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EGFR Status May Explain Depression-Survival Link in NSCLC
ANAHEIM, CALIF. – Notable survival differences between depressed and nondepressed patients with non–small cell lung cancer may be attributable to EGFR-mutation status, according to a small but provocative study conducted by Dr. William Pirl of the cancer center at Massachusetts General Hospital in Boston.
The investigation arose from the observation by Dr. Pirl that NSCLC patients with EGFR (epidermal growth factor receptor) mutations were rarely referred for psychiatric evaluation of depression, which is a common comorbid diagnosis among cancer patients and one that is increasingly linked to decreased survival.
He wondered whether the 15% of lung cancer patients with EGFR mutations might be less susceptible to depression because they would know of their more optimistic prognosis, relative to other patients with the disease. Could their avoidance of depression relate to smoking status, since more patients with EGFR mutations are nonsmokers? Or is it possible that a biological explanation might underlie the relationship between depression and survival in NSCLC?
Results of his study of newly diagnosed NSCLC patients point to biology, although potential biological pathways are still under investigation, Dr. Pirl said in an interview following a presentation of his latest findings at the annual conference of the American Psychosocial Oncology Society.
In a previous study of 43 recently diagnosed NSCLC patients, he and associates found that patients who were depressed according to Hospital Anxiety and Depression Scale scores lived a median of 2.5 months, compared with median survival rates of 10.4 months in nondepressed patients (Psychosomatics 2008 May-June [doi:10.1176/appi.psy.49.3.218]).
To determine whether tumor genotypes played a role, Dr. Pirl’s team administered the MDS (Major Depression Rating Scale, which is a scale derived from the Hamilton Rating Scale for Depression and the Melancholia Scale, and aligned with DSM-IV criteria for major depressive disorder) and the PHQ-9 (Patient Health Questionnaire–9) to 148 patients with metastatic NSCLC prior to the processing of their genotype results. They found the following:
- None of 16 patients with an EGFR mutation had depression, based on either instrument.
- In all, 4 of 27 patients (15%) with wild-type mutations as well as 17 of 105 (16%) of those with unknown mutations met criteria for depression, according to the MDS.
- Even more patients with wild-type mutations (9 of 27, or 33%) and unknown mutations (32 of 105, or 39%) met criteria for depression, according to the PHQ-9.
After 3 years, significantly more depressed patients than nondepressed patients had died (hazard ratio, 1.75; P = .03). Patients with an EGFR genotype also had a significant survival advantage over other patients in the study (P = .004).
"[If we put] genotype into the survival model, depression is no longer significantly associated with survival," reported Dr. Pirl, director of the hospital’s center of psychiatric oncology and behavioral sciences.
A study will soon be underway to explore plausible biological pathways linking EGFR, depression, and survival.
One candidate is tumor growth factor–alpha (TGF-alpha), a ligand of EGFR known to cause circadian rhythm dysfunction. Dr. Pirl noted in his talk that EGFR mutants do not produce TGF-alpha.
If replicated, the study could "raise larger questions about the biological pathways to depression and could possibly uncover a novel pathway in people with medical illnesses," he added in an interview.
Dr. Pirl’s coinvestigators included Dr. Jennifer S. Temel, a medical oncologist, and Joseph Greer, Ph.D., associate director of behavioral medicine at the hospital. They reported having no relevant financial disclosures.
ANAHEIM, CALIF. – Notable survival differences between depressed and nondepressed patients with non–small cell lung cancer may be attributable to EGFR-mutation status, according to a small but provocative study conducted by Dr. William Pirl of the cancer center at Massachusetts General Hospital in Boston.
The investigation arose from the observation by Dr. Pirl that NSCLC patients with EGFR (epidermal growth factor receptor) mutations were rarely referred for psychiatric evaluation of depression, which is a common comorbid diagnosis among cancer patients and one that is increasingly linked to decreased survival.
He wondered whether the 15% of lung cancer patients with EGFR mutations might be less susceptible to depression because they would know of their more optimistic prognosis, relative to other patients with the disease. Could their avoidance of depression relate to smoking status, since more patients with EGFR mutations are nonsmokers? Or is it possible that a biological explanation might underlie the relationship between depression and survival in NSCLC?
Results of his study of newly diagnosed NSCLC patients point to biology, although potential biological pathways are still under investigation, Dr. Pirl said in an interview following a presentation of his latest findings at the annual conference of the American Psychosocial Oncology Society.
In a previous study of 43 recently diagnosed NSCLC patients, he and associates found that patients who were depressed according to Hospital Anxiety and Depression Scale scores lived a median of 2.5 months, compared with median survival rates of 10.4 months in nondepressed patients (Psychosomatics 2008 May-June [doi:10.1176/appi.psy.49.3.218]).
To determine whether tumor genotypes played a role, Dr. Pirl’s team administered the MDS (Major Depression Rating Scale, which is a scale derived from the Hamilton Rating Scale for Depression and the Melancholia Scale, and aligned with DSM-IV criteria for major depressive disorder) and the PHQ-9 (Patient Health Questionnaire–9) to 148 patients with metastatic NSCLC prior to the processing of their genotype results. They found the following:
- None of 16 patients with an EGFR mutation had depression, based on either instrument.
- In all, 4 of 27 patients (15%) with wild-type mutations as well as 17 of 105 (16%) of those with unknown mutations met criteria for depression, according to the MDS.
- Even more patients with wild-type mutations (9 of 27, or 33%) and unknown mutations (32 of 105, or 39%) met criteria for depression, according to the PHQ-9.
After 3 years, significantly more depressed patients than nondepressed patients had died (hazard ratio, 1.75; P = .03). Patients with an EGFR genotype also had a significant survival advantage over other patients in the study (P = .004).
"[If we put] genotype into the survival model, depression is no longer significantly associated with survival," reported Dr. Pirl, director of the hospital’s center of psychiatric oncology and behavioral sciences.
A study will soon be underway to explore plausible biological pathways linking EGFR, depression, and survival.
One candidate is tumor growth factor–alpha (TGF-alpha), a ligand of EGFR known to cause circadian rhythm dysfunction. Dr. Pirl noted in his talk that EGFR mutants do not produce TGF-alpha.
If replicated, the study could "raise larger questions about the biological pathways to depression and could possibly uncover a novel pathway in people with medical illnesses," he added in an interview.
Dr. Pirl’s coinvestigators included Dr. Jennifer S. Temel, a medical oncologist, and Joseph Greer, Ph.D., associate director of behavioral medicine at the hospital. They reported having no relevant financial disclosures.
ANAHEIM, CALIF. – Notable survival differences between depressed and nondepressed patients with non–small cell lung cancer may be attributable to EGFR-mutation status, according to a small but provocative study conducted by Dr. William Pirl of the cancer center at Massachusetts General Hospital in Boston.
The investigation arose from the observation by Dr. Pirl that NSCLC patients with EGFR (epidermal growth factor receptor) mutations were rarely referred for psychiatric evaluation of depression, which is a common comorbid diagnosis among cancer patients and one that is increasingly linked to decreased survival.
He wondered whether the 15% of lung cancer patients with EGFR mutations might be less susceptible to depression because they would know of their more optimistic prognosis, relative to other patients with the disease. Could their avoidance of depression relate to smoking status, since more patients with EGFR mutations are nonsmokers? Or is it possible that a biological explanation might underlie the relationship between depression and survival in NSCLC?
Results of his study of newly diagnosed NSCLC patients point to biology, although potential biological pathways are still under investigation, Dr. Pirl said in an interview following a presentation of his latest findings at the annual conference of the American Psychosocial Oncology Society.
In a previous study of 43 recently diagnosed NSCLC patients, he and associates found that patients who were depressed according to Hospital Anxiety and Depression Scale scores lived a median of 2.5 months, compared with median survival rates of 10.4 months in nondepressed patients (Psychosomatics 2008 May-June [doi:10.1176/appi.psy.49.3.218]).
To determine whether tumor genotypes played a role, Dr. Pirl’s team administered the MDS (Major Depression Rating Scale, which is a scale derived from the Hamilton Rating Scale for Depression and the Melancholia Scale, and aligned with DSM-IV criteria for major depressive disorder) and the PHQ-9 (Patient Health Questionnaire–9) to 148 patients with metastatic NSCLC prior to the processing of their genotype results. They found the following:
- None of 16 patients with an EGFR mutation had depression, based on either instrument.
- In all, 4 of 27 patients (15%) with wild-type mutations as well as 17 of 105 (16%) of those with unknown mutations met criteria for depression, according to the MDS.
- Even more patients with wild-type mutations (9 of 27, or 33%) and unknown mutations (32 of 105, or 39%) met criteria for depression, according to the PHQ-9.
After 3 years, significantly more depressed patients than nondepressed patients had died (hazard ratio, 1.75; P = .03). Patients with an EGFR genotype also had a significant survival advantage over other patients in the study (P = .004).
"[If we put] genotype into the survival model, depression is no longer significantly associated with survival," reported Dr. Pirl, director of the hospital’s center of psychiatric oncology and behavioral sciences.
A study will soon be underway to explore plausible biological pathways linking EGFR, depression, and survival.
One candidate is tumor growth factor–alpha (TGF-alpha), a ligand of EGFR known to cause circadian rhythm dysfunction. Dr. Pirl noted in his talk that EGFR mutants do not produce TGF-alpha.
If replicated, the study could "raise larger questions about the biological pathways to depression and could possibly uncover a novel pathway in people with medical illnesses," he added in an interview.
Dr. Pirl’s coinvestigators included Dr. Jennifer S. Temel, a medical oncologist, and Joseph Greer, Ph.D., associate director of behavioral medicine at the hospital. They reported having no relevant financial disclosures.
FROM THE ANNUAL CONFERENCE OF THE AMERICAN PSYCHOSOCIAL ONCOLOGY SOCIETY
Major Finding: None of 16 patients with EGFR mutations met criteria for depression, but 4 of 27 patients (15%) with wild-type mutations and 17 of 105 (16%) with unknown mutations did so.
Data Source: A study of 148 patients newly diagnosed with NSCLC.
Disclosures: Researchers reported no relevant financial disclosures.
The Effective Management of Chronic Constipation and IBS-C
A supplement to Internal Medicine News and supported by Takeda Pharmaceuticals North America, Inc.
To view the supplement, click the image above.
This supplement has been designed to meet the educational needs of clinicians relative to the diagnosis and effective management of chronic constipation and IBS-C.
Faculty
Harold Fields, MD
Founder, Village Family Practice
Houston, TX
Wendy Wright, MS, RN, ARNP, FNP, FAANP
Owner, Wright & Associates
Family Healthcare
Amherst, NH
Copyright © 2008 Elsevier Inc.
A supplement to Internal Medicine News and supported by Takeda Pharmaceuticals North America, Inc.
To view the supplement, click the image above.
This supplement has been designed to meet the educational needs of clinicians relative to the diagnosis and effective management of chronic constipation and IBS-C.
Faculty
Harold Fields, MD
Founder, Village Family Practice
Houston, TX
Wendy Wright, MS, RN, ARNP, FNP, FAANP
Owner, Wright & Associates
Family Healthcare
Amherst, NH
Copyright © 2008 Elsevier Inc.
A supplement to Internal Medicine News and supported by Takeda Pharmaceuticals North America, Inc.
To view the supplement, click the image above.
This supplement has been designed to meet the educational needs of clinicians relative to the diagnosis and effective management of chronic constipation and IBS-C.
Faculty
Harold Fields, MD
Founder, Village Family Practice
Houston, TX
Wendy Wright, MS, RN, ARNP, FNP, FAANP
Owner, Wright & Associates
Family Healthcare
Amherst, NH
Copyright © 2008 Elsevier Inc.
A1c Management and Modest Weight Loss in Type 2 Diabetes
A supplement to Family Practice News and supported by Amylin/Lilly.
To view the supplement, click the image above.
Faculty
Anne Peters, MD, FACP, CDE
Director
University of Southern
California Clinical
Diabetes Programs
Los Angeles, Calif.
Copyright © 2008 Elsevier Inc.
A supplement to Family Practice News and supported by Amylin/Lilly.
To view the supplement, click the image above.
Faculty
Anne Peters, MD, FACP, CDE
Director
University of Southern
California Clinical
Diabetes Programs
Los Angeles, Calif.
Copyright © 2008 Elsevier Inc.
A supplement to Family Practice News and supported by Amylin/Lilly.
To view the supplement, click the image above.
Faculty
Anne Peters, MD, FACP, CDE
Director
University of Southern
California Clinical
Diabetes Programs
Los Angeles, Calif.
Copyright © 2008 Elsevier Inc.
Pricey Problems
Hospitalists working to reduce readmissions and medication errors would do well to consider a new policy report that suggests the two systemic problems cost the healthcare system $46 billion a year.
The white paper sets out to identify specific actions—such as creating detailed discharge plans, having pharmacists make follow-up calls after discharge, and using bar-code technology to verify drug dosages—that public and private decision-makers can use to help tackle the issues. While the bureaucratic checklist devised by the New England Healthcare Institute (NEHI) and the National Priorities Partnership is a good broad brush, the report's value may lie in how it prods physicians to change the way care is delivered.
"It's a quick and easy guide, but what's beneath it is quite complex," says Karen Nelson, a former nurse and senior vice president for clinical affairs for the Massachusetts Hospital Association. "We've seen terrific pockets of expertise … but the real work has to come for all providers and programs to do this at the same time."
Nelson believes hospitalists are "clearly essential team leaders" in fighting both medication errors and readmissions. Medication reconciliation is a problem in each of those silos that HM groups battle daily. To wit, the Agency for Healthcare Research and Quality (AHRQ) has awarded SHM a $1.5 million grant for a three-year, multicenter medication reconciliation QI study. “As care-transition managers, hospitalists are well-positioned to analyze the pitfalls of care coordination and develop and implement quality-improvement solutions to improve patient safety," says Joseph Miller, SHM's senior vice president and chief solutions officer.
Nelson says NEHI's "compact action briefs" suggest that payment bundling is one answer to wasteful spending. However, while hospitalists agree the payment system needs work, they caution against the potential consequences of such a drastic shift.
"It really makes the case to move away from the fee-for-service model," Nelson says. "What we need to do is redesign the system to cover the patient regardless of encounter or what the driver is."
Hospitalists working to reduce readmissions and medication errors would do well to consider a new policy report that suggests the two systemic problems cost the healthcare system $46 billion a year.
The white paper sets out to identify specific actions—such as creating detailed discharge plans, having pharmacists make follow-up calls after discharge, and using bar-code technology to verify drug dosages—that public and private decision-makers can use to help tackle the issues. While the bureaucratic checklist devised by the New England Healthcare Institute (NEHI) and the National Priorities Partnership is a good broad brush, the report's value may lie in how it prods physicians to change the way care is delivered.
"It's a quick and easy guide, but what's beneath it is quite complex," says Karen Nelson, a former nurse and senior vice president for clinical affairs for the Massachusetts Hospital Association. "We've seen terrific pockets of expertise … but the real work has to come for all providers and programs to do this at the same time."
Nelson believes hospitalists are "clearly essential team leaders" in fighting both medication errors and readmissions. Medication reconciliation is a problem in each of those silos that HM groups battle daily. To wit, the Agency for Healthcare Research and Quality (AHRQ) has awarded SHM a $1.5 million grant for a three-year, multicenter medication reconciliation QI study. “As care-transition managers, hospitalists are well-positioned to analyze the pitfalls of care coordination and develop and implement quality-improvement solutions to improve patient safety," says Joseph Miller, SHM's senior vice president and chief solutions officer.
Nelson says NEHI's "compact action briefs" suggest that payment bundling is one answer to wasteful spending. However, while hospitalists agree the payment system needs work, they caution against the potential consequences of such a drastic shift.
"It really makes the case to move away from the fee-for-service model," Nelson says. "What we need to do is redesign the system to cover the patient regardless of encounter or what the driver is."
Hospitalists working to reduce readmissions and medication errors would do well to consider a new policy report that suggests the two systemic problems cost the healthcare system $46 billion a year.
The white paper sets out to identify specific actions—such as creating detailed discharge plans, having pharmacists make follow-up calls after discharge, and using bar-code technology to verify drug dosages—that public and private decision-makers can use to help tackle the issues. While the bureaucratic checklist devised by the New England Healthcare Institute (NEHI) and the National Priorities Partnership is a good broad brush, the report's value may lie in how it prods physicians to change the way care is delivered.
"It's a quick and easy guide, but what's beneath it is quite complex," says Karen Nelson, a former nurse and senior vice president for clinical affairs for the Massachusetts Hospital Association. "We've seen terrific pockets of expertise … but the real work has to come for all providers and programs to do this at the same time."
Nelson believes hospitalists are "clearly essential team leaders" in fighting both medication errors and readmissions. Medication reconciliation is a problem in each of those silos that HM groups battle daily. To wit, the Agency for Healthcare Research and Quality (AHRQ) has awarded SHM a $1.5 million grant for a three-year, multicenter medication reconciliation QI study. “As care-transition managers, hospitalists are well-positioned to analyze the pitfalls of care coordination and develop and implement quality-improvement solutions to improve patient safety," says Joseph Miller, SHM's senior vice president and chief solutions officer.
Nelson says NEHI's "compact action briefs" suggest that payment bundling is one answer to wasteful spending. However, while hospitalists agree the payment system needs work, they caution against the potential consequences of such a drastic shift.
"It really makes the case to move away from the fee-for-service model," Nelson says. "What we need to do is redesign the system to cover the patient regardless of encounter or what the driver is."
In the Literature: Research You Need to Know
Clinical question: Is fondaparinux an effective and safe treatment for patients with acute, symptomatic superficial-vein thrombosis in the legs?
Background: Superficial-vein thrombosis of the legs is a common condition, with an estimated incidence that may exceed that of deep-vein thrombosis (DVT). Patients with superficial-vein thrombosis are at increased risk for subsequent symptomatic venous thromboembolic complications including DVT and pulmonary embolism (PE).
Study design: Industry-sponsored, randomized, double-blind placebo controlled study.
Setting: 171 hospitals and outpatient clinics in 17 countries.
Synopsis: Data were collected on 3,000 patients presenting with ultrasound-confirmed symptomatic superficial-vein thrombosis of the legs.
Compared with placebo, fondaparinux reduced the risk of the composite of DVT or PE by 85%, the risk of clot extension to the saphenofemoral junction by 92%, the need for surgery for superficial vein thrombosis by 81%, and the development of a recurrence of superficial-vein thrombosis by 79%.
The number needed to treat (NNT) to prevent one event of the primary outcomes was 20, whereas the NNT to specifically prevent DVT or a PE was 88.
The rates of clinically relevant nonmajor, minor, and total bleeding and arterial thromboembolic complications did not differ significantly between the two groups. No episodes of thrombocytopenia were reported in the fondaparinux group.
Bottom line: Fondaparinux at a dose of 2.5 mg once a day for 45 days is effective in reducing the risk DVT and PE in patients with acute, symptomatic superficial-vein thrombosis of the legs without an associated increased bleeding risk.
Citation: Decousus H, Prandoni P, Mismetti P, et al. Fondaparinux for the treatment of superficial-vein thrombosis in the legs. N Engl J Med. 2010;363(13):1222-1232.
Reviewed for TH eWire by Steven Deitelzweig, MD, MMM, SFHM, Renee Meadows, MD, SFHM, David Lee, MD, MBA, FHM, Frank Wharton, MD, FHM, Srinivas Vuppala, MD, William Carter, MD, Prasad Ravipati, MD, Department of Hospital Medicine, Ochsner Health System, New Orleans.
For more physician reviews of HM-related research, visit our website.
Clinical question: Is fondaparinux an effective and safe treatment for patients with acute, symptomatic superficial-vein thrombosis in the legs?
Background: Superficial-vein thrombosis of the legs is a common condition, with an estimated incidence that may exceed that of deep-vein thrombosis (DVT). Patients with superficial-vein thrombosis are at increased risk for subsequent symptomatic venous thromboembolic complications including DVT and pulmonary embolism (PE).
Study design: Industry-sponsored, randomized, double-blind placebo controlled study.
Setting: 171 hospitals and outpatient clinics in 17 countries.
