those who are refractory to therapy with haloperidol. Figure 2 delineates a clinical model for delirium management derived from the findings in the Results section.
FUTURE RESEARCH DIRECTIONS
We identified multiple areas without clear guidelines that could provide opportunities for future research. A role for routine delirium screening can be clarified through a well‐designed delirium screening trial investigating the benefits of delirium screening, coupled with a multicomponent intervention versus usual care. Use of pharmacotherapy in delirium prevention needs to be explored further in a large randomized trial, with 3 arms to compare typical antipsychotics, atypical antipsychotics, and placebo in patients at risk for delirium with a primary outcome of delirium incidence. In regard to delirium treatment, a large randomized trial to compare haloperidol with atypical antipsychotics, with a placebo arm focusing not only on delirium duration and severity, but also on long‐term outcomes such as rehospitalizations, institutionalization, cognitive impairment, and mortality, is warranted. Figure 3 points out potential areas for researchers to investigate hypotheses generated by our review and thereby improve delirium care.
To our knowledge, our SER presents the first summary of SERs in delirium. Prior to this review, Michaud et al9 and National Institute for Health and Clinical Excellence48 published delirium guidelines, but in both of these guidelines, evidence was collected from a multitude of studies ranging in methodology from scientific review and meta‐analysis to observational studies, and the majority of recommendations were based on expert opinion. On the contrary, our review was limited to rigorously conducted SERs; hence, we utilized the highest level, critically appraised evidence to provide guidance to clinicians and researchers.
Limitations include a diverse group of studies with a heterogeneous population of patients, preventing pooling of results. We did not review each individual study included in the 38 SERs. We excluded non‐English language SERs, studies evaluating delirium subtypes, alcohol or substance abuse‐related delirium, or delirium associated with psychiatric disorders. As we only reviewed SERs, some notable studies not included in the SERs may have been missed.
CONCLUSION
Delirium among hospitalized patients is a common syndrome with a significant burden to the healthcare system and society. The field of delirium has seen considerable advances in diagnosis, prevention, and treatment over the last decade. Even with this advancement, there are still areas of uncertainty, such as: the benefits and costs of delirium screening; the benefits and harms of single or combined pharmacological agents for delirium prevention and treatment; the development of a set of reliable biomarkers for delirium diagnosis, prognosis, and response to therapy; the long‐term effect of delirium‐specific therapeutics on patients' cognitive, physical, and psychological functions; and the relationship between delirium and the development of Alzheimer's disease. As our understanding of delirium's impact on patients and healthcare improves, delirium should be identified as an indicator of poor long‐term prognosis, and should prompt immediate and effective evidence‐based management strategies, like any other critical illness.
Note Added in Proof
Disclosure: This study was supported by the National Institute on Aging (NIA), grant R01AG054205‐02; and the National Institute of Mental Health (NIMH), grant R24MH080827‐04.
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Delirium is a syndrome of disturbance of consciousness, with reduced ability to focus, sustain, or shift attention, that occurs over a short period of time and fluctuates over the course of the day.1 It encompasses a variety of cognitive, behavioral, and psychological symptoms including inattention, short‐term memory loss, sleep disturbances, agitated behaviors, delusions, and visual hallucinations.2 Delirium complicates the care of 70% to 80% of mechanically ventilated patients in intensive care units (ICUs).3 Of 13 million patients aged 65 and older hospitalized in 2002, 10% to 52% had delirium at some point during their admission.4, 5
Patients experiencing delirium have a higher probability of death during their hospital stay, adjusted for age, gender, race, and comorbidities.3, 6, 7 They are more vulnerable to hospital‐acquired complications leading to prolonged ICU and hospital stay, new institutionalization, and higher healthcare costs.3, 6, 7 Even with such a range of poor outcomes, the rates of delirium recognition are low,8 resulting in inadequate management.9 There has been considerable growth in the number of articles published on delirium in recent years. Therefore, it is of value to provide a state‐of‐the‐art summary of robust evidence in the field to healthcare personnel and delirium investigators.
We systematically reviewed the literature to identify published systematic evidence reviews (SERs), which evaluated the evidence on delirium risk factors, diagnosis, pathogenesis, prevention, treatment, and outcomes. We then summarized the data from the methodologically sound SERs to provide the reader with a clinically oriented summary of delirium literature for patient care. We also identify current gaps in delirium literature, and present future directions for delirium investigators to design studies that will enhance delirium care.
DATA SOURCES AND REVIEW METHODS
The domains of risk factors, diagnosis, pathophysiology, prevention, treatment, and outcomes were selected a priori to capture all relevant SERs regarding delirium based on the framework suggested by the American Delirium Society task force.10 To maximize article retrieval, a 3‐step search strategy was applied. First, we searched the electronic database utilizing OVID Medline, PubMed, the Cochrane Library, and Cumulative Index of Nursing and Allied Health Literature (CINAHL) using the following delirium‐specific search terms: delirium, confusion, agitation, mental status change, inattention, encephalopathy, organic mental disorders, and disorientation. We combined the above terms with the following study design terms: technical report, systematic evidence review, systematic review, meta‐analysis, editorial, and clinical reviews. We limited our search to human subjects. We excluded studies that: a) enrolled patients aged <18; b) enrolled patients with current or past Diagnostic and Statistical Manual of Mental Disorders (DSM) Axis I psychotic disorders; c) did not have standardized delirium evaluation; d) evaluated alcohol or substance abuse‐related delirium; e) did not use a systematic search method for identifying delirium‐related articles; and f) evaluated delirium sub‐types. We searched articles published from January 1966 through April 2011. Second, a manual search of references of the retrieved papers plus an Internet search using Google Scholar was conducted to find additional SERs. Titles and abstracts were screened by 2 reviewers (B.A.K., M.Z.). Authors of the included studies were contacted as necessary. Third, a library professional at the Indiana University School of Medicine independently performed a literature search, and those results were compared with our search to retrieve any missing SERs.
The methodological quality of each SER was independently assessed by 2 reviewers (B.A.K., M.Z.) using the United States Preventive Services Task Force (USPSTF) Critical Appraisal for SER.11 This scale assesses parameters that are critical to the scientific credibility of an SER and categorizes the SER as poor, fair, or good (Table 1). The 2 reviewers (B.A.K., M.Z.) used a data extraction form to record the following information from each SER: primary author, publication year, number and type of studies, number of participants and their mean age, study population, method for delirium diagnosis, risk factors, preventive and therapeutic interventions, and outcomes. Any disagreement between reviewers in SER selection, data extraction, or SER appraisal was resolved through discussion with a third reviewer (M.A.B.). The conflicting findings among SERs were resolved by consensus and by including the findings from a good SER over a fair SER.
| Criteria | Rating Definition |
|---|---|
| Recent, relevant review with comprehensive sources and search strategies | Good: If all the criteria are met |
| Explicit and relevant selection criteria | |
| Standard appraisal of included studies | |
| Valid conclusion | |
| Recent, relevant review that is not clearly biased but lacks comprehensive sources and search strategies | Fair: If this criterion is met |
| Outdated, irrelevant, or biased review | Poor: If one or more of the criteria are met |
| There is no systematic search for studies | |
| There are no explicit selection criteria | |
| There is no standard appraisal of studies |
RESULTS
Our search yielded 76,060 potential citations, out of which we identified 38 SERs meeting our inclusion criteria (Table 2). Figure 1 outlines our search strategy. Based on the USPSTF criteria, 22 SERs graded as good or fair provided the data to establish our review.
