Predictability of buprenorphine-naloxone treatment retention: A multi-site analysis combining electronic health records and machine learning.

ABSTRACT:

Background and aims

Opioid use disorder (OUD) and opioid dependence lead to significant morbidity and mortality, yet treatment retention, crucial for the effectiveness of medications like buprenorphine-naloxone, remains unpredictable. Our objective was to determine the predictability of 6-month retention in buprenorphine-naloxone treatment using electronic health record (EHR) data from diverse clinical settings and to identify key predictors.

Design

This retrospective observational study developed and validated machine learning-based clinical risk prediction models using EHR data.

Setting and cases

Data were sourced from Stanford University's healthcare system and Holmusk's NeuroBlu database, reflecting a wide range of healthcare settings. The study analyzed 1800 Stan

SUBMITTER: Nateghi Haredasht F 

PROVIDER: S-EPMC11891486 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature