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

Abstract

Background: Prescription Drug Monitoring Programs (PDMPs) collect controlled substance prescriptions dispensed within a state. Many PDMP programs perform targeted outreach (i.e., “unsolicited reporting”) for patients who exceed numerical thresholds, however, the degree to which patients at highest risk of fatal opioid overdose are identified has not been compared with one another or with a predictive model. Methods: A retrospective analysis was performed using statewide PDMP data for Maryland residents aged 18 to 80 years with an opioid fill between April to June 2015. 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. Results: The validation cohort consisted of 170,433 individuals and 244 deaths. The predictive model captured more individuals who died (46.3% 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% of total deaths; 4.64 deaths per 1000) and high MME (n = 14,675; 24.6% of total deaths; 4.09 deaths per 1000) thresholds. Conclusions: The predictive model identified more individuals at risk of fatal opioid overdose as 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.

Keywords:

• Analgesics
• opioid
• overdose
• public health
• prescription drug monitoring programs/standards
• logistic models
• retrospective studies

Declaration of interest

The authors report no conflicts of interest.

Acknowledgments

We would like to acknowledge the Maryland Department of Health for their leadership and guidance in bring together the State datasets used in this study. Our deepest gratitude goes to the state agencies that contributed data that made this research possible, including the Maryland Prescription Drug Monitoring Program, Office of the Chief Medical Examiner, and Department of Public Safety and Correctional Services. Points of view or opinions in this document are those of the author and do not necessarily represent the official position or policies of the U.S. Department of Justice, the Maryland Department of Health, or any of the State of Maryland agencies who made their data available for this study. We are also thankful for the support from the CRISP health information exchange for providing patient matching services.

Additional information

Funding

This work was supported by the Bureau of Justice Assistance under Grant No. 2015-PM-BX-K002. The Bureau of Justice Assistance is a component of the Department of Justice's Office of Justice Programs, which also includes the Bureau of Justice Statistics, the National Institute of Justice, the Office of Juvenile Justice and Delinquency Prevention, the Office for Victims of Crime, and the SMART Office. Points of view or opinions in this document are those of the author and do not necessarily represent the official position or policies of the U.S. Department of Justice. The study sponsors had no role in determining study design; data collection, analysis, or interpretation; writing the report; or the decision to submit the report for publication. No other financial disclosures were reported by the authors of this paper.