Department of Clinical Laboratory, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China.
Department of Infectious Diseases, Youxian People's Hospital, Zhuzhou, China.
Clin Cardiol. 2023 Nov;46(11):1434-1441. doi: 10.1002/clc.24113. Epub 2023 Aug 4.
Coronary artery lesions are the most important complications of Kawasaki disease. Approximately 25-30% of untreated patients develop coronary artery disease, which can lead to long-term cardiovascular sequelae.
The aim of this study is to evaluate the risk factors for coronary artery lesions in Kawasaki disease and to construct a nomogram for predicting the likelihood of developing such lesions.
Data from 599 patients between January 2012 and June 2020 were reviewed retrospectively. Patients were randomly assigned to the training set (n = 450) and the validation set (n = 149). A comparison of clinical features and laboratory data was performed, followed by multivariate logistic regression analysis to identify independent risk factors and develop the nomogram. The predictive efficiency of the nomogram was evaluated using the calibration curve, area under the receiver operating characteristic curve (AUC), C-index, and decision curve analysis (DCA).
Intravenous immunoglobulin (IVIG) resistance, delayed IVIG treatment, C-reactive protein, and neutrophil/lymphocyte ratio were identified as independent risk factors for the development of coronary artery lesions. The nomogram was constructed based on these four variables. The calibration curve of the nomogram showed a high degree of agreement between the predicted probability and the actual probability. The AUC of the nomogram in the training and validation set was 0.790 and 0.711, respectively. In addition, DCA revealed that the nomogram provided a significant net benefit, further supporting its clinical utility.
The constructed nomogram demonstrates a strong and reliable performance in predicting coronary artery lesions, which enables clinicians to make timely and tailored clinical decisions.
冠状动脉病变是川崎病最重要的并发症。未经治疗的患者中约有 25-30%会发生冠状动脉疾病,从而导致长期心血管后遗症。
本研究旨在评估川崎病患者冠状动脉病变的危险因素,并构建预测发生此类病变可能性的列线图。
回顾性分析了 2012 年 1 月至 2020 年 6 月期间 599 例患者的数据。患者被随机分配到训练集(n=450)和验证集(n=149)。比较了临床特征和实验室数据,然后进行多变量逻辑回归分析,以确定独立的危险因素并建立列线图。通过校准曲线、接受者操作特征曲线下面积(AUC)、C 指数和决策曲线分析(DCA)评估列线图的预测效率。
静脉注射免疫球蛋白(IVIG)耐药、IVIG 延迟治疗、C 反应蛋白和中性粒细胞/淋巴细胞比值被确定为发生冠状动脉病变的独立危险因素。根据这四个变量构建了列线图。列线图的校准曲线显示预测概率与实际概率之间具有高度一致性。列线图在训练集和验证集中的 AUC 分别为 0.790 和 0.711。此外,DCA 显示列线图提供了显著的净收益,进一步支持了其临床实用性。
所构建的列线图在预测冠状动脉病变方面表现出强大而可靠的性能,使临床医生能够及时做出个性化的临床决策。