Objective ·To evaluate the predictive value of anti-neutrophil cytoplasmic antibodies (ANCA) in Kawasaki disease (KD) complicated with coronary artery lesions (CALs) and to construct a nomogram prediction model. Methods ·A retrospective study was conducted to collect the clinical data of 340 children with KD admitted to Shanghai Children's Medical Center from January 2018 to May 2024. All patients were randomly divided in a 7:3 ratio into a training set (n=237) and a validation set (n=103). Univariate analysis and least absolute shrinkage and selection operator (LASSO) were applied to screen the risk factors of CALs, which were incorporated into multifactorial Logistic regression analysis to develop the nomogram model. The model's discrimination, calibration and clinical practicability were evaluated using the receiver operating characteristic (ROC) curve, calibration curve, Hosmer-Lemeshow goodness-of-fit test, and decision curve analysis (DCA). A new predictive scoring system was obtained by assigning scores to each variable based on the coefficients of the independent variables in the Logistic regression equation, and its predictive efficacy was then compared with that of three commonly used scoring systems, Kobayashi, Egami, and Sano scoring models. Results ·Male, low serum albumin level, ANCA positivity, and intravenous immunoglobulin resistance were risk factors for the development of CALs in children with KD, based on which a nomogram model was constructed. The area under the ROC curve for the nomogram in the training set and validation set were 0.747 (95%CI 0.667‒0.821) and 0.645 (95%CI 0.500‒0.794), respectively, indicating good effectiveness. The model was verified to have good predictive accuracy through the calibration curve and Hosmer-Lemeshow goodness-of-fit test (training set: χ² =5.105, P=0.746; validation set:χ² =13.549, P=0.094). The DCA showed its clinical usefulness. A predictive scoring system for CALs was developed based on the coefficients of the Logistic regression equation, which demonstrated higher sensitivity (58.4%) and specificity (78.7%) compared to the Kobayashi, Egami, and Sano scoring models. Conclusion ·This study developed a new scoring model based on ANCA to effectively predict the risk of CALs in KD patients. The model provides valuable reference for clinicians to identify high-risk patients early, and to formulate personalized treatment plans and management strategies.