| Background: With the increase in morbidity,Kawasaki disease has become the leading cause of acquired heart disease in children.Prompt treatment with high-dose(2 g/kg)intravenous immunoglobulin(IVIG)could significantly reduce manifestations of KD and the prevalence of CALs.However,10%-20% of KD patients are resistant to IVIG(IVIG-R).The incidence of CALs and giant coronary aneurysms in IVIG-resistant KD group was significantly higher than that in the IVIG-sensitive KD group.Previous studies have found some risk factors of IVIG-resistant KD,based on those risk factors,some predicting models for IVIG-resistant KD were established.However,there is no one model can be generally used in different countries and regions.Objective: Accurate and early evaluation of individual risk of IVIG-resistant KD is critical for adopting appropriate regimens for the first treatment and prevention of CALs.This study was aimed to investigate the independent risk factors and build a model for early predicting IVIG-resistant KD in Chongqing based on big data.Materials and methodsDate collection: patients hospitalized in Children’s Hospital of Chongqing Medical University from October 2007 to December 2017 with main discharge diagnosis of KD were enrolled into the study.As for predicting the response to IVIG,patients with KD were assigned to IVIG-resistant group and IVIG-responsive group.The information of the patients was imported into SQL SEVER2008,and the demographic characteristics,imaging data and the laboratory data were collected and washed under SQL SEVER2008.For building the prediction model of IVIG-resistant KD,70% of the patients were randomly selected from the whole sample,including the IVIG--resistant KD and IVIG-responsive KD,by generating random list of number;the other 30% of the patients’ data were used for testing the new model.Data analysis was conducted using R Project for Statistical Computing(R version 3.4.1).All data were presented as count with percentage for categorical variables and mean ± standard deviation(SD)for continuous variables.For the variables with miss rate <25%,multiple imputation was used.The Mann-Whitney U test was used for the comparison of the intergroup continuous variables;the Chi-square test was used for the comparison of categorical variables between the two groups.P<0.05 was considered statistically significant.To determine independent predictors ofIVIG resistance,multivariate logistic regression analysis with least absolute shrinkage and selection operator(LASSO)was performed using the indicators with significant difference derived from the univariate analysis;the OR and 95% CI were calculated.The OR value was used to determine the score of an independent risk factor and build the new prediction model.Hosmer-Lemeshow goodness of fit(GOF)test was used to test the model,and p>0.05 indicated that the prediction model fit the sample data.Receiver operating characteristic(ROC)curve and the area under the curve(AUC)were used to determine the predictive ability,sensitivity,and specificity of the prediction model.To identify personal risk probability of IVIG-resistance that could be used in the nomogram,an equation was given.Result: A total of 5789 subjects were enrolled into the study,5277 of which met the inclusion criteria and were enrolled into the study,including348 cases of IVIG resistance(348/5277,6.59%)and 4929 cases of IVIG responder(4929/5277,93.41%).Fifty-seven variables were collected,including 4 demographic variables,1 imaging variable,and 52 laboratory variables.According to univariate analysis,42 variables were significantly different between IVIG-resistant KD and IVIG-responsive KD.24 variables were significantly higher in the IVIG-resistant group than in the IVIG-responsive group,18 items were significantly lower in theIVIG-resistant group.For multiple logistic regression analysis,the variables with statistical significance derived from univariate analysis were further selected by LASSO.Eight indicators presented statistical significance and were used for multivariate logistic regression analysis.The independent risk factors for IVIG-resistant KD were higher Red blood cell distribution width(RDW),lower platelet count(PLT),lower Percentage of lymphocyte(P-LYM),higher total bile acid(TBA),lower albumin(ALB),lower serum sodium level,higher degree of Degree of coronary artery lesions(D-CALs)and younger age.Based on the above result,a nomogram was derived for personal risk probability of IVIG-resistance and an equation underlying logistic model was given.The new predictive model for IVIG-resistance showed an AUC of 0.74,sensitivity of 76% and specificity of 59%.Multiple testing was performed to further evaluate the validity of the new prediction model.The AUC on average was 0.72(range 0.65-0.80),indicating the value of AUC was valid.Compared with previous IVIG-resistant scoring systems,the new model(AUC=0.74)presented a higher AUC,sensitivity and specificity.Conclusion: Using SQL can efficiently and accurately collect and clean medical big data.We found three new independent risk factors of IVIG-resistant KD which including RDW,D-CALs,and P-LYM.The IVIG-resistance could be predicted using the values of RDW,PLT,P-LYM,TBA,ALB,serum sodium level,D-CALs,and age.The new model of predicting IVIG-resistant KD appeared to be superior to those previous prediction models for the KD population in Chongqing city.Further study is necessary to validate the utility of this new model. |
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