A Clinically and Biologically Based Subclassification of the Idiopathic Inflammatory Myopathies Using Machine Learning
A Clinically and Biologically Based Subclassification of the Idiopathic Inflammatory Myopathies Using Machine Learning
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DOI：
10.1002/acr2.11115
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发表时间：
2020-03-01
影响因子：
3.4
通讯作者：
Reed, Ann M.
中科院分区：
文献类型：
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作者：
Eng, Simon W. M.;Olazagasti, Jeannette M.;Reed, Ann M.
目的：由于疾病的异质性，已发表的特发性炎症性肌病（IIMs）疾病结局的预测模型比较稀疏且准确性有限。计算方法可以通过根据临床和生物学表型划分患者来解决这种异质性。方法在利妥昔单抗治疗肌炎试验中，对168例肌炎患者（成人多发性肌炎[PM] 64例，成人皮肌炎[DM] 65例，青少年DM [JDM] 39例）的临床和生物学数据进行相似性网络融合（SNF）分析，以确定新的患者群体。我们使用多项回归生成了一个稀疏的概念验证床边分类器，并识别了区分这些组的特征。我们进行了chi(2)试验，将新的患者群体与肌炎亚型联系起来。结果snf在发现队列中确定了五个细分肌炎亚型的患者组。稀疏多项式回归预测患者分组分配（受者工作特征曲线下面积=[0.78,0.97]；精确召回率曲线下面积=[0.55,0.96]）发现，自身抗体浓缩定义了其中四个组：抗mi -2、抗信号识别肽（SRP）、抗核基质蛋白2 （NXP2）和抗合成酶（Syn）。第五组为免疫球蛋白M （IgM）缺失。每组均与一种亚型相关，成人DM与抗mi -2和抗syn自身抗体相关，JDM与抗nxp2自身抗体相关，成人PM与IgM耗尽和抗srp自身抗体相关。这些关联使我们能够进一步解决当前的肌炎亚型。使用无监督机器学习，我们确定了IIMs患者的临床和生物学同质组，形成了基于临床和生物学表型的综合疾病分类的基础，从而验证了其他方法和先前描述的内容。
ObjectivePublished predictive models of disease outcomes in idiopathic inflammatory myopathies (IIMs) are sparse and of limited accuracy due to disease heterogeneity. Computational methods may address this heterogeneity by partitioning patients based on clinical and biological phenotype.MethodsTo identify new patient groups, we applied similarity network fusion (SNF) to clinical and biological data from 168 patients with myositis (64 adult polymyositis [PM], 65 adult dermatomyositis [DM], and 39 juvenile DM [JDM]) in the Rituximab in Myositis trial. We generated a sparse proof-of-concept bedside classifier using multinomial regression and identified characteristics that distinguished these groups. We conducted chi (2) tests to link new patient groups with the myositis subtypes.ResultsSNF identified five patient groups in the discovery cohort that subdivided the myositis subtypes. The sparse multinomial regressor to predict patient group assignments (areas under the receiver operating characteristic curve = [0.78, 0.97]; areas under the precision-recall curve = [0.55, 0.96]) found that autoantibody enrichment defined four of these groups: anti-Mi-2, anti-signal recognition peptide (SRP), anti-nuclear matrix protein 2 (NXP2), and anti-synthetase (Syn). Depletion of immunoglobulin M (IgM) defined the fifth group. Each group was associated with one subtype, with adult DM being associated with anti-Mi-2 and anti-Syn autoantibodies, JDM being associated with anti-NXP2 autoantibodies, and adult PM being associated with IgM depletion and anti-SRP autoantibodies. These associations enabled us to further resolve the current myositis subtypes.ConclusionUsing unsupervised machine learning, we identified clinically and biologically homogeneous groups of patients with IIMs, forming the basis of an integrated disease classification based on both clinical and biological phenotype, thus validating other approaches and what has been previously described.