| Author (Year) | Studies (n)/ Participants (n) | Mean Age (Years) | Study Type | Service | Delirium/Cognition Assessment Scales | Review Objectives* | Rating |
|---|---|---|---|---|---|---|---|
| |||||||
| Van Rompaey et al15 (2008) | 6/7,114 | 61.2 | Prospective cohort, retrospective analysis | ICU (medical, surgical, coronary, mixed) | CAM‐ICU, psychiatric interview, ICU delirium screening checklist | 1/Risk factors | Fair |
| Bryson and Wyand13 (2006) | 18/3,473 | 71.93 | RCT | Surgery | MattisKovner Verbal Recall and Recognition, GDS, DST, DSM‐III, AMT, PRT, FOMTL, DCT, FPU, GEMS, WAIS‐R, Meta Memory Questionnaire, National Adult Reading Test | 1/Risk factors | Good |
| Fong et al14 (2006) | 9/1,078 | 63.1 | RCT, case control, prospective cohort, retrospective cohort | Surgery | CAM, DSM‐III, MMSE, SPMSQ, Digit Symbol Substitution Test, Trailmaking B Test | 1/Risk factors | Fair |
| Adamis et al53 (2009) | 6/882 | 54.59 | Case control | Medicine, ICU, surgery | CAM, DRS, DSM‐III‐R, DSM‐IV, ICD‐10 | 1/Risk factors | Poor |
| Balasundaram and Holmes12 (2007) | 4/364 | 66.8 | Prospective cohort | Surgery | CAM, DRS, HDS‐R, DSM‐IV | 1/Risk factors | Good |
| Dasgupta and Dumbrell49 (2006) | 25/5,175 | 72.5 | Prospective observational | Surgery | CAM, DSM‐III/IV | 1/Risk factors | Poor |
| Elie et al50 (1998) | 27/1,365 | 75.7 | Prospective | Medicine, surgery, psychiatry | CAM, NFRD, MMSE, MSQ, SPMSQ | 1/Risk factors | Poor |
| Van Munster et al52 (2009) | 5/1,099 | 77.86 | Cohort | Medicine, surgery | CAM, DRS | 1/Risk factors | Poor |
| Van der Mast and Roest51 (1996) | 57/6,129 | 48.2 | Prospective control, retrospective | Surgery | Psychiatric interview, chart review for signs of delirium, DSM‐III, MMSE | 1/Risk factors | Poor |
| Campbell et al16 (2009) | 27/8,492 | 71.35 | Longitudinal cohort, cross‐sectional, case control | Medicine, surgery, ICU, psychiatry | CAM, CAM‐ICU, DSI, DSM‐III/III‐R/IV, SDC, MMSE, Verbal N‐Back Test, BCRS, WMS | 1/Risk factors | Fair |
| Soiza et al17 (2008) | 12/764 | 72.4 | Cohort, case control, case series | Medicine, ICU, psychiatry | CAM, DSM‐III/III‐R/IV | 1/Risk factors | Good |
| Michaud et al9 (2007) | 29/NA | 76.7 | RCT, cohort | Medicine, surgery | CAM, BOMC, DRS, MDAS, ICD‐10, DSM‐IV, MMSE | 1/Risk factors, 2/Diagnosis, 4/Prevention, 5/Treatment | Fair |
| Steis and Fick54 (2008) | 10/3,059 | 72.5 | Prospective clinical trials, retrospective, observational, case study | Medicine, surgery, ICU | DSM‐III/IV | 2/Diagnosis | Poor |
| Wei et al20 (2008) | 7/1,071 | 70.17 | Validation, adaptation, translation, application | ICU, ED, medicine, surgery | CAM, CAM‐ICU, DSM‐IV, NH‐CAM, DI | 2/Diagnosis | Good |
| Wong et al18 (2010) | 25/3,027 | 72.76 | Prospective clinical studies | Medicine, surgery | CAC, CAM, DOSS, DRS, DRS‐R‐98, Digit Span Test, GAR, MDAS, MMSE, Nu‐DESC, Vigilance A Test | 2/Diagnosis | Fair |
| Devlin et al55 (2007) | 12/2,106 | 61.8 | Validation studies | ICU | CAM, ICDSC, CTD, ROC, DSM‐III/IV, DDS, MMSE | 2/Diagnosis | Poor |
| Fick et al47 (2002) | 14/7,701 | 79.51 | Prospective cohort, retrospective cohort, cross‐sectional, clinical trials | Medicine, surgery, ED | CAM, DRS, DSM‐III/III‐R/IV, CERAD, NINCDS‐ADRDA, IQCODE, MMSE | 2/Diagnosis, 4/Prevention, 6/Prognosis | Fair |
| Siddiqi et al46 (2006) | 40/12,220 | 78.8 | Prospective cohort, cross‐sectional, case‐controlled trials | Medicine | CAM, DRS, MDAS, SPMSQ, DSM‐III/III‐R/IV, MSQ, MMSE,BPRS, IQCODE, GHQ BAS | 2/Diagnosis, 6/Prognosis | Fair |
| 28/4,915 | |||||||
| Hall et al21 (2011) | 5/315 | 71.13 | Prospective cohort | Medicine, surgery, psychogeriatric | DSM‐III/III‐R/IV, MMSE, DRS, CAM, IQCODE, GDS | 3/Pathophysiology | Good |
| Cole et al56 (1996) | 10/999 | 71.6 | Randomized and nonrandomized trials | Medicine, surgery | DSM‐III, SPMSQ | 4/Prevention | Poor |
| Siddiqi et al25 (2007) | 6/833 | 76.67 | RCT | Surgery | CAM, DRS‐R‐98, DSM‐III/IV, DSI, MDAS, AMT, MMSE, OBS | 4/Prevention | Good |
| Campbell et al27 (2009) | 13/1,305 | 65.8 | RCT | Medicine, surgery, ICU | MDAS, DRS‐R‐98 | 4/Prevention, 5/Treatment | Good |
| Weber et al41 (2004) | 13/1,650 | 73.99 | RCT, non‐RCT, clinical trials, meta‐analysis, case report | Medicine, surgery | CAM, MDAS, DSI, DRS, DSM‐III‐R/IV, MMSE | 4/Prevention, 5/Treatment | Fair |
| Milisen et al22 (2005) | 7/1,683 | 80.73 | RCT, controlled trials, beforeafter study | Medicine, surgery | CAM, DSM‐III, SPMSQ, MMSE | 4/Prevention, 5/Treatment | Good |
| Lonergan et al39 (2009) | 3/629 | 74.5 | RCT | Medicine, surgery | CAM, DRS, DRS‐R‐98, MDAS, CGI, DSM‐IV | 5/Treatment | Good |
| Jackson and Lipman40 (2004) | 1/30 | 39.2 | RCT | Medicine | DRS, DSM‐III‐R | 5/Treatment | Good |
| Lonergan et al42 (2009) | 1/106 | 54.5 | RCT | ICU | CAM‐ICU | 5/Treatment | Good |
| Bourne et al57 (2008) | 33/1,880 | 60.99 | RCT, prospective trials, comparative trials | Medicine, surgery | DRS | 4/Prevention, 5/Treatment | Poor |
| Bitsch et al58 (2004) | 12/1,823 | 79.02 | Prospective, descriptive | Surgery | CAM, MDAS, DSI, OBS, MMSE | 4/Prevention, 5/Treatment | Poor |
| Overshott et al43 (2008) | 1/80 | 67 | RCT | Surgery | CAM, DSI, DSM‐IV, MMSE | 5/Treatment | Good |
| Lacasse et al59 (2006) | 4/158 | 60.8 | RCT | Medicine, surgery | CAM, DRS‐R‐98, MDAS, DI, DSM‐III‐R/IV, MMSE | 5/Treatment | Poor |
| Peritogiannis et al60 (2009) | 23/538 | 62.84 | RCT, retrospective, open label | Medicine, surgery | DRS, DRS‐R‐98, DRS‐R‐98‐J, MDAS, DI, 10‐Point Visual Analog Scale | 5/Treatment | Poor |
| Seitz et al38 (2007) | 14/448 | 63.09 | Prospective | Medicine, surgery, ICU | DSM‐III/III‐R/IV/IV‐TR, CAM, DRS‐R‐98, MDAS, DI | 5/Treatment | Good |
| Britton and Russell37 (2001/2004) | 1/227 | 82.35 | RCT | Medicine | CAM, SPMSQ, DSM‐III‐R, MMSE | 5/Treatment | Good |
| Jackson et al6 (2004) | 9/1,885 | 77.68 | Prospective, descriptive | Medicine, surgery, ICU, psychiatry | CAM, CAM‐ICU, DRS, MMSE, DSM | 6/Prognosis | Poor |
| Cole et al44 (2009) | 18/1,322 | 81.3 | Prospective cohort | Medicine, surgery | CAM, DSM‐III/III‐R/IV, ICD‐10, OBS | 6/Prognosis | Good |
| Witlox et al45 (2010) | 42/5,777 | 79.96 | Observational | Medicine, surgery | DSM, patient interview | 6/Prognosis | Good |
| Cole and Primeau61 (1993) | 8/573 | 77.25 | Prospective trials | Medicine, surgery, psychiatry | DSM‐I/III | 6/Prognosis | Poor |
1: What Are the Risk Factors for Development of Delirium in Hospitalized Patients?
We found 6 SERs1217 that evaluated risk factors for the development of delirium. Three reviews included only surgical patients,1214 1 focused on the intensive care unit (ICU),15 and the remaining 2 had both medical and surgical patients.16, 17 Risk factors identified in an elective vascular surgery population were age >64, preoperative cognitive impairment, depression, intraoperative blood transfusions, and previous amputation.12 The risk of incident delirium conferred by general anesthesia compared to regional anesthesia in non‐cardiac surgery patients was not significantly different among both groups.13 One SER14 focused on the effects of different opioid analgesics on postoperative delirium, and whether route of administration of medicines (intravenous vs epidural) had any impact on delirium. Mepiridine was consistently associated with an increased risk of delirium in elderly surgical patients, but there were no significant differences in postoperative delirium rates among those receiving morphine, fentanyl, or hydromorphone. The rates of delirium did not differ significantly between intravenous and epidural routes of analgesic administration, except in one study where epidural route had more delirium cases, but in 85% of those cases, mepiridine was used as an epidural agent. Risk factors explored in an ICU setting found multiple predisposing and precipitating risk factors, with the surprising finding that age was not a strong predictor of delirium.15 An association between delirium and drugs with anticholinergic properties was found in 1 SER.16 There was no causal relationship between structural or functional neuroimaging findings and delirium development.17
2: What Is the Clinical Utility of Bedside Tools in Delirium Diagnosis?
The accuracy of bedside instruments in diagnosing delirium was assessed in an SER of 25 prospective studies.18 Among the 11 scales reviewed, the Confusion Assessment Method (CAM) had the most evidence supporting its use as a bedside tool (+likelihood ratio [LR], 9.6; 95% CI [confidence interval], 5.816.0; LR, 0.16; 95% CI, 0.090.29). The Folstein mini‐mental status examination (MMSE)19 (score <24) was the least useful test for identifying delirium (LR, 1.6; 95% CI, 1.22.0). Another SER evaluating the psychometric properties of CAM demonstrated a sensitivity of 94% (CI, 91%97%) and specificity of 89% (CI, 85%94%).20 CAM also showed prognostic value with worsening of delirium outcomes depending on the number of CAM items present.20
3: What Is the Underlying Pathophysiology of Delirium and Is There a Role of Measuring Biomarkers for Delirium?
We found only 1 SER which examined the associations between cerebrospinal fluid biomarkers and delirium.21 Delirium was associated with raised levels of serotonin metabolites, interleukin‐8, cortisol, lactate, and protein. Additionally, higher acetylcholinesterase predicted poor outcome after delirium, and higher dopamine metabolites were associated with psychotic features. Delirium was also associated with reduced levels of somatostatin, ‐endorphin, and neuron‐specific enolase.
4: Can Delirium Be Prevented?
