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首页> 外文期刊>JMIR Medical Informatics >Identifying Opioid Use Disorder in the Emergency Department: Multi-System Electronic Health Record–Based Computable Phenotype Derivation and Validation Study
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Identifying Opioid Use Disorder in the Emergency Department: Multi-System Electronic Health Record–Based Computable Phenotype Derivation and Validation Study

机译：识别急诊部中的阿片类药物使用障碍：基于多系统电子健康记录的可增加表型推导和验证研究

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Background Deploying accurate computable phenotypes in pragmatic trials requires a trade-off between precise and clinically sensical variable selection. In particular, evaluating the medical encounter to assess a pattern leading to clinically significant impairment or distress indicative of disease is a difficult modeling challenge for the emergency department. Objective This study aimed to derive and validate an electronic health record–based computable phenotype to identify emergency department patients with opioid use disorder using physician chart review as a reference standard. Methods A two-algorithm computable phenotype was developed and evaluated using structured clinical data across 13 emergency departments in two large health care systems. Algorithm 1 combined clinician and billing codes. Algorithm 2 used chief complaint structured data suggestive of opioid use disorder. To evaluate the algorithms in both internal and external validation phases, two emergency medicine physicians, with a third acting as adjudicator, reviewed a pragmatic sample of 231 charts: 125 internal validation (75 positive and 50 negative), 106 external validation (56 positive and 50 negative). Results Cohen kappa, measuring agreement between reviewers, for the internal and external validation cohorts was 0.95 and 0.93, respectively. In the internal validation phase, Algorithm 1 had a positive predictive value (PPV) of 0.96 (95% CI 0.863-0.995) and a negative predictive value (NPV) of 0.98 (95% CI 0.893-0.999), and Algorithm 2 had a PPV of 0.8 (95% CI 0.593-0.932) and an NPV of 1.0 (one-sided 97.5% CI 0.863-1). In the external validation phase, the phenotype had a PPV of 0.95 (95% CI 0.851-0.989) and an NPV of 0.92 (95% CI 0.807-0.978). Conclusions This phenotype detected emergency department patients with opioid use disorder with high predictive values and reliability. Its algorithms were transportable across health care systems and have potential value for both clinical and research purposes.
机译：背景技术在务语试验中部署准确的可计算表型需要在精确和临床上的可变选择之间进行权衡。特别是，评估医疗遭遇以评估导致临床上显着损伤或痛苦的模式，这表明疾病是急诊部的难度建模挑战。目的本研究旨在衍生和验证基于电子健康记录的可增加表型，以识别使用医生图表审查作为参考标准的阿片类药物使用障碍的急诊科患者。方法采用两种急诊部门在两个大型医疗系统中的13个急诊部门使用结构化临床数据开发和评估了双算法可增加表型。算法1组合临床医生和结算代码。算法2使用的主要投诉结构化数据暗示阿片类药物使用障碍。为了评估内部和外部验证阶段中的算法，两个紧急医学医师，第三次发挥作用作为裁决者，审查了231张图表的务实样本：125内部验证（75个阳性和50负），106个外部验证（56个阳性和50负）。结果审查员衡量协议的科恩·卡普布分别为0.95％和0.93。在内部验证阶段，算法1的阳性预测值（PPV）为0.96（95％CI 0.863-0.995），负预测值（NPV）为0.98（95％CI 0.893-0.999），算法2具有PPV为0.8（95％CI 0.593-0.932）和1.0的NPV（单侧97.5％CI 0.863-1）。在外部验证阶段，表型具有0.95（95％CI 0.851-0.989）的PPV，NPV为0.92（95％CI 0.807-0.978）。结论这种表型检测到急诊部患者的阿片类药物使用障碍，具有高预测值和可靠性。其算法在医疗保健系统上运输，并且具有临床和研究目的的潜在价值。

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