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Noninvasive Scan Genotypes Non-Small Cell Lung Cancer
AMSTERDAM – An experimental combination of PET scanning and a positron-emitting form of erlotinib appeared to work as a noninvasive way of identifying patients with advanced non–small cell lung cancer tumors that have the right genotype to receive erlotinib therapy.
"[11C]erlotinib PET shows promise as a noninvasive, in vivo means of selecting patients who may benefit from thymidine kinase inhibitor therapy," Dr. Idris Bahce said, reporting on a pilot study of 10 patients. Erlotinib (Tarceva) is from the thymidine kinase inhibitor drug class.
In his study, uptake of 11C-labeled erlotinib was significantly linked to the patients’ having an activating mutation in their epidermal growth factor receptor (EGFR) gene, specifically an exon 19 deletion.
Patients positive for erlotinib uptake on the PET scan also showed a tendency for better clinical responses to a therapeutic erlotinib regimen, reported Dr. Bahce, a pulmonologist at VU University, Amsterdam, during the World Conference on Lung Cancer.
Until now, the only way to identify advanced non–small cell lung cancer (NSCLC) tumors that are candidates for treatment with a tyrosine kinase inhibitor has been to biopsy the tumor and run an in vitro genetic analysis on the tumor cells. That can be challenging in some patients, such as when the tumor is not easy to biopsy, a limited amount of tissue is available, or the tumor is genetically heterogeneous. To get a reliable result from biopsy and testing, at least 30% of the specimen must contain malignant cells, Dr. Bahce said at the conference, sponsored by the International Association for the Study of Lung Cancer.
"It is a very early study, but ... it’s important because personalized treatment [for cancer] has gone to the next level, where we use new agents and match them to the right patients by doing biopsies," commented Dr. Roy S. Herbst, chief of medical oncology at the Yale Cancer Center in New Haven. "The PET method also allows physicians to assess the volume of cancer carrying the EGFR mutation following treatment, a way to track treatment efficacy," said Dr. Herbst in an interview.
"Instead of getting tissue at one point in time, you can image more frequently. It’s a way to track the course of treatment noninvasively," and in real time, he said.
He also predicted that the [11C]erlotinib PET test will become commercialized, although currently Dr. Bahce’s studies do not have any commercial funding.
"This is a proof of concept study," commented Dr. Luis Paz-Ares, chief of medical oncology at University Hospital Virgin del Rocio in Seville, Spain. "We need to define the positive predictive value and the negative predictive value" of the test, he added. The long-term future of a test like this may also be limited because future testing will probably need to look at multiple biomarkers, Dr. Paz-Ares said.
The study enrolled five patients with advanced NSCLC who had exon 19 deletion EGFR mutations, and five advanced NSCLC patients with wild-type EGFR genes. Each patient underwent a pair of [11C]erlotinib PET scans, each preceded by a [15O]water PET scan to assess blood perfusion of the tumors. A 4-hour interval separated the two sets of scans.
The scan results showed that the volume of distribution of the tagged erlotinib in the patients with EGFR mutations ran about 50% higher than in the patients with wild-type tumors, a difference that was significant (P = .03).
Clinically, two of the five wild-type patients had nonetheless received erlotinib treatment prior to testing, and neither patient responded, with both showing progressive disease.
Three of the five patients with an EGFR mutation began receiving erlotinib treatment after testing and responded. In one of these patients, the tumor remained in check for 13 months. In a second patient, the tumor began to progress after 17 months of no progression on treatment. In the third patient, the tumor began to progress again after about 4 weeks of no progression on erlotinib treatment, Dr. Bahce said. A fourth patient went on erlotinib treatment before testing, and did not respond and continued to have progressive disease.
The two patient subgroups showed no difference in blood perfusion into the tumors, or in EGFR expression in cell membranes.
Dr. Bahce said he had no disclosures.
AMSTERDAM – An experimental combination of PET scanning and a positron-emitting form of erlotinib appeared to work as a noninvasive way of identifying patients with advanced non–small cell lung cancer tumors that have the right genotype to receive erlotinib therapy.
"[11C]erlotinib PET shows promise as a noninvasive, in vivo means of selecting patients who may benefit from thymidine kinase inhibitor therapy," Dr. Idris Bahce said, reporting on a pilot study of 10 patients. Erlotinib (Tarceva) is from the thymidine kinase inhibitor drug class.
In his study, uptake of 11C-labeled erlotinib was significantly linked to the patients’ having an activating mutation in their epidermal growth factor receptor (EGFR) gene, specifically an exon 19 deletion.
Patients positive for erlotinib uptake on the PET scan also showed a tendency for better clinical responses to a therapeutic erlotinib regimen, reported Dr. Bahce, a pulmonologist at VU University, Amsterdam, during the World Conference on Lung Cancer.
Until now, the only way to identify advanced non–small cell lung cancer (NSCLC) tumors that are candidates for treatment with a tyrosine kinase inhibitor has been to biopsy the tumor and run an in vitro genetic analysis on the tumor cells. That can be challenging in some patients, such as when the tumor is not easy to biopsy, a limited amount of tissue is available, or the tumor is genetically heterogeneous. To get a reliable result from biopsy and testing, at least 30% of the specimen must contain malignant cells, Dr. Bahce said at the conference, sponsored by the International Association for the Study of Lung Cancer.
"It is a very early study, but ... it’s important because personalized treatment [for cancer] has gone to the next level, where we use new agents and match them to the right patients by doing biopsies," commented Dr. Roy S. Herbst, chief of medical oncology at the Yale Cancer Center in New Haven. "The PET method also allows physicians to assess the volume of cancer carrying the EGFR mutation following treatment, a way to track treatment efficacy," said Dr. Herbst in an interview.
"Instead of getting tissue at one point in time, you can image more frequently. It’s a way to track the course of treatment noninvasively," and in real time, he said.
He also predicted that the [11C]erlotinib PET test will become commercialized, although currently Dr. Bahce’s studies do not have any commercial funding.
"This is a proof of concept study," commented Dr. Luis Paz-Ares, chief of medical oncology at University Hospital Virgin del Rocio in Seville, Spain. "We need to define the positive predictive value and the negative predictive value" of the test, he added. The long-term future of a test like this may also be limited because future testing will probably need to look at multiple biomarkers, Dr. Paz-Ares said.
The study enrolled five patients with advanced NSCLC who had exon 19 deletion EGFR mutations, and five advanced NSCLC patients with wild-type EGFR genes. Each patient underwent a pair of [11C]erlotinib PET scans, each preceded by a [15O]water PET scan to assess blood perfusion of the tumors. A 4-hour interval separated the two sets of scans.
The scan results showed that the volume of distribution of the tagged erlotinib in the patients with EGFR mutations ran about 50% higher than in the patients with wild-type tumors, a difference that was significant (P = .03).
Clinically, two of the five wild-type patients had nonetheless received erlotinib treatment prior to testing, and neither patient responded, with both showing progressive disease.
Three of the five patients with an EGFR mutation began receiving erlotinib treatment after testing and responded. In one of these patients, the tumor remained in check for 13 months. In a second patient, the tumor began to progress after 17 months of no progression on treatment. In the third patient, the tumor began to progress again after about 4 weeks of no progression on erlotinib treatment, Dr. Bahce said. A fourth patient went on erlotinib treatment before testing, and did not respond and continued to have progressive disease.
The two patient subgroups showed no difference in blood perfusion into the tumors, or in EGFR expression in cell membranes.
Dr. Bahce said he had no disclosures.
AMSTERDAM – An experimental combination of PET scanning and a positron-emitting form of erlotinib appeared to work as a noninvasive way of identifying patients with advanced non–small cell lung cancer tumors that have the right genotype to receive erlotinib therapy.
"[11C]erlotinib PET shows promise as a noninvasive, in vivo means of selecting patients who may benefit from thymidine kinase inhibitor therapy," Dr. Idris Bahce said, reporting on a pilot study of 10 patients. Erlotinib (Tarceva) is from the thymidine kinase inhibitor drug class.
In his study, uptake of 11C-labeled erlotinib was significantly linked to the patients’ having an activating mutation in their epidermal growth factor receptor (EGFR) gene, specifically an exon 19 deletion.
Patients positive for erlotinib uptake on the PET scan also showed a tendency for better clinical responses to a therapeutic erlotinib regimen, reported Dr. Bahce, a pulmonologist at VU University, Amsterdam, during the World Conference on Lung Cancer.
Until now, the only way to identify advanced non–small cell lung cancer (NSCLC) tumors that are candidates for treatment with a tyrosine kinase inhibitor has been to biopsy the tumor and run an in vitro genetic analysis on the tumor cells. That can be challenging in some patients, such as when the tumor is not easy to biopsy, a limited amount of tissue is available, or the tumor is genetically heterogeneous. To get a reliable result from biopsy and testing, at least 30% of the specimen must contain malignant cells, Dr. Bahce said at the conference, sponsored by the International Association for the Study of Lung Cancer.
"It is a very early study, but ... it’s important because personalized treatment [for cancer] has gone to the next level, where we use new agents and match them to the right patients by doing biopsies," commented Dr. Roy S. Herbst, chief of medical oncology at the Yale Cancer Center in New Haven. "The PET method also allows physicians to assess the volume of cancer carrying the EGFR mutation following treatment, a way to track treatment efficacy," said Dr. Herbst in an interview.
"Instead of getting tissue at one point in time, you can image more frequently. It’s a way to track the course of treatment noninvasively," and in real time, he said.
He also predicted that the [11C]erlotinib PET test will become commercialized, although currently Dr. Bahce’s studies do not have any commercial funding.
"This is a proof of concept study," commented Dr. Luis Paz-Ares, chief of medical oncology at University Hospital Virgin del Rocio in Seville, Spain. "We need to define the positive predictive value and the negative predictive value" of the test, he added. The long-term future of a test like this may also be limited because future testing will probably need to look at multiple biomarkers, Dr. Paz-Ares said.
The study enrolled five patients with advanced NSCLC who had exon 19 deletion EGFR mutations, and five advanced NSCLC patients with wild-type EGFR genes. Each patient underwent a pair of [11C]erlotinib PET scans, each preceded by a [15O]water PET scan to assess blood perfusion of the tumors. A 4-hour interval separated the two sets of scans.
The scan results showed that the volume of distribution of the tagged erlotinib in the patients with EGFR mutations ran about 50% higher than in the patients with wild-type tumors, a difference that was significant (P = .03).
Clinically, two of the five wild-type patients had nonetheless received erlotinib treatment prior to testing, and neither patient responded, with both showing progressive disease.
Three of the five patients with an EGFR mutation began receiving erlotinib treatment after testing and responded. In one of these patients, the tumor remained in check for 13 months. In a second patient, the tumor began to progress after 17 months of no progression on treatment. In the third patient, the tumor began to progress again after about 4 weeks of no progression on erlotinib treatment, Dr. Bahce said. A fourth patient went on erlotinib treatment before testing, and did not respond and continued to have progressive disease.
The two patient subgroups showed no difference in blood perfusion into the tumors, or in EGFR expression in cell membranes.
Dr. Bahce said he had no disclosures.
FROM THE WORLD CONFERENCE ON LUNG CANCER
Major Finding: Advanced non–small cell lung cancer tumors with an epidermal growth factor receptor (EGFR)–activating mutation bound significantly more radiolabeled erlotinib than did tumors with wild-type EGFR genes (P = .03).
Data Source: A pilot study in 10 patients.
Disclosures: Dr. Bahce said he had no disclosures.
Rivaroxaban noninferior to warfarin in AF patients
A large, multicenter, randomized study of 14, 264 patients at risk for stroke with nonvalvular atrial fibrillation (AF) found the factor Xa inhibitor rivaroxaban to be noninferior to warfarin for preventing stroke or systemic embolism.
The ROCKET AF investigators, who reported the results online August 10 in The New England Journal of Medicine, detected no significant difference between rivaroxaban and warfarin in the rates of major or nonmajor clinically relevant bleeding.
Investigators at 1178 study sites in 45 countries randomly assigned the patients to receive either fixed-dose rivaroxaban at 20 mg daily or adjusted-dose warfarin to a target of INR 2.0 – 3.0. Patients with a creatinine clearance of 30-49 mL/minute received a rivaroxaban dose of 15 mg daily.
Patients in both arms of the intent-to-treat population were a median age of 73 years and about 40% were women. The patients had considerable rates of coexisting conditions, including 90.5% with hypertension, 62.5% with heart failure, and 54.8% who had had a previous stroke, embolism, or transient ischemic attack.
After a median treatment duration of 590 days, the primary efficacy analysis showed188 patients (1.7% per year) in the rivaroxaban group had a stroke or systemic embolism, compared with 241 patients (2.2% per year) in the warfarin group (P<0.001 for noninferiority).
Rates of major bleeding were similar in the 2 groups—3.6% with rivaroxaban and 3.4% with warfarin (P=0.58). Major and clinically relevant nonmajor bleeding occurred in 1475 (14.9%) rivaroxaban-treated patients and 1449 (14.5%) warfarin-treated patients (P=0.44). Intracranial and fatal bleeding occurred less often in the rivaroxaban group.
The investigators noted that the warfarin-treated patients were in therapeutic range a mean of 55% of the time. However, the efficacy of rivaroxaban was as favorable in those centers with the best INR control as it was in those with inferior control.
Lead author Manesh R. Patel, MD, of Duke University School of Medicine in North Carolina, said, “Warfarin has been a standard treatment for decades, but requires a rigorous monitoring schedule to ensure therapeutic dosing levels, and is subject to the potential of food and drug interactions that present treatment obstacles for patients and doctors alike.”
He indicated that the result of the trial “have convincingly shown rivaroxaban to be an alternative to warfarin in treating patients with atrial fibrillation, and importantly, with no increase in bleeding.”
The study was funded by Johnson & Johnson and Bayer.
ROCKET AF stands for Rivaroxaban Once Daily Oral Direct Factor Xa Inhibition Compared with Vitamin K Antagonist for Prevention of Stroke and Embolism Trial in Atrial Fibrillation. 
A large, multicenter, randomized study of 14, 264 patients at risk for stroke with nonvalvular atrial fibrillation (AF) found the factor Xa inhibitor rivaroxaban to be noninferior to warfarin for preventing stroke or systemic embolism.
The ROCKET AF investigators, who reported the results online August 10 in The New England Journal of Medicine, detected no significant difference between rivaroxaban and warfarin in the rates of major or nonmajor clinically relevant bleeding.
Investigators at 1178 study sites in 45 countries randomly assigned the patients to receive either fixed-dose rivaroxaban at 20 mg daily or adjusted-dose warfarin to a target of INR 2.0 – 3.0. Patients with a creatinine clearance of 30-49 mL/minute received a rivaroxaban dose of 15 mg daily.
Patients in both arms of the intent-to-treat population were a median age of 73 years and about 40% were women. The patients had considerable rates of coexisting conditions, including 90.5% with hypertension, 62.5% with heart failure, and 54.8% who had had a previous stroke, embolism, or transient ischemic attack.
After a median treatment duration of 590 days, the primary efficacy analysis showed188 patients (1.7% per year) in the rivaroxaban group had a stroke or systemic embolism, compared with 241 patients (2.2% per year) in the warfarin group (P<0.001 for noninferiority).
Rates of major bleeding were similar in the 2 groups—3.6% with rivaroxaban and 3.4% with warfarin (P=0.58). Major and clinically relevant nonmajor bleeding occurred in 1475 (14.9%) rivaroxaban-treated patients and 1449 (14.5%) warfarin-treated patients (P=0.44). Intracranial and fatal bleeding occurred less often in the rivaroxaban group.
The investigators noted that the warfarin-treated patients were in therapeutic range a mean of 55% of the time. However, the efficacy of rivaroxaban was as favorable in those centers with the best INR control as it was in those with inferior control.
Lead author Manesh R. Patel, MD, of Duke University School of Medicine in North Carolina, said, “Warfarin has been a standard treatment for decades, but requires a rigorous monitoring schedule to ensure therapeutic dosing levels, and is subject to the potential of food and drug interactions that present treatment obstacles for patients and doctors alike.”
He indicated that the result of the trial “have convincingly shown rivaroxaban to be an alternative to warfarin in treating patients with atrial fibrillation, and importantly, with no increase in bleeding.”
The study was funded by Johnson & Johnson and Bayer.
ROCKET AF stands for Rivaroxaban Once Daily Oral Direct Factor Xa Inhibition Compared with Vitamin K Antagonist for Prevention of Stroke and Embolism Trial in Atrial Fibrillation.
A large, multicenter, randomized study of 14, 264 patients at risk for stroke with nonvalvular atrial fibrillation (AF) found the factor Xa inhibitor rivaroxaban to be noninferior to warfarin for preventing stroke or systemic embolism.
The ROCKET AF investigators, who reported the results online August 10 in The New England Journal of Medicine, detected no significant difference between rivaroxaban and warfarin in the rates of major or nonmajor clinically relevant bleeding.
Investigators at 1178 study sites in 45 countries randomly assigned the patients to receive either fixed-dose rivaroxaban at 20 mg daily or adjusted-dose warfarin to a target of INR 2.0 – 3.0. Patients with a creatinine clearance of 30-49 mL/minute received a rivaroxaban dose of 15 mg daily.
Patients in both arms of the intent-to-treat population were a median age of 73 years and about 40% were women. The patients had considerable rates of coexisting conditions, including 90.5% with hypertension, 62.5% with heart failure, and 54.8% who had had a previous stroke, embolism, or transient ischemic attack.
After a median treatment duration of 590 days, the primary efficacy analysis showed188 patients (1.7% per year) in the rivaroxaban group had a stroke or systemic embolism, compared with 241 patients (2.2% per year) in the warfarin group (P<0.001 for noninferiority).
Rates of major bleeding were similar in the 2 groups—3.6% with rivaroxaban and 3.4% with warfarin (P=0.58). Major and clinically relevant nonmajor bleeding occurred in 1475 (14.9%) rivaroxaban-treated patients and 1449 (14.5%) warfarin-treated patients (P=0.44). Intracranial and fatal bleeding occurred less often in the rivaroxaban group.
The investigators noted that the warfarin-treated patients were in therapeutic range a mean of 55% of the time. However, the efficacy of rivaroxaban was as favorable in those centers with the best INR control as it was in those with inferior control.
Lead author Manesh R. Patel, MD, of Duke University School of Medicine in North Carolina, said, “Warfarin has been a standard treatment for decades, but requires a rigorous monitoring schedule to ensure therapeutic dosing levels, and is subject to the potential of food and drug interactions that present treatment obstacles for patients and doctors alike.”
He indicated that the result of the trial “have convincingly shown rivaroxaban to be an alternative to warfarin in treating patients with atrial fibrillation, and importantly, with no increase in bleeding.”
The study was funded by Johnson & Johnson and Bayer.
ROCKET AF stands for Rivaroxaban Once Daily Oral Direct Factor Xa Inhibition Compared with Vitamin K Antagonist for Prevention of Stroke and Embolism Trial in Atrial Fibrillation.
Out of Control
That the largest-ever study of glucose control in U.S. hospitals found roughly 1 in 3 patients are hyperglycemic (<180 mg/dL) during their hospital stay is no surprise to hospitalist Cheryl O'Malley, MD, FACP, program director of internal medicine at the Banner Good Samaritan Medical Center, Phoenix.
The data (PDF), based on point-of-care bedside glucose tests at 575 hospitals, showed hyperglycemia in 32.2% of ICU patients and 32% in non-ICU patients. Dr. O'Malley says the findings are further evidence that HM leaders have a duty to focus on glycemic control because so many of their patients are hyperglycemic.
Dr. O'Malley does her part as a mentor for SHM's Glycemic Control Mentored Initiative (GCMI) program, which recently expanded to a second cohort of 96 sites. The mentoring program has branched out to include nurses, physician assistants, and even two leading endocrinologists as mentors: Emory University School of Medicine's Guillermo Umpierrez, MD, FACP, FACE, and HealthPartners' John MacIndoe, MD.
SHM also has launched a microsite, dubbed eQUIPS (Electronic Quality Improvement Programs), which gives HM groups not involved in the mentoring program access to data analysis, benchmarking tools, and other services.
Kendall M. Rogers, MD, CPE, FACP, SFHM, associate professor of medicine and hospital medicine division chief at the University of New Mexico Health Sciences Center's Department of Internal Medicine, says SHM has always wanted to broaden the program to as many hospitals and physicians as possible to battle glycemic-control issues. And bringing in nationally respected endocrinologists as mentors furthers the goal to build "teams of experts within local hospitals."
"Hospitalists, endocrinologists, and other specialists have to work together," Dr. O'Malley adds. "The volume of work is just too much for any one group to bear."
That the largest-ever study of glucose control in U.S. hospitals found roughly 1 in 3 patients are hyperglycemic (<180 mg/dL) during their hospital stay is no surprise to hospitalist Cheryl O'Malley, MD, FACP, program director of internal medicine at the Banner Good Samaritan Medical Center, Phoenix.
The data (PDF), based on point-of-care bedside glucose tests at 575 hospitals, showed hyperglycemia in 32.2% of ICU patients and 32% in non-ICU patients. Dr. O'Malley says the findings are further evidence that HM leaders have a duty to focus on glycemic control because so many of their patients are hyperglycemic.
Dr. O'Malley does her part as a mentor for SHM's Glycemic Control Mentored Initiative (GCMI) program, which recently expanded to a second cohort of 96 sites. The mentoring program has branched out to include nurses, physician assistants, and even two leading endocrinologists as mentors: Emory University School of Medicine's Guillermo Umpierrez, MD, FACP, FACE, and HealthPartners' John MacIndoe, MD.
SHM also has launched a microsite, dubbed eQUIPS (Electronic Quality Improvement Programs), which gives HM groups not involved in the mentoring program access to data analysis, benchmarking tools, and other services.
Kendall M. Rogers, MD, CPE, FACP, SFHM, associate professor of medicine and hospital medicine division chief at the University of New Mexico Health Sciences Center's Department of Internal Medicine, says SHM has always wanted to broaden the program to as many hospitals and physicians as possible to battle glycemic-control issues. And bringing in nationally respected endocrinologists as mentors furthers the goal to build "teams of experts within local hospitals."
"Hospitalists, endocrinologists, and other specialists have to work together," Dr. O'Malley adds. "The volume of work is just too much for any one group to bear."
That the largest-ever study of glucose control in U.S. hospitals found roughly 1 in 3 patients are hyperglycemic (<180 mg/dL) during their hospital stay is no surprise to hospitalist Cheryl O'Malley, MD, FACP, program director of internal medicine at the Banner Good Samaritan Medical Center, Phoenix.
The data (PDF), based on point-of-care bedside glucose tests at 575 hospitals, showed hyperglycemia in 32.2% of ICU patients and 32% in non-ICU patients. Dr. O'Malley says the findings are further evidence that HM leaders have a duty to focus on glycemic control because so many of their patients are hyperglycemic.
