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Working When You're Sick: Symptom of a Larger Problem?
The phenomenon of physician presenteeism, doctors coming to work even if they themselves are sick, is an opportunity for residency directors to pull back on how they schedule physicians in training, one program head says.
A study last month found that 57.9% of residents reported working while sick at least once and 31.3% had done so in the previous year (JAMA 2010;304(11);1166-1168). In one outlier hospital, every resident surveyed reported working when sick.
"Hospitals have to learn not to schedule their people to the max," says Ethan Fried, MD, MS, FACP, assistant professor of clinical medicine at Columbia University, vice chair for education in the department of medicine and director of Graduate Medical Education at St. Luke's-Roosevelt in New York City. "Just because you can go 80 hours a week and take care of 10 patients doesn't mean you should go 80 hours a week and take care of 10 patients."
Dr. Fried, president of the Association of Program Directors in Internal Medicine (APDIM), says creating schedules with little or no flexibility can hamper a program's ability to handle inevitable sick calls. Larger programs might have "sick-call pools," which are used to cover staffing shortfalls, but smaller programs might not have that luxury, he adds.
Jack Percelay, MD, MPH, SFHM, FAAP, pediatric hospitalist with ELMO Pediatrics in New York City, says the culture of residencies is to "suck it up," and some physicians carry that attitude into private practice.
"The decision of whether or not to work sick is really related to the institutions' culture," Dr. Percelay, an SHM board member, writes in an e-mail interview. "If we are to discourage physicians from working when sick, some sort of sick leave benefit or backup system needs to be in place. ... It's a real Pandora's box. I don't want my colleagues to stay home with a runny nose, nor do I want them to come in and get IV fluids in the back room."
Dr. Fried notes that the issue is further complicated by rules on how much training time residents need to be considered competent. He says the American Board of Internal Medicine (ABIM) recently gave program directors discretion in "granting credit for up to one month of missed time in a three-year period."
Still, presenteeism may be less of a problem with the current generation of residents than in the past because of culture changes tied to duty-hour rules. "We make such a big deal about working while fatigued, and that's now considered completely inappropriate," Dr. Fried says. "The trainees ... are much more willing to admit when they under the weather."
The phenomenon of physician presenteeism, doctors coming to work even if they themselves are sick, is an opportunity for residency directors to pull back on how they schedule physicians in training, one program head says.
A study last month found that 57.9% of residents reported working while sick at least once and 31.3% had done so in the previous year (JAMA 2010;304(11);1166-1168). In one outlier hospital, every resident surveyed reported working when sick.
"Hospitals have to learn not to schedule their people to the max," says Ethan Fried, MD, MS, FACP, assistant professor of clinical medicine at Columbia University, vice chair for education in the department of medicine and director of Graduate Medical Education at St. Luke's-Roosevelt in New York City. "Just because you can go 80 hours a week and take care of 10 patients doesn't mean you should go 80 hours a week and take care of 10 patients."
Dr. Fried, president of the Association of Program Directors in Internal Medicine (APDIM), says creating schedules with little or no flexibility can hamper a program's ability to handle inevitable sick calls. Larger programs might have "sick-call pools," which are used to cover staffing shortfalls, but smaller programs might not have that luxury, he adds.
Jack Percelay, MD, MPH, SFHM, FAAP, pediatric hospitalist with ELMO Pediatrics in New York City, says the culture of residencies is to "suck it up," and some physicians carry that attitude into private practice.
"The decision of whether or not to work sick is really related to the institutions' culture," Dr. Percelay, an SHM board member, writes in an e-mail interview. "If we are to discourage physicians from working when sick, some sort of sick leave benefit or backup system needs to be in place. ... It's a real Pandora's box. I don't want my colleagues to stay home with a runny nose, nor do I want them to come in and get IV fluids in the back room."
Dr. Fried notes that the issue is further complicated by rules on how much training time residents need to be considered competent. He says the American Board of Internal Medicine (ABIM) recently gave program directors discretion in "granting credit for up to one month of missed time in a three-year period."
Still, presenteeism may be less of a problem with the current generation of residents than in the past because of culture changes tied to duty-hour rules. "We make such a big deal about working while fatigued, and that's now considered completely inappropriate," Dr. Fried says. "The trainees ... are much more willing to admit when they under the weather."
The phenomenon of physician presenteeism, doctors coming to work even if they themselves are sick, is an opportunity for residency directors to pull back on how they schedule physicians in training, one program head says.
A study last month found that 57.9% of residents reported working while sick at least once and 31.3% had done so in the previous year (JAMA 2010;304(11);1166-1168). In one outlier hospital, every resident surveyed reported working when sick.
"Hospitals have to learn not to schedule their people to the max," says Ethan Fried, MD, MS, FACP, assistant professor of clinical medicine at Columbia University, vice chair for education in the department of medicine and director of Graduate Medical Education at St. Luke's-Roosevelt in New York City. "Just because you can go 80 hours a week and take care of 10 patients doesn't mean you should go 80 hours a week and take care of 10 patients."
Dr. Fried, president of the Association of Program Directors in Internal Medicine (APDIM), says creating schedules with little or no flexibility can hamper a program's ability to handle inevitable sick calls. Larger programs might have "sick-call pools," which are used to cover staffing shortfalls, but smaller programs might not have that luxury, he adds.
Jack Percelay, MD, MPH, SFHM, FAAP, pediatric hospitalist with ELMO Pediatrics in New York City, says the culture of residencies is to "suck it up," and some physicians carry that attitude into private practice.
"The decision of whether or not to work sick is really related to the institutions' culture," Dr. Percelay, an SHM board member, writes in an e-mail interview. "If we are to discourage physicians from working when sick, some sort of sick leave benefit or backup system needs to be in place. ... It's a real Pandora's box. I don't want my colleagues to stay home with a runny nose, nor do I want them to come in and get IV fluids in the back room."
Dr. Fried notes that the issue is further complicated by rules on how much training time residents need to be considered competent. He says the American Board of Internal Medicine (ABIM) recently gave program directors discretion in "granting credit for up to one month of missed time in a three-year period."
Still, presenteeism may be less of a problem with the current generation of residents than in the past because of culture changes tied to duty-hour rules. "We make such a big deal about working while fatigued, and that's now considered completely inappropriate," Dr. Fried says. "The trainees ... are much more willing to admit when they under the weather."
Technology, Follow-Up Care Concern Hospitalists
Hospitalists at the "Management of the Hospitalized Patient" conference, Oct. 14-16 in San Francisco, expressed frustrations during an interactive presentation on how to reduce preventable rehospitalizations.
Participants described the challenges of high-risk patients who lack insurance coverage and a relationship with a primary care physician (PCP), which can negate streamlined outreach to PCPs at the time of discharge. “The people who least need follow-up, I'm able to call their physician. But it seems like the ones who most need follow-up care are the hardest to reach a PCP," one hospitalist observed ruefully. Participants also acknowledged steep learning curves for electronic medical records, even though they hope these could facilitate better discharge processes in the long run.
And careful patient education might not help with cases like the 75-year-old heart failure patient described in the July 28, 2009, issue of The Wall Street Journal, cited by the presenters as a typical example of readmission risk. Despite targeted education on the need to reduce her sodium intake, the patient insisted on eating a hot dog at a Fourth of July picnic and was readmitted to the hospital the following day.
Presenter Michelle Mourad, MD, medical director of CHF and Oncology Hospitalist Services at the University of California at San Francisco, which sponsors the annual conference, challenged hospitalists to identify readmission risk factors for their patients, including diagnoses of heart failure, pneumonia and COPD, high-risk medications and polypharmacy, poor health literacy, poor social support, and advanced age. Patients at risk could then become the focus of strategies designed to minimize rehospitalizations, including follow-up phone calls post-discharge and scheduling a visit to a PCP before the patient leaves the hospital.
Hospitalists have an important role in improving the quality of discharges at their hospitals, Dr. Mourad said. They can start by convening a multidisciplinary team of stakeholders that assesses current practice and designs process improvements.
“At UCSF, our discharge process was broken,” she says. A new QI process led to implementing patient teachback strategies, a hotline phone number discharged patients could call, and "core measures" of discharge quality, as well as designing new discharge folders with a user-friendly yellow medication card for patients to bring home.
Hospitalists at the "Management of the Hospitalized Patient" conference, Oct. 14-16 in San Francisco, expressed frustrations during an interactive presentation on how to reduce preventable rehospitalizations.
Participants described the challenges of high-risk patients who lack insurance coverage and a relationship with a primary care physician (PCP), which can negate streamlined outreach to PCPs at the time of discharge. “The people who least need follow-up, I'm able to call their physician. But it seems like the ones who most need follow-up care are the hardest to reach a PCP," one hospitalist observed ruefully. Participants also acknowledged steep learning curves for electronic medical records, even though they hope these could facilitate better discharge processes in the long run.
And careful patient education might not help with cases like the 75-year-old heart failure patient described in the July 28, 2009, issue of The Wall Street Journal, cited by the presenters as a typical example of readmission risk. Despite targeted education on the need to reduce her sodium intake, the patient insisted on eating a hot dog at a Fourth of July picnic and was readmitted to the hospital the following day.
Presenter Michelle Mourad, MD, medical director of CHF and Oncology Hospitalist Services at the University of California at San Francisco, which sponsors the annual conference, challenged hospitalists to identify readmission risk factors for their patients, including diagnoses of heart failure, pneumonia and COPD, high-risk medications and polypharmacy, poor health literacy, poor social support, and advanced age. Patients at risk could then become the focus of strategies designed to minimize rehospitalizations, including follow-up phone calls post-discharge and scheduling a visit to a PCP before the patient leaves the hospital.
Hospitalists have an important role in improving the quality of discharges at their hospitals, Dr. Mourad said. They can start by convening a multidisciplinary team of stakeholders that assesses current practice and designs process improvements.
“At UCSF, our discharge process was broken,” she says. A new QI process led to implementing patient teachback strategies, a hotline phone number discharged patients could call, and "core measures" of discharge quality, as well as designing new discharge folders with a user-friendly yellow medication card for patients to bring home.
Hospitalists at the "Management of the Hospitalized Patient" conference, Oct. 14-16 in San Francisco, expressed frustrations during an interactive presentation on how to reduce preventable rehospitalizations.
Participants described the challenges of high-risk patients who lack insurance coverage and a relationship with a primary care physician (PCP), which can negate streamlined outreach to PCPs at the time of discharge. “The people who least need follow-up, I'm able to call their physician. But it seems like the ones who most need follow-up care are the hardest to reach a PCP," one hospitalist observed ruefully. Participants also acknowledged steep learning curves for electronic medical records, even though they hope these could facilitate better discharge processes in the long run.
And careful patient education might not help with cases like the 75-year-old heart failure patient described in the July 28, 2009, issue of The Wall Street Journal, cited by the presenters as a typical example of readmission risk. Despite targeted education on the need to reduce her sodium intake, the patient insisted on eating a hot dog at a Fourth of July picnic and was readmitted to the hospital the following day.
Presenter Michelle Mourad, MD, medical director of CHF and Oncology Hospitalist Services at the University of California at San Francisco, which sponsors the annual conference, challenged hospitalists to identify readmission risk factors for their patients, including diagnoses of heart failure, pneumonia and COPD, high-risk medications and polypharmacy, poor health literacy, poor social support, and advanced age. Patients at risk could then become the focus of strategies designed to minimize rehospitalizations, including follow-up phone calls post-discharge and scheduling a visit to a PCP before the patient leaves the hospital.
Hospitalists have an important role in improving the quality of discharges at their hospitals, Dr. Mourad said. They can start by convening a multidisciplinary team of stakeholders that assesses current practice and designs process improvements.
“At UCSF, our discharge process was broken,” she says. A new QI process led to implementing patient teachback strategies, a hotline phone number discharged patients could call, and "core measures" of discharge quality, as well as designing new discharge folders with a user-friendly yellow medication card for patients to bring home.
Hospitalists Should Expect More HIV Patients
Advances in treatment and ever-growing life expectancies for patients diagnosed with human immunodeficiency virus (HIV) are likely to push more HIV-positive patients into the censuses of HM groups, according to a specialist at Mount Sinai School of Medicine in New York City.
“Hospitalists … are going to be doing more and more of the HIV care because we have a growing population of aging patients who are in care or identify as being HIV-positive, and they’re not coming in with exotic or unusual opportunistic infections,” says Rich MacKay, MD, director of the inpatient HIV service at Mount Sinai Medial Center in New York. “They are coming in with the things that other 50-, 60-, 70-year-olds are coming in with, though they may have more of those.”
Dr. MacKay, who is an assistant professor and splits his time between admitted patients and an outpatient clinic, spoke to more than 100 attendees at the fifth annual Mid-Atlantic Hospital Medicine Symposium last weekend in New York. He says hospitalists who familiarize themselves with HIV indicators could press for earlier identification of HIV in patients.
“If you screen people and you’re testing them on the day of their hospitalization, I think that’s huge,” Dr. MacKay says. “Finding somebody who is early in the disease and linking them in to care, so that they don’t fall off the cliff, so that they don’t come in five years later with PCP [pneumocystis pneumonia] and die from it—I think that’s a huge part for the hospitalist.”
Dr. MacKay further notes that just being aware of HIV symptoms can provide the cognizance necessary to consider alternative diagnoses. That can be particularly relevant for cases in which standard treatments might be effective for a few days (e.g. a steroid regimen) but not actually resolve the underlying problem, he adds.
“Maybe [a patient] is coming in with what looks like an exacerbation of COPD, but they’ve only got 50 T-cells and in fact what you’re seeing is PCP,” he says. “It’s not always clear.”
Advances in treatment and ever-growing life expectancies for patients diagnosed with human immunodeficiency virus (HIV) are likely to push more HIV-positive patients into the censuses of HM groups, according to a specialist at Mount Sinai School of Medicine in New York City.
“Hospitalists … are going to be doing more and more of the HIV care because we have a growing population of aging patients who are in care or identify as being HIV-positive, and they’re not coming in with exotic or unusual opportunistic infections,” says Rich MacKay, MD, director of the inpatient HIV service at Mount Sinai Medial Center in New York. “They are coming in with the things that other 50-, 60-, 70-year-olds are coming in with, though they may have more of those.”
Dr. MacKay, who is an assistant professor and splits his time between admitted patients and an outpatient clinic, spoke to more than 100 attendees at the fifth annual Mid-Atlantic Hospital Medicine Symposium last weekend in New York. He says hospitalists who familiarize themselves with HIV indicators could press for earlier identification of HIV in patients.
“If you screen people and you’re testing them on the day of their hospitalization, I think that’s huge,” Dr. MacKay says. “Finding somebody who is early in the disease and linking them in to care, so that they don’t fall off the cliff, so that they don’t come in five years later with PCP [pneumocystis pneumonia] and die from it—I think that’s a huge part for the hospitalist.”
Dr. MacKay further notes that just being aware of HIV symptoms can provide the cognizance necessary to consider alternative diagnoses. That can be particularly relevant for cases in which standard treatments might be effective for a few days (e.g. a steroid regimen) but not actually resolve the underlying problem, he adds.
“Maybe [a patient] is coming in with what looks like an exacerbation of COPD, but they’ve only got 50 T-cells and in fact what you’re seeing is PCP,” he says. “It’s not always clear.”
Advances in treatment and ever-growing life expectancies for patients diagnosed with human immunodeficiency virus (HIV) are likely to push more HIV-positive patients into the censuses of HM groups, according to a specialist at Mount Sinai School of Medicine in New York City.
“Hospitalists … are going to be doing more and more of the HIV care because we have a growing population of aging patients who are in care or identify as being HIV-positive, and they’re not coming in with exotic or unusual opportunistic infections,” says Rich MacKay, MD, director of the inpatient HIV service at Mount Sinai Medial Center in New York. “They are coming in with the things that other 50-, 60-, 70-year-olds are coming in with, though they may have more of those.”
Dr. MacKay, who is an assistant professor and splits his time between admitted patients and an outpatient clinic, spoke to more than 100 attendees at the fifth annual Mid-Atlantic Hospital Medicine Symposium last weekend in New York. He says hospitalists who familiarize themselves with HIV indicators could press for earlier identification of HIV in patients.
“If you screen people and you’re testing them on the day of their hospitalization, I think that’s huge,” Dr. MacKay says. “Finding somebody who is early in the disease and linking them in to care, so that they don’t fall off the cliff, so that they don’t come in five years later with PCP [pneumocystis pneumonia] and die from it—I think that’s a huge part for the hospitalist.”
Dr. MacKay further notes that just being aware of HIV symptoms can provide the cognizance necessary to consider alternative diagnoses. That can be particularly relevant for cases in which standard treatments might be effective for a few days (e.g. a steroid regimen) but not actually resolve the underlying problem, he adds.
“Maybe [a patient] is coming in with what looks like an exacerbation of COPD, but they’ve only got 50 T-cells and in fact what you’re seeing is PCP,” he says. “It’s not always clear.”
In the Literature: Research You Need to Know
Clinical question: What is the prevalence of silent pulmonary embolism (PE) in patients with DVT?
Background: PE was undiagnosed or unsuspected in approximately 80% to 93% patients antemortem who were found to have a PE at autopsy. The extent to which silent PE explains the undiagnosed or unsuspected pulmonary emboli at autopsy is not certain. Prior studies have demonstrated the association of silent PE in living patients with DVT.
Study design: Systematic review.
Setting: Published trials performed worldwide.
Synopsis: Researchers performed a systematic review of all published trials addressing the prevalence of silent PE in patients with DVT. Studies were included if the methods of PE diagnosis were described, if it was an asymptomatic PE, and if raw data were presented. Twenty-eight studies were identified and were stratified according to how the PE was diagnosed (Tier 1: high-probability VQ scan based on PIOPED criteria, computerized tomographic angiography [CTA], angiography; Tier 2: VQ scans based on non-PIOPED criteria).
Among Tier 1 studies, silent PE was detected in 27% of patients with DVT. Among Tier 2 studies, silent PE was detected among 37% of patients with DVT. Combined, silent PE was diagnosed in 1,665 of 5,233 patients (32%) with DVT.
Further analysis showed that the prevalence of silent PE in patients with proximal DVT was higher in those with distal DVT and that there was a trend toward increased prevalence of silent PE with increased age.
A limitation of this study includes the heterogeneity in the methods used for diagnosis of silent PE.
Bottom line: Silent PE occurs in a third of patients with DVT, and routine screening should be considered.
Citation: Stein PD, Matta F, Musani MH, Diaczok B. Silent pulmonary embolism in patients with deep venous thrombosis: a systematic review. Am J Med. 2010;123(5):426-431.
Reviewed for TH eWireby Alexander R. Carbo, MD, SFHM, Lauren Doctoroff, MD, John Fani Srour, MD, Matthew Hill, MD, Nancy Torres-Finnerty, MD, FHM, and Anita Vanka, MD, Hospital Medicine Program, Beth Israel Deaconess Medical Center, Boston.
For more physician reviews of literature, visit our website.
Clinical question: What is the prevalence of silent pulmonary embolism (PE) in patients with DVT?
Background: PE was undiagnosed or unsuspected in approximately 80% to 93% patients antemortem who were found to have a PE at autopsy. The extent to which silent PE explains the undiagnosed or unsuspected pulmonary emboli at autopsy is not certain. Prior studies have demonstrated the association of silent PE in living patients with DVT.
Study design: Systematic review.
Setting: Published trials performed worldwide.
Synopsis: Researchers performed a systematic review of all published trials addressing the prevalence of silent PE in patients with DVT. Studies were included if the methods of PE diagnosis were described, if it was an asymptomatic PE, and if raw data were presented. Twenty-eight studies were identified and were stratified according to how the PE was diagnosed (Tier 1: high-probability VQ scan based on PIOPED criteria, computerized tomographic angiography [CTA], angiography; Tier 2: VQ scans based on non-PIOPED criteria).
Among Tier 1 studies, silent PE was detected in 27% of patients with DVT. Among Tier 2 studies, silent PE was detected among 37% of patients with DVT. Combined, silent PE was diagnosed in 1,665 of 5,233 patients (32%) with DVT.
Further analysis showed that the prevalence of silent PE in patients with proximal DVT was higher in those with distal DVT and that there was a trend toward increased prevalence of silent PE with increased age.
A limitation of this study includes the heterogeneity in the methods used for diagnosis of silent PE.
Bottom line: Silent PE occurs in a third of patients with DVT, and routine screening should be considered.
Citation: Stein PD, Matta F, Musani MH, Diaczok B. Silent pulmonary embolism in patients with deep venous thrombosis: a systematic review. Am J Med. 2010;123(5):426-431.
Reviewed for TH eWireby Alexander R. Carbo, MD, SFHM, Lauren Doctoroff, MD, John Fani Srour, MD, Matthew Hill, MD, Nancy Torres-Finnerty, MD, FHM, and Anita Vanka, MD, Hospital Medicine Program, Beth Israel Deaconess Medical Center, Boston.
For more physician reviews of literature, visit our website.
Clinical question: What is the prevalence of silent pulmonary embolism (PE) in patients with DVT?
Background: PE was undiagnosed or unsuspected in approximately 80% to 93% patients antemortem who were found to have a PE at autopsy. The extent to which silent PE explains the undiagnosed or unsuspected pulmonary emboli at autopsy is not certain. Prior studies have demonstrated the association of silent PE in living patients with DVT.
Study design: Systematic review.
Setting: Published trials performed worldwide.
Synopsis: Researchers performed a systematic review of all published trials addressing the prevalence of silent PE in patients with DVT. Studies were included if the methods of PE diagnosis were described, if it was an asymptomatic PE, and if raw data were presented. Twenty-eight studies were identified and were stratified according to how the PE was diagnosed (Tier 1: high-probability VQ scan based on PIOPED criteria, computerized tomographic angiography [CTA], angiography; Tier 2: VQ scans based on non-PIOPED criteria).
Among Tier 1 studies, silent PE was detected in 27% of patients with DVT. Among Tier 2 studies, silent PE was detected among 37% of patients with DVT. Combined, silent PE was diagnosed in 1,665 of 5,233 patients (32%) with DVT.
Further analysis showed that the prevalence of silent PE in patients with proximal DVT was higher in those with distal DVT and that there was a trend toward increased prevalence of silent PE with increased age.
A limitation of this study includes the heterogeneity in the methods used for diagnosis of silent PE.
Bottom line: Silent PE occurs in a third of patients with DVT, and routine screening should be considered.
Citation: Stein PD, Matta F, Musani MH, Diaczok B. Silent pulmonary embolism in patients with deep venous thrombosis: a systematic review. Am J Med. 2010;123(5):426-431.
Reviewed for TH eWireby Alexander R. Carbo, MD, SFHM, Lauren Doctoroff, MD, John Fani Srour, MD, Matthew Hill, MD, Nancy Torres-Finnerty, MD, FHM, and Anita Vanka, MD, Hospital Medicine Program, Beth Israel Deaconess Medical Center, Boston.
For more physician reviews of literature, visit our website.
FDA approves dabigatran for AF patients
Credit: Kevin MacKenzie
The US Food and Drug Administration (FDA) has approved dabigatran etexilate (Pradaxa) to prevent strokes and thrombosis in patients with atrial fibrillation (AF).
Dabigatran is an oral direct thrombin inhibitor that can be administered at a fixed oral dose, with no need for coagulation monitoring.
“Unlike warfarin, which requires patients to undergo periodic monitoring with blood tests, such monitoring is not necessary for Pradaxa,” said Norman Stockbridge, MD, PhD, director of the Division of Cardiovascular and Renal Products in the FDA’s Center for Drug Evaluation and Research.
The FDA has approved dabigatran based on results of the RE-LY trial, in which investigators compared dabigatran to warfarin in more than 18,000 AF patients.
Results suggested that, overall, dabigatran is noninferior to warfarin for preventing stroke and systemic embolism. And, at a 150 mg dose, dabigatran is actually more effective than warfarin.
Bleeding, including life-threatening and fatal bleeding, was among the most common adverse events observed in patients treated with dabigatran. Gastrointestinal symptoms, including dyspepsia, stomach pain, nausea, heartburn, and bloating were reported as well.
Dabigatran was approved with a medication guide that informs patients of the risk of serious bleeding. The guide will be distributed each time a patient fills a prescription for the medication.
Dabigatran will be marketed as Pradaxa by Boehringer Ingelheim Pharmaceuticals, Inc. It will be available in 75 mg and 150 mg capsules. 
Credit: Kevin MacKenzie
The US Food and Drug Administration (FDA) has approved dabigatran etexilate (Pradaxa) to prevent strokes and thrombosis in patients with atrial fibrillation (AF).
Dabigatran is an oral direct thrombin inhibitor that can be administered at a fixed oral dose, with no need for coagulation monitoring.
“Unlike warfarin, which requires patients to undergo periodic monitoring with blood tests, such monitoring is not necessary for Pradaxa,” said Norman Stockbridge, MD, PhD, director of the Division of Cardiovascular and Renal Products in the FDA’s Center for Drug Evaluation and Research.
The FDA has approved dabigatran based on results of the RE-LY trial, in which investigators compared dabigatran to warfarin in more than 18,000 AF patients.
Results suggested that, overall, dabigatran is noninferior to warfarin for preventing stroke and systemic embolism. And, at a 150 mg dose, dabigatran is actually more effective than warfarin.
Bleeding, including life-threatening and fatal bleeding, was among the most common adverse events observed in patients treated with dabigatran. Gastrointestinal symptoms, including dyspepsia, stomach pain, nausea, heartburn, and bloating were reported as well.
Dabigatran was approved with a medication guide that informs patients of the risk of serious bleeding. The guide will be distributed each time a patient fills a prescription for the medication.
Dabigatran will be marketed as Pradaxa by Boehringer Ingelheim Pharmaceuticals, Inc. It will be available in 75 mg and 150 mg capsules.
Credit: Kevin MacKenzie
The US Food and Drug Administration (FDA) has approved dabigatran etexilate (Pradaxa) to prevent strokes and thrombosis in patients with atrial fibrillation (AF).
Dabigatran is an oral direct thrombin inhibitor that can be administered at a fixed oral dose, with no need for coagulation monitoring.
“Unlike warfarin, which requires patients to undergo periodic monitoring with blood tests, such monitoring is not necessary for Pradaxa,” said Norman Stockbridge, MD, PhD, director of the Division of Cardiovascular and Renal Products in the FDA’s Center for Drug Evaluation and Research.
The FDA has approved dabigatran based on results of the RE-LY trial, in which investigators compared dabigatran to warfarin in more than 18,000 AF patients.
Results suggested that, overall, dabigatran is noninferior to warfarin for preventing stroke and systemic embolism. And, at a 150 mg dose, dabigatran is actually more effective than warfarin.
Bleeding, including life-threatening and fatal bleeding, was among the most common adverse events observed in patients treated with dabigatran. Gastrointestinal symptoms, including dyspepsia, stomach pain, nausea, heartburn, and bloating were reported as well.
Dabigatran was approved with a medication guide that informs patients of the risk of serious bleeding. The guide will be distributed each time a patient fills a prescription for the medication.
Dabigatran will be marketed as Pradaxa by Boehringer Ingelheim Pharmaceuticals, Inc. It will be available in 75 mg and 150 mg capsules.
Stick with What Works
A new study that found tighter glycemic control in ICU patients who received continuous insulin infusion (CII) via computer-guided algorithms versus paper-based protocols might not be enough to ditch paper forms just yet, one of the report's authors says.
While the review in this month's multicenter, randomized trial also reported no differences between groups in length of stay (P=0.704), ICU stay (P=0.145), or in-hospital mortality (P=0.561).
"It leaves it up to the individual physician to decide," Dr. Newton says. "'Is what we're doing working good enough to do what we need to do? Or do we need to make a change?'"
Nationwide, glycemic control is a quality initiative frequently tackled by HM groups. To wit, SHM this year enrolled the first sites into its Glycemic Control Mentored Implementation program. The pilot program addresses subcutaneous insulin protocols, transition from subcutaneous to infusion, care coordination, improving follow-up care, and hypoglycemia management.
And while those institutions and hospitalists focusing on glycemic control will be keen to see the data comparing computer-based and standard column-based algorithms, Dr. Newton says, it will require continued research to determine how each protocol performs in patient safety measures before hospitalists change their habits.
"Honestly, I don't know if [the current research] is [enough]," Dr. Newton says. "If their approach is working … then it's probably not worth making a large investment to cause an upheaval of their whole system at this time."
A new study that found tighter glycemic control in ICU patients who received continuous insulin infusion (CII) via computer-guided algorithms versus paper-based protocols might not be enough to ditch paper forms just yet, one of the report's authors says.
While the review in this month's multicenter, randomized trial also reported no differences between groups in length of stay (P=0.704), ICU stay (P=0.145), or in-hospital mortality (P=0.561).
"It leaves it up to the individual physician to decide," Dr. Newton says. "'Is what we're doing working good enough to do what we need to do? Or do we need to make a change?'"
Nationwide, glycemic control is a quality initiative frequently tackled by HM groups. To wit, SHM this year enrolled the first sites into its Glycemic Control Mentored Implementation program. The pilot program addresses subcutaneous insulin protocols, transition from subcutaneous to infusion, care coordination, improving follow-up care, and hypoglycemia management.
And while those institutions and hospitalists focusing on glycemic control will be keen to see the data comparing computer-based and standard column-based algorithms, Dr. Newton says, it will require continued research to determine how each protocol performs in patient safety measures before hospitalists change their habits.
"Honestly, I don't know if [the current research] is [enough]," Dr. Newton says. "If their approach is working … then it's probably not worth making a large investment to cause an upheaval of their whole system at this time."
A new study that found tighter glycemic control in ICU patients who received continuous insulin infusion (CII) via computer-guided algorithms versus paper-based protocols might not be enough to ditch paper forms just yet, one of the report's authors says.
While the review in this month's multicenter, randomized trial also reported no differences between groups in length of stay (P=0.704), ICU stay (P=0.145), or in-hospital mortality (P=0.561).
"It leaves it up to the individual physician to decide," Dr. Newton says. "'Is what we're doing working good enough to do what we need to do? Or do we need to make a change?'"
