A nomogram for predicting immunoglobulin-resistant Kawasaki disease in children. Issue 2 (February 2023) - Pan, Yan; Fan, Qihong
Objective: This case–control study focused on the establishment and internal validation of a risk nomogram for intravenous immunoglobulin (IVIG)-resistant Kawasaki disease (KD) using the Kawasaki Disease Database. Methods: The Kawasaki Disease Database is the first public database for KD researchers. A prediction nomogram for IVIG-resistant KD was constructed using multivariable logistic regression. Then, the C-index was used to assess the discriminating ability of the proposed prediction model, a calibration plot was drawn to evaluate its calibration, and a decision curve analysis was adopted to assess its clinical usefulness. Bootstrapping validation was performed for interval validation. Results: The median ages of IVIG-resistant and -sensitive KD groups were 3.3 and 2.9 years, respectively. Predicting factors incorporated into the nomogram were coronary artery lesions, C-reactive protein, percentage of neutrophils, platelets, aspartate aminotransferase, and alanine transaminase. Our constructed nomogram exhibited favorable discriminating ability (C-index: 0.742; 95% confidence interval: 0.673–0.812) and excellent calibration. Moreover, interval validation achieved a high C-index of 0.722. Conclusions: The as-constructed new IVIG-resistant KD nomogram that incorporated C-reactive protein, coronary artery lesions, platelets, percentage of neutrophils, alanine transaminase, and aspartate aminotransferase may be adopted for predicting the risk of IVIG-resistant KD.