High-Risk Prescribing and Opioid Overdose: Prospects for Prescription Drug Monitoring Program Based Proactive Alerts
In order to develop a simple, valid model to identify patients at high risk for opioid overdose-related hospitalization and mortality Oregon PDMP, Vital Records, and Hospital Discharge data were linked to estimate two logistic models; A first model that included a broad range of risk factors from the literature and a second simplified model. ROC curves, sensitivity and specificity of the models were analyzed. Variables retained in the final model were age categories over 35, number of prescribers, number of pharmacies, and prescriptions for long acting opioids, benzodiazepines/sedatives, or carisoprodol. The ability of the model to discriminate between patients who did and did not overdose was reasonably good (AUC = .82, Nagelkerke R2 = .11). The positive predictive value of the model was low. Computationally simple models can identify high risk patients based on prescription history alone, but improvement of the predictive value of models may require information from outside the PDMP. Patient or prescription features that predict opioid overdose may differ from those that predict diversion.
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Geissert, P., Hallvik, S., Van Otterloo, J., O'Kane, N., Alley, L., Carson, J., ... & Deyo, R. A. (2017). High-risk prescribing and opioid overdose: prospects for prescription drug monitoring program based proactive alerts. Pain.