Nationwide, glycemic control is a quality initiative frequently tackled by HM groups. To wit, SHM this year enrolled the first sites into its Glycemic Control Mentored Implementation program. The pilot program addresses subcutaneous insulin protocols, transition from subcutaneous to infusion, care coordination, improving follow-up care, and hypoglycemia management.
And while those institutions and hospitalists focusing on glycemic control will be keen to see the data comparing computer-based and standard column-based algorithms, Dr. Newton says, it will require continued research to determine how each protocol performs in patient safety measures before hospitalists change their habits.
"Honestly, I don't know if [the current research] is [enough]," Dr. Newton says. "If their approach is working … then it's probably not worth making a large investment to cause an upheaval of their whole system at this time."
Rethinking Rapid Discharge
A national study of trauma patients transferred from one hospital to another (J Trauma. 2010;69:602-606) has found significant rates of "secondary overtriage," which happens when the patient is discharged home less than a day after the transfer without undergoing a surgical procedure.
Such rapid discharge suggests that the transfer might not have been necessary in the first place, says lead author Hayley Osen, BA, research analyst at the University of California-San Diego Center for Surgical Systems and Public Health. The occurrence of secondary overtriage, which can cost nearly $6,000 ($12,000 for transfer by helicopter), was found to be higher among patients under 18 years of age (19.5%, versus 6.9% overall).
Hospitalists can be at both ends of these transfers, which often are between small or rural hospitals and regional medical centers. They can also play important roles in preventing unnecessary transfers, says Cleo Hardin, MD, SFHM, FAAP, section chief for pediatric hospital medicine and outreach at the University of Arizona in Tucson.
"Phone triage is absolutely vital as a first-line approach," Dr. Hardin says. Telemedicine links and teleradiology, the electronic transmission of X-rays for review by a specialist at the regional center, also help with the triage and management of patients at the referring institution, she adds.
Building good working relationships between the two facilities, establishing rapport between key connections, and knowing the resources within each facility can help, says Monika Gottlieb, MD, SFHM, who just left her job at Hospitalist Specialists in Spokane, Wash., to start a new position. "In these cases, a lot depends on understanding the capacity of the local facility, including nurses," she says.
It might be possible to establish mentorships with key specialists at regional centers, with mechanisms for how to reach them, Dr. Gottlieb explains, but hospitalists need to take responsibility for completing successful transfers and handoffs.
A national study of trauma patients transferred from one hospital to another (J Trauma. 2010;69:602-606) has found significant rates of "secondary overtriage," which happens when the patient is discharged home less than a day after the transfer without undergoing a surgical procedure.
Such rapid discharge suggests that the transfer might not have been necessary in the first place, says lead author Hayley Osen, BA, research analyst at the University of California-San Diego Center for Surgical Systems and Public Health. The occurrence of secondary overtriage, which can cost nearly $6,000 ($12,000 for transfer by helicopter), was found to be higher among patients under 18 years of age (19.5%, versus 6.9% overall).
Hospitalists can be at both ends of these transfers, which often are between small or rural hospitals and regional medical centers. They can also play important roles in preventing unnecessary transfers, says Cleo Hardin, MD, SFHM, FAAP, section chief for pediatric hospital medicine and outreach at the University of Arizona in Tucson.
"Phone triage is absolutely vital as a first-line approach," Dr. Hardin says. Telemedicine links and teleradiology, the electronic transmission of X-rays for review by a specialist at the regional center, also help with the triage and management of patients at the referring institution, she adds.
Building good working relationships between the two facilities, establishing rapport between key connections, and knowing the resources within each facility can help, says Monika Gottlieb, MD, SFHM, who just left her job at Hospitalist Specialists in Spokane, Wash., to start a new position. "In these cases, a lot depends on understanding the capacity of the local facility, including nurses," she says.
It might be possible to establish mentorships with key specialists at regional centers, with mechanisms for how to reach them, Dr. Gottlieb explains, but hospitalists need to take responsibility for completing successful transfers and handoffs.
A national study of trauma patients transferred from one hospital to another (J Trauma. 2010;69:602-606) has found significant rates of "secondary overtriage," which happens when the patient is discharged home less than a day after the transfer without undergoing a surgical procedure.
Such rapid discharge suggests that the transfer might not have been necessary in the first place, says lead author Hayley Osen, BA, research analyst at the University of California-San Diego Center for Surgical Systems and Public Health. The occurrence of secondary overtriage, which can cost nearly $6,000 ($12,000 for transfer by helicopter), was found to be higher among patients under 18 years of age (19.5%, versus 6.9% overall).
Hospitalists can be at both ends of these transfers, which often are between small or rural hospitals and regional medical centers. They can also play important roles in preventing unnecessary transfers, says Cleo Hardin, MD, SFHM, FAAP, section chief for pediatric hospital medicine and outreach at the University of Arizona in Tucson.
"Phone triage is absolutely vital as a first-line approach," Dr. Hardin says. Telemedicine links and teleradiology, the electronic transmission of X-rays for review by a specialist at the regional center, also help with the triage and management of patients at the referring institution, she adds.
Building good working relationships between the two facilities, establishing rapport between key connections, and knowing the resources within each facility can help, says Monika Gottlieb, MD, SFHM, who just left her job at Hospitalist Specialists in Spokane, Wash., to start a new position. "In these cases, a lot depends on understanding the capacity of the local facility, including nurses," she says.
It might be possible to establish mentorships with key specialists at regional centers, with mechanisms for how to reach them, Dr. Gottlieb explains, but hospitalists need to take responsibility for completing successful transfers and handoffs.
Predictors of Recurrent Readmissions
Hospital readmissions are recognized as both a significant contributor to health care costs and a putative indicator of healthcare quality.1, 2 Older medical patients with chronic medical comorbidities are at particularly high risk for hospital readmission3 and attendant risks of hospitalization.4 Many intervention strategies have been used in trials to reduce readmissions in such patients. Single interventions such as case management,5 care coordination,6 and self‐management7 have been disappointing. There is emerging evidence to support complex, multidisciplinary interventions which include outreach and support in the early post‐hospital period, especially in heart failure patients,8 but also in medical patients with a range of conditions.9 However, such interventions are resource intensive and it remains uncertain which patients may benefit most from interventions.
Although there are many studies of risk factors for hospital admission and readmission, few studies have reported predictors of recurrent readmission.1012 Patients with 2 or more recent hospitalizations are readily identifiable and have a substantially increased risk of hospital readmission compared to patients with only 1 recent hospitalization.10, 11, 1315 These patients may have a unique risk factor profile, and may be a group which may particularly benefit from complex interventions,16 but no previous study has specifically examined risk factors in this high‐risk group.
Previous studies of readmission predictors have largely focussed on demographic and disease characteristics which are not amenable to intervention at individual level. The results of such studies may determine a population at increased risk, but do not inform an intervention strategy.14 Psychological and behavioral factors such as depression and anxiety, perceptions of health, and adherence patterns may also contribute to hospitalizations.17, 18 However, the role of these factors in repeated admissions of medical patients has been poorly studied.
The aim of this study was to describe the association of a wider range of biological, functional, and psychosocial variables with the risk of unplanned hospital readmission within 6 months in medical patients with 2 or more recent hospitalizations. There was a particular emphasis on risk factors which might be amenable to intervention.
Methods
Setting and Participants
The study was a prospective longitudinal cohort study. Participant enrolment was undertaken from February 2006 to February 2007. The study setting was the Internal Medicine Department of a tertiary teaching hospital in Brisbane, Australia. The Internal Medicine Department admits approximately 5000 inpatients per annum; more than 95% of these are unplanned admissions (general practitioner referral or self‐referral) via the Emergency Department. Acute and some subacute care are provided by 1 of 5 medical units, each staffed by 2 to 3 consultant general physicians, 2 medical residents (post‐graduate year 3‐4), 2 interns (post‐graduate year 1), and a consistent multidisciplinary team of allied health professionals and senior nursing staff. Descriptions of the inpatient case‐mix and model of care have been published previously.19
Participants were identified by 2 trained research nurses. Daily reports were generated from the hospital admission database to identify all consecutive patients admitted to any general medical unit who had already been hospitalized at the study hospital within the previous six months. The medical record was then screened for eligibility.
Patients were considered for inclusion if they were aged 50 years or older, based on clinical consensus that different factors may be relevant in younger patients, and the demonstrated validity of the selected tools in an older medical population. For logistic reasons, patients were ineligible if they lived outside the greater Brisbane area; came from residential care; had significant language or cognitive difficulties which would preclude participation in interviews; were admitted for end‐of‐life care; or were considered otherwise unsuitable for post‐hospital interviews (eg, no fixed address). Eligible patients were invited to participate in the study. Informed consent was obtained from all participants. The study was approved by the Royal Brisbane and Women's Hospitals and University of Queensland Human Research Ethics Committees.
Outcome and Variables
The primary outcome was 1 or more unplanned readmission to any ward at the study hospital within 6 months of the date of hospital discharge. Information about the frequency and duration of planned and unplanned hospital readmissions in the 6 months after discharge was obtained from the hospital admissions database. Planned readmissions included elective surgical or procedural admissions and scheduled day‐case admissions. Unplanned readmissions included all presentations through the emergency department, except for brief emergency department attendances where the participant was not formally admitted under a consultant.
Information was abstracted from the medical record at the time of discharge using a structured audit tool, including age, sex, primary diagnosis according to the treating clinical team, Charlson comorbidity score, number of medications at discharge, living situation at time of discharge, and the number of hospitalizations in the previous 12 months. Note was made of whether a discharge summary was completed and faxed to the general practitioner within 24 hours of discharge. Weight and height were measured by the research assistant to calculate the body mass index (BMI) which was categorized according to World Health Organization recommended cut‐offs.
Within 5 days of discharge from the index admission, the participant was contacted by telephone to schedule an interview at home within the next week. Posthospital interviews were undertaken using a structured interview tool composed of validated measures of the variables of interest, as described below. Interviews took 45 to 90 minutes to complete, and were performed by one of eight postgraduate clinical psychology students from the University of Queensland, who received training and regular supervision by a senior academic psychologist (NP).
Cognitive status was tested using the 3MS cognitive screening test, a sensitive test for early cognitive impairment.20 The Cambridge Contextual Reading Test (CCRT, short version) was used as a measure of literacy and verbal intelligence, as it may be robust in the presence of early cognitive decline21 Mood disturbances were evaluated using the Geriatric Depression Scale (GDS, short version)22 and the Geriatric Anxiety Inventory (GAI).23 The Social Support Questionnaire short form (SSQ6) was used to identify satisfaction with supports.24 Self‐rated health and income adequacy were rated using a 5 point Likert scale. Compliance with prescribed medication was assessed using the Medication Adherence Rating Scale (MARS) (R Horne, personal communication). Alcohol consumption was evaluated using the alcohol use disorders identification tool (AUDIT).25 Instrumental activities of daily living (IADL: using the telephone, using transport, shopping, housework, meal preparation, medication management, managing money) and basic activities of daily living (BADL: bathing, dressing, eating, mobility, transfers, grooming) were assessed using items from the Older Americans Resources and Services (OARS) questionnaire.26 Relevant permissions were obtained from the developers.
Analysis
Data were analyzed using SPSS 17.0. The distribution of each explanatory and confounding variable was examined and summarized using appropriate statistics (mean, median or proportion). Ordinal and some continuous variables were grouped into categories according to previously validated cut‐offs and clinical meaning. Logarithmic transformation was used in analyses of length of stay due to the highly skewed distribution. ADL and IADL function were grouped into independent in all activities, dependent in IADL function only, and dependent in basic ADL function. Bivariate analysis was undertaken using contingency tables and chi‐square testing for categorical variables and independent samples t‐test or equivalent nonparametric testing for continuous variables, to identify potential associations with the primary outcome.
Dealing with diagnosis posed particular difficulties because of the large number of disease categories. Other authors have restricted the sample to a limited number of diagnoses,27, 28 explored the impact of a limited number of diagnoses compared to all others,13 or grouped diagnoses in a pre‐specified or post hoc manner.3, 15 Considering previous studies and preliminary examination of the data (Table 3), we grouped diagnoses as chronic (heart failure, chronic lung disease, diabetes) vs. other for analysis purposes.
| Follow‐Up Data (n = 142) | No Follow‐Up Data (n = 48) | P | |
|---|---|---|---|
| |||
| Age, years, mean (SD) | 74.0 (10.9) | 76.8 (10.1) | 0.13 |
| Male, % | 52.8 | 56.3 | 0.68 |
| Admissions past 12 months, median (IQR) | 2 (1, 2.25) | 1 (1,2) | 0.38 |
| Comorbidity score, median (IQR) | 2 (1,4) | 2 (1,3) | 0.48 |
| Medications on discharge, mean (SD) | 8.8 (4.0) | 9.0 (4.5) | 0.86 |
| Length of stay, median (IQR) | 6.5 (4,11) | 7 (4,14) | 0.35 |
| Discharged to | 0.30 | ||
| Independent living alone | 38.7 | 47.9 | |
| Independent living with others | 54.2 | 50.0 | |
| Assisted living/residential care | 7.1 | 2.1 | |
| Community services on discharge, % | 42.3 | 56.3 | 0.16 |
| Number (%) with characteristic | % Readmitted | P | |
|---|---|---|---|
| |||
| Age (years) | 0.78 | ||
| <65 | 31 (22) | 39 | |
| 6584.9 | 92 (65) | 40 | |
| 85 or more | 19 (13) | 32 | |
| Male | 75 (53) | 40 | 0.74 |
| Admissions past 12 months | 0.78 | ||
| 1 | 67 (47) | 39 | |
| 2 | 40 (28) | 35 | |
| 3 or more | 35 (25) | 43 | |
| Body mass index | 0.02 | ||
| Underweight | 11 (8) | 72 | |
| Normal | 55 (39) | 27 | |
| Overweight | 43 (30) | 37 | |
| Obese | 32 (23) | 50 | |
| Chronic disease diagnosis | 27 (19) | 67 | 0.001 |
| Functional dependence | 0.16 | ||
| Independent | 27 (19) | 26 | |
| Dependent in IADLs | 48 (34) | 48 | |
| Dependent in BADLs | 66 (47) | 36 | |
| Comorbidity score | 0.15 | ||
| 0 | 19 (13) | 26 | |
| 12 | 62 (44) | 34 | |
| 3 or more | 61 (43) | 48 | |
| Summary sent within 24 hours | 117 (82) | 40 | 0.93 |
| Discharge supports | 0.60 | ||
| community | 72 (51) | 35 | |
| community with supports | 60 (42) | 43 | |
| residential care | 10 (7) | 40 | |
| Poor cognition (3MS 85)* | 80 (58) | 36 | 0.68 |
| Reduced literacy (CCRT<21) | 61 (48) | 41 | 0.55 |
| Depressive symptoms (GDS 5) | 72 (51) | 47 | 0.04 |
| Anxiety symptoms (GAI 9) | 45 (32) | 38 | 0.93 |
| Poor adherence (MARS <24) | 48 (34) | 35 | 0.56 |
| Hazardous drinking (AUDIT >6) | 18 (13) | 39 | 0.98 |
| English as second language | 19 (13) | 32 | 0.50 |
| Self‐rated health fair or poor | 102 (72) | 38 | 0.68 |
| Financial hardship | 46 (32) | 35 | 0.47 |
| Total | 142 | 39 | |
| Diagnosis | Number with diagnosis (%) | % readmitted |
|---|---|---|
| Heart failure | 13 (9) | 69 |
| Diabetes | 6 (4) | 67 |
| Chronic lung disease | 8 (6) | 63 |
| Cellulitis | 8 (6) | 63 |
| Syncope/arrhythmia | 11 (8) | 46 |
| Pneumonia | 10 (7) | 40 |
| Urinary infection | 15 (11) | 33 |
| Fall or fracture | 18 (13) | 33 |
| Gastrointestinal disease | 7 (5) | 29 |
| Ischemic heart disease | 11 (8) | 18 |
| Neurological disease | 7 (5) | 0 |
| Other | 28 (20) | 29 |
| Total | 142 (100) | 39 |
Potentially important variables were chosen based on bivariate analysis (P < 0.2) and previous literature. These variables were then entered into a multiple logistic regression model, and a significant association in the adjusted model was defined as P < 0.05. The performance of the final model was assessed by constructing a receiver operating curve. Given a 40% to 50% anticipated event rate, we estimated that 150 to 200 participants would provide power to include 7 to 10 variables of interest within the model.
Results
Active screening over 12 months identified 1194 new admissions with a documented hospitalization in the previous 6 months. Of these, 85 were discharged prior to clinical review, 227 were aged less than 50 years, 16 died in hospital, and 153 had been screened previously in the study, leaving 713 individual patients for eligibility screening. Screening identified 328 of 713 (46.0%) patients eligible to participate in the study, who were approached for consent. Of these eligible patients, 190 of 328 (57.9%) agreed to participate but 48 of 190 (25%) did not complete posthospital follow‐up, leaving a total of 142 participants. Patient eligibility, consent, and follow‐up are detailed in Figure 1.
Demographic and disease characteristics of the study participants are shown in Table 1. The 48 participants without follow‐up data appeared similar to those with full data, and 25 (52.1%) of these participants without follow‐up data had an unplanned admission within 6 months.
By 6 months, 55 of 142 participants with follow‐up data (38.7%) had had a total of 102 unplanned admissions to the study hospital. Of these, 42 of 55 (76%) were readmitted to internal medicine. Of the 55 participants with an unplanned readmission, 30 had only 1 unplanned readmission, 9 had 2, and 16 had 3 or more unplanned readmissions within 6 months of the index hospitalization.
During 6 month follow‐up of all 142 participants, there were also 97 planned (scheduled) admissions, 56 (58%) of which occurred in the group with an unplanned admission. Thus the 55 participants with an unplanned readmission accounted for a total of 1055 hospital bed‐days (mean 19.2 days per patient over 6 months follow‐up) while the 87 participants without an unplanned readmission used only 147 bed‐days (mean 1.7 days per patient over 6 months).
Bivariate analysis of the association of unplanned readmissions with the study variables is detailed in Table 2. BMI showed a nonlinear relationship with readmission, with a higher risk apparent at each end of the distribution. Depressive symptoms were also associated with a higher risk of readmission.
Age, sex, number of previous admissions, and discharge supports were not significantly different between the 2 groups. There was no difference in length of the index hospital stay: median length of stay was 6 days (interquartile range [IQR] 3‐14 days) in the readmitted group and 7 (IQR 4‐10 days) in the non‐readmitted group. There was a trend to higher mean number of medications in the readmitted group (9.4 vs. 8.8, P = 0.21).
The strongest predictor of readmission was the presence of a chronic disease diagnosis. Patterns of readmission for each primary clinical diagnosis are shown in Table 3. Chronic comorbidities including heart failure, chronic renal failure, and diabetes were associated with a higher risk of readmission (Table 4). Median comorbidity score was 3 (IQR 1‐5) in the readmitted group compared to 2 (IQR 1‐3) in the nonreadmitted group (P = 0.02).
| Co‐Morbidity | Number with Co‐Morbidity (%) | % Readmitted | P |
|---|---|---|---|
| Heart failure | 30 (21) | 57 | 0.02 |
| Chronic renal impairment | 22 (15) | 59 | 0.03 |
| Diabetes | 36 (25) | 53 | 0.05 |
| Chronic lung disease | 39 (27) | 49 | 0.13 |
| Peripheral vascular disease | 25 (18) | 44 | 0.55 |
| Cerebrovascular disease | 36 (25) | 44 | 0.42 |
| Ischemic heart disease | 57 (40) | 40 | 0.75 |
| Cancer | 23 (16) | 35 | 0.67 |
On the basis of these findings and the literature, a multivariate binary logistic regression model for unplanned admission within 6 months was fitted, including chronic disease diagnosis, comorbiditiy score, BMI, functional status, and GDS as explanatory variables, and adjusting for the potential confounders of age and length of stay (as a severity surrogate). The model is shown in Table 5, and demonstrates a significant association between readmission and chronic conditions, BMI, and depressive symptoms. The area under the receiving operating curve was 0.73.
| Odds Ratio (95% CI) | P Value | |
|---|---|---|
| ||
| Body mass index (reference 18.525) | ||
| Underweight (<18.5) | 12.7 (2.370.7) | 0.004 |
| Overweight (2530) | 1.9 (0.75.1) | 0.18 |
| Obese (>30) | 2.6 (0.97.3) | 0.07 |
| Depressive symptoms (GDS 5) | 3.0 (1.36.8) | 0.01 |
| Chronic disease diagnosis | 3.4 (1.39.3) | 0.02 |
| Co‐morbidity score | 1.3 (1.01.6) | 0.02 |
| Dependency (reference independent) | 0.32 | |
| IADL dependency only | 1.7 (0.55.4) | |
| BADL dependency | 0.9 (0.32.8) | |
| Age group (reference <65 years) | 0.94 | |
| 6584 | 1.1 (0.42.8) | |
| 85 or older | 0.9 (0.23.5) | |
| Log length of stay | 0.99 (0.961.01) | 0.43 |
Discussion
This study demonstrates a number of important findings. First, 39% of this group of participants went on to further unplanned hospital readmissions in the ensuing 6 months, demonstrating the high risk in this group with more than 1 recent hospital admission. However, within this group, the risk of readmission was not related to the frequency of admission within the previous year, consistent with several previous studies.29, 30 These finding suggest that 1 or more recent previous admissions identified at the time of a medical admission is an effective identifier of high risk patients. Subgroup analysis of a recent discharge intervention study in medical patients suggests that this high risk group may particularly benefit from such an intervention.16
Second, the study describes important predictors of readmission which may inform novel interventions. The BMI showed a significant nonlinear relationship with readmission, with an increased risk both above and below the normal weight range. Almost half the group was overweight or obese, with a 2‐fold risk compared to normal weight patients. While underweight was less prevalent, it carried a markedly increased probability of readmission. Limited previous studies support the association of nutritional status and unplanned readmission.31, 32 Malnutrition may be a marker of disease stage or severity, or may be associated with other unmeasured social determinants which increase readmission risk. However, malnutrition itself may reduce physiological resilience and predispose to higher health care needs. There are no published trials of posthospital nutritional intervention programs for reducing readmission rates in general medical patients.
The risk of readmission was also increased in participants with depressive symptoms, consistent with several previous studies.3336 This effect was independent of illness type and comorbidity. Depression is increasingly recognized as an important independent predictor of a range of important outcomes in older medical patients, including posthospital functional decline,37 institutionalization and mortality.36, 38 Posthospital decline and poor self‐management might contribute to higher rehospitalization. There is some evidence that effective treatment of psychological comorbidities in medically ill patients may reduce readmissions.18, 39
Both the number and type of chronic conditions appear to be predictors of readmission in this high risk group, where there was a high baseline prevalence of chronic diseases such as heart failure, diabetes, renal impairment, and chronic lung disease which have been associated with higher readmission rates in a number of previous studies.13, 15, 30, 40 Almost all participants had one or more significant comorbid conditions in addition to their presenting complaint; single disease‐focused chronic disease management programs may not be an optimal solution in this group. Consistent with this comorbidity burden, most participants were prescribed a large number of medications. In keeping with other studies,17 about one‐third of participants reported reduced medication adherence but this was not associated with a higher readmission risk.
Like most previous studies in medical patients,10, 11, 15, 29, 30, 35, 36, 40, 41 there was no evidence of increasing readmission rates with age. Functional status impairment was not a significant predictor of readmission, probably reflecting selection of a patient subgroup with a high prevalence of disability and chronic disease. Satisfaction with social support was generally high, and not associated with readmission. This may reflect the emphasis on discharge planning and postacute social and functional support already occurring in usual care.
Measures of cognition and literacy were not associated with readmission. However, these were the items with the most missing data (see Table 2), which may have reduced our ability to detect an association. The study design excluded patients with significant cognitive or communication deficits who were unable to participate in detailed assessments. Such stringent eligibility criteria may be seen as a weakness of this study, reducing the generalizability of the findings. However, the study deliberately sampled a population of older adults suitable for a multifacetted posthospital management program, in order to inform specific intervention targets, and the eligibility criteria reflect these practical considerations. Although some previous studies have found that cognitive impairment is a predictor of readmission,10, 12 others have found no association.29, 30, 36, 40, 41
The main study weaknesses are the small sample size (reflected in the wide confidence intervals [CIs] in the multivariate analysis), and the relatively high rate of drop‐outs (25% of enrolments) for whom detailed posthospital data could not be collected. This problem reflects the age and burden of illness in the population under study. Readmission data were collected for all participants, and a similar rate of readmission was observed in patients with missing data (52% vs. 39%, P = 0.11). The heterogeneous patients mix may have concealed some important associations within individual diagnoses or other patient subgroups. This heterogeneity reflects the reality of the selected high risk subgroup, and the study deliberately avoided a disease‐specific focus for generalizability.
Conclusions
This study confirms the high rate of hospital readmission in medical patients who have already had a previous inpatient admission in the past 6 months. It shifts the emphasis from nonmodifiable disease and demographic predictors to consideration of common, nondisease specific factors which might have a plausible causative relationship with readmission and may be amenable to specific interventions. The population sampled had a high prevalence of chronic disease, and often multiple diseases. Nutritional status and depressive symptoms are emerging as important modifiers of disease course and mortality in the setting of several chronic diseases; this study also supports their potential contribution to increased hospital resource consumption in a high‐risk group. Posthospital programs which specifically address these factors in the context of optimal medical management of underlying chronic diseases have the potential to reduce hospital readmissions.
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- ,,,,.Relation between symptoms of depression and health status outcomes in acutely ill hospitalized older persons.Ann Intern Med.1997;126(6):417–425.
- ,,,.Diabetes, depression and death. A randomized controlled trial of a depression treatment program for older adults based in primary care (PROSPECT).Diabetes Care.2007;30(12):3005–3010.
- ,,.Clinical implications of a reduction in psychosocial distress in cardiac prognosis in patients participating in a psychosocial intervention programme.Psychosom Med.2001;63(2):257–266.
- ,,, et al.Predictors of immediate and 6‐month outcomes in hospitalized elderly patients.J Am Geriatr Soc.1988;36:775–783.
- ,,,,.Factors predictive of outcome on admission to an acute geriatric ward.Age Ageing.1999;28:429–432.
Hospital readmissions are recognized as both a significant contributor to health care costs and a putative indicator of healthcare quality.1, 2 Older medical patients with chronic medical comorbidities are at particularly high risk for hospital readmission3 and attendant risks of hospitalization.4 Many intervention strategies have been used in trials to reduce readmissions in such patients. Single interventions such as case management,5 care coordination,6 and self‐management7 have been disappointing. There is emerging evidence to support complex, multidisciplinary interventions which include outreach and support in the early post‐hospital period, especially in heart failure patients,8 but also in medical patients with a range of conditions.9 However, such interventions are resource intensive and it remains uncertain which patients may benefit most from interventions.
Although there are many studies of risk factors for hospital admission and readmission, few studies have reported predictors of recurrent readmission.1012 Patients with 2 or more recent hospitalizations are readily identifiable and have a substantially increased risk of hospital readmission compared to patients with only 1 recent hospitalization.10, 11, 1315 These patients may have a unique risk factor profile, and may be a group which may particularly benefit from complex interventions,16 but no previous study has specifically examined risk factors in this high‐risk group.
Previous studies of readmission predictors have largely focussed on demographic and disease characteristics which are not amenable to intervention at individual level. The results of such studies may determine a population at increased risk, but do not inform an intervention strategy.14 Psychological and behavioral factors such as depression and anxiety, perceptions of health, and adherence patterns may also contribute to hospitalizations.17, 18 However, the role of these factors in repeated admissions of medical patients has been poorly studied.
The aim of this study was to describe the association of a wider range of biological, functional, and psychosocial variables with the risk of unplanned hospital readmission within 6 months in medical patients with 2 or more recent hospitalizations. There was a particular emphasis on risk factors which might be amenable to intervention.
Methods
Setting and Participants
The study was a prospective longitudinal cohort study. Participant enrolment was undertaken from February 2006 to February 2007. The study setting was the Internal Medicine Department of a tertiary teaching hospital in Brisbane, Australia. The Internal Medicine Department admits approximately 5000 inpatients per annum; more than 95% of these are unplanned admissions (general practitioner referral or self‐referral) via the Emergency Department. Acute and some subacute care are provided by 1 of 5 medical units, each staffed by 2 to 3 consultant general physicians, 2 medical residents (post‐graduate year 3‐4), 2 interns (post‐graduate year 1), and a consistent multidisciplinary team of allied health professionals and senior nursing staff. Descriptions of the inpatient case‐mix and model of care have been published previously.19
Participants were identified by 2 trained research nurses. Daily reports were generated from the hospital admission database to identify all consecutive patients admitted to any general medical unit who had already been hospitalized at the study hospital within the previous six months. The medical record was then screened for eligibility.
Patients were considered for inclusion if they were aged 50 years or older, based on clinical consensus that different factors may be relevant in younger patients, and the demonstrated validity of the selected tools in an older medical population. For logistic reasons, patients were ineligible if they lived outside the greater Brisbane area; came from residential care; had significant language or cognitive difficulties which would preclude participation in interviews; were admitted for end‐of‐life care; or were considered otherwise unsuitable for post‐hospital interviews (eg, no fixed address). Eligible patients were invited to participate in the study. Informed consent was obtained from all participants. The study was approved by the Royal Brisbane and Women's Hospitals and University of Queensland Human Research Ethics Committees.
Outcome and Variables
The primary outcome was 1 or more unplanned readmission to any ward at the study hospital within 6 months of the date of hospital discharge. Information about the frequency and duration of planned and unplanned hospital readmissions in the 6 months after discharge was obtained from the hospital admissions database. Planned readmissions included elective surgical or procedural admissions and scheduled day‐case admissions. Unplanned readmissions included all presentations through the emergency department, except for brief emergency department attendances where the participant was not formally admitted under a consultant.
Information was abstracted from the medical record at the time of discharge using a structured audit tool, including age, sex, primary diagnosis according to the treating clinical team, Charlson comorbidity score, number of medications at discharge, living situation at time of discharge, and the number of hospitalizations in the previous 12 months. Note was made of whether a discharge summary was completed and faxed to the general practitioner within 24 hours of discharge. Weight and height were measured by the research assistant to calculate the body mass index (BMI) which was categorized according to World Health Organization recommended cut‐offs.
