Robert E Burke1,2,3, Edward Hess3,4, Anna E Barón4, Cari Levy3,5, Jacques D Donzé6,7. 1. Research and Hospital Medicine Sections, Department of Medicine, Denver Veterans Affairs Medical Center, Denver, Colorado. 2. Division of General Internal Medicine, Department of Medicine, School of Medicine, University of Colorado, Aurora, Colorado. 3. Denver-Seattle Center of Innovation for Veteran-Centered and Value-Driven Care, Denver Veterans Affairs Medical Center, Denver, Colorado. 4. Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado, Aurora, Colorado. 5. Division of Health Care Policy and Research, Department of Medicine, School of Medicine, University of Colorado, Aurora, Colorado. 6. Division of General Internal Medicine, Bern University Hospital, Bern, Switzerland. 7. Division of General Medicine, Brigham and Women's Hospital, Boston, Massachusetts.
Abstract
OBJECTIVES: To derive a risk prediction score for potential adverse outcomes in older adults transitioning to a skilled nursing facility (SNF) from the hospital. DESIGN: Retrospective analysis. SETTING: Medicare Current Beneficiary Survey (2003-11). PARTICIPANTS: Previously community-dwelling Medicare beneficiaries who were hospitalized and discharged to SNF for postacute care (N=2,043). MEASUREMENTS: Risk factors included demographic characteristics, comorbidities, health status, hospital length of stay, prior SNF stays, SNF size and ownership, treatments received, physical function, and active signs or symptoms at time of SNF admission. The primary outcome was a composite of undesirable outcomes from the patient perspective, including hospital readmission during the SNF stay, long SNF stay (≥100 days), and death during the SNF stay. RESULTS: Of the 2,043 previously community-dwelling beneficiaries hospitalized and discharged to a SNF for post-acute care, 589 (28.8%) experienced one of the three outcomes, with readmission (19.4%) most common, followed by mortality (10.5%) and long SNF stay (3.5%). A risk score including 5 factors (Barthel Index, Charlson-Deyo comorbidity score, hospital length of stay, heart failure diagnosis, presence of an indwelling catheter) demonstrated very good discrimination (C-statistic=0.75), accuracy (Brier score=0.17), and calibration for observed and expected events. CONCLUSION: Older adults frequently experience potentially adverse outcomes in transitions to a SNF from the hospital; this novel score could be used to better match resources with patient risk.
OBJECTIVES: To derive a risk prediction score for potential adverse outcomes in older adults transitioning to a skilled nursing facility (SNF) from the hospital. DESIGN: Retrospective analysis. SETTING: Medicare Current Beneficiary Survey (2003-11). PARTICIPANTS: Previously community-dwelling Medicare beneficiaries who were hospitalized and discharged to SNF for postacute care (N=2,043). MEASUREMENTS: Risk factors included demographic characteristics, comorbidities, health status, hospital length of stay, prior SNF stays, SNF size and ownership, treatments received, physical function, and active signs or symptoms at time of SNF admission. The primary outcome was a composite of undesirable outcomes from the patient perspective, including hospital readmission during the SNF stay, long SNF stay (≥100 days), and death during the SNF stay. RESULTS: Of the 2,043 previously community-dwelling beneficiaries hospitalized and discharged to a SNF for post-acute care, 589 (28.8%) experienced one of the three outcomes, with readmission (19.4%) most common, followed by mortality (10.5%) and long SNF stay (3.5%). A risk score including 5 factors (Barthel Index, Charlson-Deyo comorbidity score, hospital length of stay, heart failure diagnosis, presence of an indwelling catheter) demonstrated very good discrimination (C-statistic=0.75), accuracy (Brier score=0.17), and calibration for observed and expected events. CONCLUSION: Older adults frequently experience potentially adverse outcomes in transitions to a SNF from the hospital; this novel score could be used to better match resources with patient risk.
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