Nonpharmacologic Interventions
An SER22 reviewing multicomponent interventions to prevent delirium identified 2 studies23, 24 showing statistically significant results. In the Yale Delirium Prevention Trial,23 the intervention was targeted toward minimizing 6 risk factors in elderly patients (70 years of age) admitted to a general medicine service, who did not have delirium at the time of admission, but were at risk for delirium development. The interventions included: orientation activities for the cognitively impaired, early mobilization, preventing sleep deprivation, minimizing the use of psychoactive drugs, use of eyeglasses and hearing aids, and treating volume depletion. The incidence of delirium was 9.9% with this intervention compared with 15% in the usual care group (OR [odds ratio], 0.60; 95% CI, 0.390.92).23 The other studied patients with hip fractures, randomized to either standard care versus the addition of a geriatrics consultation preoperatively or immediately after hip repair, providing recommendations based on a structured protocol.24 The incidence of delirium during hospitalization was 32% in the geriatrics consultation group versus 50% in the standard care group (OR, 0.48; 95% CI, 0.230.98; relative risk [RR], 0.64; 95% CI, 0.370.98), but there was no difference in duration of delirium.24
Pharmacologic Interventions
A Cochrane review found 6 randomized controlled trials for preventing delirium in hospitalized surgical patients.25 Low‐dose haloperidol prophylaxis was found to be effective in reducing the severity (mean difference in delirium rating scale score of 4.0 (95% CI, 2.05.8) and duration of delirium (RR, 6.44; 95% CI, 7.64 to 5.24), along with shortening the length of hospital stay (mean difference in hospital days, 5.5; 95% CI, 1.42.3) in hip surgery patients, but it did not prevent delirium occurrence.26 A review by Campbell et al evaluated 9 studies testing pharmacological interventions in preventing delirium in surgical patients.27 Use of a single‐dose risperidone after cardiac surgery decreased delirium incidence compared to placebo.28 Donepezil and citicoline showed no benefit in preventing delirium.2931 Early restoration of sleep cycles with the use of a benzodiazepine/opiate combination and pain control with gabapentin postoperatively reduced delirium incidence.32, 33 Interventions started on day of surgery and continued for up to 3 days postoperatively were found to be effective in reducing delirium incidence.27
5: How Should Delirium Be Treated?
Nonpharmacologic Interventions
The multicomponent intervention SER22 mentioned above evaluated the efficacy of interventions ranging from a geriatric psychiatric consultation and a nursing liaison to assess patients' daily pain management, to treating hypoxemia and other metabolic derangements along with a standardized screening tool for early detection of delirium. Delirious patients randomized to a geriatrician or a geriatric psychiatrist's consultation making treatment decisions, along with daily visits by a nursing liaison, resulted in improvement in short portable mental status questionnaire scores (SPMSQ) from 8.2 to 7.9, two weeks after admission, whereas the usual care group showed a deterioration in scores (8.4 to 9.1).34 Though by week 8, the difference between both groups disappeared. While the severity and recurrence rates of delirium were unchanged, the trial by Inouye et al23 evaluating 6 standardized intervention protocols showed a significant reduction in the total number of hospital days with delirium (105 vs 161 days, P = 0.02). Training of nurses to use a delirium screening instrument to identify delirium in hip fracture patients, along with prompt implementation of interventions based on a nursing guide for evaluation of causes of delirium, resulted in a shorter duration of delirium (median = 1 day vs 4 days, P = 0.03) and severity, compared to the usual care group.35 Daily assessment by a gerontological nurse resulted in greater improvement in functional status (21% vs 10%).36 No difference in patients' length of stay or mortality was demonstrated in any of the studies included in the review.22 A Cochrane review assessing efficacy of multidisciplinary interventions for reducing delirium in cognitively impaired patients did not identify any studies.37
Pharmacologic Interventions
We identified 7 SERs,27, 3843 addressing the efficacy and safety of various pharmacological interventions to treat delirium. Campbell et al suggested that blocking the dopaminergic system with neuroleptics, and reducing the exposure to lorazepam, might reduce delirium severity and duration among hospitalized elders, including those in the ICU.27 There was no advantage of using atypical neuroleptics over haloperidol. Low‐dose haloperidol use was associated with reduced delirium severity and duration in hip surgery patients.26 Seitz et al38 evaluated the efficacy and safety of antipsychotics (haloperidol, olanzapine, quetiapine, risperidone, mianserin, and lorazepam) in treating delirium symptoms. They evaluated prospective single‐agent and comparison trials. None of the studies included a placebo group. An improvement in delirium severity was observed in the majority of studies, but there was no advantage of one agent over the other in comparison trials. Most trials were underpowered to detect a clinically significant difference and are of short duration (<7 days) to adequately assess for delirium resolution.
A Cochrane review39 comparing the efficacy of haloperidol over risperidone and olanzapine for treating delirium showed similar findings as Campbell and colleagues' SER.27 The decrease in delirium severity scores was not significantly different using low‐dose haloperidol (<3.0 mg per day) compared with olanzapine and risperidone (OR, 0.63; 95% CI, 0.291.38; P = 0.25). High‐dose haloperidol (>4.5 mg per day) was associated with an increased incidence of extrapyramidal adverse effects. The role of drug therapy for delirium in terminally ill adult patients was evaluated in a Cochrane review40 and by Weber et al.41 They suggested the use of haloperidol or chlorpromazine in reducing delirium in acquired immune deficiency syndrome (AIDS) patients. Benzodiazepines were ineffective for treatment of non‐alcohol withdrawal delirium.42 In mechanically ventilated ICU patients, dexmedetomidine treatment increased number of delirium/coma‐free days compared with lorazepam (7 vs 3 days, P = 0.01).42 Cholinesterase inhibitor donepezil did not decrease duration of delirium compared to placebo in postoperative orthopedic patients.43
6: What Is the Impact of Delirium on Patient Outcomes?
We found 4 SERs.4447 Persistent delirium defined as delirium present on admission and at the time of discharge or beyond, and its impact on outcomes in older hospitalized patients, was evaluated in 1 SER. The combined proportions of patients with persistent delirium at discharge, 1, 3, and 6 months were 44.7%, 32.8%, 25.6%, and 21%, respectively.44 Evaluation of prognosis was complicated by small number of subjects and differences in length of follow up.
Delirium in elderly (>65 years) patients was associated with an increased risk of death45, 46 compared with controls, with a mortality rate of 38% in delirious patients compared to 27.5% in controls (hazard ratio[HR], 1.95; 95% CI, 1.512.52).45 This association persisted independent of preexisting dementia. Patients with delirium compared to controls were also at increased risk of institutionalization (33.4% vs 10.7%) (OR, 2.41; 95% CI, 1.773.29) and dementia (62.5% vs 8.1%) (OR, 12.52; 95% CI, 1.8684.21).45 In patients with dementia, delirium increased the risk of 30‐day rehospitalization and admission to long‐term care, compared to patients with dementia or delirium alone.47
DISCUSSION AND CLINICAL IMPLICATIONS
Our study identified age, cognitive impairment, depression, and mepiridine use for analgesia as risk factors for delirium in surgical patients. Drugs with anticholinergic properties were implicated in delirium development in both medical and surgical patients. The CAM has the best available data to be used as a diagnostic tool for delirium. Multicomponent interventions to prevent delirium occurrence are effective in a non‐cognitively impaired population, and low‐dose haloperidol prophylaxis decreases delirium duration and severity without affecting delirium incidence in hip surgery patients. There is no advantage of using atypical antipsychotics over haloperidol in treating delirium, and low‐dose haloperidol is as effective as a higher dose without unwarranted extrapyramidal side effects. Delirium carries a poor prognosis with an increased risk of death, institutionalization, and dementia.
Hospitals may benefit from implementing multicomponent strategies, focusing on at‐risk elderly medical and surgical patients, administered by a multidisciplinary team to reduce delirium incidence. For ICU physicians and administrators, development of sedation guidelines minimizing the use of benzodiazepines will decrease the risk of delirium development.
A structured approach in diagnosing delirium is required to maximize identification. Use of the CAM, based on best available data is recommended. However, the length of time in doing the CAM (more than 10 minutes with the requisite mental status examination) and insensitivity in nonexpert hands suggest a need for alternative screening tools. Haloperidol should be the preferred first‐line pharmacological therapy for delirium, with atypical antipsychotics reserved for patients with contraindications to haloperidol or those who are refractory to therapy with haloperidol. Figure 2 delineates a clinical model for delirium management derived from the findings in the Results section.
FUTURE RESEARCH DIRECTIONS
We identified multiple areas without clear guidelines that could provide opportunities for future research. A role for routine delirium screening can be clarified through a well‐designed delirium screening trial investigating the benefits of delirium screening, coupled with a multicomponent intervention versus usual care. Use of pharmacotherapy in delirium prevention needs to be explored further in a large randomized trial, with 3 arms to compare typical antipsychotics, atypical antipsychotics, and placebo in patients at risk for delirium with a primary outcome of delirium incidence. In regard to delirium treatment, a large randomized trial to compare haloperidol with atypical antipsychotics, with a placebo arm focusing not only on delirium duration and severity, but also on long‐term outcomes such as rehospitalizations, institutionalization, cognitive impairment, and mortality, is warranted. Figure 3 points out potential areas for researchers to investigate hypotheses generated by our review and thereby improve delirium care.
To our knowledge, our SER presents the first summary of SERs in delirium. Prior to this review, Michaud et al9 and National Institute for Health and Clinical Excellence48 published delirium guidelines, but in both of these guidelines, evidence was collected from a multitude of studies ranging in methodology from scientific review and meta‐analysis to observational studies, and the majority of recommendations were based on expert opinion. On the contrary, our review was limited to rigorously conducted SERs; hence, we utilized the highest level, critically appraised evidence to provide guidance to clinicians and researchers.
Limitations include a diverse group of studies with a heterogeneous population of patients, preventing pooling of results. We did not review each individual study included in the 38 SERs. We excluded non‐English language SERs, studies evaluating delirium subtypes, alcohol or substance abuse‐related delirium, or delirium associated with psychiatric disorders. As we only reviewed SERs, some notable studies not included in the SERs may have been missed.
CONCLUSION
Delirium among hospitalized patients is a common syndrome with a significant burden to the healthcare system and society. The field of delirium has seen considerable advances in diagnosis, prevention, and treatment over the last decade. Even with this advancement, there are still areas of uncertainty, such as: the benefits and costs of delirium screening; the benefits and harms of single or combined pharmacological agents for delirium prevention and treatment; the development of a set of reliable biomarkers for delirium diagnosis, prognosis, and response to therapy; the long‐term effect of delirium‐specific therapeutics on patients' cognitive, physical, and psychological functions; and the relationship between delirium and the development of Alzheimer's disease. As our understanding of delirium's impact on patients and healthcare improves, delirium should be identified as an indicator of poor long‐term prognosis, and should prompt immediate and effective evidence‐based management strategies, like any other critical illness.
Note Added in Proof
Disclosure: This study was supported by the National Institute on Aging (NIA), grant R01AG054205‐02; and the National Institute of Mental Health (NIMH), grant R24MH080827‐04.
