验证一种在行政健康索赔数据集中识别 MS 病例的算法。
Validation of an algorithm for identifying MS cases in administrative health claims datasets.
机构信息
From the Department of Veterans Affairs Post Deployment Health Services (W.J.C., M.T.W.), Multiple Sclerosis Center of Excellence; University of Maryland (W.J.C.), Baltimore; Departments of Internal Medicine and Community Health Sciences (R.A.M., S.L.), Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Canada; Neurology Department (A.L.-G., L.H.C.), Kaiser Permanente Southern California, Los Angeles; Georgetown University School of Medicine (M.T.W.), Washington, DC; University of Colorado (J.C.), Denver; Stanford University School of Medicine (L.M.N.), CA; McKing Consulting Corp (W.E.K., L.W.), Atlanta, GA; Faculty of Medicine (Neurology) and Centre for Brain Health (H.T.), University of British Columbia, Vancouver; College of Pharmacy and Nutrition (C.E.), University of Saskatchewan; Health Quality Council (Saskatchewan) (S.Y.), Saskatoon, Canada; and National Multiple Sclerosis Society (N.G.L.), New York, NY.
出版信息
Neurology. 2019 Mar 5;92(10):e1016-e1028. doi: 10.1212/WNL.0000000000007043. Epub 2019 Feb 15.
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 claims performed better than those that did not. Sensitivity (86.6%-96.0%), specificity (66.7%-99.0%), positive predictive value (95.4%-99.0%), and interrater reliability (Youden J = 0.60-0.92) were generally stable across datasets and across strata. Some variation in performance in the stratified analyses was observed but largely reflected changes in the composition of the strata. In Saskatchewan, the preferred algorithm had a sensitivity of 96%, specificity of 99%, positive predictive value of 99%, and negative predictive value of 96%.
CONCLUSIONS
The performance of each algorithm was remarkably consistent across datasets. The preferred algorithm required ≥3 MS-related claims from any combination of inpatient, outpatient, or DMT use within 1 year. We recommend this algorithm as the standard AHC case definition for MS.
目的
开发一种有效的算法，用于在行政健康保险索赔（AHC）数据集中识别多发性硬化症（MS）病例。
方法
我们使用了来自退伍军人管理局（VA）、加利福尼亚州凯撒永久医疗集团（KPSC）、马尼托巴省（加拿大）和萨斯喀彻温省（加拿大）的 4 个 AHC 数据集。在 VA、KPSC 和马尼托巴省，我们基于住院、门诊和疾病修正治疗（DMT）索赔，与使用敏感性、特异性、阳性和阴性预测值以及组内一致性（Youden J 统计量）的病历审查相比，测试了候选算法的性能，同时按性别和年龄进行了分层。在萨斯喀彻温省，我们在从一般人群中随机选择的队列中测试了算法。
结果
首选算法需要在 1 年内从任何组合的住院、门诊或 DMT 索赔中获得≥3 项 MS 相关索赔；2 年内的表现几乎没有提高。包括 DMT 索赔的算法比不包括 DMT 索赔的算法性能更好。敏感性（86.6%-96.0%）、特异性（66.7%-99.0%）、阳性预测值（95.4%-99.0%）和组内一致性（Youden J = 0.60-0.92）在数据集和分层之间通常是稳定的。在分层分析中观察到性能的一些变化，但主要反映了分层组成的变化。在萨斯喀彻温省，首选算法的敏感性为 96%，特异性为 99%，阳性预测值为 99%，阴性预测值为 96%。
结论
每种算法的性能在不同数据集之间都非常一致。首选算法需要在 1 年内从任何组合的住院、门诊或 DMT 使用中获得≥3 项 MS 相关索赔。我们建议将该算法作为多发性硬化症的 AHC 病例定义标准。
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