[1]李红霞 朱凯 刘雯 王慧 杨涛毅.0～5岁川崎病儿童静脉注射免疫球蛋白耐药的相关因素分析及预测模型构建[J].心血管病学进展,2023,(3):283.[doi:10.16806/j.cnki.issn.1004-3934.2023.03.020]
　LI HongxiaZHU KaiLIU WenWANG HuiYANG Taoyi?/html>.Analysis of Related Factors and Prediction Model?f Intravenous Immunoglobulin Resistance in Children with Kawasaki Disease Aged 0 5 Years[J].Advances in Cardiovascular Diseases,2023,(3):283.[doi:10.16806/j.cnki.issn.1004-3934.2023.03.020]
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0～5岁川崎病儿童静脉注射免疫球蛋白耐药的相关因素分析及预测模型构建()
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《心血管病学进展》[ISSN:51-1187/R/CN:1004-3934]

卷:
期数:
2023年3期
页码:
283
栏目:
论著
出版日期:
2023-03-25

文章信息/Info

Title:
Analysis of Related Factors and Prediction Model?f Intravenous Immunoglobulin Resistance in Children with Kawasaki Disease Aged 0 5 Years
作者:
李红霞 朱凯 2 刘雯 1 王慧 1 杨涛毅 1
(1.西南交通大学附属医院 成都市第三人民医院儿科,四川 成都 610031;2.新疆医科大学护理学院,新疆 乌鲁木齐 830011)
Author(s):
LI Hongxia1ZHU Kai2LIU Wen1WANG Hui1YANG Taoyi1?/html>
?1.Department of Pediatrics,The Third People’s Hospital of Chengdu,The Affiliated Hospital of Southwest Jiaotong University,Chengdu 610031,Sichuan,China; 2.School of Nursing,Xinjiang Medical University,Urumqi 830011,Xinjiang,China)
关键词:
川崎病; 静脉注射免疫球蛋白耐药; 危险因素; 预测模型
Keywords:
Kawasaki disease; Intravenous immunoglobulin resistance; Risk factor; Prediction model
DOI:
10.16806/j.cnki.issn.1004-3934.2023.03.020
摘要:
目的 通过卡方自动交互检测（CHAID）决策树模型与逻辑回归探讨川崎病（KD）静脉注射免疫球蛋白（IVIg）耐药发生的影响因素及其对IVIg耐药的预测效果。方法 采用回顾性队列研究方法，选取2015年1月—2022年1月于成都市第三人民医院确诊并经规范治疗且随访记录完整的310例KD患儿为研究对象，根据 IVIg耐药的发生情况分为耐药组与敏感组。分别构建逻辑回归模型与CHAID决策树模型分析IVIg耐药发生的主要影响因素，采用ROC曲线比较模型的效能。结果 310例KD患儿中，出现IVIg耐药63例（20.3%），与IVIg敏感组相比，IVIg耐药组的中性粒细胞绝对值、总胆红素（TBIL）、谷草转氨酶、谷丙转氨酶、C反应蛋白（CRP）、脑钠肽（BNP）和血清铁蛋白（SF）水平显著升高（P＜0.05），10 d后开始使用IVIg，并发冠状动脉病变、心包积液、侧支血管形成的人数更多（P＜0.05），血小板计数、血钠水平显著降低（P＜0.05）。决策树模型ROC曲线下面积为0.918 6（95% CI 0.862 3～0.974 8）， 逻辑回归模型为0.855 9（95% CI 0.775 9～0.935 9），两种模型区分度均良好，但CHAID决策树模型分类效能更优（ Z＝9.191，P＜0.001）。结论 KD患儿发生 IVIg耐药的主要高危因素包括TBIL≥1.46 mg·dL﹣1、CRP≥94 mg·dL﹣1、BNP≥1 450 pg·mL﹣1和SF≥148 μg·L﹣1，其中TBIL和BNP是重要的影响因素。CHAID决策树模型可精确地识别早期IVIg耐药，为预防儿童获得性心脏病提供参考。
Abstract:
Objective The CHAID decision tree model and logistic regression were used to explore the factors influencing the occurrence of intravenous immunoglobulin(IVIg) resistance in Kawasaki disease(KD) and its predictive effect on IVIg resistance. Methods 310 KD patients who were diagnosed and rec eived treatment in The Third People’s Hospital of Chengdu between January 2015 and January 2022 and had complete follow-up records were chosen for the study using a retrospective cohort study method. The participants were split into drug-resistant group and sensitive group based on the presence of IVIg resistance. Logistic regression model and CHAID decision tree model were constructed to analyze the main factors affecting the occurrence of IVIg resistance,and the efficacy of the model was compared using the ROC curve. Results 63(20.3%) of the 310 KD patients acquired IVIg resistance. Compared with IVIg-sensitive group,the absolute neutrophil count,total bilirubin(TBIL),glutamic-oxaloacetic transaminase,glutamic-pyruvic transaminase,C-reactive protein(CRP),brain natriuretic peptide(BNP),and serum ferritin(SF) levels in IVIg-resistant group were significantly higher(P＜0.05).The number of patients with coronary artery lesion,pericardial effusion,and collateral vessel formation was higher when IVIg was used 10 days later(P＜0.05),and platelet count and blood sodium level were significantly lower(P＜0.05). Both models performed well in terms of discrimination,but the CHAID decision tree model had superior classification efficacy（Z＝9.191，P＜0.001）,with the area under the ROC curve for the decision tree model being 0.918 6(95% CI 0.862 3 to 0.974 8) and for the logistic regression model being 0.855 9(95% CI 0.775 9 to 0.935 9). Conclusion The main risk factors for IVIg resistance in KD children include TBIL≥1.46 mg·dL﹣1,CRP≥94 mg·dL﹣1,BNP≥1 450 pg·mL﹣1 and SF≥148 μg·L ﹣1,in which TBIL and BNP are important influencing factors. CHAID decision tree model can accurately identify early IVIg resistance and provide reference for prevention of acquired heart disease in children

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更新日期/Last Update: 2023-04-24