RISK PREDICTION FOR UNPLANNED READMISSIONS FROM HOME HEALTH USING MACHINE LEARNING - Falvey, J (Innov Aging, 2018)
INTRODUCTION: Utilization of home health (HH) services among older Medicare beneficiaries has increased significantly over the last 10 years. Yet, clinicians are largely unaware of risk factors for readmission from HH. METHODS: This was a secondary analysis of 2012 and 2013 Medicare claims for patients >65 years old discharged to HH following acute hospitalization. The primary outcome was unplanned readmission within 30 days of hospital discharge. A series of models were created based on a starting pool of candidate variables that HH clinicians would have accessible using a combination of backward selection (logistic regression) and a machine learning technique (GBM). The models were fit using logistic regression and relative performance was assessed based on the optimism corrected C-index and Brier score. RESULTS: The analytic sample (n=50,919) was, on average, 79.1 years old, a majority female and Caucasian, and had a 4-day hospital length of stay (LOS). Nearly 15% (n=7,409) experienced a 30-day readmission. The initial ‘full’ model included 54 candidate variables with a C-statistic of 0.64 and a Brier score of 0.12. An 8-variable predictive model had similar performance (c=0.64, Brier index=0.12) with Charlson score, LOS, surgical wound status, and multiple hospitalizations having greatest relative importance. DISCUSSION: The results are among the first to provide a quick and effective screening tool to identify older adults discharged to HH at high risk for readmission. This tool could be used to direct resources towards higher risk patients, and provide a framework for interventions needed.