From the Departments of Neurology (T.E.L., A.L.M.), Neuroscience (T.E.L.), and Medicine (A.L.M.), Johns Hopkins University School of Medicine and Johns Hopkins Bayview Myositis Center, Baltimore, MD; Department of Neurology (A.A.A., S.A.G.), Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Department of Neurology (M.D.W.), University of Washington, Seattle; Department of Neurology (M.N.), Australian Neuromuscular Research Institute, University of Western Australia; and Children's Hospital Informatics Program (S.A.G.), Boston Children's Hospital and Harvard-MIT Division of Health Sciences and Technology, Boston, MA.
Neurology. 2014 Jul 29;83(5):426-33. doi: 10.1212/WNL.0000000000000642. Epub 2014 Jun 27.
To use patient data to evaluate and construct diagnostic criteria for inclusion body myositis (IBM), a progressive disease of skeletal muscle.
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.
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).
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.
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.
利用患者数据评估和构建包涵体肌炎（IBM）的诊断标准，IBM 是一种进行性骨骼肌疾病。
回顾文献以确定所有先前提出的 IBM 诊断标准。这些标准通过对 2 家机构的神经肌肉专家诊断为 IBM 的 200 名患者和诊断为非 IBM 肌肉疾病的 171 名患者的病历进行回顾，以及对另外 2 家机构的 66 名 IBM 患者进行验证。使用机器学习技术进行无偏诊断标准的构建。
确定了 24 个先前提出的 IBM 诊断类别。12 个类别均具有高（≥97%）特异性，但敏感性差异很大（11%-84%）。表现最好的类别是欧洲神经肌肉中心 2013 年可能的（敏感性为 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。对 371 名患者的 20 个特征进行无偏数据分析导致了性能更高的数据衍生诊断标准的构建（敏感性为 90%，特异性为 96%）。
已发表的专家共识衍生 IBM 诊断类别具有统一的高特异性，但敏感性范围广泛。从患者数据的原则性无偏分析中可以直接开发高性能的 IBM 诊断类别标准。
本研究提供了 II 级证据，表明已发表的专家共识衍生 IBM 诊断标准准确地区分了 IBM 与其他肌肉疾病，特异性高，但敏感性范围广泛。
