| 小儿川崎病并发冠状动脉损伤的危险因素分析 |
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| 引用本文： | 姚小飞,潘晓明,王丽.小儿川崎病并发冠状动脉损伤的危险因素分析[J].蚌埠医学院学报,2022,47(9):1217-1221. |
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| 作者姓名： | 姚小飞 潘晓明 王丽 |
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| 作者单位： | 安徽省马鞍山十七冶医院 儿科，243000 |
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| 摘 要： | 目的探究小儿川崎病并发冠状动脉损伤(CAL)的危险因素，为临床早期预防和干预提供指导。方法采用回顾性分析法，收集82例川崎病患儿的临床资料进行研究，其中合并有CAL者纳入观察组30例，未合并有CAL者纳入对照组52例，对影响小儿川崎病并发CAL的危险因素进行单因素分析、logistic多因素分析，明确独立危险因素后构建基于logistic回归、向量机(XGB)、决策树的不同预测模型，并对模型进行优化和验证以明确预测效能最为理想的预测模型。结果单因素分析和logstic回归模型分析显示, 热持续时间、血小板计数、白细胞计数、C反应蛋白、血钠是川崎病患儿发生CAL的独立危险因素(P < 0.05~P < 0.01)；基于随机森林模型分析得到的川崎病患儿发生CAL的危险因素按照影响权重大小排列依次为白细胞计数、C反应蛋白、血小板计数、血清白蛋白和血钠；基于XGB模型分析可知，影响川崎病患儿发生CAL的独立危险因素按照影响权重大小排列依次为血小板计数、血钠、血清白蛋白、白细胞计数及发热持续时间；基于logistic回归模型分析所得的独立危险因素预测小儿川崎病合并CAL的敏感性为86.50%，特异性为80.00%，约登指数为0.665，AUC面积为0.895；基于随机森林模型分析所得的危险因素预测小儿川崎病合并CAL的敏感性为86.70%，特异性为73.10%，约登指数为0.598，AUC面积为0.841；基于XGB模型分析所得的危险因素预测小儿川崎病合并CAL的敏感性为100%，特异性为80.00%，约登指数为0.800，AUC面积为0.963，XGB模型的预测效能优于logistic回归模型和随机森林模型。结论Logistic回归分析、随机森林模型、XGB模型均可用于小儿川崎病合并CAL的危险因素的研究，其中XGB模型的预测效能最为良好，所演算出的影响因素按照权重大小依次排序为血小板计数、血钠、血清白蛋白、白细胞计数和发热持续时间。
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| 关 键 词： | 川崎病 冠状动脉损伤 logistics模型 随机森林模型 XGB模型 |
| 收稿时间： | 2022-05-30 |
Analysis of the risk factors of coronary artery lesion in children with Kawasaki disease |
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| Affiliation: | Department of Pediatrics, Maanshan Shiqiye Hospital, Maanshan Anhui 243000, China |
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| Abstract: | ObjectiveTo explore the risk factors of Kawasaki disease complicated with coronary artery lesion(CAL) in children, and provide the guidance for clinical early prevention and intervention.MethodsThe clinical data of 82 children with Kawasaki disease were retrospectively analyzed, 30 patients with CAL and 52 patients without CAL were divided into the observation group and control group, respectively.The risk factors of Kawasaki disease complicated with CAL in children were analyzed using the univariate analysis and logistic multivariate analysis.After the independent risk factors were clarified, the different prediction models based on logistic regression, vector machine and decision tree were established, and the models were optimized and validated to identify the most ideal prediction model.ResultsThe results of the univariate analysis and logstic regression model analysis showed that the heat duration, platelet count, white blood cell count, C-reactive protein and serum sodium were the independent risk factors of CAL in children with Kawasaki disease(P < 0.05 to P < 0.01).The results of the random forest model analysis showed the the risk factors of CAL in children with Kawasaki disease were the white blood cell count, C-reactive protein, platelet count, serum albumin and serum sodium in order of influence weight.The results of XGB model analysis showed that the independent risk factors of CAL in children with Kawasaki disease were the platelet count, serum sodium, serum albumin, white blood cell count and fever duration in order of the influencing weight.The results of logistic regressive model analysis showed that the sensitivity, specificity, Youden index and AUC area of the independent risk factors in predicting CAL children with Kawasaki disease were 86.50%, 80.00%, 0.665 and 0.895, respectively.The results of random forest model analysis showed that the sensitivity, specificity, Youden index and AUC area of the risk factors in predicting CAL children with Kawasaki disease were 86.70%, 73.10%, 0.598 and 0.841, respectively.The results of XGB model analysis showed that the sensitivity, specificity, Youden index and AUC area of the risk factors in predicting CAL children with Kawasaki disease were 100.00%, 80.00%, 0.800 and 0.963, respectively.The prediction efficiency of GB model was better than that of logistic regression model and random forest model.ConclusionsThe logistic regression analysis, random forest model and XGB model can be used in the study of risk factors in CAL children with Kawasaki disease, the prediction efficacy of XGB model is most good, and the influencing factors are the platelet count, serum sodium, serum albumin, white blood cell count and fever duration in order of weigh calculated by XGB model. |
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