Analysis of the risk factors of coronary artery lesion in children with Kawasaki disease

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.