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.

IF 2.4 Journal of Drug Assessment Pub Date : 2020-04-28 eCollection Date: 2020-01-01 DOI:10.1080/21556660.2020.1750419
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
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引用次数: 16

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.

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在接受长期阿片类镇痛治疗的患者中测量问题处方阿片类药物的使用:用于电子病历和索赔数据的算法的开发和评估。
目的:应对阿片类药物流行的阿片类药物监测将受益于可扩展的自动化算法,用于识别临床记录有问题处方阿片类药物使用迹象的患者。现有算法缺乏准确性。我们试图基于广泛可用的结构化健康数据开发一种高灵敏度、高特异性的分类算法,以识别接受慢性缓释/长效(ER/LA)治疗的患者,并提供问题使用的证据,以支持随后的流行病学调查。方法:对2000名在2006年1月1日至2015年6月30日的90天内接受ER/LA阿片类药物供应≥60天的门诊患者的概率样本进行人工审查,以确定是否存在临床记录的问题使用迹象,并将其作为算法开发的参考标准。使用1400名患者作为训练数据,我们从医疗索赔记录或电子健康记录(EHR)系统中提取的人口统计、登记、就诊、诊断、程序和药物数据中构建候选预测因子,并使用自适应最小绝对收缩和选择算子(LASSO)回归来开发模型。我们在600例患者验证集中评估了该模型。我们将该模型与ICD-9阿片类药物滥用、依赖和中毒的诊断代码进行了比较。该研究于2016年1月28日在ClinicalTrials.gov注册为研究NCT02667262。结果:我们操作了1126个潜在的预测因素,这些因素描述了患者的人口统计学特征、程序、诊断、时间、剂量和药物分配的位置。纳入53个预测因子的最终模型在阳性预测值(PPV)为0.572时的敏感性为0.582。在同一队列中,ICD-9阿片类药物滥用、依赖和中毒代码在PPV为0.599时的敏感性为0.390。结论:使用广泛可用的结构化EHR/索赔数据来准确识别接受长期ER/LA治疗的患者中阿片类药物使用问题的可扩展方法是不成功的。这种方法可能有助于识别需要临床评估的患者。
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Journal of Drug Assessment
Journal of Drug Assessment PHARMACOLOGY & PHARMACY-
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