Dr. O'Malley does her part as a mentor for SHM's Glycemic Control Mentored Initiative (GCMI) program, which recently expanded to a second cohort of 96 sites. The mentoring program has branched out to include nurses, physician assistants, and even two leading endocrinologists as mentors: Emory University School of Medicine's Guillermo Umpierrez, MD, FACP, FACE, and HealthPartners' John MacIndoe, MD.
SHM also has launched a microsite, dubbed eQUIPS (Electronic Quality Improvement Programs), which gives HM groups not involved in the mentoring program access to data analysis, benchmarking tools, and other services.
Kendall M. Rogers, MD, CPE, FACP, SFHM, associate professor of medicine and hospital medicine division chief at the University of New Mexico Health Sciences Center's Department of Internal Medicine, says SHM has always wanted to broaden the program to as many hospitals and physicians as possible to battle glycemic-control issues. And bringing in nationally respected endocrinologists as mentors furthers the goal to build "teams of experts within local hospitals."
"Hospitalists, endocrinologists, and other specialists have to work together," Dr. O'Malley adds. "The volume of work is just too much for any one group to bear."
Smooth Moves
A recent study in the Journal of Hospital Medicine concluded that by rescheduling fewer than 10 elective admissions per week from a weekday to a weekend, hospitals can reduce overcrowding. The report should encourage hospitalists to reconsider their own scheduling strategies, the lead author says.
"If they notice that on certain days their unit or their hospital is very crowded and on other days it's less so, it may be worth working with their organization's quality and safety or operational leadership to learn more about those patterns and see if they can improve on them," says Evan S. Fieldston, MD, MBA, MSHP, pediatric hospitalist at the Children's Hospital of Philadelphia.
The study examined 2007 daily inpatient census data from 39 tertiary-care children's hospitals. The average weekday occupancy ranged from 70.9% to 108.1%, while the average weekend occupancy ranged from 65.7% to 94.9%. After rescheduling, or "smoothing," elective admissions from days with "thresholds of high occupancy," defined as >85% occupancy, to less busy days, 39,607 patients were removed from exposure to occupancy levels greater than 95%.
Eugene Litvak, MD, president and CEO of the nonprofit Institute for Healthcare Optimization and adjunct professor of operations management at the Harvard School of Public Health in Boston, says the issue goes beyond U.S. hospitals. Dr. Litvak says he's discussed overcrowding with more than 100 hospitals in Europe, Japan, Australia, and the U.S. "In talking with their leadership in healthcare, I saw the same problem," he says.
The solution, Dr. Litvak suggests, lies with queueing theory, a mathematical formula that addresses random demand for a fixed capacity. Based on average census data, hospitals can apply queueing theory to determine how many beds and staff they need for ED admissions throughout a typical week.
A recent study in the Journal of Hospital Medicine concluded that by rescheduling fewer than 10 elective admissions per week from a weekday to a weekend, hospitals can reduce overcrowding. The report should encourage hospitalists to reconsider their own scheduling strategies, the lead author says.
"If they notice that on certain days their unit or their hospital is very crowded and on other days it's less so, it may be worth working with their organization's quality and safety or operational leadership to learn more about those patterns and see if they can improve on them," says Evan S. Fieldston, MD, MBA, MSHP, pediatric hospitalist at the Children's Hospital of Philadelphia.
The study examined 2007 daily inpatient census data from 39 tertiary-care children's hospitals. The average weekday occupancy ranged from 70.9% to 108.1%, while the average weekend occupancy ranged from 65.7% to 94.9%. After rescheduling, or "smoothing," elective admissions from days with "thresholds of high occupancy," defined as >85% occupancy, to less busy days, 39,607 patients were removed from exposure to occupancy levels greater than 95%.
Eugene Litvak, MD, president and CEO of the nonprofit Institute for Healthcare Optimization and adjunct professor of operations management at the Harvard School of Public Health in Boston, says the issue goes beyond U.S. hospitals. Dr. Litvak says he's discussed overcrowding with more than 100 hospitals in Europe, Japan, Australia, and the U.S. "In talking with their leadership in healthcare, I saw the same problem," he says.
The solution, Dr. Litvak suggests, lies with queueing theory, a mathematical formula that addresses random demand for a fixed capacity. Based on average census data, hospitals can apply queueing theory to determine how many beds and staff they need for ED admissions throughout a typical week.
A recent study in the Journal of Hospital Medicine concluded that by rescheduling fewer than 10 elective admissions per week from a weekday to a weekend, hospitals can reduce overcrowding. The report should encourage hospitalists to reconsider their own scheduling strategies, the lead author says.
"If they notice that on certain days their unit or their hospital is very crowded and on other days it's less so, it may be worth working with their organization's quality and safety or operational leadership to learn more about those patterns and see if they can improve on them," says Evan S. Fieldston, MD, MBA, MSHP, pediatric hospitalist at the Children's Hospital of Philadelphia.
The study examined 2007 daily inpatient census data from 39 tertiary-care children's hospitals. The average weekday occupancy ranged from 70.9% to 108.1%, while the average weekend occupancy ranged from 65.7% to 94.9%. After rescheduling, or "smoothing," elective admissions from days with "thresholds of high occupancy," defined as >85% occupancy, to less busy days, 39,607 patients were removed from exposure to occupancy levels greater than 95%.
Eugene Litvak, MD, president and CEO of the nonprofit Institute for Healthcare Optimization and adjunct professor of operations management at the Harvard School of Public Health in Boston, says the issue goes beyond U.S. hospitals. Dr. Litvak says he's discussed overcrowding with more than 100 hospitals in Europe, Japan, Australia, and the U.S. "In talking with their leadership in healthcare, I saw the same problem," he says.
The solution, Dr. Litvak suggests, lies with queueing theory, a mathematical formula that addresses random demand for a fixed capacity. Based on average census data, hospitals can apply queueing theory to determine how many beds and staff they need for ED admissions throughout a typical week.
CD19-redirected T cells induce remission in CLL patients
Gene therapy with a lentiviral vector expressing a chimeric antigen receptor with specificity for CD19 (CART19) has induced complete remission in 3 patients with chronic lymphocytic leukemia (CLL), according to research published simultaneously in the August 10 issues of The New England Journal of Medicine and Science Translational Medicine.
The research team, from the University of Pennsylvania, reported that the reinfused, modified T cells expanded to more than 1000 times the initial engraftment level. The patients’ remission was ongoing at 10 months after treatment.
The investigators believe the big difference between this genetically modified T cell and previous ones that had disappointing clinical activity is the addition of the CD137 (4-1BB) costimulatory signaling domain that significantly increases antitumor activity.
The team, led by Carl June, MD, described in the NEJM article the T-cell treatment of one of the patients with advanced, p53-deficient CLL.
A half year prior to enrolling in the trial, the 64-year-old patient’s T cells were collected and frozen. Before reinfusing the T cells into the patient, the investigators thawed the cells and transduced them with lentivirus expressing CD19-specific chimeric antigen receptor.
Four days prior to reinfusion, the patient received chemotherapy with pentostatin and cyclophosphamide to deplete his lymphocytes. After 3 days of chemotherapy, his bone marrow was hypercellular with approximately 40% involvement by CLL.
After 4 days of chemotherapy, the patient received an infusion of T cells, of which 5% were transduced, totaling 1.42 x 107 transduced cells, split into 3 consecutive daily infusions.
Two weeks after the infusion, the patient experienced chills, fever, and fatigue, which intensified over the subsequent days. He was diagnosed with tumor lysis syndrome on day 22 after infusion. On day 23 after the CART19-cell infusion, the patient had no evidence of CLL in the bone marrow, and by day 28, his adenopathy was not palpable.
In addition to tumor lysis syndrome, the only other grade 3/4 toxicity observed was lymphopenia.
The investigators did not expect that such a low dose of chimeric antigen receptor T cells would result in a clinically evident antitumor response. The dose was several orders of magnitude lower than that used in previous studies of modified T cells.
They speculated that the course of chemotherapy administered to the patient prior to the CART19-cell infusion may have been responsible for the increased engraftment and for “potentiating the ability of chimeric antigen receptor T cells to kill stressed tumor cells that would otherwise survive the chemotherapy.”
The researchers conclude that continued study of CD19-redirected T cells is warranted and plan to test the approach in other CD19-positive tumors, including non-Hodgkin lymphoma and acute lymphocytic leukemia.
Gene therapy with a lentiviral vector expressing a chimeric antigen receptor with specificity for CD19 (CART19) has induced complete remission in 3 patients with chronic lymphocytic leukemia (CLL), according to research published simultaneously in the August 10 issues of The New England Journal of Medicine and Science Translational Medicine.
The research team, from the University of Pennsylvania, reported that the reinfused, modified T cells expanded to more than 1000 times the initial engraftment level. The patients’ remission was ongoing at 10 months after treatment.
The investigators believe the big difference between this genetically modified T cell and previous ones that had disappointing clinical activity is the addition of the CD137 (4-1BB) costimulatory signaling domain that significantly increases antitumor activity.
The team, led by Carl June, MD, described in the NEJM article the T-cell treatment of one of the patients with advanced, p53-deficient CLL.
A half year prior to enrolling in the trial, the 64-year-old patient’s T cells were collected and frozen. Before reinfusing the T cells into the patient, the investigators thawed the cells and transduced them with lentivirus expressing CD19-specific chimeric antigen receptor.
Four days prior to reinfusion, the patient received chemotherapy with pentostatin and cyclophosphamide to deplete his lymphocytes. After 3 days of chemotherapy, his bone marrow was hypercellular with approximately 40% involvement by CLL.
After 4 days of chemotherapy, the patient received an infusion of T cells, of which 5% were transduced, totaling 1.42 x 107 transduced cells, split into 3 consecutive daily infusions.
Two weeks after the infusion, the patient experienced chills, fever, and fatigue, which intensified over the subsequent days. He was diagnosed with tumor lysis syndrome on day 22 after infusion. On day 23 after the CART19-cell infusion, the patient had no evidence of CLL in the bone marrow, and by day 28, his adenopathy was not palpable.
In addition to tumor lysis syndrome, the only other grade 3/4 toxicity observed was lymphopenia.
The investigators did not expect that such a low dose of chimeric antigen receptor T cells would result in a clinically evident antitumor response. The dose was several orders of magnitude lower than that used in previous studies of modified T cells.
They speculated that the course of chemotherapy administered to the patient prior to the CART19-cell infusion may have been responsible for the increased engraftment and for “potentiating the ability of chimeric antigen receptor T cells to kill stressed tumor cells that would otherwise survive the chemotherapy.”
The researchers conclude that continued study of CD19-redirected T cells is warranted and plan to test the approach in other CD19-positive tumors, including non-Hodgkin lymphoma and acute lymphocytic leukemia.
Gene therapy with a lentiviral vector expressing a chimeric antigen receptor with specificity for CD19 (CART19) has induced complete remission in 3 patients with chronic lymphocytic leukemia (CLL), according to research published simultaneously in the August 10 issues of The New England Journal of Medicine and Science Translational Medicine.
The research team, from the University of Pennsylvania, reported that the reinfused, modified T cells expanded to more than 1000 times the initial engraftment level. The patients’ remission was ongoing at 10 months after treatment.
The investigators believe the big difference between this genetically modified T cell and previous ones that had disappointing clinical activity is the addition of the CD137 (4-1BB) costimulatory signaling domain that significantly increases antitumor activity.
The team, led by Carl June, MD, described in the NEJM article the T-cell treatment of one of the patients with advanced, p53-deficient CLL.
A half year prior to enrolling in the trial, the 64-year-old patient’s T cells were collected and frozen. Before reinfusing the T cells into the patient, the investigators thawed the cells and transduced them with lentivirus expressing CD19-specific chimeric antigen receptor.
Four days prior to reinfusion, the patient received chemotherapy with pentostatin and cyclophosphamide to deplete his lymphocytes. After 3 days of chemotherapy, his bone marrow was hypercellular with approximately 40% involvement by CLL.
After 4 days of chemotherapy, the patient received an infusion of T cells, of which 5% were transduced, totaling 1.42 x 107 transduced cells, split into 3 consecutive daily infusions.
Two weeks after the infusion, the patient experienced chills, fever, and fatigue, which intensified over the subsequent days. He was diagnosed with tumor lysis syndrome on day 22 after infusion. On day 23 after the CART19-cell infusion, the patient had no evidence of CLL in the bone marrow, and by day 28, his adenopathy was not palpable.
In addition to tumor lysis syndrome, the only other grade 3/4 toxicity observed was lymphopenia.
The investigators did not expect that such a low dose of chimeric antigen receptor T cells would result in a clinically evident antitumor response. The dose was several orders of magnitude lower than that used in previous studies of modified T cells.
They speculated that the course of chemotherapy administered to the patient prior to the CART19-cell infusion may have been responsible for the increased engraftment and for “potentiating the ability of chimeric antigen receptor T cells to kill stressed tumor cells that would otherwise survive the chemotherapy.”
The researchers conclude that continued study of CD19-redirected T cells is warranted and plan to test the approach in other CD19-positive tumors, including non-Hodgkin lymphoma and acute lymphocytic leukemia.
Neuro-HM Gains Numbers, Momentum
“Enter The Neurohospitalist” might sound like a medical spoof of a Bruce Lee movie, but it’s really a subspecialty’s announcement that it’s here to stay.
The clever moniker was the name of a plenary session at the 8th New York Symposium on Neurological Emergencies & Neurological Care, sponsored by Columbia University’s Center for Continuing Medical Education. The two-hour presentation on neurology’s take on HM was a new feature for the annual meeting, and to presenter David Likosky, MD, SFHM, hospitalist and stroke program director at Evergreen Hospital Medical Center in Kirkland, Wash., it was the latest sign that the field of HM is cementing its future.
“The neurohospitalist world right now is where the hospital medicine world was, say, ten, fifteen years ago,” says Dr. Likosky, who is board-certified in both neurology and internal medicine.
Multiple fields have adopted the HM model, to the point that SHM is holding its first national specialty hospitalist meeting, Focused Practice in Hospital Medicine, on Nov. 4 in Las Vegas. The meeting is designed to help promote networking of people interested in the hospitalist model in various specialties, as well as to help identify issues related to those specialties. Click here for more information and registration.
But even within the growth of speciality hospitalist models, neurology might be the cohort embracing it the fastest. Dr. Likosky estimates there are 500 neurohospitalists practicing nationwide. The Neurohospitalist Society held its first meeting earlier this year, and the field’s first textbook, which he is contributing to, is set for release in November. The Academy of Neurology has a dedicated neurohospitalist section. And the subspecialty even has its own quarterly journal, The Neurohospitalist.
—David Likosky, MD, SFHM, hospitalist, stroke program director, Evergreen Hospital Medical Center, Kirkland, Wash.
“There is now a critical mass of neurohospitalists,” Dr. Likosky says. “There’s also an increasing recognition by the neurointensivists that someone has to help them take care of these patients, either before they get to the unit or when they come out of the unit. … Most hospitals don’t have neurointensivists, but they have very ill neurology patients. That’s another niche for neurohospitalists. All specialties of intensivists are looking for help with these patients.”
Another panelist at the four-day Manhattan conference, William D. Freeman, MD, assistant professor of neurology at the Mayo Clinic in Jacksonville, Fla., says the continued success of the field will be judged on data. He says three areas of potential “low-hanging fruit” to focus on are:
- Increased use of intravenous tissue plasminogen activators (tPA). The FDA-approved “clot-busting therapy” has been shown to reverse the effects of ischemic stroke if given within a time-sensitive window of therapeutic opportunity.
- Reduced length of stay for stroke patients. Adherence to best practices, Dr. Freeman says, will most effectively reduce patient stays and will be the ones that also demonstrate quality and patient-safety attributes.
- Focus on stroke patient metrics. Administrators often focus on quality measures that are easily identifiable; Dr. Likosky says new programs have to be able to show they can meet those thresholds.
“Hospital administrators are new to the concept of a neurohospitalist,” Dr. Likosky adds. “It’s easier in that they get the hospitalist model because that’s been around for so long, but figuring out the expense of a neurohospitalist program, how that functionally works, are there enough volumes, are all questions that are being asked.”
Still, Drs. Freeman and Likosky agree that the advantages of the subspecialty—everything from physicians’ quality of life to newly satisfied specialists in other departments (who will have a quicker neuro consult available)—mean the nascent specialty can continue to grow in numbers and influence.
“The future is bright for neurohospitalists,” Dr. Freeman says.
Richard Quinn is a freelance writer based in New Jersey.
“Enter The Neurohospitalist” might sound like a medical spoof of a Bruce Lee movie, but it’s really a subspecialty’s announcement that it’s here to stay.
The clever moniker was the name of a plenary session at the 8th New York Symposium on Neurological Emergencies & Neurological Care, sponsored by Columbia University’s Center for Continuing Medical Education. The two-hour presentation on neurology’s take on HM was a new feature for the annual meeting, and to presenter David Likosky, MD, SFHM, hospitalist and stroke program director at Evergreen Hospital Medical Center in Kirkland, Wash., it was the latest sign that the field of HM is cementing its future.
“The neurohospitalist world right now is where the hospital medicine world was, say, ten, fifteen years ago,” says Dr. Likosky, who is board-certified in both neurology and internal medicine.
Multiple fields have adopted the HM model, to the point that SHM is holding its first national specialty hospitalist meeting, Focused Practice in Hospital Medicine, on Nov. 4 in Las Vegas. The meeting is designed to help promote networking of people interested in the hospitalist model in various specialties, as well as to help identify issues related to those specialties. Click here for more information and registration.
But even within the growth of speciality hospitalist models, neurology might be the cohort embracing it the fastest. Dr. Likosky estimates there are 500 neurohospitalists practicing nationwide. The Neurohospitalist Society held its first meeting earlier this year, and the field’s first textbook, which he is contributing to, is set for release in November. The Academy of Neurology has a dedicated neurohospitalist section. And the subspecialty even has its own quarterly journal, The Neurohospitalist.
—David Likosky, MD, SFHM, hospitalist, stroke program director, Evergreen Hospital Medical Center, Kirkland, Wash.
“There is now a critical mass of neurohospitalists,” Dr. Likosky says. “There’s also an increasing recognition by the neurointensivists that someone has to help them take care of these patients, either before they get to the unit or when they come out of the unit. … Most hospitals don’t have neurointensivists, but they have very ill neurology patients. That’s another niche for neurohospitalists. All specialties of intensivists are looking for help with these patients.”
Another panelist at the four-day Manhattan conference, William D. Freeman, MD, assistant professor of neurology at the Mayo Clinic in Jacksonville, Fla., says the continued success of the field will be judged on data. He says three areas of potential “low-hanging fruit” to focus on are:
- Increased use of intravenous tissue plasminogen activators (tPA). The FDA-approved “clot-busting therapy” has been shown to reverse the effects of ischemic stroke if given within a time-sensitive window of therapeutic opportunity.
- Reduced length of stay for stroke patients. Adherence to best practices, Dr. Freeman says, will most effectively reduce patient stays and will be the ones that also demonstrate quality and patient-safety attributes.
- Focus on stroke patient metrics. Administrators often focus on quality measures that are easily identifiable; Dr. Likosky says new programs have to be able to show they can meet those thresholds.
“Hospital administrators are new to the concept of a neurohospitalist,” Dr. Likosky adds. “It’s easier in that they get the hospitalist model because that’s been around for so long, but figuring out the expense of a neurohospitalist program, how that functionally works, are there enough volumes, are all questions that are being asked.”
Still, Drs. Freeman and Likosky agree that the advantages of the subspecialty—everything from physicians’ quality of life to newly satisfied specialists in other departments (who will have a quicker neuro consult available)—mean the nascent specialty can continue to grow in numbers and influence.
“The future is bright for neurohospitalists,” Dr. Freeman says.
Richard Quinn is a freelance writer based in New Jersey.
“Enter The Neurohospitalist” might sound like a medical spoof of a Bruce Lee movie, but it’s really a subspecialty’s announcement that it’s here to stay.
The clever moniker was the name of a plenary session at the 8th New York Symposium on Neurological Emergencies & Neurological Care, sponsored by Columbia University’s Center for Continuing Medical Education. The two-hour presentation on neurology’s take on HM was a new feature for the annual meeting, and to presenter David Likosky, MD, SFHM, hospitalist and stroke program director at Evergreen Hospital Medical Center in Kirkland, Wash., it was the latest sign that the field of HM is cementing its future.
“The neurohospitalist world right now is where the hospital medicine world was, say, ten, fifteen years ago,” says Dr. Likosky, who is board-certified in both neurology and internal medicine.
Multiple fields have adopted the HM model, to the point that SHM is holding its first national specialty hospitalist meeting, Focused Practice in Hospital Medicine, on Nov. 4 in Las Vegas. The meeting is designed to help promote networking of people interested in the hospitalist model in various specialties, as well as to help identify issues related to those specialties. Click here for more information and registration.
But even within the growth of speciality hospitalist models, neurology might be the cohort embracing it the fastest. Dr. Likosky estimates there are 500 neurohospitalists practicing nationwide. The Neurohospitalist Society held its first meeting earlier this year, and the field’s first textbook, which he is contributing to, is set for release in November. The Academy of Neurology has a dedicated neurohospitalist section. And the subspecialty even has its own quarterly journal, The Neurohospitalist.
—David Likosky, MD, SFHM, hospitalist, stroke program director, Evergreen Hospital Medical Center, Kirkland, Wash.
“There is now a critical mass of neurohospitalists,” Dr. Likosky says. “There’s also an increasing recognition by the neurointensivists that someone has to help them take care of these patients, either before they get to the unit or when they come out of the unit. … Most hospitals don’t have neurointensivists, but they have very ill neurology patients. That’s another niche for neurohospitalists. All specialties of intensivists are looking for help with these patients.”
Another panelist at the four-day Manhattan conference, William D. Freeman, MD, assistant professor of neurology at the Mayo Clinic in Jacksonville, Fla., says the continued success of the field will be judged on data. He says three areas of potential “low-hanging fruit” to focus on are:
- Increased use of intravenous tissue plasminogen activators (tPA). The FDA-approved “clot-busting therapy” has been shown to reverse the effects of ischemic stroke if given within a time-sensitive window of therapeutic opportunity.
- Reduced length of stay for stroke patients. Adherence to best practices, Dr. Freeman says, will most effectively reduce patient stays and will be the ones that also demonstrate quality and patient-safety attributes.
- Focus on stroke patient metrics. Administrators often focus on quality measures that are easily identifiable; Dr. Likosky says new programs have to be able to show they can meet those thresholds.
“Hospital administrators are new to the concept of a neurohospitalist,” Dr. Likosky adds. “It’s easier in that they get the hospitalist model because that’s been around for so long, but figuring out the expense of a neurohospitalist program, how that functionally works, are there enough volumes, are all questions that are being asked.”
