Development of a 30-Day Readmission Risk Calculator for the Inpatient Rehabilitation Setting |
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| Affiliation: | 1. Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, MA, USA;2. Department of Physical Medicine and Rehabilitation, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA;3. Surgical Services, Shriners Hospitals for Children, Boston, MA, USA;4. Sumner Redstone Burn Center, Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA;1. Cecil G. Sheps Center for Health Services Research and Department of Family Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA;2. Cecil G. Sheps Center for Health Services Research and Schools of Social Work and Public Health, University of North Carolina at Chapel Hill, NC, USA;1. Rory Meyers College of Nursing, New York University, New York, NY, USA;2. Graduate School of Social Work, University of Denver, Denver, CO, USA;1. University La Salle, Canoas, Rio Grande do Sul, Brazil;2. Autoimmune Diseases Laboratory, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Rio Grande do Sul, Brazil;3. Division of Rheumatology, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Rio Grande do Sul, Brazil;4. Medical School, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Rio Grande do Sul, Brazil;5. Methodist University Center, Porto Alegre, Rio Grande do Sul, Brazil;6. Postgraduate Program in Health and Human Development, La Salle University, Canoas, Rio Grande do Sul, Brazil;1. Duke University School of Nursing, Durham, NC, USA;2. Geriatric Research, Education, and Clinical Center, Durham Veterans Affairs Health Care System, Durham, NC, USA;3. University of Colorado School of Medicine, Aurora, CO, USA;4. Denver-Seattle Center of Innovation for Veteran-Centered and Value-Driven Care, Rocky Mountain Regional Veterans Affairs Medical Center, Aurora, CO, USA;1. Aging Clinical Research, Department II of Internal Medicine and Center for Molecular Medicine Cologne, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany;2. Center for Molecular Medicine, University of Cologne, Cologne, Germany;3. Institute of Medical Statistics and Computational Biology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany;4. CECAD, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany |
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| Abstract: | ObjectivesReadmission to acute care from the inpatient rehabilitation facility (IRF) setting is potentially preventable and an important target of quality improvement and cost savings. The objective of this study was to develop a risk calculator to predict 30-day all-cause readmissions from the IRF setting.DesignRetrospective database analysis using the Uniform Data System for Medical Rehabilitation (UDSMR) from 2015 through 2019.Setting and ParticipantsIn total, 956 US inpatient rehabilitation facilities and 1,849,768 IRF discharges comprising patients from 14 impairment groups.MethodsLogistic regression models were developed to calculate risk-standardized 30-day all-cause hospital readmission rates for patients admitted to an IRF. Models for each impairment group were assessed using 12 common clinical and demographic variables and all but 4 models included various special variables. Models were assessed for discrimination (c-statistics), calibration (calibration plots), and internal validation (bootstrapping). A readmission risk scoring system was created for each impairment group population and was graphically validated.ResultsThe mean age of the cohort was 68.7 (15.2) years, 50.7% were women, and 78.3% were Caucasian. Medicare was the primary payer for 73.1% of the study population. The final models for each impairment group included between 4 and 13 total predictor variables. Model c-statistics ranged from 0.65 to 0.70. There was good calibration represented for most models up to a readmission risk of 30%. Internal validation of the models using bootstrap samples revealed little bias. Point systems for determining risk of 30-day readmission were developed for each impairment group.Conclusions and ImplicationsMultivariable risk factor algorithms based upon administrative data were developed to assess 30-day readmission risk for patients admitted from IRF. This report represents the development of a readmission risk calculator for the IRF setting, which could be instrumental in identifying high risk populations for readmission and targeting resources towards a diverse group of IRF impairment groups. |
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| Keywords: | Readmission rehabilitation patient outcomes calibration risk calculator |
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