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中国循证儿科杂志

中国循证儿科杂志 ›› 2019, Vol. 14 ›› Issue (3): 169-175.DOI: 10.3969/j.issn.1673-5501.2019.03.002

• 论著 • 上一篇    下一篇

对川崎病患儿静脉注射丙种球蛋白耐药临床预测模型建立的质疑

谢丽萍1, 龚娟1, 富洋1, 何岚1, 储晨1, 严卫丽2, 黄国英1, 刘芳1   

  1. 复旦大学附属儿科医院心血管中心 上海,201102,1 心血管中心;2 流行病学研究室
  • 收稿日期:2019-04-28 出版日期:2019-06-25 发布日期:2019-06-25
  • 通讯作者: 刘芳, E-mail:liufang@fudan.edu.cn

Questioning the establishment of clinical prediction model for intravenous immunoglobulin resistance in children with Kawasaki disease

XIE Li-ping1, GONG Juan1, FU Yang1, HE Lan1, CHU Chen1, YAN Wei-li2, HUANG Guo-ying1, LIU Fang1   

  1. Children's Hospital of Fudan University, Shanghai 201102, China, 1 Heart Center; 2 Department of Clinical Epidemiology
  • Received:2019-04-28 Online:2019-06-25 Published:2019-06-25
  • Contact: LIU Fang, E-mail: liufang@fudan.edu.cn

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摘要： 目的 对川崎病(KD)患儿IVIG耐药预测模型提出质疑。方法 回顾性收集经复旦大学附属儿科医院(我院)首次诊断和治疗的KD病例,全样本人群按7∶3比例随机分为建模组和验证组,通过单因素及多因素Logistic回归分析建立IVIG耐药预测模型并行验证,将KD患儿按性别、年龄、发热天数和KD类型等分层,在不同的分层中单独建模和验证;基于全样本人群验证已发表的11个IVIG耐药预测模型,考察通过临床症状、体征和实验室指标是否能满足临床预测KD患儿IVIG耐药。结果 符合本文纳入和排除标准的1 360例KD患儿进入本文分析。男875例(64.3%);年龄中位数1.8(0.9,3.2)岁;IVIG耐药组和敏感组分别为171和1 189例;建模组和验证组分别为952和408例。建模组和验证组人口学特征、主要临床表现、实验室指标、IVIG耐药率和冠脉病变率差异均无统计学意义(P >0.05);建模组中建立的IVIG耐药模型中,男性、发病年龄≥2岁、N%≥0.75、Hb≥110 g·L-1各计1分,应用首剂IVIG发热≥5 d、ALB≥34 g·L-1、Na+≥133 mmol·L-1各计2分,AUC为0.818(95% CI:0.774~0.861),总分≥5时,敏感度和特异度分别为0.767和0.726。验证组中AUC为0.777(95% CI:0.712~0.842),敏感度和特异度分别为0.627和0.776。对11个IVIG耐药预测模型验证,以相应预测界值计算敏感度0.272~0.799,特异度0.412~0.926。结论 基于KD患儿人口学特征、临床症状、体征和实验室指标行KD患儿IVIG耐药预测特异度和敏感度均＜75%,对临床预测KD患儿IVIG耐药作用有限。

关键词: IVIG耐药, 川崎病, 预测

Abstract: Objective To question the prediction model of intravenous gamma globulin (IVIG) resistance in children with Kawasaki disease (KD).Methods Medical records of KD patients, who were first diagnosed and treated in Children's Hospital of Fudan University, were retrospectively collected. All eligible KD patients were randomly assigned to the establishment group and the validation group at the ratio of 7∶3. A prediction model of IVIG resistance was constructed in the establishment group through univariate and multivariate logistic regression analysis, and then validated in the validation group. In addition, KD children were stratified by gender, age of onset, fever days before initial IVIG, KD type, etc. A new prediction model was constructed and validated separately in each layer. Finally, the published prediction models of IVIG resistance, which were established based on clinical manifestations and laboratory indicators, were applied to the whole sample population to evaluate the predictive value.Results A total of 1,360 KD children were enrolled, including 875 males (64.3%). The median age of onset was 1.8 (0.9, 3.2) years. There were 171 patients in the IVIG resistant group, 1,189 in the IVIG sensitive group, 952 in the establishment group and 408 in the validation group, respectively. Demographic characteristics, main clinical symptoms, laboratory indicators, and the rate of IVIG resistance and coronary artery lesions were not significantly different between the establishment group and the validation group (P >0.05). In the establishment group, the constructed prediction model included 1 score each for male, age of onset ≥2 years, N%≥0.75, Hb≥110 g·L-1 and 2 scores each for fever days before initial IVIG ≥5 days, ALB≥34 g·L-1, Na+≥133 mmol·L-1. The AUC was 0.818 (95% CI: 0.774-0.861). With a cutoff point of ≥5 scores, the sensitivity and specificity were 0.767 and 0.726, respectively. In the validation group, the AUC was 0.777 (95% CI: 0.712-0.842) with the sensitivity and specificity of 0.627 and 0.776, respectively. When 11 published prediction models of IVIG resistance were applied to the whole sample population, the sensitivity and specificity were 0.272-0.799 and 0.412-0.926, respectively. Of the prediction models established in our and other studies, none had both sensitivity and specificity ≥75%.Conclusion Demographic characteristics, clinical symptoms, and laboratory indicators of KD children were insufficient to establish a clinically useful prediction model of IVIG resistance.

Key words: IVIG resistance, Kawasaki disease, Prediction