Within 5 days of discharge from the index admission, the participant was contacted by telephone to schedule an interview at home within the next week. Posthospital interviews were undertaken using a structured interview tool composed of validated measures of the variables of interest, as described below. Interviews took 45 to 90 minutes to complete, and were performed by one of eight postgraduate clinical psychology students from the University of Queensland, who received training and regular supervision by a senior academic psychologist (NP).
Cognitive status was tested using the 3MS cognitive screening test, a sensitive test for early cognitive impairment.20 The Cambridge Contextual Reading Test (CCRT, short version) was used as a measure of literacy and verbal intelligence, as it may be robust in the presence of early cognitive decline21 Mood disturbances were evaluated using the Geriatric Depression Scale (GDS, short version)22 and the Geriatric Anxiety Inventory (GAI).23 The Social Support Questionnaire short form (SSQ6) was used to identify satisfaction with supports.24 Self‐rated health and income adequacy were rated using a 5 point Likert scale. Compliance with prescribed medication was assessed using the Medication Adherence Rating Scale (MARS) (R Horne, personal communication). Alcohol consumption was evaluated using the alcohol use disorders identification tool (AUDIT).25 Instrumental activities of daily living (IADL: using the telephone, using transport, shopping, housework, meal preparation, medication management, managing money) and basic activities of daily living (BADL: bathing, dressing, eating, mobility, transfers, grooming) were assessed using items from the Older Americans Resources and Services (OARS) questionnaire.26 Relevant permissions were obtained from the developers.
Analysis
Data were analyzed using SPSS 17.0. The distribution of each explanatory and confounding variable was examined and summarized using appropriate statistics (mean, median or proportion). Ordinal and some continuous variables were grouped into categories according to previously validated cut‐offs and clinical meaning. Logarithmic transformation was used in analyses of length of stay due to the highly skewed distribution. ADL and IADL function were grouped into independent in all activities, dependent in IADL function only, and dependent in basic ADL function. Bivariate analysis was undertaken using contingency tables and chi‐square testing for categorical variables and independent samples t‐test or equivalent nonparametric testing for continuous variables, to identify potential associations with the primary outcome.
Dealing with diagnosis posed particular difficulties because of the large number of disease categories. Other authors have restricted the sample to a limited number of diagnoses,27, 28 explored the impact of a limited number of diagnoses compared to all others,13 or grouped diagnoses in a pre‐specified or post hoc manner.3, 15 Considering previous studies and preliminary examination of the data (Table 3), we grouped diagnoses as chronic (heart failure, chronic lung disease, diabetes) vs. other for analysis purposes.
| Follow‐Up Data (n = 142) | No Follow‐Up Data (n = 48) | P | |
|---|---|---|---|
| |||
| Age, years, mean (SD) | 74.0 (10.9) | 76.8 (10.1) | 0.13 |
| Male, % | 52.8 | 56.3 | 0.68 |
| Admissions past 12 months, median (IQR) | 2 (1, 2.25) | 1 (1,2) | 0.38 |
| Comorbidity score, median (IQR) | 2 (1,4) | 2 (1,3) | 0.48 |
| Medications on discharge, mean (SD) | 8.8 (4.0) | 9.0 (4.5) | 0.86 |
| Length of stay, median (IQR) | 6.5 (4,11) | 7 (4,14) | 0.35 |
| Discharged to | 0.30 | ||
| Independent living alone | 38.7 | 47.9 | |
| Independent living with others | 54.2 | 50.0 | |
| Assisted living/residential care | 7.1 | 2.1 | |
| Community services on discharge, % | 42.3 | 56.3 | 0.16 |
| Number (%) with characteristic | % Readmitted | P | |
|---|---|---|---|
| |||
| Age (years) | 0.78 | ||
| <65 | 31 (22) | 39 | |
| 6584.9 | 92 (65) | 40 | |
| 85 or more | 19 (13) | 32 | |
| Male | 75 (53) | 40 | 0.74 |
| Admissions past 12 months | 0.78 | ||
| 1 | 67 (47) | 39 | |
| 2 | 40 (28) | 35 | |
| 3 or more | 35 (25) | 43 | |
| Body mass index | 0.02 | ||
| Underweight | 11 (8) | 72 | |
| Normal | 55 (39) | 27 | |
| Overweight | 43 (30) | 37 | |
| Obese | 32 (23) | 50 | |
| Chronic disease diagnosis | 27 (19) | 67 | 0.001 |
| Functional dependence | 0.16 | ||
| Independent | 27 (19) | 26 | |
| Dependent in IADLs | 48 (34) | 48 | |
| Dependent in BADLs | 66 (47) | 36 | |
| Comorbidity score | 0.15 | ||
| 0 | 19 (13) | 26 | |
| 12 | 62 (44) | 34 | |
| 3 or more | 61 (43) | 48 | |
| Summary sent within 24 hours | 117 (82) | 40 | 0.93 |
| Discharge supports | 0.60 | ||
| community | 72 (51) | 35 | |
| community with supports | 60 (42) | 43 | |
| residential care | 10 (7) | 40 | |
| Poor cognition (3MS 85)* | 80 (58) | 36 | 0.68 |
| Reduced literacy (CCRT<21) | 61 (48) | 41 | 0.55 |
| Depressive symptoms (GDS 5) | 72 (51) | 47 | 0.04 |
| Anxiety symptoms (GAI 9) | 45 (32) | 38 | 0.93 |
| Poor adherence (MARS <24) | 48 (34) | 35 | 0.56 |
| Hazardous drinking (AUDIT >6) | 18 (13) | 39 | 0.98 |
| English as second language | 19 (13) | 32 | 0.50 |
| Self‐rated health fair or poor | 102 (72) | 38 | 0.68 |
| Financial hardship | 46 (32) | 35 | 0.47 |
| Total | 142 | 39 | |
| Diagnosis | Number with diagnosis (%) | % readmitted |
|---|---|---|
| Heart failure | 13 (9) | 69 |
| Diabetes | 6 (4) | 67 |
| Chronic lung disease | 8 (6) | 63 |
| Cellulitis | 8 (6) | 63 |
| Syncope/arrhythmia | 11 (8) | 46 |
| Pneumonia | 10 (7) | 40 |
| Urinary infection | 15 (11) | 33 |
| Fall or fracture | 18 (13) | 33 |
| Gastrointestinal disease | 7 (5) | 29 |
| Ischemic heart disease | 11 (8) | 18 |
| Neurological disease | 7 (5) | 0 |
| Other | 28 (20) | 29 |
| Total | 142 (100) | 39 |
Potentially important variables were chosen based on bivariate analysis (P < 0.2) and previous literature. These variables were then entered into a multiple logistic regression model, and a significant association in the adjusted model was defined as P < 0.05. The performance of the final model was assessed by constructing a receiver operating curve. Given a 40% to 50% anticipated event rate, we estimated that 150 to 200 participants would provide power to include 7 to 10 variables of interest within the model.
Results
Active screening over 12 months identified 1194 new admissions with a documented hospitalization in the previous 6 months. Of these, 85 were discharged prior to clinical review, 227 were aged less than 50 years, 16 died in hospital, and 153 had been screened previously in the study, leaving 713 individual patients for eligibility screening. Screening identified 328 of 713 (46.0%) patients eligible to participate in the study, who were approached for consent. Of these eligible patients, 190 of 328 (57.9%) agreed to participate but 48 of 190 (25%) did not complete posthospital follow‐up, leaving a total of 142 participants. Patient eligibility, consent, and follow‐up are detailed in Figure 1.
Demographic and disease characteristics of the study participants are shown in Table 1. The 48 participants without follow‐up data appeared similar to those with full data, and 25 (52.1%) of these participants without follow‐up data had an unplanned admission within 6 months.
By 6 months, 55 of 142 participants with follow‐up data (38.7%) had had a total of 102 unplanned admissions to the study hospital. Of these, 42 of 55 (76%) were readmitted to internal medicine. Of the 55 participants with an unplanned readmission, 30 had only 1 unplanned readmission, 9 had 2, and 16 had 3 or more unplanned readmissions within 6 months of the index hospitalization.
During 6 month follow‐up of all 142 participants, there were also 97 planned (scheduled) admissions, 56 (58%) of which occurred in the group with an unplanned admission. Thus the 55 participants with an unplanned readmission accounted for a total of 1055 hospital bed‐days (mean 19.2 days per patient over 6 months follow‐up) while the 87 participants without an unplanned readmission used only 147 bed‐days (mean 1.7 days per patient over 6 months).
Bivariate analysis of the association of unplanned readmissions with the study variables is detailed in Table 2. BMI showed a nonlinear relationship with readmission, with a higher risk apparent at each end of the distribution. Depressive symptoms were also associated with a higher risk of readmission.
Age, sex, number of previous admissions, and discharge supports were not significantly different between the 2 groups. There was no difference in length of the index hospital stay: median length of stay was 6 days (interquartile range [IQR] 3‐14 days) in the readmitted group and 7 (IQR 4‐10 days) in the non‐readmitted group. There was a trend to higher mean number of medications in the readmitted group (9.4 vs. 8.8, P = 0.21).
The strongest predictor of readmission was the presence of a chronic disease diagnosis. Patterns of readmission for each primary clinical diagnosis are shown in Table 3. Chronic comorbidities including heart failure, chronic renal failure, and diabetes were associated with a higher risk of readmission (Table 4). Median comorbidity score was 3 (IQR 1‐5) in the readmitted group compared to 2 (IQR 1‐3) in the nonreadmitted group (P = 0.02).
| Co‐Morbidity | Number with Co‐Morbidity (%) | % Readmitted | P |
|---|---|---|---|
| Heart failure | 30 (21) | 57 | 0.02 |
| Chronic renal impairment | 22 (15) | 59 | 0.03 |
| Diabetes | 36 (25) | 53 | 0.05 |
| Chronic lung disease | 39 (27) | 49 | 0.13 |
| Peripheral vascular disease | 25 (18) | 44 | 0.55 |
| Cerebrovascular disease | 36 (25) | 44 | 0.42 |
| Ischemic heart disease | 57 (40) | 40 | 0.75 |
| Cancer | 23 (16) | 35 | 0.67 |
On the basis of these findings and the literature, a multivariate binary logistic regression model for unplanned admission within 6 months was fitted, including chronic disease diagnosis, comorbiditiy score, BMI, functional status, and GDS as explanatory variables, and adjusting for the potential confounders of age and length of stay (as a severity surrogate). The model is shown in Table 5, and demonstrates a significant association between readmission and chronic conditions, BMI, and depressive symptoms. The area under the receiving operating curve was 0.73.
| Odds Ratio (95% CI) | P Value | |
|---|---|---|
| ||
| Body mass index (reference 18.525) | ||
| Underweight (<18.5) | 12.7 (2.370.7) | 0.004 |
| Overweight (2530) | 1.9 (0.75.1) | 0.18 |
| Obese (>30) | 2.6 (0.97.3) | 0.07 |
| Depressive symptoms (GDS 5) | 3.0 (1.36.8) | 0.01 |
| Chronic disease diagnosis | 3.4 (1.39.3) | 0.02 |
| Co‐morbidity score | 1.3 (1.01.6) | 0.02 |
| Dependency (reference independent) | 0.32 | |
| IADL dependency only | 1.7 (0.55.4) | |
| BADL dependency | 0.9 (0.32.8) | |
| Age group (reference <65 years) | 0.94 | |
| 6584 | 1.1 (0.42.8) | |
| 85 or older | 0.9 (0.23.5) | |
| Log length of stay | 0.99 (0.961.01) | 0.43 |
Discussion
This study demonstrates a number of important findings. First, 39% of this group of participants went on to further unplanned hospital readmissions in the ensuing 6 months, demonstrating the high risk in this group with more than 1 recent hospital admission. However, within this group, the risk of readmission was not related to the frequency of admission within the previous year, consistent with several previous studies.29, 30 These finding suggest that 1 or more recent previous admissions identified at the time of a medical admission is an effective identifier of high risk patients. Subgroup analysis of a recent discharge intervention study in medical patients suggests that this high risk group may particularly benefit from such an intervention.16
Second, the study describes important predictors of readmission which may inform novel interventions. The BMI showed a significant nonlinear relationship with readmission, with an increased risk both above and below the normal weight range. Almost half the group was overweight or obese, with a 2‐fold risk compared to normal weight patients. While underweight was less prevalent, it carried a markedly increased probability of readmission. Limited previous studies support the association of nutritional status and unplanned readmission.31, 32 Malnutrition may be a marker of disease stage or severity, or may be associated with other unmeasured social determinants which increase readmission risk. However, malnutrition itself may reduce physiological resilience and predispose to higher health care needs. There are no published trials of posthospital nutritional intervention programs for reducing readmission rates in general medical patients.
The risk of readmission was also increased in participants with depressive symptoms, consistent with several previous studies.3336 This effect was independent of illness type and comorbidity. Depression is increasingly recognized as an important independent predictor of a range of important outcomes in older medical patients, including posthospital functional decline,37 institutionalization and mortality.36, 38 Posthospital decline and poor self‐management might contribute to higher rehospitalization. There is some evidence that effective treatment of psychological comorbidities in medically ill patients may reduce readmissions.18, 39
Both the number and type of chronic conditions appear to be predictors of readmission in this high risk group, where there was a high baseline prevalence of chronic diseases such as heart failure, diabetes, renal impairment, and chronic lung disease which have been associated with higher readmission rates in a number of previous studies.13, 15, 30, 40 Almost all participants had one or more significant comorbid conditions in addition to their presenting complaint; single disease‐focused chronic disease management programs may not be an optimal solution in this group. Consistent with this comorbidity burden, most participants were prescribed a large number of medications. In keeping with other studies,17 about one‐third of participants reported reduced medication adherence but this was not associated with a higher readmission risk.
Like most previous studies in medical patients,10, 11, 15, 29, 30, 35, 36, 40, 41 there was no evidence of increasing readmission rates with age. Functional status impairment was not a significant predictor of readmission, probably reflecting selection of a patient subgroup with a high prevalence of disability and chronic disease. Satisfaction with social support was generally high, and not associated with readmission. This may reflect the emphasis on discharge planning and postacute social and functional support already occurring in usual care.
Measures of cognition and literacy were not associated with readmission. However, these were the items with the most missing data (see Table 2), which may have reduced our ability to detect an association. The study design excluded patients with significant cognitive or communication deficits who were unable to participate in detailed assessments. Such stringent eligibility criteria may be seen as a weakness of this study, reducing the generalizability of the findings. However, the study deliberately sampled a population of older adults suitable for a multifacetted posthospital management program, in order to inform specific intervention targets, and the eligibility criteria reflect these practical considerations. Although some previous studies have found that cognitive impairment is a predictor of readmission,10, 12 others have found no association.29, 30, 36, 40, 41
The main study weaknesses are the small sample size (reflected in the wide confidence intervals [CIs] in the multivariate analysis), and the relatively high rate of drop‐outs (25% of enrolments) for whom detailed posthospital data could not be collected. This problem reflects the age and burden of illness in the population under study. Readmission data were collected for all participants, and a similar rate of readmission was observed in patients with missing data (52% vs. 39%, P = 0.11). The heterogeneous patients mix may have concealed some important associations within individual diagnoses or other patient subgroups. This heterogeneity reflects the reality of the selected high risk subgroup, and the study deliberately avoided a disease‐specific focus for generalizability.
Conclusions
This study confirms the high rate of hospital readmission in medical patients who have already had a previous inpatient admission in the past 6 months. It shifts the emphasis from nonmodifiable disease and demographic predictors to consideration of common, nondisease specific factors which might have a plausible causative relationship with readmission and may be amenable to specific interventions. The population sampled had a high prevalence of chronic disease, and often multiple diseases. Nutritional status and depressive symptoms are emerging as important modifiers of disease course and mortality in the setting of several chronic diseases; this study also supports their potential contribution to increased hospital resource consumption in a high‐risk group. Posthospital programs which specifically address these factors in the context of optimal medical management of underlying chronic diseases have the potential to reduce hospital readmissions.
Hospital readmissions are recognized as both a significant contributor to health care costs and a putative indicator of healthcare quality.1, 2 Older medical patients with chronic medical comorbidities are at particularly high risk for hospital readmission3 and attendant risks of hospitalization.4 Many intervention strategies have been used in trials to reduce readmissions in such patients. Single interventions such as case management,5 care coordination,6 and self‐management7 have been disappointing. There is emerging evidence to support complex, multidisciplinary interventions which include outreach and support in the early post‐hospital period, especially in heart failure patients,8 but also in medical patients with a range of conditions.9 However, such interventions are resource intensive and it remains uncertain which patients may benefit most from interventions.
Although there are many studies of risk factors for hospital admission and readmission, few studies have reported predictors of recurrent readmission.1012 Patients with 2 or more recent hospitalizations are readily identifiable and have a substantially increased risk of hospital readmission compared to patients with only 1 recent hospitalization.10, 11, 1315 These patients may have a unique risk factor profile, and may be a group which may particularly benefit from complex interventions,16 but no previous study has specifically examined risk factors in this high‐risk group.
Previous studies of readmission predictors have largely focussed on demographic and disease characteristics which are not amenable to intervention at individual level. The results of such studies may determine a population at increased risk, but do not inform an intervention strategy.14 Psychological and behavioral factors such as depression and anxiety, perceptions of health, and adherence patterns may also contribute to hospitalizations.17, 18 However, the role of these factors in repeated admissions of medical patients has been poorly studied.
The aim of this study was to describe the association of a wider range of biological, functional, and psychosocial variables with the risk of unplanned hospital readmission within 6 months in medical patients with 2 or more recent hospitalizations. There was a particular emphasis on risk factors which might be amenable to intervention.
Methods
Setting and Participants
The study was a prospective longitudinal cohort study. Participant enrolment was undertaken from February 2006 to February 2007. The study setting was the Internal Medicine Department of a tertiary teaching hospital in Brisbane, Australia. The Internal Medicine Department admits approximately 5000 inpatients per annum; more than 95% of these are unplanned admissions (general practitioner referral or self‐referral) via the Emergency Department. Acute and some subacute care are provided by 1 of 5 medical units, each staffed by 2 to 3 consultant general physicians, 2 medical residents (post‐graduate year 3‐4), 2 interns (post‐graduate year 1), and a consistent multidisciplinary team of allied health professionals and senior nursing staff. Descriptions of the inpatient case‐mix and model of care have been published previously.19
Participants were identified by 2 trained research nurses. Daily reports were generated from the hospital admission database to identify all consecutive patients admitted to any general medical unit who had already been hospitalized at the study hospital within the previous six months. The medical record was then screened for eligibility.
Patients were considered for inclusion if they were aged 50 years or older, based on clinical consensus that different factors may be relevant in younger patients, and the demonstrated validity of the selected tools in an older medical population. For logistic reasons, patients were ineligible if they lived outside the greater Brisbane area; came from residential care; had significant language or cognitive difficulties which would preclude participation in interviews; were admitted for end‐of‐life care; or were considered otherwise unsuitable for post‐hospital interviews (eg, no fixed address). Eligible patients were invited to participate in the study. Informed consent was obtained from all participants. The study was approved by the Royal Brisbane and Women's Hospitals and University of Queensland Human Research Ethics Committees.
Outcome and Variables
The primary outcome was 1 or more unplanned readmission to any ward at the study hospital within 6 months of the date of hospital discharge. Information about the frequency and duration of planned and unplanned hospital readmissions in the 6 months after discharge was obtained from the hospital admissions database. Planned readmissions included elective surgical or procedural admissions and scheduled day‐case admissions. Unplanned readmissions included all presentations through the emergency department, except for brief emergency department attendances where the participant was not formally admitted under a consultant.
Information was abstracted from the medical record at the time of discharge using a structured audit tool, including age, sex, primary diagnosis according to the treating clinical team, Charlson comorbidity score, number of medications at discharge, living situation at time of discharge, and the number of hospitalizations in the previous 12 months. Note was made of whether a discharge summary was completed and faxed to the general practitioner within 24 hours of discharge. Weight and height were measured by the research assistant to calculate the body mass index (BMI) which was categorized according to World Health Organization recommended cut‐offs.
Within 5 days of discharge from the index admission, the participant was contacted by telephone to schedule an interview at home within the next week. Posthospital interviews were undertaken using a structured interview tool composed of validated measures of the variables of interest, as described below. Interviews took 45 to 90 minutes to complete, and were performed by one of eight postgraduate clinical psychology students from the University of Queensland, who received training and regular supervision by a senior academic psychologist (NP).
Cognitive status was tested using the 3MS cognitive screening test, a sensitive test for early cognitive impairment.20 The Cambridge Contextual Reading Test (CCRT, short version) was used as a measure of literacy and verbal intelligence, as it may be robust in the presence of early cognitive decline21 Mood disturbances were evaluated using the Geriatric Depression Scale (GDS, short version)22 and the Geriatric Anxiety Inventory (GAI).23 The Social Support Questionnaire short form (SSQ6) was used to identify satisfaction with supports.24 Self‐rated health and income adequacy were rated using a 5 point Likert scale. Compliance with prescribed medication was assessed using the Medication Adherence Rating Scale (MARS) (R Horne, personal communication). Alcohol consumption was evaluated using the alcohol use disorders identification tool (AUDIT).25 Instrumental activities of daily living (IADL: using the telephone, using transport, shopping, housework, meal preparation, medication management, managing money) and basic activities of daily living (BADL: bathing, dressing, eating, mobility, transfers, grooming) were assessed using items from the Older Americans Resources and Services (OARS) questionnaire.26 Relevant permissions were obtained from the developers.
Analysis
Data were analyzed using SPSS 17.0. The distribution of each explanatory and confounding variable was examined and summarized using appropriate statistics (mean, median or proportion). Ordinal and some continuous variables were grouped into categories according to previously validated cut‐offs and clinical meaning. Logarithmic transformation was used in analyses of length of stay due to the highly skewed distribution. ADL and IADL function were grouped into independent in all activities, dependent in IADL function only, and dependent in basic ADL function. Bivariate analysis was undertaken using contingency tables and chi‐square testing for categorical variables and independent samples t‐test or equivalent nonparametric testing for continuous variables, to identify potential associations with the primary outcome.
Dealing with diagnosis posed particular difficulties because of the large number of disease categories. Other authors have restricted the sample to a limited number of diagnoses,27, 28 explored the impact of a limited number of diagnoses compared to all others,13 or grouped diagnoses in a pre‐specified or post hoc manner.3, 15 Considering previous studies and preliminary examination of the data (Table 3), we grouped diagnoses as chronic (heart failure, chronic lung disease, diabetes) vs. other for analysis purposes.
| Follow‐Up Data (n = 142) | No Follow‐Up Data (n = 48) | P | |
|---|---|---|---|
| |||
| Age, years, mean (SD) | 74.0 (10.9) | 76.8 (10.1) | 0.13 |
| Male, % | 52.8 | 56.3 | 0.68 |
| Admissions past 12 months, median (IQR) | 2 (1, 2.25) | 1 (1,2) | 0.38 |
| Comorbidity score, median (IQR) | 2 (1,4) | 2 (1,3) | 0.48 |
| Medications on discharge, mean (SD) | 8.8 (4.0) | 9.0 (4.5) | 0.86 |
| Length of stay, median (IQR) | 6.5 (4,11) | 7 (4,14) | 0.35 |
| Discharged to | 0.30 | ||
| Independent living alone | 38.7 | 47.9 | |
| Independent living with others | 54.2 | 50.0 | |
| Assisted living/residential care | 7.1 | 2.1 | |
| Community services on discharge, % | 42.3 | 56.3 | 0.16 |
| Number (%) with characteristic | % Readmitted | P | |
|---|---|---|---|
| |||
| Age (years) | 0.78 | ||
| <65 | 31 (22) | 39 | |
| 6584.9 | 92 (65) | 40 | |
| 85 or more | 19 (13) | 32 | |
| Male | 75 (53) | 40 | 0.74 |
| Admissions past 12 months | 0.78 | ||
| 1 | 67 (47) | 39 | |
| 2 | 40 (28) | 35 | |
| 3 or more | 35 (25) | 43 | |
| Body mass index | 0.02 | ||
| Underweight | 11 (8) | 72 | |
| Normal | 55 (39) | 27 | |
| Overweight | 43 (30) | 37 | |
| Obese | 32 (23) | 50 | |
| Chronic disease diagnosis | 27 (19) | 67 | 0.001 |
| Functional dependence | 0.16 | ||
| Independent | 27 (19) | 26 | |
| Dependent in IADLs | 48 (34) | 48 | |
| Dependent in BADLs | 66 (47) | 36 | |
| Comorbidity score | 0.15 | ||
| 0 | 19 (13) | 26 | |
| 12 | 62 (44) | 34 | |
| 3 or more | 61 (43) | 48 | |
| Summary sent within 24 hours | 117 (82) | 40 | 0.93 |
| Discharge supports | 0.60 | ||
| community | 72 (51) | 35 | |
| community with supports | 60 (42) | 43 | |
| residential care | 10 (7) | 40 | |
| Poor cognition (3MS 85)* | 80 (58) | 36 | 0.68 |
| Reduced literacy (CCRT<21) | 61 (48) | 41 | 0.55 |
| Depressive symptoms (GDS 5) | 72 (51) | 47 | 0.04 |
| Anxiety symptoms (GAI 9) | 45 (32) | 38 | 0.93 |
| Poor adherence (MARS <24) | 48 (34) | 35 | 0.56 |
| Hazardous drinking (AUDIT >6) | 18 (13) | 39 | 0.98 |
| English as second language | 19 (13) | 32 | 0.50 |
| Self‐rated health fair or poor | 102 (72) | 38 | 0.68 |
| Financial hardship | 46 (32) | 35 | 0.47 |
| Total | 142 | 39 | |
| Diagnosis | Number with diagnosis (%) | % readmitted |
|---|---|---|
| Heart failure | 13 (9) | 69 |
| Diabetes | 6 (4) | 67 |
| Chronic lung disease | 8 (6) | 63 |
| Cellulitis | 8 (6) | 63 |
| Syncope/arrhythmia | 11 (8) | 46 |
| Pneumonia | 10 (7) | 40 |
| Urinary infection | 15 (11) | 33 |
| Fall or fracture | 18 (13) | 33 |
| Gastrointestinal disease | 7 (5) | 29 |
| Ischemic heart disease | 11 (8) | 18 |
| Neurological disease | 7 (5) | 0 |
| Other | 28 (20) | 29 |
| Total | 142 (100) | 39 |
Potentially important variables were chosen based on bivariate analysis (P < 0.2) and previous literature. These variables were then entered into a multiple logistic regression model, and a significant association in the adjusted model was defined as P < 0.05. The performance of the final model was assessed by constructing a receiver operating curve. Given a 40% to 50% anticipated event rate, we estimated that 150 to 200 participants would provide power to include 7 to 10 variables of interest within the model.
Results
Active screening over 12 months identified 1194 new admissions with a documented hospitalization in the previous 6 months. Of these, 85 were discharged prior to clinical review, 227 were aged less than 50 years, 16 died in hospital, and 153 had been screened previously in the study, leaving 713 individual patients for eligibility screening. Screening identified 328 of 713 (46.0%) patients eligible to participate in the study, who were approached for consent. Of these eligible patients, 190 of 328 (57.9%) agreed to participate but 48 of 190 (25%) did not complete posthospital follow‐up, leaving a total of 142 participants. Patient eligibility, consent, and follow‐up are detailed in Figure 1.
Demographic and disease characteristics of the study participants are shown in Table 1. The 48 participants without follow‐up data appeared similar to those with full data, and 25 (52.1%) of these participants without follow‐up data had an unplanned admission within 6 months.
By 6 months, 55 of 142 participants with follow‐up data (38.7%) had had a total of 102 unplanned admissions to the study hospital. Of these, 42 of 55 (76%) were readmitted to internal medicine. Of the 55 participants with an unplanned readmission, 30 had only 1 unplanned readmission, 9 had 2, and 16 had 3 or more unplanned readmissions within 6 months of the index hospitalization.
During 6 month follow‐up of all 142 participants, there were also 97 planned (scheduled) admissions, 56 (58%) of which occurred in the group with an unplanned admission. Thus the 55 participants with an unplanned readmission accounted for a total of 1055 hospital bed‐days (mean 19.2 days per patient over 6 months follow‐up) while the 87 participants without an unplanned readmission used only 147 bed‐days (mean 1.7 days per patient over 6 months).
Bivariate analysis of the association of unplanned readmissions with the study variables is detailed in Table 2. BMI showed a nonlinear relationship with readmission, with a higher risk apparent at each end of the distribution. Depressive symptoms were also associated with a higher risk of readmission.
Age, sex, number of previous admissions, and discharge supports were not significantly different between the 2 groups. There was no difference in length of the index hospital stay: median length of stay was 6 days (interquartile range [IQR] 3‐14 days) in the readmitted group and 7 (IQR 4‐10 days) in the non‐readmitted group. There was a trend to higher mean number of medications in the readmitted group (9.4 vs. 8.8, P = 0.21).
