幼年皮肌炎临床特征与肌炎抗体相关性分析及重症风险预测OA
Analysis of clinical phenotypes and the myositis antibodies in juvenile dermatomyositis and risk prediction for severe cases
背景 幼年皮肌炎(juvenile dermatomyositis,JDM)合并重症可能进展迅速,预后不良,早期识别存在一定困难,目前缺乏对重症JDM的风险预测模型.目的 描述JDM不同肌炎抗体亚型的临床特征,构建及验证重症JDM的风险预测模型.设计 病例对照研究.方法 收集2015年1月至2025年1月重庆医科大学附属儿童医院收治的JDM患儿的人口学特征、临床表现、实验室指标、影像学等临床资料,分析临床特征与肌炎抗体的关系.根据中国JDM诊断与治疗指南分为重症组和非重症组,以7∶3比例将患儿随机分为训练集和验证集,对训练集采用最小绝对收缩和选择算子(least absolute shrinkage and selection operator,LASSO)识别预测性实验室指标并创建实验室风险评分,采用多因素Logistic回归分析重症JDM危险因素并构建列线图风险预测模型,对模型预测效能进行验证和评估.主要结局指标 不同肌炎抗体患儿临床表型特点;模型对JDM合并重症的预测性能.结果 (1)临床特征与肌炎抗体分析纳入236例JDM患儿,男性123例(52.1%),中位起病年龄6.42(4.31,9.67)岁.190例首次诊断即检测了肌炎抗体,阳性125例(65.8%),其中肌炎特异性抗体阳性患儿104例(54.7%),以抗核基质蛋白2(nuclear matrix protein 2,NXP2)抗体最常见(43/190,22.6%),抗黑色素瘤分化相关基因5(melanoma differentiation-associated gene 5,MDA5)抗体(35/190,18.4%)、抗转录中间因子1γ(transcription intermediary factor 1 γ,TIF1-γ)抗体(12/190,6.3%)次之.各肌炎抗体间合并重症、Gottron征、关节痛、吞咽困难、发热、钙质沉着、间质性肺病变(interstitial lung disease,ILD)、肌电图异常构成比和AST、ALT、ALB、CK差异均有统计学意义(P<0.05).其中合并重症、吞咽困难、钙质沉着、AST和CK升高、ALB降低主要见于抗NXP2抗体阳性患儿.关节痛、发热和ILD以及ALT升高主要见于抗MDA5抗体阳性患儿.Gottron征主要见于抗TIF1-γ抗体阳性患儿.(2)模型构建纳入201例,其中训练集140例(重症20例),验证集61例(重症12例),LASSO回归筛选出抗NXP2抗体阳性、ALB、PLT、AST 4个实验室指标形成实验室风险评分.多因素Logistic回归分析筛选出声音嘶哑(OR=7.493,95%CI:1.396~48.678,P=0.023)、口腔溃疡(OR=5.304,95%CI:0.840~38.149,P=0.079)、皮下水肿(OR=9.348,95%CI:2.167~47.504,P=0.004)、钙质沉着(OR=8.844,95%CI:0.798~105.194,P=0.071)、腹腔积液(OR=5.781,95%CI:0.659~48.940,P=0.099)、实验室风险评分(OR=11.739,95%CI:3.366~59.930,P=0.001)6个预测因子构建列线图预测模型.训练集和验证集AUC分别为0.960(95%CI:0.929~0.991)、0.920(95%CI:0.841~0.999).校准曲线接近理想曲线,具有良好的校准度,临床决策曲线分析提示在大部分阈值概率下,预测模型提供了更大的净收益.结论 JDM不同肌炎抗体亚型临床表现存在差异.基于抗NXP2抗体阳性、声音嘶哑、皮下水肿、钙质沉着、腹腔积液等指标构成的重症JDM预测模型具有较好的预测价值.
