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Serious Falls in Middle‐Aged Veterans: Development and Validation of a Predictive Risk Model
Author(s) -
Womack Julie A.,
Murphy Terrence E.,
Bathulapalli Harini,
Smith Alexandria,
Bates Jonathan,
Jarad Samah,
Redeker Nancy S.,
Luther Stephen L.,
Gill Thomas M.,
Brandt Cynthia A.,
Justice Amy C.
Publication year - 2020
Publication title -
journal of the american geriatrics society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.992
H-Index - 232
eISSN - 1532-5415
pISSN - 0002-8614
DOI - 10.1111/jgs.16773
Subject(s) - medicine , polypharmacy , alcohol use disorders identification test , medical prescription , logistic regression , veterans affairs , poison control , injury prevention , occupational safety and health , emergency medicine , pathology , pharmacology
BACKGROUND/OBJECTIVES Due to high rates of multimorbidity, polypharmacy, and hazardous alcohol and opioid use, middle‐aged Veterans are at risk for serious falls (those prompting a visit with a healthcare provider), posing significant risk to their forthcoming geriatric health and quality of life. We developed and validated a predictive model of the 6‐month risk of serious falls among middle‐aged Veterans. DESIGN Cohort study. SETTING Veterans Health Administration (VA). PARTICIPANTS Veterans, aged 45 to 65 years, who presented for care within the VA between 2012 and 2015 (N = 275,940). EXPOSURES The exposures of primary interest were substance use (including alcohol and prescription opioid use), multimorbidity, and polypharmacy. Hazardous alcohol use was defined as an Alcohol Use Disorders Identification Test ‐ Consumption (AUDIT‐C) score of 3 or greater for women and 4 or greater for men. We used International Classification of Diseases, Ninth Revision ( ICD‐9 ), codes to identify alcohol and illicit substance use disorders and identified prescription opioid use from pharmacy fill‐refill data. We included counts of chronic medications and of physical and mental health comorbidities. MEASUREMENTS We identified serious falls using external cause of injury codes and a machine‐learning algorithm that identified serious falls in radiology reports. We used multivariable logistic regression with general estimating equations to calculate risk. We used an integrated predictiveness curve to identify intervention thresholds. RESULTS Most of our sample (54%) was aged 60 years or younger. Duration of follow‐up was up to 4 years. Veterans who fell were more likely to be female (11% vs 7%) and White (72% vs 68%). They experienced 43,641 serious falls during follow‐up. We identified 16 key predictors of serious falls and five interaction terms. Model performance was enhanced by addition of opioid use, as evidenced by overall category‐free net reclassification improvement of 0.32 ( P < .001). Discrimination (C‐statistic = 0.76) and calibration were excellent for both development and validation data sets. CONCLUSION We developed and internally validated a model to predict 6‐month risk of serious falls among middle‐aged Veterans with excellent discrimination and calibration.