Synopsis: Data were collected on 3,000 patients presenting with ultrasound-confirmed symptomatic superficial-vein thrombosis of the legs.
Compared with placebo, fondaparinux reduced the risk of the composite of DVT or PE by 85%, the risk of clot extension to the saphenofemoral junction by 92%, the need for surgery for superficial vein thrombosis by 81%, and the development of a recurrence of superficial-vein thrombosis by 79%.
The number needed to treat (NNT) to prevent one event of the primary outcomes was 20, whereas the NNT to specifically prevent DVT or a PE was 88.
The rates of clinically relevant nonmajor, minor, and total bleeding and arterial thromboembolic complications did not differ significantly between the two groups. No episodes of thrombocytopenia were reported in the fondaparinux group.
Bottom line: Fondaparinux at a dose of 2.5 mg once a day for 45 days is effective in reducing the risk DVT and PE in patients with acute, symptomatic superficial-vein thrombosis of the legs without an associated increased bleeding risk.
Citation: Decousus H, Prandoni P, Mismetti P, et al. Fondaparinux for the treatment of superficial-vein thrombosis in the legs. N Engl J Med. 2010;363(13):1222-1232.
Reviewed for TH eWire by Steven Deitelzweig, MD, MMM, SFHM, Renee Meadows, MD, SFHM, David Lee, MD, MBA, FHM, Frank Wharton, MD, FHM, Srinivas Vuppala, MD, William Carter, MD, Prasad Ravipati, MD, Department of Hospital Medicine, Ochsner Health System, New Orleans.
For more physician reviews of HM-related research, visit our website.
Clinical question: Is fondaparinux an effective and safe treatment for patients with acute, symptomatic superficial-vein thrombosis in the legs?
Background: Superficial-vein thrombosis of the legs is a common condition, with an estimated incidence that may exceed that of deep-vein thrombosis (DVT). Patients with superficial-vein thrombosis are at increased risk for subsequent symptomatic venous thromboembolic complications including DVT and pulmonary embolism (PE).
Study design: Industry-sponsored, randomized, double-blind placebo controlled study.
Setting: 171 hospitals and outpatient clinics in 17 countries.
Synopsis: Data were collected on 3,000 patients presenting with ultrasound-confirmed symptomatic superficial-vein thrombosis of the legs.
Compared with placebo, fondaparinux reduced the risk of the composite of DVT or PE by 85%, the risk of clot extension to the saphenofemoral junction by 92%, the need for surgery for superficial vein thrombosis by 81%, and the development of a recurrence of superficial-vein thrombosis by 79%.
The number needed to treat (NNT) to prevent one event of the primary outcomes was 20, whereas the NNT to specifically prevent DVT or a PE was 88.
The rates of clinically relevant nonmajor, minor, and total bleeding and arterial thromboembolic complications did not differ significantly between the two groups. No episodes of thrombocytopenia were reported in the fondaparinux group.
Bottom line: Fondaparinux at a dose of 2.5 mg once a day for 45 days is effective in reducing the risk DVT and PE in patients with acute, symptomatic superficial-vein thrombosis of the legs without an associated increased bleeding risk.
Citation: Decousus H, Prandoni P, Mismetti P, et al. Fondaparinux for the treatment of superficial-vein thrombosis in the legs. N Engl J Med. 2010;363(13):1222-1232.
Reviewed for TH eWire by Steven Deitelzweig, MD, MMM, SFHM, Renee Meadows, MD, SFHM, David Lee, MD, MBA, FHM, Frank Wharton, MD, FHM, Srinivas Vuppala, MD, William Carter, MD, Prasad Ravipati, MD, Department of Hospital Medicine, Ochsner Health System, New Orleans.
For more physician reviews of HM-related research, visit our website.
BEST PRACTICES IN:Treating Rosacea: Current Insights
Treating Rosacea: Current Insights
A supplement to Family Practice News. This supplement was funded by Galderma.
To view the supplement, click the image above.
•Topics
•Faculty/Faculty Disclosures
Topics
• Is it Rosacea?
• Managing Rosacea: Target Potential Triggers
• Treatment Options for Rosacea
• Newer Topical Formulations
• Surgical Therapies
• Summary
Faculty/Faculty Disclosures
Richard Odom, MD
Chair
Professor, Clinical Dermatology
University of California
San Francisco School of Medicine
San Francisco, California
Advisory committee for Johnson & Johnson.
Terry Arnold, MA, PA-C
Advanced Practice Consultants, LLC
Tulsa, Oklahoma
Speaker for Medicis; advisory board of Novartis, Warner-Chilcott; advisory board/speaker for Abbott Laboratories, Astellas, Collagenex; advisory board, speaker and consultant for Amgen, Coria Labs, Ranbaxy.
Stephen Brunton, MD
Adjunct Clinical Professor
Department of Family Medicine
University of North Carolina
Chapel Hill
Chapel Hill, North Carolina
Nothing to disclose.
Mary Knudtson, DNSc, NP
Professor, Family Medicine
Director of Family Nurse Practitioner Program
University of California, Irvine
Irvine, California
Speaker's bureau for Proctor & Gamble; consultant for sanofi aventis, Galderma.
John E. Wolf, Jr., MD, MA
Professor and Chairman
Department of Dermatology
Baylor College of Medicine
Houston, Texas
Consultant, speaker, and on the advisory board for Stiefel, PharmaDerm, Galderma, Medicis, Novartis, Warner-Chilcott, consultant for QLT and Peplin; speaker for Stiefel, sanofi-aventis (Dermik).
Copyright © 2008 Elsevier Inc.
A supplement to Family Practice News. This supplement was funded by Galderma.
To view the supplement, click the image above.
•Topics
•Faculty/Faculty Disclosures
Topics
• Is it Rosacea?
• Managing Rosacea: Target Potential Triggers
• Treatment Options for Rosacea
• Newer Topical Formulations
• Surgical Therapies
• Summary
Faculty/Faculty Disclosures
Richard Odom, MD
Chair
Professor, Clinical Dermatology
University of California
San Francisco School of Medicine
San Francisco, California
Advisory committee for Johnson & Johnson.
Terry Arnold, MA, PA-C
Advanced Practice Consultants, LLC
Tulsa, Oklahoma
Speaker for Medicis; advisory board of Novartis, Warner-Chilcott; advisory board/speaker for Abbott Laboratories, Astellas, Collagenex; advisory board, speaker and consultant for Amgen, Coria Labs, Ranbaxy.
Stephen Brunton, MD
Adjunct Clinical Professor
Department of Family Medicine
University of North Carolina
Chapel Hill
Chapel Hill, North Carolina
Nothing to disclose.
Mary Knudtson, DNSc, NP
Professor, Family Medicine
Director of Family Nurse Practitioner Program
University of California, Irvine
Irvine, California
Speaker's bureau for Proctor & Gamble; consultant for sanofi aventis, Galderma.
John E. Wolf, Jr., MD, MA
Professor and Chairman
Department of Dermatology
Baylor College of Medicine
Houston, Texas
Consultant, speaker, and on the advisory board for Stiefel, PharmaDerm, Galderma, Medicis, Novartis, Warner-Chilcott, consultant for QLT and Peplin; speaker for Stiefel, sanofi-aventis (Dermik).
Copyright © 2008 Elsevier Inc.
A supplement to Family Practice News. This supplement was funded by Galderma.
To view the supplement, click the image above.
•Topics
•Faculty/Faculty Disclosures
Topics
• Is it Rosacea?
• Managing Rosacea: Target Potential Triggers
• Treatment Options for Rosacea
• Newer Topical Formulations
• Surgical Therapies
• Summary
Faculty/Faculty Disclosures
Richard Odom, MD
Chair
Professor, Clinical Dermatology
University of California
San Francisco School of Medicine
San Francisco, California
Advisory committee for Johnson & Johnson.
Terry Arnold, MA, PA-C
Advanced Practice Consultants, LLC
Tulsa, Oklahoma
Speaker for Medicis; advisory board of Novartis, Warner-Chilcott; advisory board/speaker for Abbott Laboratories, Astellas, Collagenex; advisory board, speaker and consultant for Amgen, Coria Labs, Ranbaxy.
Stephen Brunton, MD
Adjunct Clinical Professor
Department of Family Medicine
University of North Carolina
Chapel Hill
Chapel Hill, North Carolina
Nothing to disclose.
Mary Knudtson, DNSc, NP
Professor, Family Medicine
Director of Family Nurse Practitioner Program
University of California, Irvine
Irvine, California
Speaker's bureau for Proctor & Gamble; consultant for sanofi aventis, Galderma.
John E. Wolf, Jr., MD, MA
Professor and Chairman
Department of Dermatology
Baylor College of Medicine
Houston, Texas
Consultant, speaker, and on the advisory board for Stiefel, PharmaDerm, Galderma, Medicis, Novartis, Warner-Chilcott, consultant for QLT and Peplin; speaker for Stiefel, sanofi-aventis (Dermik).
Copyright © 2008 Elsevier Inc.
Treating Rosacea: Current Insights
Treating Rosacea: Current Insights
Immediate-Release Therapy for Control of Nocturnal GERD
A supplement to Family Practice News supported by Supported by Santarus, Inc. The supplement is based on a faculty interview.
To view the supplement, click the image above.
A supplement to Family Practice News supported by Supported by Santarus, Inc. The supplement is based on a faculty interview.
To view the supplement, click the image above.
A supplement to Family Practice News supported by Supported by Santarus, Inc. The supplement is based on a faculty interview.
To view the supplement, click the image above.
Outcomes of Care by Hospitalists
The hospitalist care model as described by Drs Robert Wachter and Lee Goldman in 1996 has seen rapid growth in terms of the number of hospitalists and the number of hospitals served by hospitalists' groups in North America.16 Hospitalists were initially defined as physicians who spend a minimum of 25% to 50% of their time caring for inpatients.7 The Society of Hospital Medicine later identified hospitalists as physicians whose primary professional focus is the general medical care of hospitalized patients. Their activities include patient care, teaching, research, and leadership related to hospital care.7, 8
Previous study results have consistently demonstrated that hospitalists may decrease overall cost and length of stay (LOS) for patients without compromising clinical outcomes.912
Like many countries around the world, tertiary hospitals in the Singapore health system grapple with the challenges of providing highly subspecialized care to patients who at the same time requires general medical care for multiple complex comorbidities. Unlike the United States, the shortage of generalists in Singapore's hospitals is partly historical, due to the fact that its health system was developed based on the British health system. Under this model of care, family doctors or general practitioners (GP) do not have a tradition of attending to their patients after they are admitted to the hospital. Care of patient is handed over to hospital specialist on admission. In the interest of improving patient care amidst increasing fragmentation of care due to subspecialization, Singapore General Hospital (SGH) developed a hospitalist care model that is adapted to the health system of Singapore.13 The hospital conducted an extensive study of the hospitalist care model which included study visits to the hospitalist programs at the Calgary Health Region in Canada and the University of California, San Francisco, CA, in the United States.14
The aim of this study was to assess the clinical outcomes of the adapted hospitalist program which uses family physicians as generalists in the hospital and compare this with the traditional model of using specialists to provide general medical care in the Singapore General Hospital. The care outcome indicators we used were hospital LOS, 30 day all‐cause readmissions, mortality, and cost of care.
Methods
Setting
SGH is the largest acute tertiary care hospital in Singapore. It has 29 clinical departments many of which are established as national referral centers. It has 1600‐beds in Singapore, serving approximately one‐fourth of its population of 4.59 million.15 It is affiliated with the Yong Loo Lin School of Medicine at the National University of Singapore (NUS) and the Duke‐NUS Graduate Medical School. Forty‐eight percent of the medical staff hold teaching appointments. It is also a major training hospital for residents, accounting for approximately 60% of the total amount of residency training in Singapore.
Intervention
In May 2006, a clinical family medicine department was established in the Singapore General Hospital with the intention of developing a local adaptation of the hospitalist care model.16 The important distinctive features of the adapted hospitalist model as compared to the model commonly found in North America include:
All attending physicians in the hospitalist teams are family physicians by training. The family medicine training program in Singapore resembles a hospital based residency program in the United States. It comprises a minimum of 2 years of structured training based in an acute hospital and 1 year in a primary care setting. The attending physicians in the hospitalist program spend a minimum of 25% of their time providing general medical care for hospitalized patients. The average time spent in inpatient care by the attending physicians was 33%(unpublished internal data). The rest of the time, they are rotated to run hospital‐based ambulatory care clinics, preoperative evaluation clinics, health assessment clinics and a home‐based intermediate care service. All the attending physicians hold teaching appointments and are involved in the teaching of medical students and residents.
Bringing family physicians into the hospital. In Singapore as it is in many health system modeled after the British system, internists work almost exclusively in the hospitals while primary care is almost exclusively provided for by family physicians who are referred to as GP. Primary physicians in Singapore do not have a tradition of managing their patients after admission to the hospital. The pace of specialization of medicine intensified after the mid‐1980s when all public hospitals in Singapore were restructured. The number of full‐time general internist dwindled dramatically and presently general medical care of patients in most hospitals in Singapore are provided for mainly by specialists.17 There had been criticism of the hospitalist care model in the United States for introducing care discontinuity between the hospital and the community.18, 19 Unlike the situation in the United States, the use of family physicians as hospitalist in Singapore is seen as a disruption of a different kind of tradition where there is a distinct divide between physicians who care for patients in the community and those who care for patients in the hospital setting.
Discharged patients with unresolved issues are followed up by the family medicine hospitalist at the ambulatory care clinics in the hospital. Patients are reviewed until they can be handed off to suitable health care providers in the community, to continue with their long term care outside the hospital. Specialist physicians of the hospital provide a similar system of follow‐up of discharged patients although they are more likely to be referred for review by different specialties according to the patients' comorbidities.
Traditionally, patients who are admitted to the Singapore General Hospital come under the care of single system specialists. Many of them are subspecialists who spend a large part of their time running specialist outpatient services and performing procedures and interventions related to their specialty. Approximately 66% of cases admitted from the emergency department to be cared for by the medical departments require specialty care and are admitted directly to the specialist departments. The rest of the patients (34%) are admitted under the category of internal medicine. The admitting physicians who work in Department of Emergency Medicine make these decisions independently. All physicians from the medical specialty departments go on a roster to provide general medical care for such patients usually with the help of specialist consultations. The reason for this form of usual care is again historically related to the fact that the Singapore health system was developed and modeled after the British system.
Patients admitted under the category of internal medicine were assigned hospital beds based on availability of beds for this category of admissions by the bed management unit of the hospital. A total of 34 beds out of 232 beds that are available for use under this category of admission are assigned to be cared for by the family medicine hospitalists in this program. The assignment is administrative and these beds are no different from other beds that accept patients admitted under the same admission category. The assignment of patients to the family medicine hospitalist or the specialist is therefore determined by the bed availability at the time of admission.
Data Source
Data of all hospitalized patients aged 18 years and above who were admitted into the Singapore General Hospital from January 1, 2008 to December 31, 2008 were collected from the hospital data warehouse at the Information Technology Department, SingHealth Group, Singapore. The collected data included demographic information such as ethnic group, gender, age, marital status, admission date, discharge date, intensive care unit (ICU) admission, disease codes under ICD‐9‐AM (International Statistical Classification of Diseases and Related Health Problems, 9th Revision, Australian Modification), and ICD‐9‐AM procedure codes. Data of patients who were readmitted to the hospital within 30‐days after the first discharge were also extracted. Available cost of care data including investigation, medication, treatment, and facility charges were also extracted based on hospital service codes. The protocol was approved by the Ethics Committee of the Singapore General Hospital with exemption from the requirement for informed consent.
Case Definition
Patients cared for by hospitalists were inpatients who were under the care of attending physicians from the Department of Family Medicine and Continuing Care using the adapted hospitalist care model. Patients cared for by nonhospitalists who were inpatients and who were cared for by all other attending specialist physicians from the other medical departments go on a roster to provide general medical care under the usual care model.
Surgical and medical conditions were determined based on the presence of ICD‐9‐AM diagnostic codes and procedure codes.20
Chronic comorbid conditions were identified by ICD‐9‐AM codes and were then studied using the Charlson Index.2123 The Charlson score was then classified into 4 previously defined grades known as the Charlson Comorbidity Index (CCI): 0 points (none), 1 to 2 points (low), 3 to 4 points (moderate), and 5 points (high).2123
Organ failure was defined by a combination of ICD‐9‐AM diagnosis and procedure codes, as outlined previously.24, 25
Statistical Analysis
Categorical variables were reported as percentages, continuous variables were reported as mean and standard deviation (SD). Hospital LOS and hospitalization cost of care were reported using geometric mean (GM) and 95% confidence interval (CI) because of their skewed distribution.
Hospital mortality rate was defined as the ratio of inpatient deaths to the total number of inpatients. Age was categorized into quintiles (18‐54, 55‐64, 65‐74, 75‐84, and >84 years) to capture the nonlinear effect of age on hospital service quality and resource utilization. Parameters were compared between patients cared for by hospitalists and nonhospitalists by chi‐square test for hospital mortality and readmission rate and by Mann‐Whitney U test for LOS and hospital cost. All hospital costs were converted to the year 2008 US dollars for presentation based on the Annual Average Rates of Exchange, Singapore (2008).26 Logistic regression was used to assess the association of hospital mortality and 30‐day all‐cause unscheduled readmission after adjusting for gender, ethnic group, ICU admission, CCI groups, number of organ failures, and the interaction among age groups, CCI groups, and numbers of organ failures. Analysis of hospital LOS and hospitalization costs were restricted to cases with values within 3 SD of the mean because of the extremely skewed nature of these data. Generalized linear model was used to assess log‐transformed LOS and cost with hospitalists care and adjusted for the above‐mentioned factors. The HosmerLemeshow chi‐square goodness‐of‐fit tests were used to exclude variables and identify clinically plausible interaction terms. Using the estimates from the regression models, we presented differences in adjusted LOS, hospitalization costs, hospital mortality, and 30‐day all‐cause unscheduled readmission between hospitalists and nonhospitalists. All P values were 2 sided. The level of statistical significance was considered to be the conventional alpha = 0.05. The data analysis was performed with SPSS 17.0 software (SPSS, Chicago, IL).
