Hylan Timothy R, Von Korff Michael, Saunders Kathleen, Masters Elizabeth, Palmer Roy E, Carrell David, Cronkite David, Mardekian Jack, Gross David
North America Medical Affairs, Global Innovative Pharma, Pfizer Inc, New York, New York.
Group Health Research Institute, Seattle, Washington.
J Pain. 2015 Apr;16(4):380-7. doi: 10.1016/j.jpain.2015.01.011. Epub 2015 Jan 29.
Identification of patients at increased risk for problem opioid use is recommended by chronic opioid therapy (COT) guidelines, but clinical assessment of risks often does not occur on a timely basis. This research assessed whether structured electronic health record (EHR) data could accurately predict subsequent problem opioid use. This research was conducted among 2,752 chronic noncancer pain patients initiating COT (≥70 days' supply of an opioid in a calendar quarter) during 2008 to 2010. Patients were followed through the end of 2012 or until disenrollment from the health plan, whichever was earlier. Baseline risk indicators were derived from structured EHR data for a 2-year period prior to COT initiation. Problem opioid use after COT initiation was assessed by reviewing clinician-documented problem opioid use in EHR clinical notes identified using natural language processing techniques followed by computer-assisted manual review of natural language processing-positive clinical notes. Multivariate analyses in learning and validation samples assessed prediction of subsequent problem opioid use. The area under the receiver operating characteristic curve (c-statistic) for problem opioid use was .739 (95% confidence interval = .688, .790) in the validation sample. A measure of problem opioid use derived from a simple weighted count of risk indicators was found to be comparably predictive of the natural language processing measure of problem opioid use, with 60% sensitivity and 72% specificity for a weighted count of ≥4 risk indicators.
An automated surveillance method utilizing baseline risk indicators from structured EHR data was moderately accurate in identifying COT patients who had subsequent problem opioid use.
慢性阿片类药物治疗（COT）指南建议识别阿片类药物使用问题风险增加的患者，但风险的临床评估往往未及时进行。本研究评估了结构化电子健康记录（EHR）数据能否准确预测随后的阿片类药物使用问题。本研究在2008年至2010年期间启动COT（一个日历季度内阿片类药物供应≥70天）的2752例慢性非癌性疼痛患者中进行。对患者进行随访至2012年底或直至从健康计划中退出，以较早者为准。基线风险指标来自COT启动前2年的结构化EHR数据。通过审查使用自然语言处理技术识别的EHR临床记录中临床医生记录的阿片类药物使用问题，然后对自然语言处理呈阳性的临床记录进行计算机辅助人工审查，评估COT启动后的阿片类药物使用问题。在学习样本和验证样本中进行多变量分析，评估对随后阿片类药物使用问题的预测。验证样本中阿片类药物使用问题的受试者工作特征曲线下面积（c统计量）为0.739（95%置信区间=0.688，0.790）。发现一种从风险指标的简单加权计数得出的阿片类药物使用问题测量方法对阿片类药物使用问题的自然语言处理测量方法具有相当的预测性，对于≥4个风险指标的加权计数，敏感性为60%，特异性为72%。
利用结构化EHR数据中的基线风险指标的自动监测方法在识别随后有阿片类药物使用问题的COT患者方面具有中等准确性。
