Predicting Functional Decline and Recovery for Residents in Veterans Affairs Nursing Homes.

PURPOSE OF THE STUDY: This article describes methods and accuracy of predicting change in activities of daily living (ADLs) for nursing home patients following hospitalization. DESIGN AND METHODS: Electronic Health Record data for 5,595 residents of Veterans Affairs' (VAs') Community Living Centers (CLCs) aged 70 years and older were analyzed within the VA Informatics and Computing Infrastructure. Data included diagnoses from 7,106 inpatient records, 21,318 functional status evaluations, and 69,140 inpatient diagnoses. The Barthel Index extracted from CLC's Minimum Data Set was used to assess ADLs loss and recovery. Patients' diagnoses on hospital admission, ADL status prior to hospitalization, age, and gender were used alone or in combination to predict ADL loss/gain following hospitalization. Area under the Receiver-Operator Curve (AUC) was used to report accuracy of predictions in short (14 days) and long-term (15-365 days) follow-up post-hospitalization.
RESULTS: Admissions fell into 7 distinct patterns of recovery and loss: early recovery 19%, delayed recovery 9%, delayed recovery after temporary decline 9%, early decline 29%, delayed decline 10%, delayed decline after temporary recovery 6%, and no change 18%. Models accurately predicted ADL's 14-day post-hospitalization (AUC for bathing 0.917, bladder 0.842, bowels 0.875, dressing 0.871, eating 0.867, grooming 0.902, toileting 0.882, transfer 0.852, and walking deficits was 0.882). Accuracy declined but remained relatively high when predicting 14-365 days post-hospitalization (AUC ranging from 0.798 to 0.875). IMPLICATIONS: Predictive modeling may allow development of more personalized predictions of functional loss and recovery after hospitalization among nursing home patients. Published by Oxford University Press on behalf of the Gerontological Society of America 2015.