Measuring problem prescription opioid use among patients receiving long-term opioid analgesic treatment: development and evaluation of an algorithm for use in EHR and claims data. (1st January 2020)

• Record Type: Journal Article
• Authors: Carrell, David S.; Albertson-Junkans, Ladia; Ramaprasan, Arvind; Scull, Grant; Mackwood, Matt; Johnson, Eric; Cronkite, David J.; Baer, Andrew; Hansen, Kris; Green, Carla A.; Hazlehurst, Brian L.; Janoff, Shannon L.; Coplan, Paul M.; DeVeaugh-Geiss, Angela; Grijalva, Carlos G.; Liang, Caihua; Enger, Cheryl L.; Lange, Jane; Shortreed, Susan M.; Von Korff, Michael
• Is Part Of: Journal of drug assessment. Volume 9(2020)Supplement 1
• Page Start: 97
• Page End: 105
• Publication Date: 2020-01-01
• Subjects: Algorithms -- electronic health records -- opioid-related disorders -- population surveillance; Drugs -- Testing -- Periodicals; 615.1901
• DOI: 10.1080/21556660.2020.1750419
• Languages: English
• ISSNs: 2155-6660

Abstract

Abstract: Objective: Opioid surveillance in response to the opioid epidemic will benefit from scalable, automated algorithms for identifying patients with clinically documented signs of problem prescription opioid use. Existing algorithms lack accuracy. We sought to develop a high-sensitivity, high-specificity classification algorithm based on widely available structured health data to identify patients receiving chronic extended-release/long-acting (ER/LA) therapy with evidence of problem use to support subsequent epidemiologic investigations. Methods: Outpatient medical records of a probability sample of 2, 000 Kaiser Permanente Washington patients receiving ≥60 days' supply of ER/LA opioids in a 90-day period from 1 January 2006 to 30 June 2015 were manually reviewed to determine the presence of clinically documented signs of problem use and used as a reference standard for algorithm development. Using 1, 400 patients as training data, we constructed candidate predictors from demographic, enrollment, encounter, diagnosis, procedure, and medication data extracted from medical claims records or the equivalent from electronic health record (EHR) systems, and we used adaptive least absolute shrinkage and selection operator (LASSO) regression to develop a model. We evaluated this model in a comparable 600-patient validation set. We compared this model to ICD-9 diagnostic codes for opioid abuse, dependence, and poisoning. This study was registered with ClinicalTrials.gov as study NCT02667262 on 28 January 2016. Results: We operationalized 1, 126 potential predictors characterizing patient demographics, procedures, diagnoses, timing, dose, and location of medication dispensing. The final model incorporating 53 predictors had a sensitivity of 0.582 at positive predictive value (PPV) of 0.572. ICD-9 codes for opioid abuse, dependence, and poisoning had a sensitivity of 0.390 at PPV of 0.599 in the same cohort. Conclusions: Scalable methods using widely available structured EHR/claims data to accurately identify problem opioid use among patients receiving long-term ER/LA therapy were unsuccessful. This approach may be useful for identifying patients needing clinical evaluation.