Delirium is a syndrome of disturbance of consciousness, with reduced ability to focus, sustain, or shift attention, that occurs over a short period of time and fluctuates over the course of the day.1 It encompasses a variety of cognitive, behavioral, and psychological symptoms including inattention, short‐term memory loss, sleep disturbances, agitated behaviors, delusions, and visual hallucinations.2 Delirium complicates the care of 70% to 80% of mechanically ventilated patients in intensive care units (ICUs).3 Of 13 million patients aged 65 and older hospitalized in 2002, 10% to 52% had delirium at some point during their admission.4, 5
Patients experiencing delirium have a higher probability of death during their hospital stay, adjusted for age, gender, race, and comorbidities.3, 6, 7 They are more vulnerable to hospital‐acquired complications leading to prolonged ICU and hospital stay, new institutionalization, and higher healthcare costs.3, 6, 7 Even with such a range of poor outcomes, the rates of delirium recognition are low,8 resulting in inadequate management.9 There has been considerable growth in the number of articles published on delirium in recent years. Therefore, it is of value to provide a state‐of‐the‐art summary of robust evidence in the field to healthcare personnel and delirium investigators.
We systematically reviewed the literature to identify published systematic evidence reviews (SERs), which evaluated the evidence on delirium risk factors, diagnosis, pathogenesis, prevention, treatment, and outcomes. We then summarized the data from the methodologically sound SERs to provide the reader with a clinically oriented summary of delirium literature for patient care. We also identify current gaps in delirium literature, and present future directions for delirium investigators to design studies that will enhance delirium care.
DATA SOURCES AND REVIEW METHODS
The domains of risk factors, diagnosis, pathophysiology, prevention, treatment, and outcomes were selected a priori to capture all relevant SERs regarding delirium based on the framework suggested by the American Delirium Society task force.10 To maximize article retrieval, a 3‐step search strategy was applied. First, we searched the electronic database utilizing OVID Medline, PubMed, the Cochrane Library, and Cumulative Index of Nursing and Allied Health Literature (CINAHL) using the following delirium‐specific search terms: delirium, confusion, agitation, mental status change, inattention, encephalopathy, organic mental disorders, and disorientation. We combined the above terms with the following study design terms: technical report, systematic evidence review, systematic review, meta‐analysis, editorial, and clinical reviews. We limited our search to human subjects. We excluded studies that: a) enrolled patients aged <18; b) enrolled patients with current or past Diagnostic and Statistical Manual of Mental Disorders (DSM) Axis I psychotic disorders; c) did not have standardized delirium evaluation; d) evaluated alcohol or substance abuse‐related delirium; e) did not use a systematic search method for identifying delirium‐related articles; and f) evaluated delirium sub‐types. We searched articles published from January 1966 through April 2011. Second, a manual search of references of the retrieved papers plus an Internet search using Google Scholar was conducted to find additional SERs. Titles and abstracts were screened by 2 reviewers (B.A.K., M.Z.). Authors of the included studies were contacted as necessary. Third, a library professional at the Indiana University School of Medicine independently performed a literature search, and those results were compared with our search to retrieve any missing SERs.
The methodological quality of each SER was independently assessed by 2 reviewers (B.A.K., M.Z.) using the United States Preventive Services Task Force (USPSTF) Critical Appraisal for SER.11 This scale assesses parameters that are critical to the scientific credibility of an SER and categorizes the SER as poor, fair, or good (Table 1). The 2 reviewers (B.A.K., M.Z.) used a data extraction form to record the following information from each SER: primary author, publication year, number and type of studies, number of participants and their mean age, study population, method for delirium diagnosis, risk factors, preventive and therapeutic interventions, and outcomes. Any disagreement between reviewers in SER selection, data extraction, or SER appraisal was resolved through discussion with a third reviewer (M.A.B.). The conflicting findings among SERs were resolved by consensus and by including the findings from a good SER over a fair SER.
| Criteria | Rating Definition |
|---|---|
| Recent, relevant review with comprehensive sources and search strategies | Good: If all the criteria are met |
| Explicit and relevant selection criteria | |
| Standard appraisal of included studies | |
| Valid conclusion | |
| Recent, relevant review that is not clearly biased but lacks comprehensive sources and search strategies | Fair: If this criterion is met |
| Outdated, irrelevant, or biased review | Poor: If one or more of the criteria are met |
| There is no systematic search for studies | |
| There are no explicit selection criteria | |
| There is no standard appraisal of studies |
RESULTS
Our search yielded 76,060 potential citations, out of which we identified 38 SERs meeting our inclusion criteria (Table 2). Figure 1 outlines our search strategy. Based on the USPSTF criteria, 22 SERs graded as good or fair provided the data to establish our review.
| Author (Year) | Studies (n)/ Participants (n) | Mean Age (Years) | Study Type | Service | Delirium/Cognition Assessment Scales | Review Objectives* | Rating |
|---|---|---|---|---|---|---|---|
| |||||||
| Van Rompaey et al15 (2008) | 6/7,114 | 61.2 | Prospective cohort, retrospective analysis | ICU (medical, surgical, coronary, mixed) | CAM‐ICU, psychiatric interview, ICU delirium screening checklist | 1/Risk factors | Fair |
| Bryson and Wyand13 (2006) | 18/3,473 | 71.93 | RCT | Surgery | MattisKovner Verbal Recall and Recognition, GDS, DST, DSM‐III, AMT, PRT, FOMTL, DCT, FPU, GEMS, WAIS‐R, Meta Memory Questionnaire, National Adult Reading Test | 1/Risk factors | Good |
| Fong et al14 (2006) | 9/1,078 | 63.1 | RCT, case control, prospective cohort, retrospective cohort | Surgery | CAM, DSM‐III, MMSE, SPMSQ, Digit Symbol Substitution Test, Trailmaking B Test | 1/Risk factors | Fair |
| Adamis et al53 (2009) | 6/882 | 54.59 | Case control | Medicine, ICU, surgery | CAM, DRS, DSM‐III‐R, DSM‐IV, ICD‐10 | 1/Risk factors | Poor |
| Balasundaram and Holmes12 (2007) | 4/364 | 66.8 | Prospective cohort | Surgery | CAM, DRS, HDS‐R, DSM‐IV | 1/Risk factors | Good |
| Dasgupta and Dumbrell49 (2006) | 25/5,175 | 72.5 | Prospective observational | Surgery | CAM, DSM‐III/IV | 1/Risk factors | Poor |
| Elie et al50 (1998) | 27/1,365 | 75.7 | Prospective | Medicine, surgery, psychiatry | CAM, NFRD, MMSE, MSQ, SPMSQ | 1/Risk factors | Poor |
| Van Munster et al52 (2009) | 5/1,099 | 77.86 | Cohort | Medicine, surgery | CAM, DRS | 1/Risk factors | Poor |
| Van der Mast and Roest51 (1996) | 57/6,129 | 48.2 | Prospective control, retrospective | Surgery | Psychiatric interview, chart review for signs of delirium, DSM‐III, MMSE | 1/Risk factors | Poor |
| Campbell et al16 (2009) | 27/8,492 | 71.35 | Longitudinal cohort, cross‐sectional, case control | Medicine, surgery, ICU, psychiatry | CAM, CAM‐ICU, DSI, DSM‐III/III‐R/IV, SDC, MMSE, Verbal N‐Back Test, BCRS, WMS | 1/Risk factors | Fair |
| Soiza et al17 (2008) | 12/764 | 72.4 | Cohort, case control, case series | Medicine, ICU, psychiatry | CAM, DSM‐III/III‐R/IV | 1/Risk factors | Good |
| Michaud et al9 (2007) | 29/NA | 76.7 | RCT, cohort | Medicine, surgery | CAM, BOMC, DRS, MDAS, ICD‐10, DSM‐IV, MMSE | 1/Risk factors, 2/Diagnosis, 4/Prevention, 5/Treatment | Fair |
| Steis and Fick54 (2008) | 10/3,059 | 72.5 | Prospective clinical trials, retrospective, observational, case study | Medicine, surgery, ICU | DSM‐III/IV | 2/Diagnosis | Poor |
| Wei et al20 (2008) | 7/1,071 | 70.17 | Validation, adaptation, translation, application | ICU, ED, medicine, surgery | CAM, CAM‐ICU, DSM‐IV, NH‐CAM, DI | 2/Diagnosis | Good |
| Wong et al18 (2010) | 25/3,027 | 72.76 | Prospective clinical studies | Medicine, surgery | CAC, CAM, DOSS, DRS, DRS‐R‐98, Digit Span Test, GAR, MDAS, MMSE, Nu‐DESC, Vigilance A Test | 2/Diagnosis | Fair |
| Devlin et al55 (2007) | 12/2,106 | 61.8 | Validation studies | ICU | CAM, ICDSC, CTD, ROC, DSM‐III/IV, DDS, MMSE | 2/Diagnosis | Poor |
| Fick et al47 (2002) | 14/7,701 | 79.51 | Prospective cohort, retrospective cohort, cross‐sectional, clinical trials | Medicine, surgery, ED | CAM, DRS, DSM‐III/III‐R/IV, CERAD, NINCDS‐ADRDA, IQCODE, MMSE | 2/Diagnosis, 4/Prevention, 6/Prognosis | Fair |
| Siddiqi et al46 (2006) | 40/12,220 | 78.8 | Prospective cohort, cross‐sectional, case‐controlled trials | Medicine | CAM, DRS, MDAS, SPMSQ, DSM‐III/III‐R/IV, MSQ, MMSE,BPRS, IQCODE, GHQ BAS | 2/Diagnosis, 6/Prognosis | Fair |
| 28/4,915 | |||||||
| Hall et al21 (2011) | 5/315 | 71.13 | Prospective cohort | Medicine, surgery, psychogeriatric | DSM‐III/III‐R/IV, MMSE, DRS, CAM, IQCODE, GDS | 3/Pathophysiology | Good |
| Cole et al56 (1996) | 10/999 | 71.6 | Randomized and nonrandomized trials | Medicine, surgery | DSM‐III, SPMSQ | 4/Prevention | Poor |
| Siddiqi et al25 (2007) | 6/833 | 76.67 | RCT | Surgery | CAM, DRS‐R‐98, DSM‐III/IV, DSI, MDAS, AMT, MMSE, OBS | 4/Prevention | Good |
| Campbell et al27 (2009) | 13/1,305 | 65.8 | RCT | Medicine, surgery, ICU | MDAS, DRS‐R‐98 | 4/Prevention, 5/Treatment | Good |
| Weber et al41 (2004) | 13/1,650 | 73.99 | RCT, non‐RCT, clinical trials, meta‐analysis, case report | Medicine, surgery | CAM, MDAS, DSI, DRS, DSM‐III‐R/IV, MMSE | 4/Prevention, 5/Treatment | Fair |
| Milisen et al22 (2005) | 7/1,683 | 80.73 | RCT, controlled trials, beforeafter study | Medicine, surgery | CAM, DSM‐III, SPMSQ, MMSE | 4/Prevention, 5/Treatment | Good |
| Lonergan et al39 (2009) | 3/629 | 74.5 | RCT | Medicine, surgery | CAM, DRS, DRS‐R‐98, MDAS, CGI, DSM‐IV | 5/Treatment | Good |
| Jackson and Lipman40 (2004) | 1/30 | 39.2 | RCT | Medicine | DRS, DSM‐III‐R | 5/Treatment | Good |
