U.S. flag

An official website of the United States government, Department of Justice.

NCJRS Virtual Library

The Virtual Library houses over 235,000 criminal justice resources, including all known OJP works.
Click here to search the NCJRS Virtual Library

Performance of a Predictive Model versus Prescription-Based Thresholds in Identifying Patients at Risk of Fatal Opioid Overdose

NCJ Number
302456
Journal
Substance Use & Misuse Volume: 56 Issue: 3 Dated: January 2021 Pages: 396-403
Author(s)
Lindsey M. Ferris; Brendan Saloner; Kate Jackson ; B. Casey Lyons ; Vijay Murthy ; Hadi Kharrazi ; Amanda Latimore ; Elizabeth A. Stuart ; Jonathan P. Weiner
Date Published
January 2021
Length
8 pages
Annotation

Since Prescription Drug Monitoring Programs (PDMPs) have not been compared on their ability to identify patients at highest risk for fatal opioid overdose, this article reported on a retrospective analysis that used statewide PDMP for Maryland residents ages 18 to 80 years old with an opioid fill between April to June 2015.

Abstract

The outcome was opioid-related overdose death in 2015 or 2016. A multivariable logistic regression model and three PDMP thresholds were evaluated: (1) multiple provider episodes; (2) high daily average morphine milligram equivalents (MME); and (3) overlapping opioid and benzodiazepine prescriptions. The validation cohort consisted of 170,433 individuals and 244 deaths. The predictive model captured more individuals who died (46.3 percent of total deaths) and had a higher death rate (7.12 per 1000) when the risk score cutoff (0.0030) was selected for a comparable size of high-risk individuals (n = 15,881) than those meeting the overlapping opioid/benzodiazepine prescriptions (n = 17,440; 33.2 percent of total deaths; 4.64 deaths per 1000) and high MME (n = 14,675; 24.6 percent of total deaths; 4.09 deaths per 1000) thresholds. The predictive model identified more individuals at risk of fatal opioid overdose compared with PDMP thresholds commonly used for unsolicited reporting. PDMP programs could improve their targeting of unsolicited reports to reach more individuals at risk of overdose by using predictive models instead of simple threshold-based approaches. (publisher abstract modified)

Downloads