Advanced models for improved prediction of opioid-related overdose and suicide events among Veterans using administrative healthcare data.

ABSTRACT: Veterans suffer disproportionate health impacts from the opioid epidemic, including overdose, suicide, and death. Prediction models based on electronic medical record data can be powerful tools for identifying patients at greatest risk of such outcomes. The Veterans Health Administration implemented the Stratification Tool for Opioid Risk Mitigation (STORM) in 2018. In this study we propose changes to the original STORM model and propose alternative models that improve risk prediction performance. The best of these proposed models uses a multivariate generalized linear mixed modeling (mGLMM) approach to produce separate predictions for overdose and suicide-related events (SRE) rather than a single prediction for combined outcomes. Further improvements include incorporation of additional da

SUBMITTER: Ward R 

PROVIDER: S-EPMC8561350 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature