High-risk prescribing and opioid overdose: Prospects for prescription drug monitoring program-based proactive alerts

Peter Geissert, Sara Hallvik, Joshua Van Otterloo, Nicole O'Kane, Lindsey Alley, Jody Carson, Gillian Leichtling, Christi Hildebran, Wayne Wakeland, Richard A. Deyo

Research output: Contribution to journalArticlepeer-review

32 Scopus citations


To develop a simple, valid model to identify patients at high risk of opioid overdose-related hospitalization and mortality, Oregon prescription drug monitoring program, Vital Records, and Hospital Discharge data were linked to estimate 2 logistic models; a first model that included a broad range of risk factors from the literature and a second simplified model. Receiver operating characteristic curves, sensitivity, and specificity of the models were analyzed. Variables retained in the final model were categories such as older than 35 years, number of prescribers, number of pharmacies, and prescriptions for long-acting opioids, benzodiazepines or sedatives, or carisoprodol. The ability of the model to discriminate between patients who did and did not overdose was reasonably good (area under the receiver operating characteristic curve = 0.82, Nagelkerke R 2 = 0.11). 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 prescription drug monitoring program. Patient or prescription features that predict opioid overdose may differ from those that predict diversion.

Original languageEnglish (US)
Pages (from-to)150-156
Number of pages7
Issue number1
StatePublished - Jan 1 2018


  • Addiction medicine
  • Chronic pain management
  • Opioid overdose
  • Opioid prescription
  • Predictive modeling

ASJC Scopus subject areas

  • Neurology
  • Clinical Neurology
  • Anesthesiology and Pain Medicine


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