丹麦国家登记册中的耐药性癫痫算法

IF 10.6 1区 医学 Q1 CLINICAL NEUROLOGY Brain Pub Date : 2024-09-10 DOI:10.1093/brain/awae286
Eva Bølling-Ladegaard, Julie W Dreier, Jakob Christensen
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引用次数: 0

摘要

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。从行政数据中识别出 DRE 患者可以有效地开展大规模研究,因此我们的目标是构建一种算法,用于在丹麦全国范围内的健康登记册中识别 DRE 患者。我们使用了之前生成的一个样本,该样本包含 525 名在 2010-2019 年间有医疗记录验证的癫痫患者,其中 80 人(15%)在最近一次联系时符合国际抗癫痫联盟 (ILAE) 的 DRE 标准 - 该队列被视为黄金标准。我们将经过验证的队列中的信息与丹麦国家健康登记册联系起来,并构建了基于登记册的 DRE 病例识别算法。每种算法的准确性都与经过医疗记录验证的黄金标准进行了比对。我们将根据测试准确性(F1 分数)得出的最佳算法应用于 1995 年至 2013 年期间在丹麦全国患者登记册中发现的一大批偶发性癫痫患者,并进行了描述性分析和逻辑回归分析,以描述算法识别出的 DRE 患者群的特征。就F1得分而言,表现最佳的算法定义为 "3年内开具≥3种不同的抗癫痫药物(ASM)处方,或开具2种不同的ASM处方后因癫痫/抽搐到医院就诊"(灵敏度为0.59,特异性为0.93,阳性预测值为0.59,阴性预测值为0.92,接收者工作特征曲线下面积为0.77,F1得分为0.595)。将该算法应用于以登记簿为基础的 83,682 例偶发性癫痫患者队列中,发现 8650 例(10.3%)患者有 DRE。在多变量逻辑回归分析中,癫痫早发、局灶性或全身性癫痫、躯体共病和药物滥用与被归类为 DRE 的风险独立相关。我们开发了一种在丹麦国家登记册中识别 DRE 的算法,可用于各种研究问题。我们将癫痫早发、局灶性或全身性癫痫、躯体共病和药物滥用确定为 DRE 的风险因素。
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An algorithm for drug-resistant epilepsy in Danish national registers
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.
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来源期刊
Brain
Brain 医学-临床神经学
CiteScore
20.30
自引率
4.10%
发文量
458
审稿时长
3-6 weeks
期刊介绍: Brain, a journal focused on clinical neurology and translational neuroscience, has been publishing landmark papers since 1878. The journal aims to expand its scope by including studies that shed light on disease mechanisms and conducting innovative clinical trials for brain disorders. With a wide range of topics covered, the Editorial Board represents the international readership and diverse coverage of the journal. Accepted articles are promptly posted online, typically within a few weeks of acceptance. As of 2022, Brain holds an impressive impact factor of 14.5, according to the Journal Citation Reports.
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