The strongest predictor of readmission was the presence of a chronic disease diagnosis. Patterns of readmission for each primary clinical diagnosis are shown in Table 3. Chronic comorbidities including heart failure, chronic renal failure, and diabetes were associated with a higher risk of readmission (Table 4). Median comorbidity score was 3 (IQR 1‐5) in the readmitted group compared to 2 (IQR 1‐3) in the nonreadmitted group (P = 0.02).
| Co‐Morbidity | Number with Co‐Morbidity (%) | % Readmitted | P |
|---|---|---|---|
| Heart failure | 30 (21) | 57 | 0.02 |
| Chronic renal impairment | 22 (15) | 59 | 0.03 |
| Diabetes | 36 (25) | 53 | 0.05 |
| Chronic lung disease | 39 (27) | 49 | 0.13 |
| Peripheral vascular disease | 25 (18) | 44 | 0.55 |
| Cerebrovascular disease | 36 (25) | 44 | 0.42 |
| Ischemic heart disease | 57 (40) | 40 | 0.75 |
| Cancer | 23 (16) | 35 | 0.67 |
On the basis of these findings and the literature, a multivariate binary logistic regression model for unplanned admission within 6 months was fitted, including chronic disease diagnosis, comorbiditiy score, BMI, functional status, and GDS as explanatory variables, and adjusting for the potential confounders of age and length of stay (as a severity surrogate). The model is shown in Table 5, and demonstrates a significant association between readmission and chronic conditions, BMI, and depressive symptoms. The area under the receiving operating curve was 0.73.
| Odds Ratio (95% CI) | P Value | |
|---|---|---|
| ||
| Body mass index (reference 18.525) | ||
| Underweight (<18.5) | 12.7 (2.370.7) | 0.004 |
| Overweight (2530) | 1.9 (0.75.1) | 0.18 |
| Obese (>30) | 2.6 (0.97.3) | 0.07 |
| Depressive symptoms (GDS 5) | 3.0 (1.36.8) | 0.01 |
| Chronic disease diagnosis | 3.4 (1.39.3) | 0.02 |
| Co‐morbidity score | 1.3 (1.01.6) | 0.02 |
| Dependency (reference independent) | 0.32 | |
| IADL dependency only | 1.7 (0.55.4) | |
| BADL dependency | 0.9 (0.32.8) | |
| Age group (reference <65 years) | 0.94 | |
| 6584 | 1.1 (0.42.8) | |
| 85 or older | 0.9 (0.23.5) | |
| Log length of stay | 0.99 (0.961.01) | 0.43 |
Discussion
This study demonstrates a number of important findings. First, 39% of this group of participants went on to further unplanned hospital readmissions in the ensuing 6 months, demonstrating the high risk in this group with more than 1 recent hospital admission. However, within this group, the risk of readmission was not related to the frequency of admission within the previous year, consistent with several previous studies.29, 30 These finding suggest that 1 or more recent previous admissions identified at the time of a medical admission is an effective identifier of high risk patients. Subgroup analysis of a recent discharge intervention study in medical patients suggests that this high risk group may particularly benefit from such an intervention.16
Second, the study describes important predictors of readmission which may inform novel interventions. The BMI showed a significant nonlinear relationship with readmission, with an increased risk both above and below the normal weight range. Almost half the group was overweight or obese, with a 2‐fold risk compared to normal weight patients. While underweight was less prevalent, it carried a markedly increased probability of readmission. Limited previous studies support the association of nutritional status and unplanned readmission.31, 32 Malnutrition may be a marker of disease stage or severity, or may be associated with other unmeasured social determinants which increase readmission risk. However, malnutrition itself may reduce physiological resilience and predispose to higher health care needs. There are no published trials of posthospital nutritional intervention programs for reducing readmission rates in general medical patients.
The risk of readmission was also increased in participants with depressive symptoms, consistent with several previous studies.3336 This effect was independent of illness type and comorbidity. Depression is increasingly recognized as an important independent predictor of a range of important outcomes in older medical patients, including posthospital functional decline,37 institutionalization and mortality.36, 38 Posthospital decline and poor self‐management might contribute to higher rehospitalization. There is some evidence that effective treatment of psychological comorbidities in medically ill patients may reduce readmissions.18, 39
Both the number and type of chronic conditions appear to be predictors of readmission in this high risk group, where there was a high baseline prevalence of chronic diseases such as heart failure, diabetes, renal impairment, and chronic lung disease which have been associated with higher readmission rates in a number of previous studies.13, 15, 30, 40 Almost all participants had one or more significant comorbid conditions in addition to their presenting complaint; single disease‐focused chronic disease management programs may not be an optimal solution in this group. Consistent with this comorbidity burden, most participants were prescribed a large number of medications. In keeping with other studies,17 about one‐third of participants reported reduced medication adherence but this was not associated with a higher readmission risk.
Like most previous studies in medical patients,10, 11, 15, 29, 30, 35, 36, 40, 41 there was no evidence of increasing readmission rates with age. Functional status impairment was not a significant predictor of readmission, probably reflecting selection of a patient subgroup with a high prevalence of disability and chronic disease. Satisfaction with social support was generally high, and not associated with readmission. This may reflect the emphasis on discharge planning and postacute social and functional support already occurring in usual care.
Measures of cognition and literacy were not associated with readmission. However, these were the items with the most missing data (see Table 2), which may have reduced our ability to detect an association. The study design excluded patients with significant cognitive or communication deficits who were unable to participate in detailed assessments. Such stringent eligibility criteria may be seen as a weakness of this study, reducing the generalizability of the findings. However, the study deliberately sampled a population of older adults suitable for a multifacetted posthospital management program, in order to inform specific intervention targets, and the eligibility criteria reflect these practical considerations. Although some previous studies have found that cognitive impairment is a predictor of readmission,10, 12 others have found no association.29, 30, 36, 40, 41
The main study weaknesses are the small sample size (reflected in the wide confidence intervals [CIs] in the multivariate analysis), and the relatively high rate of drop‐outs (25% of enrolments) for whom detailed posthospital data could not be collected. This problem reflects the age and burden of illness in the population under study. Readmission data were collected for all participants, and a similar rate of readmission was observed in patients with missing data (52% vs. 39%, P = 0.11). The heterogeneous patients mix may have concealed some important associations within individual diagnoses or other patient subgroups. This heterogeneity reflects the reality of the selected high risk subgroup, and the study deliberately avoided a disease‐specific focus for generalizability.
Conclusions
This study confirms the high rate of hospital readmission in medical patients who have already had a previous inpatient admission in the past 6 months. It shifts the emphasis from nonmodifiable disease and demographic predictors to consideration of common, nondisease specific factors which might have a plausible causative relationship with readmission and may be amenable to specific interventions. The population sampled had a high prevalence of chronic disease, and often multiple diseases. Nutritional status and depressive symptoms are emerging as important modifiers of disease course and mortality in the setting of several chronic diseases; this study also supports their potential contribution to increased hospital resource consumption in a high‐risk group. Posthospital programs which specifically address these factors in the context of optimal medical management of underlying chronic diseases have the potential to reduce hospital readmissions.
- ,,.Rehospitalizations among patients in the Medicare fee‐for‐service program.N Engl J Med.2009;360:1418–1428.
- ,,,,,.Hospital readmissions and quality of care.Med Care.1999;37(5):490–501.
- ,,.Clinical and sociodemographic risk factors for reamdission of Medicare benficiaries.Health Care Financ Rev.1988;10(1):27–36.
- .Hazards of hospitalization of the elderly.Ann Intern Med.1993;118:219–223.
- ,,,,.A case manager intervention to reduce readmissions.Arch Intern Med.1995;154(15):1721–1729.
- ,,, et al.A transitional care service for elderly chronic disease patients at risk of readmission.Aust Health Rev.2004;28(3):275–284.
- ,,,,.Self‐management programmes by lay leaders for people with chronic conditions.Cochrane Database Syst Rev.2007(4):Art No.CD005108.
- ,,,.The effectiveness of disease management programmes in reducing hospital re‐admission in older patients with heart failure: a systematic review and meta‐analysis of published reports.Eur Heart J.2004;25:1570–1595.
- ,,, et al.Discharge planning from hospital to home.Cochrane Database Syst Rev.2010(Issue 1):Art No.CD000313.
- ,,, et al.Characteristics of geriatric patients related to early and late readmissions to hospital.Aging Clin Exp Res.1998;10:339–346.
- ,,, et al.Hospital readmission among older medical patietns in Hong Kong.J R Coll Physicians Lond.1999;33(2):153–156.
- ,,, et al.Early re‐hospitalization of elderly people discharged from a geriatric ward.Aging Clin Exp Res.2006;18(1):63–69.
- ,,,,.Readmission patterns in patients with chronic obstructive pulmonary disease, chronic heart failure and diabetes mellitus: an administrative dataset analysis.Intern Med J.2005;35:296–299.
- ,,,.Case finding for patients at risk of readmission to hospital: development of algorithm to identify high risk patients.BMJ.2006;333:327–330.
- ,,,.Predicting emergency readmission for patients discharged from the medical service of a teaching hospital.J Gen Intern Med.1987;2:400–405.
- ,,, et al.A reengineered hospital discharge program to decrease hospitalization.Ann Intern Med.2009;150:178–187.
- ,,.The role of medication noncompliance and adverse drug reactions in hospitalisations in the elderly.Arch Intern Med.1990;150:841–845.
- ,,, et al.UPBEAT: the impact of a psychogeriatric intervention in VA medical centers.Med Care.2001;39(5):500–512.
- ,,,.Controlled trial of multidisciplinary care teams for acutely ill medical inpatients: enhanced multidisciplinary care.Intern Med J.2006;36:558–563.
- ,.The modified mini‐mental state (3MS) examination.J Clin Psychiatry.1987;48:314–318.
- .Development of the Cambridge Contextual Reading Test for improving the examination of premorbid verbal intelligence in older persons with dementia.Br J Clin Psychol.1998;37:229–240.
- ,.Geriatric Depression Scale (GDS): recent evidence and development of a shorter version.Clinics in Gerontology.1986;5:165–172.
- ,,,,,.Development and validation of the Geriatric Anxiety Inventory.Int Psychogeriatr.2007;19(1):103–114.
- ,,.A brief measure of social support: practical and theoretical implications.J Soc Pers Relat.1987;4:497–510.
- ,,,,.Screening for problem drinking: comparison of the CAGE and AUDIT.J Gen Intern Med.1998;13(6):379–388.
- ,.OARS methodology: a decade of experience in geriatric assessment.J Am Geriatr Soc.1985;33:607–615.
- ,.Hospital readmissions among the elderly.J Am Geriatr Soc.1985;33:595–601.
- ,,,,,.Discharge destination and repeat hospitalizations.Med Care.1997;35:756–767.
- ,,, et al.Measuring frailty in the hospitalized elderly. Concept of functional homeostasis.Am J Phys Med Rehab.1998;77(3):252–257.
- ,.Factors predicting readmission of older general medicine patients.J Gen Intern Med.1991;6(5):389–393.
- .Risk factors for early hospital readmission in a select population of geriatric rehabilitation patients: the significance of functional status.J Am Geriatr Soc.1992;40:792–798.
- ,,,.Predicting early nonelective hospital readmission in nutritionally compromised older adults.Am J Clin Nutr.1997;65:1714–1720.
- ,,,,,.Factors associated with unplanned hospital readmission among patients 65 years of age and older in a Medicare managed care plan.Am J Med.1999;107(1):13–17.
- ,,,.Depression and activities of daily living predict rehospitalisation within 6 months of discharge from geriatric rehabilitation.Rehabil Psychol.2004;49(3):219–223.
- ,,,.Depressive symptoms and negative outcomes in older hospitalized patients.Arch Intern Med.2002;162:948–949.
- ,,,.Depressive symptoms as a predictor of 6‐month outcomes and services utilization in elderly medical inpatients.Arch Intern Med.2001;161:2609–2615.
- ,,,,.Relation between symptoms of depression and health status outcomes in acutely ill hospitalized older persons.Ann Intern Med.1997;126(6):417–425.
- ,,,.Diabetes, depression and death. A randomized controlled trial of a depression treatment program for older adults based in primary care (PROSPECT).Diabetes Care.2007;30(12):3005–3010.
- ,,.Clinical implications of a reduction in psychosocial distress in cardiac prognosis in patients participating in a psychosocial intervention programme.Psychosom Med.2001;63(2):257–266.
- ,,, et al.Predictors of immediate and 6‐month outcomes in hospitalized elderly patients.J Am Geriatr Soc.1988;36:775–783.
- ,,,,.Factors predictive of outcome on admission to an acute geriatric ward.Age Ageing.1999;28:429–432.
- ,,.Rehospitalizations among patients in the Medicare fee‐for‐service program.N Engl J Med.2009;360:1418–1428.
- ,,,,,.Hospital readmissions and quality of care.Med Care.1999;37(5):490–501.
- ,,.Clinical and sociodemographic risk factors for reamdission of Medicare benficiaries.Health Care Financ Rev.1988;10(1):27–36.
- .Hazards of hospitalization of the elderly.Ann Intern Med.1993;118:219–223.
- ,,,,.A case manager intervention to reduce readmissions.Arch Intern Med.1995;154(15):1721–1729.
- ,,, et al.A transitional care service for elderly chronic disease patients at risk of readmission.Aust Health Rev.2004;28(3):275–284.
- ,,,,.Self‐management programmes by lay leaders for people with chronic conditions.Cochrane Database Syst Rev.2007(4):Art No.CD005108.
- ,,,.The effectiveness of disease management programmes in reducing hospital re‐admission in older patients with heart failure: a systematic review and meta‐analysis of published reports.Eur Heart J.2004;25:1570–1595.
- ,,, et al.Discharge planning from hospital to home.Cochrane Database Syst Rev.2010(Issue 1):Art No.CD000313.
- ,,, et al.Characteristics of geriatric patients related to early and late readmissions to hospital.Aging Clin Exp Res.1998;10:339–346.
- ,,, et al.Hospital readmission among older medical patietns in Hong Kong.J R Coll Physicians Lond.1999;33(2):153–156.
- ,,, et al.Early re‐hospitalization of elderly people discharged from a geriatric ward.Aging Clin Exp Res.2006;18(1):63–69.
- ,,,,.Readmission patterns in patients with chronic obstructive pulmonary disease, chronic heart failure and diabetes mellitus: an administrative dataset analysis.Intern Med J.2005;35:296–299.
- ,,,.Case finding for patients at risk of readmission to hospital: development of algorithm to identify high risk patients.BMJ.2006;333:327–330.
- ,,,.Predicting emergency readmission for patients discharged from the medical service of a teaching hospital.J Gen Intern Med.1987;2:400–405.
- ,,, et al.A reengineered hospital discharge program to decrease hospitalization.Ann Intern Med.2009;150:178–187.
- ,,.The role of medication noncompliance and adverse drug reactions in hospitalisations in the elderly.Arch Intern Med.1990;150:841–845.
- ,,, et al.UPBEAT: the impact of a psychogeriatric intervention in VA medical centers.Med Care.2001;39(5):500–512.
- ,,,.Controlled trial of multidisciplinary care teams for acutely ill medical inpatients: enhanced multidisciplinary care.Intern Med J.2006;36:558–563.
- ,.The modified mini‐mental state (3MS) examination.J Clin Psychiatry.1987;48:314–318.
- .Development of the Cambridge Contextual Reading Test for improving the examination of premorbid verbal intelligence in older persons with dementia.Br J Clin Psychol.1998;37:229–240.
- ,.Geriatric Depression Scale (GDS): recent evidence and development of a shorter version.Clinics in Gerontology.1986;5:165–172.
- ,,,,,.Development and validation of the Geriatric Anxiety Inventory.Int Psychogeriatr.2007;19(1):103–114.
- ,,.A brief measure of social support: practical and theoretical implications.J Soc Pers Relat.1987;4:497–510.
- ,,,,.Screening for problem drinking: comparison of the CAGE and AUDIT.J Gen Intern Med.1998;13(6):379–388.
- ,.OARS methodology: a decade of experience in geriatric assessment.J Am Geriatr Soc.1985;33:607–615.
- ,.Hospital readmissions among the elderly.J Am Geriatr Soc.1985;33:595–601.
- ,,,,,.Discharge destination and repeat hospitalizations.Med Care.1997;35:756–767.
- ,,, et al.Measuring frailty in the hospitalized elderly. Concept of functional homeostasis.Am J Phys Med Rehab.1998;77(3):252–257.
- ,.Factors predicting readmission of older general medicine patients.J Gen Intern Med.1991;6(5):389–393.
- .Risk factors for early hospital readmission in a select population of geriatric rehabilitation patients: the significance of functional status.J Am Geriatr Soc.1992;40:792–798.
- ,,,.Predicting early nonelective hospital readmission in nutritionally compromised older adults.Am J Clin Nutr.1997;65:1714–1720.
- ,,,,,.Factors associated with unplanned hospital readmission among patients 65 years of age and older in a Medicare managed care plan.Am J Med.1999;107(1):13–17.
- ,,,.Depression and activities of daily living predict rehospitalisation within 6 months of discharge from geriatric rehabilitation.Rehabil Psychol.2004;49(3):219–223.
- ,,,.Depressive symptoms and negative outcomes in older hospitalized patients.Arch Intern Med.2002;162:948–949.
- ,,,.Depressive symptoms as a predictor of 6‐month outcomes and services utilization in elderly medical inpatients.Arch Intern Med.2001;161:2609–2615.
- ,,,,.Relation between symptoms of depression and health status outcomes in acutely ill hospitalized older persons.Ann Intern Med.1997;126(6):417–425.
- ,,,.Diabetes, depression and death. A randomized controlled trial of a depression treatment program for older adults based in primary care (PROSPECT).Diabetes Care.2007;30(12):3005–3010.
- ,,.Clinical implications of a reduction in psychosocial distress in cardiac prognosis in patients participating in a psychosocial intervention programme.Psychosom Med.2001;63(2):257–266.
- ,,, et al.Predictors of immediate and 6‐month outcomes in hospitalized elderly patients.J Am Geriatr Soc.1988;36:775–783.
- ,,,,.Factors predictive of outcome on admission to an acute geriatric ward.Age Ageing.1999;28:429–432.
Copyright © 2010 Society of Hospital Medicine
Redefining Readmission Risk Factors
Within Medicare recipients, an astounding one in five medical patients (19.6%) is readmitted within 30 days, accounting for $15 billion in spending.1, 2 Amidst the current healthcare system crisis, reducing these hospital readmissions has risen to the highest priority. Reducing readmissions is the newest addition to multiple quality dashboards, both institutional and national, as a measure of the care delivered during hospitalization.3 One of the most notable of these reporting entities, Hospital Compare, now publicly reports Medicare readmission rates for a few common diagnoses.4 While Medicare already withholds payment to hospitals for readmissions within 24 hours for the same diagnosis, Medicare may soon reduce payment to hospitals with the highest rates of readmission within 30 days, a powerful incentive for hospitals to intervene. Readmissions have also reached the radar of additional stakeholders, even making its way onto Obama's budget considerations, given the potential cost savings to the system overall.5
To develop systems which reduce readmissions, one must first gain understanding of the characteristics of readmissions. A few clinical risk factors (such as age, number of prior admissions, and comorbidities) have been well defined in subgroups of general medicine inpatients.612 Likewise, interventions aiming to reduce readmissions have also focused on subgroups, excluding a large portion of hospitalized patients (for example, non‐English speakers and younger patients).1320 Other data have been derived in veterans or within non‐US populations that have inherently different payer, race, ethnicity, and primary language composition, and may not be applicable outside those settings.7, 8, 10, 11, 21 Lastly, little is known regarding risk that may be associated with operational factors, such as weekend discharge or admission source. As a result, there are few data describing the clinical, operational, and demographic factors associated with readmission in a heterogeneous population of hospitalized general medicine patientsthe patient population of most generalists in the United States.
To understand the impact of a variety of risk factors in a diverse general medicine population, we evaluated the characteristics of readmitted patients in a large urban university medical center over a 2‐year period. We hypothesized that a number of clinical, operational, and sociodemographic factors would be associated with readmission.
Methods
Sites and Subjects
Our data were collected on general medicine patients during hospitalization between June 1, 2006 and May 31, 2008, at the University of California San Francisco. The University of California, San Francisco (UCSF) Medical Center is composed of Moffitt‐Long Hospital (a 400‐bed center) and UCSF‐Mount Zion Hospital (a 200‐bed facility) located in San Francisco, CA.
Medical patients at Moffitt‐Long Hospital are admitted to 1 of 8 medical teams composed of a resident, 1 to 2 interns, and 0 to 3 medical students, supervised by an attending physician who is most often a hospitalist. At Moffitt‐Long Hospital, housestaff write all orders and provide 24‐hour coverage to inpatients. Mount Zion medical patients are cared for by 1 of 2 teams and staffed by a hospitalist on each team who is responsible for all elements of care. Both services care for common inpatient diagnoses, as well as specialty‐associated diagnoses such as cancer, pneumonia, and chronic obstructive pulmonary disease (COPD). Of note, at Moffitt‐Long Hospital, those patients with primary cardiac diagnoses are cared for by a separate team composed of housestaff and students supervised by a cardiologist.
The discharge process at both sites utilizes a multidisciplinary teamincluding physicians, case managers, nurses, pharmacists, and discharge coordinatorsworking in concert. Key components include arranging follow‐up care, faxing the discharge summary to the primary care provider, and educating the patient and caregivers, especially regarding medications. While these goals are clearly delineated, significant variability exists in how these tasks are actually accomplished. The multidisciplinary approach, components of the discharge process, and lack of a systematic approach are representative of the discharge process around the country.22
Data
Data regarding patient demographics, age, comorbidities, and insurance status were collected from administrative data systems at UCSF, reflecting the patient's status at the time of index admission. These same systems were used to collect a date‐stamped log of all medications (eg, anticoagulants) for which the patient was billed during the last 48 hours of hospitalization. Specifically, data were obtained for medications previously shown to cause adverse drug events following hospital discharge.23, 24 These medication groups include corticosteroids, anticoagulants, antibiotics, narcotics, nonsteroidal anti‐inflammatory drugs (NSAIDs), cardiovascular medications, antiepileptics, anticholinergics, antidepressants, and antidiabetics. Operational factors that we hypothesized would affect readmission risk included admission source, discharge disposition, and weekday vs. weekend discharge. Case management, social work, and pharmacy services operate with limited staffing on weekends. Likewise, resident and intern physicians are more likely to be off on a weekend day than a weekday; covering attending physicians care for about half of patients during the weekend. Data were obtained from Transition Systems International (TSI, Boston, MA) administrative databases, a cost‐accounting system that collects data abstracted from patient charts upon discharge from UCSF.
Definition of Readmission Measure
Using TSI, we detected readmission to UCSF by screening for any inpatient encounters on any service (not just medicine) within the 30 days following discharge from the general medicine service at the 2 UCSF campuses. We excluded elective readmissions, such as for scheduled chemotherapy. Patients who died at the index admission were excluded from the cohort.
Adjustment Variables
Age, gender, payer status and APR risk of mortality (3M Health Information Systems, St. Paul, MN) were collected from administrative data. The All Patient Refined (APR) risk of mortality is the all patients risk of mortality score developed by 3M which divides patients into 4 subclasses of risk based on clinical problems and comorbidities.25 We used secondary diagnosis codes in administrative data to classify comorbidities using the method of Elixhauser.26
Using the log of medication charges, we grouped high‐risk medications according to the classification scheme of Forster et al.23 and Hanlon et al.24 We then created a count representing the total number of medications administered to patients within the final 48 hours of stay.
Analysis
We first described study patients and hospitals using univariable methods. Multivariable generalized estimating equations (SAS PROC GENMOD) were used to account for clustering of patients within physicians and calculate adjusted odds ratios (ORs). As there were 2 sites within UCSF Medical Center (Moffitt‐Long and Mount Zion hospitals), we included site as a fixed effect in our model. Models were constructed using manual variable selection methods with final selection being made based on whether the covariate was associated with readmission at P < 0.05. All analyses were carried out using SAS version 9.2 (SAS Institute, Inc. Cary, NC).
Results
Baseline Characteristics
During the 2‐year accrual period, 295 attending physicians admitted 6805 unique patients for a total of 10,359 admissions. Seventeen percent of these 10,359 admissions were readmitted within 30 days. The cohort of all patients had a mean age of 59.6 years 19.5 standard deviation (SD), with 52.8% women. The mean length of stay was 5.6 days 10.4 SD. Medicare was the payer source for approximately half of the admissions. The majority of admissions (90.4%) were billed for at least 1 high risk medication, with narcotics, cardiac medications, and antibiotics being the most common. Regarding disposition, 79.5% of admissions were discharged to home; 9.1% were discharged to a skilled nursing facility (SNF).
Baseline sociodemographic, operational, and clinical characteristics for patients readmitted and not readmitted are shown in Table 1. Demographic characteristics with significant differences (P < 0.05) between readmitted and nonreadmitted groups included mean age, race, payer status, and primary language other than English. Regarding operational characteristics, readmitted patients had a higher median length of stay and were more likely to be admitted through the emergency room during their index admission. Discharge to an SNF was higher in the readmitted group versus the nonreadmitted group (9.7% vs. 9.0%). Several clinical factors were more prevalent in the readmitted group: high‐risk medications, specifically steroids, narcotics, and cardiovascular medications; high‐risk medication count of 3 or greater; and comorbidities including congestive heart failure, renal disease, cancer, anemia, and depression.
| Characteristic | Patients Readmitted (n = 1762 17.0%), n (%) | Patients Not Readmitted (n = 8597 83.0%), n (%) | P Value |
|---|---|---|---|
| |||
| Mean age (years) (SD) | 58.8 (19.3) | 59.8 (19.6) | 0.0491 |
| Female | 930 (52.8) | 4548 (52.9) | 0.9260 |
| Race* | |||
| White | 785 (44.6) | 4166 (48.8) | <0.0001 |
| Black | 442 (25.1) | 1401 (16.4) | |
| Asian | 323 (18.4) | 1726 (20.2) | |
| Other | 209 (11.9) | 1240 (14.5) | |
| Hispanic ethnicity | 140 (8.1) | 734 (8.9) | 0.2737 |
| Payer status | |||
| Medicare | 905 (51.4) | 4266 (49.6) | <0.0001 |
| Medicaid/Medi‐cal | 458 (26.0) | 1578 (18.4) | |
| Private | 370 (21.0) | 2092 (24.3) | |
| Other | 29 (1.7) | 661 (7.7) | |
| Primary language other than English | 242 (17.1) | 1394 (19.5) | 0.0359 |
| Median length of stay (days) (interquartile range) | 4 (2, 7) | 3 (2, 6) | <0.0001 |
| Admit source | |||
| Emergency room | 1506 (85.5) | 6898 (80.2) | <0.0001 |
| Outside hospital | 38 (2.2) | 271 (3.2) | |
| Direct admission/other (jail) | 218 (12.4) | 1428 (16.6) | |
| Discharge to | |||
| Home | 1461 (82.9) | 6773 (78.8) | <0.0001 |
| SNF | 170 (9.7) | 774 (9.0) | |
| Other | 131 (7.4) | 1050 (12.2) | |
| Discharged on weekend | 381 (21.6) | 1904 (22.1) | 0.6288 |
| Patient medications | |||
| Any high‐risk medication | 1679 (95.3) | 7684 (89.4) | <0.0001 |
| High‐risk medication count | |||
| 02 | 577 (32.8) | 3666 (42.6) | <0.0001 |
| 34 | 692 (39.3) | 2968 (34.5) | |
| 5 | 493 (28) | 1963 (22.8) | |
| Any corticosteroids | 399 (22.6) | 1571 (18.3) | <0.0001 |
| Anticoagulant | 120 (6.8) | 559 (6.5) | 0.6340 |
| Any antibiotic | 904 (51.3) | 4203 (48.9) | 0.0646 |
| Any narcotic | 1036 (58.8) | 4206 (48.9) | <0.0001 |
| Any NSAID | 68 (3.9) | 320 (3.7) | 0.7826 |
| Any cardiovascular med | 887 (50.3) | 3806 (44.3) | <0.0001 |
| Any antiepileptic | 93 (5.3) | 470 (5.5) | 0.7500 |
| Any anticholinergic | 47 (2.7) | 354 (4.1) | 0.0040 |
| Any antidepressant | 455 (25.8) | 1863 (25.8) | 0.0001 |
| Any antidiabetic | 198 (11.2) | 994 (11.6) | 0.6970 |
| Elixhauser comorbidities | |||
| Congestive heart failure | 219 (12.4) | 795 (9.3) | <0.0001 |
| Pulmonary circulation disease | 72 (4.1) | 178 (2.1) | <0.0001 |
| Peripheral vascular disease | 84 (4.8) | 331 (3.9) | 0.0737 |
| Hypertension | 745 (42.3) | 3741 (43.5) | 0.3411 |
| Other neurological disease | 101 (5.7) | 696 (8.1) | 0.0007 |
| Chronic pulmonary disease | 317 (18.0) | 1442 (16.8) | 0.2149 |
| Diabetes | 303 (17.2) | 1333 (15.5) | 0.0762 |
| Renal failure | 339 (19.2) | 1286 (15.0) | <0.0001 |
| Liver disease | 188 (10.7) | 774 (9.0) | 0.0281 |
| Metastatic cancer | 160 (9.1) | 530 (6.2) | <0.0001 |
| Solid tumor w/o metastases | 100 (5.7) | 277 (3.2) | <0.0001 |
| Rheumatoid arthritis/collagen vas | 81 (4.6) | 303 (3.5) | 0.0299 |
| Weight loss | 153 (8.7) | 584 (6.8) | 0.0449 |
| Deficiency anemia | 522 (29.6) | 1979 (23.0) | <0.0001 |
| Alcohol abuse | 101 (5.7) | 428 (5.0) | 0.1905 |
| Drug abuse | 148 (8.4) | 619 (7.2) | 0.0798 |
| Depression | 244 (13.9) | 963 (11.2) | 0.0016 |
| APR risk of mortality | |||
| 1 | 451 (25.6) | 3101 (36.1) | <0.0001 |
| 2 | 619 (35.1) | 2797 (32.5) | |
| 3 | 536 (30.4) | 1907 (22.2) | |
| 4 | 156 (8.9) | 792 (9.2) | |
Frequency of Readmission
The 30‐day readmission rate was 17.0% (1762 patients), with 49.7% (875 patients) of the readmissions occurring within 10 days of discharge. Of patients readmitted, the general medicine service was the readmitting team in 78.2%. A quarter of readmissions (26.2%) had the same primary diagnosis on initial and repeat admission.
