Clin Transl Sci. 2026 Aug;19(8):e70690. doi: 10.1111/cts.70690.
ABSTRACT
Identification of modifiable risk factors for prescription opioid use disorder (OUD)-related emergency department (ED) visits (ICD-10 F11.xx) is a clinical priority; however, most published models remain cross-sectional and lack pharmacogenomic (PGx) enrichment or dual-method feature confirmation. Using Virginia All-Payer Claims Database (APCD) data (2016-2019; 6,929,576 patients), we analyzed prescription OUD-related ED visits ( n = 1,505,138 cases; 5:1 matched controls) across seven age bands. CatBoost, XGBoost, and XGBoost-RF ensembles were trained within four utilization-density strata (25-split MCCV; 2016-2018 training; 2019 holdout) using PGx burden and pre-index temporal dynamics. Risk features were identified via a Consensus Filter (SHAP ∩ FFA) requiring SHAP values ≥ 75th percentile and FFA rule support ≥ 0.05. Dynamic Time Warping (DTW) characterized pre-index trajectories. On the 2019 low-density holdout, PR-AUC lift over prevalence ranged from 2.3× to 3.4×; the 25-44 band achieved an AUROC of 0.800 and 3.3× lift ( n = 4942 holdout cases). Top Consensus-Risk features included gabapentin, long-term opioid use (Z79.891), and pgx_num_drugs. DTW yielded three utilization archetypes in the 25-44 band ( n = 59,813), with a mean pre-index time-to-target of 6.8 months. While SHAP/FFA specify how to intervene (medication and care targets), DTW specifies when to act (surveillance window). These findings provide an observational risk-attribution framework for Consensus-Risk priority features and pre-index trajectory windows in claims-based prescription OUD-related ED care rather than identified causal treatment effects. We maintain the association-versus-causation distinction to support safer clinical use and preserve a path to prospective causal validation.
PMID:42543496 | DOI:10.1111/cts.70690