Background Severe juvenile dermatomyositis(JDM)may progress rapidly with a poor prognosis.Early identification is somewhat difficult,and currently,there is a lack of a risk prediction model for severe JDM.Objective To describe the clinical characteristics of different myositis antibody subtypes in JDM,to construct and validate a risk prediction model for severe JDM.Design A case-control study.Methods Clinical data including demographic characteristics,clinical manifestations,laboratory indices,and imaging findings of JDM patients admitted to Children's Hospital of Chongqing Medical University from January 2015 to January 2025 were collected.The relationship between clinical characteristics and myositis antibodies was analyzed.According to the Chinese guidelines for the diagnosis and treatment of JDM,patients were divided into the severe group and the non-severe group.The study population were randomly divided into a development set and a validation set at a ratio of 7∶3.The least absolute shrinkage and selection operator(LASSO)was used in the development set to identify predictive laboratory indices and create a laboratory risk score.Multivariate logistic regression analysis was performed to analyze the risk factors for severe JDM and construct a nomogram risk prediction model,the predictive performance of the model was then validated and evaluated.Main Outcome Measures Clinical phenotypic characteristics of children with different myositis antibodies;the predictive performance of the model for JDM complicated with severity.Results(1)A total of 236 JDM children were included in the analysis of clinical characteristics and myositis antibodies.There were 123 males(52.1%),the median age of onset was 6.42(4.31,9.67)years.Myositis antibodies were detected in 190 children at the first diagnosis,and 125(65.8%)were positive for myositis antibodies.The positive rate of myositis-specific autoantibodies was 54.7%(104/190).The anti-nuclear matrix protein 2(NXP2)antibody was the most common(43/190,22.6%),followed by the anti-melanoma differentiation-associated gene 5(MDA5)antibody(35/190,18.4%)and the anti-transcriptional intermediate factor 1γ(TIF1-γ)antibody(12/190,6.3%).There were significant differences in the composition ratios of severe conditions,Gottron sign,joint pain,dysphagia,fever,calcinosis,interstitial lung disease(ILD),abnormal electromyogram,and the counts of AST,ALT,ALB,and CK among different myositis antibodies(P<0.05).Among them,severe conditions,dysphagia,calcinosis,increased counts of AST and CK,and decreased count of ALB were mainly seen in children with anti-NXP2 antibody.Joint pain,fever,ILD,and increased ALT count were mainly seen in children with anti-MDA5 antibody.Gottron sign was mainly seen in children with anti-TIF1-γ antibody.(2)A total of 201 cases were included in model construction,with 140 cases in the development set(20 severe cases)and 61 cases in the validation set(12 severe cases).LASSO regression identified four optimal laboratory index characteristics in the development set:anti-NXP2 antibody,ALB,PLT,and AST,and formed a laboratory risk score.Multivariate logistic regression analysis identified 6 predictors,including hoarseness(OR=7.493,95%CI:1.396-48.678,P=0.023),oral ulcers(OR=5.304,95%CI:0.840-38.149,P=0.079),subcutaneous edema(OR=9.348,95%CI:2.167-47.504,P=0.004),calcinosis(OR=8.844,95%CI:0.798-105.194,P=0.071),ascites(OR=5.781,95%CI:0.659-48.940,P=0.099),and laboratory risk score(OR=11.739,95%CI:3.366-59.930,P=0.001),to construct a nomogram prediction model.The AUC of the development set and the validation set were 0.960(95%CI:0.929-0.991)and 0.920(95%CI:0.841-0.999)respectively.The calibration curve was close to the ideal curve,indicating good calibration.Clinical decision curve analysis(DCA)suggested that the prediction model provided a greater net benefit at most threshold probabilities.Conclusion There are differences in the clinical manifestations of different myositis antibody subtypes in JDM.The prediction model for severe JDM based on indicators such as positive anti-NXP2 antibody,hoarseness,subcutaneous edema,calcinosis,and ascites has good predictive value.
曾新;唐雪梅
重庆医科大学附属儿童医院风湿免疫科 儿童发育疾病研究教育部重点实验室 国家儿童健康与疾病临床医学研究中心 儿童感染与免疫罕见病重庆市重点实验室 中国儿童风湿免疫病联盟 重庆,400014重庆医科大学附属儿童医院风湿免疫科 儿童发育疾病研究教育部重点实验室 国家儿童健康与疾病临床医学研究中心 儿童感染与免疫罕见病重庆市重点实验室 中国儿童风湿免疫病联盟 重庆,400014
幼年型皮肌炎重症肌炎抗体预测模型列线图
Juvenile dermatomyositisSevereMyositis antibodiesPrediction modelNomogram
《中国循证儿科杂志》 2026 (1)
62-71,10
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