Results
A total of 3493 patients met the inclusion/exclusion criteria, in which 601 patients were under the care of hospitalists. The demographic and clinical characteristics of all patients are shown in Table 1. There were no differences in age (67.0 20.4 vs. 66.1 19.8 years, P= 0.311) and ethnic group (P= 0.253) between patients cared for by hospitalists and nonhospitalists. Patients cared for by hospitalists were less likely to be admitted to ICU (0.5% vs. 2.5%, P= 0.002) than patients cared for by nonhospitalists. There were similar CCI grades between patients cared for by hospitalists and nonhospitalists (P= 0.872). Patients cared for by hospitalists had similar numbers of organ failures as compared to patients cared for by nonhospitalists.
| Non Hospitalist, n = 2892 | Hospitalist, n = 601 | P* | |
|---|---|---|---|
| |||
| Age, year | 66.1 19.8 | 67.0 20.4 | 0.311 |
| Male sex, % | 49.2 | 44.4 | 0.033 |
| Ethnicity, % | 0.253 | ||
| Chinese | 79.2 | 76.9 | |
| Malay | 7.8 | 7.7 | |
| Indian | 9.8 | 12.5 | |
| Others | 3.3 | 3.0 | |
| ICU admission, % | 2.5 | 0.5 | 0.002 |
| Charlson Comorbidity Index, % | 0.872 | ||
| None | 43.5 | 42.3 | |
| Low | 35.5 | 36.6 | |
| Moderate | 14.0 | 14.6 | |
| High | 7.0 | 6.5 | |
| Organ failure No, % | 0.192 | ||
| 0 | 73.7 | 75.9 | |
| 1 | 21.6 | 20.5 | |
| 2 | 3.7 | 3.5 | |
| 3 | 1.0 | 0.2 | |
Unadjusted Clinical Outcomes and Resource Utilization
The in‐hospital mortality rates in patients cared for by hospitalists were similar (4.0% vs. 5.3%, P= 0.187) compared to patients cared for by nonhospitalists (Table 2). Also, there were no statistically significant differences in the 30‐day all‐cause unscheduled readmission rates between patients cared for by hospitalists and nonhospitalists (7.5% vs. 7.3%, p= 0.854). As for resource utilization, patients cared for by hospitalists had significantly shorter hospital LOS (GM, 4.6 vs. 5.7 days, P < 0.001) and lower cost (GM, $2301.0 vs. $2656.6, P= 0.001) compared with that in patients cared for by nonhospitalists (Table 2).
| Non hospitalist, n = 2892 | Hospitalist, n = 601 | Difference | P | |
|---|---|---|---|---|
| ||||
| Length of stay, day | ||||
| Un‐adjusted mean (95% CI) | 5.7 (5.4, 5.9) | 4.6 (4.2, 5.0) | 1.1 | <0.001 |
| Adjusted mean (95% CI) | 5.3 (5.3, 5.4) | 4.4 (4.3, 4.5) | 0.9 | <0.001 |
| Total hospitalization cost, $ | ||||
| Un‐adjusted mean (95% CI) | 2656.6 (2569.8, 2746.4) | 2301.0 (2125.2, 2460.1) | 335.6 | 0.001 |
| Adjusted mean (95% CI) | 2500.6 (2471.5, 2530.1) | 2250.7 (2195.9, 2306.8) | 250 | 0.003 |
| Unscheduled readmission, %* | ||||
| Un‐adjusted | 7.5 | 7.3 | 0.2 | 0.854 |
| Adjusted | 8.4 | 7.5 | 0.9 | 0.761 |
| Hospital mortality, %* | ||||
| Un‐adjusted | 5.3 | 4.0 | 1.3 | 0.187 |
| Adjusted | 5.3 | 4.2 | 1.1 | 0.307 |
Adjusted (Multivariate) Results of Hospital Clinical Outcomes and Resource Utilization
Multivariate analysis was used with adjustment for age groups, ethnic group, gender, ICU admission, numbers of organ failures, and CCI grades. Patients cared for by hospitalists, as compared with patients cared for by nonhospitalists, had a statistically significant shorter hospital LOS by 0.9 days (GM, 4.4 vs. 5.3 days, P < 0.001) and lower cost by $250 (GM, $2250.7 vs. $2500.0, P= 0.003), but similar in‐hospital mortality and 30‐day all‐cause readmission rates (both P > 0.05; Table 2). The goodness of fit of the regression model used for hospital LOS was assessed with the Hosmer‐Lemeshow test (2= 546.1, degree of freedom (df) = 3406, P= 0.160).
As for the detailed secondary cost, patients cared for by hospitalists had significantly lower treatment costs by $66 (GM, $462.9 vs. $528.1 P < 0.001) and facility costs by $127 (GM, $827.1 vs. $953.7 P < 0.001) compared with patients cared for by nonhospitalists after adjustment for age groups, ethnic group, gender, ICU admission, presence of organ failures, and CCI grades. The investigation and medication costs were comparable between the 2 groups (all P > 0.05) even after similar adjustments (Table 3).
| Non hospitalist, n = 2892 | Hospitalist, n = 601 | Difference | P | |
|---|---|---|---|---|
| ||||
| Investigation Cost, $ | ||||
| Un‐adjusted mean (95% CI) | 828.3 (802.2, 855.3) | 772.3 (722.2, 825.8) | 24.7 | 0.196 |
| Adjusted mean (95% CI) | 776.5 (767.8, 785.2) | 759.0 (741.4, 776.9) | 17.5 | 0.673 |
| Medication Cost, $ | ||||
| Un‐adjusted mean (95% CI) | 62.8 (58.7, 67.2) | 68.2 (59.6, 78.2) | 9.0 | 0.409 |
| Adjusted mean (95% CI) | 50.5 (49.1, 52.0) | 56.9 (53.5, 60.6) | 6.4 | 0.184 |
| Treatment Cost, $ | ||||
| Un‐adjusted mean (95% CI) | 561.2 (542.3, 580.7) | 472.4 (442.1, 504.8) | 93.4 | <0.001 |
| Adjusted mean (95% CI) | 528.1 (522.1, 534.3) | 461.9 (450.8, 473.3) | 66.2 | <0.001 |
| Facility Cost, $ | ||||
| Un‐adjusted mean (95% CI) | 1015.9 (978.8, 1054.4) | 847.6 (787.8, 912.0) | 178.5 | <0.001 |
| Adjusted mean (95% CI) | 953.7 (942.2, 965.4) | 827.1 (806.0, 848.9) | 126.6 | <0.001 |
| Total hospitalization cost, $ | ||||
| Un‐adjusted mean (95% CI) | 2656.6 (2569.8, 2746.4) | 2301.0 (2125.2, 2460.1) | 335.6 | 0.001 |
| Adjusted mean (95% CI) | 2500.6 (2471.5, 2530.1) | 2250.7 (2195.9, 2306.8) | 250 | 0.003 |
Discussion
We found that patients cared for by hospitalists had shorter hospital LOS (reduced by 0.9 days), and reduced cost (reduced by $250), compared with that of patients cared for by nonhospitalists. This occurred without any compromise of the clinical outcomes as measured by hospital mortality and 30‐day all‐cause unscheduled readmission. The mortality rates were 4.0%(hospitalist) and 5.3%(nonhospitalist). The difference was not statistically significant (P= 0.187). The differences in hospital LOS and cost still existed even after adjustment for age, gender, ethnic group, ICU admission, CCI, and the numbers of organ failure.
Our findings are consistent with previous reports of reduced hospital LOS and hospitalization costs in patients cared for by hospitalists in the United States.2730
Several explanations have been proposed for the favorable results. First, hospitalists' accessibility and holistic approach to the multiple problems of individual patients provide a more coordinated response to the patient's changing needs during the hospital stay. Second, studies had suggested that better coordination of care by hospitalist results in a lower use of resources and fewer specialist consultations.31 Reduced patients hospital LOS and cost may lead to an overall reduction in total revenue to the hospital, resulting in an advantage to the patients and a disadvantage to the hospital. However, this reduction in total revenue may have been partially offset by an increase in the number of patients receiving hospital care services.32 Third, fewer patients in the hospitalists group had an ICU stay and this could be another reason for an increased chance of earlier discharge from the hospital.33 The reduced LOS still existed even after adjustment for ICU admission and other factors. The total number of patients with ICU admissions was small in both groups and did not make a difference to the outcome measures used in our study. The use of ICU is significantly higher by the nonhospitalist group. It is unlikely that the difference was due to severity of illness on admission because the admitting physicians at the emergency department do not specify whether patients are to be managed by hospitalists or nonhospitalists. Assignment of the patient to the 2 groups was based only on the availability of bed at the time of admission. There is a possibility that the difference had occurred by chance due to the small number of patients requiring ICU admission. Discussion of end of life care issues and palliation as an option is known to result in lowered utilization of ICU.34 There is a possibility that hospitalists are more likely to engage the patient and family in end of life care discussions although this would require further studies to confirm. Finally, hospitalists might be more effective in navigating the complex environment of the hospital and might develop greater clinical expertise as a result of repeated experience in caring for patients in a similar setting.35 All these favorable factors were present in our adapted hospitalist care model.
While some studies of hospitalist care have shown reduced LOS without reduction of cost, our results showed significant reduction in the cost of care ($250, P= 0.001) for patient who are managed under the hospitalist program. The differences lie mainly in the cost of treatment and facilities. There were no significant differences in the cost of investigations and drugs.
There were several limitations in our study. Although hospital discharge data had numerous advantages including reliability and extensive use in various analyses in health services and health policy research,36 the dataset was administrative in nature and was not created specifically for our study. The hospital discharge data was also at risk of coding errors and omissions of important diagnoses and complications, which may affect the disease complexity estimates.37 In our study this was partly mitigated by the quality control processes instituted by the hospital and government agencies that use this data for planning and health care financing purposes. As all the data came from a single hospital, there was less risk of coding discrepancies and nonuniformity in calculating hospitalization costs. Our study was retrospective and observational in nature. Nevertheless we did use a number of statistical methods and analyses to minimize the risk of bias. Underlying differences in the patient populations of the 2 comparison groups led us to rely heavily on multivariate analysis and adjustment to minimize confounding factors. Nevertheless it is still possible that factors not included in administrative data might still exist as unmeasured confounders.28 In the period of study, we did not identify significant changes in the process of care for the patients apart from the introduction of the adapted hospitalist care model. This study was done in a single hospital. The hospital is an acute tertiary hospital. It is also a teaching hospital with affiliation to 2 medical schools (Duke‐NUS Graduate Medical School and Yong Loo Lin School of Medicine). The results might not be generalizable to other hospitals with different characteristics. Finally, this study was limited to information concerning in‐hospital deaths, and we did not have data that would have allowed us to detect differences in deaths that occurred after discharge. Similarly, we could only assess readmissions within our hospital. We do not know how frequently patients were admitted to other hospitals, a potential problem in all research of this type reported to date.30
In conclusion, our study showed that the hospitalist care model can be adapted to work in health systems outside North America with favorable outcomes. Our adapted hospitalist care model using family physicians as generalists in the hospital was associated with improvements in areas of LOS and cost reductions. There were no difference in mortality and readmissions. In health systems where hospital and primary care providers are historically divided, the deployment of family physicians as hospitalist may have the additional benefit of bridging the gap and support care continuity across the entire spectrum of care settings for patients.
Acknowledgements
The authors acknowledge Mr. Jeffrey Fong, Ms. Bessie Ang, Ms. LiSee Lou and Ms. Huicheng Tan of their contribution with the database preparation. We also thank all other staff of Family Medicine and Continuing Care and Epidemiology Unit, SGH for logistical support, and the patients who made the study possible.
- ,,,Hospitalists and the practice of inpatient medicine: results of a survey of the National Association of Inpatient Physicians.Ann Intern Med.1999;130(4 Pt 2):343–349.
- ,The emerging role of “hospitalists” in the American health care system.N Engl J Med.1996;335(7):514–517.
- ,,,The status of hospital medicine groups in the United States.J Hosp Med.2006;1(2):75–80.
- ,,,. The rise of the hospitalist in California. Oakland: California Healthcare Foundation, July 2007. Available at: http://www.chcf.org/topics/hospitals/index.cfm?itemID=133365. Accessed June 2010.
- Society of Hospital Medicine. Hospital medicine specialty shows 20 percent growth 2007. Available at: http://www.hospitalmedicine.org/AM/Template.cfm?Section=Press_Releases 2005. Available at: http://www.hospitalmedicine.org/AM/Template.cfm?Section=General_ Information111(8):627–632.
- ,,, et al.Effects of physician experience on costs and outcomes on an academic general medicine service: results of a trial of hospitalists.Ann Intern Med.2002;137(11):866–874.
- ,,, et al.The positive impact of initiation of hospitalist clinician educators.J Gen Intern Med.2004;19(4):293–301.
- ,The hospitalist movement 5 years later.JAMA.2002;287(4):487–494.
- The hospitalist movement‐‐a complex adaptive response to fragmentation of care in hospitals.Ann Acad Med Singapore.2008;37(2):145–150.
- UCSF Today. Singapore Executives Learn About UCSF's Hospitalist Program. 2008. Available at: http://today.ucsf.edu/stories/singapore‐ executives‐learn‐about‐ucsfs‐hospitalist‐program/. Accessed June 2010.
- Statistics Singapore. 2007 Population (Mid‐Year Estimates). Singapore Department of Statistics,2007.
- Launch of Family Medicine Department.The College Mirror.2006;32:1–2‐.
- 1st college of physicians lecture: the role of internal medicine as a specialty in the era of subspecialisation.Ann Acad Med Singapore.2004;33(6):725–732.
- AAFP. AAFP Guidelines for Interaction in “Hospitalist” Models ‐ Communication Between the Receiving Inpatient Care Management Physician and the Referring Primary Care Physician. 2003, April Board 2009‐Reaffirmed. Available at: http://www.aafp.org/online/en/home/policy/ policies/h/hospitalists.html. Accessed June 2010.
- ,,,Continuity of care and intensive care unit use at the end of life.Arch Intern Med.2009;169(1):81–86.
- ,,,,,Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care.Crit Care Med.2001;29(7):1303–1310.
- ,,,A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.J Chronic Dis.1987;40(5):373–383.
- ,,Adapting a clinical comorbidity index for use with ICD‐9‐CM administrative databases.J Clin Epidemiol.1992;45(6):613–619.
- ,,,Validation of a combined comorbidity index.J Clin Epidemiol.1994;47(11):1245–1251.
- ,,,,The effect of comorbidity and age on hospital mortality and length of stay in patients with sepsis.J Crit Care.2009.
- ,,,The epidemiology of sepsis in the United States from 1979 through 2000.N Engl J Med.2003;348(16):1546–1554.
- Inland Revenue Authority of Singapore. Annual Average Rates of Exchange 2008. Available at:http://www.iras.gov.sg/irasHome/page.aspx? id=806. Accessed June 2010.
- ,,,A comparison of two hospitalist models with traditional care in a community teaching hospital.Am J Med.2005;118(5):536–543.
- ,,,,,Outcomes of care by hospitalists, general internists, and family physicians.N Engl J Med.2007;357(25):2589–2600.
- ,Comparison of a family practice teaching service and a hospitalist model: costs, charges, length of stay, and mortality.Fam Med.2001;33(7):511–515.
- ,,Comparison of hospital costs and length of stay for community internists, hospitalists, and academicians.J Gen Intern Med.2007;22(5):662–667.
- ,,Hospital economics of the hospitalist.Health Serv Res.2003;38(3):905–918; discussion 919‐922.
- ,,, et al.Impact of a hospitalist system on length of stay and cost for children with common conditions.Pediatrics.2007;120(2):267–274.
- ,,3rd, et al.Effects of a hospitalist model on elderly patients with hip fracture.Arch Intern Med.2005;165(7):796–801‐.
- ,Admission to intensive care unit at the end‐of‐life: is it an informed decision?Palliat Med.2004;18(8):705–711.
- ,,Is volume related to outcome in health care? A systematic review and methodologic critique of the literature.Ann Intern Med.2002;137(6):511–520.
- ,,,,The Value of Hospital Discharge Databases: Final Report. NORC at the University of Chicago, The National Association of Health Data Organizations.2005. Available at: http://www.hcup‐us.ahrq.gov/reports/final_report.pdf. Accessed June 2010.
- ,,Diagnosis‐dependent misclassification of infections using administrative data variably affected incidence and mortality estimates in ICU patients.J Clin Epidemiol.2007;60(2):155–162.
The hospitalist care model as described by Drs Robert Wachter and Lee Goldman in 1996 has seen rapid growth in terms of the number of hospitalists and the number of hospitals served by hospitalists' groups in North America.16 Hospitalists were initially defined as physicians who spend a minimum of 25% to 50% of their time caring for inpatients.7 The Society of Hospital Medicine later identified hospitalists as physicians whose primary professional focus is the general medical care of hospitalized patients. Their activities include patient care, teaching, research, and leadership related to hospital care.7, 8
Previous study results have consistently demonstrated that hospitalists may decrease overall cost and length of stay (LOS) for patients without compromising clinical outcomes.912
Like many countries around the world, tertiary hospitals in the Singapore health system grapple with the challenges of providing highly subspecialized care to patients who at the same time requires general medical care for multiple complex comorbidities. Unlike the United States, the shortage of generalists in Singapore's hospitals is partly historical, due to the fact that its health system was developed based on the British health system. Under this model of care, family doctors or general practitioners (GP) do not have a tradition of attending to their patients after they are admitted to the hospital. Care of patient is handed over to hospital specialist on admission. In the interest of improving patient care amidst increasing fragmentation of care due to subspecialization, Singapore General Hospital (SGH) developed a hospitalist care model that is adapted to the health system of Singapore.13 The hospital conducted an extensive study of the hospitalist care model which included study visits to the hospitalist programs at the Calgary Health Region in Canada and the University of California, San Francisco, CA, in the United States.14
The aim of this study was to assess the clinical outcomes of the adapted hospitalist program which uses family physicians as generalists in the hospital and compare this with the traditional model of using specialists to provide general medical care in the Singapore General Hospital. The care outcome indicators we used were hospital LOS, 30 day all‐cause readmissions, mortality, and cost of care.
Methods
Setting
SGH is the largest acute tertiary care hospital in Singapore. It has 29 clinical departments many of which are established as national referral centers. It has 1600‐beds in Singapore, serving approximately one‐fourth of its population of 4.59 million.15 It is affiliated with the Yong Loo Lin School of Medicine at the National University of Singapore (NUS) and the Duke‐NUS Graduate Medical School. Forty‐eight percent of the medical staff hold teaching appointments. It is also a major training hospital for residents, accounting for approximately 60% of the total amount of residency training in Singapore.
Intervention
In May 2006, a clinical family medicine department was established in the Singapore General Hospital with the intention of developing a local adaptation of the hospitalist care model.16 The important distinctive features of the adapted hospitalist model as compared to the model commonly found in North America include:
All attending physicians in the hospitalist teams are family physicians by training. The family medicine training program in Singapore resembles a hospital based residency program in the United States. It comprises a minimum of 2 years of structured training based in an acute hospital and 1 year in a primary care setting. The attending physicians in the hospitalist program spend a minimum of 25% of their time providing general medical care for hospitalized patients. The average time spent in inpatient care by the attending physicians was 33%(unpublished internal data). The rest of the time, they are rotated to run hospital‐based ambulatory care clinics, preoperative evaluation clinics, health assessment clinics and a home‐based intermediate care service. All the attending physicians hold teaching appointments and are involved in the teaching of medical students and residents.
Bringing family physicians into the hospital. In Singapore as it is in many health system modeled after the British system, internists work almost exclusively in the hospitals while primary care is almost exclusively provided for by family physicians who are referred to as GP. Primary physicians in Singapore do not have a tradition of managing their patients after admission to the hospital. The pace of specialization of medicine intensified after the mid‐1980s when all public hospitals in Singapore were restructured. The number of full‐time general internist dwindled dramatically and presently general medical care of patients in most hospitals in Singapore are provided for mainly by specialists.17 There had been criticism of the hospitalist care model in the United States for introducing care discontinuity between the hospital and the community.18, 19 Unlike the situation in the United States, the use of family physicians as hospitalist in Singapore is seen as a disruption of a different kind of tradition where there is a distinct divide between physicians who care for patients in the community and those who care for patients in the hospital setting.
Discharged patients with unresolved issues are followed up by the family medicine hospitalist at the ambulatory care clinics in the hospital. Patients are reviewed until they can be handed off to suitable health care providers in the community, to continue with their long term care outside the hospital. Specialist physicians of the hospital provide a similar system of follow‐up of discharged patients although they are more likely to be referred for review by different specialties according to the patients' comorbidities.
Traditionally, patients who are admitted to the Singapore General Hospital come under the care of single system specialists. Many of them are subspecialists who spend a large part of their time running specialist outpatient services and performing procedures and interventions related to their specialty. Approximately 66% of cases admitted from the emergency department to be cared for by the medical departments require specialty care and are admitted directly to the specialist departments. The rest of the patients (34%) are admitted under the category of internal medicine. The admitting physicians who work in Department of Emergency Medicine make these decisions independently. All physicians from the medical specialty departments go on a roster to provide general medical care for such patients usually with the help of specialist consultations. The reason for this form of usual care is again historically related to the fact that the Singapore health system was developed and modeled after the British system.
Patients admitted under the category of internal medicine were assigned hospital beds based on availability of beds for this category of admissions by the bed management unit of the hospital. A total of 34 beds out of 232 beds that are available for use under this category of admission are assigned to be cared for by the family medicine hospitalists in this program. The assignment is administrative and these beds are no different from other beds that accept patients admitted under the same admission category. The assignment of patients to the family medicine hospitalist or the specialist is therefore determined by the bed availability at the time of admission.
Data Source
Data of all hospitalized patients aged 18 years and above who were admitted into the Singapore General Hospital from January 1, 2008 to December 31, 2008 were collected from the hospital data warehouse at the Information Technology Department, SingHealth Group, Singapore. The collected data included demographic information such as ethnic group, gender, age, marital status, admission date, discharge date, intensive care unit (ICU) admission, disease codes under ICD‐9‐AM (International Statistical Classification of Diseases and Related Health Problems, 9th Revision, Australian Modification), and ICD‐9‐AM procedure codes. Data of patients who were readmitted to the hospital within 30‐days after the first discharge were also extracted. Available cost of care data including investigation, medication, treatment, and facility charges were also extracted based on hospital service codes. The protocol was approved by the Ethics Committee of the Singapore General Hospital with exemption from the requirement for informed consent.