| Lonergan et al42 (2009) | 1/106 | 54.5 | RCT | ICU | CAM‐ICU | 5/Treatment | Good |
| Bourne et al57 (2008) | 33/1,880 | 60.99 | RCT, prospective trials, comparative trials | Medicine, surgery | DRS | 4/Prevention, 5/Treatment | Poor |
| Bitsch et al58 (2004) | 12/1,823 | 79.02 | Prospective, descriptive | Surgery | CAM, MDAS, DSI, OBS, MMSE | 4/Prevention, 5/Treatment | Poor |
| Overshott et al43 (2008) | 1/80 | 67 | RCT | Surgery | CAM, DSI, DSM‐IV, MMSE | 5/Treatment | Good |
| Lacasse et al59 (2006) | 4/158 | 60.8 | RCT | Medicine, surgery | CAM, DRS‐R‐98, MDAS, DI, DSM‐III‐R/IV, MMSE | 5/Treatment | Poor |
| Peritogiannis et al60 (2009) | 23/538 | 62.84 | RCT, retrospective, open label | Medicine, surgery | DRS, DRS‐R‐98, DRS‐R‐98‐J, MDAS, DI, 10‐Point Visual Analog Scale | 5/Treatment | Poor |
| Seitz et al38 (2007) | 14/448 | 63.09 | Prospective | Medicine, surgery, ICU | DSM‐III/III‐R/IV/IV‐TR, CAM, DRS‐R‐98, MDAS, DI | 5/Treatment | Good |
| Britton and Russell37 (2001/2004) | 1/227 | 82.35 | RCT | Medicine | CAM, SPMSQ, DSM‐III‐R, MMSE | 5/Treatment | Good |
| Jackson et al6 (2004) | 9/1,885 | 77.68 | Prospective, descriptive | Medicine, surgery, ICU, psychiatry | CAM, CAM‐ICU, DRS, MMSE, DSM | 6/Prognosis | Poor |
| Cole et al44 (2009) | 18/1,322 | 81.3 | Prospective cohort | Medicine, surgery | CAM, DSM‐III/III‐R/IV, ICD‐10, OBS | 6/Prognosis | Good |
| Witlox et al45 (2010) | 42/5,777 | 79.96 | Observational | Medicine, surgery | DSM, patient interview | 6/Prognosis | Good |
| Cole and Primeau61 (1993) | 8/573 | 77.25 | Prospective trials | Medicine, surgery, psychiatry | DSM‐I/III | 6/Prognosis | Poor |
1: What Are the Risk Factors for Development of Delirium in Hospitalized Patients?
We found 6 SERs1217 that evaluated risk factors for the development of delirium. Three reviews included only surgical patients,1214 1 focused on the intensive care unit (ICU),15 and the remaining 2 had both medical and surgical patients.16, 17 Risk factors identified in an elective vascular surgery population were age >64, preoperative cognitive impairment, depression, intraoperative blood transfusions, and previous amputation.12 The risk of incident delirium conferred by general anesthesia compared to regional anesthesia in non‐cardiac surgery patients was not significantly different among both groups.13 One SER14 focused on the effects of different opioid analgesics on postoperative delirium, and whether route of administration of medicines (intravenous vs epidural) had any impact on delirium. Mepiridine was consistently associated with an increased risk of delirium in elderly surgical patients, but there were no significant differences in postoperative delirium rates among those receiving morphine, fentanyl, or hydromorphone. The rates of delirium did not differ significantly between intravenous and epidural routes of analgesic administration, except in one study where epidural route had more delirium cases, but in 85% of those cases, mepiridine was used as an epidural agent. Risk factors explored in an ICU setting found multiple predisposing and precipitating risk factors, with the surprising finding that age was not a strong predictor of delirium.15 An association between delirium and drugs with anticholinergic properties was found in 1 SER.16 There was no causal relationship between structural or functional neuroimaging findings and delirium development.17
2: What Is the Clinical Utility of Bedside Tools in Delirium Diagnosis?
The accuracy of bedside instruments in diagnosing delirium was assessed in an SER of 25 prospective studies.18 Among the 11 scales reviewed, the Confusion Assessment Method (CAM) had the most evidence supporting its use as a bedside tool (+likelihood ratio [LR], 9.6; 95% CI [confidence interval], 5.816.0; LR, 0.16; 95% CI, 0.090.29). The Folstein mini‐mental status examination (MMSE)19 (score <24) was the least useful test for identifying delirium (LR, 1.6; 95% CI, 1.22.0). Another SER evaluating the psychometric properties of CAM demonstrated a sensitivity of 94% (CI, 91%97%) and specificity of 89% (CI, 85%94%).20 CAM also showed prognostic value with worsening of delirium outcomes depending on the number of CAM items present.20
3: What Is the Underlying Pathophysiology of Delirium and Is There a Role of Measuring Biomarkers for Delirium?
We found only 1 SER which examined the associations between cerebrospinal fluid biomarkers and delirium.21 Delirium was associated with raised levels of serotonin metabolites, interleukin‐8, cortisol, lactate, and protein. Additionally, higher acetylcholinesterase predicted poor outcome after delirium, and higher dopamine metabolites were associated with psychotic features. Delirium was also associated with reduced levels of somatostatin, ‐endorphin, and neuron‐specific enolase.
4: Can Delirium Be Prevented?
Nonpharmacologic Interventions
An SER22 reviewing multicomponent interventions to prevent delirium identified 2 studies23, 24 showing statistically significant results. In the Yale Delirium Prevention Trial,23 the intervention was targeted toward minimizing 6 risk factors in elderly patients (70 years of age) admitted to a general medicine service, who did not have delirium at the time of admission, but were at risk for delirium development. The interventions included: orientation activities for the cognitively impaired, early mobilization, preventing sleep deprivation, minimizing the use of psychoactive drugs, use of eyeglasses and hearing aids, and treating volume depletion. The incidence of delirium was 9.9% with this intervention compared with 15% in the usual care group (OR [odds ratio], 0.60; 95% CI, 0.390.92).23 The other studied patients with hip fractures, randomized to either standard care versus the addition of a geriatrics consultation preoperatively or immediately after hip repair, providing recommendations based on a structured protocol.24 The incidence of delirium during hospitalization was 32% in the geriatrics consultation group versus 50% in the standard care group (OR, 0.48; 95% CI, 0.230.98; relative risk [RR], 0.64; 95% CI, 0.370.98), but there was no difference in duration of delirium.24
Pharmacologic Interventions
A Cochrane review found 6 randomized controlled trials for preventing delirium in hospitalized surgical patients.25 Low‐dose haloperidol prophylaxis was found to be effective in reducing the severity (mean difference in delirium rating scale score of 4.0 (95% CI, 2.05.8) and duration of delirium (RR, 6.44; 95% CI, 7.64 to 5.24), along with shortening the length of hospital stay (mean difference in hospital days, 5.5; 95% CI, 1.42.3) in hip surgery patients, but it did not prevent delirium occurrence.26 A review by Campbell et al evaluated 9 studies testing pharmacological interventions in preventing delirium in surgical patients.27 Use of a single‐dose risperidone after cardiac surgery decreased delirium incidence compared to placebo.28 Donepezil and citicoline showed no benefit in preventing delirium.2931 Early restoration of sleep cycles with the use of a benzodiazepine/opiate combination and pain control with gabapentin postoperatively reduced delirium incidence.32, 33 Interventions started on day of surgery and continued for up to 3 days postoperatively were found to be effective in reducing delirium incidence.27
5: How Should Delirium Be Treated?
Nonpharmacologic Interventions
The multicomponent intervention SER22 mentioned above evaluated the efficacy of interventions ranging from a geriatric psychiatric consultation and a nursing liaison to assess patients' daily pain management, to treating hypoxemia and other metabolic derangements along with a standardized screening tool for early detection of delirium. Delirious patients randomized to a geriatrician or a geriatric psychiatrist's consultation making treatment decisions, along with daily visits by a nursing liaison, resulted in improvement in short portable mental status questionnaire scores (SPMSQ) from 8.2 to 7.9, two weeks after admission, whereas the usual care group showed a deterioration in scores (8.4 to 9.1).34 Though by week 8, the difference between both groups disappeared. While the severity and recurrence rates of delirium were unchanged, the trial by Inouye et al23 evaluating 6 standardized intervention protocols showed a significant reduction in the total number of hospital days with delirium (105 vs 161 days, P = 0.02). Training of nurses to use a delirium screening instrument to identify delirium in hip fracture patients, along with prompt implementation of interventions based on a nursing guide for evaluation of causes of delirium, resulted in a shorter duration of delirium (median = 1 day vs 4 days, P = 0.03) and severity, compared to the usual care group.35 Daily assessment by a gerontological nurse resulted in greater improvement in functional status (21% vs 10%).36 No difference in patients' length of stay or mortality was demonstrated in any of the studies included in the review.22 A Cochrane review assessing efficacy of multidisciplinary interventions for reducing delirium in cognitively impaired patients did not identify any studies.37
Pharmacologic Interventions
We identified 7 SERs,27, 3843 addressing the efficacy and safety of various pharmacological interventions to treat delirium. Campbell et al suggested that blocking the dopaminergic system with neuroleptics, and reducing the exposure to lorazepam, might reduce delirium severity and duration among hospitalized elders, including those in the ICU.27 There was no advantage of using atypical neuroleptics over haloperidol. Low‐dose haloperidol use was associated with reduced delirium severity and duration in hip surgery patients.26 Seitz et al38 evaluated the efficacy and safety of antipsychotics (haloperidol, olanzapine, quetiapine, risperidone, mianserin, and lorazepam) in treating delirium symptoms. They evaluated prospective single‐agent and comparison trials. None of the studies included a placebo group. An improvement in delirium severity was observed in the majority of studies, but there was no advantage of one agent over the other in comparison trials. Most trials were underpowered to detect a clinically significant difference and are of short duration (<7 days) to adequately assess for delirium resolution.
