PT -期刊文章盟William j . Culpepper AU -露丝安上盟安妮特Langer-Gould AU -米切尔·t·沃林AU -乔纳森·d·坎贝尔盟Lorene m . Nelson AU -温迪·e·凯盟劳里瓦格纳AU -海伦Tremlett盟谎言h·陈盟-斯特拉梁盟慈善埃文斯盟-深圳姚明AU -尼古拉斯·g·LaRocca盟代表美国多发性硬化患病率工作组(MSPWG) TI -验证算法的识别女士病例管理援助- 10.1212 / WNL健康声明数据集。0000000000007043 DP - 2019年3月05 TA -神经病首页学PG - e1016 e1028 VI - 92 IP - 10 4099 - //www.ez-admanager.com/content/92/10/e1016.short 4100 - //www.ez-admanager.com/content/92/10/e1016.full所以Neurology2019 3月05;92 AB -目的开发一个有效的算法识别多发性硬化症(MS)的病例管理健康声明(AHC)数据集。方法我们使用4 AHC数据集从退伍军人管理局(VA)、Kaiser Permanente南加州(KPSC),马尼托巴省(加拿大),萨斯喀彻温(加拿大)。VA, KPSC,马尼托巴省,我们测试了候选算法的性能根据住院,门诊,和疾病修饰治疗(DMT)声称相比,医疗记录审查使用灵敏度,特异性,阳性和阴性预测值,评分者间信度(Youden J统计)整体和分层按性别和年龄。在萨斯喀彻温省,我们测试了算法在一群随机选择从一般人群。结果的首选算法要求≥3医学相关索赔的任何组合住院,门诊,或DMT声称在一年时间内;2年时间内提供小增益性能。算法包括DMT声称比那些没有做的更好。敏感性(86.6% - -96.0%),特异性(66.7% - -99.0%)、阳性预测值(95.4% - -99.0%),和评分者间信度(Youden J = 0.60 - -0.92)通常是稳定的跨越数据集和地层。分层分析观察的一些性能的变化但是很大程度上反映了地层的成分的变化。 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.AHC=administrative health claims; CI=confidence interval; DMT=disease-modifying therapy; ICD-9=International Classification of Disease, 9th revision; ICD-9-CM=International Classification of Disease, 9th revision, clinical modification; ICD-10-CA=International Classification of Disease, 10th revision, Canadian version; ICD-10-CM=International Classification of Disease, 10th revision, clinical modification; IMS=Intercontinental Marketing Services; IP=inpatient; KPSC=Kaiser Permanente Southern California; MS=multiple sclerosis; NPV=negative predictive value; OP=outpatient; PPV=positive predictive value; VA=Department of Veterans Affairs