Evaluation and construction of diagnostic criteria for inclusion body myositis
Evaluation and construction of diagnostic criteria for inclusion body myositis
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DOI：
10.1212/wnl.0000000000000642
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发表时间：
2014-07-29
期刊：
影响因子：
9.9
通讯作者：
Greenberg, Steven A.
中科院分区：
文献类型：
--
作者：
Lloyd, Thomas E.;Mammen, Andrew L.;Greenberg, Steven A.
目的：使用患者的数据，以评估和构建诊断标准包涵体肌炎（IBM），进行性疾病的骨骼muscle.Methods：文献进行了审查，以确定所有以前提出的IBM诊断标准。这些标准通过病历审查应用于2个机构的神经肌肉专家诊断为患有IBM的200名患者和诊断为患有IBM以外的肌肉疾病的171名患者，以及来自其他2个机构的66名IBM患者的验证组。机器学习技术被用于无偏建设的诊断criteris.Results：二十四个以前提出的IBM诊断类别进行了鉴定。12个类别均具有高特异性（>= 97%），但其灵敏度差异很大（11%-84%）。表现最好的类别是欧洲神经肌肉中心2013年可能（灵敏度为84%）。专门的病理特征和新引入的强度标准（比较膝关节伸展/髋关节屈曲强度）表现不佳。无偏见的数据导向的分析20个功能，在371例导致建设更高性能的数据衍生的诊断标准（90%的灵敏度和96%的特异性）。结论：发表的专家共识衍生IBM诊断类别具有一致的高特异性，但范围广泛的敏感性。高性能的IBM诊断类别标准可以直接从原则性的无偏分析患者data.Classification的证据：这项研究提供了II类证据，发表的专家共识派生的IBM诊断类别准确区分IBM从其他肌肉疾病具有高特异性，但广泛的敏感性。
Objective: To use patient data to evaluate and construct diagnostic criteria for inclusion body myositis (IBM), a progressive disease of skeletal muscle.Methods: The literature was reviewed to identify all previously proposed IBM diagnostic criteria. These criteria were applied through medical records review to 200 patients diagnosed as having IBM and 171 patients diagnosed as having a muscle disease other than IBM by neuromuscular specialists at 2 institutions, and to a validating set of 66 additional patients with IBM from 2 other institutions. Machine learning techniques were used for unbiased construction of diagnostic criteria.Results: Twenty-four previously proposed IBM diagnostic categories were identified. Twelve categories all performed with high (>= 97%) specificity but varied substantially in their sensitivities (11%-84%). The best performing category was European Neuromuscular Centre 2013 probable (sensitivity of 84%). Specialized pathologic features and newly introduced strength criteria (comparative knee extension/hip flexion strength) performed poorly. Unbiased data-directed analysis of 20 features in 371 patients resulted in construction of higher-performing data-derived diagnostic criteria (90% sensitivity and 96% specificity).Conclusions: Published expert consensus-derived IBM diagnostic categories have uniformly high specificity but wide-ranging sensitivities. High-performing IBM diagnostic category criteria can be developed directly from principled unbiased analysis of patient data.Classification of evidence: This study provides Class II evidence that published expert consensus-derived IBM diagnostic categories accurately distinguish IBM from other muscle disease with high specificity but wide-ranging sensitivities.