OBJECTIVE: Large administrative datasets enable investigation of rare outcomes, but accurate individual-level prediction remains challenging. Emerging evidence suggests that glucagon-like peptide-1 receptor agonists (GLP-1 RAs), used to treat diabetes and obesity, may reduce alcohol craving and intake. We examined predictors of incident alcohol-related disorder (ARD) using inverse probability-weighted (IPW) machine learning models, focusing on GLP-1 RA use.
METHODS: We conducted a retrospective cohort study using the MarketScan Multi-State Medicaid Database (N = 96 460). Type 2 diabetic adults aged 18-64 years with GLP-1 RA prescription coverage (2022-2023) and no ARD in 2022 were included. Baseline variables included demographics, social risk factors (Z-codes), comorbidities, insurance characteristics, and GLP-1 RA use. Propensity scores were used to derive stabilized IPW weights to adjust for confounding in GLP-1 RA exposure. An Extreme Gradient Boosting model with IPW was trained using a 70/30 train-test split with five-fold cross-validation and probability calibration. Performance was evaluated using Area Under the Receiver Operating Characteristics Curve (AUROC), recall, and precision. Model interpretability was assessed using SHapley Additive exPlanations (SHAP).
RESULTS: GLP-1 RAs were used by 21.7% of individuals; 2.1% (N = 2004) developed incident ARD. The model demonstrated modest discrimination (AUROC = 0.67). Recall and precision were 0.28 and 0.05, respectively. SHAP analysis ranked GLP-1 RA use as the sixth most influential predictor and showed an association with lower predicted ARD risk.
CONCLUSION: Predicting rare outcomes remains difficult, particularly for identifying positive cases. Interpretable machine learning identified GLP-1 RA use as an important feature associated with lower predicted ARD risk, warranting further investigation.
