title: Predicting Functional Decline and Recovery for Residents in Veterans Affairs Nursing Homes.
abstract: OBJECTIVE
  This article describes methods and accuracy of predicting change in activities of daily living (ADLs) for nursing home patients following hospitalization.
  
  
  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).
  
  
  CONCLUSIONS
  Predictive modeling may allow development of more personalized predictions of functional loss and recovery after hospitalization among nursing home patients.
authors.affiliation: Department of Health Administration and Policy, George Mason University, Fairfax, Virginia.
authors.forename: Janusz
authors.initials: J
authors.lastname: Wojtusiak
authors.affiliation: Department of Internal Medicine, Palliative Care, Veterans Affairs Medical Center Eastern Colorado Health Care System, Denver.
authors.forename: Cari R
authors.initials: CR
authors.lastname: Levy
authors.affiliation: Department of Research, Bay Pines Veterans Affairs Healthcare System, Bay Pines, Florida. Allison.williams3@va.gov.
authors.forename: Allison E
authors.initials: AE
authors.lastname: Williams
authors.affiliation: Department of Health Administration and Policy, George Mason University, Fairfax, Virginia. Office of Chief of Staff, District of Columbia Veterans Affairs Medical Center, Washington, DC.
authors.forename: Farrokh
authors.initials: F
authors.lastname: Alemi
mesh_terms: D000203:Activities of Daily Living / Q000523:psychology*; D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D015577:Geriatric Assessment / Q000379:methods*; D006801:Humans; D008297:Male; D009735:Nursing Homes; D012648:Self Care / Q000706:statistics & numerical data*; D014481:United States; D014493:United States Department of Veterans Affairs; D014728:Veterans / Q000523:psychology*; D016138:Walking / Q000502:physiology*
keywords: Activities of daily living; Community living centers; Data analysis; Hospitalization; Predictive modeling