Case Definition
Patients cared for by hospitalists were inpatients who were under the care of attending physicians from the Department of Family Medicine and Continuing Care using the adapted hospitalist care model. Patients cared for by nonhospitalists who were inpatients and who were cared for by all other attending specialist physicians from the other medical departments go on a roster to provide general medical care under the usual care model.
Surgical and medical conditions were determined based on the presence of ICD‐9‐AM diagnostic codes and procedure codes.20
Chronic comorbid conditions were identified by ICD‐9‐AM codes and were then studied using the Charlson Index.2123 The Charlson score was then classified into 4 previously defined grades known as the Charlson Comorbidity Index (CCI): 0 points (none), 1 to 2 points (low), 3 to 4 points (moderate), and 5 points (high).2123
Organ failure was defined by a combination of ICD‐9‐AM diagnosis and procedure codes, as outlined previously.24, 25
Statistical Analysis
Categorical variables were reported as percentages, continuous variables were reported as mean and standard deviation (SD). Hospital LOS and hospitalization cost of care were reported using geometric mean (GM) and 95% confidence interval (CI) because of their skewed distribution.
Hospital mortality rate was defined as the ratio of inpatient deaths to the total number of inpatients. Age was categorized into quintiles (18‐54, 55‐64, 65‐74, 75‐84, and >84 years) to capture the nonlinear effect of age on hospital service quality and resource utilization. Parameters were compared between patients cared for by hospitalists and nonhospitalists by chi‐square test for hospital mortality and readmission rate and by Mann‐Whitney U test for LOS and hospital cost. All hospital costs were converted to the year 2008 US dollars for presentation based on the Annual Average Rates of Exchange, Singapore (2008).26 Logistic regression was used to assess the association of hospital mortality and 30‐day all‐cause unscheduled readmission after adjusting for gender, ethnic group, ICU admission, CCI groups, number of organ failures, and the interaction among age groups, CCI groups, and numbers of organ failures. Analysis of hospital LOS and hospitalization costs were restricted to cases with values within 3 SD of the mean because of the extremely skewed nature of these data. Generalized linear model was used to assess log‐transformed LOS and cost with hospitalists care and adjusted for the above‐mentioned factors. The HosmerLemeshow chi‐square goodness‐of‐fit tests were used to exclude variables and identify clinically plausible interaction terms. Using the estimates from the regression models, we presented differences in adjusted LOS, hospitalization costs, hospital mortality, and 30‐day all‐cause unscheduled readmission between hospitalists and nonhospitalists. All P values were 2 sided. The level of statistical significance was considered to be the conventional alpha = 0.05. The data analysis was performed with SPSS 17.0 software (SPSS, Chicago, IL).
Results
A total of 3493 patients met the inclusion/exclusion criteria, in which 601 patients were under the care of hospitalists. The demographic and clinical characteristics of all patients are shown in Table 1. There were no differences in age (67.0 20.4 vs. 66.1 19.8 years, P= 0.311) and ethnic group (P= 0.253) between patients cared for by hospitalists and nonhospitalists. Patients cared for by hospitalists were less likely to be admitted to ICU (0.5% vs. 2.5%, P= 0.002) than patients cared for by nonhospitalists. There were similar CCI grades between patients cared for by hospitalists and nonhospitalists (P= 0.872). Patients cared for by hospitalists had similar numbers of organ failures as compared to patients cared for by nonhospitalists.
| Non Hospitalist, n = 2892 | Hospitalist, n = 601 | P* | |
|---|---|---|---|
| |||
| Age, year | 66.1 19.8 | 67.0 20.4 | 0.311 |
| Male sex, % | 49.2 | 44.4 | 0.033 |
| Ethnicity, % | 0.253 | ||
| Chinese | 79.2 | 76.9 | |
| Malay | 7.8 | 7.7 | |
| Indian | 9.8 | 12.5 | |
| Others | 3.3 | 3.0 | |
| ICU admission, % | 2.5 | 0.5 | 0.002 |
| Charlson Comorbidity Index, % | 0.872 | ||
| None | 43.5 | 42.3 | |
| Low | 35.5 | 36.6 | |
| Moderate | 14.0 | 14.6 | |
| High | 7.0 | 6.5 | |
| Organ failure No, % | 0.192 | ||
| 0 | 73.7 | 75.9 | |
| 1 | 21.6 | 20.5 | |
| 2 | 3.7 | 3.5 | |
| 3 | 1.0 | 0.2 | |
Unadjusted Clinical Outcomes and Resource Utilization
The in‐hospital mortality rates in patients cared for by hospitalists were similar (4.0% vs. 5.3%, P= 0.187) compared to patients cared for by nonhospitalists (Table 2). Also, there were no statistically significant differences in the 30‐day all‐cause unscheduled readmission rates between patients cared for by hospitalists and nonhospitalists (7.5% vs. 7.3%, p= 0.854). As for resource utilization, patients cared for by hospitalists had significantly shorter hospital LOS (GM, 4.6 vs. 5.7 days, P < 0.001) and lower cost (GM, $2301.0 vs. $2656.6, P= 0.001) compared with that in patients cared for by nonhospitalists (Table 2).
| Non hospitalist, n = 2892 | Hospitalist, n = 601 | Difference | P | |
|---|---|---|---|---|
| ||||
| Length of stay, day | ||||
| Un‐adjusted mean (95% CI) | 5.7 (5.4, 5.9) | 4.6 (4.2, 5.0) | 1.1 | <0.001 |
| Adjusted mean (95% CI) | 5.3 (5.3, 5.4) | 4.4 (4.3, 4.5) | 0.9 | <0.001 |
| Total hospitalization cost, $ | ||||
| Un‐adjusted mean (95% CI) | 2656.6 (2569.8, 2746.4) | 2301.0 (2125.2, 2460.1) | 335.6 | 0.001 |
| Adjusted mean (95% CI) | 2500.6 (2471.5, 2530.1) | 2250.7 (2195.9, 2306.8) | 250 | 0.003 |
| Unscheduled readmission, %* | ||||
| Un‐adjusted | 7.5 | 7.3 | 0.2 | 0.854 |
| Adjusted | 8.4 | 7.5 | 0.9 | 0.761 |
| Hospital mortality, %* | ||||
| Un‐adjusted | 5.3 | 4.0 | 1.3 | 0.187 |
| Adjusted | 5.3 | 4.2 | 1.1 | 0.307 |
Adjusted (Multivariate) Results of Hospital Clinical Outcomes and Resource Utilization
Multivariate analysis was used with adjustment for age groups, ethnic group, gender, ICU admission, numbers of organ failures, and CCI grades. Patients cared for by hospitalists, as compared with patients cared for by nonhospitalists, had a statistically significant shorter hospital LOS by 0.9 days (GM, 4.4 vs. 5.3 days, P < 0.001) and lower cost by $250 (GM, $2250.7 vs. $2500.0, P= 0.003), but similar in‐hospital mortality and 30‐day all‐cause readmission rates (both P > 0.05; Table 2). The goodness of fit of the regression model used for hospital LOS was assessed with the Hosmer‐Lemeshow test (2= 546.1, degree of freedom (df) = 3406, P= 0.160).
As for the detailed secondary cost, patients cared for by hospitalists had significantly lower treatment costs by $66 (GM, $462.9 vs. $528.1 P < 0.001) and facility costs by $127 (GM, $827.1 vs. $953.7 P < 0.001) compared with patients cared for by nonhospitalists after adjustment for age groups, ethnic group, gender, ICU admission, presence of organ failures, and CCI grades. The investigation and medication costs were comparable between the 2 groups (all P > 0.05) even after similar adjustments (Table 3).
| Non hospitalist, n = 2892 | Hospitalist, n = 601 | Difference | P | |
|---|---|---|---|---|
| ||||
| Investigation Cost, $ | ||||
| Un‐adjusted mean (95% CI) | 828.3 (802.2, 855.3) | 772.3 (722.2, 825.8) | 24.7 | 0.196 |
| Adjusted mean (95% CI) | 776.5 (767.8, 785.2) | 759.0 (741.4, 776.9) | 17.5 | 0.673 |
| Medication Cost, $ | ||||
| Un‐adjusted mean (95% CI) | 62.8 (58.7, 67.2) | 68.2 (59.6, 78.2) | 9.0 | 0.409 |
| Adjusted mean (95% CI) | 50.5 (49.1, 52.0) | 56.9 (53.5, 60.6) | 6.4 | 0.184 |
| Treatment Cost, $ | ||||
| Un‐adjusted mean (95% CI) | 561.2 (542.3, 580.7) | 472.4 (442.1, 504.8) | 93.4 | <0.001 |
| Adjusted mean (95% CI) | 528.1 (522.1, 534.3) | 461.9 (450.8, 473.3) | 66.2 | <0.001 |
| Facility Cost, $ | ||||
| Un‐adjusted mean (95% CI) | 1015.9 (978.8, 1054.4) | 847.6 (787.8, 912.0) | 178.5 | <0.001 |
| Adjusted mean (95% CI) | 953.7 (942.2, 965.4) | 827.1 (806.0, 848.9) | 126.6 | <0.001 |
| Total hospitalization cost, $ | ||||
| Un‐adjusted mean (95% CI) | 2656.6 (2569.8, 2746.4) | 2301.0 (2125.2, 2460.1) | 335.6 | 0.001 |
| Adjusted mean (95% CI) | 2500.6 (2471.5, 2530.1) | 2250.7 (2195.9, 2306.8) | 250 | 0.003 |
Discussion
We found that patients cared for by hospitalists had shorter hospital LOS (reduced by 0.9 days), and reduced cost (reduced by $250), compared with that of patients cared for by nonhospitalists. This occurred without any compromise of the clinical outcomes as measured by hospital mortality and 30‐day all‐cause unscheduled readmission. The mortality rates were 4.0%(hospitalist) and 5.3%(nonhospitalist). The difference was not statistically significant (P= 0.187). The differences in hospital LOS and cost still existed even after adjustment for age, gender, ethnic group, ICU admission, CCI, and the numbers of organ failure.
Our findings are consistent with previous reports of reduced hospital LOS and hospitalization costs in patients cared for by hospitalists in the United States.2730
Several explanations have been proposed for the favorable results. First, hospitalists' accessibility and holistic approach to the multiple problems of individual patients provide a more coordinated response to the patient's changing needs during the hospital stay. Second, studies had suggested that better coordination of care by hospitalist results in a lower use of resources and fewer specialist consultations.31 Reduced patients hospital LOS and cost may lead to an overall reduction in total revenue to the hospital, resulting in an advantage to the patients and a disadvantage to the hospital. However, this reduction in total revenue may have been partially offset by an increase in the number of patients receiving hospital care services.32 Third, fewer patients in the hospitalists group had an ICU stay and this could be another reason for an increased chance of earlier discharge from the hospital.33 The reduced LOS still existed even after adjustment for ICU admission and other factors. The total number of patients with ICU admissions was small in both groups and did not make a difference to the outcome measures used in our study. The use of ICU is significantly higher by the nonhospitalist group. It is unlikely that the difference was due to severity of illness on admission because the admitting physicians at the emergency department do not specify whether patients are to be managed by hospitalists or nonhospitalists. Assignment of the patient to the 2 groups was based only on the availability of bed at the time of admission. There is a possibility that the difference had occurred by chance due to the small number of patients requiring ICU admission. Discussion of end of life care issues and palliation as an option is known to result in lowered utilization of ICU.34 There is a possibility that hospitalists are more likely to engage the patient and family in end of life care discussions although this would require further studies to confirm. Finally, hospitalists might be more effective in navigating the complex environment of the hospital and might develop greater clinical expertise as a result of repeated experience in caring for patients in a similar setting.35 All these favorable factors were present in our adapted hospitalist care model.
While some studies of hospitalist care have shown reduced LOS without reduction of cost, our results showed significant reduction in the cost of care ($250, P= 0.001) for patient who are managed under the hospitalist program. The differences lie mainly in the cost of treatment and facilities. There were no significant differences in the cost of investigations and drugs.
There were several limitations in our study. Although hospital discharge data had numerous advantages including reliability and extensive use in various analyses in health services and health policy research,36 the dataset was administrative in nature and was not created specifically for our study. The hospital discharge data was also at risk of coding errors and omissions of important diagnoses and complications, which may affect the disease complexity estimates.37 In our study this was partly mitigated by the quality control processes instituted by the hospital and government agencies that use this data for planning and health care financing purposes. As all the data came from a single hospital, there was less risk of coding discrepancies and nonuniformity in calculating hospitalization costs. Our study was retrospective and observational in nature. Nevertheless we did use a number of statistical methods and analyses to minimize the risk of bias. Underlying differences in the patient populations of the 2 comparison groups led us to rely heavily on multivariate analysis and adjustment to minimize confounding factors. Nevertheless it is still possible that factors not included in administrative data might still exist as unmeasured confounders.28 In the period of study, we did not identify significant changes in the process of care for the patients apart from the introduction of the adapted hospitalist care model. This study was done in a single hospital. The hospital is an acute tertiary hospital. It is also a teaching hospital with affiliation to 2 medical schools (Duke‐NUS Graduate Medical School and Yong Loo Lin School of Medicine). The results might not be generalizable to other hospitals with different characteristics. Finally, this study was limited to information concerning in‐hospital deaths, and we did not have data that would have allowed us to detect differences in deaths that occurred after discharge. Similarly, we could only assess readmissions within our hospital. We do not know how frequently patients were admitted to other hospitals, a potential problem in all research of this type reported to date.30
In conclusion, our study showed that the hospitalist care model can be adapted to work in health systems outside North America with favorable outcomes. Our adapted hospitalist care model using family physicians as generalists in the hospital was associated with improvements in areas of LOS and cost reductions. There were no difference in mortality and readmissions. In health systems where hospital and primary care providers are historically divided, the deployment of family physicians as hospitalist may have the additional benefit of bridging the gap and support care continuity across the entire spectrum of care settings for patients.
Acknowledgements
The authors acknowledge Mr. Jeffrey Fong, Ms. Bessie Ang, Ms. LiSee Lou and Ms. Huicheng Tan of their contribution with the database preparation. We also thank all other staff of Family Medicine and Continuing Care and Epidemiology Unit, SGH for logistical support, and the patients who made the study possible.
The hospitalist care model as described by Drs Robert Wachter and Lee Goldman in 1996 has seen rapid growth in terms of the number of hospitalists and the number of hospitals served by hospitalists' groups in North America.16 Hospitalists were initially defined as physicians who spend a minimum of 25% to 50% of their time caring for inpatients.7 The Society of Hospital Medicine later identified hospitalists as physicians whose primary professional focus is the general medical care of hospitalized patients. Their activities include patient care, teaching, research, and leadership related to hospital care.7, 8
Previous study results have consistently demonstrated that hospitalists may decrease overall cost and length of stay (LOS) for patients without compromising clinical outcomes.912
Like many countries around the world, tertiary hospitals in the Singapore health system grapple with the challenges of providing highly subspecialized care to patients who at the same time requires general medical care for multiple complex comorbidities. Unlike the United States, the shortage of generalists in Singapore's hospitals is partly historical, due to the fact that its health system was developed based on the British health system. Under this model of care, family doctors or general practitioners (GP) do not have a tradition of attending to their patients after they are admitted to the hospital. Care of patient is handed over to hospital specialist on admission. In the interest of improving patient care amidst increasing fragmentation of care due to subspecialization, Singapore General Hospital (SGH) developed a hospitalist care model that is adapted to the health system of Singapore.13 The hospital conducted an extensive study of the hospitalist care model which included study visits to the hospitalist programs at the Calgary Health Region in Canada and the University of California, San Francisco, CA, in the United States.14
The aim of this study was to assess the clinical outcomes of the adapted hospitalist program which uses family physicians as generalists in the hospital and compare this with the traditional model of using specialists to provide general medical care in the Singapore General Hospital. The care outcome indicators we used were hospital LOS, 30 day all‐cause readmissions, mortality, and cost of care.
Methods
Setting
SGH is the largest acute tertiary care hospital in Singapore. It has 29 clinical departments many of which are established as national referral centers. It has 1600‐beds in Singapore, serving approximately one‐fourth of its population of 4.59 million.15 It is affiliated with the Yong Loo Lin School of Medicine at the National University of Singapore (NUS) and the Duke‐NUS Graduate Medical School. Forty‐eight percent of the medical staff hold teaching appointments. It is also a major training hospital for residents, accounting for approximately 60% of the total amount of residency training in Singapore.
Intervention
In May 2006, a clinical family medicine department was established in the Singapore General Hospital with the intention of developing a local adaptation of the hospitalist care model.16 The important distinctive features of the adapted hospitalist model as compared to the model commonly found in North America include:
All attending physicians in the hospitalist teams are family physicians by training. The family medicine training program in Singapore resembles a hospital based residency program in the United States. It comprises a minimum of 2 years of structured training based in an acute hospital and 1 year in a primary care setting. The attending physicians in the hospitalist program spend a minimum of 25% of their time providing general medical care for hospitalized patients. The average time spent in inpatient care by the attending physicians was 33%(unpublished internal data). The rest of the time, they are rotated to run hospital‐based ambulatory care clinics, preoperative evaluation clinics, health assessment clinics and a home‐based intermediate care service. All the attending physicians hold teaching appointments and are involved in the teaching of medical students and residents.
Bringing family physicians into the hospital. In Singapore as it is in many health system modeled after the British system, internists work almost exclusively in the hospitals while primary care is almost exclusively provided for by family physicians who are referred to as GP. Primary physicians in Singapore do not have a tradition of managing their patients after admission to the hospital. The pace of specialization of medicine intensified after the mid‐1980s when all public hospitals in Singapore were restructured. The number of full‐time general internist dwindled dramatically and presently general medical care of patients in most hospitals in Singapore are provided for mainly by specialists.17 There had been criticism of the hospitalist care model in the United States for introducing care discontinuity between the hospital and the community.18, 19 Unlike the situation in the United States, the use of family physicians as hospitalist in Singapore is seen as a disruption of a different kind of tradition where there is a distinct divide between physicians who care for patients in the community and those who care for patients in the hospital setting.
Discharged patients with unresolved issues are followed up by the family medicine hospitalist at the ambulatory care clinics in the hospital. Patients are reviewed until they can be handed off to suitable health care providers in the community, to continue with their long term care outside the hospital. Specialist physicians of the hospital provide a similar system of follow‐up of discharged patients although they are more likely to be referred for review by different specialties according to the patients' comorbidities.
Traditionally, patients who are admitted to the Singapore General Hospital come under the care of single system specialists. Many of them are subspecialists who spend a large part of their time running specialist outpatient services and performing procedures and interventions related to their specialty. Approximately 66% of cases admitted from the emergency department to be cared for by the medical departments require specialty care and are admitted directly to the specialist departments. The rest of the patients (34%) are admitted under the category of internal medicine. The admitting physicians who work in Department of Emergency Medicine make these decisions independently. All physicians from the medical specialty departments go on a roster to provide general medical care for such patients usually with the help of specialist consultations. The reason for this form of usual care is again historically related to the fact that the Singapore health system was developed and modeled after the British system.
Patients admitted under the category of internal medicine were assigned hospital beds based on availability of beds for this category of admissions by the bed management unit of the hospital. A total of 34 beds out of 232 beds that are available for use under this category of admission are assigned to be cared for by the family medicine hospitalists in this program. The assignment is administrative and these beds are no different from other beds that accept patients admitted under the same admission category. The assignment of patients to the family medicine hospitalist or the specialist is therefore determined by the bed availability at the time of admission.
Data Source
Data of all hospitalized patients aged 18 years and above who were admitted into the Singapore General Hospital from January 1, 2008 to December 31, 2008 were collected from the hospital data warehouse at the Information Technology Department, SingHealth Group, Singapore. The collected data included demographic information such as ethnic group, gender, age, marital status, admission date, discharge date, intensive care unit (ICU) admission, disease codes under ICD‐9‐AM (International Statistical Classification of Diseases and Related Health Problems, 9th Revision, Australian Modification), and ICD‐9‐AM procedure codes. Data of patients who were readmitted to the hospital within 30‐days after the first discharge were also extracted. Available cost of care data including investigation, medication, treatment, and facility charges were also extracted based on hospital service codes. The protocol was approved by the Ethics Committee of the Singapore General Hospital with exemption from the requirement for informed consent.
