Our objective was to undertake a systematic review ascertaining the accuracy of using administrative healthcare data to identify epilepsy cases. We searched MEDLINE and Embase from 01/01/1975 to 03/07/2018 for studies evaluating the diagnostic accuracy of routinely collected healthcare data in identifying epilepsy cases. Any disease coding system in use since the International Classification of Diseases, Ninth Revision (ICD‐9) was permissible. Two authors independently screened studies, extracted data, and quality‐assessed studies. We assessed positive predictive value (PPV), sensitivity, negative predictive value (NPV), and specificity. The primary analysis was a narrative synthesis of review findings. Thirty studies were included, published between 1989 and 2018. Risks of bias were low, high, and unclear in 4, 14, and 12 studies, respectively. Coding systems included ICD‐9, ICD‐10, and Read Codes, with or without antiepileptic drugs (AEDs). PPVs included ranges of 5.2%–100% (Canada), 32.7%–96.0% (USA), 47.0%–100% (UK), and 37.0%–88.0% (Norway). Sensitivities included ranges of 22.2%–99.7% (Canada), 12.2%–97.3% (USA), and 79.0%–94.0% (UK). Nineteen studies contained at least one algorithm with a PPV >80%. Sixteen studies contained at least one algorithm with a sensitivity >80%. PPV was highest in algorithms consisting of disease codes (ICD‐10 G40‐41, ICD‐9 345) in combination with one or more AEDs. The addition of symptom codes to this (ICD‐10 R56; ICD‐9 780.3, 780.39) lowered PPV. Sensitivity was highest in algorithms consisting of symptom codes with one or more AEDs. Although using AEDs alone achieved high sensitivities, the associated PPVs were low. Most NPVs and specificities were >90%. We conclude that it is reasonable to use administrative data to identify people with epilepsy (PWE) in epidemiological research. Studies prioritizing high PPVs should focus on combining disease codes with AEDs. Studies prioritizing high sensitivities should focus on combining symptom codes with AEDs. We caution against the use of AEDs alone to identify PWE.

中文翻译：

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使用行政医疗保健数据识别癫痫病例的准确性：验证研究的系统评价

我们的目标是进行系统评价，以确定使用行政医疗保健数据识别癫痫病例的准确性。我们在 1975 年 1 月 1 日至 2018 年 3 月 7 日期间检索了 MEDLINE 和 Embase，以评估常规收集的医疗保健数据在识别癫痫病例方面的诊断准确性的研究。自国际疾病分类第九次修订版 (ICD-9) 以来使用的任何疾病编码系统都是允许的。两位作者独立筛选研究、提取数据和质量评估研究。我们评估了阳性预测值 (PPV)、敏感性、阴性预测值 (NPV) 和特异性。主要分析是综述结果的叙述性综合。纳入了 1989 年至 2018 年间发表的 30 项研究。分别有 4、14 和 12 项研究的偏倚风险低、高和不明确。编码系统包括 ICD-9、ICD-10 和 Read Codes，有或没有抗癫痫药物 (AED)。PPV 包括 5.2%–100%（加拿大）、32.7%–96.0%（美国）、47.0%–100%（英国）和 37.0%–88.0%（挪威）的范围。敏感性包括 22.2%–99.7%（加拿大）、12.2%–97.3%（美国）和 79.0%–94.0%（英国）的范围。19 项研究至少包含一种 PPV > 80% 的算法。16 项研究包含至少一种灵敏度 > 80% 的算法。PPV 在由疾病代码（ICD-10 G40-41、ICD-9 345）与一种或多种 AED 组合组成的算法中最高。对此添加症状代码（ICD-10 R56；ICD-9 780.3、780.39）降低了 PPV。由带有一种或多种 AED 的症状代码组成的算法的敏感性最高。尽管单独使用 AED 获得了高灵敏度，但相关的 PPV 却很低。大多数 NPV 和特异性 >90%。我们得出的结论是，在流行病学研究中使用行政数据来识别癫痫患者 (PWE) 是合理的。优先考虑高 PPV 的研究应侧重于将疾病代码与 AED 相结合。优先考虑高敏感性的研究应侧重于将症状代码与 AED 相结合。我们告诫不要单独使用 AED 来识别 PWE。