Serious Falls in Middle‐Aged Veterans: Development and Validation of a Predictive Risk Model

• Published in: Journal of the American Geriatrics Society (JAGS) Vol. 68; no. 12; pp. 2847 - 2854
• Main Authors: 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.
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01-12-2020; Wiley Subscription Services, Inc
• Subjects: Accidental Falls - statistics & numerical data; Alcohol use; Algorithms; Body Mass Index; Cohort Studies; Comorbidity; Comorbidity - trends; Drug use; Female; Humans; Learning algorithms; Machine learning; Male; middle age; Middle Aged; Narcotics; Opioids; Polypharmacy; Prediction models; Quality of Life; Reclassification; Reproducibility of Results; Risk Assessment; serious falls; Sex Factors; Substance-Related Disorders - epidemiology; United States; United States Department of Veterans Affairs; Veterans; Veterans - statistics & numerical data; United States; Veterans; serious falls; middle age
• ISSN: 0002-8614; 1532-5415
• DOI: 10.1111/jgs.16773

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.