Case Definition
Patients cared for by hospitalists were inpatients who were under the care of attending physicians from the Department of Family Medicine and Continuing Care using the adapted hospitalist care model. Patients cared for by nonhospitalists who were inpatients and who were cared for by all other attending specialist physicians from the other medical departments go on a roster to provide general medical care under the usual care model.
Surgical and medical conditions were determined based on the presence of ICD‐9‐AM diagnostic codes and procedure codes.20
Chronic comorbid conditions were identified by ICD‐9‐AM codes and were then studied using the Charlson Index.2123 The Charlson score was then classified into 4 previously defined grades known as the Charlson Comorbidity Index (CCI): 0 points (none), 1 to 2 points (low), 3 to 4 points (moderate), and 5 points (high).2123
Organ failure was defined by a combination of ICD‐9‐AM diagnosis and procedure codes, as outlined previously.24, 25
Statistical Analysis
Categorical variables were reported as percentages, continuous variables were reported as mean and standard deviation (SD). Hospital LOS and hospitalization cost of care were reported using geometric mean (GM) and 95% confidence interval (CI) because of their skewed distribution.
Hospital mortality rate was defined as the ratio of inpatient deaths to the total number of inpatients. Age was categorized into quintiles (18‐54, 55‐64, 65‐74, 75‐84, and >84 years) to capture the nonlinear effect of age on hospital service quality and resource utilization. Parameters were compared between patients cared for by hospitalists and nonhospitalists by chi‐square test for hospital mortality and readmission rate and by Mann‐Whitney U test for LOS and hospital cost. All hospital costs were converted to the year 2008 US dollars for presentation based on the Annual Average Rates of Exchange, Singapore (2008).26 Logistic regression was used to assess the association of hospital mortality and 30‐day all‐cause unscheduled readmission after adjusting for gender, ethnic group, ICU admission, CCI groups, number of organ failures, and the interaction among age groups, CCI groups, and numbers of organ failures. Analysis of hospital LOS and hospitalization costs were restricted to cases with values within 3 SD of the mean because of the extremely skewed nature of these data. Generalized linear model was used to assess log‐transformed LOS and cost with hospitalists care and adjusted for the above‐mentioned factors. The HosmerLemeshow chi‐square goodness‐of‐fit tests were used to exclude variables and identify clinically plausible interaction terms. Using the estimates from the regression models, we presented differences in adjusted LOS, hospitalization costs, hospital mortality, and 30‐day all‐cause unscheduled readmission between hospitalists and nonhospitalists. All P values were 2 sided. The level of statistical significance was considered to be the conventional alpha = 0.05. The data analysis was performed with SPSS 17.0 software (SPSS, Chicago, IL).
Results
A total of 3493 patients met the inclusion/exclusion criteria, in which 601 patients were under the care of hospitalists. The demographic and clinical characteristics of all patients are shown in Table 1. There were no differences in age (67.0 20.4 vs. 66.1 19.8 years, P= 0.311) and ethnic group (P= 0.253) between patients cared for by hospitalists and nonhospitalists. Patients cared for by hospitalists were less likely to be admitted to ICU (0.5% vs. 2.5%, P= 0.002) than patients cared for by nonhospitalists. There were similar CCI grades between patients cared for by hospitalists and nonhospitalists (P= 0.872). Patients cared for by hospitalists had similar numbers of organ failures as compared to patients cared for by nonhospitalists.
| Non Hospitalist, n = 2892 | Hospitalist, n = 601 | P* | |
|---|---|---|---|
| |||
| Age, year | 66.1 19.8 | 67.0 20.4 | 0.311 |
| Male sex, % | 49.2 | 44.4 | 0.033 |
| Ethnicity, % | 0.253 | ||
| Chinese | 79.2 | 76.9 | |
| Malay | 7.8 | 7.7 | |
| Indian | 9.8 | 12.5 | |
| Others | 3.3 | 3.0 | |
| ICU admission, % | 2.5 | 0.5 | 0.002 |
| Charlson Comorbidity Index, % | 0.872 | ||
| None | 43.5 | 42.3 | |
| Low | 35.5 | 36.6 | |
| Moderate | 14.0 | 14.6 | |
| High | 7.0 | 6.5 | |
| Organ failure No, % | 0.192 | ||
| 0 | 73.7 | 75.9 | |
| 1 | 21.6 | 20.5 | |
| 2 | 3.7 | 3.5 | |
| 3 | 1.0 | 0.2 | |
Unadjusted Clinical Outcomes and Resource Utilization
The in‐hospital mortality rates in patients cared for by hospitalists were similar (4.0% vs. 5.3%, P= 0.187) compared to patients cared for by nonhospitalists (Table 2). Also, there were no statistically significant differences in the 30‐day all‐cause unscheduled readmission rates between patients cared for by hospitalists and nonhospitalists (7.5% vs. 7.3%, p= 0.854). As for resource utilization, patients cared for by hospitalists had significantly shorter hospital LOS (GM, 4.6 vs. 5.7 days, P < 0.001) and lower cost (GM, $2301.0 vs. $2656.6, P= 0.001) compared with that in patients cared for by nonhospitalists (Table 2).
| Non hospitalist, n = 2892 | Hospitalist, n = 601 | Difference | P | |
|---|---|---|---|---|
| ||||
| Length of stay, day | ||||
| Un‐adjusted mean (95% CI) | 5.7 (5.4, 5.9) | 4.6 (4.2, 5.0) | 1.1 | <0.001 |
| Adjusted mean (95% CI) | 5.3 (5.3, 5.4) | 4.4 (4.3, 4.5) | 0.9 | <0.001 |
| Total hospitalization cost, $ | ||||
| Un‐adjusted mean (95% CI) | 2656.6 (2569.8, 2746.4) | 2301.0 (2125.2, 2460.1) | 335.6 | 0.001 |
| Adjusted mean (95% CI) | 2500.6 (2471.5, 2530.1) | 2250.7 (2195.9, 2306.8) | 250 | 0.003 |
| Unscheduled readmission, %* | ||||
| Un‐adjusted | 7.5 | 7.3 | 0.2 | 0.854 |
| Adjusted | 8.4 | 7.5 | 0.9 | 0.761 |
| Hospital mortality, %* | ||||
| Un‐adjusted | 5.3 | 4.0 | 1.3 | 0.187 |
| Adjusted | 5.3 | 4.2 | 1.1 | 0.307 |
Adjusted (Multivariate) Results of Hospital Clinical Outcomes and Resource Utilization
Multivariate analysis was used with adjustment for age groups, ethnic group, gender, ICU admission, numbers of organ failures, and CCI grades. Patients cared for by hospitalists, as compared with patients cared for by nonhospitalists, had a statistically significant shorter hospital LOS by 0.9 days (GM, 4.4 vs. 5.3 days, P < 0.001) and lower cost by $250 (GM, $2250.7 vs. $2500.0, P= 0.003), but similar in‐hospital mortality and 30‐day all‐cause readmission rates (both P > 0.05; Table 2). The goodness of fit of the regression model used for hospital LOS was assessed with the Hosmer‐Lemeshow test (2= 546.1, degree of freedom (df) = 3406, P= 0.160).
As for the detailed secondary cost, patients cared for by hospitalists had significantly lower treatment costs by $66 (GM, $462.9 vs. $528.1 P < 0.001) and facility costs by $127 (GM, $827.1 vs. $953.7 P < 0.001) compared with patients cared for by nonhospitalists after adjustment for age groups, ethnic group, gender, ICU admission, presence of organ failures, and CCI grades. The investigation and medication costs were comparable between the 2 groups (all P > 0.05) even after similar adjustments (Table 3).
| Non hospitalist, n = 2892 | Hospitalist, n = 601 | Difference | P | |
|---|---|---|---|---|
| ||||
| Investigation Cost, $ | ||||
| Un‐adjusted mean (95% CI) | 828.3 (802.2, 855.3) | 772.3 (722.2, 825.8) | 24.7 | 0.196 |
| Adjusted mean (95% CI) | 776.5 (767.8, 785.2) | 759.0 (741.4, 776.9) | 17.5 | 0.673 |
| Medication Cost, $ | ||||
| Un‐adjusted mean (95% CI) | 62.8 (58.7, 67.2) | 68.2 (59.6, 78.2) | 9.0 | 0.409 |
| Adjusted mean (95% CI) | 50.5 (49.1, 52.0) | 56.9 (53.5, 60.6) | 6.4 | 0.184 |
| Treatment Cost, $ | ||||
| Un‐adjusted mean (95% CI) | 561.2 (542.3, 580.7) | 472.4 (442.1, 504.8) | 93.4 | <0.001 |
| Adjusted mean (95% CI) | 528.1 (522.1, 534.3) | 461.9 (450.8, 473.3) | 66.2 | <0.001 |
| Facility Cost, $ | ||||
| Un‐adjusted mean (95% CI) | 1015.9 (978.8, 1054.4) | 847.6 (787.8, 912.0) | 178.5 | <0.001 |
| Adjusted mean (95% CI) | 953.7 (942.2, 965.4) | 827.1 (806.0, 848.9) | 126.6 | <0.001 |
| Total hospitalization cost, $ | ||||
| Un‐adjusted mean (95% CI) | 2656.6 (2569.8, 2746.4) | 2301.0 (2125.2, 2460.1) | 335.6 | 0.001 |
| Adjusted mean (95% CI) | 2500.6 (2471.5, 2530.1) | 2250.7 (2195.9, 2306.8) | 250 | 0.003 |
Discussion
We found that patients cared for by hospitalists had shorter hospital LOS (reduced by 0.9 days), and reduced cost (reduced by $250), compared with that of patients cared for by nonhospitalists. This occurred without any compromise of the clinical outcomes as measured by hospital mortality and 30‐day all‐cause unscheduled readmission. The mortality rates were 4.0%(hospitalist) and 5.3%(nonhospitalist). The difference was not statistically significant (P= 0.187). The differences in hospital LOS and cost still existed even after adjustment for age, gender, ethnic group, ICU admission, CCI, and the numbers of organ failure.
Our findings are consistent with previous reports of reduced hospital LOS and hospitalization costs in patients cared for by hospitalists in the United States.2730
Several explanations have been proposed for the favorable results. First, hospitalists' accessibility and holistic approach to the multiple problems of individual patients provide a more coordinated response to the patient's changing needs during the hospital stay. Second, studies had suggested that better coordination of care by hospitalist results in a lower use of resources and fewer specialist consultations.31 Reduced patients hospital LOS and cost may lead to an overall reduction in total revenue to the hospital, resulting in an advantage to the patients and a disadvantage to the hospital. However, this reduction in total revenue may have been partially offset by an increase in the number of patients receiving hospital care services.32 Third, fewer patients in the hospitalists group had an ICU stay and this could be another reason for an increased chance of earlier discharge from the hospital.33 The reduced LOS still existed even after adjustment for ICU admission and other factors. The total number of patients with ICU admissions was small in both groups and did not make a difference to the outcome measures used in our study. The use of ICU is significantly higher by the nonhospitalist group. It is unlikely that the difference was due to severity of illness on admission because the admitting physicians at the emergency department do not specify whether patients are to be managed by hospitalists or nonhospitalists. Assignment of the patient to the 2 groups was based only on the availability of bed at the time of admission. There is a possibility that the difference had occurred by chance due to the small number of patients requiring ICU admission. Discussion of end of life care issues and palliation as an option is known to result in lowered utilization of ICU.34 There is a possibility that hospitalists are more likely to engage the patient and family in end of life care discussions although this would require further studies to confirm. Finally, hospitalists might be more effective in navigating the complex environment of the hospital and might develop greater clinical expertise as a result of repeated experience in caring for patients in a similar setting.35 All these favorable factors were present in our adapted hospitalist care model.
While some studies of hospitalist care have shown reduced LOS without reduction of cost, our results showed significant reduction in the cost of care ($250, P= 0.001) for patient who are managed under the hospitalist program. The differences lie mainly in the cost of treatment and facilities. There were no significant differences in the cost of investigations and drugs.
There were several limitations in our study. Although hospital discharge data had numerous advantages including reliability and extensive use in various analyses in health services and health policy research,36 the dataset was administrative in nature and was not created specifically for our study. The hospital discharge data was also at risk of coding errors and omissions of important diagnoses and complications, which may affect the disease complexity estimates.37 In our study this was partly mitigated by the quality control processes instituted by the hospital and government agencies that use this data for planning and health care financing purposes. As all the data came from a single hospital, there was less risk of coding discrepancies and nonuniformity in calculating hospitalization costs. Our study was retrospective and observational in nature. Nevertheless we did use a number of statistical methods and analyses to minimize the risk of bias. Underlying differences in the patient populations of the 2 comparison groups led us to rely heavily on multivariate analysis and adjustment to minimize confounding factors. Nevertheless it is still possible that factors not included in administrative data might still exist as unmeasured confounders.28 In the period of study, we did not identify significant changes in the process of care for the patients apart from the introduction of the adapted hospitalist care model. This study was done in a single hospital. The hospital is an acute tertiary hospital. It is also a teaching hospital with affiliation to 2 medical schools (Duke‐NUS Graduate Medical School and Yong Loo Lin School of Medicine). The results might not be generalizable to other hospitals with different characteristics. Finally, this study was limited to information concerning in‐hospital deaths, and we did not have data that would have allowed us to detect differences in deaths that occurred after discharge. Similarly, we could only assess readmissions within our hospital. We do not know how frequently patients were admitted to other hospitals, a potential problem in all research of this type reported to date.30
In conclusion, our study showed that the hospitalist care model can be adapted to work in health systems outside North America with favorable outcomes. Our adapted hospitalist care model using family physicians as generalists in the hospital was associated with improvements in areas of LOS and cost reductions. There were no difference in mortality and readmissions. In health systems where hospital and primary care providers are historically divided, the deployment of family physicians as hospitalist may have the additional benefit of bridging the gap and support care continuity across the entire spectrum of care settings for patients.
Acknowledgements
The authors acknowledge Mr. Jeffrey Fong, Ms. Bessie Ang, Ms. LiSee Lou and Ms. Huicheng Tan of their contribution with the database preparation. We also thank all other staff of Family Medicine and Continuing Care and Epidemiology Unit, SGH for logistical support, and the patients who made the study possible.
- ,,,Hospitalists and the practice of inpatient medicine: results of a survey of the National Association of Inpatient Physicians.Ann Intern Med.1999;130(4 Pt 2):343–349.
- ,The emerging role of “hospitalists” in the American health care system.N Engl J Med.1996;335(7):514–517.
- ,,,The status of hospital medicine groups in the United States.J Hosp Med.2006;1(2):75–80.
- ,,,. The rise of the hospitalist in California. Oakland: California Healthcare Foundation, July 2007. Available at: http://www.chcf.org/topics/hospitals/index.cfm?itemID=133365. Accessed June 2010.
- Society of Hospital Medicine. Hospital medicine specialty shows 20 percent growth 2007. Available at: http://www.hospitalmedicine.org/AM/Template.cfm?Section=Press_Releases 2005. Available at: http://www.hospitalmedicine.org/AM/Template.cfm?Section=General_ Information111(8):627–632.
- ,,, et al.Effects of physician experience on costs and outcomes on an academic general medicine service: results of a trial of hospitalists.Ann Intern Med.2002;137(11):866–874.
- ,,, et al.The positive impact of initiation of hospitalist clinician educators.J Gen Intern Med.2004;19(4):293–301.
- ,The hospitalist movement 5 years later.JAMA.2002;287(4):487–494.
- The hospitalist movement‐‐a complex adaptive response to fragmentation of care in hospitals.Ann Acad Med Singapore.2008;37(2):145–150.
- UCSF Today. Singapore Executives Learn About UCSF's Hospitalist Program. 2008. Available at: http://today.ucsf.edu/stories/singapore‐ executives‐learn‐about‐ucsfs‐hospitalist‐program/. Accessed June 2010.
- Statistics Singapore. 2007 Population (Mid‐Year Estimates). Singapore Department of Statistics,2007.
- Launch of Family Medicine Department.The College Mirror.2006;32:1–2‐.
- 1st college of physicians lecture: the role of internal medicine as a specialty in the era of subspecialisation.Ann Acad Med Singapore.2004;33(6):725–732.
- AAFP. AAFP Guidelines for Interaction in “Hospitalist” Models ‐ Communication Between the Receiving Inpatient Care Management Physician and the Referring Primary Care Physician. 2003, April Board 2009‐Reaffirmed. Available at: http://www.aafp.org/online/en/home/policy/ policies/h/hospitalists.html. Accessed June 2010.
- ,,,Continuity of care and intensive care unit use at the end of life.Arch Intern Med.2009;169(1):81–86.
- ,,,,,Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care.Crit Care Med.2001;29(7):1303–1310.
- ,,,A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.J Chronic Dis.1987;40(5):373–383.
- ,,Adapting a clinical comorbidity index for use with ICD‐9‐CM administrative databases.J Clin Epidemiol.1992;45(6):613–619.
- ,,,Validation of a combined comorbidity index.J Clin Epidemiol.1994;47(11):1245–1251.
- ,,,,The effect of comorbidity and age on hospital mortality and length of stay in patients with sepsis.J Crit Care.2009.
- ,,,The epidemiology of sepsis in the United States from 1979 through 2000.N Engl J Med.2003;348(16):1546–1554.
- Inland Revenue Authority of Singapore. Annual Average Rates of Exchange 2008. Available at:http://www.iras.gov.sg/irasHome/page.aspx? id=806. Accessed June 2010.
- ,,,A comparison of two hospitalist models with traditional care in a community teaching hospital.Am J Med.2005;118(5):536–543.
- ,,,,,Outcomes of care by hospitalists, general internists, and family physicians.N Engl J Med.2007;357(25):2589–2600.
- ,Comparison of a family practice teaching service and a hospitalist model: costs, charges, length of stay, and mortality.Fam Med.2001;33(7):511–515.
- ,,Comparison of hospital costs and length of stay for community internists, hospitalists, and academicians.J Gen Intern Med.2007;22(5):662–667.
- ,,Hospital economics of the hospitalist.Health Serv Res.2003;38(3):905–918; discussion 919‐922.
- ,,, et al.Impact of a hospitalist system on length of stay and cost for children with common conditions.Pediatrics.2007;120(2):267–274.
- ,,3rd, et al.Effects of a hospitalist model on elderly patients with hip fracture.Arch Intern Med.2005;165(7):796–801‐.
- ,Admission to intensive care unit at the end‐of‐life: is it an informed decision?Palliat Med.2004;18(8):705–711.
- ,,Is volume related to outcome in health care? A systematic review and methodologic critique of the literature.Ann Intern Med.2002;137(6):511–520.
- ,,,,The Value of Hospital Discharge Databases: Final Report. NORC at the University of Chicago, The National Association of Health Data Organizations.2005. Available at: http://www.hcup‐us.ahrq.gov/reports/final_report.pdf. Accessed June 2010.
- ,,Diagnosis‐dependent misclassification of infections using administrative data variably affected incidence and mortality estimates in ICU patients.J Clin Epidemiol.2007;60(2):155–162.
- ,,,Hospitalists and the practice of inpatient medicine: results of a survey of the National Association of Inpatient Physicians.Ann Intern Med.1999;130(4 Pt 2):343–349.
- ,The emerging role of “hospitalists” in the American health care system.N Engl J Med.1996;335(7):514–517.
- ,,,The status of hospital medicine groups in the United States.J Hosp Med.2006;1(2):75–80.
- ,,,. The rise of the hospitalist in California. Oakland: California Healthcare Foundation, July 2007. Available at: http://www.chcf.org/topics/hospitals/index.cfm?itemID=133365. Accessed June 2010.
- Society of Hospital Medicine. Hospital medicine specialty shows 20 percent growth 2007. Available at: http://www.hospitalmedicine.org/AM/Template.cfm?Section=Press_Releases 2005. Available at: http://www.hospitalmedicine.org/AM/Template.cfm?Section=General_ Information111(8):627–632.
