Predictors and a novel predictive model for intravascular immunoglobulin resistance in Kawasaki disease

Background Early identification of intravenous immunoglobulin (IVIG) resistance contributes to better management of Kawasaki disease (KD). This study aims to establish an effective prediction model for IVIG resistance in the Chinese population. Methods A total of 658 eligible patients diagnosed with KD were enrolled in this study, with 461 in the training cohort and 197 in the validation cohort. The demographic characteristics and potential risk factors were compared between IVIG-responsive and resistant groups. Predictors were selected by the Akaike information criterion. The nomogram’s performance was evaluated by calibration curve, decision curve analysis, and operating characteristic curve. Results White blood cell counts (WBC), neutrophil-lymphocyte ratio (N/L ratio), hematocrit (HCT), albumin (ALB), total bilirubin (TBIL), lactate dehydrogenase (LDH), and creatinine (Cr) were detected as predictors of IVIG resistance. A predictive nomogram incorporating these predictors was constructed using the training cohort. The calibration curve and decision curve analysis showed good discrimination and calibration of the proposed nomogram in both training and validation sets, and the area under the receiver operating characteristic curve (AUROC) in both sets was 75.8% and 74.2%, respectively. Conclusion This study identified WBC, N/L ratio, HCT, ALB, TBIL, LDH, and Cr as predictors for IVIG resistance in patients with KD. The proposed novel nomogram with a high level of accuracy and reliability may benefit clinical decision-making upon treatment initiation.