ArticleClinical and translational science2026
A SHAP-Informed Formal Feature Attribution Framework for Drug-Drug Interaction Risk in Large-Scale Claims Data.
Article in Clinical and translational science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Pairwise drug-drug interaction databases flag co-prescribed pairs, but they under-weight multi-drug combinations that drive adverse drug events in older adults. We studied that gap in Virginia All-Payer Claims Database records (2016-2019) for adults aged 65-114 years with non-opioid emergency department visits (n = 1182 cases; 16,105 matched controls across three geriatric age bands). Features came from pharmacy and medical claims in a short pre-index window optimized for acute ADE timing (21-30 days by age band; 21 days for ages 65-84). We trained gradient-boosting models separately among patients with similar pre-index healthcare contact volume (2016-2018 training; 2019 holdout), then used Formal Feature Attribution to score drug pairs and triplets and Intervention Rate ranks to order deprescribing review. On the 2019 holdout, geriatric AUPRC was 0.101-0.335 (PR lift 1.6×-4.2×). FFA flagged 115 synergistic pairs and 312 high-confidence triplets (e.g., furosemide + hydrochlorothiazide + lisinopril; digoxin + furosemide + amiodarone, IE = 8.7). Top Intervention Rate drugs included simvastatin, furosemide, and alprazolam. Moderate preventive Z-code monitoring (Q2) was protective (OR = 0.25; 95% CI 0.18-0.34) versus no monitoring, while fragmented high-intensity monitoring (Q4) was not. The framework prioritizes medication combinations for pharmacist review in claims data; it does not replace pharmacokinetic confirmation or prove that changing a drug caused fewer ED visits.
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