A Cochrane review39 comparing the efficacy of haloperidol over risperidone and olanzapine for treating delirium showed similar findings as Campbell and colleagues' SER.27 The decrease in delirium severity scores was not significantly different using low‐dose haloperidol (<3.0 mg per day) compared with olanzapine and risperidone (OR, 0.63; 95% CI, 0.291.38; P = 0.25). High‐dose haloperidol (>4.5 mg per day) was associated with an increased incidence of extrapyramidal adverse effects. The role of drug therapy for delirium in terminally ill adult patients was evaluated in a Cochrane review40 and by Weber et al.41 They suggested the use of haloperidol or chlorpromazine in reducing delirium in acquired immune deficiency syndrome (AIDS) patients. Benzodiazepines were ineffective for treatment of non‐alcohol withdrawal delirium.42 In mechanically ventilated ICU patients, dexmedetomidine treatment increased number of delirium/coma‐free days compared with lorazepam (7 vs 3 days, P = 0.01).42 Cholinesterase inhibitor donepezil did not decrease duration of delirium compared to placebo in postoperative orthopedic patients.43
6: What Is the Impact of Delirium on Patient Outcomes?
We found 4 SERs.4447 Persistent delirium defined as delirium present on admission and at the time of discharge or beyond, and its impact on outcomes in older hospitalized patients, was evaluated in 1 SER. The combined proportions of patients with persistent delirium at discharge, 1, 3, and 6 months were 44.7%, 32.8%, 25.6%, and 21%, respectively.44 Evaluation of prognosis was complicated by small number of subjects and differences in length of follow up.
Delirium in elderly (>65 years) patients was associated with an increased risk of death45, 46 compared with controls, with a mortality rate of 38% in delirious patients compared to 27.5% in controls (hazard ratio[HR], 1.95; 95% CI, 1.512.52).45 This association persisted independent of preexisting dementia. Patients with delirium compared to controls were also at increased risk of institutionalization (33.4% vs 10.7%) (OR, 2.41; 95% CI, 1.773.29) and dementia (62.5% vs 8.1%) (OR, 12.52; 95% CI, 1.8684.21).45 In patients with dementia, delirium increased the risk of 30‐day rehospitalization and admission to long‐term care, compared to patients with dementia or delirium alone.47
DISCUSSION AND CLINICAL IMPLICATIONS
Our study identified age, cognitive impairment, depression, and mepiridine use for analgesia as risk factors for delirium in surgical patients. Drugs with anticholinergic properties were implicated in delirium development in both medical and surgical patients. The CAM has the best available data to be used as a diagnostic tool for delirium. Multicomponent interventions to prevent delirium occurrence are effective in a non‐cognitively impaired population, and low‐dose haloperidol prophylaxis decreases delirium duration and severity without affecting delirium incidence in hip surgery patients. There is no advantage of using atypical antipsychotics over haloperidol in treating delirium, and low‐dose haloperidol is as effective as a higher dose without unwarranted extrapyramidal side effects. Delirium carries a poor prognosis with an increased risk of death, institutionalization, and dementia.
Hospitals may benefit from implementing multicomponent strategies, focusing on at‐risk elderly medical and surgical patients, administered by a multidisciplinary team to reduce delirium incidence. For ICU physicians and administrators, development of sedation guidelines minimizing the use of benzodiazepines will decrease the risk of delirium development.
A structured approach in diagnosing delirium is required to maximize identification. Use of the CAM, based on best available data is recommended. However, the length of time in doing the CAM (more than 10 minutes with the requisite mental status examination) and insensitivity in nonexpert hands suggest a need for alternative screening tools. Haloperidol should be the preferred first‐line pharmacological therapy for delirium, with atypical antipsychotics reserved for patients with contraindications to haloperidol or those who are refractory to therapy with haloperidol. Figure 2 delineates a clinical model for delirium management derived from the findings in the Results section.
FUTURE RESEARCH DIRECTIONS
We identified multiple areas without clear guidelines that could provide opportunities for future research. A role for routine delirium screening can be clarified through a well‐designed delirium screening trial investigating the benefits of delirium screening, coupled with a multicomponent intervention versus usual care. Use of pharmacotherapy in delirium prevention needs to be explored further in a large randomized trial, with 3 arms to compare typical antipsychotics, atypical antipsychotics, and placebo in patients at risk for delirium with a primary outcome of delirium incidence. In regard to delirium treatment, a large randomized trial to compare haloperidol with atypical antipsychotics, with a placebo arm focusing not only on delirium duration and severity, but also on long‐term outcomes such as rehospitalizations, institutionalization, cognitive impairment, and mortality, is warranted. Figure 3 points out potential areas for researchers to investigate hypotheses generated by our review and thereby improve delirium care.
To our knowledge, our SER presents the first summary of SERs in delirium. Prior to this review, Michaud et al9 and National Institute for Health and Clinical Excellence48 published delirium guidelines, but in both of these guidelines, evidence was collected from a multitude of studies ranging in methodology from scientific review and meta‐analysis to observational studies, and the majority of recommendations were based on expert opinion. On the contrary, our review was limited to rigorously conducted SERs; hence, we utilized the highest level, critically appraised evidence to provide guidance to clinicians and researchers.
Limitations include a diverse group of studies with a heterogeneous population of patients, preventing pooling of results. We did not review each individual study included in the 38 SERs. We excluded non‐English language SERs, studies evaluating delirium subtypes, alcohol or substance abuse‐related delirium, or delirium associated with psychiatric disorders. As we only reviewed SERs, some notable studies not included in the SERs may have been missed.
CONCLUSION
Delirium among hospitalized patients is a common syndrome with a significant burden to the healthcare system and society. The field of delirium has seen considerable advances in diagnosis, prevention, and treatment over the last decade. Even with this advancement, there are still areas of uncertainty, such as: the benefits and costs of delirium screening; the benefits and harms of single or combined pharmacological agents for delirium prevention and treatment; the development of a set of reliable biomarkers for delirium diagnosis, prognosis, and response to therapy; the long‐term effect of delirium‐specific therapeutics on patients' cognitive, physical, and psychological functions; and the relationship between delirium and the development of Alzheimer's disease. As our understanding of delirium's impact on patients and healthcare improves, delirium should be identified as an indicator of poor long‐term prognosis, and should prompt immediate and effective evidence‐based management strategies, like any other critical illness.
Note Added in Proof
Disclosure: This study was supported by the National Institute on Aging (NIA), grant R01AG054205‐02; and the National Institute of Mental Health (NIMH), grant R24MH080827‐04.
- ,,,,,.Clarifying delirium: the confusion assessment method. A new method for detection of delirium.Ann Intern Med.1990;113(12):941–948.
- ,,.Delirium; a subcortical phenomenon?J Neuropsychiatry Clin Neurosci.1989;1(3):283–290.
- ,,, et al.The impact of delirium on the survival of mechanically ventilated patients.Crit Care Med.2004;32(11):2254–2259.
- ,.2002 National Hospital Discharge Survey.Adv Data.2004;342:1–29.
- ,.Delirium in hospitalized older adults. In: Ham R, Sloane P, Warshaw G, Bernard M, Flaherty E, eds.Primary Care Geriatrics, a Case‐Based Approach.5th ed.Philadelphia, PA:Mosby/Elsevier;2007:210–218.
- ,,,,.The association between delirium and cognitive decline: a review of the empirical literature.Neuropsychol Rev.2004;14(2):87–98.
- ,,, et al.Costs associated with delirium in mechanically ventilated patients.Crit Care Med.2004;32(4):955–962.
- ,,,.Detection in delirium in the acute hospital.Age Ageing.2010;39(1):131–135.
- ,,, et al;for the Delirium Guidelines Development Group.Delirium: guidelines for general hospitals.J Psychosom Res.2007;62(3):371–383.
- ,,,,.Delirium: a strategic plan to bring an ancient disease into the 21st century.J Am Geriatr Soc.2011;59:S237–S240.
- ,,, et al;for the Methods Work Group.Third US Preventive Services Task Force. Current methods of the US Preventive Services Task Force: a review of the process.Am J Prev Med.2001;20:21–35.
- ,.Delirium in vascular surgery.Eur J Vasc Endovasc Surg.2007;34(2):131–134.
- ,.Evidence‐based clinical update: general anesthesia and the risk of delirium and postoperative cognitive dysfunction.Can J Anaesth.2006;53(7):669–677.
- ,,.The role of postoperative analgesia in delirium and cognitive decline in elderly patients: a systematic review.Anesth Analg.2006(4):1255–1266.
- ,,,,.Risk factors for intensive care delirium: a systematic review.Intensive Crit Care Nurs.2008;24(2):98–107.
- ,,, et al.The cognitive impact of anticholinergics: a clinical review.Clin Interv Aging.2009;4:225–233.
- ,,,,,.Neuroimaging studies of delirium: a systematic review.J Psychosom Res.2008;65(3):239–248.
- ,,,.Does this patient have delirium? Value of bedside instruments.JAMA.2010;304(7):779–786.
- ,,.“Mini‐mental state.” A practical method for grading the cognitive state of patients for the clinician.J Psychiatr Res.1975;12(3):189–198.
- ,,,.The confusion assessment method: a systematic review of current usage.J Am Geriatr Soc.2008;56(5):823–830.
- ,,.A systematic literature review of cerebrospinal fluid biomarkers in delirium.Dement Geriatr Cogn Disord.2011;32:9–93.
- ,,,.Multicomponent intervention strategies for managing delirium in hospitalized older people; systematic review.J Adv Nurs.2005;52(1):79–90.
