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

David S Carrell, Ladia Albertson-Junkans, Arvind Ramaprasan, Grant Scull, Matt Mackwood, Eric Johnson, David J Cronkite, Andrew Baer, Kris Hansen, Carla A Green, Brian L Hazlehurst, Shannon L Janoff, Paul M Coplan, Angela DeVeaugh-Geiss, Carlos G Grijalva, Caihua Liang, Cheryl L Enger, Jane Lange, Susan M Shortreed, Michael Von Korff, David S Carrell, Ladia Albertson-Junkans, Arvind Ramaprasan, Grant Scull, Matt Mackwood, Eric Johnson, David J Cronkite, Andrew Baer, Kris Hansen, Carla A Green, Brian L Hazlehurst, Shannon L Janoff, Paul M Coplan, Angela DeVeaugh-Geiss, Carlos G Grijalva, Caihua Liang, Cheryl L Enger, Jane Lange, Susan M Shortreed, Michael Von Korff

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.

Keywords: Algorithms; electronic health records; opioid-related disorders; population surveillance.

© 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Figures

Figure 1.
Figure 1.
Receiver operating characteristic (ROC) curve for the problem opioid use classification algorithm in the training set (solid line), validation set (dashed lines), and sensitivity and specificity of the simple binary algorithm based on ICD-9 diagnosis codes for opioid abuse, dependence and poisoning (circle).

