Establishment and validation of a nomogram for predicting intravenous immunoglobulin resistance and coronary artery lesions involvement in Kawasaki disease: A retrospective study

Objective We aimed to develop a useful nomogram for early identification of Kawasaki disease (KD) children at a high risk of intravenous immunoglobulin (IVIG) resistance and coronary artery lesions (CAL) complications to improve KD management. Methods: Clinical data from 400 patients treated at our hospital between January 1, 2016, and December 31, 2023, were collected. Lasso regression was utilized to screen risk factors for IVIG resistance and CAL involvement. Subsequently, a Logistic regression model incorporating parameters screened by Lasso regression was established and visualized as a nomogram. The discrimination, calibration, clinical applicability, and universality of the model were evaluated using receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and internal validation. Results NEU%, HDL-C and MHR were identified as predictors of IVIG resistance by Lasso regression, with C-index of the Logistic model being 0.886 for the training set and 0.855 for the validation set. For predicting CAL development, sex, fever date before the first IVIG administration, KD type, the level of HDL-C and MHR were the optimal variables, yielding C-index of 0.915 and 0.866 for the training and validation set, respectively. Calibration curves for both validation sets performed well, indicating strong predictive abilities of the models. Conclusions We established two nomograms for predicting IVIG resistance and CAL complications in KD patients, based on the Lasso-Logistic regression model. These nomograms were of guiding significance for screening KD children at high risk of developing IVIG resistance and CAL complications, thereby improving prognosis.