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Statistical Models and Patient Predictors of Readmission for Heart FailureA Systematic Review
Joseph S. Ross, MD, MHS;
Gregory K. Mulvey, BA;
Brett Stauffer, MD;
Vishnu Patlolla, MD, MPH;
Susannah M. Bernheim, MD, MHS;
Patricia S. Keenan, PhD;
Harlan M. Krumholz, MD, SM
Arch Intern Med. 2008;168(13):1371-1386.
Background Readmission after heart failure (HF) hospitalization is an increasing focus for physicians and policy makers, but statistical models are needed to assess patient risk and to compare hospital performance. We performed a systematic review to describe models designed to compare hospital rates of readmission or to predict patients' risk of readmission, as well as to identify studies evaluating patient characteristics associated with hospital readmission, all among patients admitted for HF.
Methods We identified relevant studies published between January 1, 1950, and November 19, 2007, by searching MEDLINE, Scopus, PsycINFO, and all 4 Ovid Evidence-Based Medicine Reviews. Eligible English-language publications reported on readmission after HF hospitalization among adult patients. We excluded experimental studies and publications without original data or quantitative outcomes.
Results From 941 potentially relevant articles, 117 met inclusion criteria: none contained models to compare readmission rates among hospitals, 5 (4.3%) presented models to predict patients' risk of readmission, and 112 (95.7%) examined patient characteristics associated with readmission. Studies varied in case identification, used multiple types of data sources, found few patient characteristics consistently associated with readmission, and examined differing outcomes, often either readmission alone or a combined outcome of readmission or death, measured across varying periods (from 14 days to 4 years). Two articles reported model discriminations of patient readmission risk, both of which were modest (C statistic, 0.60 for both).
Conclusions Our systematic review identified no model designed to compare hospital rates of readmission, while models designed to predict patients' readmission risk used heterogeneous approaches and found substantial inconsistencies regarding which patient characteristics were predictive. Clinically, patient risk stratification is challenging. From a policy perspective, a validated risk-standardized statistical model to accurately profile hospitals using readmission rates is unavailable in the published English-language literature to date.
Author Affiliations: Departments of Geriatrics and Adult Development and Medicine, Mount Sinai School of Medicine, New York, and Health Services Research and Development Targeted Research Enhancement Program and Geriatrics Research, Education, and Clinical Center, James J. Peters Veterans Administration Medical Center, Bronx, New York (Dr Ross); and Robert Wood Johnson Clinical Scholars Program and Department of Medicine (Mr Mulvey and Drs Stauffer and Krumholz), Sections of Cardiovascular Medicine (Drs Patlolla and Krumholz) and Geriatrics (Dr Bernheim), Department of Medicine, and Section of Health Policy and Administration, Department of Epidemiology and Public Health (Drs Keenan and Krumholz), Yale University School of Medicine, and Center for Outcomes Research and Evaluation, Yale–New Haven Hospital (Drs Krumholz), New Haven, Connecticut. Dr Bernheim is now with the Department of Internal Medicine, Yale University School of Medicine, Yale–New Haven Health System, New Haven, Connecticut.
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