- ,,, et al.Effects of physician experience on costs and outcomes on an academic general medicine service: results of a trial of hospitalists.Ann Intern Med.2002;137(11):866–874.
- ,,, et al.The positive impact of initiation of hospitalist clinician educators.J Gen Intern Med.2004;19(4):293–301.
- ,The hospitalist movement 5 years later.JAMA.2002;287(4):487–494.
- The hospitalist movement‐‐a complex adaptive response to fragmentation of care in hospitals.Ann Acad Med Singapore.2008;37(2):145–150.
- UCSF Today. Singapore Executives Learn About UCSF's Hospitalist Program. 2008. Available at: http://today.ucsf.edu/stories/singapore‐ executives‐learn‐about‐ucsfs‐hospitalist‐program/. Accessed June 2010.
- Statistics Singapore. 2007 Population (Mid‐Year Estimates). Singapore Department of Statistics,2007.
- Launch of Family Medicine Department.The College Mirror.2006;32:1–2‐.
- 1st college of physicians lecture: the role of internal medicine as a specialty in the era of subspecialisation.Ann Acad Med Singapore.2004;33(6):725–732.
- AAFP. AAFP Guidelines for Interaction in “Hospitalist” Models ‐ Communication Between the Receiving Inpatient Care Management Physician and the Referring Primary Care Physician. 2003, April Board 2009‐Reaffirmed. Available at: http://www.aafp.org/online/en/home/policy/ policies/h/hospitalists.html. Accessed June 2010.
- ,,,Continuity of care and intensive care unit use at the end of life.Arch Intern Med.2009;169(1):81–86.
- ,,,,,Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care.Crit Care Med.2001;29(7):1303–1310.
- ,,,A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.J Chronic Dis.1987;40(5):373–383.
- ,,Adapting a clinical comorbidity index for use with ICD‐9‐CM administrative databases.J Clin Epidemiol.1992;45(6):613–619.
- ,,,Validation of a combined comorbidity index.J Clin Epidemiol.1994;47(11):1245–1251.
- ,,,,The effect of comorbidity and age on hospital mortality and length of stay in patients with sepsis.J Crit Care.2009.
- ,,,The epidemiology of sepsis in the United States from 1979 through 2000.N Engl J Med.2003;348(16):1546–1554.
- Inland Revenue Authority of Singapore. Annual Average Rates of Exchange 2008. Available at:http://www.iras.gov.sg/irasHome/page.aspx? id=806. Accessed June 2010.
- ,,,A comparison of two hospitalist models with traditional care in a community teaching hospital.Am J Med.2005;118(5):536–543.
- ,,,,,Outcomes of care by hospitalists, general internists, and family physicians.N Engl J Med.2007;357(25):2589–2600.
- ,Comparison of a family practice teaching service and a hospitalist model: costs, charges, length of stay, and mortality.Fam Med.2001;33(7):511–515.
- ,,Comparison of hospital costs and length of stay for community internists, hospitalists, and academicians.J Gen Intern Med.2007;22(5):662–667.
- ,,Hospital economics of the hospitalist.Health Serv Res.2003;38(3):905–918; discussion 919‐922.
- ,,, et al.Impact of a hospitalist system on length of stay and cost for children with common conditions.Pediatrics.2007;120(2):267–274.
- ,,3rd, et al.Effects of a hospitalist model on elderly patients with hip fracture.Arch Intern Med.2005;165(7):796–801‐.
- ,Admission to intensive care unit at the end‐of‐life: is it an informed decision?Palliat Med.2004;18(8):705–711.
- ,,Is volume related to outcome in health care? A systematic review and methodologic critique of the literature.Ann Intern Med.2002;137(6):511–520.
- ,,,,The Value of Hospital Discharge Databases: Final Report. NORC at the University of Chicago, The National Association of Health Data Organizations.2005. Available at: http://www.hcup‐us.ahrq.gov/reports/final_report.pdf. Accessed June 2010.
- ,,Diagnosis‐dependent misclassification of infections using administrative data variably affected incidence and mortality estimates in ICU patients.J Clin Epidemiol.2007;60(2):155–162.
Copyright © 2011 Society of Hospital Medicine
Continuing Medical Education Program in
If you wish to receive credit for this activity, which beginson the next page, please refer to the website:
Accreditation and Designation Statement
Blackwell Futura Media Services designates this educational activity for a 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
Upon completion of this educational activity, participants will be better able to:
-
Identify the approximate 30‐day readmission rate of Medicare patient hospitalized initially for pneumonia.
-
Distinguish which variables were accounted and unaccounted for in the development of a pneumonia readmission model.
Continuous participation in the Journal of Hospital Medicine CME program will enable learners to be better able to:
-
Interpret clinical guidelines and their applications for higher quality and more efficient care for all hospitalized patients.
-
Describe the standard of care for common illnesses and conditions treated in the hospital; such as pneumonia, COPD exacerbation, acute coronary syndrome, HF exacerbation, glycemic control, venous thromboembolic disease, stroke, etc.
-
Discuss evidence‐based recommendations involving transitions of care, including the hospital discharge process.
-
Gain insights into the roles of hospitalists as medical educators, researchers, medical ethicists, palliative care providers, and hospital‐based geriatricians.
-
Incorporate best practices for hospitalist administration, including quality improvement, patient safety, practice management, leadership, and demonstrating hospitalist value.
-
Identify evidence‐based best practices and trends for both adult and pediatric hospital medicine.
Instructions on Receiving Credit
For information on applicability and acceptance of continuing medical education credit for this activity, please consult your professional licensing board.
This activity is designed to be completed within the time designated on the title page; 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 that is noted on the title page.
Follow these steps to earn credit:
-
Log on to
www.blackwellpublishing.com/cme . -
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.
If you wish to receive credit for this activity, which beginson the next page, please refer to the website:
Accreditation and Designation Statement
Blackwell Futura Media Services designates this educational activity for a 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
Upon completion of this educational activity, participants will be better able to:
-
Identify the approximate 30‐day readmission rate of Medicare patient hospitalized initially for pneumonia.
-
Distinguish which variables were accounted and unaccounted for in the development of a pneumonia readmission model.
Continuous participation in the Journal of Hospital Medicine CME program will enable learners to be better able to:
-
Interpret clinical guidelines and their applications for higher quality and more efficient care for all hospitalized patients.
-
Describe the standard of care for common illnesses and conditions treated in the hospital; such as pneumonia, COPD exacerbation, acute coronary syndrome, HF exacerbation, glycemic control, venous thromboembolic disease, stroke, etc.
-
Discuss evidence‐based recommendations involving transitions of care, including the hospital discharge process.
-
Gain insights into the roles of hospitalists as medical educators, researchers, medical ethicists, palliative care providers, and hospital‐based geriatricians.
-
Incorporate best practices for hospitalist administration, including quality improvement, patient safety, practice management, leadership, and demonstrating hospitalist value.
-
Identify evidence‐based best practices and trends for both adult and pediatric hospital medicine.
Instructions on Receiving Credit
For information on applicability and acceptance of continuing medical education credit for this activity, please consult your professional licensing board.
This activity is designed to be completed within the time designated on the title page; 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 that is noted on the title page.
Follow these steps to earn credit:
-
Log on to
www.blackwellpublishing.com/cme . -
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.
If you wish to receive credit for this activity, which beginson the next page, please refer to the website:
Accreditation and Designation Statement
Blackwell Futura Media Services designates this educational activity for a 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
Upon completion of this educational activity, participants will be better able to:
-
Identify the approximate 30‐day readmission rate of Medicare patient hospitalized initially for pneumonia.
-
Distinguish which variables were accounted and unaccounted for in the development of a pneumonia readmission model.
Continuous participation in the Journal of Hospital Medicine CME program will enable learners to be better able to:
-
Interpret clinical guidelines and their applications for higher quality and more efficient care for all hospitalized patients.
-
Describe the standard of care for common illnesses and conditions treated in the hospital; such as pneumonia, COPD exacerbation, acute coronary syndrome, HF exacerbation, glycemic control, venous thromboembolic disease, stroke, etc.
-
Discuss evidence‐based recommendations involving transitions of care, including the hospital discharge process.
-
Gain insights into the roles of hospitalists as medical educators, researchers, medical ethicists, palliative care providers, and hospital‐based geriatricians.
-
Incorporate best practices for hospitalist administration, including quality improvement, patient safety, practice management, leadership, and demonstrating hospitalist value.
-
Identify evidence‐based best practices and trends for both adult and pediatric hospital medicine.
Instructions on Receiving Credit
For information on applicability and acceptance of continuing medical education credit for this activity, please consult your professional licensing board.
This activity is designed to be completed within the time designated on the title page; 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 that is noted on the title page.
Follow these steps to earn credit:
-
Log on to
www.blackwellpublishing.com/cme . -
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.
SPS in Hyperkalemia
Hyperkalemia is a common and potentially life‐threatening problem encountered in many clinical settings. The incidence of hyperkalemia in hospitalized patients has been estimated to be between 1% and 10% in the United States each year.16 Medications are thought to contribute to the development of hyperkalemia in 35% to 75% of hospitalized patients.12, 9 Medications known to cause hyperkalemia include angiotensin‐converting enzyme (ACE) inhibitors, angiotensin II receptor antagonists (ARBs), potassium‐sparing diuretics, nonsteroidal anti‐inflammatory drugs (NSAIDs), potassium supplements, and antibiotics such as trimethoprim/sulfamethoxazole. Patients with hyperkalemia may develop neuromuscular symptoms such as fatigue, muscle weakness, tingling, numbness, and cramping. The most deleterious effect of hyperkalemia is cardiac toxicity, including life‐threatening arrhythmias.3, 711
Sodium polystyrene sulfonate (SPS) or Kayexalate is a cation‐exchange resin that is commonly employed to lower total body potassium in patients with mild to moderate hyperkalemia.1214 It removes potassium from the gut in exchange for sodium. SPS can be given orally or as an enema. When given orally, it is commonly administered with sorbitol to promote diarrhea. The onset of action is within 1 to 2 hours and lasts approximately 4 to 6 hours. The recommended average daily dose is 15 gm to 60 gm given as a single dose or in divided doses. An in vitro study indicated that each gram of resin binds to approximately 3.1 mEq of potassium.14 However, the majority of the exchange capacity is utilized for cations other than potassium. Therefore, the in vivo exchange capacity is thought to be no greater than 1 mEq of potassium per gram of resin.
There are conflicting data regarding the effectiveness of exchange resins.1, 13 In 1998, Guy‐Kapral et al.15 found that a 30‐gm dose of SPS failed to lower serum potassium concentration in end‐stage renal disease patients who were normokalemic. In contrast, Flinn et al.16 demonstrated effective lowering of serum potassium over a period of several days in which repeated doses of SPS were given to hyperkalemic patients. Moreover, in another study, serum potassium concentration was reduced by 1.0 mEq/L in 24 hours.17 However, this study was limited since subjects also received other potassium‐lowering agents such as insulin/glucose or sodium bicarbonate.
In 2004, Mikrut and Brockmiller‐Sell18 studied the dose response between SPS and serum potassium concentration. Their data demonstrated an average reduction in potassium concentration of approximately 1 mmol/L and 1.48 mmol/L following a 30 gm and a 60 gm dose of SPS, respectively. However, the study was small (n = 39) and only evaluated 2 dosages. In clinical practice, there is wide variability in SPS dosing. Furthermore, studies assessing the effects of different doses of SPS are lacking. Often, the dose chosen is dependent on comorbid conditions, concomitant medications and provider experience in treating hyperkalemia. The aim of our study was to examine the single‐dose effect of SPS on potassium concentration in hospitalized, hyperkalemic patients and to compare the potassium lowering effects of SPS doses commonly used.
Methods
Study Population
A retrospective cohort study involving a review of electronic medical records of inpatients receiving SPS for the treatment of hyperkalemia was conducted at the Jesse Brown Veteran Affairs Medical Center, between January 1, 2006 and December 31, 2006. Hyperkalemia was defined as a serum potassium concentration >5.1 mmol/L, as this concentration corresponded to the upper limit of normal for serum potassium at our hospital. The patient list was generated from prescription databases. Exclusion criteria included: chronic SPS use, rectal administration of SPS, multiple doses of SPS spaced less than 6 hours apart, a hemolyzed laboratory specimen, current hemodialysis, lack of a follow‐up serum potassium concentration, or other treatments started for hyperkalemia at the time of the event, specifically insulin, dextrose, albuterol, and/or furosemide. This study protocol received approval from the local Institutional Review Board and the hospital's research and development committee.
Data Collection
The Computerized Patient Record System (CPRS), the electronic medical record system used at Jesse Brown VA Medical Center (JBVAMC), was utilized to gather patient baseline demographic information consisting of age, gender, weight, and height. Data on comorbid conditions such as hypertension (HTN), diabetes mellitus (DM), chronic heart failure (CHF), chronic kidney disease (CKD), and acute renal failure (ARF) was identified from the patient's problem list in the medical record. Additionally, the authors abstracted information about concomitant hyperkalemia‐precipitating medications including ACE inhibitors, ARBs, potassium‐sparing diuretics, aldosterone antagonists, NSAIDs, potassium supplements, and antibiotics such as trimethoprim/sulfamethoxazole. Progress notes and discharge summaries were reviewed for documentation of ARF. Laboratory information including serum creatinine, blood urea nitrogen, potassium, sodium, and calcium concentrations before and after SPS administration was recorded. The date and time of laboratory draws were also collected. Data on SPS administration regarding time and date, as well as dosing information, was obtained from the medication administration records.
Outcomes
The primary outcome were the mean change in serum potassium after a single dose of SPS compared to the baseline. The data were also evaluated based on the dose of SPS that was administered, 15 gm, 30 gm, 45 gm, or 60 gm doses of SPS. Secondary outcomes were the mean change in sodium and calcium concentrations.
Statistical Analysis
In order to complete the statistical analysis for the study, continuous variables were summarized as mean standard deviation, and categorical variables were summarized as frequency counts and proportions. Group comparisons for categorical data in baseline characteristics were performed using the Fisher's exact test and the chi‐square test. The comparisons for continuous data in baseline characteristics were performed using analysis of variance (ANOVA). A paired t‐test was then used to compare changes in potassium, calcium, and sodium concentrations and again to compare the changes in potassium concentrations by different dosage groups. Subset analysis also required the paired t‐test. The differences in the change of potassium concentration between dosage groups were analyzed by ANOVA. Post hoc comparisons were performed to identify which groups were statistically different from each other. A P value less than 0.05 was considered statistically significant. The effect of dosage on primary outcome was also assessed by adjusting baseline characteristics using analysis of covariance (ANCOVA). Least square means (LSMEAN) of the primary outcome were calculated using the ANCOVA model with the SPS dosage groups as the main categorical independent variable and the baseline characteristics in Table 1 as the covariates. The LSMEAN are group means after controlling for covariates (they are the predicted group means, predicted as the population mean values of the covariates in the model). All statistical analyses were done using SAS software, version 9.1 (Cary, NC).
| All (n = 122) | 15 gm (n = 30) | 30 gm (n = 60) | 45 gm (n = 19) | 60 gm (n = 13) | P Value | |
|---|---|---|---|---|---|---|
| ||||||
| Male, n (%) | 121 (99) | 30 (100) | 60 (100) | 18 (95) | 13 (100) | 0.14 |
| Age (year) | 69.0 11.4 | 70.71 0.5 | 70.2 11.5 | 68.0 1.7 | 63.8 12.2 | 0.25 |
| Height (inch) | 69.0 3.5 | 69.2 4.6 | 68.8 3.3 | 68.2 3.0 | 70.5 1.3 | 0.31 |
| Weight (kg) | 81.4 20.7 | 77.3 15.0 | 83.1 23.3 | 82.7 21.7 | 81.6 18.1 | 0.64 |
| Hypertension, n (%) | 96 (79) | 29*(97) | 47 (78) | 12 (63) | 8 (62) | 0.01* |
| Diabetes Mellitus, n (%) | 41 (34) | 10 (33) | 21 (35) | 6 (32) | 4 (31) | 0.99 |
| Chronic kidney disease, n (%) | 46 (38) | 10 (33) | 21 (35) | 8 (42) | 7 (54) | 0.57 |
| Heart failure, n (%) | 32 (26) | 12 (40) | 13 (22) | 6 (32) | 1 (8) | 0.10 |
| Serum creatinine (mg/dL) | 2.57 2.4 | 2.09 1.5 | 2.93 3.0 | 2.37 1.2 | 2.29 2.1 | 0.41 |
| Acute renal failure, n (%) | 84 (69) | 19 (63) | 45 (75) | 12 (63) | 8 (62) | 0.55 |
| Concomitant hyperkalemia‐precipitating medications, n (%) | 60 (49) | 19 (63) | 23 (38) | 10 (53) | 8 (61) | 0.11 |
Results
Patient Characteristics
Study subjects consisted of patients who received SPS in the hospital for treatment of hyperkalemia. A total of 140 patients were identified and 122 met the inclusion criteria. Eighteen patients were excluded for current hemodialysis, receiving other treatments for hyperkalemia, and specimen hemolysis. Of those who were included in the study analysis, 30 patients received 15 gm of SPS, 60 patients received 30 gm, 19 patients received 45 gm, and 13 patients received 60 gm. Baseline characteristics of included patients are shown in Table 1. The majority of patients were male (99%) and had comorbid conditions.
At baseline, the mean serum creatinine concentration was 2.57 2.36 mg/dL. Documented acute renal dysfunction was present at the time of the hyperkalemic event in 69% of patients. Forty‐nine percent of patients were taking at least one medication that can precipitate hyperkalemia. The most common medications were ACE inhibitors (52%). All baseline characteristics were similar among the different cohorts except for HTN. There were significantly more patients with HTN in the 15 gm dosage group compared to the others. There was no effect of baseline characteristics on the primary or secondary outcomes.
Clinical Outcome
A total of 115 patients (94%) achieved normalization of potassium concentration with a single dose of SPS. The mean SPS dose given was 31.84 13.58 gm. The changes in mean electrolyte values for all patients are shown in Table 2. The mean reduction in potassium concentration was 0.99 0.51 mmol/L (P < 0.0001). The follow‐up potassium concentration was obtained on an average of 10 hours after administration of SPS. The mean reduction in calcium concentration was 0.07 0.53 mg/dL (P = 0.13). Further, the mean reduction in sodium concentration was 0.09 3.33 mg/dL (P = 0.004). Figure 1 depicts the change in serum potassium concentration in each of the SPS dosage groups. The mean change in potassium concentrations compared to baseline was statistically significant in all dosage groups. The mean reduction in potassium concentration achieved statistical significance between the 15 gm and 60 gm groups (P < 0.05) and between the 30 gm and 60 gm groups (P < 0.05) (Table 3). To adjust for the potential covariate effects in these group mean comparisons, we further calculated the LSMEAN. Table 3 presents the least square mean changes in serum potassium concentration in each of the SPS dosage groups. As shown in Table 4, the least square mean changes in potassium concentrations compared to the baseline were also statistically significant in all dosage groups. These group differences remain significant, even after the adjustment by the baseline variables.
| Electrolytes | Pre‐SPS | Post‐SPS | Mean Change | P Value |
|---|---|---|---|---|
| ||||
| K+(mmol/L) | 5.57 0.35 | 4.59 0.46 | 0.99 0.51 | <0.0001 |
| Ca2+(mg/dL) | 8.54 0.81 | 8.46 0.64 | 0.07 0.53 | 0.13 |
| Na+(mmol/L) | 136.7 4.66 | 137.6 4.65 | 0.89 3.33 | 0.004 |
| Dose (gm) | Difference Between Means | CI for Difference Between Means |
|---|---|---|
| ||
| 15‐30 | 0.13 | 0.43, 0.16 |
| 15‐45 | 0.27 | 0.66, 0.11 |
| 15‐60* | 0.64 | 1.09, 0.19 |
| 30‐45 | 0.14 | 0.48, 0.20 |
| 30‐60* | 0.50 | 0.91, 0.10 |
| 45‐60 | 0.36 | 0.82, 0.10 |
| SPS Dose | LSMEAN of Potassium Reduction | 95% Confidence Limits | |
|---|---|---|---|
| |||
| 15 gm | 0.676566 | 0.119091 | 1.234041 |
| 30 gm | 0.808860 | 0.260418 | 1.357302 |
| 45 gm | 0.951205 | 0.431039 | 1.471371 |
| 60 gm | 1.313806 | 0.718793 | 1.908818 |
Subset Analysis
To address cointervention bias, an analysis of patients who were on at least 1 medication that predisposed them to hyperkalemia such as ACE inhibitors, ARBs, potassium sparing diuretics, aldosterone antagonists, NSAIDs, potassium supplements, and antibiotics such as trimethoprim/sulfamethoxazole (n = 60) were reviewed. Of the 60 patients, 30 received SPS and had hyperkalemia‐precipitating medications adjusted and 29 patients received SPS alone. Of those who had medications adjusted, the majority of them (83%) had these medications discontinued. The mean potassium concentration before the intervention was approximately 5.6 mmol/L in both groups. The mean SPS dose given was also similar as well as the mean potassium reduction of approximately 1 mmol/L.
