Wang Shuhui, Ding Chuxin, Zhang Qiyue, Hou Miao, Chen Ye, Huang Hongbiao, Qian Guanghui, Yang Daoping, Tang Changqing, Zheng Yiming, Huang Li, Xu Lei, Zhang Jiaying, Gao Yang, Zhuo Wenyu, Zeng Bihe, Lv Haitao
Department of Cardiology, Children's Hospital of Soochow University, Suzhou, China.
Department of Pediatrics, Institute of Pediatric Research, Children's Hospital of Soochow University, Suzhou, China.
Front Cardiovasc Med. 2023 Jul 28;10:1226592. doi: 10.3389/fcvm.2023.1226592. eCollection 2023.
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.
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.
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.
The nomogram can well predict IVIG resistance in KD. CAR was an important marker in predicting IVIG resistance in Kawasaki disease.
预测静脉注射免疫球蛋白（IVIG）抵抗性川崎病（KD）有助于早期治疗并预防冠状动脉病变。已针对KD中的IVIG抵抗性建立了一个临床一致性预测模型。
在这项对2016年1月1日至2021年12月31日诊断为KD的儿童进行的回顾性队列研究中，构建了一个评分系统。使用2022年1月1日至6月诊断为KD的儿童数据集进行前瞻性模型验证。最小绝对收缩和选择算子（LASSO）回归分析最佳地选择了基线变量。多变量逻辑回归纳入了LASSO回归分析中的预测因子以构建模型。使用选定变量，绘制了列线图。校准图、受试者操作特征曲线下面积（AUC）和临床影响曲线（CIC）用于评估模型性能。
在1975名儿童中，1259名儿童（1177名IVIG敏感型和82名IVIG抵抗型KD）被纳入训练集。淋巴细胞百分比；C反应蛋白/白蛋白比值（CAR）；以及天冬氨酸转氨酶、钠和总胆红素水平是IVIG抵抗的危险因素。训练集AUC为0.825（敏感性，0.723；特异性，0.744）。CIC表明列线图具有良好的临床应用价值。
列线图可以很好地预测KD中的IVIG抵抗性。CAR是预测川崎病IVIG抵抗性的重要标志物。
