Predicting overdose among individuals prescribed opioids using routinely collected healthcare utilization data.

ABSTRACT:

Introduction

With increasing rates of opioid overdoses in the US, a surveillance tool to identify high-risk patients may help facilitate early intervention.

Objective

To develop an algorithm to predict overdose using routinely-collected healthcare databases.

Methods

Within a US commercial claims database (2011-2015), patients with ≥1 opioid prescription were identified. Patients were randomly allocated into the training (50%), validation (25%), or test set (25%). For each month of follow-up, pooled logistic regression was used to predict the odds of incident overdose in the next month based on patient history from the preceding 3-6 months (time-updated), using elastic net for variable selection. As secondary analyses, we explored whether using simpler models (few pred

SUBMITTER: Sun JW 

PROVIDER: S-EPMC7575098 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature