PREDICTING 30-DAY MORTALITY AND 30-DAY RE-HOSPITALIZATION RISKS IN MEDICARE PATIENTS WITH HEART FAILURE DISCHARGED TO SKILLED NURSING FACILITIES: DEVELOPMENT AND VALIDATION OF MODELS USING ADMINISTRATIVE DATA

Abstract

Despite the growing importance of skilled nursing facility care for Medicare patients hospitalized with heart failure, no risk prediction models for these patients exist. To develop and validate separate predictive models for 30-day all-cause mortality and 30-day all-cause re-hospitalization. Retrospective cohort study using a nationwide Medicare claims data cross-linked with Minimum Data Set 3.0. 11,529 skilled nursing facilities in the United States (2011-2013). 77,670 hospitalized heart failure patients discharged to skilled nursing facilities (randomly split into development (2/3) and validation (1/3) cohorts). Using data on patient sociodemographic and clinical characteristics, health service use, functional status, and facility-level factors, we developed separate prediction models for 30-day mortality and 30-day re-hospitalization using logistic regression models in the development cohort. Within 30 days, 6.8% died and 24.2% were re-hospitalized. Thirteen patient-level factors remained in the final model for 30-day mortality and 10 patient-level factors for re-hospitalization with good calibration. The area under receiver operating characteristic curves were 0.71 for 30-day mortality and 0.63 for re-hospitalization in the validation cohort. Among Medicare patients with heart failure discharged to skilled nursing facilities, predicting 30-day mortality and re-hospitalization using administrative data is challenging. Further work identifying factors for re-hospitalization remains needed.