Factors Associated With Readmission
Factors associated with readmission were categorized as sociodemographic, operational, and clinical. Factors associated with readmission with P < 0.05 and present in at least 5% of admissions are presented in Table 2. Of the sociodemographic factors, black race was significantly associated with readmission. Within the Medicare cohort, risk for readmission was similar for white vs. nonwhite race, with relative risk of 1.0 (95% confidence interval [CI], 0.86‐1.18). Medicaid as payer status was significantly associated in the unadjusted model, and in the adjusted model showed a trend toward readmission. Mean age was significantly different in the readmitted and nonreadmitted groups, but the difference was small (1.0 year). Moreover, when we evaluated age in 5‐year categories (ex. 65‐70, 71‐75, etc.), age was not associated with readmission. In the adjusted model, none of the operational factors were significantly associated with readmission, including discharge to SNF, weekend discharge, or admit source.
| Covariate | Unadjusted OR (95% CI) | Adjusted OR (95% CI) |
|---|---|---|
| ||
| Age | 1.00 (0.991.00) | 1.00 (0.991.00) |
| Race | ||
| White | Referent | Referent |
| Black | 1.67 (1.471.91) | 1.43 (1.241.65) |
| Asian | 0.99 (0.861.14) | 0.95 (0.821.11) |
| Other | 0.89 (0.761.06) | 0.84 (0.671.06) |
| Payer | ||
| Medicare | Referent | Referent |
| Medicaid/medical | 1.37 (1.211.55) | 1.15 (0.971.36) |
| Private | 0.83 (0.730.95) | 0.78 (0.650.95) |
| Other | 0.21 (0.140.30) | 0.23 (0.110.45) |
| Disposition | ||
| To home | Referent | Referent |
| SNF | 1.02 (0.851.21) | 0.98 (0.821.18) |
| Other | 0.58 (0.480.70) | 0.53 (0.430.66) |
| Highrisk medications | ||
| Corticosteroids | 1.31 (1.161.48) | 1.24 (1.091.42) |
| Narcotics | 1.49 (1.341.65) | 1.33 (1.161.53) |
| Anticholinergics | 0.64 (0.470.87) | 0.66 (0.480.90) |
| Comorbidities | ||
| Congestive heart failure | 1.39 (1.191.63) | 1.30 (1.091.56) |
| Neurological disorders | 0.69 (0.560.86) | 0.70 (0.570.87) |
| Renal failure | 1.35 (1.191.55) | 1.19 (1.051.36) |
| Metastatic cancer | 1.52 (1.261.83) | 1.61 (1.331.95) |
| Solid tumor w/o metastasis | 1.81 (1.432.29) | 1.95 (1.542.47) |
| Deficiency anemia | 1.41 (1.261.58) | 1.27 (1.131.44) |
| Weight loss | 1.30 (1.081.57) | 1.26 (1.091.47) |
Of the clinical factors, high‐risk medications and 6 comorbidities were associated with readmission. High‐risk medication categories associated with readmission were steroids and narcotics; anticholinergics medications were protective. The 6 comorbidities associated with readmission were congestive heart failure, renal disease, cancer (with and without metastasis), weight loss, and iron deficiency anemia. While APR risk of mortality was associated with readmission at P < 0.05, including APR in our final model did not alter which other factors were significantly associated with readmission. When site (Moffitt‐Long vs. Mount Zion Hospitals) was added to the model, the ORs for factors associated with readmission did not change appreciably (0.01).
Discussion
In this retrospective observational study of hospitalized patients, we found that readmission was common and associated with a number of risk factors that could be easily identified early in hospitalization. Nonclinical factors associated with readmission were black race and Medicaid payer status (in the unadjusted model). Clinical factors were high risk medications including steroids and narcotics; and comorbidities including congestive heart failure, renal disease, cancer, anemia, and weight loss. In contrast, other potential riskssuch as discharge on a weekend and discharge to an SNFwere not independently associated with readmission. This cohortwith a mix of clinical scenarios, payers, age, etc.represents the inherently heterogeneous population of inpatient general medicine across the country and abroad. Hospitalists provided care for over 65% of the general medicine service, again representative of the trend in US inpatient medicine.27, 28 Lastly, our cohort did not have the benefit of a systematic and consistent discharge process with interventions focused on reducing readmissions. This gap, which is common across hospitals, highlights the utility of this data in targeting quality improvement efforts.
Reducing risks for readmission requires identification of patient populations at highest risk; in those patients, one can further identify factors which are potentially modifiable via education or patient‐engagement interventions. While in the hospital, more intensive predischarge counseling and efforts to increase mobility may be most useful if targeted early and often on those at highest risk.15, 16, 29, 30 Finally, broader‐based support in the form of better home services, more access to longitudinal care, or targeted postdischarge efforts may be required.14, 31
Though current strategies focus largely on clinical risk factors, this study shows that nonclinical factors play an equally important but underappreciated role in contributing to readmission. While prior studies have shown variable results on association of black race with readmission,2, 9, 11 none have evaluated or linked Medicaid to readmission, which just missed statistical significance in this study (OR, 1.15; 95% CI, 0.971.36). Both black race and Medicaid as payer are proxies for the underlying root cause aspects leading to readmission, such as access to longitudinal care. Following this trail to the root cause will require in‐depth qualitative evaluation that includes the patient perspective as a source of data.32 For example, risk for readmission may not stem solely from being on warfarin, but in combination with not having transportation to get an international normalized ratio (INR) checked, a suboptimal understanding of how to take the medication, or not recognizing potential side effects until too late to avoid inpatient admission.
Several of the strongest associations, and perhaps most conducive to targeted interventions, were high‐risk medications at discharge. Risk related to medications and adverse drug events following discharge have been a consistent theme in readmission literature.24, 33 Our current system, which includes mandated inpatient medicine reconciliation, does not encourage discontinuation of unnecessary medications to combat polypharmacy, address affordability of medications, provide consistent medication counseling, or focus on the highest risk medications. In fact, bundled interventions which implement pharmacists to focus on these measures have been successful in decreasing readmission,14, 16, 29 but unfortunately are not yet part of the standard of care. The challenge remains transforming a mandatory policy such as medicine reconciliation into a valuable and systematic tool in the discharge process.
Two factors were surprisingly protective against readmission: neurologic diagnosis and anticholinergic medications. This first may be explained by the presence of a separate neurology service at our institution which skews our data. For example, a patient with acute stroke, who has a 20% rate of bounce‐back to a higher level of care within 30 days of discharge,34 would be admitted to the neurology service, not general medicine, and therefore would not nr part of our cohort. Regarding anticholinergics, several factors may explain this unexpected result. First, use of anticholinergics was relatively rare in our sample (2.7% in readmitted patients, 4.1% in patients not readmitted), possibly creating a false positive result. Second, Hanlon et al.24 showed only a weak association at best between anticholinergics and postdischarge adverse drug reactions (hazard ratio, 1.11; 95% CI, 0.86‐1.43). Lastly, anticholinergics include a varied group of medications, therefore diluting possible relative risk of specific medications.
While this study allows providers to identify patients at increased risk of readmission, the identified factors do not fully account for readmission risk; we did not aim to produce a risk‐prediction rule with our study. Prior readmission studies have been unable to create a tool to predict which patients will be readmitted with much success.3537 These results underscore the complexities and variability of readmission, which often lack a clear single cause and effect relationship. Given the breadth of risk factors we identified, it seems likely that more intensive interventions will require a multidisciplinary approach, one which might be costly if applied broadly. Our study does not attempt to predict who will be readmitted and who will not, but rather provides a list of risk factors which might be used to deploy resources more efficiently.
This study had several limitations. We did not capture readmissions to outside hospitals, which account for 22% to 24% of all readmissions in prior studies, and therefore have underestimated the readmission rate in our population.2, 8 However, by limiting our data to 2 hospitals within 1 institution, we were able to include more detailed patient level data, which is not accurately available in other large databases. Also, while studies of risk factors in a managed care population (such as within Medicare, the Veterans Affairs medical centers, or countries with national integrated medical records) are able to capture all readmissions, this study is the first to evaluate readmissions risk factors in a truly heterogeneous U.S. inpatient medicine population without limitation by age or payer status. Second, we did not have access to outpatient medications lists; however use of these same medications within the last 48 hours of admission is likely a reasonable proxy for outpatient use and more conducive to potential interventions (such as medication reconciliation or patient education) that could flag patients prior to discharge. Payer data was limited to only the primary payer, so patients who were dual eligible (ie, have both Medicare and Medicaid) were categorized as Medicare. Regarding sociodemographic factors, while primary language other than English was not associated with readmission, language data was missing in 17.4% of admissions, thereby limiting our ability to evaluate this factor. Our data did not include access to outpatient or primary care, and therefore we were unable to evaluate access to postdischarge follow‐up care as a risk factor for readmission. Lastly, while this study did not include outpatient deaths, we did exclude patients who died in the hospital.
Conclusions
Readmission is common among general medicine patients, with approximately 1 in 5 patients being readmitted within 30 days. While the identified associated factors do not account for all the potential reasons for readmission, our study suggests a spectrum of risk factors which might be used to target more intensive multidisciplinary interventions. Specifically, the nonclinical factors of race and payer status merit further in depth research incorporating the patient experience to truly determine causation of readmission. Hospitalists, who are at nexus of the discharge process and uniquely invested in quality inpatient care, are ideally positioned to lead efforts to reduce readmissions. How to use our study's results to develop and implement effective interventions to reduce readmissions remains a subject for future studies.
- A path to bundled payment around a rehospitalization.: Medicare payment Advisory Commission; June2005.
- ,,.Rehospitalizations among patients in the Medicare fee‐for‐service program.N Engl J Med.2009;360(14):1418–1428.
- University HealthSystem Consortium. Available at: https://www.uhc.edu. Accessed May2010.
- U.S. Department of Health 15(5):599–606.
- ,,,.Development and validation of a model for predicting emergency admissions over the next year (PEONY): a UK historical cohort study.Arch Intern Med.2008;168(13):1416–1422.
- ,,, et al.Incidence and main factors associated with early unplanned hospital readmission among French medical inpatients aged 75 and over admitted through emergency units.Age Ageing.2008;37(4):416–422.
- ,,,,,.Factors associated with unplanned hospital readmission among patients 65 years of age and older in a Medicare managed care plan.Am J Med.1999;107(1):13–17.
- ,,.Risk factors for early unplanned hospital readmission in the elderly.J Gen Intern Med.1991;6(3):223–228.
- ,,, et al.Predicting non‐elective hospital readmissions: a multi‐site study. Department of Veterans Affairs Cooperative Study Group on Primary Care and Readmissions.J Clin Epidemiol.2000;53(11):1113–1118.
- ,.Effect of gender, ethnicity, pulmonary disease, and symptom stability on rehospitalization in patients with heart failure.Am J Cardiol.2007;100(7):1139–1144.
- ,,,.The care transitions intervention: results of a randomized controlled trial.Arch Intern Med.2006;166(17):1822–1828.
- ,,,.The impact of follow‐up telephone calls to patients after hospitalization.Am J Med.2001;111(9B):26S–30S.
- ,,, et al.A reengineered hospital discharge program to decrease rehospitalization: a randomized trial.Ann Intern Med.2009;150(3):178–187.
- ,,, et al.Reduction of 30‐day postdischarge hospital readmission or emergency department (ED) visit rates in high‐risk elderly medical patients through delivery of a targeted care bundle.J Hosp Med.2009;4(4):211–218.
- ,,,,,.Comprehensive discharge planning with postdischarge support for older patients with congestive heart failure: a meta‐analysis.JAMA.2004;291(11):1358–1367.
- ,.Home‐based intervention in congestive heart failure: long‐term implications on readmission and survival.Circulation.2002;105(24):2861–2866.
- ,,,,,.Effect of a standardized nurse case‐management telephone intervention on resource use in patients with chronic heart failure.Arch Intern Med.2002;162(6):705–712.
- ,,,.The impact of follow‐up physician visits on emergency readmissions for patients with asthma and chronic obstructive pulmonary disease: a population‐based study.Am J Med.2002;112(2):120–125.
- ,.Factors predicting readmission of older general medicine patients.J Gen Intern Med.1991;6(5):389–393.
- BOOSTing Care Transitions. Available at: http://www.hospitalmedicine.org/ResourceRoomRedesign/RR_CareTransitions/CT_Home.cfm. Accessed May2010.
- ,,,,.Adverse drug events occurring following hospital discharge.J Gen Intern Med.2005;20(4):317–323.
- ,,, et al.Incidence and predictors of all and preventable adverse drug reactions in frail elderly persons after hospital stay.J Gerontol A Biol Sci Med Sci.2006;61(5):511–515.
- . Development of the 3M™ All Patient Refined Diagnosis Related Groups (APR DRGs). Available at: http://www.ahrq.gov/qual/mortality/Hughes.htm. Accessed May2010.
- ,,.Volume thresholds and hospital characteristics in the United States.Health Aff (Millwood).2003;22(2):167–177.
- ,,,.The status of hospital medicine groups in the United States.J Hosp Med.2006;1(2):75–80.
- ,,,.Growth in the care of older patients by hospitalists in the United States.N Engl J Med.2009;360(11):1102–1112.
- ,,, et al.Role of pharmacist counseling in preventing adverse drug events after hospitalization.Arch Intern Med.2006;166(5):565–571.
- ,,, et al.Effects of a multicomponent intervention on functional outcomes and process of care in hospitalized older patients: a randomized controlled trial of Acute Care for Elders (ACE) in a community hospital.J Am Geriatr Soc.2000;48(12):1572–1581.
- ,,,,,.Telehome monitoring in patients with cardiac disease who are at high risk of readmission.Heart Lung.2008;37(1):36–45.
- ,,.Understanding rehospitalization risk: can hospital discharge be modified to reduce recurrent hospitalization?J Hosp Med.2007;2(5):297–304.
- ,,,,.The incidence and severity of adverse events affecting patients after discharge from the hospital.Ann Intern Med.2003;138(3):161–167.
- ,,,.Bouncing back: patterns and predictors of complicated transitions 30 days after hospitalization for acute ischemic stroke.J Am Geriatr Soc.2007;55(3):365–373.
- ,,,.Case finding for patients at risk of readmission to hospital: development of algorithm to identify high risk patients.BMJ.2006;333(7563):327.
- ,,,,,.Screening elders for risk of hospital admission.J Am Geriatr Soc.1993;41(8):811–817.
- ,,,.Using routine inpatient data to identify patients at risk of hospital readmission.BMC Health Serv Res.2009;9:96.
Within Medicare recipients, an astounding one in five medical patients (19.6%) is readmitted within 30 days, accounting for $15 billion in spending.1, 2 Amidst the current healthcare system crisis, reducing these hospital readmissions has risen to the highest priority. Reducing readmissions is the newest addition to multiple quality dashboards, both institutional and national, as a measure of the care delivered during hospitalization.3 One of the most notable of these reporting entities, Hospital Compare, now publicly reports Medicare readmission rates for a few common diagnoses.4 While Medicare already withholds payment to hospitals for readmissions within 24 hours for the same diagnosis, Medicare may soon reduce payment to hospitals with the highest rates of readmission within 30 days, a powerful incentive for hospitals to intervene. Readmissions have also reached the radar of additional stakeholders, even making its way onto Obama's budget considerations, given the potential cost savings to the system overall.5
To develop systems which reduce readmissions, one must first gain understanding of the characteristics of readmissions. A few clinical risk factors (such as age, number of prior admissions, and comorbidities) have been well defined in subgroups of general medicine inpatients.612 Likewise, interventions aiming to reduce readmissions have also focused on subgroups, excluding a large portion of hospitalized patients (for example, non‐English speakers and younger patients).1320 Other data have been derived in veterans or within non‐US populations that have inherently different payer, race, ethnicity, and primary language composition, and may not be applicable outside those settings.7, 8, 10, 11, 21 Lastly, little is known regarding risk that may be associated with operational factors, such as weekend discharge or admission source. As a result, there are few data describing the clinical, operational, and demographic factors associated with readmission in a heterogeneous population of hospitalized general medicine patientsthe patient population of most generalists in the United States.
To understand the impact of a variety of risk factors in a diverse general medicine population, we evaluated the characteristics of readmitted patients in a large urban university medical center over a 2‐year period. We hypothesized that a number of clinical, operational, and sociodemographic factors would be associated with readmission.
Methods
Sites and Subjects
Our data were collected on general medicine patients during hospitalization between June 1, 2006 and May 31, 2008, at the University of California San Francisco. The University of California, San Francisco (UCSF) Medical Center is composed of Moffitt‐Long Hospital (a 400‐bed center) and UCSF‐Mount Zion Hospital (a 200‐bed facility) located in San Francisco, CA.
Medical patients at Moffitt‐Long Hospital are admitted to 1 of 8 medical teams composed of a resident, 1 to 2 interns, and 0 to 3 medical students, supervised by an attending physician who is most often a hospitalist. At Moffitt‐Long Hospital, housestaff write all orders and provide 24‐hour coverage to inpatients. Mount Zion medical patients are cared for by 1 of 2 teams and staffed by a hospitalist on each team who is responsible for all elements of care. Both services care for common inpatient diagnoses, as well as specialty‐associated diagnoses such as cancer, pneumonia, and chronic obstructive pulmonary disease (COPD). Of note, at Moffitt‐Long Hospital, those patients with primary cardiac diagnoses are cared for by a separate team composed of housestaff and students supervised by a cardiologist.
The discharge process at both sites utilizes a multidisciplinary teamincluding physicians, case managers, nurses, pharmacists, and discharge coordinatorsworking in concert. Key components include arranging follow‐up care, faxing the discharge summary to the primary care provider, and educating the patient and caregivers, especially regarding medications. While these goals are clearly delineated, significant variability exists in how these tasks are actually accomplished. The multidisciplinary approach, components of the discharge process, and lack of a systematic approach are representative of the discharge process around the country.22
Data
Data regarding patient demographics, age, comorbidities, and insurance status were collected from administrative data systems at UCSF, reflecting the patient's status at the time of index admission. These same systems were used to collect a date‐stamped log of all medications (eg, anticoagulants) for which the patient was billed during the last 48 hours of hospitalization. Specifically, data were obtained for medications previously shown to cause adverse drug events following hospital discharge.23, 24 These medication groups include corticosteroids, anticoagulants, antibiotics, narcotics, nonsteroidal anti‐inflammatory drugs (NSAIDs), cardiovascular medications, antiepileptics, anticholinergics, antidepressants, and antidiabetics. Operational factors that we hypothesized would affect readmission risk included admission source, discharge disposition, and weekday vs. weekend discharge. Case management, social work, and pharmacy services operate with limited staffing on weekends. Likewise, resident and intern physicians are more likely to be off on a weekend day than a weekday; covering attending physicians care for about half of patients during the weekend. Data were obtained from Transition Systems International (TSI, Boston, MA) administrative databases, a cost‐accounting system that collects data abstracted from patient charts upon discharge from UCSF.
Definition of Readmission Measure
Using TSI, we detected readmission to UCSF by screening for any inpatient encounters on any service (not just medicine) within the 30 days following discharge from the general medicine service at the 2 UCSF campuses. We excluded elective readmissions, such as for scheduled chemotherapy. Patients who died at the index admission were excluded from the cohort.
Adjustment Variables
Age, gender, payer status and APR risk of mortality (3M Health Information Systems, St. Paul, MN) were collected from administrative data. The All Patient Refined (APR) risk of mortality is the all patients risk of mortality score developed by 3M which divides patients into 4 subclasses of risk based on clinical problems and comorbidities.25 We used secondary diagnosis codes in administrative data to classify comorbidities using the method of Elixhauser.26
Using the log of medication charges, we grouped high‐risk medications according to the classification scheme of Forster et al.23 and Hanlon et al.24 We then created a count representing the total number of medications administered to patients within the final 48 hours of stay.
Analysis
We first described study patients and hospitals using univariable methods. Multivariable generalized estimating equations (SAS PROC GENMOD) were used to account for clustering of patients within physicians and calculate adjusted odds ratios (ORs). As there were 2 sites within UCSF Medical Center (Moffitt‐Long and Mount Zion hospitals), we included site as a fixed effect in our model. Models were constructed using manual variable selection methods with final selection being made based on whether the covariate was associated with readmission at P < 0.05. All analyses were carried out using SAS version 9.2 (SAS Institute, Inc. Cary, NC).
Results
Baseline Characteristics
During the 2‐year accrual period, 295 attending physicians admitted 6805 unique patients for a total of 10,359 admissions. Seventeen percent of these 10,359 admissions were readmitted within 30 days. The cohort of all patients had a mean age of 59.6 years 19.5 standard deviation (SD), with 52.8% women. The mean length of stay was 5.6 days 10.4 SD. Medicare was the payer source for approximately half of the admissions. The majority of admissions (90.4%) were billed for at least 1 high risk medication, with narcotics, cardiac medications, and antibiotics being the most common. Regarding disposition, 79.5% of admissions were discharged to home; 9.1% were discharged to a skilled nursing facility (SNF).
Baseline sociodemographic, operational, and clinical characteristics for patients readmitted and not readmitted are shown in Table 1. Demographic characteristics with significant differences (P < 0.05) between readmitted and nonreadmitted groups included mean age, race, payer status, and primary language other than English. Regarding operational characteristics, readmitted patients had a higher median length of stay and were more likely to be admitted through the emergency room during their index admission. Discharge to an SNF was higher in the readmitted group versus the nonreadmitted group (9.7% vs. 9.0%). Several clinical factors were more prevalent in the readmitted group: high‐risk medications, specifically steroids, narcotics, and cardiovascular medications; high‐risk medication count of 3 or greater; and comorbidities including congestive heart failure, renal disease, cancer, anemia, and depression.
| Characteristic | Patients Readmitted (n = 1762 17.0%), n (%) | Patients Not Readmitted (n = 8597 83.0%), n (%) | P Value |
|---|---|---|---|
| |||
| Mean age (years) (SD) | 58.8 (19.3) | 59.8 (19.6) | 0.0491 |
| Female | 930 (52.8) | 4548 (52.9) | 0.9260 |
| Race* | |||
| White | 785 (44.6) | 4166 (48.8) | <0.0001 |
| Black | 442 (25.1) | 1401 (16.4) | |
| Asian | 323 (18.4) | 1726 (20.2) | |
| Other | 209 (11.9) | 1240 (14.5) | |
| Hispanic ethnicity | 140 (8.1) | 734 (8.9) | 0.2737 |
| Payer status | |||
| Medicare | 905 (51.4) | 4266 (49.6) | <0.0001 |
| Medicaid/Medi‐cal | 458 (26.0) | 1578 (18.4) | |
| Private | 370 (21.0) | 2092 (24.3) | |
| Other | 29 (1.7) | 661 (7.7) | |
| Primary language other than English | 242 (17.1) | 1394 (19.5) | 0.0359 |
| Median length of stay (days) (interquartile range) | 4 (2, 7) | 3 (2, 6) | <0.0001 |
| Admit source | |||
| Emergency room | 1506 (85.5) | 6898 (80.2) | <0.0001 |
| Outside hospital | 38 (2.2) | 271 (3.2) | |
| Direct admission/other (jail) | 218 (12.4) | 1428 (16.6) | |
| Discharge to | |||
| Home | 1461 (82.9) | 6773 (78.8) | <0.0001 |
| SNF | 170 (9.7) | 774 (9.0) | |
| Other | 131 (7.4) | 1050 (12.2) | |
| Discharged on weekend | 381 (21.6) | 1904 (22.1) | 0.6288 |
| Patient medications | |||
| Any high‐risk medication | 1679 (95.3) | 7684 (89.4) | <0.0001 |
| High‐risk medication count | |||
| 02 | 577 (32.8) | 3666 (42.6) | <0.0001 |
| 34 | 692 (39.3) | 2968 (34.5) | |
| 5 | 493 (28) | 1963 (22.8) | |
| Any corticosteroids | 399 (22.6) | 1571 (18.3) | <0.0001 |
| Anticoagulant | 120 (6.8) | 559 (6.5) | 0.6340 |
| Any antibiotic | 904 (51.3) | 4203 (48.9) | 0.0646 |
| Any narcotic | 1036 (58.8) | 4206 (48.9) | <0.0001 |
| Any NSAID | 68 (3.9) | 320 (3.7) | 0.7826 |
| Any cardiovascular med | 887 (50.3) | 3806 (44.3) | <0.0001 |
| Any antiepileptic | 93 (5.3) | 470 (5.5) | 0.7500 |
| Any anticholinergic | 47 (2.7) | 354 (4.1) | 0.0040 |
| Any antidepressant | 455 (25.8) | 1863 (25.8) | 0.0001 |
| Any antidiabetic | 198 (11.2) | 994 (11.6) | 0.6970 |
| Elixhauser comorbidities | |||
| Congestive heart failure | 219 (12.4) | 795 (9.3) | <0.0001 |
| Pulmonary circulation disease | 72 (4.1) | 178 (2.1) | <0.0001 |
| Peripheral vascular disease | 84 (4.8) | 331 (3.9) | 0.0737 |
| Hypertension | 745 (42.3) | 3741 (43.5) | 0.3411 |
| Other neurological disease | 101 (5.7) | 696 (8.1) | 0.0007 |
| Chronic pulmonary disease | 317 (18.0) | 1442 (16.8) | 0.2149 |
| Diabetes | 303 (17.2) | 1333 (15.5) | 0.0762 |
| Renal failure | 339 (19.2) | 1286 (15.0) | <0.0001 |
| Liver disease | 188 (10.7) | 774 (9.0) | 0.0281 |
| Metastatic cancer | 160 (9.1) | 530 (6.2) | <0.0001 |
| Solid tumor w/o metastases | 100 (5.7) | 277 (3.2) | <0.0001 |
| Rheumatoid arthritis/collagen vas | 81 (4.6) | 303 (3.5) | 0.0299 |
| Weight loss | 153 (8.7) | 584 (6.8) | 0.0449 |
| Deficiency anemia | 522 (29.6) | 1979 (23.0) | <0.0001 |
| Alcohol abuse | 101 (5.7) | 428 (5.0) | 0.1905 |
| Drug abuse | 148 (8.4) | 619 (7.2) | 0.0798 |
| Depression | 244 (13.9) | 963 (11.2) | 0.0016 |
| APR risk of mortality | |||
| 1 | 451 (25.6) | 3101 (36.1) | <0.0001 |
| 2 | 619 (35.1) | 2797 (32.5) | |
| 3 | 536 (30.4) | 1907 (22.2) | |
| 4 | 156 (8.9) | 792 (9.2) | |
Frequency of Readmission
The 30‐day readmission rate was 17.0% (1762 patients), with 49.7% (875 patients) of the readmissions occurring within 10 days of discharge. Of patients readmitted, the general medicine service was the readmitting team in 78.2%. A quarter of readmissions (26.2%) had the same primary diagnosis on initial and repeat admission.
Factors Associated With Readmission
Factors associated with readmission were categorized as sociodemographic, operational, and clinical. Factors associated with readmission with P < 0.05 and present in at least 5% of admissions are presented in Table 2. Of the sociodemographic factors, black race was significantly associated with readmission. Within the Medicare cohort, risk for readmission was similar for white vs. nonwhite race, with relative risk of 1.0 (95% confidence interval [CI], 0.86‐1.18). Medicaid as payer status was significantly associated in the unadjusted model, and in the adjusted model showed a trend toward readmission. Mean age was significantly different in the readmitted and nonreadmitted groups, but the difference was small (1.0 year). Moreover, when we evaluated age in 5‐year categories (ex. 65‐70, 71‐75, etc.), age was not associated with readmission. In the adjusted model, none of the operational factors were significantly associated with readmission, including discharge to SNF, weekend discharge, or admit source.
| Covariate | Unadjusted OR (95% CI) | Adjusted OR (95% CI) |
|---|---|---|
| ||
| Age | 1.00 (0.991.00) | 1.00 (0.991.00) |
| Race | ||
| White | Referent | Referent |
| Black | 1.67 (1.471.91) | 1.43 (1.241.65) |
| Asian | 0.99 (0.861.14) | 0.95 (0.821.11) |
| Other | 0.89 (0.761.06) | 0.84 (0.671.06) |
| Payer | ||
| Medicare | Referent | Referent |
| Medicaid/medical | 1.37 (1.211.55) | 1.15 (0.971.36) |
| Private | 0.83 (0.730.95) | 0.78 (0.650.95) |
| Other | 0.21 (0.140.30) | 0.23 (0.110.45) |
| Disposition | ||
| To home | Referent | Referent |
| SNF | 1.02 (0.851.21) | 0.98 (0.821.18) |
| Other | 0.58 (0.480.70) | 0.53 (0.430.66) |
| Highrisk medications | ||
| Corticosteroids | 1.31 (1.161.48) | 1.24 (1.091.42) |
| Narcotics | 1.49 (1.341.65) | 1.33 (1.161.53) |
| Anticholinergics | 0.64 (0.470.87) | 0.66 (0.480.90) |
| Comorbidities | ||
| Congestive heart failure | 1.39 (1.191.63) | 1.30 (1.091.56) |
| Neurological disorders | 0.69 (0.560.86) | 0.70 (0.570.87) |
| Renal failure | 1.35 (1.191.55) | 1.19 (1.051.36) |
| Metastatic cancer | 1.52 (1.261.83) | 1.61 (1.331.95) |
| Solid tumor w/o metastasis | 1.81 (1.432.29) | 1.95 (1.542.47) |
| Deficiency anemia | 1.41 (1.261.58) | 1.27 (1.131.44) |
| Weight loss | 1.30 (1.081.57) | 1.26 (1.091.47) |
Of the clinical factors, high‐risk medications and 6 comorbidities were associated with readmission. High‐risk medication categories associated with readmission were steroids and narcotics; anticholinergics medications were protective. The 6 comorbidities associated with readmission were congestive heart failure, renal disease, cancer (with and without metastasis), weight loss, and iron deficiency anemia. While APR risk of mortality was associated with readmission at P < 0.05, including APR in our final model did not alter which other factors were significantly associated with readmission. When site (Moffitt‐Long vs. Mount Zion Hospitals) was added to the model, the ORs for factors associated with readmission did not change appreciably (0.01).
Discussion
In this retrospective observational study of hospitalized patients, we found that readmission was common and associated with a number of risk factors that could be easily identified early in hospitalization. Nonclinical factors associated with readmission were black race and Medicaid payer status (in the unadjusted model). Clinical factors were high risk medications including steroids and narcotics; and comorbidities including congestive heart failure, renal disease, cancer, anemia, and weight loss. In contrast, other potential riskssuch as discharge on a weekend and discharge to an SNFwere not independently associated with readmission. This cohortwith a mix of clinical scenarios, payers, age, etc.represents the inherently heterogeneous population of inpatient general medicine across the country and abroad. Hospitalists provided care for over 65% of the general medicine service, again representative of the trend in US inpatient medicine.27, 28 Lastly, our cohort did not have the benefit of a systematic and consistent discharge process with interventions focused on reducing readmissions. This gap, which is common across hospitals, highlights the utility of this data in targeting quality improvement efforts.
