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

BACKGROUND: Despite the growing importance of skilled nursing facility care for Medicare patients hospitalized with heart failure, no risk prediction models for these patients exist., OBJECTIVES: To develop and validate separate predictive models for 30-day all-cause mortality and 30-day all-cause re-hospitalization., DESIGN: Retrospective cohort study using a nationwide Medicare claims data cross-linked with Minimum Data Set 3.0., SETTING: 11,529 skilled nursing facilities in the United States (2011-2013)., PARTICIPANTS: 77,670 hospitalized heart failure patients discharged to skilled nursing facilities (randomly split into development (2/3) and validation (1/3) cohorts)., MEASUREMENTS: 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., RESULTS: 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., CONCLUSIONS: 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.