Patients with drug-resistant epilepsy (DRE) have increased risks of premature death, injuries, psychosocial dysfunction, and a reduced quality of life. Identification of persons with DRE in administrative data can allow for effective large-scale research, and we therefore aimed to construct an algorithm for identification of DRE in Danish nation-wide health registers. We used a previously generated sample of 525 persons with medical record-validated incident epilepsy between 2010-2019, of which 80 (15%) fulfilled International League Against Epilepsy (ILAE) criteria of DRE at the time of the latest contact – this cohort was considered the gold standard. We linked information in the validated cohort to Danish national health registers and constructed register-based algorithms for identification of DRE-cases. The accuracy of each algorithm was validated against the medical record-validated gold standard. We applied the best performing algorithm according to test accuracy (F1 score) to a large cohort with incident epilepsy identified in the Danish National Patient Registry between 1995 and 2013 and performed descriptive and logistic regression analyses to characterize the cohort with DRE as identified by the algorithm. The best performing algorithm in terms of F1 score was defined as ‘fillings of prescriptions for ≥ 3 distinct antiseizure medications (ASMs) within 3 years or acute hospital visit with epilepsy/convulsions following fillings of prescriptions for two distinct ASMs’ (sensitivity 0.59, specificity 0.93, positive predictive value 0.59, negative predictive value 0.92, area under the receiver operating characteristic curve 0.77, and F1 score 0.595). Applying the algorithm to a register-based cohort of 83,682 individuals with incident epilepsy yielded 8,650 cases (10.3 %) with DRE. In multivariable logistic regression analysis, early onset of epilepsy, focal or generalized epilepsy, somatic co-morbidity, and substance abuse, were independently associated with risk of being classified with DRE. We developed an algorithm for the identification of DRE in Danish national registers, which can be applied for a variety of research questions. We identified early onset of epilepsy, focal or generalized epilepsy, somatic co-morbidity, and substance abuse as risk factors for DRE.

中文翻译：

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丹麦国家登记册中耐药性癫痫的算法


耐药性癫痫 (DRE) 患者过早死亡、受伤、心理社会功能障碍和生活质量下降的风险增加。在管理数据中识别患有 DRE 的人可以进行有效的大规模研究，因此我们的目标是构建一种在丹麦全国健康登记册中识别 DRE 的算法。我们使用了之前生成的 2010 年至 2019 年间 525 名经病历验证的癫痫发作患者样本，其中 80 人 (15%) 在最近一次接触时符合国际抗癫痫联盟 (ILAE) 的 DRE 标准——该队列是被认为是黄金标准。我们将经过验证的队列中的信息与丹麦国家健康登记册相关联，并构建了基于登记册的算法来识别 DRE 病例。每个算法的准确性都根据医疗记录验证的黄金标准进行了验证。我们根据测试准确性（F1 分数）对丹麦国家患者登记处 1995 年至 2013 年间发现的癫痫发作大型队列应用了性能最佳的算法，并进行了描述性和逻辑回归分析，以表征算法所确定的 DRE 队列的特征。就 F1 评分而言，表现最佳的算法被定义为“在 3 年内配药 ≥ 3 种不同的抗癫痫药物 (ASM) 的处方，或在配药两种不同的 ASM 的处方后因癫痫/惊厥而急性住院”（敏感性 0.59，特异性0.93，阳性预测值 0.59，阴性预测值 0.92，受试者工作特征曲线下面积 0.77，F1 评分 0.595）。 将该算法应用到由 83,682 名癫痫患者组成的基于登记的队列中，得出 8,650 例 (10.3%) 的 DRE 病例。在多变量逻辑回归分析中，癫痫的早发、局灶性或全身性癫痫、躯体共病和药物滥用与被分类为 DRE 的风险独立相关。我们开发了一种用于识别丹麦国家登记册中 DRE 的算法，该算法可应用于各种研究问题。我们确定癫痫早发、局灶性或全身性癫痫、躯体共病和药物滥用是 DRE 的危险因素。