Reducing risks for readmission requires identification of patient populations at highest risk; in those patients, one can further identify factors which are potentially modifiable via education or patient‐engagement interventions. While in the hospital, more intensive predischarge counseling and efforts to increase mobility may be most useful if targeted early and often on those at highest risk.15, 16, 29, 30 Finally, broader‐based support in the form of better home services, more access to longitudinal care, or targeted postdischarge efforts may be required.14, 31
Though current strategies focus largely on clinical risk factors, this study shows that nonclinical factors play an equally important but underappreciated role in contributing to readmission. While prior studies have shown variable results on association of black race with readmission,2, 9, 11 none have evaluated or linked Medicaid to readmission, which just missed statistical significance in this study (OR, 1.15; 95% CI, 0.971.36). Both black race and Medicaid as payer are proxies for the underlying root cause aspects leading to readmission, such as access to longitudinal care. Following this trail to the root cause will require in‐depth qualitative evaluation that includes the patient perspective as a source of data.32 For example, risk for readmission may not stem solely from being on warfarin, but in combination with not having transportation to get an international normalized ratio (INR) checked, a suboptimal understanding of how to take the medication, or not recognizing potential side effects until too late to avoid inpatient admission.
Several of the strongest associations, and perhaps most conducive to targeted interventions, were high‐risk medications at discharge. Risk related to medications and adverse drug events following discharge have been a consistent theme in readmission literature.24, 33 Our current system, which includes mandated inpatient medicine reconciliation, does not encourage discontinuation of unnecessary medications to combat polypharmacy, address affordability of medications, provide consistent medication counseling, or focus on the highest risk medications. In fact, bundled interventions which implement pharmacists to focus on these measures have been successful in decreasing readmission,14, 16, 29 but unfortunately are not yet part of the standard of care. The challenge remains transforming a mandatory policy such as medicine reconciliation into a valuable and systematic tool in the discharge process.
Two factors were surprisingly protective against readmission: neurologic diagnosis and anticholinergic medications. This first may be explained by the presence of a separate neurology service at our institution which skews our data. For example, a patient with acute stroke, who has a 20% rate of bounce‐back to a higher level of care within 30 days of discharge,34 would be admitted to the neurology service, not general medicine, and therefore would not nr part of our cohort. Regarding anticholinergics, several factors may explain this unexpected result. First, use of anticholinergics was relatively rare in our sample (2.7% in readmitted patients, 4.1% in patients not readmitted), possibly creating a false positive result. Second, Hanlon et al.24 showed only a weak association at best between anticholinergics and postdischarge adverse drug reactions (hazard ratio, 1.11; 95% CI, 0.86‐1.43). Lastly, anticholinergics include a varied group of medications, therefore diluting possible relative risk of specific medications.
While this study allows providers to identify patients at increased risk of readmission, the identified factors do not fully account for readmission risk; we did not aim to produce a risk‐prediction rule with our study. Prior readmission studies have been unable to create a tool to predict which patients will be readmitted with much success.3537 These results underscore the complexities and variability of readmission, which often lack a clear single cause and effect relationship. Given the breadth of risk factors we identified, it seems likely that more intensive interventions will require a multidisciplinary approach, one which might be costly if applied broadly. Our study does not attempt to predict who will be readmitted and who will not, but rather provides a list of risk factors which might be used to deploy resources more efficiently.
This study had several limitations. We did not capture readmissions to outside hospitals, which account for 22% to 24% of all readmissions in prior studies, and therefore have underestimated the readmission rate in our population.2, 8 However, by limiting our data to 2 hospitals within 1 institution, we were able to include more detailed patient level data, which is not accurately available in other large databases. Also, while studies of risk factors in a managed care population (such as within Medicare, the Veterans Affairs medical centers, or countries with national integrated medical records) are able to capture all readmissions, this study is the first to evaluate readmissions risk factors in a truly heterogeneous U.S. inpatient medicine population without limitation by age or payer status. Second, we did not have access to outpatient medications lists; however use of these same medications within the last 48 hours of admission is likely a reasonable proxy for outpatient use and more conducive to potential interventions (such as medication reconciliation or patient education) that could flag patients prior to discharge. Payer data was limited to only the primary payer, so patients who were dual eligible (ie, have both Medicare and Medicaid) were categorized as Medicare. Regarding sociodemographic factors, while primary language other than English was not associated with readmission, language data was missing in 17.4% of admissions, thereby limiting our ability to evaluate this factor. Our data did not include access to outpatient or primary care, and therefore we were unable to evaluate access to postdischarge follow‐up care as a risk factor for readmission. Lastly, while this study did not include outpatient deaths, we did exclude patients who died in the hospital.
Conclusions
Readmission is common among general medicine patients, with approximately 1 in 5 patients being readmitted within 30 days. While the identified associated factors do not account for all the potential reasons for readmission, our study suggests a spectrum of risk factors which might be used to target more intensive multidisciplinary interventions. Specifically, the nonclinical factors of race and payer status merit further in depth research incorporating the patient experience to truly determine causation of readmission. Hospitalists, who are at nexus of the discharge process and uniquely invested in quality inpatient care, are ideally positioned to lead efforts to reduce readmissions. How to use our study's results to develop and implement effective interventions to reduce readmissions remains a subject for future studies.
Within Medicare recipients, an astounding one in five medical patients (19.6%) is readmitted within 30 days, accounting for $15 billion in spending.1, 2 Amidst the current healthcare system crisis, reducing these hospital readmissions has risen to the highest priority. Reducing readmissions is the newest addition to multiple quality dashboards, both institutional and national, as a measure of the care delivered during hospitalization.3 One of the most notable of these reporting entities, Hospital Compare, now publicly reports Medicare readmission rates for a few common diagnoses.4 While Medicare already withholds payment to hospitals for readmissions within 24 hours for the same diagnosis, Medicare may soon reduce payment to hospitals with the highest rates of readmission within 30 days, a powerful incentive for hospitals to intervene. Readmissions have also reached the radar of additional stakeholders, even making its way onto Obama's budget considerations, given the potential cost savings to the system overall.5
To develop systems which reduce readmissions, one must first gain understanding of the characteristics of readmissions. A few clinical risk factors (such as age, number of prior admissions, and comorbidities) have been well defined in subgroups of general medicine inpatients.612 Likewise, interventions aiming to reduce readmissions have also focused on subgroups, excluding a large portion of hospitalized patients (for example, non‐English speakers and younger patients).1320 Other data have been derived in veterans or within non‐US populations that have inherently different payer, race, ethnicity, and primary language composition, and may not be applicable outside those settings.7, 8, 10, 11, 21 Lastly, little is known regarding risk that may be associated with operational factors, such as weekend discharge or admission source. As a result, there are few data describing the clinical, operational, and demographic factors associated with readmission in a heterogeneous population of hospitalized general medicine patientsthe patient population of most generalists in the United States.
To understand the impact of a variety of risk factors in a diverse general medicine population, we evaluated the characteristics of readmitted patients in a large urban university medical center over a 2‐year period. We hypothesized that a number of clinical, operational, and sociodemographic factors would be associated with readmission.
Methods
Sites and Subjects
Our data were collected on general medicine patients during hospitalization between June 1, 2006 and May 31, 2008, at the University of California San Francisco. The University of California, San Francisco (UCSF) Medical Center is composed of Moffitt‐Long Hospital (a 400‐bed center) and UCSF‐Mount Zion Hospital (a 200‐bed facility) located in San Francisco, CA.
Medical patients at Moffitt‐Long Hospital are admitted to 1 of 8 medical teams composed of a resident, 1 to 2 interns, and 0 to 3 medical students, supervised by an attending physician who is most often a hospitalist. At Moffitt‐Long Hospital, housestaff write all orders and provide 24‐hour coverage to inpatients. Mount Zion medical patients are cared for by 1 of 2 teams and staffed by a hospitalist on each team who is responsible for all elements of care. Both services care for common inpatient diagnoses, as well as specialty‐associated diagnoses such as cancer, pneumonia, and chronic obstructive pulmonary disease (COPD). Of note, at Moffitt‐Long Hospital, those patients with primary cardiac diagnoses are cared for by a separate team composed of housestaff and students supervised by a cardiologist.
The discharge process at both sites utilizes a multidisciplinary teamincluding physicians, case managers, nurses, pharmacists, and discharge coordinatorsworking in concert. Key components include arranging follow‐up care, faxing the discharge summary to the primary care provider, and educating the patient and caregivers, especially regarding medications. While these goals are clearly delineated, significant variability exists in how these tasks are actually accomplished. The multidisciplinary approach, components of the discharge process, and lack of a systematic approach are representative of the discharge process around the country.22
Data
Data regarding patient demographics, age, comorbidities, and insurance status were collected from administrative data systems at UCSF, reflecting the patient's status at the time of index admission. These same systems were used to collect a date‐stamped log of all medications (eg, anticoagulants) for which the patient was billed during the last 48 hours of hospitalization. Specifically, data were obtained for medications previously shown to cause adverse drug events following hospital discharge.23, 24 These medication groups include corticosteroids, anticoagulants, antibiotics, narcotics, nonsteroidal anti‐inflammatory drugs (NSAIDs), cardiovascular medications, antiepileptics, anticholinergics, antidepressants, and antidiabetics. Operational factors that we hypothesized would affect readmission risk included admission source, discharge disposition, and weekday vs. weekend discharge. Case management, social work, and pharmacy services operate with limited staffing on weekends. Likewise, resident and intern physicians are more likely to be off on a weekend day than a weekday; covering attending physicians care for about half of patients during the weekend. Data were obtained from Transition Systems International (TSI, Boston, MA) administrative databases, a cost‐accounting system that collects data abstracted from patient charts upon discharge from UCSF.
Definition of Readmission Measure
Using TSI, we detected readmission to UCSF by screening for any inpatient encounters on any service (not just medicine) within the 30 days following discharge from the general medicine service at the 2 UCSF campuses. We excluded elective readmissions, such as for scheduled chemotherapy. Patients who died at the index admission were excluded from the cohort.
Adjustment Variables
Age, gender, payer status and APR risk of mortality (3M Health Information Systems, St. Paul, MN) were collected from administrative data. The All Patient Refined (APR) risk of mortality is the all patients risk of mortality score developed by 3M which divides patients into 4 subclasses of risk based on clinical problems and comorbidities.25 We used secondary diagnosis codes in administrative data to classify comorbidities using the method of Elixhauser.26
Using the log of medication charges, we grouped high‐risk medications according to the classification scheme of Forster et al.23 and Hanlon et al.24 We then created a count representing the total number of medications administered to patients within the final 48 hours of stay.
Analysis
We first described study patients and hospitals using univariable methods. Multivariable generalized estimating equations (SAS PROC GENMOD) were used to account for clustering of patients within physicians and calculate adjusted odds ratios (ORs). As there were 2 sites within UCSF Medical Center (Moffitt‐Long and Mount Zion hospitals), we included site as a fixed effect in our model. Models were constructed using manual variable selection methods with final selection being made based on whether the covariate was associated with readmission at P < 0.05. All analyses were carried out using SAS version 9.2 (SAS Institute, Inc. Cary, NC).
Results
Baseline Characteristics
During the 2‐year accrual period, 295 attending physicians admitted 6805 unique patients for a total of 10,359 admissions. Seventeen percent of these 10,359 admissions were readmitted within 30 days. The cohort of all patients had a mean age of 59.6 years 19.5 standard deviation (SD), with 52.8% women. The mean length of stay was 5.6 days 10.4 SD. Medicare was the payer source for approximately half of the admissions. The majority of admissions (90.4%) were billed for at least 1 high risk medication, with narcotics, cardiac medications, and antibiotics being the most common. Regarding disposition, 79.5% of admissions were discharged to home; 9.1% were discharged to a skilled nursing facility (SNF).
Baseline sociodemographic, operational, and clinical characteristics for patients readmitted and not readmitted are shown in Table 1. Demographic characteristics with significant differences (P < 0.05) between readmitted and nonreadmitted groups included mean age, race, payer status, and primary language other than English. Regarding operational characteristics, readmitted patients had a higher median length of stay and were more likely to be admitted through the emergency room during their index admission. Discharge to an SNF was higher in the readmitted group versus the nonreadmitted group (9.7% vs. 9.0%). Several clinical factors were more prevalent in the readmitted group: high‐risk medications, specifically steroids, narcotics, and cardiovascular medications; high‐risk medication count of 3 or greater; and comorbidities including congestive heart failure, renal disease, cancer, anemia, and depression.
| Characteristic | Patients Readmitted (n = 1762 17.0%), n (%) | Patients Not Readmitted (n = 8597 83.0%), n (%) | P Value |
|---|---|---|---|
| |||
| Mean age (years) (SD) | 58.8 (19.3) | 59.8 (19.6) | 0.0491 |
| Female | 930 (52.8) | 4548 (52.9) | 0.9260 |
| Race* | |||
| White | 785 (44.6) | 4166 (48.8) | <0.0001 |
| Black | 442 (25.1) | 1401 (16.4) | |
| Asian | 323 (18.4) | 1726 (20.2) | |
| Other | 209 (11.9) | 1240 (14.5) | |
| Hispanic ethnicity | 140 (8.1) | 734 (8.9) | 0.2737 |
| Payer status | |||
| Medicare | 905 (51.4) | 4266 (49.6) | <0.0001 |
| Medicaid/Medi‐cal | 458 (26.0) | 1578 (18.4) | |
| Private | 370 (21.0) | 2092 (24.3) | |
| Other | 29 (1.7) | 661 (7.7) | |
| Primary language other than English | 242 (17.1) | 1394 (19.5) | 0.0359 |
| Median length of stay (days) (interquartile range) | 4 (2, 7) | 3 (2, 6) | <0.0001 |
| Admit source | |||
| Emergency room | 1506 (85.5) | 6898 (80.2) | <0.0001 |
| Outside hospital | 38 (2.2) | 271 (3.2) | |
| Direct admission/other (jail) | 218 (12.4) | 1428 (16.6) | |
| Discharge to | |||
| Home | 1461 (82.9) | 6773 (78.8) | <0.0001 |
| SNF | 170 (9.7) | 774 (9.0) | |
| Other | 131 (7.4) | 1050 (12.2) | |
| Discharged on weekend | 381 (21.6) | 1904 (22.1) | 0.6288 |
| Patient medications | |||
| Any high‐risk medication | 1679 (95.3) | 7684 (89.4) | <0.0001 |
| High‐risk medication count | |||
| 02 | 577 (32.8) | 3666 (42.6) | <0.0001 |
| 34 | 692 (39.3) | 2968 (34.5) | |
| 5 | 493 (28) | 1963 (22.8) | |
| Any corticosteroids | 399 (22.6) | 1571 (18.3) | <0.0001 |
| Anticoagulant | 120 (6.8) | 559 (6.5) | 0.6340 |
| Any antibiotic | 904 (51.3) | 4203 (48.9) | 0.0646 |
| Any narcotic | 1036 (58.8) | 4206 (48.9) | <0.0001 |
| Any NSAID | 68 (3.9) | 320 (3.7) | 0.7826 |
| Any cardiovascular med | 887 (50.3) | 3806 (44.3) | <0.0001 |
| Any antiepileptic | 93 (5.3) | 470 (5.5) | 0.7500 |
| Any anticholinergic | 47 (2.7) | 354 (4.1) | 0.0040 |
| Any antidepressant | 455 (25.8) | 1863 (25.8) | 0.0001 |
| Any antidiabetic | 198 (11.2) | 994 (11.6) | 0.6970 |
| Elixhauser comorbidities | |||
| Congestive heart failure | 219 (12.4) | 795 (9.3) | <0.0001 |
| Pulmonary circulation disease | 72 (4.1) | 178 (2.1) | <0.0001 |
| Peripheral vascular disease | 84 (4.8) | 331 (3.9) | 0.0737 |
| Hypertension | 745 (42.3) | 3741 (43.5) | 0.3411 |
| Other neurological disease | 101 (5.7) | 696 (8.1) | 0.0007 |
| Chronic pulmonary disease | 317 (18.0) | 1442 (16.8) | 0.2149 |
| Diabetes | 303 (17.2) | 1333 (15.5) | 0.0762 |
| Renal failure | 339 (19.2) | 1286 (15.0) | <0.0001 |
| Liver disease | 188 (10.7) | 774 (9.0) | 0.0281 |
| Metastatic cancer | 160 (9.1) | 530 (6.2) | <0.0001 |
| Solid tumor w/o metastases | 100 (5.7) | 277 (3.2) | <0.0001 |
| Rheumatoid arthritis/collagen vas | 81 (4.6) | 303 (3.5) | 0.0299 |
| Weight loss | 153 (8.7) | 584 (6.8) | 0.0449 |
| Deficiency anemia | 522 (29.6) | 1979 (23.0) | <0.0001 |
| Alcohol abuse | 101 (5.7) | 428 (5.0) | 0.1905 |
| Drug abuse | 148 (8.4) | 619 (7.2) | 0.0798 |
| Depression | 244 (13.9) | 963 (11.2) | 0.0016 |
| APR risk of mortality | |||
| 1 | 451 (25.6) | 3101 (36.1) | <0.0001 |
| 2 | 619 (35.1) | 2797 (32.5) | |
| 3 | 536 (30.4) | 1907 (22.2) | |
| 4 | 156 (8.9) | 792 (9.2) | |
Frequency of Readmission
The 30‐day readmission rate was 17.0% (1762 patients), with 49.7% (875 patients) of the readmissions occurring within 10 days of discharge. Of patients readmitted, the general medicine service was the readmitting team in 78.2%. A quarter of readmissions (26.2%) had the same primary diagnosis on initial and repeat admission.
Factors Associated With Readmission
Factors associated with readmission were categorized as sociodemographic, operational, and clinical. Factors associated with readmission with P < 0.05 and present in at least 5% of admissions are presented in Table 2. Of the sociodemographic factors, black race was significantly associated with readmission. Within the Medicare cohort, risk for readmission was similar for white vs. nonwhite race, with relative risk of 1.0 (95% confidence interval [CI], 0.86‐1.18). Medicaid as payer status was significantly associated in the unadjusted model, and in the adjusted model showed a trend toward readmission. Mean age was significantly different in the readmitted and nonreadmitted groups, but the difference was small (1.0 year). Moreover, when we evaluated age in 5‐year categories (ex. 65‐70, 71‐75, etc.), age was not associated with readmission. In the adjusted model, none of the operational factors were significantly associated with readmission, including discharge to SNF, weekend discharge, or admit source.
| Covariate | Unadjusted OR (95% CI) | Adjusted OR (95% CI) |
|---|---|---|
| ||
| Age | 1.00 (0.991.00) | 1.00 (0.991.00) |
| Race | ||
| White | Referent | Referent |
| Black | 1.67 (1.471.91) | 1.43 (1.241.65) |
| Asian | 0.99 (0.861.14) | 0.95 (0.821.11) |
| Other | 0.89 (0.761.06) | 0.84 (0.671.06) |
| Payer | ||
| Medicare | Referent | Referent |
| Medicaid/medical | 1.37 (1.211.55) | 1.15 (0.971.36) |
| Private | 0.83 (0.730.95) | 0.78 (0.650.95) |
| Other | 0.21 (0.140.30) | 0.23 (0.110.45) |
| Disposition | ||
| To home | Referent | Referent |
| SNF | 1.02 (0.851.21) | 0.98 (0.821.18) |
| Other | 0.58 (0.480.70) | 0.53 (0.430.66) |
| Highrisk medications | ||
| Corticosteroids | 1.31 (1.161.48) | 1.24 (1.091.42) |
| Narcotics | 1.49 (1.341.65) | 1.33 (1.161.53) |
| Anticholinergics | 0.64 (0.470.87) | 0.66 (0.480.90) |
| Comorbidities | ||
| Congestive heart failure | 1.39 (1.191.63) | 1.30 (1.091.56) |
| Neurological disorders | 0.69 (0.560.86) | 0.70 (0.570.87) |
| Renal failure | 1.35 (1.191.55) | 1.19 (1.051.36) |
| Metastatic cancer | 1.52 (1.261.83) | 1.61 (1.331.95) |
| Solid tumor w/o metastasis | 1.81 (1.432.29) | 1.95 (1.542.47) |
| Deficiency anemia | 1.41 (1.261.58) | 1.27 (1.131.44) |
| Weight loss | 1.30 (1.081.57) | 1.26 (1.091.47) |
Of the clinical factors, high‐risk medications and 6 comorbidities were associated with readmission. High‐risk medication categories associated with readmission were steroids and narcotics; anticholinergics medications were protective. The 6 comorbidities associated with readmission were congestive heart failure, renal disease, cancer (with and without metastasis), weight loss, and iron deficiency anemia. While APR risk of mortality was associated with readmission at P < 0.05, including APR in our final model did not alter which other factors were significantly associated with readmission. When site (Moffitt‐Long vs. Mount Zion Hospitals) was added to the model, the ORs for factors associated with readmission did not change appreciably (0.01).
Discussion
In this retrospective observational study of hospitalized patients, we found that readmission was common and associated with a number of risk factors that could be easily identified early in hospitalization. Nonclinical factors associated with readmission were black race and Medicaid payer status (in the unadjusted model). Clinical factors were high risk medications including steroids and narcotics; and comorbidities including congestive heart failure, renal disease, cancer, anemia, and weight loss. In contrast, other potential riskssuch as discharge on a weekend and discharge to an SNFwere not independently associated with readmission. This cohortwith a mix of clinical scenarios, payers, age, etc.represents the inherently heterogeneous population of inpatient general medicine across the country and abroad. Hospitalists provided care for over 65% of the general medicine service, again representative of the trend in US inpatient medicine.27, 28 Lastly, our cohort did not have the benefit of a systematic and consistent discharge process with interventions focused on reducing readmissions. This gap, which is common across hospitals, highlights the utility of this data in targeting quality improvement efforts.
Reducing risks for readmission requires identification of patient populations at highest risk; in those patients, one can further identify factors which are potentially modifiable via education or patient‐engagement interventions. While in the hospital, more intensive predischarge counseling and efforts to increase mobility may be most useful if targeted early and often on those at highest risk.15, 16, 29, 30 Finally, broader‐based support in the form of better home services, more access to longitudinal care, or targeted postdischarge efforts may be required.14, 31
Though current strategies focus largely on clinical risk factors, this study shows that nonclinical factors play an equally important but underappreciated role in contributing to readmission. While prior studies have shown variable results on association of black race with readmission,2, 9, 11 none have evaluated or linked Medicaid to readmission, which just missed statistical significance in this study (OR, 1.15; 95% CI, 0.971.36). Both black race and Medicaid as payer are proxies for the underlying root cause aspects leading to readmission, such as access to longitudinal care. Following this trail to the root cause will require in‐depth qualitative evaluation that includes the patient perspective as a source of data.32 For example, risk for readmission may not stem solely from being on warfarin, but in combination with not having transportation to get an international normalized ratio (INR) checked, a suboptimal understanding of how to take the medication, or not recognizing potential side effects until too late to avoid inpatient admission.
Several of the strongest associations, and perhaps most conducive to targeted interventions, were high‐risk medications at discharge. Risk related to medications and adverse drug events following discharge have been a consistent theme in readmission literature.24, 33 Our current system, which includes mandated inpatient medicine reconciliation, does not encourage discontinuation of unnecessary medications to combat polypharmacy, address affordability of medications, provide consistent medication counseling, or focus on the highest risk medications. In fact, bundled interventions which implement pharmacists to focus on these measures have been successful in decreasing readmission,14, 16, 29 but unfortunately are not yet part of the standard of care. The challenge remains transforming a mandatory policy such as medicine reconciliation into a valuable and systematic tool in the discharge process.
Two factors were surprisingly protective against readmission: neurologic diagnosis and anticholinergic medications. This first may be explained by the presence of a separate neurology service at our institution which skews our data. For example, a patient with acute stroke, who has a 20% rate of bounce‐back to a higher level of care within 30 days of discharge,34 would be admitted to the neurology service, not general medicine, and therefore would not nr part of our cohort. Regarding anticholinergics, several factors may explain this unexpected result. First, use of anticholinergics was relatively rare in our sample (2.7% in readmitted patients, 4.1% in patients not readmitted), possibly creating a false positive result. Second, Hanlon et al.24 showed only a weak association at best between anticholinergics and postdischarge adverse drug reactions (hazard ratio, 1.11; 95% CI, 0.86‐1.43). Lastly, anticholinergics include a varied group of medications, therefore diluting possible relative risk of specific medications.
While this study allows providers to identify patients at increased risk of readmission, the identified factors do not fully account for readmission risk; we did not aim to produce a risk‐prediction rule with our study. Prior readmission studies have been unable to create a tool to predict which patients will be readmitted with much success.3537 These results underscore the complexities and variability of readmission, which often lack a clear single cause and effect relationship. Given the breadth of risk factors we identified, it seems likely that more intensive interventions will require a multidisciplinary approach, one which might be costly if applied broadly. Our study does not attempt to predict who will be readmitted and who will not, but rather provides a list of risk factors which might be used to deploy resources more efficiently.
This study had several limitations. We did not capture readmissions to outside hospitals, which account for 22% to 24% of all readmissions in prior studies, and therefore have underestimated the readmission rate in our population.2, 8 However, by limiting our data to 2 hospitals within 1 institution, we were able to include more detailed patient level data, which is not accurately available in other large databases. Also, while studies of risk factors in a managed care population (such as within Medicare, the Veterans Affairs medical centers, or countries with national integrated medical records) are able to capture all readmissions, this study is the first to evaluate readmissions risk factors in a truly heterogeneous U.S. inpatient medicine population without limitation by age or payer status. Second, we did not have access to outpatient medications lists; however use of these same medications within the last 48 hours of admission is likely a reasonable proxy for outpatient use and more conducive to potential interventions (such as medication reconciliation or patient education) that could flag patients prior to discharge. Payer data was limited to only the primary payer, so patients who were dual eligible (ie, have both Medicare and Medicaid) were categorized as Medicare. Regarding sociodemographic factors, while primary language other than English was not associated with readmission, language data was missing in 17.4% of admissions, thereby limiting our ability to evaluate this factor. Our data did not include access to outpatient or primary care, and therefore we were unable to evaluate access to postdischarge follow‐up care as a risk factor for readmission. Lastly, while this study did not include outpatient deaths, we did exclude patients who died in the hospital.
Conclusions
Readmission is common among general medicine patients, with approximately 1 in 5 patients being readmitted within 30 days. While the identified associated factors do not account for all the potential reasons for readmission, our study suggests a spectrum of risk factors which might be used to target more intensive multidisciplinary interventions. Specifically, the nonclinical factors of race and payer status merit further in depth research incorporating the patient experience to truly determine causation of readmission. Hospitalists, who are at nexus of the discharge process and uniquely invested in quality inpatient care, are ideally positioned to lead efforts to reduce readmissions. How to use our study's results to develop and implement effective interventions to reduce readmissions remains a subject for future studies.
- A path to bundled payment around a rehospitalization.: Medicare payment Advisory Commission; June2005.
- ,,.Rehospitalizations among patients in the Medicare fee‐for‐service program.N Engl J Med.2009;360(14):1418–1428.
- University HealthSystem Consortium. Available at: https://www.uhc.edu. Accessed May2010.
- U.S. Department of Health 15(5):599–606.
- ,,,.Development and validation of a model for predicting emergency admissions over the next year (PEONY): a UK historical cohort study.Arch Intern Med.2008;168(13):1416–1422.
- ,,, et al.Incidence and main factors associated with early unplanned hospital readmission among French medical inpatients aged 75 and over admitted through emergency units.Age Ageing.2008;37(4):416–422.
- ,,,,,.Factors associated with unplanned hospital readmission among patients 65 years of age and older in a Medicare managed care plan.Am J Med.1999;107(1):13–17.
- ,,.Risk factors for early unplanned hospital readmission in the elderly.J Gen Intern Med.1991;6(3):223–228.
- ,,, et al.Predicting non‐elective hospital readmissions: a multi‐site study. Department of Veterans Affairs Cooperative Study Group on Primary Care and Readmissions.J Clin Epidemiol.2000;53(11):1113–1118.
- ,.Effect of gender, ethnicity, pulmonary disease, and symptom stability on rehospitalization in patients with heart failure.Am J Cardiol.2007;100(7):1139–1144.
- ,,,.The care transitions intervention: results of a randomized controlled trial.Arch Intern Med.2006;166(17):1822–1828.
- ,,,.The impact of follow‐up telephone calls to patients after hospitalization.Am J Med.2001;111(9B):26S–30S.
- ,,, et al.A reengineered hospital discharge program to decrease rehospitalization: a randomized trial.Ann Intern Med.2009;150(3):178–187.
- ,,, et al.Reduction of 30‐day postdischarge hospital readmission or emergency department (ED) visit rates in high‐risk elderly medical patients through delivery of a targeted care bundle.J Hosp Med.2009;4(4):211–218.
- ,,,,,.Comprehensive discharge planning with postdischarge support for older patients with congestive heart failure: a meta‐analysis.JAMA.2004;291(11):1358–1367.
- ,.Home‐based intervention in congestive heart failure: long‐term implications on readmission and survival.Circulation.2002;105(24):2861–2866.
- ,,,,,.Effect of a standardized nurse case‐management telephone intervention on resource use in patients with chronic heart failure.Arch Intern Med.2002;162(6):705–712.
- ,,,.The impact of follow‐up physician visits on emergency readmissions for patients with asthma and chronic obstructive pulmonary disease: a population‐based study.Am J Med.2002;112(2):120–125.
- ,.Factors predicting readmission of older general medicine patients.J Gen Intern Med.1991;6(5):389–393.
- BOOSTing Care Transitions. Available at: http://www.hospitalmedicine.org/ResourceRoomRedesign/RR_CareTransitions/CT_Home.cfm. Accessed May2010.
- ,,,,.Adverse drug events occurring following hospital discharge.J Gen Intern Med.2005;20(4):317–323.