References

    1. Guy GP, Jr., Zhang K, Bohm MK, et al. . Vital signs: changes in opioid prescribing in the United States, 2006-2015. MMWR Morb Mortal Wkly Rep. 2017;66(26):697–704.
    1. Manchikanti L, Fellows B, Ailinani H, et al. . Therapeutic use, abuse, and nonmedical use of opioids: a ten-year perspective. Pain Physician. 2010;13(5):401–435.
    1. Substance Abuse and Mental Health Services Administration (SAMHSA) [Internet] . Rockville (MD): SAMHSA. Results from the 2013 National Survey on Drug Use and Health: Summary of national findings; 2014. [cited 2017 Nov 1]; Available from: .
    1. Rudd RA, Seth P, David F, et al. . Increases in drug and opioid-involved overdose deaths – United States, 2010-2015. MMWR Morb Mortal Wkly Rep. 2016;65(5051):1445–1452.
    1. Hirschfield DJ. Trump declares opioid crisis a ‘health emergency’ but requests no funds. New York (NY): The New York Times Company; 2017.
    1. National Academies of Sciences Engineering and Medicine. Pain management and the opioid epidemic: balancing societal and individual benefits and risks of prescription opioid use. Washington (DC): The National Academies Press; 2017.
    1. Vowles KE, McEntee ML, Julnes PS, et al. . Rates of opioid misuse, abuse, and addiction in chronic pain: a systematic review and data synthesis. Pain. 2015;156(4):569–576.
    1. Sehgal N, Manchikanti L, Smith HS.. Prescription opioid abuse in chronic pain: a review of opioid abuse predictors and strategies to curb opioid abuse. Pain Physician. 2012;15(3):ES67–ES92.
    1. Sullivan MD, Edlund MJ, Fan MY, et al. . Risks for possible and probable opioid misuse among recipients of chronic opioid therapy in commercial and medicaid insurance plans: the TROUP Study. Pain. 2010;150(2):332–339.
    1. White AG, Birnbaum H, Schiller M.. Economic impact of opioid abuse, dependence, and misuse. Am J Pharm Benefits. 2011;3:e59–e70.
    1. White AG, Birnbaum HG, Mareva MN, et al. . Direct costs of opioid abuse in an insured population in the United States. J Managed Care Special Pharma. 2005;11(6):469–479.
    1. White AG, Birnbaum HG, Schiller M, et al. . Analytic models to identify patients at risk for prescription opioid abuse. Am J Manag Care. 2009;15:897–906.
    1. Rice JB, White AG, Birnbaum HG, et al. . A model to identify patients at risk for prescription opioid abuse, dependence, and misuse. Pain Med. 2012;13(9):1162–1173.
    1. Shei A, Rice JB, Kirson NY, et al. . Sources of prescription opioids among diagnosed opioid abusers. Curr Med Res Opin. 2015;31(4):779–784.
    1. Shei A, Rice JB, Kirson NY, et al. . Characteristics of high-cost patients diagnosed with opioid abuse. J Managed Care Special Pharma. 2015;21(10):902–912.
    1. Rowe C, Vittinghoff E, Santos GM, et al. . Performance measures of diagnostic codes for detecting opioid overdose in the emergency department. Acad Emerg Med. 2017;24(4):475–483.
    1. Carrell DS, Cronkite D, Palmer RE, et al. . Using natural language processing to identify problem usage of prescription opioids. Int J Med Inform. 2015;84(12):1057–1064.
    1. United States Food and Drug Administration [Internet] . Silver Spring (MD): FDA. New safety measures announced for extended-release and long-acting opioids. Updated 5 November 17; 2017. [cited 2017 Nov 1]; Available from: .
    1. United States Food and Drug Administration [Internet] . Silver Spring (MD): FDA. Labeling supplement and PMR required; 2017. [cited 2017 Nov 1]; Available from: .
    1. Coplan PM, Cepeda MS, Petronis KR, et al. . Postmarketing studies program to assess the risks and benefits of long-term use of extended-release/long-acting opioids among chronic pain patients. Postgrad Med. 2020;132(1):44–51.
    1. Federal Register [Internet]. Washington (DC): Federal Register. Postmarketing requirements for the class-wide extended-release/long-acting opioid analgesics; Public Meeting; Request for Comments; 2014. [cited 2017 Nov 1]; Available from: .
    1. [Internet] . Bethesda (MD): U.S. National Library of Medicine. An observational study to develop algorithms for identifying opioid abuse and addiction based on admin claims data; 2017. [cited 2017 Nov 1]:[ identifier: NCT02667262]. Available from: .
    1. [Internet] . Bethesda (MD): U.S. National Library of Medicine. Incidence and predictors of opioid overdose and death in ER/LA opioid users as measured by diagnoses and death records; 2017. [cited 2017 Nov 1]; Available from: .
    1. Yu S, Ma Y, Gronsbell J, et al. . Enabling phenotypic big data with PheNorm. J Am Med Inform Assoc. 2018;25(1):54–60.
    1. Kaur H, Sohn S, Wi CI, et al. . Automated chart review utilizing natural language processing algorithm for asthma predictive index. BMC Pulm Med. 2018;18(1):34.
    1. Martin S, Wagner J, Lupulescu-Mann N, et al. . Comparison of EHR-based diagnosis documentation locations to a gold standard for risk stratification in patients with multiple chronic conditions. Appl Clin Inform. 2017;08(03):794–809.
    1. Carrell DS, Albertson-Junkans L, Ramaprasan A, et al. Problem opioid use among patients receiving long-term opioid therapy established by manual chart review. 2020. (Under Review).
    1. Epic Systems Corporation . Verona (WI): Epic.; 1979. [cited 2014 Oct 6]; Available from: .
    1. Curtis LH, Brown J, Platt R.. Four health data networks illustrate the potential for a shared national multipurpose big-data network. Health Aff (Millwood). 2014;33(7):1178–1186.
