From the Health Services Research Program (C.E.H., C.C.L., S.W.T., L.E.S., J.F.B.), Department of Neurology, University of Michigan; Department of Neurology (S.R., J.M.P.), University of Michigan; Veterans Affairs Healthcare System (J.M.P., J.F.B.); and Michigan Neuroscience Institute (J.M.P.), Ann Arbor.
Neurology. 2021 Sep 28;97(13):e1343-e1350. doi: 10.1212/WNL.0000000000012514. Epub 2021 Jul 15.
To assess the accuracy of definitions of drug-resistant epilepsy applied to administrative claims data.
We randomly sampled 450 patients from a tertiary health system with ≥1 epilepsy/convulsion encounter, ≥2 distinct antiseizure medications (ASMs) from 2014 to 2020, and ≥2 years of electronic medical records (EMR) data. We established a drug-resistant epilepsy diagnosis at a specific visit by reviewing EMR data and using a rubric based on the 2010 International League Against Epilepsy definition. We performed logistic regressions to assess clinically relevant predictors of drug-resistant epilepsy and to inform claims-based definitions.
Of 450 patients reviewed, 150 were excluded for insufficient EMR data. Of the 300 patients included, 98 (33%) met criteria for current drug-resistant epilepsy. The strongest predictors of current drug-resistant epilepsy were drug-resistant epilepsy diagnosis code (odds ratio [OR] 16.9, 95% confidence interval [CI] 8.8-32.2), ≥2 ASMs in the prior 2 years (OR 13.0, 95% CI 5.1-33.3), ≥3 nongabapentinoid ASMs (OR 10.3, 95% CI 5.4-19.6), neurosurgery visit (OR 45.2, 95% CI 5.9-344.3), and epilepsy surgery (OR 30.7, 95% CI 7.1-133.3). We created claims-based drug-resistant epilepsy definitions (1) to maximize overall predictiveness (drug-resistant epilepsy diagnosis; sensitivity 0.86, specificity 0.74, area under the receiver operating characteristics curve [AUROC] 0.80), (2) to maximize sensitivity (drug-resistant epilepsy diagnosis or ≥3 ASMs; sensitivity 0.98, specificity 0.47, AUROC 0.72), and (3) to maximize specificity (drug-resistant epilepsy diagnosis and ≥3 nongabapentinoid ASMs; sensitivity 0.42, specificity 0.98, AUROC 0.70).
Our findings provide validation for several claims-based definitions of drug-resistant epilepsy that can be applied to a variety of research questions.
评估在行政索赔数据中应用耐药性癫痫定义的准确性。
我们从一家三级医疗机构中随机抽取了 450 名患者,这些患者在 2014 年至 2020 年间至少有一次癫痫/抽搐发作,至少使用了两种不同的抗癫痫药物(ASMs),并且至少有两年的电子病历(EMR)数据。我们通过回顾 EMR 数据并使用基于 2010 年国际抗癫痫联盟定义的量表,在特定就诊时建立了耐药性癫痫诊断。我们进行了逻辑回归分析,以评估耐药性癫痫的临床相关预测因素,并为基于索赔的定义提供信息。
在审查的 450 名患者中,有 150 名因 EMR 数据不足而被排除。在纳入的 300 名患者中,有 98 名(33%)符合当前耐药性癫痫的标准。当前耐药性癫痫的最强预测因素是耐药性癫痫诊断代码(比值比 [OR] 16.9,95%置信区间 [CI] 8.8-32.2)、过去 2 年内使用≥2 种 ASMs(OR 13.0,95% CI 5.1-33.3)、≥3 种非加巴喷丁类 ASMs(OR 10.3,95% CI 5.4-19.6)、神经外科就诊(OR 45.2,95% CI 5.9-344.3)和癫痫手术(OR 30.7,95% CI 7.1-133.3)。我们创建了基于索赔的耐药性癫痫定义(1)以最大限度地提高整体预测性(耐药性癫痫诊断;敏感性 0.86,特异性 0.74,接收者操作特征曲线下面积 [AUROC] 0.80),(2)以最大限度地提高敏感性(耐药性癫痫诊断或≥3 种 ASMs;敏感性 0.98,特异性 0.47,AUROC 0.72),和(3)以最大限度地提高特异性(耐药性癫痫诊断和≥3 种非加巴喷丁类 ASMs;敏感性 0.42,特异性 0.98,AUROC 0.70)。
我们的研究结果为几种基于索赔的耐药性癫痫定义提供了验证,这些定义可应用于各种研究问题。