- ,,, et al.Incidence and predictors of all and preventable adverse drug reactions in frail elderly persons after hospital stay.J Gerontol A Biol Sci Med Sci.2006;61(5):511–515.
- . Development of the 3M™ All Patient Refined Diagnosis Related Groups (APR DRGs). Available at: http://www.ahrq.gov/qual/mortality/Hughes.htm. Accessed May2010.
- ,,.Volume thresholds and hospital characteristics in the United States.Health Aff (Millwood).2003;22(2):167–177.
- ,,,.The status of hospital medicine groups in the United States.J Hosp Med.2006;1(2):75–80.
- ,,,.Growth in the care of older patients by hospitalists in the United States.N Engl J Med.2009;360(11):1102–1112.
- ,,, et al.Role of pharmacist counseling in preventing adverse drug events after hospitalization.Arch Intern Med.2006;166(5):565–571.
- ,,, et al.Effects of a multicomponent intervention on functional outcomes and process of care in hospitalized older patients: a randomized controlled trial of Acute Care for Elders (ACE) in a community hospital.J Am Geriatr Soc.2000;48(12):1572–1581.
- ,,,,,.Telehome monitoring in patients with cardiac disease who are at high risk of readmission.Heart Lung.2008;37(1):36–45.
- ,,.Understanding rehospitalization risk: can hospital discharge be modified to reduce recurrent hospitalization?J Hosp Med.2007;2(5):297–304.
- ,,,,.The incidence and severity of adverse events affecting patients after discharge from the hospital.Ann Intern Med.2003;138(3):161–167.
- ,,,.Bouncing back: patterns and predictors of complicated transitions 30 days after hospitalization for acute ischemic stroke.J Am Geriatr Soc.2007;55(3):365–373.
- ,,,.Case finding for patients at risk of readmission to hospital: development of algorithm to identify high risk patients.BMJ.2006;333(7563):327.
- ,,,,,.Screening elders for risk of hospital admission.J Am Geriatr Soc.1993;41(8):811–817.
- ,,,.Using routine inpatient data to identify patients at risk of hospital readmission.BMC Health Serv Res.2009;9:96.
- A path to bundled payment around a rehospitalization.: Medicare payment Advisory Commission; June2005.
- ,,.Rehospitalizations among patients in the Medicare fee‐for‐service program.N Engl J Med.2009;360(14):1418–1428.
- University HealthSystem Consortium. Available at: https://www.uhc.edu. Accessed May2010.
- U.S. Department of Health 15(5):599–606.
- ,,,.Development and validation of a model for predicting emergency admissions over the next year (PEONY): a UK historical cohort study.Arch Intern Med.2008;168(13):1416–1422.
- ,,, et al.Incidence and main factors associated with early unplanned hospital readmission among French medical inpatients aged 75 and over admitted through emergency units.Age Ageing.2008;37(4):416–422.
- ,,,,,.Factors associated with unplanned hospital readmission among patients 65 years of age and older in a Medicare managed care plan.Am J Med.1999;107(1):13–17.
- ,,.Risk factors for early unplanned hospital readmission in the elderly.J Gen Intern Med.1991;6(3):223–228.
- ,,, et al.Predicting non‐elective hospital readmissions: a multi‐site study. Department of Veterans Affairs Cooperative Study Group on Primary Care and Readmissions.J Clin Epidemiol.2000;53(11):1113–1118.
- ,.Effect of gender, ethnicity, pulmonary disease, and symptom stability on rehospitalization in patients with heart failure.Am J Cardiol.2007;100(7):1139–1144.
- ,,,.The care transitions intervention: results of a randomized controlled trial.Arch Intern Med.2006;166(17):1822–1828.
- ,,,.The impact of follow‐up telephone calls to patients after hospitalization.Am J Med.2001;111(9B):26S–30S.
- ,,, et al.A reengineered hospital discharge program to decrease rehospitalization: a randomized trial.Ann Intern Med.2009;150(3):178–187.
- ,,, et al.Reduction of 30‐day postdischarge hospital readmission or emergency department (ED) visit rates in high‐risk elderly medical patients through delivery of a targeted care bundle.J Hosp Med.2009;4(4):211–218.
- ,,,,,.Comprehensive discharge planning with postdischarge support for older patients with congestive heart failure: a meta‐analysis.JAMA.2004;291(11):1358–1367.
- ,.Home‐based intervention in congestive heart failure: long‐term implications on readmission and survival.Circulation.2002;105(24):2861–2866.
- ,,,,,.Effect of a standardized nurse case‐management telephone intervention on resource use in patients with chronic heart failure.Arch Intern Med.2002;162(6):705–712.
- ,,,.The impact of follow‐up physician visits on emergency readmissions for patients with asthma and chronic obstructive pulmonary disease: a population‐based study.Am J Med.2002;112(2):120–125.
- ,.Factors predicting readmission of older general medicine patients.J Gen Intern Med.1991;6(5):389–393.
- BOOSTing Care Transitions. Available at: http://www.hospitalmedicine.org/ResourceRoomRedesign/RR_CareTransitions/CT_Home.cfm. Accessed May2010.
- ,,,,.Adverse drug events occurring following hospital discharge.J Gen Intern Med.2005;20(4):317–323.
- ,,, et al.Incidence and predictors of all and preventable adverse drug reactions in frail elderly persons after hospital stay.J Gerontol A Biol Sci Med Sci.2006;61(5):511–515.
- . Development of the 3M™ All Patient Refined Diagnosis Related Groups (APR DRGs). Available at: http://www.ahrq.gov/qual/mortality/Hughes.htm. Accessed May2010.
- ,,.Volume thresholds and hospital characteristics in the United States.Health Aff (Millwood).2003;22(2):167–177.
- ,,,.The status of hospital medicine groups in the United States.J Hosp Med.2006;1(2):75–80.
- ,,,.Growth in the care of older patients by hospitalists in the United States.N Engl J Med.2009;360(11):1102–1112.
- ,,, et al.Role of pharmacist counseling in preventing adverse drug events after hospitalization.Arch Intern Med.2006;166(5):565–571.
- ,,, et al.Effects of a multicomponent intervention on functional outcomes and process of care in hospitalized older patients: a randomized controlled trial of Acute Care for Elders (ACE) in a community hospital.J Am Geriatr Soc.2000;48(12):1572–1581.
- ,,,,,.Telehome monitoring in patients with cardiac disease who are at high risk of readmission.Heart Lung.2008;37(1):36–45.
- ,,.Understanding rehospitalization risk: can hospital discharge be modified to reduce recurrent hospitalization?J Hosp Med.2007;2(5):297–304.
- ,,,,.The incidence and severity of adverse events affecting patients after discharge from the hospital.Ann Intern Med.2003;138(3):161–167.
- ,,,.Bouncing back: patterns and predictors of complicated transitions 30 days after hospitalization for acute ischemic stroke.J Am Geriatr Soc.2007;55(3):365–373.
- ,,,.Case finding for patients at risk of readmission to hospital: development of algorithm to identify high risk patients.BMJ.2006;333(7563):327.
- ,,,,,.Screening elders for risk of hospital admission.J Am Geriatr Soc.1993;41(8):811–817.
- ,,,.Using routine inpatient data to identify patients at risk of hospital readmission.BMC Health Serv Res.2009;9:96.
Copyright © 2010 Society of Hospital Medicine
Unforgettable
The approach to clinical conundrums by an expert clinician is revealed through presentation of an actual patient's case in an approach typical of morning report. Similar to patient care, sequential pieces of information are provided to the clinician who is unfamiliar with the case. The focus is on the thought processes of both the clinical team caring the patient and the discussant.
A 27‐year‐old woman with a history of asthma presented to her primary care physician (PCP) with a sore throat which began after attending a party where she shared alcoholic beverages with friends. She denied any high‐risk sexual behavior. Her PCP prescribed azithromycin and methylprednisolone empirically for tonsillitis. The throat pain subsided, but in the next several days she experienced increased weakness, lethargy, poor appetite, and chills, and she returned to her PCP for reevaluation.
Two months prior she had been treated at a walk‐in clinic with a course of penicillin for a presumed streptococcal pharyngitis. Her symptoms resolved until her current presentation.
In a young woman with 2 episodes of pharyngitis in 2 months followed by an acute systemic illness, one must consider an immunocompromised state such as human immunodeficiency virus (HIV), hematologic malignancy, or autoimmune diseases. Weakness, lethargy, anorexia, and chills in the setting of pharyngitis suggest a local process in the neck, most likely infection associated with systemic toxicity. As neck abscess and bacteremia warrant early consideration, the physical examination should focus on the neck and oropharynx, as well as neurologic exam to evaluate for bacterial spreading into the central nervous system. In addition to routine laboratory studies, a chest x‐ray (CXR) would be appropriate as upper respiratory infections may be complicated by pneumonia and present with signs and symptoms of systemic illness.
On examination by her PCP, her temperature was 99.2F and her blood pressure was 118/68. She had bilateral oropharyngeal erythema without exudates and bilateral tonsillar and anterior triangle lymphadenopathy (LAD). An oropharyngeal rapid Streptococcal antigen detection test was negative, but a Monospot test was positive for heterophile antibodies. Azithromycin and methylprednisolone were discontinued, and the patient was informed she most likely had Epstein Barr Virus (EBV) infection.
The following day, the complete blood count results returned. The platelet count was 50 K/L and the white blood cell (WBC) count was 13.0 K/L. The patient stated she had developed right‐sided flank pain upon deep inspiration and used her albuterol inhaler with minimal relief. She continued to have fever, decreased appetite, chest and abdominal pain, and difficulty swallowing due to odynophagia. She was instructed to go to the emergency department (ED) for further evaluation. In the ED, she denied any shortness of breath, but reported a slight cough and right‐sided abdominal pain.
Acute tonsillar pharyngitis and fever, as well as systemic symptoms of fatigue and abdominal pain along with positive heterophile screen are highly suggestive of EBV infection in this young female. The episode of pharyngitis 2 months prior remains unexplained and may be unrelated. Right‐sided pleuritic pain and abdominal pain may be related to EBV hepatitis. Odynophagia is consistent with EBV infection as well. Profound lethargy, however, is not a common presenting feature in mononucleosis unless infected patients are profoundly dehydrated due to inability to swallow. Her pain symptoms may be secondary to other signs of EBV infection, such as hepatomegaly, splenomegaly, ascites, and/or right pleural effusion. A history of rash should be investigated. Initial assessment in this acutely ill patient should focus on evaluation for the presence of severe sepsis and for a primary source of infection. Given the severity of her illness, I would consider early computed tomography (CT) of her chest, abdomen, and pelvis, as well as CT of the neck to exclude a possibility of peritonsillar abscess. The complaint of chills indicates a possible bacteremia, so coverage with broad‐spectrum antibiotics is indicated. Symptomatic relief with acetaminophen and intravenous fluid rehydration is appropriate.
On exam, temperature was 101.9F, blood pressure was 111/74, heart rate was 140 beats per minute, respiratory rate was 18 per minute, and oxygen saturation was 99% on room air. She appeared drowsy, but answered questions appropriately. She had bilateral swollen tonsils, as well as anterior and posterior cervical adenopathy, with tenderness greater on the left side. Her chest exam had slightly diminished breath sounds at the bases bilaterally. Heart rhythm was regular, and there were no murmurs appreciated. On abdominal exam, she was tender to palpation in both right‐upper and left‐upper quadrants, without obvious hepatosplenomegaly. There were no petechiae noted on her skin.
The WBC was 17.6 K/L, with 89% neutrophils and 5% lymphocytes, platelet count was 22 K/L, and hemoglobin was 13.8 g/dL. A D‐dimer test was elevated at 1344 ng/mL. Peripheral blood smear showed thrombocytopenia and neutrophilia, but demonstrated no schistocytes. The serum potassium was 3.2 mEq/L, bicarbonate was 29 mEq/L, blood urea nitrogen was 15 mg/dL and the creatinine was 1.29 mg/dL. Transaminases were within normal limits, but total bilirubin was 1.8 mg/dL. Her urinalysis was normal. Blood cultures were sent. A CXR showed bibasilar consolidations and pleural effusions (Figure 1). A CT of the chest with contrast was obtained that showed multiple confluent and patchy foci of consolidation in the lung bases, with trace bilateral pleural effusions (Figure 2). A CT of the abdomen showed a spleen at the upper limits of normal in size, measuring 13 cm in length, but was otherwise normal.
Leukocytosis with lymphopenia is not consistent with EBV infection and another process needs to be considered. This patient meets criteria for sepsis syndrome and should receive broad spectrum antibiotics, such as vancomycin and piperacillin‐tazobactam immediately after the blood cultures are sent, in addition to further evaluation to determine the source of sepsis. Depending on her mental status response to initial measures such as acetaminophen and hydration, one should consider a lumbar puncture, which would require platelet transfusion and may therefore not be done immediately. HIV serology should be performed, since acute retroviral syndrome can mimic this presentation. With neck tenderness that is more localized to her left side, a CT of her neck to evaluate for an abscess may be helpful.
She was admitted for presumed community‐acquired pneumonia complicating an upper respiratory tract infection. Her pharyngitis was thought to be of viral etiology. Moxifloxacin was started and intravenous fluids were administered. She was started on prednisone 60 mg daily for presumed immune‐mediated thrombocytopenia related to EBV infection. An HIV antibody test and quantitative polymerase chain reaction (PCR) were both negative. The EBV immunoglobulin G (IgG) titer was positive (>1:10), but the IgM titer was negative. Her mental status improved after starting moxifloxacin and fluids. Her creatinine and bilirubin normalized to 0.97 mg/dL and 0.8 mg/dL respectively. She continued to have a tender left‐sided submandibular swelling. Blood cultures grew Gram‐negative bacilli in 2 anaerobic bottles.
I am uncomfortable with moxifloxacin as initial empiric therapy because at presentation she had sepsis syndrome as well as a suspected immunocompromised state. In addition, moxifloxacin would not be adequate coverage for anaerobic organisms if a peritonsillar abscess was involved. At this point, she needs a CT of her neck to look for a focus of infection which may require surgical management and, if negative, further imaging such as a tagged white blood scan to identify the source of the anaerobes.
Moxifloxacin was switched to piperacillin‐tazobactam and prednisone was discontinued. By day 4 of hospitalization her platelet count had risen to 261 K/L. Her WBC continued to rise to a peak of 21.5 K/L and she continued to have fevers and diffuse pains, although her repeat blood cultures were negative. She continued to have tenderness of the cervical lymph nodes, left greater than right. A repeat CXR showed patchy air space disease bilaterally and pleural effusions, both of which had progressed compared with the prior film. Clindamycin was empirically added to her antibiotic regimen in light of her progressing pneumonia and evidence of anaerobic infection. A repeat CT scan of her chest revealed multiple nodular opacities scattered throughout the lung fields, some of which were cavitary, predominating in the lung bases. The CT scan of her neck revealed a left peritonsillar abscess and phlegmon in the left retropharyngeal and deep neck area along the sternocleidomastoid and internal jugular vein (IJV). There also was noted a large thrombus within the left IJV extending superiorly to involve the jugular bulb, sigmoid sinus, and distal left transverse sinus; and inferiorly to near the origin of the brachiocephalic vein (see Figure 3). An echocardiogram did not reveal any vegetations.
The combination of recent pharyngitis, septic pulmonary emboli, and IJV thrombosis is consistent with a diagnosis of Lemierre's syndrome (LS). This is a life threatening condition, even if diagnosis is made early and appropriate treatment is started. The most likely causative agent is Fusobacterium necrophorum. In this case it was important to realize that clinical presentation was not consistent with EBV infection, even though heterophile screen was positive. Early initiation of broad spectrum antibiotics as well as CT scan of the neck would have been appropriate.
The diagnosis of LS was made. The blood culture speciation revealed Fusobacterium nucleatum, which was too fastidious to perform antimicrobial sensitivities. Her symptoms improved significantly with the addition of clindamycin to piperacillin‐tazobactam, which was postulated to be the result of bacterial beta‐lactamase activity mitigating the efficacy of piperacillin‐tazobactam. Thoracentesis of her pleural effusion did not reveal an empyema. Due to her large thrombus burden, she was started on anticoagulation with heparin and transitioned to outpatient coumadin. She was switched to metronidazole as a single agent antibiotic for 6 weeks, and on outpatient follow‐up was doing well.
Commentary
LS was described by Dr. Andre Lemierre in 1936.6 The syndrome consists of a primary oropharyngeal infection, thrombosis of the IJV, bacteremia, and septic metastatic foci, usually involving the lungs.1, 2 LS is a form of necrobacillosis, which is a systemic infection resulting from F. necrophorum.3, 4 In classic LS, the initial pharyngitis is usually a tonsillar or peritonsillar abscess, and is followed by intense fever and rigors after 4 days to 2 weeks.1, 3 This is followed by a unilateral painful submaxillary LAD and IJV thrombophlebitisthe cord sign.2 Finally, bacteremia and distant metastatic pyogenic abscesses develop.1 (see Table 1).
| Lemierre's Syndrome typical features |
| Antecedent head and neck infection, typically an oropharyngeal infection prior to deterioration |
| Thrombophlebitis, typically of internal jugular vein (present in only 1/3 of cases) |
| Bacteremia (Fusobacterium necrophorum most commonly) |
| Septic metastatic foci, typically to lungs |
| Usual Presentation |
| Pharyngitis |
| Fevers |
| Rigors |
| Neck involvement: tenderness, swelling, tender internal jugular vein thrombus (cord sign) |
| Pulmonary infiltrates which cavitate |
With the advent of antibiotics, LS is now rare with an incidence of 0.9 per million persons per year. In Lemierre's time, the disease was fulminant and led to death within 2 weeks, but in the antibiotic age the mortality rate is 4.9%.1, 3 The median age of an LS patient is 19 years, with a higher incidence in males.1, 35 Although in the literature it is referred to as the forgotten disease, there is evidence the incidence is increasing.3, 4, 6, 8
There are variations of classic LS. Bacteremia may occur much later than the initial pharyngitis, the disease may be less aggressive, the thrombus may be in the external jugular vein, or there may be no identified thrombus.3, 4, 8 In fact, a thrombus is only identified in 36% of cases.9 The primary infection may be a head and neck infection that is not pharyngitis, such as an odontogenic infection,4 or may not be identified.10 Despite variations, the fundamentals of diagnosis are prior head and neck infection, presumed thrombophlebitis and bacteremia, and evidence of septic metastatic foci.
The genus Fusobacterium comprises anaerobic, nonspore forming gram negative bacilli.1, 35, 11 F. necrophorum and F. nucleatum are 2 species within this genus. F. nucleatum causes the majority of reported human bacteremias by Fusobacterium species, but it is F. necrophorum that is most associated with anaerobic oropharyngeal infections, thrombocytopenia, clot formation, and LS.35, 8, 9
It is unknown if Fusobacterium species directly cause the sore throat, or rather are bystanders which thrive once a favorable anaerobic environment is created via endotoxins and exotoxins.35 A break in oral mucosa via trauma or coinfection with bacteria/viruses (especially EBV) is also thought to play a role with infection.2, 3, 5 One‐third of LS cases have coinfection with other oropharyngeal flora. Thus, one must reexamine the anaerobic blood cultures after an organism has been identified in suspect cases.3, 4
There is an increased association of LS with EBV infection, likely due to viral‐induced and steroid‐induced immunosuppression.24 False positive heterophile tests are reported with LS, so the specific antibody tests for EBV must be checked.3, 4
Once thrombophlebitis occurs, the bacteria can metastasize to distant sites. In 80% to 92% of LS cases, the metastatic complication is a pleuro‐pulmonary infection, consisting of septic pulmonary emboli, empyema, and pleural effusions, but extra‐pulmonary lesions occur.1, 3, 9, 12 Abdominal pain usually results from abdominal microabscesses or thrombophlebitis.4 Mild renal impairment and abnormal liver function tests are common.3, 4 Cranial nerve palsies and Horner's syndrome are rare and indicate carotid sheath involvement.3, 12 An elevated C‐reactive protein can distinguish bacterial from uncomplicated viral pharyngitis.3, 4 Also, rigors are unusual in tonsillitis, and their presence indicate bacterial entry into the circulation.3
CXRs may reveal the pulmonary septic emboli. Ultrasound of the IJV is inexpensive and noninvasive, but may have limited sensitivity for an acute thrombus. CT scan allows increased visualization of anatomy, but can have decreased sensitivity and specificity for thrombosis.3 Magnetic resonance imaging (MRI) is recommended if LS results from mastoiditis, to exclude an intracerebral vein thrombosis.9
Antibiotics have both dramatically decreased the incidence of LS and improved its prognosis. The recent rise in incidence may be due to a renewed interest in restricting the use of antibiotics in cases of pharyngitis, as well as an increased use of macrolides, to which F. necrophorum is frequently resistant.3 Decreased tonsillectomies may also have a role, as LS is more common with retained tonsils.1, 3
No trials have evaluated the optimal antibiotic regimen. Fusobacterium species are sensitive to penicillin, but 23% have beta‐lactamase activity as reported clinically by several authors.3, 5 F. necrophorum is also sensitive to metronidazole, ticarcillin‐clavulanate, cefoxitin, amoxicillin‐clavulanate, imipenem, and clindamycin. There is a high resistance to macrolides and gentamicin, and the activity of tetracyclines is poor. For treatment, most authors suggest a carbapenem, a penicillin/beta‐lactamase inhibitor combination, or metronidazole. Clindamycin has weaker bactericidal activity than metronidazole or imipenem. Metronidazole is preferred because of its activity against all Fusobacterium species, good penetration into tissues, bactericidal activity, low minimum inhibitory concentration, and ability to achieve high concentration in the cerebrospinal fluid if meningitis occurs. An effective regimen is metronidazole with a penicillinase‐resistant penicillin to cover for mixed coinfection with streptococci or staphylococci.3, 4, 12 A 6‐week antibiotic course is given for adequate penetration into the protective fibrin clots.4
Reports have shown good outcomes both with and without the use of anticoagulation.3, 4, 8 Support for anticoagulation is extrapolated from experience with septic pelvic thrombophlebitis, in which anticoagulation results in more rapid resolution of symptoms.13 Given the lack of firm evidence in cases of LS, anticoagulation is typically reserved for poor clinical response despite 2 to 3 days of antibiotic therapy or propagation of thromboses into the cavernous sinus. It is generally given for 3 months.4, 13
Prior to the antibiotic era, surgical ligation or excision of the IJV was done without clear benefit. Today, surgery is reserved for cases of continued septic emboli or extension of thrombus despite aggressive medical therapy.3 If mediastinitis develops, then surgical intervention is essential.4
Lemierre stated that the symptoms and signs of LS are so characteristic that it permits diagnosis before bacteriological examination.1 However, today it may go unrecognized by physicians until a blood culture shows anaerobes or Fusobacterium species. For a young patient admitted with pneumonia preceded by pharyngitis, hospitalists must remain vigilant for the presence of LS.
Key Points for Hospitalists/Teaching Points
-
The triad of LS is pharyngitis, thrombophlebitis, and distant metastatic pyogenic emboli.
-
Suspect LS in a young, otherwise healthy patient who clinically deteriorates in the setting of a recent pharyngeal infection.
-
With the modern decrease in antibiotic use for pharyngitis, LS may be on the rise.
- .On certain septicaemias due to anaerobic organisms.Lancet.1936;1:701–703.
- ,.Lemierre's syndrome: more judicious antibiotic prescribing habits may lead to the clinical reappearance of this often forgotten disease.Am J Med.2006;119(3):e7–e9.
- .Human infection with Fusobacterium necrophorum (necrobacillosis), with a focus on Lemierre's syndrome.Clin Microbiol Rev.2007;20(4):622–659.
- ,.Human necrobacillosis, with emphasis on Lemierre's syndrome.Clin Infect Dis.2000;31(2):524–532.
- ,.Fusobacterial infections: clinical spectrum and incidence of invasive disease.J Infect.2008;57(4):283–289.
- .Human infections with Fusobacterium necrophorum.Anaerobe.2006;12(4):165–172.
- ,,, et al.Increased diagnosis of Lemierre Syndrome and other Fusobacterium necrophorum infections at a Children's Hospital.Pediatrics.2003;112(5):e380.
- ,,.Unusual presentation of Lemierre's syndrome due to Fusobacterium nucleatum.J Clin Microbiol.2003;41(7):3445–3448.
- ,,,.The evolution of Lemierre Syndrome: report of 2 cases and review of the literature.Medicine (Baltimore).2002;81(6):458–465.
- ,.An unusual case of Lemierre's syndrome presenting as pyomyositis.Am J Med Sci.2008;335(6):499–501.
- .Update on the taxonomy and clinical aspects of the genus Fusobacterium.Clin Infect Dis.2002;35(Suppl 1):S22–S27.
- ,,,,.Lemierre syndrome: two cases and a review.Laryngosope.2007;117(9):1605–1610.
- ,,.Lemierre's syndrome (necrobacillosis).Postgrad Med J.1999;75(881):141–144.
The approach to clinical conundrums by an expert clinician is revealed through presentation of an actual patient's case in an approach typical of morning report. Similar to patient care, sequential pieces of information are provided to the clinician who is unfamiliar with the case. The focus is on the thought processes of both the clinical team caring the patient and the discussant.
A 27‐year‐old woman with a history of asthma presented to her primary care physician (PCP) with a sore throat which began after attending a party where she shared alcoholic beverages with friends. She denied any high‐risk sexual behavior. Her PCP prescribed azithromycin and methylprednisolone empirically for tonsillitis. The throat pain subsided, but in the next several days she experienced increased weakness, lethargy, poor appetite, and chills, and she returned to her PCP for reevaluation.
Two months prior she had been treated at a walk‐in clinic with a course of penicillin for a presumed streptococcal pharyngitis. Her symptoms resolved until her current presentation.
In a young woman with 2 episodes of pharyngitis in 2 months followed by an acute systemic illness, one must consider an immunocompromised state such as human immunodeficiency virus (HIV), hematologic malignancy, or autoimmune diseases. Weakness, lethargy, anorexia, and chills in the setting of pharyngitis suggest a local process in the neck, most likely infection associated with systemic toxicity. As neck abscess and bacteremia warrant early consideration, the physical examination should focus on the neck and oropharynx, as well as neurologic exam to evaluate for bacterial spreading into the central nervous system. In addition to routine laboratory studies, a chest x‐ray (CXR) would be appropriate as upper respiratory infections may be complicated by pneumonia and present with signs and symptoms of systemic illness.
On examination by her PCP, her temperature was 99.2F and her blood pressure was 118/68. She had bilateral oropharyngeal erythema without exudates and bilateral tonsillar and anterior triangle lymphadenopathy (LAD). An oropharyngeal rapid Streptococcal antigen detection test was negative, but a Monospot test was positive for heterophile antibodies. Azithromycin and methylprednisolone were discontinued, and the patient was informed she most likely had Epstein Barr Virus (EBV) infection.
The following day, the complete blood count results returned. The platelet count was 50 K/L and the white blood cell (WBC) count was 13.0 K/L. The patient stated she had developed right‐sided flank pain upon deep inspiration and used her albuterol inhaler with minimal relief. She continued to have fever, decreased appetite, chest and abdominal pain, and difficulty swallowing due to odynophagia. She was instructed to go to the emergency department (ED) for further evaluation. In the ED, she denied any shortness of breath, but reported a slight cough and right‐sided abdominal pain.
Acute tonsillar pharyngitis and fever, as well as systemic symptoms of fatigue and abdominal pain along with positive heterophile screen are highly suggestive of EBV infection in this young female. The episode of pharyngitis 2 months prior remains unexplained and may be unrelated. Right‐sided pleuritic pain and abdominal pain may be related to EBV hepatitis. Odynophagia is consistent with EBV infection as well. Profound lethargy, however, is not a common presenting feature in mononucleosis unless infected patients are profoundly dehydrated due to inability to swallow. Her pain symptoms may be secondary to other signs of EBV infection, such as hepatomegaly, splenomegaly, ascites, and/or right pleural effusion. A history of rash should be investigated. Initial assessment in this acutely ill patient should focus on evaluation for the presence of severe sepsis and for a primary source of infection. Given the severity of her illness, I would consider early computed tomography (CT) of her chest, abdomen, and pelvis, as well as CT of the neck to exclude a possibility of peritonsillar abscess. The complaint of chills indicates a possible bacteremia, so coverage with broad‐spectrum antibiotics is indicated. Symptomatic relief with acetaminophen and intravenous fluid rehydration is appropriate.
On exam, temperature was 101.9F, blood pressure was 111/74, heart rate was 140 beats per minute, respiratory rate was 18 per minute, and oxygen saturation was 99% on room air. She appeared drowsy, but answered questions appropriately. She had bilateral swollen tonsils, as well as anterior and posterior cervical adenopathy, with tenderness greater on the left side. Her chest exam had slightly diminished breath sounds at the bases bilaterally. Heart rhythm was regular, and there were no murmurs appreciated. On abdominal exam, she was tender to palpation in both right‐upper and left‐upper quadrants, without obvious hepatosplenomegaly. There were no petechiae noted on her skin.
The WBC was 17.6 K/L, with 89% neutrophils and 5% lymphocytes, platelet count was 22 K/L, and hemoglobin was 13.8 g/dL. A D‐dimer test was elevated at 1344 ng/mL. Peripheral blood smear showed thrombocytopenia and neutrophilia, but demonstrated no schistocytes. The serum potassium was 3.2 mEq/L, bicarbonate was 29 mEq/L, blood urea nitrogen was 15 mg/dL and the creatinine was 1.29 mg/dL. Transaminases were within normal limits, but total bilirubin was 1.8 mg/dL. Her urinalysis was normal. Blood cultures were sent. A CXR showed bibasilar consolidations and pleural effusions (Figure 1). A CT of the chest with contrast was obtained that showed multiple confluent and patchy foci of consolidation in the lung bases, with trace bilateral pleural effusions (Figure 2). A CT of the abdomen showed a spleen at the upper limits of normal in size, measuring 13 cm in length, but was otherwise normal.
