目的·评估抗中性粒细胞胞质抗体（anti-neutrophil cytoplasmic antibody，ANCA）对川崎病（Kawasaki disease，KD）患儿并发冠状动脉病变（coronary artery lesion，CAL）的预测价值。方法·回顾性收集2018年1月至2024年5月上海交通大学医学院附属上海儿童医学中心收治的340例KD患儿的临床资料，按7∶3的比例随机分为训练集（n=237）和验证集（n=103）。通过单因素分析、最小绝对收缩和选择算法（least absolute shrinkage and selection operator，LASSO）筛选出CAL的危险因素，并将其纳入多因素Logistic回归分析，构建列线图预测模型。分别采用受试者操作特征（receiver operating characteristic，ROC）曲线、校准曲线及Hosmer-Lemeshow拟合优度检验、决策曲线分析（decision curve analysis，DCA）评价模型的区分度、校准度和临床适用性。根据Logistic回归方程中的自变量系数对各变量赋分，得到一个预测评分系统，并将其与目前3个常用评分系统（Kobayashi评分、Egami评分和Sano评分）的预测效能进行比较。结果·男性、低白蛋白血症、ANCA阳性和静脉注射免疫球蛋白抵抗是KD患儿发生CAL的危险因素，据此构建列线图预测模型。模型在训练集和验证集中的ROC曲线下面积分别为0.747（95%CI 0.667~0.821）和0.645（95%CI 0.500~0.794），表明模型预测效能良好；模型经校准曲线和Hosmer-Lemeshow拟合优度检验（训练集χ² =5.105，P=0.746；验证集χ² =13.549，P=0.094）验证，预测准确性良好；DCA显示模型具有一定的临床适用性。根据Logistic回归方程系数建立CAL的预测评分体系，与Kobayashi评分、Egami评分、Sano评分模型相比，其灵敏度（58.4%）和特异度（78.7%）均较高。结论·研究基于ANCA建立了一个可有效预测KD患儿发生CAL风险的评分模型，可为临床上早期识别高危患儿、制定个性化治疗方案和管理策略提供参考。

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.