School of Medicine Publications and Presentations
Document Type
Article
Publication Date
9-11-2023
Abstract
Background
Accurate identification of opioid overdose (OOD) cases in electronic healthcare record (EHR) data is an important element in surveillance, empirical research, and clinical intervention. We sought to improve existing OOD electronic phenotypes by incorporating new data types beyond diagnostic codes and by applying several statistical and machine learning methods.
Materials and Methods
We developed an EHR dataset of emergency department visits involving OOD cases or patients considered at risk for an OOD and ascertained true OOD status through manual chart reviews. We developed and validated prediction models using Random Forest, Extreme Gradient Boost, and Elastic Net models that incorporated 717 features involving primary and second diagnoses, chief complaints, medications prescribed, vital signs, laboratory results, and procedural codes. We also developed models limited to single data types.
Results
A total of 1718 records involving 1485 patients were manually reviewed; 541 (36.4%) patients had one or more OOD. Prediction performance was similar for all models; sensitivity varied from 94% to 97%; and area under the receiver operating characteristic curve (AUC) was 98% for all methods. The primary diagnosis and chief complaint were the most important contributors to AUC performance; primary diagnoses and medication class contributed most to sensitivity; chief complaint, primary diagnosis, and vital signs were most important for specificity. Models limited to decision support data types available in real time demonstrated robust prediction performance.
Conclusions
Substantial prediction performance improvements were demonstrated for identifying OODs in EHR data. Our e-phenotypes could be applied in surveillance, retrospective empirical applications, or clinical decision support systems.
Recommended Citation
Ralph Ward, Jihad S Obeid, Lindsey Jennings, Elizabeth Szwast, William Garrett Hayes, Royal Pipaliya, Cameron Bailey, Skylar Faul, Brianna Polyak, George Hamilton Baker, Jenna L McCauley, Leslie A Lenert, Enhanced phenotypes for identifying opioid overdose in emergency department visit electronic health record data, JAMIA Open, Volume 6, Issue 3, October 2023, ooad081, https://doi.org/10.1093/jamiaopen/ooad081
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Publication Title
JAMIA Open
DOI
10.1093/jamiaopen/ooad081
Academic Level
medical student
Comments
Copyright © 2023 The Author(s)
This article is available under the Creative Commons CC-BY-NC license and permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited.