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
Creators: name: Carrell, David S.
name: Ramaprasan, Arvind
name: Scull, Grant
name: Mackwood, Matt
name: Johnson, Eric
name: Cronkite, David J.
name: Baer, Andrew
name: Hansen, Kris
name: Green, Carla A.
name: Hazlehurst, Brian
name: Janoff, Shannon L.
name: Coplan, Paul M.
name: DeVeaugh-Geiss, Angela
name: Grijalva, Carlos G.
name: Caihua Liang
name: Enger, Cheryl L.
name: Lange, Jane
name: Shortreed, Susan M.
name: Von Korff, Michael
Publisher: name: Taylor & Francis
Year: 2020
Subjects: subject: Medicine
subject: Biotechnology
subject: 69999 Biological Sciences not elsewhere classified
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: 80699 Information Systems not elsewhere classified
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences
subject: Cancer
subject: Science Policy

description: <b><i>Objective:</i></b> 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. <b><i>Methods:</i></b> 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 during 1/1/2006-6/30/2015 were manually reviewed to determine 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, enrolment, 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 January 28, 2016. <b><i>Results:</i></b> 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 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. <b><i>Conclusions:</i></b> 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.