A Joint Survival Modeling and Therapy Knowledge Graph Framework to Characterize Opioid Use Disorder Trajectories

TL;DR

This study develops a multi-stage modeling framework using EHR and survey data to understand opioid use disorder trajectories, identifying key risk factors and linking them to potential treatments via a comprehensive therapy knowledge graph.

Contribution

It introduces a novel multi-stage survival analysis framework combined with a therapy knowledge graph to characterize OUD progression and inform treatment prioritization.

Findings

Chronic pain, mental health, and polysubstance use are key predictors across stages.

Tobacco dependence during remission predicts transition to remission.

The therapy knowledge graph links risk factors to candidate treatments.

Abstract

Motivation: Opioid use disorder (OUD) often arises after prescription opioid exposure and follows transitions among onset, remission, and relapse. Linked EHR-survey resources such as the All of Us Research Program enable stage-specific risk modeling and connection to intervention options. Results: We built a multi-stage framework to model time-to-onset, time-to-remission, and time-to-relapse after remission using All of Us EHR and survey data. For each participant we derived longitudinal predictors from clinical conditions and survey concepts, including recent (1/3/12-month) event counts, cumulative exposures, and time since last event. We fit regularized Cox models for each transition and aggregated selection frequencies and hazard ratios to identify a compact set of high-confidence predictors. Pain, mental health, and polysubstance use contributed across stages: chronic pain syndromes, tobacco/nicotine dependence, anxiety and depressive disorders, and cannabis dependence prominently predicted onset and relapse, whereas tobacco dependence during remission and other remission-coded conditions were strongly associated with transition to remission. To support therapeutic prioritization, we constructed a therapy knowledge graph integrating genetic targets, biological pathways, and published evidence to map identified risk factors to candidate treatments in recent OUD studies and clinical guidelines.