Still, Drs. Freeman and Likosky agree that the advantages of the subspecialty—everything from physicians’ quality of life to newly satisfied specialists in other departments (who will have a quicker neuro consult available)—mean the nascent specialty can continue to grow in numbers and influence.
“The future is bright for neurohospitalists,” Dr. Freeman says.
Richard Quinn is a freelance writer based in New Jersey.
Roth Spots—More than Meets the Eye
A 50‐year‐old female patient with a past medical history of Sjogren's syndrome and polymyositis presented with fever, rash, swelling, and pain in her extremities. Skin biopsy confirmed vasculitis. She was treated with steroids and azathioprine. However, she developed sudden‐onset central visual blurring in her right eye on the fifth day of hospitalization. Fundoscopic exam showed multiple central white‐centered retinal hemorrhages (Roth spots, Figures 1, 2) and vascular sheathing, consistent with retinal vasculitis. Blood cultures were negative. Transthoracic and transesophageal echocardiograms were normal. She was treated with high‐dose intravenous steroids and cyclophosphamide, with visual improvement and a marked reduction in the number of Roth spots.
Roth spots 1 are nonspecific intraretinal hemorrhagic lesions with a white center due to fibrin deposition. Although historically associated with infective endocarditis, they can also occur in other systemic diseases such as connective tissue disorders, vasculitis, leukemia, diabetes, hypertension, anemia, trauma, as well as disseminated bacterial and fungal infections.
- , , .White centered hemorrhages: their significance.Ophthalmology.1980;87:66–69.
A 50‐year‐old female patient with a past medical history of Sjogren's syndrome and polymyositis presented with fever, rash, swelling, and pain in her extremities. Skin biopsy confirmed vasculitis. She was treated with steroids and azathioprine. However, she developed sudden‐onset central visual blurring in her right eye on the fifth day of hospitalization. Fundoscopic exam showed multiple central white‐centered retinal hemorrhages (Roth spots, Figures 1, 2) and vascular sheathing, consistent with retinal vasculitis. Blood cultures were negative. Transthoracic and transesophageal echocardiograms were normal. She was treated with high‐dose intravenous steroids and cyclophosphamide, with visual improvement and a marked reduction in the number of Roth spots.
Roth spots 1 are nonspecific intraretinal hemorrhagic lesions with a white center due to fibrin deposition. Although historically associated with infective endocarditis, they can also occur in other systemic diseases such as connective tissue disorders, vasculitis, leukemia, diabetes, hypertension, anemia, trauma, as well as disseminated bacterial and fungal infections.
A 50‐year‐old female patient with a past medical history of Sjogren's syndrome and polymyositis presented with fever, rash, swelling, and pain in her extremities. Skin biopsy confirmed vasculitis. She was treated with steroids and azathioprine. However, she developed sudden‐onset central visual blurring in her right eye on the fifth day of hospitalization. Fundoscopic exam showed multiple central white‐centered retinal hemorrhages (Roth spots, Figures 1, 2) and vascular sheathing, consistent with retinal vasculitis. Blood cultures were negative. Transthoracic and transesophageal echocardiograms were normal. She was treated with high‐dose intravenous steroids and cyclophosphamide, with visual improvement and a marked reduction in the number of Roth spots.
Roth spots 1 are nonspecific intraretinal hemorrhagic lesions with a white center due to fibrin deposition. Although historically associated with infective endocarditis, they can also occur in other systemic diseases such as connective tissue disorders, vasculitis, leukemia, diabetes, hypertension, anemia, trauma, as well as disseminated bacterial and fungal infections.
- , , .White centered hemorrhages: their significance.Ophthalmology.1980;87:66–69.
- , , .White centered hemorrhages: their significance.Ophthalmology.1980;87:66–69.
Pharmacist‐Directed Anticoagulation
Anticoagulants are one of the most common drug classes involved in medication errors and adverse events. Warfarin, an anticoagulant that plays a key role in the management of many disease states, is implicated in approximately 30% of reported anticoagulant‐related errors.1 Anticoagulation with warfarin is complicated by inter‐individual variability in response to therapy, clinically significant drug interactions, a narrow therapeutic window, and the need for frequent and lifelong monitoring.2
In the hospital setting, warfarin use is complicated due to patient handoff among health care providers, and acute illnesses that impact sensitivity and response to warfarin. Common causes of errors with anticoagulants are knowledge deficits, failure to follow policy/procedure/protocol, and communication issues.1 An added opportunity for warfarin‐related medication errors is the risk associated with the transition from the inpatient‐to‐outpatient setting. Due to the risk and complexity associated with anticoagulant medications, the Joint Commission instituted National Patient Safety Goal (NPSG) 03.05.01 (formerly NPSG 3E): a series of requirements intended to Reduce the likelihood of patient harm with the use of anticoagulation therapy.3 In order to optimally address this National Patient Safety Goal, a systematic intervention would be required to impact each step of the medication use process for anticoagulants.
Several studies have suggested that dedicated anticoagulation management services or clinics improve anticoagulation management in the outpatient setting.2 Non‐physician providers, primarily pharmacists and nurses, frequently manage outpatient anticoagulation management services or clinics. However, very few studies have evaluated the impact of a warfarin management service in the inpatient hospital setting.48 While the few available studies suggest some benefit associated with an inpatient anticoagulation management service, a minority of these studies have assessed the role of these services in facilitating the transition of the anticoagulated patient to the outpatient setting.7
In order to improve anticoagulation management and safety, our institution implemented an inpatient Pharmacist‐Directed Anticoagulation Service (PDAS). The purpose of this study was to evaluate the impact of this service on both transition of care and safety of patients receiving warfarin anticoagulation.
METHODS
This study was completed at Henry Ford Hospital, an 802‐bed, tertiary care, level 1 trauma and academic medical center in Detroit, MI. The study was carried out between November 2007 and June 2009. The study was approved by the Henry Ford Hospital Institutional Review Board with waiver of consent.
Patients
This was a prospective cluster randomized study. All patients admitted to two internal medicine units (IM1 or IM2) or two cardiology units (Card1 or Card2), who received at least one inpatient dose of warfarin, were eligible for inclusion. Patients were included regardless of whether warfarin was newly initiated during the index admission (newly initiated patients) or was continuation of existing anticoagulation (existing warfarin patients). In order to ensure that patient data following discharge would be available for analysis, patients were excluded from this analysis if they were not scheduled to follow‐up in the Henry Ford Medical Group outpatient anticoagulation clinics after discharge, however, these patients were cared for by the PDAS service in the usual manner.
Study Design
Prior to implementation of the PDAS, one internal medicine and one cardiology unit was randomly selected to receive the PDAS intervention (IM1 and Card1), while the other two units (IM2 and Card2) served as control units. These hospital units were selected because anticoagulants are frequently used on these units and the patient population is generally similar between the two internal medicine and two cardiology unitswith exception that Card1 unit also contains a specialized service for advanced heart failure and left ventricular assist device (LVAD) patients. Of note, there was significant expansion of the heart failure service and LVAD program during the time frame of the study, accounting for a greater number of more complicated patients on the Card1 (PDAS) unit.
Specific responsibilities of the PDAS related to warfarin are detailed in Table 1. The PDAS was implemented in September 2007 as a system‐based change to improve anticoagulant safety at our institution. The goals of this service were to improve communication regarding anticoagulation; to improve safety as patients transition from the inpatient‐to‐outpatient settings; and to standardize anticoagulant dosing, monitoring, and patient education. For patients taking warfarin, who are cared for by a health system‐affiliated physician, the PDAS collaborates with our outpatient anticoagulation clinics in order to facilitate transition from the inpatient‐to‐outpatient setting. The Henry Ford Health System has an established, multisite outpatient Anticoagulation Clinic with >5000 patients actively receiving warfarin dosing and monitoring. The anticoagulation clinics are staffed by nurses and pharmacists who provide standardized management of warfarin for patients of all physicians within our health system and provide consistent high‐quality care (average time in international normalized ratio [INR] goal range = 68.2%). The anticoagulation clinics have been in existence since 1992. The PDAS is comprised of three full‐time and two part‐time pharmacists whose responsibilities are limited to the management of anticoagulation throughout the hospital.
| Inpatient Care | Patient Education | Transition of Care |
|---|---|---|
| ||
| Initial dose selection and daily dose adjustments after warfarin is initiated by primary team | Comprehensive education provided verbally and via written communication utilizing the Krames database. | Contact anticoagulation‐responsible physician and anticoagulation clinic via phone. |
| Provide written dosing regimen to patient and provide date for first INR postdischarge. | ||
| Daily laboratory monitoring | Education provided is standardized between inpatient and outpatient settings. | Create electronic Anticoagulation Discharge Summary. Document communication with the outpatient clinicians, reason for admission, steps taken to manage warfarin drug interactions, and warfarin doses administered during stay, discharge warfarin dose and follow‐up date. |
The PDAS was staffed by repurposing pharmacist staff. All pharmacists had either several years of general medicine‐based clinical practice experience or residency training, or both. Pharmacists were oriented to service responsibilities by spending approximately one week in the outpatient anticoagulation clinic and completing focused review of internal and external anticoagulation guidelines.
In the control group, management of anticoagulation and transition of care occurred at the discretion of the primary care team. The primary team had access to a clinical pharmacist, who was not part of the PDAS, seven days per week. However, the primary team was not able to consult the PDAS.
This study was primarily designed to assess the impact of the PDAS on both transition of care and patient safety. For study endpoint purposes, transition of care was assessed by satisfactory completion and documentation of four important metrics: 1) appropriate enrollment in the anticoagulation clinic; 2) documented communication between the inpatient service responsible for anticoagulation and the outpatient anticoagulation clinic prior to patient discharge; 3) documented communication between the inpatient service responsible for anticoagulation and the physician responsible for outpatient management of the patient; 4) INR drawn within five days of hospital discharge. Documentation of communication for metric #2 and #3 was obtained by reviewing the electronic medical record system, particularly electronic discharge summaries and telephone encounter notes.
The primary safety endpoint was defined as a composite of any INR >5, any episode of major bleeding, or development of new thrombosis. This endpoint was met if any of these events occurred either during the index hospitalization or within 30 days of hospital discharge. Major bleeding was identified by review of outpatient anticoagulation clinic encounters and the patient's electronic medical record (includes all inpatient and outpatient encounters within Henry Ford Health System) by using the International Society of Thrombosis and Haemostasis standard and was defined as fatal bleeding or symptomatic bleeding in a critical area or organ (intracranial, intraspinal, intraocular, retroperitoneal, intraarticular, pericardial, or intramuscular with compartment syndrome), or bleeding causing a reduction in hemoglobin levels of 2 g/dL or more, or leading to transfusion of two or more units of blood or red cells.9 New thrombosis was defined as documentation of any of the following: deep vein thrombosis, pulmonary embolism, or cardioembolic stroke. Need for dose adjustment at the first anticoagulation clinic visit after discharge was evaluated as a secondary endpoint.
All analyses compared the PDAS to the control group. In addition, a planned comparison of patients in the PDAS and control groups who were newly initiated to warfarin during the study hospitalization (newly initiated subgroup) and those who were taking warfarin on admission (existing warfarin subgroup) was also undertaken. It was expected that these subgroup analyses would likely be underpowered, however, the potential implications of a service such as this could differ based on history of warfarin use. Therefore, these analyses were planned for exploratory purposes. In order to determine the impact of risk factors for altered warfarin pharmacodynamic response on the safety endpoint, post hoc subgroup analyses were performed based on demographics and clinical characteristics.
Data Analysis
Data are presented as mean standard deviation or proportion, as appropriate. A P‐value of less than 0.05 was considered significant for all comparisons and all tests were two‐tailed.
Intervention and control groups were compared with Student's t test, MannWhitney U test, chi‐square or Fishers exact test, as appropriate. Relative risk (RR) and 95% confidence intervals (CI) were calculated for all primary analyses. All statistical analyses were performed with SPSS v.12.0 (SPSS Inc, Chicago, IL).
It was estimated that a sample size of 250 patients per group would provide greater than 80% power to detect at least a 50% improvement in both the transition of care and primary safety endpoints, with implementation of the PDAS. This calculation is based on the following assumptions: alpha = 0.05; expected control group achievement of the four transition of care metrics = 50%; rate of safety endpoint for the control group = 20%.4
RESULTS
Baseline Characteristics
During the study period, 1360 patients were admitted to the study units. A total of 377 and 483 patients were found to be ineligible for inclusion on the PDAS and control units, respectively. These patients were ineligible because they did not follow up in the Henry Ford Medical Group outpatient anticoagulation clinic. In total, 500 patients were included in the analysis. Patients (n = 145) who were newly initiated on warfarin made up 29% of the total population. Table 2 presents baseline clinical characteristics for patients in the PDAS and control groups, showing increased age, and a greater proportion of patients with heart failure and LVADs in the PDAS group. Patients in the PDAS group had significantly longer hospital stays, however, these increases were driven by a longer length of stay among the advanced heart failure service patients that were managed by the PDAS.
| PDAS (n = 250) | Control (n = 250) | P Value | |
|---|---|---|---|
| |||
| Demographic data | |||
| Age (mean SD) | 64.1 15.6 | 68.0 14.9 | 0.004 |
| Male gender | 54.0% | 56.4% | 0.589 |
| Caucasian race | 44.4% | 50.4% | 0.179 |
| Admitted to a cardiology unit | 78.8% | 74.8% | 0.289 |
| Length of stay (mean SD) | 8.13 7.04 | 6.29 5.63 | 0.001 |
| No heart failure history: length of stay (mean SD) | 6.83 4.53 | 6.15 5.14 | 0.288 |
| Heart failure history: length of stay (mean SD) | 9.09 8.31 | 6.45 6.15 | 0.004 |
| History of heart failure* | 57.6% | 47.6% | 0.025 |
| Heart failure with an LVAD | 14.0% | 0.4% | <0.001 |
| Indication for anticoagulation | |||
| Venous thromboembolism | 21.6% | 18.4% | 0.371 |
| Atrial fibrillation | 54.4% | 66.4% | 0.006 |
| Other | 24.0% | 15.2% | 0.013 |
| Primary admission diagnosis* | |||
| Heart failure | 25.6%* | 21.6% | 0.292 |
| Atrial fibrillation | 16.4% | 20.8% | 0.206 |
| Acute coronary syndrome | 13.6% | 17.6% | 0.218 |
| Venous thromboembolism | 4.8% | 4.8% | 1.00 |
| Infection | 12.4% | 10.0% | 0.395 |
| Bleeding | 1.6% | 1.2% | 0.703 |
Early Warfarin Management
Warfarin management metrics are presented in Table 3. The number of inpatient days prescribed warfarin was increased in the PDAS group by greater than one day while PDAS patients required significantly less dosage adjustment at first outpatient follow‐up visit. Similar to increases noted with length of stay, increases in inpatient warfarin days were likely driven by patients with severe heart failure managed by the PDAS.
| Warfarin Dosing | PDAS (n = 250) | Control (n = 250) | P Value |
|---|---|---|---|
| |||
| Initial dose (mean SD) | 5.23 2.37 | 4.99 2.07 | 0.245 |
| Discharge dose (mean SD) | 5.15 2.52 | 4.91 2.14 | 0.258 |
| INR at discharge (mean SD) | 2.07 0.73 | 2.04 0.73 | 0.660 |
| Therapeutic INR at discharge | 40.8% | 38.0% | 0.522 |
| Inpatient warfarin days (mean SD) | 4.97 4.30 | 3.68 2.69 | <0.001 |
| No heart failure: inpatient warfarin days (mean SD) | 4.09 2.49 | 3.60 2.67 | 0.148 |
| Heart failure: inpatient warfarin days (mean SD) | 5.62 5.16 | 3.76 2.71 | <0.001 |
| Dose change required at first follow‐up visit | 44.8% | 72.6% | <0.001 |
Transition of Care
Transition of care results are presented in Table 4. Full compliance and achievement of the transition of care metrics occurred significantly more often in the PDAS versus control patients with markedly increased rates of documented communication between inpatient providers and both outpatient anticoagulation clinic staff and outpatient physicians. Early follow‐up INR monitoring also occurred more frequently in the PDAS patients. The PDAS patients experienced greater compliance with the transition of care metrics regardless of whether they were in the newly initiated or existing warfarin subgroups (data not shown).
| Transition of Care | PDAS (n = 250) | Control (n = 250) | Relative Risk (95% CI) | P Value |
|---|---|---|---|---|
| ||||
| 100% Communication bundle* compliance, % (n) | 75.6% (189) | 2.8% (7) | 27.0 (13.056.2) | <0.001 |
| Appropriately enrolled in the AC clinic, % (n) | 97.2% (243) | 95.2% (238) | 1.02 (0.991.06) | 0.242 |
| Communication: inpatient service and outpatient physician, % (n) | 99.6% (249) | 12.4% (31) | 8.03 (5.7811.2) | <0.001 |
| Communication: inpatient clinicians and AC clinic staff, % (n) | 98.8% (247) | 14.8% (37) | 6.68 (4.969.00) | <0.001 |
| INR drawn within five days of hospital discharge, % (n) | 78.4% (196) | 66.4% (166) | 1.18 (1.061.32) | 0.003 |
| 30‐Day Composite safety endpoint, % (n) | 10.0% (25) | 14.8% (37) | 0.68 (0.421.09) | 0.103 |
| Inpatient + 30‐day INR >5, % (n) | 9.6% (24) | 14.8% (37) | 0.65 (0.401.05) | 0.076 |
| Inpatient + 30‐day major bleeding, % (n) | 0.8% (2) | 0.4% (1) | 2.00 (0.1821.9) | 0.563 |
| Inpatient + 30‐day thrombosis, % (n) | 0% (0) | 0% (0) | N/A | N/A |
Anticoagulant Safety
Safety endpoint data is presented in Table 4. The composite safety outcome of INR >5, major bleeding event, or thrombosis occurred in 12.4% of all patients with no early thrombotic events and only three major bleeding events recorded. Excessive INR values >5 occurred less frequently in the PDAS patients, however, differences in this metric and the composite safety outcome were not significantly different. Safety endpoint results in the overall population were driven by a reduction in INR values >5 among newly initiated warfarin patients in the PDAS group (PDAS: 9.5% vs control: 19.7%; P = 0.079; Figure 1). Other subgroup analyses relating to the safety endpoint are presented in Figure 1.
DISCUSSION
This article describes a systematic intervention designed to improve anticoagulation safety and efficacy in the hospital and during the transition to the postdischarge setting. Implementation of a PDAS did not impact patient bleeding and thrombotic outcomes, but did result in improved coordination and documentation of warfarin management and subsequent enhancement in the transition of the anticoagulated patient from the inpatient‐to‐outpatient setting with the Pharmacist‐Directed Anticoagulation Service.
Limited previous work has investigated the role of an anticoagulation service in inpatient management of anticoagulation.48 Only one published study has investigated the impact of this type of service on transition of care issues with warfarin, as was done in our study.7 In that study, management by an inpatient anticoagulation service resulted in a greater proportion of patients referred to an anticoagulation clinic for management (P = 0.001), more patients presenting to the anticoagulation clinic with a therapeutic INR (P = 0.001), and fewer patients presenting to the clinic with supratherapeutic levels of anticoagulation (P = 0.002). These results are somewhat analogous to our findings, in that patients in our study were less likely to require a dose change at the first clinic follow‐up visit after discharge or to have INR values 5.
We completed several subgroup analyses to thoroughly explore the impact of the PDAS on the safety endpoint. While firm conclusions cannot be drawn from these subgroup analyses, some hypothesis‐generating observations can be made. First, there was a greater impact of the PDAS on the safety endpoint in patients who are usually more sensitive to the effects of warfarin and therefore more challenging to manage.2 The impact of the PDAS was also greater among patients whose length of stay was more than five days (population median). This is significant because it suggests that when the opportunity for adverse events and miscommunication is greatest (ie, during hospitalizations of longer duration), there appears to be improvement in the safety endpoint with the PDAS.
To our knowledge, this study was the first to explore the impact of an inpatient anticoagulation service on the care of both newly initiated and existing warfarin patients, rather than only patients newly initiated on warfarin. As expected, the greatest influence of the PDAS on the safety endpoint was observed among the newly initiated patients. While the safety impact of the PDAS was noted most significantly among the newly initiated patients, the PDAS had a positive effect on the transition of care metrics regardless of previous warfarin use.
A limitation of our study should be mentioned. While we employed a design in which randomly selected units were exposed to the PDAS, individual patients were not randomized to the service. This cluster randomized design was chosen because it mimics quality improvement processes that would be rolled out to a hospital nursing unit. While lack of randomization at the patient level is a limitation, our cluster randomized study design is pragmatic and represents an improvement over the existing published before and after quasi‐experimental studies in this area.48
An improved system for documentation of communication was built into the PDAS processes on implementation of the service. However, it should also be noted that inadequate communication between inpatient and outpatient providers was an identified root cause for adverse events with warfarin in our institution prior to implementation of the PDAS. Therefore, improvement in the transition of care metrics with the PDAS were likely due to a combination of both improved documentation and true improvement in communication.
The approach of the PDAS was refined in several ways early after implementation of the service. Some major notable improvements included the development of a systematic approach to ensuring appropriate follow‐up and transition of care for patients being discharged to a skilled nursing facility, and creation of a system that required a mandatory conversation between the PDAS and a surgical service if anticoagulation is ordered within 48 hours of a major procedure. An upcoming improvement to the service will be to transition from a home grown electronic database, which was built for the purposes of streamlining clinical workflow and data collection, to a commercially available software program that has recently become available for management of inpatient anticoagulation. The major advantage of the new program will be the clinical decision support capabilities that will help to further streamline the service and allow for greater efficiency.
To understand implications of our PDAS intervention, it is important to remember that the majority of study patients were prescribed warfarin prior to hospital admission, that patients in both study groups were enrolled in an established, multisite anticoagulation clinic, and that patients in both groups were managed with a comprehensive inpatientoutpatient electronic medical record. Therefore, all providers caring for these patients had real‐time access to all warfarin dosing and dose adjustments, INR results, and anticoagulation clinic encounters, even though formal communication between providers was infrequently documented for control group patients in our electronic medical record. Study patients had low rates of bleeding and no adverse thrombotic outcomes across both treatment groups. Therefore, this type of model is likely to produce larger gains in communication and safety outcomes in health care systems without established anticoagulation clinics or comprehensive electronic medical records.
