High-risk prescribing and opioid overdose: prospects for prescription drug monitoring program–based proactive alerts. Issue 1 (January 2018)

• Record Type: Journal Article
• Authors: Geissert, Peter; Hallvik, Sara; Van Otterloo, Joshua; O'Kane, Nicole; Alley, Lindsey; Carson, Jody; Leichtling, Gillian; Hildebran, Christi; Wakeland, Wayne; Deyo, Richard A.
• Is Part Of: Pain. Volume 159:Issue 1(2018)
• Publication Date: 2018-01
• Subjects: Chronic pain management -- Opioid prescription -- Opioid overdose -- Addiction medicine -- Predictive modeling; Pain -- Periodicals; Douleur -- Périodiques; Anesthésie -- Périodiques; Pain; Electronic journals; Periodicals; Electronic journals; 616.0472
• DOI: 10.1097/j.pain.0000000000001078
• Languages: English
• ISSNs: 0304-3959

Abstract

Abstract : Abstract: 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. Abstract : Supplemental Digital Content is Available in the Text.Computationally simple models can identify high-risk patients from prescription history alone, but improving specificity of models may require information from outside the PDMP.