- ,,, et al.A multicomponent intervention to prevent delirium in hospitalized older patients.N Engl J Med.1999;340(9):669–676.
- ,,,.Reducing delirium after hip fracture: a randomized trial.J Am Geriatr Soc.2001;49(5):516–522.
- ,,,.Interventions for preventing delirium in hospitalized patients.Cochrane Database Syst Rev.2007;2:CD005563. DOI: 10.1002/14651858.CD005563.
- ,,, et al.Haloperidol prophylaxis for elderly hip‐surgery patients at risk for delirium: a randomized placebo‐controlled study.J Am Geriatr Soc.2005;53(10):1658–1666.
- ,,, et al.Pharmacological management of delirium in hospitalized adults: a systematic evidence review.J Gen Intern Med.2009;24:848–853.
- ,.Efficacy of risperidone for prevention of postoperative delirium in cardiac surgery.Anaesth Intensive Care.2007;35(5):714–719.
- ,,,,.Donepezil in the prevention and treatment of post‐surgical delirium.Am J Geriatr Psychiatry.2005;13:1100–1106.
- ,,, et al.A randomized,doubleblind, placebo‐controlled trial of donepezil hydrochloride (Aricept) for reducing the incidence of postoperative delirium after elective total hip replacement.Int J Geriatr Psychiatry.2007;22:343–349.
- ,,, et al.Use of procholinergics in the prevention of postoperative delirium in hip fracture surgery in the elderly. A randomized controlled trial [in Spanish].Rev Neurol.2001;33(8):716–719.
- ,,, et al.A novel approach to the prevention of postoperative delirium in the elderly after gastrointestinal surgery.Surg Today.2002;32:310–314.
- ,,, et al.Pilot clinical trial of gabapentin to decrease postoperative delirium in older patients.Neurology.2006;67(7):1251–1253.
- ,,, et al.Systematic intervention for elderly inpatients with delirium: a randomized clinical trial.Can Med Assoc J.1994;151:965–970.
- ,,, et al.A nurse‐led interdisciplinary intervention program for delirium in elderly hip‐fracture patients.J Am Geriatr Soc.2001;49:523–532.
- ,,,.Functional status outcomes of a nursing intervention in hospitalized elderly.Image J Nurs Sch.1992;24:201–220.
- ,.Multidisciplinary team interventions for delirium in patients with chronic cognitive impairment.Cochrane Database Syst Rev.2001;1:CD000395. Update in: Cochrane Database Syst Rev. year="2004"2004;2:CD000395.
- ,,.Antipsychotics in the treatment of delirium: a systematic review.J Clin Psychiatry.2007;68(1):11–21.
- ,,.Antipsychotics for delirium. The Cochrane Collaboration.The Cochrane Library.2009;1:1–117.
- ,.Drug therapy for delirium in terminally ill patients.Cochrane Database Syst Rev.2004;2:CD004770.
- ,,.Delirium: current trends in prevention and treatment.J Intern Med.2004;34(3):115–121.
- ,,,.Benzodiazepines for delirium.Cochrane Database Syst Rev.2009;1:CD006379. Update in: Cochrane Database Syst Rev.year="2009"2009;4:CD006379.
- ,,.Cholinesterase inhibitors for delirium.Cochrane Database Syst Rev.2008;1:CD005317.
- ,,,.Persistent delirium in older hospital patients: a systematic review of frequency and prognosis.Age Ageing.2009;38(1):19–26.
- ,,,,,.Delirium in elderly patients and the risk of postdischarge mortality, institutionalization, and dementia: a meta‐analysis.JAMA.2010;304(4):443–451.
- ,,.Occurrence and outcome of delirium in medical in‐patients: a systematic literature review.Age Ageing.2006;35(4):350–364.
- ,,.Delirium superimposed on dementia: a systematic review.J Am Geriatr Soc.2002;50(10):1723–1732.
- National Institute for Health and Clinical Excellence. NICE guidelines for delirium diagnosis, prevention and management. Available at: http://www.ice.ork.uk/guidelines. Accessed October 1,2011.
- ,.Preoperative risk assessment for delirium after noncardiac surgery: a systematic review.J Am Geriatr Soc.2006;54(10):1578–1589.
- ,,,.Delirium risk factors in elderly hospitalized patients.J Gen Intern Med.1998;13(3):204–212.
- ,.Delirium after cardiac surgery: a critical review.J Psychosom Res.1996;41(1):13–30.
- ,,,,.The association between delirium and the apolipoprotein E epsilon 4 allele: new study results and a meta‐analysis.Am J Geriatr Psychiatry.2009;17:856–862.
- ,,.The genetics of deliria.Int Rev Psychiatry.2009;21(1):20–29.
- ,.Are nurses recognizing delirium? A systematic review.J Gerontol Nurs.2008;34(9):40–48.
- ,,,.Delirium assessment in the critically ill.Intensive Care Med.2007;33(6):929–940.
- ,,.Effectiveness of interventions to prevent delirium in hospitalized patients: a systematic review.Can Med Assoc J.1996;155(9):1263–1268.
- ,,,.Drug treatment of delirium: past, present and future.J Psychosom Res.2008;65(3):273–282.
- ,,,.Pathogensis of and management strategies for postoperative delirium after hip fracture: a review.Acta Orthop Scand.2004;75(4):378–389.
- ,,.Systematic review of antipsychotics for the treatment of hospital‐associated delirium in medically or surgically ill patients.Ann Pharmacother.2006;40(11):1966–1973.
- ,,,,.Atypical antipsychotics in the treatment of delirium.Psychiatry Clin Neurosci.2009;63(5):623–631.
- ,.Prognosis of delirium in elderly hospital patients.Can Med Assoc J.1993;149(1):41–46.
- ,,,,,.Clarifying delirium: the confusion assessment method. A new method for detection of delirium.Ann Intern Med.1990;113(12):941–948.
- ,,.Delirium; a subcortical phenomenon?J Neuropsychiatry Clin Neurosci.1989;1(3):283–290.
- ,,, et al.The impact of delirium on the survival of mechanically ventilated patients.Crit Care Med.2004;32(11):2254–2259.
- ,.2002 National Hospital Discharge Survey.Adv Data.2004;342:1–29.
- ,.Delirium in hospitalized older adults. In: Ham R, Sloane P, Warshaw G, Bernard M, Flaherty E, eds.Primary Care Geriatrics, a Case‐Based Approach.5th ed.Philadelphia, PA:Mosby/Elsevier;2007:210–218.
- ,,,,.The association between delirium and cognitive decline: a review of the empirical literature.Neuropsychol Rev.2004;14(2):87–98.
- ,,, et al.Costs associated with delirium in mechanically ventilated patients.Crit Care Med.2004;32(4):955–962.
- ,,,.Detection in delirium in the acute hospital.Age Ageing.2010;39(1):131–135.
- ,,, et al;for the Delirium Guidelines Development Group.Delirium: guidelines for general hospitals.J Psychosom Res.2007;62(3):371–383.
- ,,,,.Delirium: a strategic plan to bring an ancient disease into the 21st century.J Am Geriatr Soc.2011;59:S237–S240.
- ,,, et al;for the Methods Work Group.Third US Preventive Services Task Force. Current methods of the US Preventive Services Task Force: a review of the process.Am J Prev Med.2001;20:21–35.
- ,.Delirium in vascular surgery.Eur J Vasc Endovasc Surg.2007;34(2):131–134.
- ,.Evidence‐based clinical update: general anesthesia and the risk of delirium and postoperative cognitive dysfunction.Can J Anaesth.2006;53(7):669–677.
- ,,.The role of postoperative analgesia in delirium and cognitive decline in elderly patients: a systematic review.Anesth Analg.2006(4):1255–1266.
- ,,,,.Risk factors for intensive care delirium: a systematic review.Intensive Crit Care Nurs.2008;24(2):98–107.
- ,,, et al.The cognitive impact of anticholinergics: a clinical review.Clin Interv Aging.2009;4:225–233.
- ,,,,,.Neuroimaging studies of delirium: a systematic review.J Psychosom Res.2008;65(3):239–248.
- ,,,.Does this patient have delirium? Value of bedside instruments.JAMA.2010;304(7):779–786.
- ,,.“Mini‐mental state.” A practical method for grading the cognitive state of patients for the clinician.J Psychiatr Res.1975;12(3):189–198.
- ,,,.The confusion assessment method: a systematic review of current usage.J Am Geriatr Soc.2008;56(5):823–830.
- ,,.A systematic literature review of cerebrospinal fluid biomarkers in delirium.Dement Geriatr Cogn Disord.2011;32:9–93.
- ,,,.Multicomponent intervention strategies for managing delirium in hospitalized older people; systematic review.J Adv Nurs.2005;52(1):79–90.
- ,,, et al.A multicomponent intervention to prevent delirium in hospitalized older patients.N Engl J Med.1999;340(9):669–676.
- ,,,.Reducing delirium after hip fracture: a randomized trial.J Am Geriatr Soc.2001;49(5):516–522.
- ,,,.Interventions for preventing delirium in hospitalized patients.Cochrane Database Syst Rev.2007;2:CD005563. DOI: 10.1002/14651858.CD005563.
- ,,, et al.Haloperidol prophylaxis for elderly hip‐surgery patients at risk for delirium: a randomized placebo‐controlled study.J Am Geriatr Soc.2005;53(10):1658–1666.
- ,,, et al.Pharmacological management of delirium in hospitalized adults: a systematic evidence review.J Gen Intern Med.2009;24:848–853.
- ,.Efficacy of risperidone for prevention of postoperative delirium in cardiac surgery.Anaesth Intensive Care.2007;35(5):714–719.
- ,,,,.Donepezil in the prevention and treatment of post‐surgical delirium.Am J Geriatr Psychiatry.2005;13:1100–1106.
- ,,, et al.A randomized,doubleblind, placebo‐controlled trial of donepezil hydrochloride (Aricept) for reducing the incidence of postoperative delirium after elective total hip replacement.Int J Geriatr Psychiatry.2007;22:343–349.
- ,,, et al.Use of procholinergics in the prevention of postoperative delirium in hip fracture surgery in the elderly. A randomized controlled trial [in Spanish].Rev Neurol.2001;33(8):716–719.