The PDAS was enthusiastically accepted by providers at our institution and expanded hospital‐wide after completion of this pilot. The PDAS model is a viable approach to standardize anticoagulant management with a goal of improving anticoagulant safety in the inpatient setting. Assessment of the effectiveness of models such as the PDAS for improving anticoagulant safety in the inpatient setting is particularly relevant with the current expectations for hospitals set by The Joint Commission's NPSG.03.05.01.3 More importantly the PDAS model can be an option for improving the transition of the anticoagulated patient from the inpatient‐to‐outpatient setting. Follow‐up with the anticoagulation clinic occurred earlier with the PDAS and, while this study was not designed to evaluate the impact of this new service on rehospitalization, recent literature suggests that earlier follow‐up after discharge leads to less rehospitalization.10 Finally, it may be possible to adapt this model to provide more intensive medication therapy management and monitoring for hospitalized patients with other complicated medication regimens or chronic disease.
CONCLUSION
The clinical pharmacist is uniquely prepared to manage inter‐individual variability in pharmacodynamic response to drug therapy, as well as to provide high‐quality patient education. This study evaluated a new model of inpatient warfarin management, in which warfarin dosing, monitoring, patient education, and transition of care was coordinated by a specialized team of clinical pharmacists that worked in collaboration with physicians and outpatient anticoagulation clinic staff. Safety and efficiency of the care provided by this new service was improved in certain subsets of more complex patients. The major advantage of this service was improvement in patient handoff, improved communication, and earlier patient follow‐up after discharge. Therefore, implementation of a Pharmacist‐Directed Anticoagulation Service provides a net improvement in quality of care for the patient taking warfarin in the inpatient setting.
Acknowledgements
The authors acknowledge the efforts of the PDAS staff: Nassif Abi‐Samra, Pam Holland, Sara Lanfear, and Gail Washington. This work would not have been possible without the dedication of these pharmacists. All authors had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
- U.S. Pharmacopeia. USP Patient Safety CapsLink: January 2008. Available at: http://www.usp.org/pdf/EN/patientSafety/capsLink2008–01‐01.pdf. Accessed March 19,2010.
- ,,,,,.Pharmacology and management of the vitamin K antagonists: American College of Chest Physicians evidence‐based clinical practice guidelines (8th ed.).Chest.2008;133:160S–198S.
- The Joint Commission. 2009 National Patient Safety Goals. Available at: http://www.jointcommission.org/NR/rdonlyres/31666E86‐E7F4–423E‐9BE8‐F05BD1CB0AA8/0/HAP_NPSG.pdf. Accessed May 6,2010.
- ,,, et al.Optimization of inpatient warfarin therapy: impact of a daily consultation by a pharmacist‐managed anticoagulation service.Ann Pharmacother.2000;34:567–572.
- ,,, et al.Pharmacy‐managed protocol for warfarin use in orthopedic surgery patients.Am J Health‐Syst Pharm.1995;52:1310–1316.
- .Pharmacist involvement with warfarin dosing for inpatients.Pharm World Sci.2001;23:31–35.
- ,,.Evaluation of a pharmacy‐managed warfarin‐monitoring service to coordinate inpatient and outpatient therapy.Am J Hosp Pharm.1992;49:387–394.
- ,.Implementation and evaluation of a pharmacist‐assisted warfarin dosing program.Can J Hosp Pharm.1997;50:169–175.
- ,.Definition of major bleeding in clinical investigations of antihemostatic medicinal products in non‐surgical patients.J Thromb Haemostasis.2005;3:692–694.
- ,,.Rehospitalizations among patients in the Medicare fee‐for‐service program.N Engl J Med.2009;360:1418–1428.
Anticoagulants are one of the most common drug classes involved in medication errors and adverse events. Warfarin, an anticoagulant that plays a key role in the management of many disease states, is implicated in approximately 30% of reported anticoagulant‐related errors.1 Anticoagulation with warfarin is complicated by inter‐individual variability in response to therapy, clinically significant drug interactions, a narrow therapeutic window, and the need for frequent and lifelong monitoring.2
In the hospital setting, warfarin use is complicated due to patient handoff among health care providers, and acute illnesses that impact sensitivity and response to warfarin. Common causes of errors with anticoagulants are knowledge deficits, failure to follow policy/procedure/protocol, and communication issues.1 An added opportunity for warfarin‐related medication errors is the risk associated with the transition from the inpatient‐to‐outpatient setting. Due to the risk and complexity associated with anticoagulant medications, the Joint Commission instituted National Patient Safety Goal (NPSG) 03.05.01 (formerly NPSG 3E): a series of requirements intended to Reduce the likelihood of patient harm with the use of anticoagulation therapy.3 In order to optimally address this National Patient Safety Goal, a systematic intervention would be required to impact each step of the medication use process for anticoagulants.
Several studies have suggested that dedicated anticoagulation management services or clinics improve anticoagulation management in the outpatient setting.2 Non‐physician providers, primarily pharmacists and nurses, frequently manage outpatient anticoagulation management services or clinics. However, very few studies have evaluated the impact of a warfarin management service in the inpatient hospital setting.48 While the few available studies suggest some benefit associated with an inpatient anticoagulation management service, a minority of these studies have assessed the role of these services in facilitating the transition of the anticoagulated patient to the outpatient setting.7
In order to improve anticoagulation management and safety, our institution implemented an inpatient Pharmacist‐Directed Anticoagulation Service (PDAS). The purpose of this study was to evaluate the impact of this service on both transition of care and safety of patients receiving warfarin anticoagulation.
METHODS
This study was completed at Henry Ford Hospital, an 802‐bed, tertiary care, level 1 trauma and academic medical center in Detroit, MI. The study was carried out between November 2007 and June 2009. The study was approved by the Henry Ford Hospital Institutional Review Board with waiver of consent.
Patients
This was a prospective cluster randomized study. All patients admitted to two internal medicine units (IM1 or IM2) or two cardiology units (Card1 or Card2), who received at least one inpatient dose of warfarin, were eligible for inclusion. Patients were included regardless of whether warfarin was newly initiated during the index admission (newly initiated patients) or was continuation of existing anticoagulation (existing warfarin patients). In order to ensure that patient data following discharge would be available for analysis, patients were excluded from this analysis if they were not scheduled to follow‐up in the Henry Ford Medical Group outpatient anticoagulation clinics after discharge, however, these patients were cared for by the PDAS service in the usual manner.
Study Design
Prior to implementation of the PDAS, one internal medicine and one cardiology unit was randomly selected to receive the PDAS intervention (IM1 and Card1), while the other two units (IM2 and Card2) served as control units. These hospital units were selected because anticoagulants are frequently used on these units and the patient population is generally similar between the two internal medicine and two cardiology unitswith exception that Card1 unit also contains a specialized service for advanced heart failure and left ventricular assist device (LVAD) patients. Of note, there was significant expansion of the heart failure service and LVAD program during the time frame of the study, accounting for a greater number of more complicated patients on the Card1 (PDAS) unit.
Specific responsibilities of the PDAS related to warfarin are detailed in Table 1. The PDAS was implemented in September 2007 as a system‐based change to improve anticoagulant safety at our institution. The goals of this service were to improve communication regarding anticoagulation; to improve safety as patients transition from the inpatient‐to‐outpatient settings; and to standardize anticoagulant dosing, monitoring, and patient education. For patients taking warfarin, who are cared for by a health system‐affiliated physician, the PDAS collaborates with our outpatient anticoagulation clinics in order to facilitate transition from the inpatient‐to‐outpatient setting. The Henry Ford Health System has an established, multisite outpatient Anticoagulation Clinic with >5000 patients actively receiving warfarin dosing and monitoring. The anticoagulation clinics are staffed by nurses and pharmacists who provide standardized management of warfarin for patients of all physicians within our health system and provide consistent high‐quality care (average time in international normalized ratio [INR] goal range = 68.2%). The anticoagulation clinics have been in existence since 1992. The PDAS is comprised of three full‐time and two part‐time pharmacists whose responsibilities are limited to the management of anticoagulation throughout the hospital.
| Inpatient Care | Patient Education | Transition of Care |
|---|---|---|
| ||
| Initial dose selection and daily dose adjustments after warfarin is initiated by primary team | Comprehensive education provided verbally and via written communication utilizing the Krames database. | Contact anticoagulation‐responsible physician and anticoagulation clinic via phone. |
| Provide written dosing regimen to patient and provide date for first INR postdischarge. | ||
| Daily laboratory monitoring | Education provided is standardized between inpatient and outpatient settings. | Create electronic Anticoagulation Discharge Summary. Document communication with the outpatient clinicians, reason for admission, steps taken to manage warfarin drug interactions, and warfarin doses administered during stay, discharge warfarin dose and follow‐up date. |
The PDAS was staffed by repurposing pharmacist staff. All pharmacists had either several years of general medicine‐based clinical practice experience or residency training, or both. Pharmacists were oriented to service responsibilities by spending approximately one week in the outpatient anticoagulation clinic and completing focused review of internal and external anticoagulation guidelines.
In the control group, management of anticoagulation and transition of care occurred at the discretion of the primary care team. The primary team had access to a clinical pharmacist, who was not part of the PDAS, seven days per week. However, the primary team was not able to consult the PDAS.
This study was primarily designed to assess the impact of the PDAS on both transition of care and patient safety. For study endpoint purposes, transition of care was assessed by satisfactory completion and documentation of four important metrics: 1) appropriate enrollment in the anticoagulation clinic; 2) documented communication between the inpatient service responsible for anticoagulation and the outpatient anticoagulation clinic prior to patient discharge; 3) documented communication between the inpatient service responsible for anticoagulation and the physician responsible for outpatient management of the patient; 4) INR drawn within five days of hospital discharge. Documentation of communication for metric #2 and #3 was obtained by reviewing the electronic medical record system, particularly electronic discharge summaries and telephone encounter notes.
The primary safety endpoint was defined as a composite of any INR >5, any episode of major bleeding, or development of new thrombosis. This endpoint was met if any of these events occurred either during the index hospitalization or within 30 days of hospital discharge. Major bleeding was identified by review of outpatient anticoagulation clinic encounters and the patient's electronic medical record (includes all inpatient and outpatient encounters within Henry Ford Health System) by using the International Society of Thrombosis and Haemostasis standard and was defined as fatal bleeding or symptomatic bleeding in a critical area or organ (intracranial, intraspinal, intraocular, retroperitoneal, intraarticular, pericardial, or intramuscular with compartment syndrome), or bleeding causing a reduction in hemoglobin levels of 2 g/dL or more, or leading to transfusion of two or more units of blood or red cells.9 New thrombosis was defined as documentation of any of the following: deep vein thrombosis, pulmonary embolism, or cardioembolic stroke. Need for dose adjustment at the first anticoagulation clinic visit after discharge was evaluated as a secondary endpoint.
All analyses compared the PDAS to the control group. In addition, a planned comparison of patients in the PDAS and control groups who were newly initiated to warfarin during the study hospitalization (newly initiated subgroup) and those who were taking warfarin on admission (existing warfarin subgroup) was also undertaken. It was expected that these subgroup analyses would likely be underpowered, however, the potential implications of a service such as this could differ based on history of warfarin use. Therefore, these analyses were planned for exploratory purposes. In order to determine the impact of risk factors for altered warfarin pharmacodynamic response on the safety endpoint, post hoc subgroup analyses were performed based on demographics and clinical characteristics.
Data Analysis
Data are presented as mean standard deviation or proportion, as appropriate. A P‐value of less than 0.05 was considered significant for all comparisons and all tests were two‐tailed.
Intervention and control groups were compared with Student's t test, MannWhitney U test, chi‐square or Fishers exact test, as appropriate. Relative risk (RR) and 95% confidence intervals (CI) were calculated for all primary analyses. All statistical analyses were performed with SPSS v.12.0 (SPSS Inc, Chicago, IL).
It was estimated that a sample size of 250 patients per group would provide greater than 80% power to detect at least a 50% improvement in both the transition of care and primary safety endpoints, with implementation of the PDAS. This calculation is based on the following assumptions: alpha = 0.05; expected control group achievement of the four transition of care metrics = 50%; rate of safety endpoint for the control group = 20%.4
RESULTS
Baseline Characteristics
During the study period, 1360 patients were admitted to the study units. A total of 377 and 483 patients were found to be ineligible for inclusion on the PDAS and control units, respectively. These patients were ineligible because they did not follow up in the Henry Ford Medical Group outpatient anticoagulation clinic. In total, 500 patients were included in the analysis. Patients (n = 145) who were newly initiated on warfarin made up 29% of the total population. Table 2 presents baseline clinical characteristics for patients in the PDAS and control groups, showing increased age, and a greater proportion of patients with heart failure and LVADs in the PDAS group. Patients in the PDAS group had significantly longer hospital stays, however, these increases were driven by a longer length of stay among the advanced heart failure service patients that were managed by the PDAS.
| PDAS (n = 250) | Control (n = 250) | P Value | |
|---|---|---|---|
| |||
| Demographic data | |||
| Age (mean SD) | 64.1 15.6 | 68.0 14.9 | 0.004 |
| Male gender | 54.0% | 56.4% | 0.589 |
| Caucasian race | 44.4% | 50.4% | 0.179 |
| Admitted to a cardiology unit | 78.8% | 74.8% | 0.289 |
| Length of stay (mean SD) | 8.13 7.04 | 6.29 5.63 | 0.001 |
| No heart failure history: length of stay (mean SD) | 6.83 4.53 | 6.15 5.14 | 0.288 |
| Heart failure history: length of stay (mean SD) | 9.09 8.31 | 6.45 6.15 | 0.004 |
| History of heart failure* | 57.6% | 47.6% | 0.025 |
| Heart failure with an LVAD | 14.0% | 0.4% | <0.001 |
| Indication for anticoagulation | |||
| Venous thromboembolism | 21.6% | 18.4% | 0.371 |
| Atrial fibrillation | 54.4% | 66.4% | 0.006 |
| Other | 24.0% | 15.2% | 0.013 |
| Primary admission diagnosis* | |||
| Heart failure | 25.6%* | 21.6% | 0.292 |
| Atrial fibrillation | 16.4% | 20.8% | 0.206 |
| Acute coronary syndrome | 13.6% | 17.6% | 0.218 |
| Venous thromboembolism | 4.8% | 4.8% | 1.00 |
| Infection | 12.4% | 10.0% | 0.395 |
| Bleeding | 1.6% | 1.2% | 0.703 |
Early Warfarin Management
Warfarin management metrics are presented in Table 3. The number of inpatient days prescribed warfarin was increased in the PDAS group by greater than one day while PDAS patients required significantly less dosage adjustment at first outpatient follow‐up visit. Similar to increases noted with length of stay, increases in inpatient warfarin days were likely driven by patients with severe heart failure managed by the PDAS.
| Warfarin Dosing | PDAS (n = 250) | Control (n = 250) | P Value |
|---|---|---|---|
| |||
| Initial dose (mean SD) | 5.23 2.37 | 4.99 2.07 | 0.245 |
| Discharge dose (mean SD) | 5.15 2.52 | 4.91 2.14 | 0.258 |
| INR at discharge (mean SD) | 2.07 0.73 | 2.04 0.73 | 0.660 |
| Therapeutic INR at discharge | 40.8% | 38.0% | 0.522 |
| Inpatient warfarin days (mean SD) | 4.97 4.30 | 3.68 2.69 | <0.001 |
| No heart failure: inpatient warfarin days (mean SD) | 4.09 2.49 | 3.60 2.67 | 0.148 |
| Heart failure: inpatient warfarin days (mean SD) | 5.62 5.16 | 3.76 2.71 | <0.001 |
| Dose change required at first follow‐up visit | 44.8% | 72.6% | <0.001 |
Transition of Care
Transition of care results are presented in Table 4. Full compliance and achievement of the transition of care metrics occurred significantly more often in the PDAS versus control patients with markedly increased rates of documented communication between inpatient providers and both outpatient anticoagulation clinic staff and outpatient physicians. Early follow‐up INR monitoring also occurred more frequently in the PDAS patients. The PDAS patients experienced greater compliance with the transition of care metrics regardless of whether they were in the newly initiated or existing warfarin subgroups (data not shown).
| Transition of Care | PDAS (n = 250) | Control (n = 250) | Relative Risk (95% CI) | P Value |
|---|---|---|---|---|
| ||||
| 100% Communication bundle* compliance, % (n) | 75.6% (189) | 2.8% (7) | 27.0 (13.056.2) | <0.001 |
| Appropriately enrolled in the AC clinic, % (n) | 97.2% (243) | 95.2% (238) | 1.02 (0.991.06) | 0.242 |
| Communication: inpatient service and outpatient physician, % (n) | 99.6% (249) | 12.4% (31) | 8.03 (5.7811.2) | <0.001 |
| Communication: inpatient clinicians and AC clinic staff, % (n) | 98.8% (247) | 14.8% (37) | 6.68 (4.969.00) | <0.001 |
| INR drawn within five days of hospital discharge, % (n) | 78.4% (196) | 66.4% (166) | 1.18 (1.061.32) | 0.003 |
| 30‐Day Composite safety endpoint, % (n) | 10.0% (25) | 14.8% (37) | 0.68 (0.421.09) | 0.103 |
| Inpatient + 30‐day INR >5, % (n) | 9.6% (24) | 14.8% (37) | 0.65 (0.401.05) | 0.076 |
| Inpatient + 30‐day major bleeding, % (n) | 0.8% (2) | 0.4% (1) | 2.00 (0.1821.9) | 0.563 |
| Inpatient + 30‐day thrombosis, % (n) | 0% (0) | 0% (0) | N/A | N/A |
Anticoagulant Safety
Safety endpoint data is presented in Table 4. The composite safety outcome of INR >5, major bleeding event, or thrombosis occurred in 12.4% of all patients with no early thrombotic events and only three major bleeding events recorded. Excessive INR values >5 occurred less frequently in the PDAS patients, however, differences in this metric and the composite safety outcome were not significantly different. Safety endpoint results in the overall population were driven by a reduction in INR values >5 among newly initiated warfarin patients in the PDAS group (PDAS: 9.5% vs control: 19.7%; P = 0.079; Figure 1). Other subgroup analyses relating to the safety endpoint are presented in Figure 1.
DISCUSSION
This article describes a systematic intervention designed to improve anticoagulation safety and efficacy in the hospital and during the transition to the postdischarge setting. Implementation of a PDAS did not impact patient bleeding and thrombotic outcomes, but did result in improved coordination and documentation of warfarin management and subsequent enhancement in the transition of the anticoagulated patient from the inpatient‐to‐outpatient setting with the Pharmacist‐Directed Anticoagulation Service.
Limited previous work has investigated the role of an anticoagulation service in inpatient management of anticoagulation.48 Only one published study has investigated the impact of this type of service on transition of care issues with warfarin, as was done in our study.7 In that study, management by an inpatient anticoagulation service resulted in a greater proportion of patients referred to an anticoagulation clinic for management (P = 0.001), more patients presenting to the anticoagulation clinic with a therapeutic INR (P = 0.001), and fewer patients presenting to the clinic with supratherapeutic levels of anticoagulation (P = 0.002). These results are somewhat analogous to our findings, in that patients in our study were less likely to require a dose change at the first clinic follow‐up visit after discharge or to have INR values 5.
We completed several subgroup analyses to thoroughly explore the impact of the PDAS on the safety endpoint. While firm conclusions cannot be drawn from these subgroup analyses, some hypothesis‐generating observations can be made. First, there was a greater impact of the PDAS on the safety endpoint in patients who are usually more sensitive to the effects of warfarin and therefore more challenging to manage.2 The impact of the PDAS was also greater among patients whose length of stay was more than five days (population median). This is significant because it suggests that when the opportunity for adverse events and miscommunication is greatest (ie, during hospitalizations of longer duration), there appears to be improvement in the safety endpoint with the PDAS.
To our knowledge, this study was the first to explore the impact of an inpatient anticoagulation service on the care of both newly initiated and existing warfarin patients, rather than only patients newly initiated on warfarin. As expected, the greatest influence of the PDAS on the safety endpoint was observed among the newly initiated patients. While the safety impact of the PDAS was noted most significantly among the newly initiated patients, the PDAS had a positive effect on the transition of care metrics regardless of previous warfarin use.
A limitation of our study should be mentioned. While we employed a design in which randomly selected units were exposed to the PDAS, individual patients were not randomized to the service. This cluster randomized design was chosen because it mimics quality improvement processes that would be rolled out to a hospital nursing unit. While lack of randomization at the patient level is a limitation, our cluster randomized study design is pragmatic and represents an improvement over the existing published before and after quasi‐experimental studies in this area.48
An improved system for documentation of communication was built into the PDAS processes on implementation of the service. However, it should also be noted that inadequate communication between inpatient and outpatient providers was an identified root cause for adverse events with warfarin in our institution prior to implementation of the PDAS. Therefore, improvement in the transition of care metrics with the PDAS were likely due to a combination of both improved documentation and true improvement in communication.
The approach of the PDAS was refined in several ways early after implementation of the service. Some major notable improvements included the development of a systematic approach to ensuring appropriate follow‐up and transition of care for patients being discharged to a skilled nursing facility, and creation of a system that required a mandatory conversation between the PDAS and a surgical service if anticoagulation is ordered within 48 hours of a major procedure. An upcoming improvement to the service will be to transition from a home grown electronic database, which was built for the purposes of streamlining clinical workflow and data collection, to a commercially available software program that has recently become available for management of inpatient anticoagulation. The major advantage of the new program will be the clinical decision support capabilities that will help to further streamline the service and allow for greater efficiency.
To understand implications of our PDAS intervention, it is important to remember that the majority of study patients were prescribed warfarin prior to hospital admission, that patients in both study groups were enrolled in an established, multisite anticoagulation clinic, and that patients in both groups were managed with a comprehensive inpatientoutpatient electronic medical record. Therefore, all providers caring for these patients had real‐time access to all warfarin dosing and dose adjustments, INR results, and anticoagulation clinic encounters, even though formal communication between providers was infrequently documented for control group patients in our electronic medical record. Study patients had low rates of bleeding and no adverse thrombotic outcomes across both treatment groups. Therefore, this type of model is likely to produce larger gains in communication and safety outcomes in health care systems without established anticoagulation clinics or comprehensive electronic medical records.
The PDAS was enthusiastically accepted by providers at our institution and expanded hospital‐wide after completion of this pilot. The PDAS model is a viable approach to standardize anticoagulant management with a goal of improving anticoagulant safety in the inpatient setting. Assessment of the effectiveness of models such as the PDAS for improving anticoagulant safety in the inpatient setting is particularly relevant with the current expectations for hospitals set by The Joint Commission's NPSG.03.05.01.3 More importantly the PDAS model can be an option for improving the transition of the anticoagulated patient from the inpatient‐to‐outpatient setting. Follow‐up with the anticoagulation clinic occurred earlier with the PDAS and, while this study was not designed to evaluate the impact of this new service on rehospitalization, recent literature suggests that earlier follow‐up after discharge leads to less rehospitalization.10 Finally, it may be possible to adapt this model to provide more intensive medication therapy management and monitoring for hospitalized patients with other complicated medication regimens or chronic disease.
