• Amit Kumar, Amol M Karmarkar, James E Graham, Linda Resnik, Alai Tan, Anne Deutsch, and Kenneth J Ottenbacher.
• Department of Health Services, Policy and Practice, Brown University Providence, Rhode Island.
• J. Gerontol. A Biol. Sci. Med. Sci. 2017 Feb 1; 72 (2): 223-228.

BackgroundInformation regarding the association of comorbidity indices with readmission risk for older adults receiving postacute care is limited. The purpose of this study was to compare the discriminatory ability of five comorbidity indices in predicting 30-day all-cause hospital readmission following discharge to the community from postacute inpatient rehabilitation facilities (IRF).MethodsThe sample included Medicare fee-for-service beneficiaries with stroke, lower extremity joint replacement, and fracture, discharged from IRF in 2011 (N = 75,582). Logistic regression models were used to predict 30-day all-cause readmission. Impairment-specific base models included demographic characteristics and length of stay. Subsequent models included individual comorbidity indices: Tier, Charlson, Elixhauser, functional comorbidity index (FCI), and the hierarchical condition category (HCC). We then added discharge functional status to each model. Results were compared using C-statistics.ResultsThirty-day readmission rates following discharge from an IRF ranged from 6.5% (joint replacement) to 14% (stroke). The C-statistics were 0.53, 0.56, and 0.55 for the base models in the stroke, joint replacement, and fracture groups, respectively. Adding the Tier, Charlson, FCI, or Elixhauser variables increased the C-statistics by 0.03-0.07 across the three impairment categories. Adding the HCC increased the C-statistics by 0.06-0.09. With the addition of discharge functional status in the model, the C-statistics further increased by 0.06-0.09.ConclusionsComorbidity indices were weakly associated with 30-day readmission in older adults discharged from postacute inpatient rehabilitation. Adding patient-level functional status to the comorbidity indices further improved the discriminatory ability to predict readmission in our sample.© The Author 2016. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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