A novel model for predicting intravenous immunoglobulin-resistance in Kawasaki disease: a large cohort study - Wang, Shuhui; Ding, Chuxin; Zhang, Qiyue; Hou, Miao; Chen, Ye; Huang, Hongbiao; Qian, Guanghui; Yang, Daoping; Tang, Changqing; Zheng, Yiming; Huang, Li; Xu, Lei; et al. (Frontiers in Cardiovascular Medicine, 2023)
Background Predicting intravenous immunoglobulin (IVIG)-resistant Kawasaki disease (KD) can aid early treatment and prevent coronary artery lesions. A clinically consistent predictive model was developed for IVIG resistance in KD. Methods In this retrospective cohort study of children diagnosed with KD from January 1, 2016 to December 31, 2021, a scoring system was constructed. A prospective model validation was performed using the dataset of children with KD diagnosed from January 1 to June 2022. The least absolute shrinkage and selection operator (LASSO) regression analysis optimally selected baseline variables. Multivariate logistic regression incorporated predictors from the LASSO regression analysis to construct the model. Using selected variables, a nomogram was developed. The calibration plot, area under the receiver operating characteristic curve (AUC), and clinical impact curve (CIC) were used to evaluate model performance. Results Of 1975, 1,259 children (1,177 IVIG-sensitive and 82 IVIG-resistant KD) were included in the training set. Lymphocyte percentage; C-reactive protein/albumin ratio (CAR); and aspartate aminotransferase, sodium, and total bilirubin levels, were risk factors for IVIG resistance. The training set AUC was 0.825 (sensitivity, 0.723; specificity, 0.744). CIC indicated good clinical application of the nomogram. Conclusion The nomogram can well predict IVIG resistance in KD. CAR was an important marker in predicting IVIG resistance in Kawasaki disease.