CONCLUSION
The clinical pharmacist is uniquely prepared to manage inter‐individual variability in pharmacodynamic response to drug therapy, as well as to provide high‐quality patient education. This study evaluated a new model of inpatient warfarin management, in which warfarin dosing, monitoring, patient education, and transition of care was coordinated by a specialized team of clinical pharmacists that worked in collaboration with physicians and outpatient anticoagulation clinic staff. Safety and efficiency of the care provided by this new service was improved in certain subsets of more complex patients. The major advantage of this service was improvement in patient handoff, improved communication, and earlier patient follow‐up after discharge. Therefore, implementation of a Pharmacist‐Directed Anticoagulation Service provides a net improvement in quality of care for the patient taking warfarin in the inpatient setting.
Acknowledgements
The authors acknowledge the efforts of the PDAS staff: Nassif Abi‐Samra, Pam Holland, Sara Lanfear, and Gail Washington. This work would not have been possible without the dedication of these pharmacists. All authors had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Anticoagulants are one of the most common drug classes involved in medication errors and adverse events. Warfarin, an anticoagulant that plays a key role in the management of many disease states, is implicated in approximately 30% of reported anticoagulant‐related errors.1 Anticoagulation with warfarin is complicated by inter‐individual variability in response to therapy, clinically significant drug interactions, a narrow therapeutic window, and the need for frequent and lifelong monitoring.2
In the hospital setting, warfarin use is complicated due to patient handoff among health care providers, and acute illnesses that impact sensitivity and response to warfarin. Common causes of errors with anticoagulants are knowledge deficits, failure to follow policy/procedure/protocol, and communication issues.1 An added opportunity for warfarin‐related medication errors is the risk associated with the transition from the inpatient‐to‐outpatient setting. Due to the risk and complexity associated with anticoagulant medications, the Joint Commission instituted National Patient Safety Goal (NPSG) 03.05.01 (formerly NPSG 3E): a series of requirements intended to Reduce the likelihood of patient harm with the use of anticoagulation therapy.3 In order to optimally address this National Patient Safety Goal, a systematic intervention would be required to impact each step of the medication use process for anticoagulants.
Several studies have suggested that dedicated anticoagulation management services or clinics improve anticoagulation management in the outpatient setting.2 Non‐physician providers, primarily pharmacists and nurses, frequently manage outpatient anticoagulation management services or clinics. However, very few studies have evaluated the impact of a warfarin management service in the inpatient hospital setting.48 While the few available studies suggest some benefit associated with an inpatient anticoagulation management service, a minority of these studies have assessed the role of these services in facilitating the transition of the anticoagulated patient to the outpatient setting.7
In order to improve anticoagulation management and safety, our institution implemented an inpatient Pharmacist‐Directed Anticoagulation Service (PDAS). The purpose of this study was to evaluate the impact of this service on both transition of care and safety of patients receiving warfarin anticoagulation.
METHODS
This study was completed at Henry Ford Hospital, an 802‐bed, tertiary care, level 1 trauma and academic medical center in Detroit, MI. The study was carried out between November 2007 and June 2009. The study was approved by the Henry Ford Hospital Institutional Review Board with waiver of consent.
Patients
This was a prospective cluster randomized study. All patients admitted to two internal medicine units (IM1 or IM2) or two cardiology units (Card1 or Card2), who received at least one inpatient dose of warfarin, were eligible for inclusion. Patients were included regardless of whether warfarin was newly initiated during the index admission (newly initiated patients) or was continuation of existing anticoagulation (existing warfarin patients). In order to ensure that patient data following discharge would be available for analysis, patients were excluded from this analysis if they were not scheduled to follow‐up in the Henry Ford Medical Group outpatient anticoagulation clinics after discharge, however, these patients were cared for by the PDAS service in the usual manner.
Study Design
Prior to implementation of the PDAS, one internal medicine and one cardiology unit was randomly selected to receive the PDAS intervention (IM1 and Card1), while the other two units (IM2 and Card2) served as control units. These hospital units were selected because anticoagulants are frequently used on these units and the patient population is generally similar between the two internal medicine and two cardiology unitswith exception that Card1 unit also contains a specialized service for advanced heart failure and left ventricular assist device (LVAD) patients. Of note, there was significant expansion of the heart failure service and LVAD program during the time frame of the study, accounting for a greater number of more complicated patients on the Card1 (PDAS) unit.
Specific responsibilities of the PDAS related to warfarin are detailed in Table 1. The PDAS was implemented in September 2007 as a system‐based change to improve anticoagulant safety at our institution. The goals of this service were to improve communication regarding anticoagulation; to improve safety as patients transition from the inpatient‐to‐outpatient settings; and to standardize anticoagulant dosing, monitoring, and patient education. For patients taking warfarin, who are cared for by a health system‐affiliated physician, the PDAS collaborates with our outpatient anticoagulation clinics in order to facilitate transition from the inpatient‐to‐outpatient setting. The Henry Ford Health System has an established, multisite outpatient Anticoagulation Clinic with >5000 patients actively receiving warfarin dosing and monitoring. The anticoagulation clinics are staffed by nurses and pharmacists who provide standardized management of warfarin for patients of all physicians within our health system and provide consistent high‐quality care (average time in international normalized ratio [INR] goal range = 68.2%). The anticoagulation clinics have been in existence since 1992. The PDAS is comprised of three full‐time and two part‐time pharmacists whose responsibilities are limited to the management of anticoagulation throughout the hospital.
| Inpatient Care | Patient Education | Transition of Care |
|---|---|---|
| ||
| Initial dose selection and daily dose adjustments after warfarin is initiated by primary team | Comprehensive education provided verbally and via written communication utilizing the Krames database. | Contact anticoagulation‐responsible physician and anticoagulation clinic via phone. |
| Provide written dosing regimen to patient and provide date for first INR postdischarge. | ||
| Daily laboratory monitoring | Education provided is standardized between inpatient and outpatient settings. | Create electronic Anticoagulation Discharge Summary. Document communication with the outpatient clinicians, reason for admission, steps taken to manage warfarin drug interactions, and warfarin doses administered during stay, discharge warfarin dose and follow‐up date. |
The PDAS was staffed by repurposing pharmacist staff. All pharmacists had either several years of general medicine‐based clinical practice experience or residency training, or both. Pharmacists were oriented to service responsibilities by spending approximately one week in the outpatient anticoagulation clinic and completing focused review of internal and external anticoagulation guidelines.
In the control group, management of anticoagulation and transition of care occurred at the discretion of the primary care team. The primary team had access to a clinical pharmacist, who was not part of the PDAS, seven days per week. However, the primary team was not able to consult the PDAS.
This study was primarily designed to assess the impact of the PDAS on both transition of care and patient safety. For study endpoint purposes, transition of care was assessed by satisfactory completion and documentation of four important metrics: 1) appropriate enrollment in the anticoagulation clinic; 2) documented communication between the inpatient service responsible for anticoagulation and the outpatient anticoagulation clinic prior to patient discharge; 3) documented communication between the inpatient service responsible for anticoagulation and the physician responsible for outpatient management of the patient; 4) INR drawn within five days of hospital discharge. Documentation of communication for metric #2 and #3 was obtained by reviewing the electronic medical record system, particularly electronic discharge summaries and telephone encounter notes.
The primary safety endpoint was defined as a composite of any INR >5, any episode of major bleeding, or development of new thrombosis. This endpoint was met if any of these events occurred either during the index hospitalization or within 30 days of hospital discharge. Major bleeding was identified by review of outpatient anticoagulation clinic encounters and the patient's electronic medical record (includes all inpatient and outpatient encounters within Henry Ford Health System) by using the International Society of Thrombosis and Haemostasis standard and was defined as fatal bleeding or symptomatic bleeding in a critical area or organ (intracranial, intraspinal, intraocular, retroperitoneal, intraarticular, pericardial, or intramuscular with compartment syndrome), or bleeding causing a reduction in hemoglobin levels of 2 g/dL or more, or leading to transfusion of two or more units of blood or red cells.9 New thrombosis was defined as documentation of any of the following: deep vein thrombosis, pulmonary embolism, or cardioembolic stroke. Need for dose adjustment at the first anticoagulation clinic visit after discharge was evaluated as a secondary endpoint.
All analyses compared the PDAS to the control group. In addition, a planned comparison of patients in the PDAS and control groups who were newly initiated to warfarin during the study hospitalization (newly initiated subgroup) and those who were taking warfarin on admission (existing warfarin subgroup) was also undertaken. It was expected that these subgroup analyses would likely be underpowered, however, the potential implications of a service such as this could differ based on history of warfarin use. Therefore, these analyses were planned for exploratory purposes. In order to determine the impact of risk factors for altered warfarin pharmacodynamic response on the safety endpoint, post hoc subgroup analyses were performed based on demographics and clinical characteristics.
Data Analysis
Data are presented as mean standard deviation or proportion, as appropriate. A P‐value of less than 0.05 was considered significant for all comparisons and all tests were two‐tailed.
Intervention and control groups were compared with Student's t test, MannWhitney U test, chi‐square or Fishers exact test, as appropriate. Relative risk (RR) and 95% confidence intervals (CI) were calculated for all primary analyses. All statistical analyses were performed with SPSS v.12.0 (SPSS Inc, Chicago, IL).
It was estimated that a sample size of 250 patients per group would provide greater than 80% power to detect at least a 50% improvement in both the transition of care and primary safety endpoints, with implementation of the PDAS. This calculation is based on the following assumptions: alpha = 0.05; expected control group achievement of the four transition of care metrics = 50%; rate of safety endpoint for the control group = 20%.4
RESULTS
Baseline Characteristics
During the study period, 1360 patients were admitted to the study units. A total of 377 and 483 patients were found to be ineligible for inclusion on the PDAS and control units, respectively. These patients were ineligible because they did not follow up in the Henry Ford Medical Group outpatient anticoagulation clinic. In total, 500 patients were included in the analysis. Patients (n = 145) who were newly initiated on warfarin made up 29% of the total population. Table 2 presents baseline clinical characteristics for patients in the PDAS and control groups, showing increased age, and a greater proportion of patients with heart failure and LVADs in the PDAS group. Patients in the PDAS group had significantly longer hospital stays, however, these increases were driven by a longer length of stay among the advanced heart failure service patients that were managed by the PDAS.
| PDAS (n = 250) | Control (n = 250) | P Value | |
|---|---|---|---|
| |||
| Demographic data | |||
| Age (mean SD) | 64.1 15.6 | 68.0 14.9 | 0.004 |
| Male gender | 54.0% | 56.4% | 0.589 |
| Caucasian race | 44.4% | 50.4% | 0.179 |
| Admitted to a cardiology unit | 78.8% | 74.8% | 0.289 |
| Length of stay (mean SD) | 8.13 7.04 | 6.29 5.63 | 0.001 |
| No heart failure history: length of stay (mean SD) | 6.83 4.53 | 6.15 5.14 | 0.288 |
| Heart failure history: length of stay (mean SD) | 9.09 8.31 | 6.45 6.15 | 0.004 |
| History of heart failure* | 57.6% | 47.6% | 0.025 |
| Heart failure with an LVAD | 14.0% | 0.4% | <0.001 |
| Indication for anticoagulation | |||
| Venous thromboembolism | 21.6% | 18.4% | 0.371 |
| Atrial fibrillation | 54.4% | 66.4% | 0.006 |
| Other | 24.0% | 15.2% | 0.013 |
| Primary admission diagnosis* | |||
| Heart failure | 25.6%* | 21.6% | 0.292 |
| Atrial fibrillation | 16.4% | 20.8% | 0.206 |
| Acute coronary syndrome | 13.6% | 17.6% | 0.218 |
| Venous thromboembolism | 4.8% | 4.8% | 1.00 |
| Infection | 12.4% | 10.0% | 0.395 |
| Bleeding | 1.6% | 1.2% | 0.703 |
Early Warfarin Management
Warfarin management metrics are presented in Table 3. The number of inpatient days prescribed warfarin was increased in the PDAS group by greater than one day while PDAS patients required significantly less dosage adjustment at first outpatient follow‐up visit. Similar to increases noted with length of stay, increases in inpatient warfarin days were likely driven by patients with severe heart failure managed by the PDAS.
| Warfarin Dosing | PDAS (n = 250) | Control (n = 250) | P Value |
|---|---|---|---|
| |||
| Initial dose (mean SD) | 5.23 2.37 | 4.99 2.07 | 0.245 |
| Discharge dose (mean SD) | 5.15 2.52 | 4.91 2.14 | 0.258 |
| INR at discharge (mean SD) | 2.07 0.73 | 2.04 0.73 | 0.660 |
| Therapeutic INR at discharge | 40.8% | 38.0% | 0.522 |
| Inpatient warfarin days (mean SD) | 4.97 4.30 | 3.68 2.69 | <0.001 |
| No heart failure: inpatient warfarin days (mean SD) | 4.09 2.49 | 3.60 2.67 | 0.148 |
| Heart failure: inpatient warfarin days (mean SD) | 5.62 5.16 | 3.76 2.71 | <0.001 |
| Dose change required at first follow‐up visit | 44.8% | 72.6% | <0.001 |
Transition of Care
Transition of care results are presented in Table 4. Full compliance and achievement of the transition of care metrics occurred significantly more often in the PDAS versus control patients with markedly increased rates of documented communication between inpatient providers and both outpatient anticoagulation clinic staff and outpatient physicians. Early follow‐up INR monitoring also occurred more frequently in the PDAS patients. The PDAS patients experienced greater compliance with the transition of care metrics regardless of whether they were in the newly initiated or existing warfarin subgroups (data not shown).
| Transition of Care | PDAS (n = 250) | Control (n = 250) | Relative Risk (95% CI) | P Value |
|---|---|---|---|---|
| ||||
| 100% Communication bundle* compliance, % (n) | 75.6% (189) | 2.8% (7) | 27.0 (13.056.2) | <0.001 |
| Appropriately enrolled in the AC clinic, % (n) | 97.2% (243) | 95.2% (238) | 1.02 (0.991.06) | 0.242 |
| Communication: inpatient service and outpatient physician, % (n) | 99.6% (249) | 12.4% (31) | 8.03 (5.7811.2) | <0.001 |
| Communication: inpatient clinicians and AC clinic staff, % (n) | 98.8% (247) | 14.8% (37) | 6.68 (4.969.00) | <0.001 |
| INR drawn within five days of hospital discharge, % (n) | 78.4% (196) | 66.4% (166) | 1.18 (1.061.32) | 0.003 |
| 30‐Day Composite safety endpoint, % (n) | 10.0% (25) | 14.8% (37) | 0.68 (0.421.09) | 0.103 |
| Inpatient + 30‐day INR >5, % (n) | 9.6% (24) | 14.8% (37) | 0.65 (0.401.05) | 0.076 |
| Inpatient + 30‐day major bleeding, % (n) | 0.8% (2) | 0.4% (1) | 2.00 (0.1821.9) | 0.563 |
| Inpatient + 30‐day thrombosis, % (n) | 0% (0) | 0% (0) | N/A | N/A |
Anticoagulant Safety
Safety endpoint data is presented in Table 4. The composite safety outcome of INR >5, major bleeding event, or thrombosis occurred in 12.4% of all patients with no early thrombotic events and only three major bleeding events recorded. Excessive INR values >5 occurred less frequently in the PDAS patients, however, differences in this metric and the composite safety outcome were not significantly different. Safety endpoint results in the overall population were driven by a reduction in INR values >5 among newly initiated warfarin patients in the PDAS group (PDAS: 9.5% vs control: 19.7%; P = 0.079; Figure 1). Other subgroup analyses relating to the safety endpoint are presented in Figure 1.
DISCUSSION
This article describes a systematic intervention designed to improve anticoagulation safety and efficacy in the hospital and during the transition to the postdischarge setting. Implementation of a PDAS did not impact patient bleeding and thrombotic outcomes, but did result in improved coordination and documentation of warfarin management and subsequent enhancement in the transition of the anticoagulated patient from the inpatient‐to‐outpatient setting with the Pharmacist‐Directed Anticoagulation Service.
Limited previous work has investigated the role of an anticoagulation service in inpatient management of anticoagulation.48 Only one published study has investigated the impact of this type of service on transition of care issues with warfarin, as was done in our study.7 In that study, management by an inpatient anticoagulation service resulted in a greater proportion of patients referred to an anticoagulation clinic for management (P = 0.001), more patients presenting to the anticoagulation clinic with a therapeutic INR (P = 0.001), and fewer patients presenting to the clinic with supratherapeutic levels of anticoagulation (P = 0.002). These results are somewhat analogous to our findings, in that patients in our study were less likely to require a dose change at the first clinic follow‐up visit after discharge or to have INR values 5.
We completed several subgroup analyses to thoroughly explore the impact of the PDAS on the safety endpoint. While firm conclusions cannot be drawn from these subgroup analyses, some hypothesis‐generating observations can be made. First, there was a greater impact of the PDAS on the safety endpoint in patients who are usually more sensitive to the effects of warfarin and therefore more challenging to manage.2 The impact of the PDAS was also greater among patients whose length of stay was more than five days (population median). This is significant because it suggests that when the opportunity for adverse events and miscommunication is greatest (ie, during hospitalizations of longer duration), there appears to be improvement in the safety endpoint with the PDAS.
To our knowledge, this study was the first to explore the impact of an inpatient anticoagulation service on the care of both newly initiated and existing warfarin patients, rather than only patients newly initiated on warfarin. As expected, the greatest influence of the PDAS on the safety endpoint was observed among the newly initiated patients. While the safety impact of the PDAS was noted most significantly among the newly initiated patients, the PDAS had a positive effect on the transition of care metrics regardless of previous warfarin use.
A limitation of our study should be mentioned. While we employed a design in which randomly selected units were exposed to the PDAS, individual patients were not randomized to the service. This cluster randomized design was chosen because it mimics quality improvement processes that would be rolled out to a hospital nursing unit. While lack of randomization at the patient level is a limitation, our cluster randomized study design is pragmatic and represents an improvement over the existing published before and after quasi‐experimental studies in this area.48
An improved system for documentation of communication was built into the PDAS processes on implementation of the service. However, it should also be noted that inadequate communication between inpatient and outpatient providers was an identified root cause for adverse events with warfarin in our institution prior to implementation of the PDAS. Therefore, improvement in the transition of care metrics with the PDAS were likely due to a combination of both improved documentation and true improvement in communication.
The approach of the PDAS was refined in several ways early after implementation of the service. Some major notable improvements included the development of a systematic approach to ensuring appropriate follow‐up and transition of care for patients being discharged to a skilled nursing facility, and creation of a system that required a mandatory conversation between the PDAS and a surgical service if anticoagulation is ordered within 48 hours of a major procedure. An upcoming improvement to the service will be to transition from a home grown electronic database, which was built for the purposes of streamlining clinical workflow and data collection, to a commercially available software program that has recently become available for management of inpatient anticoagulation. The major advantage of the new program will be the clinical decision support capabilities that will help to further streamline the service and allow for greater efficiency.
To understand implications of our PDAS intervention, it is important to remember that the majority of study patients were prescribed warfarin prior to hospital admission, that patients in both study groups were enrolled in an established, multisite anticoagulation clinic, and that patients in both groups were managed with a comprehensive inpatientoutpatient electronic medical record. Therefore, all providers caring for these patients had real‐time access to all warfarin dosing and dose adjustments, INR results, and anticoagulation clinic encounters, even though formal communication between providers was infrequently documented for control group patients in our electronic medical record. Study patients had low rates of bleeding and no adverse thrombotic outcomes across both treatment groups. Therefore, this type of model is likely to produce larger gains in communication and safety outcomes in health care systems without established anticoagulation clinics or comprehensive electronic medical records.
The PDAS was enthusiastically accepted by providers at our institution and expanded hospital‐wide after completion of this pilot. The PDAS model is a viable approach to standardize anticoagulant management with a goal of improving anticoagulant safety in the inpatient setting. Assessment of the effectiveness of models such as the PDAS for improving anticoagulant safety in the inpatient setting is particularly relevant with the current expectations for hospitals set by The Joint Commission's NPSG.03.05.01.3 More importantly the PDAS model can be an option for improving the transition of the anticoagulated patient from the inpatient‐to‐outpatient setting. Follow‐up with the anticoagulation clinic occurred earlier with the PDAS and, while this study was not designed to evaluate the impact of this new service on rehospitalization, recent literature suggests that earlier follow‐up after discharge leads to less rehospitalization.10 Finally, it may be possible to adapt this model to provide more intensive medication therapy management and monitoring for hospitalized patients with other complicated medication regimens or chronic disease.
CONCLUSION
The clinical pharmacist is uniquely prepared to manage inter‐individual variability in pharmacodynamic response to drug therapy, as well as to provide high‐quality patient education. This study evaluated a new model of inpatient warfarin management, in which warfarin dosing, monitoring, patient education, and transition of care was coordinated by a specialized team of clinical pharmacists that worked in collaboration with physicians and outpatient anticoagulation clinic staff. Safety and efficiency of the care provided by this new service was improved in certain subsets of more complex patients. The major advantage of this service was improvement in patient handoff, improved communication, and earlier patient follow‐up after discharge. Therefore, implementation of a Pharmacist‐Directed Anticoagulation Service provides a net improvement in quality of care for the patient taking warfarin in the inpatient setting.
Acknowledgements
The authors acknowledge the efforts of the PDAS staff: Nassif Abi‐Samra, Pam Holland, Sara Lanfear, and Gail Washington. This work would not have been possible without the dedication of these pharmacists. All authors had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
- U.S. Pharmacopeia. USP Patient Safety CapsLink: January 2008. Available at: http://www.usp.org/pdf/EN/patientSafety/capsLink2008–01‐01.pdf. Accessed March 19,2010.
- ,,,,,.Pharmacology and management of the vitamin K antagonists: American College of Chest Physicians evidence‐based clinical practice guidelines (8th ed.).Chest.2008;133:160S–198S.
- The Joint Commission. 2009 National Patient Safety Goals. Available at: http://www.jointcommission.org/NR/rdonlyres/31666E86‐E7F4–423E‐9BE8‐F05BD1CB0AA8/0/HAP_NPSG.pdf. Accessed May 6,2010.
- ,,, et al.Optimization of inpatient warfarin therapy: impact of a daily consultation by a pharmacist‐managed anticoagulation service.Ann Pharmacother.2000;34:567–572.
- ,,, et al.Pharmacy‐managed protocol for warfarin use in orthopedic surgery patients.Am J Health‐Syst Pharm.1995;52:1310–1316.
- .Pharmacist involvement with warfarin dosing for inpatients.Pharm World Sci.2001;23:31–35.
- ,,.Evaluation of a pharmacy‐managed warfarin‐monitoring service to coordinate inpatient and outpatient therapy.Am J Hosp Pharm.1992;49:387–394.