    1. United States Food and Drug Administration [Internet] . Silver Spring (MD): FDA. FDA’s sentinel initiative: transforming how we monitor the safety of FDA-regulated products; 2019. [cited 2012 Oct 29]; Available from: .
    1. United States Food and Drug Administration [Internet] . Silver Spring (MD): FDA. Sentinel distributed database and common data model; 2017. [cited 2017 Jul 21]; Available from: .
    1. Platt R, Wilson M, Chan KA, et al. . The new Sentinel Network–improving the evidence of medical-product safety. N Engl J Med. 2009;361(7):645–647.
    1. Edlund MJ, Steffick D, Hudson T, et al. . Risk factors for clinically recognized opioid abuse and dependence among veterans using opioids for chronic non-cancer pain. Pain. 2007;129:355–362.
    1. Chou R, Fanciullo GJ, Fine PG, et al. . Opioids for chronic noncancer pain: prediction and identification of aberrant drug-related behaviors: a review of the evidence for an American Pain Society and American Academy of Pain Medicine clinical practice guideline. J Pain. 2009;10(2):131–146.
    1. Passik SD, Kirsh KL, Donaghy KB, et al. . Pain and aberrant drug-related behaviors in medically ill patients with and without histories of substance abuse. Clin J Pain. 2006;22:173–181.
    1. Chou R, Turner JA, Devine EB, et al. . The effectiveness and risks of long-term opioid therapy for chronic pain: a systematic review for a National Institutes of Health Pathways to Prevention Workshop. Ann Intern Med. 2015;162(4):276–286.
    1. Katz C, El-Gabalawy R, Keyes KM, et al. . Risk factors for incident nonmedical prescription opioid use and abuse and dependence: results from a longitudinal nationally representative sample. Drug Alcohol Depend. 2013;132(1–2):107–113.
    1. Saha TD, Kerridge BT, Goldstein RB, et al. . Nonmedical prescription opioid use and DSM-5 nonmedical prescription opioid use disorder in the United States. J Clin Psychiatry. 2016;77(06):772–780.
    1. Goldner EM, Lusted A, Roerecke M, et al. . Prevalence of Axis-1 psychiatric (with focus on depression and anxiety) disorder and symptomatology among non-medical prescription opioid users in substance use treatment: systematic review and meta-analyses. Addict Behav. 2014;39(3):520–531.
    1. Amari E, Rehm J, Goldner E, et al. . Nonmedical prescription opioid use and mental health and pain comorbidities: a narrative review. Can J Psychiatry. 2011;56(8):495–502.
    1. Tibshirani RJ. Regression shrinkage and selection via the lasso. J R Stat Soc Series B Stat Methodol. 1996;58:14.
    1. Hastie T, Tibshirani RJ, Freidman J.. The elements of statistical learning. New York (NY): Springer; 2009.
    1. Ulbricht J. lqa: Penalized Likelihood Inference for GLMs, version 1.0-3. 2012. [updated 2012 Oct 29; cited 2017 Jul 21]; Available from: .
    1. Zou H. The adaptive lasso and its oracle properties. J Am Stat Assoc. 2006;101:12.
    1. Kim JK, Skinner CJ.. Weighting in survey analysis under informative sampling. Biometrika. 2013;100(2):385–398.
    1. Fuller W. Sampling statistics. Hoboken (NJ): Wiley; 2009.
    1. Chambers RL, Skinner CJ.. Analysis of survey data. Chichester (UK): Wiley; 2003.
    1. Vertelney H, Yarger J, Tilley L, et al. Health insurance churning among young adults in California: by the numbers. San Francisco (CA): Phillip R. Lee Institute for Health Policy Studies; 2017. [cited 2017 November 1]; Available from: .
    1. Callahan ST, Cooper WO.. Continuity of health insurance coverage among young adults with disabilities. Pediatrics. 2007;119(6):1175–1180.
    1. Green CA, Perrin NA, Hazlehurst B, et al. . Identifying and classifying opioid-related overdoses: a validation study. Pharmacoepidemiol Drug Saf. 2019;28(8):1127–1137.
    1. Canan C, Polinski JM, Alexander GC, et al. . Automatable algorithms to identify nonmedical opioid use using electronic data: a systematic review. J Am Med Inform Assoc. 2017;24(6):1204–1210.
    1. Maxim LD, Niebo R, Utell MJ.. Screening tests: a review with examples. Inhal Toxicol. 2014;26(13):811–828.
    1. Smith DC, Bennett KM, Dennis ML, et al. . Sensitivity and specificity of the gain short-screener for predicting substance use disorders in a large national sample of emerging adults. Addict Behav. 2017;68:14–17.
    1. Cicero TJ, Ellis MS, Kasper ZA.. Relative preferences in the abuse of immediate-release versus extended-release opioids in a sample of treatment-seeking opioid abusers. Pharmacoepidemiol Drug Saf. 2017;26(1):56–62.
    1. Juurlink DN, Dhalla IA.. Dependence and addiction during chronic opioid therapy. J Med Toxicol. 2012;8(4):393–399.
    1. American Psychiatric Association . Diagnostic and statistical manual of mental disorders (DSM-5). 5th (revised) ed. Arlington (VA): American Psychiatric Publishing; 2013.
    1. Smith SM, Dart RC, Katz NP, et al. . Classification and definition of misuse, abuse, and related events in clinical trials: ACTTION systematic review and recommendations. Pain. 2013;154(11):2287–2296.
    1. Vowles KE, McEntee ML, Siyahhan Julnes P, et al. . On the importance of clear comparisons and a methodologically rigorous empirical literature in evaluating opioid use in chronic pain: a response to Scholten and Henningfield. Pain. 2015;156(8):1577–1578.
    1. Nikulina V, Guarino H, Acosta MC, et al. . Patient vs provider reports of aberrant medication-taking behavior among opioid-treated patients with chronic pain who report misusing opioid medication. Pain. 2016;157(8):1791–1798.

Source: PubMed

Sponsorer og samarbejdspartnere

Medicinske tilstande

Narkotikainterventioner

3
Abonner