Functional Status Outperforms Comorbidities in Predicting Acute Care Readmissions in Medically Complex Patients

Shirley L. Shih, Paul Gerrard, Richard Goldstein, Jacqueline Mix, Colleen M. Ryan, Paulette Niewczyk, Lewis Kazis, Jaye Hefner, D. Clay Ackerly, Ross Zafonte, Jeffrey C. Schneider

To examine functional status versus medical comorbidities as predictors of acute care readmissions in medically complex patients. Retrospective database study. U.S. inpatient rehabilitation facilities. Subjects included 120,957 patients in the Uniform Data System for Medical Rehabilitation admitted to inpatient rehabilitation facilities under the medically complex impairment group code between 2002 and 2011. A Basic Model based on gender and functional status was developed using logistic regression to predict the odds of 3-, 7-, and 30-day readmission from inpatient rehabilitation facilities to acute care hospitals. Functional status was measured by the FIM(®) motor score. The Basic Model was compared to six other predictive models-three Basic Plus Models that added a comorbidity measure to the Basic Model and three Gender-Comorbidity Models that included only gender and a comorbidity measure. The three comorbidity measures used were the Elixhauser index, Deyo-Charlson index, and Medicare comorbidity tier system. The c-statistic was the primary measure of model performance. We investigated 3-, 7-, and 30-day readmission to acute care hospitals from inpatient rehabilitation facilities. Basic Model c-statistics predicting 3-, 7-, and 30-day readmissions were 0.69, 0.64, and 0.65, respectively. The best-performing Basic Plus Model (Basic+Elixhauser) c-statistics were only 0.02 better than the Basic Model, and the best-performing Gender-Comorbidity Model (Gender+Elixhauser) c-statistics were more than 0.07 worse than the Basic Model. Readmission models based on functional status consistently outperform models based on medical comorbidities. There is opportunity to improve current national readmission risk models to more accurately predict readmissions by incorporating functional data.