- ,.Implementation and evaluation of a pharmacist‐assisted warfarin dosing program.Can J Hosp Pharm.1997;50:169–175.
- ,.Definition of major bleeding in clinical investigations of antihemostatic medicinal products in non‐surgical patients.J Thromb Haemostasis.2005;3:692–694.
- ,,.Rehospitalizations among patients in the Medicare fee‐for‐service program.N Engl J Med.2009;360:1418–1428.
- U.S. Pharmacopeia. USP Patient Safety CapsLink: January 2008. Available at: http://www.usp.org/pdf/EN/patientSafety/capsLink2008–01‐01.pdf. Accessed March 19,2010.
- ,,,,,.Pharmacology and management of the vitamin K antagonists: American College of Chest Physicians evidence‐based clinical practice guidelines (8th ed.).Chest.2008;133:160S–198S.
- The Joint Commission. 2009 National Patient Safety Goals. Available at: http://www.jointcommission.org/NR/rdonlyres/31666E86‐E7F4–423E‐9BE8‐F05BD1CB0AA8/0/HAP_NPSG.pdf. Accessed May 6,2010.
- ,,, et al.Optimization of inpatient warfarin therapy: impact of a daily consultation by a pharmacist‐managed anticoagulation service.Ann Pharmacother.2000;34:567–572.
- ,,, et al.Pharmacy‐managed protocol for warfarin use in orthopedic surgery patients.Am J Health‐Syst Pharm.1995;52:1310–1316.
- .Pharmacist involvement with warfarin dosing for inpatients.Pharm World Sci.2001;23:31–35.
- ,,.Evaluation of a pharmacy‐managed warfarin‐monitoring service to coordinate inpatient and outpatient therapy.Am J Hosp Pharm.1992;49:387–394.
- ,.Implementation and evaluation of a pharmacist‐assisted warfarin dosing program.Can J Hosp Pharm.1997;50:169–175.
- ,.Definition of major bleeding in clinical investigations of antihemostatic medicinal products in non‐surgical patients.J Thromb Haemostasis.2005;3:692–694.
- ,,.Rehospitalizations among patients in the Medicare fee‐for‐service program.N Engl J Med.2009;360:1418–1428.
Copyright © 2011 Society of Hospital Medicine
Characteristics of High Cost/LOS Patients
Patients with advanced illness frequently do not receive care that meets their physical and emotional needs at the end of life,1 despite significant expenditures. Palliative care has been recommended as an approach to improve the quality of care for patients with advanced illness,26 while achieving hospital cost savings.7 Studies show that palliative care consults are associated with decreased hospitalization cost712 and length of stay13, 14 in the acute care setting.
Identifying which hospitalized patients are likely to benefit most from palliative care has not been well defined. The Hamilton Chart Audit tool was developed to estimate the number of patients that would benefit from a palliative care consult, in order to determine hospital palliative care staffing and financial needs.15 The CARING criteria identifies patients on admission to the hospital who are at high risk of death within one year and may, therefore, benefit from palliative care.16 The literature from the medical intensive care unit (MICU) identifies palliative care core competencies and quality measures, but does not describe patient factors that should trigger a palliative care consult.1719 Norton et al. studied proactive palliative care consultation in the MICU, finding that palliative care consultation in the high‐risk group (serious illness and high risk of dying) was associated with a shorter MICU length of stay without a significant difference in mortality rates.14
The most specific triggers for a palliative care consult comes from the surgical intensive care guidelines. The American College of Surgeons Surgical Palliative Care Task Force published a consensus guideline based on expert opinion identifying the top ten triggers for a palliative care consultation in the surgical intensive care unit (SICU).20 The top 10 criteria to identify SICU patients for palliative care consultation listed in order of priority were: 1) family request; 2) futility considered or declared by the medical team; 3) family disagreement with the team, advance directive, or each other lasting greater than seven days; 4) death expected during the same SICU stay; 5) SICU stay of greater than one month; 6) diagnosis with a median survival of less than six months; 7) greater than three SICU admissions during the same hospitalization; 8) Glasgow Coma Score of less than eight for greater than one week in a patient greater than 75 years old; 9) Glasgow Outcome Score of less than three (i.e., persistent vegetative state); and 10) multisystem organ failure of greater than three systems.
Studies are lacking that identify hospitalized patients who are more likely to have higher cost per day or length of stay, as these are patients who may benefit from palliative care. We sought to identify patient characteristics that are associated with higher cost per day or longer length of stay in hospitalized patients who died during the hospitalization or were discharged to hospicepatients likely to benefit from targeted palliative care services. We hypothesized that hospitalized patients with the following characteristics who died during the hospitalization or were discharged to hospice would have a higher cost per day or longer length of stay: older patients, lack of insurance, and patients receiving care from a critical care specialty.
METHODS
Study Design
We analyzed administrative data from a single academic hospital, the University of Colorado Hospital, a tertiary care, academic hospital with approximately 400 beds. The study population consisted of hospitalized adult patients (age 18 years) who died during hospitalization or were discharged to hospice in 2006 and 2007. We included both patients discharged to hospice and those who died during hospitalization, as we were seeking to identify a hospitalized patient population who might be expected to benefit from palliative care: those at high risk of death in the near future. Predictors were selected on the basis of clinical experience and the literature. Cost per day and length of stay were the outcome variables. Institutional Review Board (IRB) approval was not necessary because all of the study patients were deceased at the time of analysis.
Due to resource limitations, we were only able to gather clinical information (presence of organ failure [cardiac, respiratory, renal, hepatic, neurologic] or sepsis on admission, and presence or absence of palliative care consultation during hospitalization) from chart review in a subset of the sample population: those that had the highest 10% total hospitalization costs (n = 115). Organ failure was defined as chart documentation of any of the following: 1) cardiac: ST segment elevation myocardial infarction, non‐ST segment elevation myocardial infarction, congestive heart failure, heart failure (n = 28); 2) respiratory: respiratory failure (n = 36); 3) renal: acute kidney injury, acute renal failure, chronic renal failure, dialysis, end‐stage renal disease (n = 42); 4) hepatic: hepatic failure, end‐stage liver disease (n = 10); and 5) neurologic: altered mental status, delirium (n = 4). Sepsis was defined as chart documentation of any of the following: sepsis, severe sepsis, or septic shock.
Outcomes
We found total cost and length of stay to be correlated. Therefore, we used cost per day in lieu of total cost as the primary outcome. Length of stay was the secondary outcome. Using cost per day as the primary outcome reduced the correlation between our primary and secondary outcomes.
Predictors
Potential predictors (age, insurance status, and attending physician specialty) were selected on the basis of clinical experience, the literature, and patient variables available from the administrative data. We also considered diagnosis‐related group (DRG), however, the wide range of unique DRGs for this population did not allow for sensible groupings, so DRG was excluded from further analyses. For descriptive purposes, mean (standard deviation, SD) age was reported. For modeling, age was centered at 65 years, because this is the age of Medicare eligibility and thus a likely point at which insurance status would change. Sixty‐five was also close to the mean age of the full population, 62 years, therefore ensuring that interactions were assessed over the bulk of the data, rather than at outlying points. We also divided age into ten‐year increments for easier interpretation of model estimates. The relationship between age and primary and secondary outcomes differed among younger vs older patients. Therefore, age was included as a piecewise term in the final multivariate linear model which allowed a separate slope to be fit for patients age <65 years vs those 65 years.
Insurance status was dichotomized as insured vs uninsured. Attending physician specialty categories (internal medicine, pulmonary critical care, neurosurgery, surgical oncology, and cardiothoracic surgery) were selected because they were the five most common specialties. The remaining specialties were grouped together as other, which was used as a reference group in the multivariate analyses as it constituted a nontrivial proportion of the study population.
Statistical Analyses
Univariate analyses were performed separately for the primary and secondary outcomes. Univariate associations between the outcomes and categorical predictors were tested using analysis of variance (ANOVA) models with adjustment for multiple comparisons. Associations between the outcomes and the binary predictors were assessed with t‐tests. Predictors that were significant at the 0.10 level and considered clinically relevant were included in the multivariate model. Interaction terms between predictors were examined and included in the final multivariate piecewise linear models, when inclusion of the interaction terms altered the magnitude of the model estimates.
RESULTS
The study population comprised 1155 hospitalizations. Nine hospitalizations were excluded from analysis (five for organ donation, three were erroneousthe patients were not discharged to hospice or did not die during the hospitalization, and one was a pediatric patient), resulting in a study population of n = 1146 hospitalizations.
Table 1 depicts study population characteristics. The average patient age was 62 years (SD = 16), and 96% of patients were insured. The average length of stay was 10.7 days (SD = 14.1), with an average total cost per admission of $44,410 (SD = 76,355), as compared to an overall hospital admission (excluding obstetrics/neonatology) average length of stay of 5.7 days (SD = 8.5) and average total cost per admission of $17,410 (SD = 36,633) during the same time period. The average cost per day was $5095 (SD = $8546). About one‐third of patients were admitted to internal medicine, 20% to pulmonary critical care, and 18% to surgical specialties. The remaining 29% belonged to other specialties.
| Number of patients, n (%) | 1146 |
| Death in hospital | 730 (63.7) |
| Discharged with hospice | 416 (36.3) |
| Age (years), mean (SD) | 61.7 (15.9) |
| Insurance, n (%) | |
| Uninsured | 52 (4.5) |
| Insured | 1,094 (95.5) |
| Length of stay (days), mean (SD) | 10.7 (14.1) |
| Total cost, mean (SD) | $44,410 (76,355) |
| Cost per day, mean (SD) | $5,095 (8,546) |
| Attending MD specialty, n (%) | |
| Cardiothoracic Surgery | 56 (4.9) |
| Pulmonary Critical Care | 230 (20.1) |
| Surgical Oncology | 70 (6.1) |
| Internal Medicine | 383 (33.4) |
| Neurosurgery | 77 (6.7) |
| Other | 330 (28.8) |
Univariate Analyses
Overall, younger patients had a higher cost per day (Pearson 0.09; P = 0.02) and longer length of stay (Pearson 0.15; P < 0.0001) than older patients (data not shown). According to age groups defined by quartiles, patients who were age <51 and between 61‐72 years had significantly higher cost per day than patients age 73 years ($5787 and $5826 vs $3649, respectively; ANOVA P = 0.005; pairwise P < 0.05). The length of stay for the age groups under 73 years of age were significantly longer than for the patients who were 73 years of age and older (11.9, 11.9, and 11.2 vs 8.0 days, respectively; ANOVA P = 0.001; pairwise P < 0.05; Table 2). Uninsured patients had a higher cost per day ($6618 vs $5023; P = 0.02) than insured patients. In pairwise comparisons, patients on the cardiothoracic surgery service had a higher cost per day ($17,942) than any other specialty (ANOVA P < 0.0001; pairwise P < 0.05). Neurosurgery patients had a higher cost per day ($7089) than the internal medicine patients ($3173; pairwise P < 0.05). Cardiothoracic surgery patients also had a significantly higher LOS (18.3 days) than internal medicine (8.0 days), critical care (11.6 days), neurosurgery (10.0 days), and the other (10.9 days) specialties (ANOVA P < 0.0001; pairwise P < 0.05). The LOS for internal medicine (8.0 days) was significantly lower than critical care (11.6 days), surgical oncology (15.9 days), and cardiothoracic surgery (18.3 days; pairwise P < 0.05).
| Variable | N | Cost per day [$] (mean [SD]) | P Value | Length of stay [days] (mean [SD]) | P Value |
|---|---|---|---|---|---|
| |||||
| 1146 | |||||
| Age group, quartiles | |||||
| <51 years | 281 | 5,787 (8,008) | 0.005 | 11.9 (16.4) | 0.001 |
| 51‐60 | 264 | 5,202 (7,643) | 11.9 (15.4) | ||
| 61‐72 | 297 | 5,826 (12,272) | 11.2 (14.1) | ||
| 73 | 304 | 3,649 (3,978) | 8.0 (9.7) | ||
| Insurance | |||||
| Insured | 1094 | 5,023 (8,691) | 0.02 | 10.8 (14.2) | 0.23 |
| Uninsured | 52 | 6,618 (4,297) | 8.4 (13.5) | ||
| Attending MD specialty | |||||
| Internal Medicine | 383 | 3,173 (2,647) | <0.0001* | 8.0 (11.0) | <0.0001 |
| Pulmonary Critical Care | 230 | 4,671 (2,734) | 11.6 (14.3) | ||
| Neurosurgery | 77 | 7,089 (6,103) | 10.0 (13.5) | ||
| Surgical Oncology | 70 | 5,768 (3,521) | 15.9 (17.9) | ||
| Cardiothoracic Surgery | 56 | 17,942 (26,943) | 18.3 (23.6) | ||
| Other | 330 | 4,833 (8,641) | 10.9 (13.6) | ||
Multivariate Analyses
Cost per Day
The final multivariate linear model included age and attending physician specialty. Insurance status was excluded because it lost significant association with cost per day when it was added to the model (Table 3). Compared to the other specialty, internal medicine decreased cost per day by $1531 (P = 0.01), neurosurgery increased cost per day by $2255 (P = 0.03), and cardiothoracic surgery increased cost per day by $12,937 (P < 0.0001). Cost per day decreased by $811 (SE = 349; P = 0.02) for each age decade 65 years, however, no effect was observed on cost per day for those younger than 65 years.
| Predictors | Estimated Effect ($) | 95% Confidence Interval | P Value to Test if = 0 |
|---|---|---|---|
| Other specialty (reference group at 65 yr) | 5209 | (4,133, 6,284) | |
| Internal Medicine | 1,531 | (2,709, 353) | 0.01 |
| Pulmonary Critical Care | 217 | (1,562, 1,128) | 0.75 |
| Neurosurgery | 2255 | (278, 4,232) | 0.03 |
| Surgical Oncology | 1064 | (994, 3,122) | 0.31 |
| Cardiothoracic Surgery | 12937 | (10,676, 15,198) | <0.0001 |
| Age per 10 yr/age <65 | 7 | (506, 519) | 0.98 |
| Age per 10 yr/age 65 | 811 | (1,497, 125) | 0.02 |
Length of Stay
Because age and attending physician specialty had a significant effect on length of stay, multivariate analyses were performed with these two predictor variables (Table 4). Compared to the other specialty, internal medicine decreased length of stay by 2.4 days (P = 0.02), surgical oncology increased LOS by 5.3 days (P = 0.003), and cardiothoracic surgery increased length of stay by 6.9 days (P = 0.001). Length of stay was significantly decreased by 1.8 days (SE = 0.61; P = 0.003) for each age decade 65 years.
| Predictors | Estimated Effect (days) | 95% Confidence Interval | P Value to Test if = 0 |
|---|---|---|---|
| Other specialty (reference group at 65 yr) | 11 | (9.1, 12.9) | |
| Internal Medicine | 2.4 | (4.4, 0.3) | 0.02 |
| Pulmonary Critical Care | 0.5 | (1.9, 2.8) | 0.7 |
| Neurosurgery | 0.9 | (4.3, 2.6) | 0.62 |
| Surgical Oncology | 5.3 | (1.8, 8.9) | 0.003 |
| Cardiothoracic Surgery | 6.9 | (3.0, 10.9) | 0.001 |
| Age per 10 yr/age <65 | 0.8 | (1.7, 0.1) | 0.08 |
| Age per 10 yr/age 65 | 1.8 | (3.0, 0.6) | 0.003 |
DISCUSSION
We found several characteristics that were significantly associated with higher cost per day or longer length of stay in patients who died during hospitalization or were discharged to hospice. Among this patient population, the surgical specialty services had overall higher cost per day and length of stay than other services. Patients cared for on the cardiothoracic surgery service had higher cost per day and length of stay; in contrast, internal medicine patients had lower cost per day and length of stay. Neurosurgery patients had higher cost per day, while surgical oncology patients had higher length of stay. Patients age 65 years and older had a significantly lower cost per day and shorter length of stay than those less than 65 years of age.
Higher cost per day for cardiothoracic surgery and neurosurgery patients may partially be explained by cardiothoracic surgery patients' usage of clinical services, including operating room services, which are higher in costs compared with those of nonsurgical specialties. Some patients may require repeat surgeries in the same hospitalization which further increases the cost per day. Longer length of stay in surgical oncology patients may be related to complex surgeries and possible postoperative complications that may take longer to recover from than standard surgeries.
Our findings that older patients have lower cost per day and shorter length of stay are corroborated by other studies. Lubitz and Riley21 found that in 1976 and 1988, Medicare payments per person year decreased with age. Levinsky et al.22 had similar findings in a review of Medicare data in 2001, but noted smaller reductions in total costabout $400 decrease for each year above 65. Their explanation of the lower cost is that older patients receive less aggressive care. Physicians, as well as patients and families, may continue to pursue expensive, invasive therapies for terminally ill patients who are younger for a longer period of time than with older patients, which would increase cost per day as well as length of stay.
The finding that patients on the surgical specialty services may be a focus for active palliative care intervention has many implications. The American College of Surgeons Surgical Palliative Care Task Force consensus guideline triggers for a palliative care consultation in SICU applied clinically did not result in a change in palliative care consultation rate.23 The use of triggers for palliative care consultation may be an ineffective approach because knowledge and application of the triggers did not change behavior. Focusing on integrating palliative care interventions or consultation for all high‐risk surgical patients, as opposed to relying upon triggers, may be a more effective approach to meeting these patients' palliative care needs while lowering cost per day and length of stay and warrants further study. For instance, palliative care consult teams may consider routine or daily rounds with the surgical specialty services in order to effectively integrate palliative care for these patients. Such an integrative approach may foster familiarity and comfort with palliative care approaches, facilitating access to palliative care services for those patients with palliative needs.
Our study is limited in that it is a retrospective, single‐center study. Our results may not be applicable to the general population. The experience of additional centers analyzed prospectively would provide additional context. The available administrative data limited the analyses to only a small number of predictors. In the subset population with the highest 10% total hospitalization costs, from which clinical information was gathered, the presence of respiratory failure was associated with shorter LOS (33 days vs 42 days; P = 0.03), but not associated with cost per day. Having sepsis at admission was associated with lower cost per day ($5783 vs $10,071; P = 0.04); however, this finding was based on only four patients with sepsis at admission. Patients who were evaluated by the palliative care service (n = 35) had a significantly lower cost per day ($4896 vs $12,210; P = 0.01) but longer LOS (46.5 vs 35.7 days; P = 0.03) than those who were not. These, and other, clinical characteristics need further testing in larger samples. An additional limitation is that we combined hospital decedents with patients discharged to hospice as our study population. These groups were combined since they are both at high risk of death in the near future; the median hospice length of stay in Colorado is 20 days.24 There may exist important differences in these populations that are not accounted for in our findings. Despite these unidentified differences, both populations are at high risk of death in the near future, making it likely that they would benefit from palliative care. Those who died during hospitalization did have a longer LOS (11.5 vs 9.2 days; P = 0.003) and higher cost per day ($6734 vs $2221; P < 0.0001) than those who were discharged to hospice.
Palliative care consultations can lead to improved quality of care for patients and families by addressing suffering and addressing quality of life measures (2, 4, 5, 6). We sought to identify characteristics associated with high cost and prolonged hospitalizations in patients who died during hospitalization, or were discharged to hospice, in order to inform targeting of palliative care services. Our data suggest that younger patients and those cared for by surgical specialty services may have the most palliative needs. Palliative care teams may consider focusing efforts at integrating palliative care with surgical specialty services to address these needs. These findings need to be corroborated in other centers, and include clinical outcomes.
- ,,, et al.Family perspectives on end‐of‐life care at the last place of care.JAMA.2004;291:88–93.
- ,.Do specialist palliative care teams improve outcomes for cancer patients? A systematic literature review.Palliat Med.1998;12:317–332.
- ,,, et al.Evidence‐based interventions to improve the palliative care of pain, dyspnea, and depression at the end of life: a clinical practice guideline from the American College of Physicians.Ann Intern Med.2008;148:141–146.
- ,,, et al.Do palliative consultations improve patient outcomes?JAGS.2008;56:593–599.
- ,,, et al.Effects of a palliative cafe intervention on clinical outcomes in patients with advanced cancer.JAMA.2009;302:741–749.
- ,,, et al.Early palliative care for patients with metastatic non‐small‐cell lung cancer.N Engl J Med.2010;363:733–742.
- ,,, et al.Cost savings associated with US hospital palliative care consultation programs.Arch Intern Med.2008;168(16):1783–1790.
- ,,.Impact of palliative care case management on resource use by patients dying of cancer at a Veterans Affairs Medical Center.J Palliat Med.2005;8(1):26–35.
- ,,, et al.Cost and utilization outcomes of patients receiving hospital‐based palliative care consultation.J Palliat Med.2006;9(4):855–860.
- ,,, et al.A high‐volume specialist palliative care unit and team may reduce in‐hospital end‐of‐life care costs.J Palliat Med.2003;6(5):699–705.
- ,,, et al.The economic and clinical impact of an inpatient palliative care consultation service: a multifaceted approach.J Palliat Med.2007;10(6):1347–1355.
- ,,, et al.Hospital‐based palliative care consultation: effects on hospital cost.J Palliat Med.2010;13(8):1–7.
- ,.Impact of a proactive approach to improve end‐of‐life care in a medical ICU.Chest.2003;123:266–271.
- ,,, et al.Proactive palliative care in the medical intensive care unit: effects on length of stay for selected high‐risk patients.Crit Care Med.2007;35:1530–1535.
- ,,,,.Who needs a palliative care consult? The Hamilton Chart Audit tool.J Palliat Med.2007;10(2):304–307.
- ,,,,,.A practical tool to identify patients who may benefit from a palliative care approach: the CARING criteria.J Pain Symptom Manage.2006;31:285–292.
- ,,, et al.An Official American Thoracic Society Clinical Policy Statement: palliative care for patients with respiratory diseases and critical illnesses.Am J Respir Crit Care Med.2008;177:912–927.
- ,,, et al.Proposed quality measures for palliative care in the critically ill: a consensus from the Robert Wood Johnson Foundation Critical Care Workgroup.Crit Care Med.2006;34:S404–S411.
- ,,, et al.Recommendations for end‐of‐life care in the intensive care unit: a consensus statement by the American Academy of Critical Care Medicine.Crit Care Med.2008;36:953–963.
- ,.Developing guidelines that identify patients who would benefit from palliative care services in the surgical intensive care unit.Crit Care Med.2009;37:946–950.
- ,.Trends in Medicare payments in the last year of life.N Engl J Med.1993;328:1092–1096.
- ,,, et al.Influence of age on Medicare expenditures and medical care in the last year of life.JAMA.2001;286:1349–1355.
- ,,.Addressing access to palliative care services in the surgical intensive care unit.Surgery.2010;147:871–877.
