Validation of an algorithm for identifying MS cases in administrative health claims datasets

Objective: To develop a valid algorithm for identifying multiple sclerosis (MS) cases in administrative health claims (AHC) datasets. Methods: We used 4 AHC datasets from the Veterans Administration (VA), Kaiser Permanente Southern California (KPSC), Manitoba (Canada), and Saskatchewan (Canada). In the VA, KPSC, and Manitoba, we tested the performance of candidate algorithms based on inpatient, outpatient, and disease-modifying therapy (DMT) claims compared to medical records review using sensitivity, specificity, positive and negative predictive values, and interrater reliability (Youden J statistic) both overall and stratified by sex and age. In Saskatchewan, we tested the algorithms in a cohort randomly selected from the general population. Results: The preferred algorithm required ≥3 MS-related claims from any combination of inpatient, outpatient, or DMT claims within a 1-year time period; a 2-year time period provided little gain in performance. Algorithms including DMT claim...研究目标：开发一种可在行政健康索赔（Administrative Health Claims, AHC）数据集中识别多发性硬化（Multiple Sclerosis, MS）病例的有效算法。 研究方法：本研究纳入来自美国退伍军人事务部（Veterans Administration, VA）、南加州凯撒医疗机构（Kaiser Permanente Southern California, KPSC）、加拿大曼尼托巴省及加拿大萨斯喀彻温省的4套行政健康索赔数据集。在VA、KPSC及曼尼托巴数据集队列中，我们基于住院、门诊及疾病修正治疗（disease-modifying therapy, DMT）索赔项，将候选算法的性能与病历审查结果进行对比，并从整体层面及按性别、年龄分层两个维度，采用灵敏度、特异度、阳性预测值、阴性预测值以及评定者间信度（尤登J统计量（Youden J statistic））评估其性能。在萨斯喀彻温省数据集队列中，我们在从普通人群中随机抽取的队列内对该类算法进行了性能测试。 研究结果：优选算法要求在1年时限内，来自住院、门诊或DMT索赔项的任意组合中，存在≥3条MS相关索赔；2年时限对算法性能的提升微乎其微。纳入DMT索赔项的算法……