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
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
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DOI：
10.1080/21556660.2020.1750419
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发表时间：
2020-01-01
影响因子：
2.4
通讯作者：
Von Korff, Michael
中科院分区：
文献类型：
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作者：
Carrell, David S.;Albertson-Junkans, Ladia;Von Korff, Michael
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，以识别有临床记录的问题处方阿片类药物使用迹象的患者。现有的算法缺乏准确性。我们试图开发一种基于广泛可用的结构化健康数据的高敏感性、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，并使用问题证据支持后续的流行病学调查。方法：对2006年1月1日至2015年6月30日期间接受60天ER/LA阿片类药物供应的2000名Kaiser Permanente Washington患者的门诊病历进行手动审查，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。我们使用1400名患者作为训练数据，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、遭遇、诊断、过程和用药数据构建候选预测因子，并使用自适应最小绝对收缩和选择算子(LASSO)回归建立模型。我们在一组可比的600名患者验证集中对该模型进行了评估。我们将这个模型与ICD-9阿片类药物滥用、依赖和中毒诊断代码进行了比较。这项研究于2016年1月28日在ClinicalTrials.gov注册，研究名称为NCT02667262。结果：我们操作了1,126个潜在的预测因子，这些因子表征了患者的人口统计、程序、诊断、时间、剂量和药物分配的地点。包含53个预测因子的最终模型的敏感度为0.582，阳性预测值为0.572。在同一队列中，用于阿片类药物滥用、依赖和中毒的ICD-9代码的敏感度为0.390，PPV为0.599。结论：使用广泛可用的结构化EHR/Claims数据来准确识别接受长期ER/LA治疗的患者中问题阿片类药物使用的可扩展方法是不成功的。这种方法可能有助于确定需要进行临床评估的患者。
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.