- http://www.coloradocancercoalition.org/…/CCCConferenceKassner Slides11.13.07.ppt. Accessed August 16,2010.
- ,,.The impact of implementing referral criteria on the patterns of referrals and admissions to a palliative care program in Saudi Arabia.J Support Oncol.2010;8:78–81.
Patients with advanced illness frequently do not receive care that meets their physical and emotional needs at the end of life,1 despite significant expenditures. Palliative care has been recommended as an approach to improve the quality of care for patients with advanced illness,26 while achieving hospital cost savings.7 Studies show that palliative care consults are associated with decreased hospitalization cost712 and length of stay13, 14 in the acute care setting.
Identifying which hospitalized patients are likely to benefit most from palliative care has not been well defined. The Hamilton Chart Audit tool was developed to estimate the number of patients that would benefit from a palliative care consult, in order to determine hospital palliative care staffing and financial needs.15 The CARING criteria identifies patients on admission to the hospital who are at high risk of death within one year and may, therefore, benefit from palliative care.16 The literature from the medical intensive care unit (MICU) identifies palliative care core competencies and quality measures, but does not describe patient factors that should trigger a palliative care consult.1719 Norton et al. studied proactive palliative care consultation in the MICU, finding that palliative care consultation in the high‐risk group (serious illness and high risk of dying) was associated with a shorter MICU length of stay without a significant difference in mortality rates.14
The most specific triggers for a palliative care consult comes from the surgical intensive care guidelines. The American College of Surgeons Surgical Palliative Care Task Force published a consensus guideline based on expert opinion identifying the top ten triggers for a palliative care consultation in the surgical intensive care unit (SICU).20 The top 10 criteria to identify SICU patients for palliative care consultation listed in order of priority were: 1) family request; 2) futility considered or declared by the medical team; 3) family disagreement with the team, advance directive, or each other lasting greater than seven days; 4) death expected during the same SICU stay; 5) SICU stay of greater than one month; 6) diagnosis with a median survival of less than six months; 7) greater than three SICU admissions during the same hospitalization; 8) Glasgow Coma Score of less than eight for greater than one week in a patient greater than 75 years old; 9) Glasgow Outcome Score of less than three (i.e., persistent vegetative state); and 10) multisystem organ failure of greater than three systems.
Studies are lacking that identify hospitalized patients who are more likely to have higher cost per day or length of stay, as these are patients who may benefit from palliative care. We sought to identify patient characteristics that are associated with higher cost per day or longer length of stay in hospitalized patients who died during the hospitalization or were discharged to hospicepatients likely to benefit from targeted palliative care services. We hypothesized that hospitalized patients with the following characteristics who died during the hospitalization or were discharged to hospice would have a higher cost per day or longer length of stay: older patients, lack of insurance, and patients receiving care from a critical care specialty.
METHODS
Study Design
We analyzed administrative data from a single academic hospital, the University of Colorado Hospital, a tertiary care, academic hospital with approximately 400 beds. The study population consisted of hospitalized adult patients (age 18 years) who died during hospitalization or were discharged to hospice in 2006 and 2007. We included both patients discharged to hospice and those who died during hospitalization, as we were seeking to identify a hospitalized patient population who might be expected to benefit from palliative care: those at high risk of death in the near future. Predictors were selected on the basis of clinical experience and the literature. Cost per day and length of stay were the outcome variables. Institutional Review Board (IRB) approval was not necessary because all of the study patients were deceased at the time of analysis.
Due to resource limitations, we were only able to gather clinical information (presence of organ failure [cardiac, respiratory, renal, hepatic, neurologic] or sepsis on admission, and presence or absence of palliative care consultation during hospitalization) from chart review in a subset of the sample population: those that had the highest 10% total hospitalization costs (n = 115). Organ failure was defined as chart documentation of any of the following: 1) cardiac: ST segment elevation myocardial infarction, non‐ST segment elevation myocardial infarction, congestive heart failure, heart failure (n = 28); 2) respiratory: respiratory failure (n = 36); 3) renal: acute kidney injury, acute renal failure, chronic renal failure, dialysis, end‐stage renal disease (n = 42); 4) hepatic: hepatic failure, end‐stage liver disease (n = 10); and 5) neurologic: altered mental status, delirium (n = 4). Sepsis was defined as chart documentation of any of the following: sepsis, severe sepsis, or septic shock.
Outcomes
We found total cost and length of stay to be correlated. Therefore, we used cost per day in lieu of total cost as the primary outcome. Length of stay was the secondary outcome. Using cost per day as the primary outcome reduced the correlation between our primary and secondary outcomes.
Predictors
Potential predictors (age, insurance status, and attending physician specialty) were selected on the basis of clinical experience, the literature, and patient variables available from the administrative data. We also considered diagnosis‐related group (DRG), however, the wide range of unique DRGs for this population did not allow for sensible groupings, so DRG was excluded from further analyses. For descriptive purposes, mean (standard deviation, SD) age was reported. For modeling, age was centered at 65 years, because this is the age of Medicare eligibility and thus a likely point at which insurance status would change. Sixty‐five was also close to the mean age of the full population, 62 years, therefore ensuring that interactions were assessed over the bulk of the data, rather than at outlying points. We also divided age into ten‐year increments for easier interpretation of model estimates. The relationship between age and primary and secondary outcomes differed among younger vs older patients. Therefore, age was included as a piecewise term in the final multivariate linear model which allowed a separate slope to be fit for patients age <65 years vs those 65 years.
Insurance status was dichotomized as insured vs uninsured. Attending physician specialty categories (internal medicine, pulmonary critical care, neurosurgery, surgical oncology, and cardiothoracic surgery) were selected because they were the five most common specialties. The remaining specialties were grouped together as other, which was used as a reference group in the multivariate analyses as it constituted a nontrivial proportion of the study population.
Statistical Analyses
Univariate analyses were performed separately for the primary and secondary outcomes. Univariate associations between the outcomes and categorical predictors were tested using analysis of variance (ANOVA) models with adjustment for multiple comparisons. Associations between the outcomes and the binary predictors were assessed with t‐tests. Predictors that were significant at the 0.10 level and considered clinically relevant were included in the multivariate model. Interaction terms between predictors were examined and included in the final multivariate piecewise linear models, when inclusion of the interaction terms altered the magnitude of the model estimates.
RESULTS
The study population comprised 1155 hospitalizations. Nine hospitalizations were excluded from analysis (five for organ donation, three were erroneousthe patients were not discharged to hospice or did not die during the hospitalization, and one was a pediatric patient), resulting in a study population of n = 1146 hospitalizations.
Table 1 depicts study population characteristics. The average patient age was 62 years (SD = 16), and 96% of patients were insured. The average length of stay was 10.7 days (SD = 14.1), with an average total cost per admission of $44,410 (SD = 76,355), as compared to an overall hospital admission (excluding obstetrics/neonatology) average length of stay of 5.7 days (SD = 8.5) and average total cost per admission of $17,410 (SD = 36,633) during the same time period. The average cost per day was $5095 (SD = $8546). About one‐third of patients were admitted to internal medicine, 20% to pulmonary critical care, and 18% to surgical specialties. The remaining 29% belonged to other specialties.
| Number of patients, n (%) | 1146 |
| Death in hospital | 730 (63.7) |
| Discharged with hospice | 416 (36.3) |
| Age (years), mean (SD) | 61.7 (15.9) |
| Insurance, n (%) | |
| Uninsured | 52 (4.5) |
| Insured | 1,094 (95.5) |
| Length of stay (days), mean (SD) | 10.7 (14.1) |
| Total cost, mean (SD) | $44,410 (76,355) |
| Cost per day, mean (SD) | $5,095 (8,546) |
| Attending MD specialty, n (%) | |
| Cardiothoracic Surgery | 56 (4.9) |
| Pulmonary Critical Care | 230 (20.1) |
| Surgical Oncology | 70 (6.1) |
| Internal Medicine | 383 (33.4) |
| Neurosurgery | 77 (6.7) |
| Other | 330 (28.8) |
Univariate Analyses
Overall, younger patients had a higher cost per day (Pearson 0.09; P = 0.02) and longer length of stay (Pearson 0.15; P < 0.0001) than older patients (data not shown). According to age groups defined by quartiles, patients who were age <51 and between 61‐72 years had significantly higher cost per day than patients age 73 years ($5787 and $5826 vs $3649, respectively; ANOVA P = 0.005; pairwise P < 0.05). The length of stay for the age groups under 73 years of age were significantly longer than for the patients who were 73 years of age and older (11.9, 11.9, and 11.2 vs 8.0 days, respectively; ANOVA P = 0.001; pairwise P < 0.05; Table 2). Uninsured patients had a higher cost per day ($6618 vs $5023; P = 0.02) than insured patients. In pairwise comparisons, patients on the cardiothoracic surgery service had a higher cost per day ($17,942) than any other specialty (ANOVA P < 0.0001; pairwise P < 0.05). Neurosurgery patients had a higher cost per day ($7089) than the internal medicine patients ($3173; pairwise P < 0.05). Cardiothoracic surgery patients also had a significantly higher LOS (18.3 days) than internal medicine (8.0 days), critical care (11.6 days), neurosurgery (10.0 days), and the other (10.9 days) specialties (ANOVA P < 0.0001; pairwise P < 0.05). The LOS for internal medicine (8.0 days) was significantly lower than critical care (11.6 days), surgical oncology (15.9 days), and cardiothoracic surgery (18.3 days; pairwise P < 0.05).
| Variable | N | Cost per day [$] (mean [SD]) | P Value | Length of stay [days] (mean [SD]) | P Value |
|---|---|---|---|---|---|
| |||||
| 1146 | |||||
| Age group, quartiles | |||||
| <51 years | 281 | 5,787 (8,008) | 0.005 | 11.9 (16.4) | 0.001 |
| 51‐60 | 264 | 5,202 (7,643) | 11.9 (15.4) | ||
| 61‐72 | 297 | 5,826 (12,272) | 11.2 (14.1) | ||
| 73 | 304 | 3,649 (3,978) | 8.0 (9.7) | ||
| Insurance | |||||
| Insured | 1094 | 5,023 (8,691) | 0.02 | 10.8 (14.2) | 0.23 |
| Uninsured | 52 | 6,618 (4,297) | 8.4 (13.5) | ||
| Attending MD specialty | |||||
| Internal Medicine | 383 | 3,173 (2,647) | <0.0001* | 8.0 (11.0) | <0.0001 |
| Pulmonary Critical Care | 230 | 4,671 (2,734) | 11.6 (14.3) | ||
| Neurosurgery | 77 | 7,089 (6,103) | 10.0 (13.5) | ||
| Surgical Oncology | 70 | 5,768 (3,521) | 15.9 (17.9) | ||
| Cardiothoracic Surgery | 56 | 17,942 (26,943) | 18.3 (23.6) | ||
| Other | 330 | 4,833 (8,641) | 10.9 (13.6) | ||
Multivariate Analyses
Cost per Day
The final multivariate linear model included age and attending physician specialty. Insurance status was excluded because it lost significant association with cost per day when it was added to the model (Table 3). Compared to the other specialty, internal medicine decreased cost per day by $1531 (P = 0.01), neurosurgery increased cost per day by $2255 (P = 0.03), and cardiothoracic surgery increased cost per day by $12,937 (P < 0.0001). Cost per day decreased by $811 (SE = 349; P = 0.02) for each age decade 65 years, however, no effect was observed on cost per day for those younger than 65 years.
| Predictors | Estimated Effect ($) | 95% Confidence Interval | P Value to Test if = 0 |
|---|---|---|---|
| Other specialty (reference group at 65 yr) | 5209 | (4,133, 6,284) | |
| Internal Medicine | 1,531 | (2,709, 353) | 0.01 |
| Pulmonary Critical Care | 217 | (1,562, 1,128) | 0.75 |
| Neurosurgery | 2255 | (278, 4,232) | 0.03 |
| Surgical Oncology | 1064 | (994, 3,122) | 0.31 |
| Cardiothoracic Surgery | 12937 | (10,676, 15,198) | <0.0001 |
| Age per 10 yr/age <65 | 7 | (506, 519) | 0.98 |
| Age per 10 yr/age 65 | 811 | (1,497, 125) | 0.02 |
Length of Stay
Because age and attending physician specialty had a significant effect on length of stay, multivariate analyses were performed with these two predictor variables (Table 4). Compared to the other specialty, internal medicine decreased length of stay by 2.4 days (P = 0.02), surgical oncology increased LOS by 5.3 days (P = 0.003), and cardiothoracic surgery increased length of stay by 6.9 days (P = 0.001). Length of stay was significantly decreased by 1.8 days (SE = 0.61; P = 0.003) for each age decade 65 years.
| Predictors | Estimated Effect (days) | 95% Confidence Interval | P Value to Test if = 0 |
|---|---|---|---|
| Other specialty (reference group at 65 yr) | 11 | (9.1, 12.9) | |
| Internal Medicine | 2.4 | (4.4, 0.3) | 0.02 |
| Pulmonary Critical Care | 0.5 | (1.9, 2.8) | 0.7 |
| Neurosurgery | 0.9 | (4.3, 2.6) | 0.62 |
| Surgical Oncology | 5.3 | (1.8, 8.9) | 0.003 |
| Cardiothoracic Surgery | 6.9 | (3.0, 10.9) | 0.001 |
| Age per 10 yr/age <65 | 0.8 | (1.7, 0.1) | 0.08 |
| Age per 10 yr/age 65 | 1.8 | (3.0, 0.6) | 0.003 |
DISCUSSION
We found several characteristics that were significantly associated with higher cost per day or longer length of stay in patients who died during hospitalization or were discharged to hospice. Among this patient population, the surgical specialty services had overall higher cost per day and length of stay than other services. Patients cared for on the cardiothoracic surgery service had higher cost per day and length of stay; in contrast, internal medicine patients had lower cost per day and length of stay. Neurosurgery patients had higher cost per day, while surgical oncology patients had higher length of stay. Patients age 65 years and older had a significantly lower cost per day and shorter length of stay than those less than 65 years of age.
Higher cost per day for cardiothoracic surgery and neurosurgery patients may partially be explained by cardiothoracic surgery patients' usage of clinical services, including operating room services, which are higher in costs compared with those of nonsurgical specialties. Some patients may require repeat surgeries in the same hospitalization which further increases the cost per day. Longer length of stay in surgical oncology patients may be related to complex surgeries and possible postoperative complications that may take longer to recover from than standard surgeries.
Our findings that older patients have lower cost per day and shorter length of stay are corroborated by other studies. Lubitz and Riley21 found that in 1976 and 1988, Medicare payments per person year decreased with age. Levinsky et al.22 had similar findings in a review of Medicare data in 2001, but noted smaller reductions in total costabout $400 decrease for each year above 65. Their explanation of the lower cost is that older patients receive less aggressive care. Physicians, as well as patients and families, may continue to pursue expensive, invasive therapies for terminally ill patients who are younger for a longer period of time than with older patients, which would increase cost per day as well as length of stay.
The finding that patients on the surgical specialty services may be a focus for active palliative care intervention has many implications. The American College of Surgeons Surgical Palliative Care Task Force consensus guideline triggers for a palliative care consultation in SICU applied clinically did not result in a change in palliative care consultation rate.23 The use of triggers for palliative care consultation may be an ineffective approach because knowledge and application of the triggers did not change behavior. Focusing on integrating palliative care interventions or consultation for all high‐risk surgical patients, as opposed to relying upon triggers, may be a more effective approach to meeting these patients' palliative care needs while lowering cost per day and length of stay and warrants further study. For instance, palliative care consult teams may consider routine or daily rounds with the surgical specialty services in order to effectively integrate palliative care for these patients. Such an integrative approach may foster familiarity and comfort with palliative care approaches, facilitating access to palliative care services for those patients with palliative needs.
Our study is limited in that it is a retrospective, single‐center study. Our results may not be applicable to the general population. The experience of additional centers analyzed prospectively would provide additional context. The available administrative data limited the analyses to only a small number of predictors. In the subset population with the highest 10% total hospitalization costs, from which clinical information was gathered, the presence of respiratory failure was associated with shorter LOS (33 days vs 42 days; P = 0.03), but not associated with cost per day. Having sepsis at admission was associated with lower cost per day ($5783 vs $10,071; P = 0.04); however, this finding was based on only four patients with sepsis at admission. Patients who were evaluated by the palliative care service (n = 35) had a significantly lower cost per day ($4896 vs $12,210; P = 0.01) but longer LOS (46.5 vs 35.7 days; P = 0.03) than those who were not. These, and other, clinical characteristics need further testing in larger samples. An additional limitation is that we combined hospital decedents with patients discharged to hospice as our study population. These groups were combined since they are both at high risk of death in the near future; the median hospice length of stay in Colorado is 20 days.24 There may exist important differences in these populations that are not accounted for in our findings. Despite these unidentified differences, both populations are at high risk of death in the near future, making it likely that they would benefit from palliative care. Those who died during hospitalization did have a longer LOS (11.5 vs 9.2 days; P = 0.003) and higher cost per day ($6734 vs $2221; P < 0.0001) than those who were discharged to hospice.
Palliative care consultations can lead to improved quality of care for patients and families by addressing suffering and addressing quality of life measures (2, 4, 5, 6). We sought to identify characteristics associated with high cost and prolonged hospitalizations in patients who died during hospitalization, or were discharged to hospice, in order to inform targeting of palliative care services. Our data suggest that younger patients and those cared for by surgical specialty services may have the most palliative needs. Palliative care teams may consider focusing efforts at integrating palliative care with surgical specialty services to address these needs. These findings need to be corroborated in other centers, and include clinical outcomes.
Patients with advanced illness frequently do not receive care that meets their physical and emotional needs at the end of life,1 despite significant expenditures. Palliative care has been recommended as an approach to improve the quality of care for patients with advanced illness,26 while achieving hospital cost savings.7 Studies show that palliative care consults are associated with decreased hospitalization cost712 and length of stay13, 14 in the acute care setting.
Identifying which hospitalized patients are likely to benefit most from palliative care has not been well defined. The Hamilton Chart Audit tool was developed to estimate the number of patients that would benefit from a palliative care consult, in order to determine hospital palliative care staffing and financial needs.15 The CARING criteria identifies patients on admission to the hospital who are at high risk of death within one year and may, therefore, benefit from palliative care.16 The literature from the medical intensive care unit (MICU) identifies palliative care core competencies and quality measures, but does not describe patient factors that should trigger a palliative care consult.1719 Norton et al. studied proactive palliative care consultation in the MICU, finding that palliative care consultation in the high‐risk group (serious illness and high risk of dying) was associated with a shorter MICU length of stay without a significant difference in mortality rates.14
The most specific triggers for a palliative care consult comes from the surgical intensive care guidelines. The American College of Surgeons Surgical Palliative Care Task Force published a consensus guideline based on expert opinion identifying the top ten triggers for a palliative care consultation in the surgical intensive care unit (SICU).20 The top 10 criteria to identify SICU patients for palliative care consultation listed in order of priority were: 1) family request; 2) futility considered or declared by the medical team; 3) family disagreement with the team, advance directive, or each other lasting greater than seven days; 4) death expected during the same SICU stay; 5) SICU stay of greater than one month; 6) diagnosis with a median survival of less than six months; 7) greater than three SICU admissions during the same hospitalization; 8) Glasgow Coma Score of less than eight for greater than one week in a patient greater than 75 years old; 9) Glasgow Outcome Score of less than three (i.e., persistent vegetative state); and 10) multisystem organ failure of greater than three systems.
Studies are lacking that identify hospitalized patients who are more likely to have higher cost per day or length of stay, as these are patients who may benefit from palliative care. We sought to identify patient characteristics that are associated with higher cost per day or longer length of stay in hospitalized patients who died during the hospitalization or were discharged to hospicepatients likely to benefit from targeted palliative care services. We hypothesized that hospitalized patients with the following characteristics who died during the hospitalization or were discharged to hospice would have a higher cost per day or longer length of stay: older patients, lack of insurance, and patients receiving care from a critical care specialty.
METHODS
Study Design
We analyzed administrative data from a single academic hospital, the University of Colorado Hospital, a tertiary care, academic hospital with approximately 400 beds. The study population consisted of hospitalized adult patients (age 18 years) who died during hospitalization or were discharged to hospice in 2006 and 2007. We included both patients discharged to hospice and those who died during hospitalization, as we were seeking to identify a hospitalized patient population who might be expected to benefit from palliative care: those at high risk of death in the near future. Predictors were selected on the basis of clinical experience and the literature. Cost per day and length of stay were the outcome variables. Institutional Review Board (IRB) approval was not necessary because all of the study patients were deceased at the time of analysis.
Due to resource limitations, we were only able to gather clinical information (presence of organ failure [cardiac, respiratory, renal, hepatic, neurologic] or sepsis on admission, and presence or absence of palliative care consultation during hospitalization) from chart review in a subset of the sample population: those that had the highest 10% total hospitalization costs (n = 115). Organ failure was defined as chart documentation of any of the following: 1) cardiac: ST segment elevation myocardial infarction, non‐ST segment elevation myocardial infarction, congestive heart failure, heart failure (n = 28); 2) respiratory: respiratory failure (n = 36); 3) renal: acute kidney injury, acute renal failure, chronic renal failure, dialysis, end‐stage renal disease (n = 42); 4) hepatic: hepatic failure, end‐stage liver disease (n = 10); and 5) neurologic: altered mental status, delirium (n = 4). Sepsis was defined as chart documentation of any of the following: sepsis, severe sepsis, or septic shock.
Outcomes
We found total cost and length of stay to be correlated. Therefore, we used cost per day in lieu of total cost as the primary outcome. Length of stay was the secondary outcome. Using cost per day as the primary outcome reduced the correlation between our primary and secondary outcomes.
Predictors
Potential predictors (age, insurance status, and attending physician specialty) were selected on the basis of clinical experience, the literature, and patient variables available from the administrative data. We also considered diagnosis‐related group (DRG), however, the wide range of unique DRGs for this population did not allow for sensible groupings, so DRG was excluded from further analyses. For descriptive purposes, mean (standard deviation, SD) age was reported. For modeling, age was centered at 65 years, because this is the age of Medicare eligibility and thus a likely point at which insurance status would change. Sixty‐five was also close to the mean age of the full population, 62 years, therefore ensuring that interactions were assessed over the bulk of the data, rather than at outlying points. We also divided age into ten‐year increments for easier interpretation of model estimates. The relationship between age and primary and secondary outcomes differed among younger vs older patients. Therefore, age was included as a piecewise term in the final multivariate linear model which allowed a separate slope to be fit for patients age <65 years vs those 65 years.