- ,,, et al.A novel approach to the prevention of postoperative delirium in the elderly after gastrointestinal surgery.Surg Today.2002;32:310–314.
- ,,, et al.Pilot clinical trial of gabapentin to decrease postoperative delirium in older patients.Neurology.2006;67(7):1251–1253.
- ,,, et al.Systematic intervention for elderly inpatients with delirium: a randomized clinical trial.Can Med Assoc J.1994;151:965–970.
- ,,, et al.A nurse‐led interdisciplinary intervention program for delirium in elderly hip‐fracture patients.J Am Geriatr Soc.2001;49:523–532.
- ,,,.Functional status outcomes of a nursing intervention in hospitalized elderly.Image J Nurs Sch.1992;24:201–220.
- ,.Multidisciplinary team interventions for delirium in patients with chronic cognitive impairment.Cochrane Database Syst Rev.2001;1:CD000395. Update in: Cochrane Database Syst Rev. year="2004"2004;2:CD000395.
- ,,.Antipsychotics in the treatment of delirium: a systematic review.J Clin Psychiatry.2007;68(1):11–21.
- ,,.Antipsychotics for delirium. The Cochrane Collaboration.The Cochrane Library.2009;1:1–117.
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Report Highlights Strategies for Reducing AMI Mortality Rates
A new report on acute myocardial infarction (AMI) suggests that implementing a handful of relatively easy strategies can improve mortality rates.
The research, "Hospital Strategies for Reducing Risk-Standardized Mortality Rates in Acute Myocardial Infarction," highlights several techniques for lowering risk-standardized mortality rates (RSMR) in this patient population:
• Holding monthly meetings to review AMI cases (lowered RSMR by 0.7%);
• Fostering an environment that encourages clinicians to solve problems creatively (lowered RSMR by 0.84%);
• Having 24-hour coverage by cardiologists (lowered RSMR by 0.54%);
• Having both a nurse and physician champion for quality in AMI (lowered RSMR by 0.88%); and
• Avoiding cross-training nurses from ICUs for cardiac catheterization laboratories (lowered RSMR by 0.44%).
Fewer than 10% of the 537 hospitals in the cross-sectional survey reported using at least four of the five strategies. Lead author Elizabeth H. Bradley, PhD, faculty director of the Global Health Leadership Institute and professor of public health at Yale University, says the challenge in implementing the strategies lies in changing the often-obstinate culture of healthcare institutions.
"The root of this is the culture," she says, adding if nothing else, "begin with the problems, begin with an analytical mind when errors occur." Dr. Bradley adds that culture of teamwork works only when it has buy-in from in-the-trenches physicians, such as hospitalists and C-suite executives.
"It has to come from the front line and from the top," she says. "In all of our studies over the last decade, [physicians and administrators] need to be supportive of an environment in which problem solving can happen."
A new report on acute myocardial infarction (AMI) suggests that implementing a handful of relatively easy strategies can improve mortality rates.
The research, "Hospital Strategies for Reducing Risk-Standardized Mortality Rates in Acute Myocardial Infarction," highlights several techniques for lowering risk-standardized mortality rates (RSMR) in this patient population:
• Holding monthly meetings to review AMI cases (lowered RSMR by 0.7%);
• Fostering an environment that encourages clinicians to solve problems creatively (lowered RSMR by 0.84%);
• Having 24-hour coverage by cardiologists (lowered RSMR by 0.54%);
• Having both a nurse and physician champion for quality in AMI (lowered RSMR by 0.88%); and
• Avoiding cross-training nurses from ICUs for cardiac catheterization laboratories (lowered RSMR by 0.44%).
Fewer than 10% of the 537 hospitals in the cross-sectional survey reported using at least four of the five strategies. Lead author Elizabeth H. Bradley, PhD, faculty director of the Global Health Leadership Institute and professor of public health at Yale University, says the challenge in implementing the strategies lies in changing the often-obstinate culture of healthcare institutions.
"The root of this is the culture," she says, adding if nothing else, "begin with the problems, begin with an analytical mind when errors occur." Dr. Bradley adds that culture of teamwork works only when it has buy-in from in-the-trenches physicians, such as hospitalists and C-suite executives.
"It has to come from the front line and from the top," she says. "In all of our studies over the last decade, [physicians and administrators] need to be supportive of an environment in which problem solving can happen."
A new report on acute myocardial infarction (AMI) suggests that implementing a handful of relatively easy strategies can improve mortality rates.
The research, "Hospital Strategies for Reducing Risk-Standardized Mortality Rates in Acute Myocardial Infarction," highlights several techniques for lowering risk-standardized mortality rates (RSMR) in this patient population:
• Holding monthly meetings to review AMI cases (lowered RSMR by 0.7%);
• Fostering an environment that encourages clinicians to solve problems creatively (lowered RSMR by 0.84%);
• Having 24-hour coverage by cardiologists (lowered RSMR by 0.54%);
• Having both a nurse and physician champion for quality in AMI (lowered RSMR by 0.88%); and
• Avoiding cross-training nurses from ICUs for cardiac catheterization laboratories (lowered RSMR by 0.44%).
Fewer than 10% of the 537 hospitals in the cross-sectional survey reported using at least four of the five strategies. Lead author Elizabeth H. Bradley, PhD, faculty director of the Global Health Leadership Institute and professor of public health at Yale University, says the challenge in implementing the strategies lies in changing the often-obstinate culture of healthcare institutions.
"The root of this is the culture," she says, adding if nothing else, "begin with the problems, begin with an analytical mind when errors occur." Dr. Bradley adds that culture of teamwork works only when it has buy-in from in-the-trenches physicians, such as hospitalists and C-suite executives.
"It has to come from the front line and from the top," she says. "In all of our studies over the last decade, [physicians and administrators] need to be supportive of an environment in which problem solving can happen."
In the Literature: Research You Need to Know
Clinical question: Does intensive-care-unit (ICU) bed availability impact outcomes for hospitalized patients with sudden clinical deterioration?
Background: ICU beds are a scarce resource, and their availability might affect the care delivered to hospitalized patients who deteriorate clinically.
Study design: Retrospective cohort analysis.
Setting: Three hospitals in Calgary, Alberta.
Synopsis: This study analyzed data for a retrospective cohort of 3,494 hospitalized patients who had a sudden clinical deterioration triggering a medical emergency team. The associations between ICU bed availability and three outcomes—likelihood of ICU admission, change in goals of care (from "resuscitative" to either "medical" or "comfort"), and hospital mortality—were examined.
Reduced ICU bed availability was associated with decreased likelihood of ICU admission and increased likelihood of a change in goals of care. When more than two ICU beds were available, 21.4% of patients with a clinical deterioration were admitted to the ICU, compared with 11.6% and 14.5% if zero or one bed was available. When more than two ICU beds were available, 8.5% of patients had a change in their goals of care, compared with 14.9% of patients who had a change in goals of care when zero ICU beds were available. ICU bed availability was not associated with in-hospital mortality.
Bottom line: Reduced ICU bed availability is associated with decreased likelihood of ICU admission and increased likelihood of changing goals of care to a less aggressive strategy.
Citation: Stelfox HT, Hemmelgarn BR, Bagshaw SM, et al. Intensive care unit bed availability and outcomes for hospitalized patients with sudden clinical deterioration. Arch Intern Med. 2012;172:467-474.
Clinical question: Does intensive-care-unit (ICU) bed availability impact outcomes for hospitalized patients with sudden clinical deterioration?
Background: ICU beds are a scarce resource, and their availability might affect the care delivered to hospitalized patients who deteriorate clinically.
Study design: Retrospective cohort analysis.
Setting: Three hospitals in Calgary, Alberta.
Synopsis: This study analyzed data for a retrospective cohort of 3,494 hospitalized patients who had a sudden clinical deterioration triggering a medical emergency team. The associations between ICU bed availability and three outcomes—likelihood of ICU admission, change in goals of care (from "resuscitative" to either "medical" or "comfort"), and hospital mortality—were examined.
Reduced ICU bed availability was associated with decreased likelihood of ICU admission and increased likelihood of a change in goals of care. When more than two ICU beds were available, 21.4% of patients with a clinical deterioration were admitted to the ICU, compared with 11.6% and 14.5% if zero or one bed was available. When more than two ICU beds were available, 8.5% of patients had a change in their goals of care, compared with 14.9% of patients who had a change in goals of care when zero ICU beds were available. ICU bed availability was not associated with in-hospital mortality.
Bottom line: Reduced ICU bed availability is associated with decreased likelihood of ICU admission and increased likelihood of changing goals of care to a less aggressive strategy.
Citation: Stelfox HT, Hemmelgarn BR, Bagshaw SM, et al. Intensive care unit bed availability and outcomes for hospitalized patients with sudden clinical deterioration. Arch Intern Med. 2012;172:467-474.
Clinical question: Does intensive-care-unit (ICU) bed availability impact outcomes for hospitalized patients with sudden clinical deterioration?
Background: ICU beds are a scarce resource, and their availability might affect the care delivered to hospitalized patients who deteriorate clinically.
Study design: Retrospective cohort analysis.
Setting: Three hospitals in Calgary, Alberta.
Synopsis: This study analyzed data for a retrospective cohort of 3,494 hospitalized patients who had a sudden clinical deterioration triggering a medical emergency team. The associations between ICU bed availability and three outcomes—likelihood of ICU admission, change in goals of care (from "resuscitative" to either "medical" or "comfort"), and hospital mortality—were examined.
Reduced ICU bed availability was associated with decreased likelihood of ICU admission and increased likelihood of a change in goals of care. When more than two ICU beds were available, 21.4% of patients with a clinical deterioration were admitted to the ICU, compared with 11.6% and 14.5% if zero or one bed was available. When more than two ICU beds were available, 8.5% of patients had a change in their goals of care, compared with 14.9% of patients who had a change in goals of care when zero ICU beds were available. ICU bed availability was not associated with in-hospital mortality.
Bottom line: Reduced ICU bed availability is associated with decreased likelihood of ICU admission and increased likelihood of changing goals of care to a less aggressive strategy.
Citation: Stelfox HT, Hemmelgarn BR, Bagshaw SM, et al. Intensive care unit bed availability and outcomes for hospitalized patients with sudden clinical deterioration. Arch Intern Med. 2012;172:467-474.