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Performance of a Predictive Model versus Prescription-Based Thresholds in Identifying Patients at Risk of Fatal
Lindsey M Ferris1,2, Brendan Saloner1,3, Kate Jackson4
1Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Prescription Drug Monitoring Programs (PDMPs) can better identify patients at high risk of fatal opioid overdose by using a predictive model instead of simple numerical thresholds. This approach identifies more at-risk individuals for targeted interventions.
Area of Science:
- Public Health
- Pharmacology
- Data Science
Background:
- Prescription Drug Monitoring Programs (PDMPs) track controlled substance prescriptions.
- Current PDMP outreach often relies on numerical thresholds, but their effectiveness in identifying high-risk individuals for fatal opioid overdose is unclear.
Purpose of the Study:
- To compare the effectiveness of a predictive model versus common PDMP thresholds in identifying individuals at high risk of fatal opioid overdose.
Main Methods:
- Retrospective analysis of Maryland PDMP data (April-June 2015).
- Evaluated a multivariable logistic regression model and three PDMP thresholds: multiple providers, high MME, and overlapping opioid/benzodiazepine prescriptions.
- Outcome was opioid-related overdose death in 2015-2016.
Main Results:
- The predictive model identified 46.3% of total deaths, with a death rate of 7.12 per 1000, for a comparable number of high-risk individuals.
- Overlapping opioid/benzodiazepine prescriptions identified 33.2% of deaths (4.64 per 1000).
- High MME thresholds identified 24.6% of deaths (4.09 per 1000).
Conclusions:
- A predictive model is more effective than common PDMP thresholds in identifying individuals at risk of fatal opioid overdose.
- PDMPs can enhance unsolicited reporting by adopting predictive models to better target at-risk populations.
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