Discussion
In this study, we retrospectively examined inpatients who received a single dose of SPS for the treatment of hyperkalemia. In addition, we established a possible dose response relationship in the treatment of mild to moderate hyperkalemia with SPS monotherapy. Our data revealed that 94% of patients achieved normalization of serum potassium concentration with a single dose of SPS. None of the patients experienced hypokalemia as a result of SPS administration. Moreover, when compared to the work by Mikrut and Brockmiller‐Sell,18 we found a similar reduction in serum potassium associated with the 30 gm (0.95 mmol/L vs. 1 mmol/L) and 60 gm (1.40 vs. 1.48 mEq/L) doses of SPS.
As expected, patients with higher serum potassium concentrations received higher SPS doses. Our data illustrated a possible direct, dose‐response relationship between the SPS dose and the reduction in serum potassium concentration. All of the dosage groups produced a statistically significant reduction in potassium concentrations compared to baseline. Moreover, a statistically significant difference was found in the 60 gm group when compared to the 15 gm and 30 gm groups. The mean increase change in sodium of 0.89 3.33 mmol/L was found to be statistically significant (P = 0.004) but not felt to be clinically relevant. The mean change in calcium concentration of 0.07 0.53 was not statistically significant (P = 0.13).
As with any retrospective chart review, incomplete documentation limits data collection. This may have led to an underestimation of the presence of ARF or other risk factors for hyperkalemia at the time of the event. We excluded patients who received other treatment for hyperkalemia in order to truly assess the effects of SPS on potassium concentration. However, we did not account for all potential confounding variables that are likely to affect potassium concentration such as acid‐base status and glycemic control at the time of the hyperkalemic episode. Also, dietary intake of potassium was not ascertained. Absence of a control group is also a limitation of the study, as it does not allow for complete causality to be established. The sample size, although significantly larger than previous studies, was small especially in regards to the subset analysis. Results cannot be generalized to women since the sample of females was very small or to those who were excluded from the analysis by design. Finally, this study evaluated hospitalized patients and direct extrapolation of the results to outpatient settings should be done cautiously, particularly in light of nutritional considerations.
Our study demonstrates a possible dose‐response relationship in lowering potassium concentrations with SPS. The data presented in this paper can be used to develop a potential dosing guideline for the use of SPS in the clinical management of hyperkalemia in a variety of clinical settings. It provides a basis for a much needed prospective study to finalize a dosing scheme for the use of SPS. Having a dosing scheme would serve as a resource for determining what dose of SPS to give for varying degrees of hyperkalemia while being mindful that individual responses to potassium lowering effect of SPS might be variable.
Acknowledgements
The authors thank the editors and reviewers of The Journal of Hospital Medicine for their comments and suggestions. Jaclyn Ng and Kathya Valdez worked equally on manuscript as second authors.
- ,Hyperkalemia: a review.J Intensive Care Med.2005;20(5):272–290.
- Drug‐induced hyperkalemia: old culprits and new offenders.Am J Med.2000;109(4):307–314.
- ,Hyperkalemia.Am Fam Physician.2006;73(2):283–290.
- ,Indications for hospitalization of patients with hyperkalemia.Arch Intern Med.2000;160(11):1605–1611.
- Hyperkalemia: a potentially lethal clinical condition.Acta Clin Croat.2001;40(3):215–225.
- Hypokalemia and hyperkalemia.Med Clin North Am.1997;81(3):611–639.
- ,,,,Hyperkalemia in patients in hospital.BMJ.1983;286(6372):1189–1192.
- ,,,Treatment of electrolyte disorders in adult patients in the intensive care unit.Am J Health Syst Pharm.2005;62(16):1663–1682.
- ,,,Hyperkalemia revisited.Tex Heart Inst J.2006;33(1):40–47.
- Disorders of potassium homeostasis.Hypokalemia and hyperkalemia. Crit Care Clin.2002;18(2):273–288.
- ,,,Drug‐induced hyperkalemia.Medicine.1985;64(6):357–370.
- ,Therapeutic approach to hyperkalemia.Nephron.2002;92(1):33–40.
- ,Controversial issues in the treatment of hyperkalemia.Nephrol Dial Transplant.2003;18(11):2215–2218.
- Sodium polystyrene sulfonate: a cation exchange resin used in treating hyperkalemia.ANNA J.1993;20(1):93–95.
- ,,,,,Effect of single dose resin‐cathartic therapy on serum potassium concentration in patients with end‐stage renal disease.J Am Soc Nephrol.1998;9(10):1924–1930.
- ,,Treatment of the oliguric patient with a new sodium‐exchange resin and sorbitol, a preliminary report.N Engl J Med.1961;264(3):111–115.
- ,,Management of hyperkalemia with a cation exchange resin.N Engl J Med.1961;264(3):115–119.
- ,Sodium polystyrene sulfonate dosing guidelines for the treatment of adult hyperkalemia.Hosp Pharm.2004;39(8):765–771.
Hyperkalemia is a common and potentially life‐threatening problem encountered in many clinical settings. The incidence of hyperkalemia in hospitalized patients has been estimated to be between 1% and 10% in the United States each year.16 Medications are thought to contribute to the development of hyperkalemia in 35% to 75% of hospitalized patients.12, 9 Medications known to cause hyperkalemia include angiotensin‐converting enzyme (ACE) inhibitors, angiotensin II receptor antagonists (ARBs), potassium‐sparing diuretics, nonsteroidal anti‐inflammatory drugs (NSAIDs), potassium supplements, and antibiotics such as trimethoprim/sulfamethoxazole. Patients with hyperkalemia may develop neuromuscular symptoms such as fatigue, muscle weakness, tingling, numbness, and cramping. The most deleterious effect of hyperkalemia is cardiac toxicity, including life‐threatening arrhythmias.3, 711
Sodium polystyrene sulfonate (SPS) or Kayexalate is a cation‐exchange resin that is commonly employed to lower total body potassium in patients with mild to moderate hyperkalemia.1214 It removes potassium from the gut in exchange for sodium. SPS can be given orally or as an enema. When given orally, it is commonly administered with sorbitol to promote diarrhea. The onset of action is within 1 to 2 hours and lasts approximately 4 to 6 hours. The recommended average daily dose is 15 gm to 60 gm given as a single dose or in divided doses. An in vitro study indicated that each gram of resin binds to approximately 3.1 mEq of potassium.14 However, the majority of the exchange capacity is utilized for cations other than potassium. Therefore, the in vivo exchange capacity is thought to be no greater than 1 mEq of potassium per gram of resin.
There are conflicting data regarding the effectiveness of exchange resins.1, 13 In 1998, Guy‐Kapral et al.15 found that a 30‐gm dose of SPS failed to lower serum potassium concentration in end‐stage renal disease patients who were normokalemic. In contrast, Flinn et al.16 demonstrated effective lowering of serum potassium over a period of several days in which repeated doses of SPS were given to hyperkalemic patients. Moreover, in another study, serum potassium concentration was reduced by 1.0 mEq/L in 24 hours.17 However, this study was limited since subjects also received other potassium‐lowering agents such as insulin/glucose or sodium bicarbonate.
In 2004, Mikrut and Brockmiller‐Sell18 studied the dose response between SPS and serum potassium concentration. Their data demonstrated an average reduction in potassium concentration of approximately 1 mmol/L and 1.48 mmol/L following a 30 gm and a 60 gm dose of SPS, respectively. However, the study was small (n = 39) and only evaluated 2 dosages. In clinical practice, there is wide variability in SPS dosing. Furthermore, studies assessing the effects of different doses of SPS are lacking. Often, the dose chosen is dependent on comorbid conditions, concomitant medications and provider experience in treating hyperkalemia. The aim of our study was to examine the single‐dose effect of SPS on potassium concentration in hospitalized, hyperkalemic patients and to compare the potassium lowering effects of SPS doses commonly used.
Methods
Study Population
A retrospective cohort study involving a review of electronic medical records of inpatients receiving SPS for the treatment of hyperkalemia was conducted at the Jesse Brown Veteran Affairs Medical Center, between January 1, 2006 and December 31, 2006. Hyperkalemia was defined as a serum potassium concentration >5.1 mmol/L, as this concentration corresponded to the upper limit of normal for serum potassium at our hospital. The patient list was generated from prescription databases. Exclusion criteria included: chronic SPS use, rectal administration of SPS, multiple doses of SPS spaced less than 6 hours apart, a hemolyzed laboratory specimen, current hemodialysis, lack of a follow‐up serum potassium concentration, or other treatments started for hyperkalemia at the time of the event, specifically insulin, dextrose, albuterol, and/or furosemide. This study protocol received approval from the local Institutional Review Board and the hospital's research and development committee.
Data Collection
The Computerized Patient Record System (CPRS), the electronic medical record system used at Jesse Brown VA Medical Center (JBVAMC), was utilized to gather patient baseline demographic information consisting of age, gender, weight, and height. Data on comorbid conditions such as hypertension (HTN), diabetes mellitus (DM), chronic heart failure (CHF), chronic kidney disease (CKD), and acute renal failure (ARF) was identified from the patient's problem list in the medical record. Additionally, the authors abstracted information about concomitant hyperkalemia‐precipitating medications including ACE inhibitors, ARBs, potassium‐sparing diuretics, aldosterone antagonists, NSAIDs, potassium supplements, and antibiotics such as trimethoprim/sulfamethoxazole. Progress notes and discharge summaries were reviewed for documentation of ARF. Laboratory information including serum creatinine, blood urea nitrogen, potassium, sodium, and calcium concentrations before and after SPS administration was recorded. The date and time of laboratory draws were also collected. Data on SPS administration regarding time and date, as well as dosing information, was obtained from the medication administration records.
Outcomes
The primary outcome were the mean change in serum potassium after a single dose of SPS compared to the baseline. The data were also evaluated based on the dose of SPS that was administered, 15 gm, 30 gm, 45 gm, or 60 gm doses of SPS. Secondary outcomes were the mean change in sodium and calcium concentrations.
Statistical Analysis
In order to complete the statistical analysis for the study, continuous variables were summarized as mean standard deviation, and categorical variables were summarized as frequency counts and proportions. Group comparisons for categorical data in baseline characteristics were performed using the Fisher's exact test and the chi‐square test. The comparisons for continuous data in baseline characteristics were performed using analysis of variance (ANOVA). A paired t‐test was then used to compare changes in potassium, calcium, and sodium concentrations and again to compare the changes in potassium concentrations by different dosage groups. Subset analysis also required the paired t‐test. The differences in the change of potassium concentration between dosage groups were analyzed by ANOVA. Post hoc comparisons were performed to identify which groups were statistically different from each other. A P value less than 0.05 was considered statistically significant. The effect of dosage on primary outcome was also assessed by adjusting baseline characteristics using analysis of covariance (ANCOVA). Least square means (LSMEAN) of the primary outcome were calculated using the ANCOVA model with the SPS dosage groups as the main categorical independent variable and the baseline characteristics in Table 1 as the covariates. The LSMEAN are group means after controlling for covariates (they are the predicted group means, predicted as the population mean values of the covariates in the model). All statistical analyses were done using SAS software, version 9.1 (Cary, NC).
| All (n = 122) | 15 gm (n = 30) | 30 gm (n = 60) | 45 gm (n = 19) | 60 gm (n = 13) | P Value | |
|---|---|---|---|---|---|---|
| ||||||
| Male, n (%) | 121 (99) | 30 (100) | 60 (100) | 18 (95) | 13 (100) | 0.14 |
| Age (year) | 69.0 11.4 | 70.71 0.5 | 70.2 11.5 | 68.0 1.7 | 63.8 12.2 | 0.25 |
| Height (inch) | 69.0 3.5 | 69.2 4.6 | 68.8 3.3 | 68.2 3.0 | 70.5 1.3 | 0.31 |
| Weight (kg) | 81.4 20.7 | 77.3 15.0 | 83.1 23.3 | 82.7 21.7 | 81.6 18.1 | 0.64 |
| Hypertension, n (%) | 96 (79) | 29*(97) | 47 (78) | 12 (63) | 8 (62) | 0.01* |
| Diabetes Mellitus, n (%) | 41 (34) | 10 (33) | 21 (35) | 6 (32) | 4 (31) | 0.99 |
| Chronic kidney disease, n (%) | 46 (38) | 10 (33) | 21 (35) | 8 (42) | 7 (54) | 0.57 |
| Heart failure, n (%) | 32 (26) | 12 (40) | 13 (22) | 6 (32) | 1 (8) | 0.10 |
| Serum creatinine (mg/dL) | 2.57 2.4 | 2.09 1.5 | 2.93 3.0 | 2.37 1.2 | 2.29 2.1 | 0.41 |
| Acute renal failure, n (%) | 84 (69) | 19 (63) | 45 (75) | 12 (63) | 8 (62) | 0.55 |
| Concomitant hyperkalemia‐precipitating medications, n (%) | 60 (49) | 19 (63) | 23 (38) | 10 (53) | 8 (61) | 0.11 |
Results
Patient Characteristics
Study subjects consisted of patients who received SPS in the hospital for treatment of hyperkalemia. A total of 140 patients were identified and 122 met the inclusion criteria. Eighteen patients were excluded for current hemodialysis, receiving other treatments for hyperkalemia, and specimen hemolysis. Of those who were included in the study analysis, 30 patients received 15 gm of SPS, 60 patients received 30 gm, 19 patients received 45 gm, and 13 patients received 60 gm. Baseline characteristics of included patients are shown in Table 1. The majority of patients were male (99%) and had comorbid conditions.
At baseline, the mean serum creatinine concentration was 2.57 2.36 mg/dL. Documented acute renal dysfunction was present at the time of the hyperkalemic event in 69% of patients. Forty‐nine percent of patients were taking at least one medication that can precipitate hyperkalemia. The most common medications were ACE inhibitors (52%). All baseline characteristics were similar among the different cohorts except for HTN. There were significantly more patients with HTN in the 15 gm dosage group compared to the others. There was no effect of baseline characteristics on the primary or secondary outcomes.
Clinical Outcome
A total of 115 patients (94%) achieved normalization of potassium concentration with a single dose of SPS. The mean SPS dose given was 31.84 13.58 gm. The changes in mean electrolyte values for all patients are shown in Table 2. The mean reduction in potassium concentration was 0.99 0.51 mmol/L (P < 0.0001). The follow‐up potassium concentration was obtained on an average of 10 hours after administration of SPS. The mean reduction in calcium concentration was 0.07 0.53 mg/dL (P = 0.13). Further, the mean reduction in sodium concentration was 0.09 3.33 mg/dL (P = 0.004). Figure 1 depicts the change in serum potassium concentration in each of the SPS dosage groups. The mean change in potassium concentrations compared to baseline was statistically significant in all dosage groups. The mean reduction in potassium concentration achieved statistical significance between the 15 gm and 60 gm groups (P < 0.05) and between the 30 gm and 60 gm groups (P < 0.05) (Table 3). To adjust for the potential covariate effects in these group mean comparisons, we further calculated the LSMEAN. Table 3 presents the least square mean changes in serum potassium concentration in each of the SPS dosage groups. As shown in Table 4, the least square mean changes in potassium concentrations compared to the baseline were also statistically significant in all dosage groups. These group differences remain significant, even after the adjustment by the baseline variables.
| Electrolytes | Pre‐SPS | Post‐SPS | Mean Change | P Value |
|---|---|---|---|---|
| ||||
| K+(mmol/L) | 5.57 0.35 | 4.59 0.46 | 0.99 0.51 | <0.0001 |
| Ca2+(mg/dL) | 8.54 0.81 | 8.46 0.64 | 0.07 0.53 | 0.13 |
| Na+(mmol/L) | 136.7 4.66 | 137.6 4.65 | 0.89 3.33 | 0.004 |
| Dose (gm) | Difference Between Means | CI for Difference Between Means |
|---|---|---|
| ||
| 15‐30 | 0.13 | 0.43, 0.16 |
| 15‐45 | 0.27 | 0.66, 0.11 |
| 15‐60* | 0.64 | 1.09, 0.19 |
| 30‐45 | 0.14 | 0.48, 0.20 |
| 30‐60* | 0.50 | 0.91, 0.10 |
| 45‐60 | 0.36 | 0.82, 0.10 |
| SPS Dose | LSMEAN of Potassium Reduction | 95% Confidence Limits | |
|---|---|---|---|
| |||
| 15 gm | 0.676566 | 0.119091 | 1.234041 |
| 30 gm | 0.808860 | 0.260418 | 1.357302 |
| 45 gm | 0.951205 | 0.431039 | 1.471371 |
| 60 gm | 1.313806 | 0.718793 | 1.908818 |
Subset Analysis
To address cointervention bias, an analysis of patients who were on at least 1 medication that predisposed them to hyperkalemia such as ACE inhibitors, ARBs, potassium sparing diuretics, aldosterone antagonists, NSAIDs, potassium supplements, and antibiotics such as trimethoprim/sulfamethoxazole (n = 60) were reviewed. Of the 60 patients, 30 received SPS and had hyperkalemia‐precipitating medications adjusted and 29 patients received SPS alone. Of those who had medications adjusted, the majority of them (83%) had these medications discontinued. The mean potassium concentration before the intervention was approximately 5.6 mmol/L in both groups. The mean SPS dose given was also similar as well as the mean potassium reduction of approximately 1 mmol/L.
Discussion
In this study, we retrospectively examined inpatients who received a single dose of SPS for the treatment of hyperkalemia. In addition, we established a possible dose response relationship in the treatment of mild to moderate hyperkalemia with SPS monotherapy. Our data revealed that 94% of patients achieved normalization of serum potassium concentration with a single dose of SPS. None of the patients experienced hypokalemia as a result of SPS administration. Moreover, when compared to the work by Mikrut and Brockmiller‐Sell,18 we found a similar reduction in serum potassium associated with the 30 gm (0.95 mmol/L vs. 1 mmol/L) and 60 gm (1.40 vs. 1.48 mEq/L) doses of SPS.
As expected, patients with higher serum potassium concentrations received higher SPS doses. Our data illustrated a possible direct, dose‐response relationship between the SPS dose and the reduction in serum potassium concentration. All of the dosage groups produced a statistically significant reduction in potassium concentrations compared to baseline. Moreover, a statistically significant difference was found in the 60 gm group when compared to the 15 gm and 30 gm groups. The mean increase change in sodium of 0.89 3.33 mmol/L was found to be statistically significant (P = 0.004) but not felt to be clinically relevant. The mean change in calcium concentration of 0.07 0.53 was not statistically significant (P = 0.13).
As with any retrospective chart review, incomplete documentation limits data collection. This may have led to an underestimation of the presence of ARF or other risk factors for hyperkalemia at the time of the event. We excluded patients who received other treatment for hyperkalemia in order to truly assess the effects of SPS on potassium concentration. However, we did not account for all potential confounding variables that are likely to affect potassium concentration such as acid‐base status and glycemic control at the time of the hyperkalemic episode. Also, dietary intake of potassium was not ascertained. Absence of a control group is also a limitation of the study, as it does not allow for complete causality to be established. The sample size, although significantly larger than previous studies, was small especially in regards to the subset analysis. Results cannot be generalized to women since the sample of females was very small or to those who were excluded from the analysis by design. Finally, this study evaluated hospitalized patients and direct extrapolation of the results to outpatient settings should be done cautiously, particularly in light of nutritional considerations.