Leukocytosis with lymphopenia is not consistent with EBV infection and another process needs to be considered. This patient meets criteria for sepsis syndrome and should receive broad spectrum antibiotics, such as vancomycin and piperacillin‐tazobactam immediately after the blood cultures are sent, in addition to further evaluation to determine the source of sepsis. Depending on her mental status response to initial measures such as acetaminophen and hydration, one should consider a lumbar puncture, which would require platelet transfusion and may therefore not be done immediately. HIV serology should be performed, since acute retroviral syndrome can mimic this presentation. With neck tenderness that is more localized to her left side, a CT of her neck to evaluate for an abscess may be helpful.
She was admitted for presumed community‐acquired pneumonia complicating an upper respiratory tract infection. Her pharyngitis was thought to be of viral etiology. Moxifloxacin was started and intravenous fluids were administered. She was started on prednisone 60 mg daily for presumed immune‐mediated thrombocytopenia related to EBV infection. An HIV antibody test and quantitative polymerase chain reaction (PCR) were both negative. The EBV immunoglobulin G (IgG) titer was positive (>1:10), but the IgM titer was negative. Her mental status improved after starting moxifloxacin and fluids. Her creatinine and bilirubin normalized to 0.97 mg/dL and 0.8 mg/dL respectively. She continued to have a tender left‐sided submandibular swelling. Blood cultures grew Gram‐negative bacilli in 2 anaerobic bottles.
I am uncomfortable with moxifloxacin as initial empiric therapy because at presentation she had sepsis syndrome as well as a suspected immunocompromised state. In addition, moxifloxacin would not be adequate coverage for anaerobic organisms if a peritonsillar abscess was involved. At this point, she needs a CT of her neck to look for a focus of infection which may require surgical management and, if negative, further imaging such as a tagged white blood scan to identify the source of the anaerobes.
Moxifloxacin was switched to piperacillin‐tazobactam and prednisone was discontinued. By day 4 of hospitalization her platelet count had risen to 261 K/L. Her WBC continued to rise to a peak of 21.5 K/L and she continued to have fevers and diffuse pains, although her repeat blood cultures were negative. She continued to have tenderness of the cervical lymph nodes, left greater than right. A repeat CXR showed patchy air space disease bilaterally and pleural effusions, both of which had progressed compared with the prior film. Clindamycin was empirically added to her antibiotic regimen in light of her progressing pneumonia and evidence of anaerobic infection. A repeat CT scan of her chest revealed multiple nodular opacities scattered throughout the lung fields, some of which were cavitary, predominating in the lung bases. The CT scan of her neck revealed a left peritonsillar abscess and phlegmon in the left retropharyngeal and deep neck area along the sternocleidomastoid and internal jugular vein (IJV). There also was noted a large thrombus within the left IJV extending superiorly to involve the jugular bulb, sigmoid sinus, and distal left transverse sinus; and inferiorly to near the origin of the brachiocephalic vein (see Figure 3). An echocardiogram did not reveal any vegetations.
The combination of recent pharyngitis, septic pulmonary emboli, and IJV thrombosis is consistent with a diagnosis of Lemierre's syndrome (LS). This is a life threatening condition, even if diagnosis is made early and appropriate treatment is started. The most likely causative agent is Fusobacterium necrophorum. In this case it was important to realize that clinical presentation was not consistent with EBV infection, even though heterophile screen was positive. Early initiation of broad spectrum antibiotics as well as CT scan of the neck would have been appropriate.
The diagnosis of LS was made. The blood culture speciation revealed Fusobacterium nucleatum, which was too fastidious to perform antimicrobial sensitivities. Her symptoms improved significantly with the addition of clindamycin to piperacillin‐tazobactam, which was postulated to be the result of bacterial beta‐lactamase activity mitigating the efficacy of piperacillin‐tazobactam. Thoracentesis of her pleural effusion did not reveal an empyema. Due to her large thrombus burden, she was started on anticoagulation with heparin and transitioned to outpatient coumadin. She was switched to metronidazole as a single agent antibiotic for 6 weeks, and on outpatient follow‐up was doing well.
Commentary
LS was described by Dr. Andre Lemierre in 1936.6 The syndrome consists of a primary oropharyngeal infection, thrombosis of the IJV, bacteremia, and septic metastatic foci, usually involving the lungs.1, 2 LS is a form of necrobacillosis, which is a systemic infection resulting from F. necrophorum.3, 4 In classic LS, the initial pharyngitis is usually a tonsillar or peritonsillar abscess, and is followed by intense fever and rigors after 4 days to 2 weeks.1, 3 This is followed by a unilateral painful submaxillary LAD and IJV thrombophlebitisthe cord sign.2 Finally, bacteremia and distant metastatic pyogenic abscesses develop.1 (see Table 1).
| Lemierre's Syndrome typical features |
| Antecedent head and neck infection, typically an oropharyngeal infection prior to deterioration |
| Thrombophlebitis, typically of internal jugular vein (present in only 1/3 of cases) |
| Bacteremia (Fusobacterium necrophorum most commonly) |
| Septic metastatic foci, typically to lungs |
| Usual Presentation |
| Pharyngitis |
| Fevers |
| Rigors |
| Neck involvement: tenderness, swelling, tender internal jugular vein thrombus (cord sign) |
| Pulmonary infiltrates which cavitate |
With the advent of antibiotics, LS is now rare with an incidence of 0.9 per million persons per year. In Lemierre's time, the disease was fulminant and led to death within 2 weeks, but in the antibiotic age the mortality rate is 4.9%.1, 3 The median age of an LS patient is 19 years, with a higher incidence in males.1, 35 Although in the literature it is referred to as the forgotten disease, there is evidence the incidence is increasing.3, 4, 6, 8
There are variations of classic LS. Bacteremia may occur much later than the initial pharyngitis, the disease may be less aggressive, the thrombus may be in the external jugular vein, or there may be no identified thrombus.3, 4, 8 In fact, a thrombus is only identified in 36% of cases.9 The primary infection may be a head and neck infection that is not pharyngitis, such as an odontogenic infection,4 or may not be identified.10 Despite variations, the fundamentals of diagnosis are prior head and neck infection, presumed thrombophlebitis and bacteremia, and evidence of septic metastatic foci.
The genus Fusobacterium comprises anaerobic, nonspore forming gram negative bacilli.1, 35, 11 F. necrophorum and F. nucleatum are 2 species within this genus. F. nucleatum causes the majority of reported human bacteremias by Fusobacterium species, but it is F. necrophorum that is most associated with anaerobic oropharyngeal infections, thrombocytopenia, clot formation, and LS.35, 8, 9
It is unknown if Fusobacterium species directly cause the sore throat, or rather are bystanders which thrive once a favorable anaerobic environment is created via endotoxins and exotoxins.35 A break in oral mucosa via trauma or coinfection with bacteria/viruses (especially EBV) is also thought to play a role with infection.2, 3, 5 One‐third of LS cases have coinfection with other oropharyngeal flora. Thus, one must reexamine the anaerobic blood cultures after an organism has been identified in suspect cases.3, 4
There is an increased association of LS with EBV infection, likely due to viral‐induced and steroid‐induced immunosuppression.24 False positive heterophile tests are reported with LS, so the specific antibody tests for EBV must be checked.3, 4
Once thrombophlebitis occurs, the bacteria can metastasize to distant sites. In 80% to 92% of LS cases, the metastatic complication is a pleuro‐pulmonary infection, consisting of septic pulmonary emboli, empyema, and pleural effusions, but extra‐pulmonary lesions occur.1, 3, 9, 12 Abdominal pain usually results from abdominal microabscesses or thrombophlebitis.4 Mild renal impairment and abnormal liver function tests are common.3, 4 Cranial nerve palsies and Horner's syndrome are rare and indicate carotid sheath involvement.3, 12 An elevated C‐reactive protein can distinguish bacterial from uncomplicated viral pharyngitis.3, 4 Also, rigors are unusual in tonsillitis, and their presence indicate bacterial entry into the circulation.3
CXRs may reveal the pulmonary septic emboli. Ultrasound of the IJV is inexpensive and noninvasive, but may have limited sensitivity for an acute thrombus. CT scan allows increased visualization of anatomy, but can have decreased sensitivity and specificity for thrombosis.3 Magnetic resonance imaging (MRI) is recommended if LS results from mastoiditis, to exclude an intracerebral vein thrombosis.9
Antibiotics have both dramatically decreased the incidence of LS and improved its prognosis. The recent rise in incidence may be due to a renewed interest in restricting the use of antibiotics in cases of pharyngitis, as well as an increased use of macrolides, to which F. necrophorum is frequently resistant.3 Decreased tonsillectomies may also have a role, as LS is more common with retained tonsils.1, 3
No trials have evaluated the optimal antibiotic regimen. Fusobacterium species are sensitive to penicillin, but 23% have beta‐lactamase activity as reported clinically by several authors.3, 5 F. necrophorum is also sensitive to metronidazole, ticarcillin‐clavulanate, cefoxitin, amoxicillin‐clavulanate, imipenem, and clindamycin. There is a high resistance to macrolides and gentamicin, and the activity of tetracyclines is poor. For treatment, most authors suggest a carbapenem, a penicillin/beta‐lactamase inhibitor combination, or metronidazole. Clindamycin has weaker bactericidal activity than metronidazole or imipenem. Metronidazole is preferred because of its activity against all Fusobacterium species, good penetration into tissues, bactericidal activity, low minimum inhibitory concentration, and ability to achieve high concentration in the cerebrospinal fluid if meningitis occurs. An effective regimen is metronidazole with a penicillinase‐resistant penicillin to cover for mixed coinfection with streptococci or staphylococci.3, 4, 12 A 6‐week antibiotic course is given for adequate penetration into the protective fibrin clots.4
Reports have shown good outcomes both with and without the use of anticoagulation.3, 4, 8 Support for anticoagulation is extrapolated from experience with septic pelvic thrombophlebitis, in which anticoagulation results in more rapid resolution of symptoms.13 Given the lack of firm evidence in cases of LS, anticoagulation is typically reserved for poor clinical response despite 2 to 3 days of antibiotic therapy or propagation of thromboses into the cavernous sinus. It is generally given for 3 months.4, 13
Prior to the antibiotic era, surgical ligation or excision of the IJV was done without clear benefit. Today, surgery is reserved for cases of continued septic emboli or extension of thrombus despite aggressive medical therapy.3 If mediastinitis develops, then surgical intervention is essential.4
Lemierre stated that the symptoms and signs of LS are so characteristic that it permits diagnosis before bacteriological examination.1 However, today it may go unrecognized by physicians until a blood culture shows anaerobes or Fusobacterium species. For a young patient admitted with pneumonia preceded by pharyngitis, hospitalists must remain vigilant for the presence of LS.
Key Points for Hospitalists/Teaching Points
-
The triad of LS is pharyngitis, thrombophlebitis, and distant metastatic pyogenic emboli.
-
Suspect LS in a young, otherwise healthy patient who clinically deteriorates in the setting of a recent pharyngeal infection.
-
With the modern decrease in antibiotic use for pharyngitis, LS may be on the rise.
The approach to clinical conundrums by an expert clinician is revealed through presentation of an actual patient's case in an approach typical of morning report. Similar to patient care, sequential pieces of information are provided to the clinician who is unfamiliar with the case. The focus is on the thought processes of both the clinical team caring the patient and the discussant.
A 27‐year‐old woman with a history of asthma presented to her primary care physician (PCP) with a sore throat which began after attending a party where she shared alcoholic beverages with friends. She denied any high‐risk sexual behavior. Her PCP prescribed azithromycin and methylprednisolone empirically for tonsillitis. The throat pain subsided, but in the next several days she experienced increased weakness, lethargy, poor appetite, and chills, and she returned to her PCP for reevaluation.
Two months prior she had been treated at a walk‐in clinic with a course of penicillin for a presumed streptococcal pharyngitis. Her symptoms resolved until her current presentation.
In a young woman with 2 episodes of pharyngitis in 2 months followed by an acute systemic illness, one must consider an immunocompromised state such as human immunodeficiency virus (HIV), hematologic malignancy, or autoimmune diseases. Weakness, lethargy, anorexia, and chills in the setting of pharyngitis suggest a local process in the neck, most likely infection associated with systemic toxicity. As neck abscess and bacteremia warrant early consideration, the physical examination should focus on the neck and oropharynx, as well as neurologic exam to evaluate for bacterial spreading into the central nervous system. In addition to routine laboratory studies, a chest x‐ray (CXR) would be appropriate as upper respiratory infections may be complicated by pneumonia and present with signs and symptoms of systemic illness.
On examination by her PCP, her temperature was 99.2F and her blood pressure was 118/68. She had bilateral oropharyngeal erythema without exudates and bilateral tonsillar and anterior triangle lymphadenopathy (LAD). An oropharyngeal rapid Streptococcal antigen detection test was negative, but a Monospot test was positive for heterophile antibodies. Azithromycin and methylprednisolone were discontinued, and the patient was informed she most likely had Epstein Barr Virus (EBV) infection.
The following day, the complete blood count results returned. The platelet count was 50 K/L and the white blood cell (WBC) count was 13.0 K/L. The patient stated she had developed right‐sided flank pain upon deep inspiration and used her albuterol inhaler with minimal relief. She continued to have fever, decreased appetite, chest and abdominal pain, and difficulty swallowing due to odynophagia. She was instructed to go to the emergency department (ED) for further evaluation. In the ED, she denied any shortness of breath, but reported a slight cough and right‐sided abdominal pain.
Acute tonsillar pharyngitis and fever, as well as systemic symptoms of fatigue and abdominal pain along with positive heterophile screen are highly suggestive of EBV infection in this young female. The episode of pharyngitis 2 months prior remains unexplained and may be unrelated. Right‐sided pleuritic pain and abdominal pain may be related to EBV hepatitis. Odynophagia is consistent with EBV infection as well. Profound lethargy, however, is not a common presenting feature in mononucleosis unless infected patients are profoundly dehydrated due to inability to swallow. Her pain symptoms may be secondary to other signs of EBV infection, such as hepatomegaly, splenomegaly, ascites, and/or right pleural effusion. A history of rash should be investigated. Initial assessment in this acutely ill patient should focus on evaluation for the presence of severe sepsis and for a primary source of infection. Given the severity of her illness, I would consider early computed tomography (CT) of her chest, abdomen, and pelvis, as well as CT of the neck to exclude a possibility of peritonsillar abscess. The complaint of chills indicates a possible bacteremia, so coverage with broad‐spectrum antibiotics is indicated. Symptomatic relief with acetaminophen and intravenous fluid rehydration is appropriate.
On exam, temperature was 101.9F, blood pressure was 111/74, heart rate was 140 beats per minute, respiratory rate was 18 per minute, and oxygen saturation was 99% on room air. She appeared drowsy, but answered questions appropriately. She had bilateral swollen tonsils, as well as anterior and posterior cervical adenopathy, with tenderness greater on the left side. Her chest exam had slightly diminished breath sounds at the bases bilaterally. Heart rhythm was regular, and there were no murmurs appreciated. On abdominal exam, she was tender to palpation in both right‐upper and left‐upper quadrants, without obvious hepatosplenomegaly. There were no petechiae noted on her skin.
The WBC was 17.6 K/L, with 89% neutrophils and 5% lymphocytes, platelet count was 22 K/L, and hemoglobin was 13.8 g/dL. A D‐dimer test was elevated at 1344 ng/mL. Peripheral blood smear showed thrombocytopenia and neutrophilia, but demonstrated no schistocytes. The serum potassium was 3.2 mEq/L, bicarbonate was 29 mEq/L, blood urea nitrogen was 15 mg/dL and the creatinine was 1.29 mg/dL. Transaminases were within normal limits, but total bilirubin was 1.8 mg/dL. Her urinalysis was normal. Blood cultures were sent. A CXR showed bibasilar consolidations and pleural effusions (Figure 1). A CT of the chest with contrast was obtained that showed multiple confluent and patchy foci of consolidation in the lung bases, with trace bilateral pleural effusions (Figure 2). A CT of the abdomen showed a spleen at the upper limits of normal in size, measuring 13 cm in length, but was otherwise normal.
Leukocytosis with lymphopenia is not consistent with EBV infection and another process needs to be considered. This patient meets criteria for sepsis syndrome and should receive broad spectrum antibiotics, such as vancomycin and piperacillin‐tazobactam immediately after the blood cultures are sent, in addition to further evaluation to determine the source of sepsis. Depending on her mental status response to initial measures such as acetaminophen and hydration, one should consider a lumbar puncture, which would require platelet transfusion and may therefore not be done immediately. HIV serology should be performed, since acute retroviral syndrome can mimic this presentation. With neck tenderness that is more localized to her left side, a CT of her neck to evaluate for an abscess may be helpful.
She was admitted for presumed community‐acquired pneumonia complicating an upper respiratory tract infection. Her pharyngitis was thought to be of viral etiology. Moxifloxacin was started and intravenous fluids were administered. She was started on prednisone 60 mg daily for presumed immune‐mediated thrombocytopenia related to EBV infection. An HIV antibody test and quantitative polymerase chain reaction (PCR) were both negative. The EBV immunoglobulin G (IgG) titer was positive (>1:10), but the IgM titer was negative. Her mental status improved after starting moxifloxacin and fluids. Her creatinine and bilirubin normalized to 0.97 mg/dL and 0.8 mg/dL respectively. She continued to have a tender left‐sided submandibular swelling. Blood cultures grew Gram‐negative bacilli in 2 anaerobic bottles.
I am uncomfortable with moxifloxacin as initial empiric therapy because at presentation she had sepsis syndrome as well as a suspected immunocompromised state. In addition, moxifloxacin would not be adequate coverage for anaerobic organisms if a peritonsillar abscess was involved. At this point, she needs a CT of her neck to look for a focus of infection which may require surgical management and, if negative, further imaging such as a tagged white blood scan to identify the source of the anaerobes.
Moxifloxacin was switched to piperacillin‐tazobactam and prednisone was discontinued. By day 4 of hospitalization her platelet count had risen to 261 K/L. Her WBC continued to rise to a peak of 21.5 K/L and she continued to have fevers and diffuse pains, although her repeat blood cultures were negative. She continued to have tenderness of the cervical lymph nodes, left greater than right. A repeat CXR showed patchy air space disease bilaterally and pleural effusions, both of which had progressed compared with the prior film. Clindamycin was empirically added to her antibiotic regimen in light of her progressing pneumonia and evidence of anaerobic infection. A repeat CT scan of her chest revealed multiple nodular opacities scattered throughout the lung fields, some of which were cavitary, predominating in the lung bases. The CT scan of her neck revealed a left peritonsillar abscess and phlegmon in the left retropharyngeal and deep neck area along the sternocleidomastoid and internal jugular vein (IJV). There also was noted a large thrombus within the left IJV extending superiorly to involve the jugular bulb, sigmoid sinus, and distal left transverse sinus; and inferiorly to near the origin of the brachiocephalic vein (see Figure 3). An echocardiogram did not reveal any vegetations.
The combination of recent pharyngitis, septic pulmonary emboli, and IJV thrombosis is consistent with a diagnosis of Lemierre's syndrome (LS). This is a life threatening condition, even if diagnosis is made early and appropriate treatment is started. The most likely causative agent is Fusobacterium necrophorum. In this case it was important to realize that clinical presentation was not consistent with EBV infection, even though heterophile screen was positive. Early initiation of broad spectrum antibiotics as well as CT scan of the neck would have been appropriate.
The diagnosis of LS was made. The blood culture speciation revealed Fusobacterium nucleatum, which was too fastidious to perform antimicrobial sensitivities. Her symptoms improved significantly with the addition of clindamycin to piperacillin‐tazobactam, which was postulated to be the result of bacterial beta‐lactamase activity mitigating the efficacy of piperacillin‐tazobactam. Thoracentesis of her pleural effusion did not reveal an empyema. Due to her large thrombus burden, she was started on anticoagulation with heparin and transitioned to outpatient coumadin. She was switched to metronidazole as a single agent antibiotic for 6 weeks, and on outpatient follow‐up was doing well.
Commentary
LS was described by Dr. Andre Lemierre in 1936.6 The syndrome consists of a primary oropharyngeal infection, thrombosis of the IJV, bacteremia, and septic metastatic foci, usually involving the lungs.1, 2 LS is a form of necrobacillosis, which is a systemic infection resulting from F. necrophorum.3, 4 In classic LS, the initial pharyngitis is usually a tonsillar or peritonsillar abscess, and is followed by intense fever and rigors after 4 days to 2 weeks.1, 3 This is followed by a unilateral painful submaxillary LAD and IJV thrombophlebitisthe cord sign.2 Finally, bacteremia and distant metastatic pyogenic abscesses develop.1 (see Table 1).
| Lemierre's Syndrome typical features |
| Antecedent head and neck infection, typically an oropharyngeal infection prior to deterioration |
| Thrombophlebitis, typically of internal jugular vein (present in only 1/3 of cases) |
| Bacteremia (Fusobacterium necrophorum most commonly) |
| Septic metastatic foci, typically to lungs |
| Usual Presentation |
| Pharyngitis |
| Fevers |
| Rigors |
| Neck involvement: tenderness, swelling, tender internal jugular vein thrombus (cord sign) |
| Pulmonary infiltrates which cavitate |
With the advent of antibiotics, LS is now rare with an incidence of 0.9 per million persons per year. In Lemierre's time, the disease was fulminant and led to death within 2 weeks, but in the antibiotic age the mortality rate is 4.9%.1, 3 The median age of an LS patient is 19 years, with a higher incidence in males.1, 35 Although in the literature it is referred to as the forgotten disease, there is evidence the incidence is increasing.3, 4, 6, 8
There are variations of classic LS. Bacteremia may occur much later than the initial pharyngitis, the disease may be less aggressive, the thrombus may be in the external jugular vein, or there may be no identified thrombus.3, 4, 8 In fact, a thrombus is only identified in 36% of cases.9 The primary infection may be a head and neck infection that is not pharyngitis, such as an odontogenic infection,4 or may not be identified.10 Despite variations, the fundamentals of diagnosis are prior head and neck infection, presumed thrombophlebitis and bacteremia, and evidence of septic metastatic foci.
The genus Fusobacterium comprises anaerobic, nonspore forming gram negative bacilli.1, 35, 11 F. necrophorum and F. nucleatum are 2 species within this genus. F. nucleatum causes the majority of reported human bacteremias by Fusobacterium species, but it is F. necrophorum that is most associated with anaerobic oropharyngeal infections, thrombocytopenia, clot formation, and LS.35, 8, 9
It is unknown if Fusobacterium species directly cause the sore throat, or rather are bystanders which thrive once a favorable anaerobic environment is created via endotoxins and exotoxins.35 A break in oral mucosa via trauma or coinfection with bacteria/viruses (especially EBV) is also thought to play a role with infection.2, 3, 5 One‐third of LS cases have coinfection with other oropharyngeal flora. Thus, one must reexamine the anaerobic blood cultures after an organism has been identified in suspect cases.3, 4
There is an increased association of LS with EBV infection, likely due to viral‐induced and steroid‐induced immunosuppression.24 False positive heterophile tests are reported with LS, so the specific antibody tests for EBV must be checked.3, 4
Once thrombophlebitis occurs, the bacteria can metastasize to distant sites. In 80% to 92% of LS cases, the metastatic complication is a pleuro‐pulmonary infection, consisting of septic pulmonary emboli, empyema, and pleural effusions, but extra‐pulmonary lesions occur.1, 3, 9, 12 Abdominal pain usually results from abdominal microabscesses or thrombophlebitis.4 Mild renal impairment and abnormal liver function tests are common.3, 4 Cranial nerve palsies and Horner's syndrome are rare and indicate carotid sheath involvement.3, 12 An elevated C‐reactive protein can distinguish bacterial from uncomplicated viral pharyngitis.3, 4 Also, rigors are unusual in tonsillitis, and their presence indicate bacterial entry into the circulation.3
CXRs may reveal the pulmonary septic emboli. Ultrasound of the IJV is inexpensive and noninvasive, but may have limited sensitivity for an acute thrombus. CT scan allows increased visualization of anatomy, but can have decreased sensitivity and specificity for thrombosis.3 Magnetic resonance imaging (MRI) is recommended if LS results from mastoiditis, to exclude an intracerebral vein thrombosis.9
Antibiotics have both dramatically decreased the incidence of LS and improved its prognosis. The recent rise in incidence may be due to a renewed interest in restricting the use of antibiotics in cases of pharyngitis, as well as an increased use of macrolides, to which F. necrophorum is frequently resistant.3 Decreased tonsillectomies may also have a role, as LS is more common with retained tonsils.1, 3
No trials have evaluated the optimal antibiotic regimen. Fusobacterium species are sensitive to penicillin, but 23% have beta‐lactamase activity as reported clinically by several authors.3, 5 F. necrophorum is also sensitive to metronidazole, ticarcillin‐clavulanate, cefoxitin, amoxicillin‐clavulanate, imipenem, and clindamycin. There is a high resistance to macrolides and gentamicin, and the activity of tetracyclines is poor. For treatment, most authors suggest a carbapenem, a penicillin/beta‐lactamase inhibitor combination, or metronidazole. Clindamycin has weaker bactericidal activity than metronidazole or imipenem. Metronidazole is preferred because of its activity against all Fusobacterium species, good penetration into tissues, bactericidal activity, low minimum inhibitory concentration, and ability to achieve high concentration in the cerebrospinal fluid if meningitis occurs. An effective regimen is metronidazole with a penicillinase‐resistant penicillin to cover for mixed coinfection with streptococci or staphylococci.3, 4, 12 A 6‐week antibiotic course is given for adequate penetration into the protective fibrin clots.4
Reports have shown good outcomes both with and without the use of anticoagulation.3, 4, 8 Support for anticoagulation is extrapolated from experience with septic pelvic thrombophlebitis, in which anticoagulation results in more rapid resolution of symptoms.13 Given the lack of firm evidence in cases of LS, anticoagulation is typically reserved for poor clinical response despite 2 to 3 days of antibiotic therapy or propagation of thromboses into the cavernous sinus. It is generally given for 3 months.4, 13
Prior to the antibiotic era, surgical ligation or excision of the IJV was done without clear benefit. Today, surgery is reserved for cases of continued septic emboli or extension of thrombus despite aggressive medical therapy.3 If mediastinitis develops, then surgical intervention is essential.4
Lemierre stated that the symptoms and signs of LS are so characteristic that it permits diagnosis before bacteriological examination.1 However, today it may go unrecognized by physicians until a blood culture shows anaerobes or Fusobacterium species. For a young patient admitted with pneumonia preceded by pharyngitis, hospitalists must remain vigilant for the presence of LS.
Key Points for Hospitalists/Teaching Points
-
The triad of LS is pharyngitis, thrombophlebitis, and distant metastatic pyogenic emboli.
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Suspect LS in a young, otherwise healthy patient who clinically deteriorates in the setting of a recent pharyngeal infection.
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With the modern decrease in antibiotic use for pharyngitis, LS may be on the rise.
- .On certain septicaemias due to anaerobic organisms.Lancet.1936;1:701–703.
- ,.Lemierre's syndrome: more judicious antibiotic prescribing habits may lead to the clinical reappearance of this often forgotten disease.Am J Med.2006;119(3):e7–e9.
- .Human infection with Fusobacterium necrophorum (necrobacillosis), with a focus on Lemierre's syndrome.Clin Microbiol Rev.2007;20(4):622–659.
- ,.Human necrobacillosis, with emphasis on Lemierre's syndrome.Clin Infect Dis.2000;31(2):524–532.
- ,.Fusobacterial infections: clinical spectrum and incidence of invasive disease.J Infect.2008;57(4):283–289.
- .Human infections with Fusobacterium necrophorum.Anaerobe.2006;12(4):165–172.
- ,,, et al.Increased diagnosis of Lemierre Syndrome and other Fusobacterium necrophorum infections at a Children's Hospital.Pediatrics.2003;112(5):e380.
- ,,.Unusual presentation of Lemierre's syndrome due to Fusobacterium nucleatum.J Clin Microbiol.2003;41(7):3445–3448.
- ,,,.The evolution of Lemierre Syndrome: report of 2 cases and review of the literature.Medicine (Baltimore).2002;81(6):458–465.
- ,.An unusual case of Lemierre's syndrome presenting as pyomyositis.Am J Med Sci.2008;335(6):499–501.
- .Update on the taxonomy and clinical aspects of the genus Fusobacterium.Clin Infect Dis.2002;35(Suppl 1):S22–S27.
- ,,,,.Lemierre syndrome: two cases and a review.Laryngosope.2007;117(9):1605–1610.
- ,,.Lemierre's syndrome (necrobacillosis).Postgrad Med J.1999;75(881):141–144.
- .On certain septicaemias due to anaerobic organisms.Lancet.1936;1:701–703.
- ,.Lemierre's syndrome: more judicious antibiotic prescribing habits may lead to the clinical reappearance of this often forgotten disease.Am J Med.2006;119(3):e7–e9.
- .Human infection with Fusobacterium necrophorum (necrobacillosis), with a focus on Lemierre's syndrome.Clin Microbiol Rev.2007;20(4):622–659.
- ,.Human necrobacillosis, with emphasis on Lemierre's syndrome.Clin Infect Dis.2000;31(2):524–532.
- ,.Fusobacterial infections: clinical spectrum and incidence of invasive disease.J Infect.2008;57(4):283–289.
- .Human infections with Fusobacterium necrophorum.Anaerobe.2006;12(4):165–172.
- ,,, et al.Increased diagnosis of Lemierre Syndrome and other Fusobacterium necrophorum infections at a Children's Hospital.Pediatrics.2003;112(5):e380.
- ,,.Unusual presentation of Lemierre's syndrome due to Fusobacterium nucleatum.J Clin Microbiol.2003;41(7):3445–3448.
- ,,,.The evolution of Lemierre Syndrome: report of 2 cases and review of the literature.Medicine (Baltimore).2002;81(6):458–465.
- ,.An unusual case of Lemierre's syndrome presenting as pyomyositis.Am J Med Sci.2008;335(6):499–501.
- .Update on the taxonomy and clinical aspects of the genus Fusobacterium.Clin Infect Dis.2002;35(Suppl 1):S22–S27.
- ,,,,.Lemierre syndrome: two cases and a review.Laryngosope.2007;117(9):1605–1610.
- ,,.Lemierre's syndrome (necrobacillosis).Postgrad Med J.1999;75(881):141–144.
