Multisite validation of a simple electronic health record algorithm for identifying diagnosed obstructive sleep apnea
Multisite validation of a simple electronic health record algorithm for identifying diagnosed obstructive sleep apnea
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DOI：
10.5664/jcsm.8160
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发表时间：
2020-02-15
影响因子：
4.3
通讯作者：
Derose, Stephen F.
中科院分区：
文献类型：
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作者：
Keenan, Brendan T.;Kirchner, H. Lester;Derose, Stephen F.
研究目标：我们检查了一种简单算法的性能，该算法使用美国六个卫生系统的电子健康记录 (EHR) 准确区分确诊的阻塞性睡眠呼吸暂停 (OSA) 病例和非病例。方法：对 EHR 数据进行回顾性分析。该算法将病例定义为在 EHR 中不同日期具有 2 个与睡眠呼吸暂停相关的特定国际疾病分类 (ICD)-9 和/或 ICD-10 诊断代码（327.20、327.23、327.29、780.51、780.53、780.57、G4730、G4733 和 G4739）实例的个体。非案例是通过缺少这些代码来定义的。使用每个中心的 120 个病例和 100 个非病例（总共 n = 1,320）的图表审查，计算阳性预测值 (PPV) 和阴性预测值 (NPV)。 结果：该算法在各个中心表现出优异的性能，PPV（95% 置信区间）为 97.1（95.6，98.2），NPV 为 95.5（93.5，97.0）。每个站点的性能类似，所有 NPV 和 PPV 估计值 >= 90%，但某个站点的 PPV 稍低，为 87.5 (80.2, 92.8)。 >= 3 个实例的修改算法将该站点的 PPV 提高到 94.9 (88.5, 98.3)，但排除了额外的 18.3% 的案例。因此，通过需要额外的代码可以进一步提高性能，但这会减少确定病例的数量。结论：用于诊断 OSA 的简单的基于 EHR 的病例识别算法在来自美国的多站点样本中显示出出色的预测特性。未来应进行分析，以了解未确诊疾病对 EHR 定义的非病例的影响。该算法对于基于 EHR 的 OSA 研究具有广泛的应用。
Study Objectives: We examined the performance of a simple algorithm to accurately distinguish cases of diagnosed obstructive sleep apnea (OSA) and noncases using the electronic health record (EHR) across six health systems in the United States.Methods: Retrospective analysis of EHR data was performed. The algorithm defined cases as individuals with 2 instances of specific International Classification of Diseases (ICD)-9 and/or ICD-10 diagnostic codes (327.20, 327.23, 327.29, 780.51, 780.53, 780.57, G4730, G4733 and G4739) related to sleep apnea on separate dates in their EHR. Noncases were defined by the absence of these codes. Using chart reviews on 120 cases and 100 noncases at each site (n = 1,320 total), positive predictive value (PPV) and negative predictive value (NPV) were calculated.Results: The algorithm showed excellent performance across sites, with a PPV (95% confidence interval) of 97.1 (95.6, 98.2) and NPV of 95.5 (93.5, 97.0). Similar performance was seen at each site, with all NPV and PPV estimates >= 90% apart from a somewhat lower PPV of 87.5 (80.2, 92.8) at one site. A modified algorithm of >= 3 instances improved PPV to 94.9 (88.5, 98.3) at this site, but excluded an additional 18.3% of cases. Thus, performance may be further improved by requiring additional codes, but this reduces the number of determinate cases.Conclusions: A simple EHR-based case-identification algorithm for diagnosed OSA showed excellent predictive characteristics in a multisite sample from the United States. Future analyses should be performed to understand the effect of undiagnosed disease in EHR-defined noncases. This algorithm has wide-ranging applications for EHR-based OSA research.