Our study demonstrates a possible dose‐response relationship in lowering potassium concentrations with SPS. The data presented in this paper can be used to develop a potential dosing guideline for the use of SPS in the clinical management of hyperkalemia in a variety of clinical settings. It provides a basis for a much needed prospective study to finalize a dosing scheme for the use of SPS. Having a dosing scheme would serve as a resource for determining what dose of SPS to give for varying degrees of hyperkalemia while being mindful that individual responses to potassium lowering effect of SPS might be variable.
Acknowledgements
The authors thank the editors and reviewers of The Journal of Hospital Medicine for their comments and suggestions. Jaclyn Ng and Kathya Valdez worked equally on manuscript as second authors.
Hyperkalemia is a common and potentially life‐threatening problem encountered in many clinical settings. The incidence of hyperkalemia in hospitalized patients has been estimated to be between 1% and 10% in the United States each year.16 Medications are thought to contribute to the development of hyperkalemia in 35% to 75% of hospitalized patients.12, 9 Medications known to cause hyperkalemia include angiotensin‐converting enzyme (ACE) inhibitors, angiotensin II receptor antagonists (ARBs), potassium‐sparing diuretics, nonsteroidal anti‐inflammatory drugs (NSAIDs), potassium supplements, and antibiotics such as trimethoprim/sulfamethoxazole. Patients with hyperkalemia may develop neuromuscular symptoms such as fatigue, muscle weakness, tingling, numbness, and cramping. The most deleterious effect of hyperkalemia is cardiac toxicity, including life‐threatening arrhythmias.3, 711
Sodium polystyrene sulfonate (SPS) or Kayexalate is a cation‐exchange resin that is commonly employed to lower total body potassium in patients with mild to moderate hyperkalemia.1214 It removes potassium from the gut in exchange for sodium. SPS can be given orally or as an enema. When given orally, it is commonly administered with sorbitol to promote diarrhea. The onset of action is within 1 to 2 hours and lasts approximately 4 to 6 hours. The recommended average daily dose is 15 gm to 60 gm given as a single dose or in divided doses. An in vitro study indicated that each gram of resin binds to approximately 3.1 mEq of potassium.14 However, the majority of the exchange capacity is utilized for cations other than potassium. Therefore, the in vivo exchange capacity is thought to be no greater than 1 mEq of potassium per gram of resin.
There are conflicting data regarding the effectiveness of exchange resins.1, 13 In 1998, Guy‐Kapral et al.15 found that a 30‐gm dose of SPS failed to lower serum potassium concentration in end‐stage renal disease patients who were normokalemic. In contrast, Flinn et al.16 demonstrated effective lowering of serum potassium over a period of several days in which repeated doses of SPS were given to hyperkalemic patients. Moreover, in another study, serum potassium concentration was reduced by 1.0 mEq/L in 24 hours.17 However, this study was limited since subjects also received other potassium‐lowering agents such as insulin/glucose or sodium bicarbonate.
In 2004, Mikrut and Brockmiller‐Sell18 studied the dose response between SPS and serum potassium concentration. Their data demonstrated an average reduction in potassium concentration of approximately 1 mmol/L and 1.48 mmol/L following a 30 gm and a 60 gm dose of SPS, respectively. However, the study was small (n = 39) and only evaluated 2 dosages. In clinical practice, there is wide variability in SPS dosing. Furthermore, studies assessing the effects of different doses of SPS are lacking. Often, the dose chosen is dependent on comorbid conditions, concomitant medications and provider experience in treating hyperkalemia. The aim of our study was to examine the single‐dose effect of SPS on potassium concentration in hospitalized, hyperkalemic patients and to compare the potassium lowering effects of SPS doses commonly used.
Methods
Study Population
A retrospective cohort study involving a review of electronic medical records of inpatients receiving SPS for the treatment of hyperkalemia was conducted at the Jesse Brown Veteran Affairs Medical Center, between January 1, 2006 and December 31, 2006. Hyperkalemia was defined as a serum potassium concentration >5.1 mmol/L, as this concentration corresponded to the upper limit of normal for serum potassium at our hospital. The patient list was generated from prescription databases. Exclusion criteria included: chronic SPS use, rectal administration of SPS, multiple doses of SPS spaced less than 6 hours apart, a hemolyzed laboratory specimen, current hemodialysis, lack of a follow‐up serum potassium concentration, or other treatments started for hyperkalemia at the time of the event, specifically insulin, dextrose, albuterol, and/or furosemide. This study protocol received approval from the local Institutional Review Board and the hospital's research and development committee.
Data Collection
The Computerized Patient Record System (CPRS), the electronic medical record system used at Jesse Brown VA Medical Center (JBVAMC), was utilized to gather patient baseline demographic information consisting of age, gender, weight, and height. Data on comorbid conditions such as hypertension (HTN), diabetes mellitus (DM), chronic heart failure (CHF), chronic kidney disease (CKD), and acute renal failure (ARF) was identified from the patient's problem list in the medical record. Additionally, the authors abstracted information about concomitant hyperkalemia‐precipitating medications including ACE inhibitors, ARBs, potassium‐sparing diuretics, aldosterone antagonists, NSAIDs, potassium supplements, and antibiotics such as trimethoprim/sulfamethoxazole. Progress notes and discharge summaries were reviewed for documentation of ARF. Laboratory information including serum creatinine, blood urea nitrogen, potassium, sodium, and calcium concentrations before and after SPS administration was recorded. The date and time of laboratory draws were also collected. Data on SPS administration regarding time and date, as well as dosing information, was obtained from the medication administration records.
Outcomes
The primary outcome were the mean change in serum potassium after a single dose of SPS compared to the baseline. The data were also evaluated based on the dose of SPS that was administered, 15 gm, 30 gm, 45 gm, or 60 gm doses of SPS. Secondary outcomes were the mean change in sodium and calcium concentrations.
Statistical Analysis
In order to complete the statistical analysis for the study, continuous variables were summarized as mean standard deviation, and categorical variables were summarized as frequency counts and proportions. Group comparisons for categorical data in baseline characteristics were performed using the Fisher's exact test and the chi‐square test. The comparisons for continuous data in baseline characteristics were performed using analysis of variance (ANOVA). A paired t‐test was then used to compare changes in potassium, calcium, and sodium concentrations and again to compare the changes in potassium concentrations by different dosage groups. Subset analysis also required the paired t‐test. The differences in the change of potassium concentration between dosage groups were analyzed by ANOVA. Post hoc comparisons were performed to identify which groups were statistically different from each other. A P value less than 0.05 was considered statistically significant. The effect of dosage on primary outcome was also assessed by adjusting baseline characteristics using analysis of covariance (ANCOVA). Least square means (LSMEAN) of the primary outcome were calculated using the ANCOVA model with the SPS dosage groups as the main categorical independent variable and the baseline characteristics in Table 1 as the covariates. The LSMEAN are group means after controlling for covariates (they are the predicted group means, predicted as the population mean values of the covariates in the model). All statistical analyses were done using SAS software, version 9.1 (Cary, NC).
| All (n = 122) | 15 gm (n = 30) | 30 gm (n = 60) | 45 gm (n = 19) | 60 gm (n = 13) | P Value | |
|---|---|---|---|---|---|---|
| ||||||
| Male, n (%) | 121 (99) | 30 (100) | 60 (100) | 18 (95) | 13 (100) | 0.14 |
| Age (year) | 69.0 11.4 | 70.71 0.5 | 70.2 11.5 | 68.0 1.7 | 63.8 12.2 | 0.25 |
| Height (inch) | 69.0 3.5 | 69.2 4.6 | 68.8 3.3 | 68.2 3.0 | 70.5 1.3 | 0.31 |
| Weight (kg) | 81.4 20.7 | 77.3 15.0 | 83.1 23.3 | 82.7 21.7 | 81.6 18.1 | 0.64 |
| Hypertension, n (%) | 96 (79) | 29*(97) | 47 (78) | 12 (63) | 8 (62) | 0.01* |
| Diabetes Mellitus, n (%) | 41 (34) | 10 (33) | 21 (35) | 6 (32) | 4 (31) | 0.99 |
| Chronic kidney disease, n (%) | 46 (38) | 10 (33) | 21 (35) | 8 (42) | 7 (54) | 0.57 |
| Heart failure, n (%) | 32 (26) | 12 (40) | 13 (22) | 6 (32) | 1 (8) | 0.10 |
| Serum creatinine (mg/dL) | 2.57 2.4 | 2.09 1.5 | 2.93 3.0 | 2.37 1.2 | 2.29 2.1 | 0.41 |
| Acute renal failure, n (%) | 84 (69) | 19 (63) | 45 (75) | 12 (63) | 8 (62) | 0.55 |
| Concomitant hyperkalemia‐precipitating medications, n (%) | 60 (49) | 19 (63) | 23 (38) | 10 (53) | 8 (61) | 0.11 |
Results
Patient Characteristics
Study subjects consisted of patients who received SPS in the hospital for treatment of hyperkalemia. A total of 140 patients were identified and 122 met the inclusion criteria. Eighteen patients were excluded for current hemodialysis, receiving other treatments for hyperkalemia, and specimen hemolysis. Of those who were included in the study analysis, 30 patients received 15 gm of SPS, 60 patients received 30 gm, 19 patients received 45 gm, and 13 patients received 60 gm. Baseline characteristics of included patients are shown in Table 1. The majority of patients were male (99%) and had comorbid conditions.
At baseline, the mean serum creatinine concentration was 2.57 2.36 mg/dL. Documented acute renal dysfunction was present at the time of the hyperkalemic event in 69% of patients. Forty‐nine percent of patients were taking at least one medication that can precipitate hyperkalemia. The most common medications were ACE inhibitors (52%). All baseline characteristics were similar among the different cohorts except for HTN. There were significantly more patients with HTN in the 15 gm dosage group compared to the others. There was no effect of baseline characteristics on the primary or secondary outcomes.
Clinical Outcome
A total of 115 patients (94%) achieved normalization of potassium concentration with a single dose of SPS. The mean SPS dose given was 31.84 13.58 gm. The changes in mean electrolyte values for all patients are shown in Table 2. The mean reduction in potassium concentration was 0.99 0.51 mmol/L (P < 0.0001). The follow‐up potassium concentration was obtained on an average of 10 hours after administration of SPS. The mean reduction in calcium concentration was 0.07 0.53 mg/dL (P = 0.13). Further, the mean reduction in sodium concentration was 0.09 3.33 mg/dL (P = 0.004). Figure 1 depicts the change in serum potassium concentration in each of the SPS dosage groups. The mean change in potassium concentrations compared to baseline was statistically significant in all dosage groups. The mean reduction in potassium concentration achieved statistical significance between the 15 gm and 60 gm groups (P < 0.05) and between the 30 gm and 60 gm groups (P < 0.05) (Table 3). To adjust for the potential covariate effects in these group mean comparisons, we further calculated the LSMEAN. Table 3 presents the least square mean changes in serum potassium concentration in each of the SPS dosage groups. As shown in Table 4, the least square mean changes in potassium concentrations compared to the baseline were also statistically significant in all dosage groups. These group differences remain significant, even after the adjustment by the baseline variables.
| Electrolytes | Pre‐SPS | Post‐SPS | Mean Change | P Value |
|---|---|---|---|---|
| ||||
| K+(mmol/L) | 5.57 0.35 | 4.59 0.46 | 0.99 0.51 | <0.0001 |
| Ca2+(mg/dL) | 8.54 0.81 | 8.46 0.64 | 0.07 0.53 | 0.13 |
| Na+(mmol/L) | 136.7 4.66 | 137.6 4.65 | 0.89 3.33 | 0.004 |
| Dose (gm) | Difference Between Means | CI for Difference Between Means |
|---|---|---|
| ||
| 15‐30 | 0.13 | 0.43, 0.16 |
| 15‐45 | 0.27 | 0.66, 0.11 |
| 15‐60* | 0.64 | 1.09, 0.19 |
| 30‐45 | 0.14 | 0.48, 0.20 |
| 30‐60* | 0.50 | 0.91, 0.10 |
| 45‐60 | 0.36 | 0.82, 0.10 |
| SPS Dose | LSMEAN of Potassium Reduction | 95% Confidence Limits | |
|---|---|---|---|
| |||
| 15 gm | 0.676566 | 0.119091 | 1.234041 |
| 30 gm | 0.808860 | 0.260418 | 1.357302 |
| 45 gm | 0.951205 | 0.431039 | 1.471371 |
| 60 gm | 1.313806 | 0.718793 | 1.908818 |
Subset Analysis
To address cointervention bias, an analysis of patients who were on at least 1 medication that predisposed them to hyperkalemia such as ACE inhibitors, ARBs, potassium sparing diuretics, aldosterone antagonists, NSAIDs, potassium supplements, and antibiotics such as trimethoprim/sulfamethoxazole (n = 60) were reviewed. Of the 60 patients, 30 received SPS and had hyperkalemia‐precipitating medications adjusted and 29 patients received SPS alone. Of those who had medications adjusted, the majority of them (83%) had these medications discontinued. The mean potassium concentration before the intervention was approximately 5.6 mmol/L in both groups. The mean SPS dose given was also similar as well as the mean potassium reduction of approximately 1 mmol/L.
Discussion
In this study, we retrospectively examined inpatients who received a single dose of SPS for the treatment of hyperkalemia. In addition, we established a possible dose response relationship in the treatment of mild to moderate hyperkalemia with SPS monotherapy. Our data revealed that 94% of patients achieved normalization of serum potassium concentration with a single dose of SPS. None of the patients experienced hypokalemia as a result of SPS administration. Moreover, when compared to the work by Mikrut and Brockmiller‐Sell,18 we found a similar reduction in serum potassium associated with the 30 gm (0.95 mmol/L vs. 1 mmol/L) and 60 gm (1.40 vs. 1.48 mEq/L) doses of SPS.
As expected, patients with higher serum potassium concentrations received higher SPS doses. Our data illustrated a possible direct, dose‐response relationship between the SPS dose and the reduction in serum potassium concentration. All of the dosage groups produced a statistically significant reduction in potassium concentrations compared to baseline. Moreover, a statistically significant difference was found in the 60 gm group when compared to the 15 gm and 30 gm groups. The mean increase change in sodium of 0.89 3.33 mmol/L was found to be statistically significant (P = 0.004) but not felt to be clinically relevant. The mean change in calcium concentration of 0.07 0.53 was not statistically significant (P = 0.13).
As with any retrospective chart review, incomplete documentation limits data collection. This may have led to an underestimation of the presence of ARF or other risk factors for hyperkalemia at the time of the event. We excluded patients who received other treatment for hyperkalemia in order to truly assess the effects of SPS on potassium concentration. However, we did not account for all potential confounding variables that are likely to affect potassium concentration such as acid‐base status and glycemic control at the time of the hyperkalemic episode. Also, dietary intake of potassium was not ascertained. Absence of a control group is also a limitation of the study, as it does not allow for complete causality to be established. The sample size, although significantly larger than previous studies, was small especially in regards to the subset analysis. Results cannot be generalized to women since the sample of females was very small or to those who were excluded from the analysis by design. Finally, this study evaluated hospitalized patients and direct extrapolation of the results to outpatient settings should be done cautiously, particularly in light of nutritional considerations.
Our study demonstrates a possible dose‐response relationship in lowering potassium concentrations with SPS. The data presented in this paper can be used to develop a potential dosing guideline for the use of SPS in the clinical management of hyperkalemia in a variety of clinical settings. It provides a basis for a much needed prospective study to finalize a dosing scheme for the use of SPS. Having a dosing scheme would serve as a resource for determining what dose of SPS to give for varying degrees of hyperkalemia while being mindful that individual responses to potassium lowering effect of SPS might be variable.
Acknowledgements
The authors thank the editors and reviewers of The Journal of Hospital Medicine for their comments and suggestions. Jaclyn Ng and Kathya Valdez worked equally on manuscript as second authors.
- ,Hyperkalemia: a review.J Intensive Care Med.2005;20(5):272–290.
- Drug‐induced hyperkalemia: old culprits and new offenders.Am J Med.2000;109(4):307–314.
- ,Hyperkalemia.Am Fam Physician.2006;73(2):283–290.
- ,Indications for hospitalization of patients with hyperkalemia.Arch Intern Med.2000;160(11):1605–1611.
- Hyperkalemia: a potentially lethal clinical condition.Acta Clin Croat.2001;40(3):215–225.
- Hypokalemia and hyperkalemia.Med Clin North Am.1997;81(3):611–639.
- ,,,,Hyperkalemia in patients in hospital.BMJ.1983;286(6372):1189–1192.
- ,,,Treatment of electrolyte disorders in adult patients in the intensive care unit.Am J Health Syst Pharm.2005;62(16):1663–1682.
- ,,,Hyperkalemia revisited.Tex Heart Inst J.2006;33(1):40–47.
- Disorders of potassium homeostasis.Hypokalemia and hyperkalemia. Crit Care Clin.2002;18(2):273–288.
- ,,,Drug‐induced hyperkalemia.Medicine.1985;64(6):357–370.
- ,Therapeutic approach to hyperkalemia.Nephron.2002;92(1):33–40.
- ,Controversial issues in the treatment of hyperkalemia.Nephrol Dial Transplant.2003;18(11):2215–2218.
- Sodium polystyrene sulfonate: a cation exchange resin used in treating hyperkalemia.ANNA J.1993;20(1):93–95.
- ,,,,,Effect of single dose resin‐cathartic therapy on serum potassium concentration in patients with end‐stage renal disease.J Am Soc Nephrol.1998;9(10):1924–1930.
- ,,Treatment of the oliguric patient with a new sodium‐exchange resin and sorbitol, a preliminary report.N Engl J Med.1961;264(3):111–115.
- ,,Management of hyperkalemia with a cation exchange resin.N Engl J Med.1961;264(3):115–119.
- ,Sodium polystyrene sulfonate dosing guidelines for the treatment of adult hyperkalemia.Hosp Pharm.2004;39(8):765–771.
- ,Hyperkalemia: a review.J Intensive Care Med.2005;20(5):272–290.
- Drug‐induced hyperkalemia: old culprits and new offenders.Am J Med.2000;109(4):307–314.
- ,Hyperkalemia.Am Fam Physician.2006;73(2):283–290.
- ,Indications for hospitalization of patients with hyperkalemia.Arch Intern Med.2000;160(11):1605–1611.
- Hyperkalemia: a potentially lethal clinical condition.Acta Clin Croat.2001;40(3):215–225.
- Hypokalemia and hyperkalemia.Med Clin North Am.1997;81(3):611–639.
- ,,,,Hyperkalemia in patients in hospital.BMJ.1983;286(6372):1189–1192.
- ,,,Treatment of electrolyte disorders in adult patients in the intensive care unit.Am J Health Syst Pharm.2005;62(16):1663–1682.
- ,,,Hyperkalemia revisited.Tex Heart Inst J.2006;33(1):40–47.
- Disorders of potassium homeostasis.Hypokalemia and hyperkalemia. Crit Care Clin.2002;18(2):273–288.
- ,,,Drug‐induced hyperkalemia.Medicine.1985;64(6):357–370.
- ,Therapeutic approach to hyperkalemia.Nephron.2002;92(1):33–40.
- ,Controversial issues in the treatment of hyperkalemia.Nephrol Dial Transplant.2003;18(11):2215–2218.
- Sodium polystyrene sulfonate: a cation exchange resin used in treating hyperkalemia.ANNA J.1993;20(1):93–95.
- ,,,,,Effect of single dose resin‐cathartic therapy on serum potassium concentration in patients with end‐stage renal disease.J Am Soc Nephrol.1998;9(10):1924–1930.
- ,,Treatment of the oliguric patient with a new sodium‐exchange resin and sorbitol, a preliminary report.N Engl J Med.1961;264(3):111–115.
- ,,Management of hyperkalemia with a cation exchange resin.N Engl J Med.1961;264(3):115–119.
- ,Sodium polystyrene sulfonate dosing guidelines for the treatment of adult hyperkalemia.Hosp Pharm.2004;39(8):765–771.
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