Muir Maxwell Epilepsy Centre, Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK.
Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, UK.
Epilepsia. 2020 Jul;61(7):1319-1335. doi: 10.1111/epi.16547. Epub 2020 May 31.
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.
我们的目标是进行系统评价，以确定使用常规医疗保健数据识别癫痫病例的准确性。我们检索了 MEDLINE 和 Embase 从 1975 年 1 月 1 日至 2018 年 3 月 7 日，以评估自国际疾病分类，第九版（ICD-9）以来使用的任何疾病编码系统用于识别癫痫病例的诊断准确性。两名作者独立筛选研究，提取数据并对研究进行质量评估。我们评估了阳性预测值（PPV）、敏感性、阴性预测值（NPV）和特异性。主要分析是对综述结果的叙述性综合。30 项研究纳入，发表于 1989 年至 2018 年。风险偏倚分别为低、高和不明确的 4、14 和 12 项研究。编码系统包括 ICD-9、ICD-10 和 Read 码，有或没有抗癫痫药物（AEDs）。PPV 包括加拿大的 5.2%-100%、美国的 32.7%-96.0%、英国的 47.0%-100%和挪威的 37.0%-88.0%。敏感性包括加拿大的 22.2%-99.7%、美国的 12.2%-97.3%和英国的 79.0%-94.0%。19 项研究至少有一个 PPV>80%的算法。16 项研究至少有一个敏感性>80%的算法。由疾病编码（ICD-10 G40-41、ICD-9 345）与一种或多种 AED 联合组成的算法中 PPV 最高。在此基础上添加症状编码（ICD-10 R56；ICD-9 780.3、780.39）降低了 PPV。由一种或多种 AED 组成的症状编码算法中敏感性最高。尽管单独使用 AEDs 可获得较高的敏感性，但相关的 PPV 较低。大多数 NPV 和特异性均>90%。我们的结论是，使用行政数据在流行病学研究中识别癫痫患者（PWE）是合理的。应优先考虑高 PPV 的研究侧重于将疾病编码与 AED 相结合。应优先考虑高敏感性的研究侧重于将症状编码与 AED 相结合。我们警告不要单独使用 AED 来识别 PWE。