Insurance status was dichotomized as insured vs uninsured. Attending physician specialty categories (internal medicine, pulmonary critical care, neurosurgery, surgical oncology, and cardiothoracic surgery) were selected because they were the five most common specialties. The remaining specialties were grouped together as other, which was used as a reference group in the multivariate analyses as it constituted a nontrivial proportion of the study population.
Statistical Analyses
Univariate analyses were performed separately for the primary and secondary outcomes. Univariate associations between the outcomes and categorical predictors were tested using analysis of variance (ANOVA) models with adjustment for multiple comparisons. Associations between the outcomes and the binary predictors were assessed with t‐tests. Predictors that were significant at the 0.10 level and considered clinically relevant were included in the multivariate model. Interaction terms between predictors were examined and included in the final multivariate piecewise linear models, when inclusion of the interaction terms altered the magnitude of the model estimates.
RESULTS
The study population comprised 1155 hospitalizations. Nine hospitalizations were excluded from analysis (five for organ donation, three were erroneousthe patients were not discharged to hospice or did not die during the hospitalization, and one was a pediatric patient), resulting in a study population of n = 1146 hospitalizations.
Table 1 depicts study population characteristics. The average patient age was 62 years (SD = 16), and 96% of patients were insured. The average length of stay was 10.7 days (SD = 14.1), with an average total cost per admission of $44,410 (SD = 76,355), as compared to an overall hospital admission (excluding obstetrics/neonatology) average length of stay of 5.7 days (SD = 8.5) and average total cost per admission of $17,410 (SD = 36,633) during the same time period. The average cost per day was $5095 (SD = $8546). About one‐third of patients were admitted to internal medicine, 20% to pulmonary critical care, and 18% to surgical specialties. The remaining 29% belonged to other specialties.
| Number of patients, n (%) | 1146 |
| Death in hospital | 730 (63.7) |
| Discharged with hospice | 416 (36.3) |
| Age (years), mean (SD) | 61.7 (15.9) |
| Insurance, n (%) | |
| Uninsured | 52 (4.5) |
| Insured | 1,094 (95.5) |
| Length of stay (days), mean (SD) | 10.7 (14.1) |
| Total cost, mean (SD) | $44,410 (76,355) |
| Cost per day, mean (SD) | $5,095 (8,546) |
| Attending MD specialty, n (%) | |
| Cardiothoracic Surgery | 56 (4.9) |
| Pulmonary Critical Care | 230 (20.1) |
| Surgical Oncology | 70 (6.1) |
| Internal Medicine | 383 (33.4) |
| Neurosurgery | 77 (6.7) |
| Other | 330 (28.8) |
Univariate Analyses
Overall, younger patients had a higher cost per day (Pearson 0.09; P = 0.02) and longer length of stay (Pearson 0.15; P < 0.0001) than older patients (data not shown). According to age groups defined by quartiles, patients who were age <51 and between 61‐72 years had significantly higher cost per day than patients age 73 years ($5787 and $5826 vs $3649, respectively; ANOVA P = 0.005; pairwise P < 0.05). The length of stay for the age groups under 73 years of age were significantly longer than for the patients who were 73 years of age and older (11.9, 11.9, and 11.2 vs 8.0 days, respectively; ANOVA P = 0.001; pairwise P < 0.05; Table 2). Uninsured patients had a higher cost per day ($6618 vs $5023; P = 0.02) than insured patients. In pairwise comparisons, patients on the cardiothoracic surgery service had a higher cost per day ($17,942) than any other specialty (ANOVA P < 0.0001; pairwise P < 0.05). Neurosurgery patients had a higher cost per day ($7089) than the internal medicine patients ($3173; pairwise P < 0.05). Cardiothoracic surgery patients also had a significantly higher LOS (18.3 days) than internal medicine (8.0 days), critical care (11.6 days), neurosurgery (10.0 days), and the other (10.9 days) specialties (ANOVA P < 0.0001; pairwise P < 0.05). The LOS for internal medicine (8.0 days) was significantly lower than critical care (11.6 days), surgical oncology (15.9 days), and cardiothoracic surgery (18.3 days; pairwise P < 0.05).
| Variable | N | Cost per day [$] (mean [SD]) | P Value | Length of stay [days] (mean [SD]) | P Value |
|---|---|---|---|---|---|
| |||||
| 1146 | |||||
| Age group, quartiles | |||||
| <51 years | 281 | 5,787 (8,008) | 0.005 | 11.9 (16.4) | 0.001 |
| 51‐60 | 264 | 5,202 (7,643) | 11.9 (15.4) | ||
| 61‐72 | 297 | 5,826 (12,272) | 11.2 (14.1) | ||
| 73 | 304 | 3,649 (3,978) | 8.0 (9.7) | ||
| Insurance | |||||
| Insured | 1094 | 5,023 (8,691) | 0.02 | 10.8 (14.2) | 0.23 |
| Uninsured | 52 | 6,618 (4,297) | 8.4 (13.5) | ||
| Attending MD specialty | |||||
| Internal Medicine | 383 | 3,173 (2,647) | <0.0001* | 8.0 (11.0) | <0.0001 |
| Pulmonary Critical Care | 230 | 4,671 (2,734) | 11.6 (14.3) | ||
| Neurosurgery | 77 | 7,089 (6,103) | 10.0 (13.5) | ||
| Surgical Oncology | 70 | 5,768 (3,521) | 15.9 (17.9) | ||
| Cardiothoracic Surgery | 56 | 17,942 (26,943) | 18.3 (23.6) | ||
| Other | 330 | 4,833 (8,641) | 10.9 (13.6) | ||
Multivariate Analyses
Cost per Day
The final multivariate linear model included age and attending physician specialty. Insurance status was excluded because it lost significant association with cost per day when it was added to the model (Table 3). Compared to the other specialty, internal medicine decreased cost per day by $1531 (P = 0.01), neurosurgery increased cost per day by $2255 (P = 0.03), and cardiothoracic surgery increased cost per day by $12,937 (P < 0.0001). Cost per day decreased by $811 (SE = 349; P = 0.02) for each age decade 65 years, however, no effect was observed on cost per day for those younger than 65 years.
| Predictors | Estimated Effect ($) | 95% Confidence Interval | P Value to Test if = 0 |
|---|---|---|---|
| Other specialty (reference group at 65 yr) | 5209 | (4,133, 6,284) | |
| Internal Medicine | 1,531 | (2,709, 353) | 0.01 |
| Pulmonary Critical Care | 217 | (1,562, 1,128) | 0.75 |
| Neurosurgery | 2255 | (278, 4,232) | 0.03 |
| Surgical Oncology | 1064 | (994, 3,122) | 0.31 |
| Cardiothoracic Surgery | 12937 | (10,676, 15,198) | <0.0001 |
| Age per 10 yr/age <65 | 7 | (506, 519) | 0.98 |
| Age per 10 yr/age 65 | 811 | (1,497, 125) | 0.02 |
Length of Stay
Because age and attending physician specialty had a significant effect on length of stay, multivariate analyses were performed with these two predictor variables (Table 4). Compared to the other specialty, internal medicine decreased length of stay by 2.4 days (P = 0.02), surgical oncology increased LOS by 5.3 days (P = 0.003), and cardiothoracic surgery increased length of stay by 6.9 days (P = 0.001). Length of stay was significantly decreased by 1.8 days (SE = 0.61; P = 0.003) for each age decade 65 years.
| Predictors | Estimated Effect (days) | 95% Confidence Interval | P Value to Test if = 0 |
|---|---|---|---|
| Other specialty (reference group at 65 yr) | 11 | (9.1, 12.9) | |
| Internal Medicine | 2.4 | (4.4, 0.3) | 0.02 |
| Pulmonary Critical Care | 0.5 | (1.9, 2.8) | 0.7 |
| Neurosurgery | 0.9 | (4.3, 2.6) | 0.62 |
| Surgical Oncology | 5.3 | (1.8, 8.9) | 0.003 |
| Cardiothoracic Surgery | 6.9 | (3.0, 10.9) | 0.001 |
| Age per 10 yr/age <65 | 0.8 | (1.7, 0.1) | 0.08 |
| Age per 10 yr/age 65 | 1.8 | (3.0, 0.6) | 0.003 |
DISCUSSION
We found several characteristics that were significantly associated with higher cost per day or longer length of stay in patients who died during hospitalization or were discharged to hospice. Among this patient population, the surgical specialty services had overall higher cost per day and length of stay than other services. Patients cared for on the cardiothoracic surgery service had higher cost per day and length of stay; in contrast, internal medicine patients had lower cost per day and length of stay. Neurosurgery patients had higher cost per day, while surgical oncology patients had higher length of stay. Patients age 65 years and older had a significantly lower cost per day and shorter length of stay than those less than 65 years of age.
Higher cost per day for cardiothoracic surgery and neurosurgery patients may partially be explained by cardiothoracic surgery patients' usage of clinical services, including operating room services, which are higher in costs compared with those of nonsurgical specialties. Some patients may require repeat surgeries in the same hospitalization which further increases the cost per day. Longer length of stay in surgical oncology patients may be related to complex surgeries and possible postoperative complications that may take longer to recover from than standard surgeries.
Our findings that older patients have lower cost per day and shorter length of stay are corroborated by other studies. Lubitz and Riley21 found that in 1976 and 1988, Medicare payments per person year decreased with age. Levinsky et al.22 had similar findings in a review of Medicare data in 2001, but noted smaller reductions in total costabout $400 decrease for each year above 65. Their explanation of the lower cost is that older patients receive less aggressive care. Physicians, as well as patients and families, may continue to pursue expensive, invasive therapies for terminally ill patients who are younger for a longer period of time than with older patients, which would increase cost per day as well as length of stay.
The finding that patients on the surgical specialty services may be a focus for active palliative care intervention has many implications. The American College of Surgeons Surgical Palliative Care Task Force consensus guideline triggers for a palliative care consultation in SICU applied clinically did not result in a change in palliative care consultation rate.23 The use of triggers for palliative care consultation may be an ineffective approach because knowledge and application of the triggers did not change behavior. Focusing on integrating palliative care interventions or consultation for all high‐risk surgical patients, as opposed to relying upon triggers, may be a more effective approach to meeting these patients' palliative care needs while lowering cost per day and length of stay and warrants further study. For instance, palliative care consult teams may consider routine or daily rounds with the surgical specialty services in order to effectively integrate palliative care for these patients. Such an integrative approach may foster familiarity and comfort with palliative care approaches, facilitating access to palliative care services for those patients with palliative needs.
Our study is limited in that it is a retrospective, single‐center study. Our results may not be applicable to the general population. The experience of additional centers analyzed prospectively would provide additional context. The available administrative data limited the analyses to only a small number of predictors. In the subset population with the highest 10% total hospitalization costs, from which clinical information was gathered, the presence of respiratory failure was associated with shorter LOS (33 days vs 42 days; P = 0.03), but not associated with cost per day. Having sepsis at admission was associated with lower cost per day ($5783 vs $10,071; P = 0.04); however, this finding was based on only four patients with sepsis at admission. Patients who were evaluated by the palliative care service (n = 35) had a significantly lower cost per day ($4896 vs $12,210; P = 0.01) but longer LOS (46.5 vs 35.7 days; P = 0.03) than those who were not. These, and other, clinical characteristics need further testing in larger samples. An additional limitation is that we combined hospital decedents with patients discharged to hospice as our study population. These groups were combined since they are both at high risk of death in the near future; the median hospice length of stay in Colorado is 20 days.24 There may exist important differences in these populations that are not accounted for in our findings. Despite these unidentified differences, both populations are at high risk of death in the near future, making it likely that they would benefit from palliative care. Those who died during hospitalization did have a longer LOS (11.5 vs 9.2 days; P = 0.003) and higher cost per day ($6734 vs $2221; P < 0.0001) than those who were discharged to hospice.
Palliative care consultations can lead to improved quality of care for patients and families by addressing suffering and addressing quality of life measures (2, 4, 5, 6). We sought to identify characteristics associated with high cost and prolonged hospitalizations in patients who died during hospitalization, or were discharged to hospice, in order to inform targeting of palliative care services. Our data suggest that younger patients and those cared for by surgical specialty services may have the most palliative needs. Palliative care teams may consider focusing efforts at integrating palliative care with surgical specialty services to address these needs. These findings need to be corroborated in other centers, and include clinical outcomes.
- ,,, et al.Family perspectives on end‐of‐life care at the last place of care.JAMA.2004;291:88–93.
- ,.Do specialist palliative care teams improve outcomes for cancer patients? A systematic literature review.Palliat Med.1998;12:317–332.
- ,,, et al.Evidence‐based interventions to improve the palliative care of pain, dyspnea, and depression at the end of life: a clinical practice guideline from the American College of Physicians.Ann Intern Med.2008;148:141–146.
- ,,, et al.Do palliative consultations improve patient outcomes?JAGS.2008;56:593–599.
- ,,, et al.Effects of a palliative cafe intervention on clinical outcomes in patients with advanced cancer.JAMA.2009;302:741–749.
- ,,, et al.Early palliative care for patients with metastatic non‐small‐cell lung cancer.N Engl J Med.2010;363:733–742.
- ,,, et al.Cost savings associated with US hospital palliative care consultation programs.Arch Intern Med.2008;168(16):1783–1790.
- ,,.Impact of palliative care case management on resource use by patients dying of cancer at a Veterans Affairs Medical Center.J Palliat Med.2005;8(1):26–35.
- ,,, et al.Cost and utilization outcomes of patients receiving hospital‐based palliative care consultation.J Palliat Med.2006;9(4):855–860.
- ,,, et al.A high‐volume specialist palliative care unit and team may reduce in‐hospital end‐of‐life care costs.J Palliat Med.2003;6(5):699–705.
- ,,, et al.The economic and clinical impact of an inpatient palliative care consultation service: a multifaceted approach.J Palliat Med.2007;10(6):1347–1355.
- ,,, et al.Hospital‐based palliative care consultation: effects on hospital cost.J Palliat Med.2010;13(8):1–7.
- ,.Impact of a proactive approach to improve end‐of‐life care in a medical ICU.Chest.2003;123:266–271.
- ,,, et al.Proactive palliative care in the medical intensive care unit: effects on length of stay for selected high‐risk patients.Crit Care Med.2007;35:1530–1535.
- ,,,,.Who needs a palliative care consult? The Hamilton Chart Audit tool.J Palliat Med.2007;10(2):304–307.
- ,,,,,.A practical tool to identify patients who may benefit from a palliative care approach: the CARING criteria.J Pain Symptom Manage.2006;31:285–292.
- ,,, et al.An Official American Thoracic Society Clinical Policy Statement: palliative care for patients with respiratory diseases and critical illnesses.Am J Respir Crit Care Med.2008;177:912–927.
- ,,, et al.Proposed quality measures for palliative care in the critically ill: a consensus from the Robert Wood Johnson Foundation Critical Care Workgroup.Crit Care Med.2006;34:S404–S411.
- ,,, et al.Recommendations for end‐of‐life care in the intensive care unit: a consensus statement by the American Academy of Critical Care Medicine.Crit Care Med.2008;36:953–963.
- ,.Developing guidelines that identify patients who would benefit from palliative care services in the surgical intensive care unit.Crit Care Med.2009;37:946–950.
- ,.Trends in Medicare payments in the last year of life.N Engl J Med.1993;328:1092–1096.
- ,,, et al.Influence of age on Medicare expenditures and medical care in the last year of life.JAMA.2001;286:1349–1355.
- ,,.Addressing access to palliative care services in the surgical intensive care unit.Surgery.2010;147:871–877.
- http://www.coloradocancercoalition.org/…/CCCConferenceKassner Slides11.13.07.ppt. Accessed August 16,2010.
- ,,.The impact of implementing referral criteria on the patterns of referrals and admissions to a palliative care program in Saudi Arabia.J Support Oncol.2010;8:78–81.
- ,,, et al.Family perspectives on end‐of‐life care at the last place of care.JAMA.2004;291:88–93.
- ,.Do specialist palliative care teams improve outcomes for cancer patients? A systematic literature review.Palliat Med.1998;12:317–332.
- ,,, et al.Evidence‐based interventions to improve the palliative care of pain, dyspnea, and depression at the end of life: a clinical practice guideline from the American College of Physicians.Ann Intern Med.2008;148:141–146.
- ,,, et al.Do palliative consultations improve patient outcomes?JAGS.2008;56:593–599.
- ,,, et al.Effects of a palliative cafe intervention on clinical outcomes in patients with advanced cancer.JAMA.2009;302:741–749.
- ,,, et al.Early palliative care for patients with metastatic non‐small‐cell lung cancer.N Engl J Med.2010;363:733–742.
- ,,, et al.Cost savings associated with US hospital palliative care consultation programs.Arch Intern Med.2008;168(16):1783–1790.
- ,,.Impact of palliative care case management on resource use by patients dying of cancer at a Veterans Affairs Medical Center.J Palliat Med.2005;8(1):26–35.
- ,,, et al.Cost and utilization outcomes of patients receiving hospital‐based palliative care consultation.J Palliat Med.2006;9(4):855–860.
- ,,, et al.A high‐volume specialist palliative care unit and team may reduce in‐hospital end‐of‐life care costs.J Palliat Med.2003;6(5):699–705.
- ,,, et al.The economic and clinical impact of an inpatient palliative care consultation service: a multifaceted approach.J Palliat Med.2007;10(6):1347–1355.
- ,,, et al.Hospital‐based palliative care consultation: effects on hospital cost.J Palliat Med.2010;13(8):1–7.
- ,.Impact of a proactive approach to improve end‐of‐life care in a medical ICU.Chest.2003;123:266–271.
- ,,, et al.Proactive palliative care in the medical intensive care unit: effects on length of stay for selected high‐risk patients.Crit Care Med.2007;35:1530–1535.
- ,,,,.Who needs a palliative care consult? The Hamilton Chart Audit tool.J Palliat Med.2007;10(2):304–307.
- ,,,,,.A practical tool to identify patients who may benefit from a palliative care approach: the CARING criteria.J Pain Symptom Manage.2006;31:285–292.
- ,,, et al.An Official American Thoracic Society Clinical Policy Statement: palliative care for patients with respiratory diseases and critical illnesses.Am J Respir Crit Care Med.2008;177:912–927.
- ,,, et al.Proposed quality measures for palliative care in the critically ill: a consensus from the Robert Wood Johnson Foundation Critical Care Workgroup.Crit Care Med.2006;34:S404–S411.
- ,,, et al.Recommendations for end‐of‐life care in the intensive care unit: a consensus statement by the American Academy of Critical Care Medicine.Crit Care Med.2008;36:953–963.
- ,.Developing guidelines that identify patients who would benefit from palliative care services in the surgical intensive care unit.Crit Care Med.2009;37:946–950.
- ,.Trends in Medicare payments in the last year of life.N Engl J Med.1993;328:1092–1096.
- ,,, et al.Influence of age on Medicare expenditures and medical care in the last year of life.JAMA.2001;286:1349–1355.
- ,,.Addressing access to palliative care services in the surgical intensive care unit.Surgery.2010;147:871–877.
- http://www.coloradocancercoalition.org/…/CCCConferenceKassner Slides11.13.07.ppt. Accessed August 16,2010.
- ,,.The impact of implementing referral criteria on the patterns of referrals and admissions to a palliative care program in Saudi Arabia.J Support Oncol.2010;8:78–81.
Copyright © 2011 Society of Hospital Medicine
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Accreditation and Designation Statement
Blackwell Futura Media Services designates this journal‐based CME activity for a maximum of 1 AMA PRA Category 1 Credit.. Physicians should only claim credit commensurate with the extent of their participation in the activity.
Blackwell Futura Media Services is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.
Educational Objectives
The objectives need to be changed. Please remove the existing ones, and include these two:
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Identify complications elderly patients are at risk for during hospitalization.
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Suggest evidence‐based strategies to prevent and treat common causes of hospitalization‐related complications in geriatric patients.
This manuscript underwent peer review in line with the standards of editorial integrity and publication ethics maintained by Journal of Hospital Medicine. The peer reviewers have no relevant financial relationships. The peer review process for Journal of Hospital Medicine is single‐blinded. As such, the identities of the reviewers are not disclosed in line with the standard accepted practices of medical journal peer review.
Conflicts of interest have been identified and resolved in accordance with Blackwell Futura Media Services's Policy on Activity Disclosure and Conflict of Interest. The primary resolution method used was peer review and review by a non‐conflicted expert.
Instructions on Receiving Credit
For information on applicability and acceptance of CME credit for this activity, please consult your professional licensing board.
This activity is designed to be completed within an hour; physicians should claim only those credits that reflect the time actually spent in the activity. To successfully earn credit, participants must complete the activity during the valid credit period, which is up to two years from initial publication.
Follow these steps to earn credit:
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Log on to www.wileyblackwellcme.com
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Read the target audience, learning objectives, and author disclosures.
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Read the article in print or online format.
-
Reflect on the article.
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Access the CME Exam, and choose the best answer to each question.
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Complete the required evaluation component of the activity.
This activity will be available for CME credit for twelve months following its publication date. At that time, it will be reviewed and potentially updated and extended for an additional twelve months.
If you wish to receive credit for this activity, please refer to the website: www.wileyblackwellcme.com.
Accreditation and Designation Statement
Blackwell Futura Media Services designates this journal‐based CME activity for a maximum of 1 AMA PRA Category 1 Credit.. Physicians should only claim credit commensurate with the extent of their participation in the activity.
Blackwell Futura Media Services is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.
Educational Objectives
The objectives need to be changed. Please remove the existing ones, and include these two:
-
Identify complications elderly patients are at risk for during hospitalization.
-
Suggest evidence‐based strategies to prevent and treat common causes of hospitalization‐related complications in geriatric patients.
This manuscript underwent peer review in line with the standards of editorial integrity and publication ethics maintained by Journal of Hospital Medicine. The peer reviewers have no relevant financial relationships. The peer review process for Journal of Hospital Medicine is single‐blinded. As such, the identities of the reviewers are not disclosed in line with the standard accepted practices of medical journal peer review.
Conflicts of interest have been identified and resolved in accordance with Blackwell Futura Media Services's Policy on Activity Disclosure and Conflict of Interest. The primary resolution method used was peer review and review by a non‐conflicted expert.
Instructions on Receiving Credit
For information on applicability and acceptance of CME credit for this activity, please consult your professional licensing board.
This activity is designed to be completed within an hour; physicians should claim only those credits that reflect the time actually spent in the activity. To successfully earn credit, participants must complete the activity during the valid credit period, which is up to two years from initial publication.
Follow these steps to earn credit:
-
Log on to www.wileyblackwellcme.com
-
Read the target audience, learning objectives, and author disclosures.
-
Read the article in print or online format.
-
Reflect on the article.
-
Access the CME Exam, and choose the best answer to each question.
-
Complete the required evaluation component of the activity.
This activity will be available for CME credit for twelve months following its publication date. At that time, it will be reviewed and potentially updated and extended for an additional twelve months.