Li Changjian, Zhang Huayong, Yin Wei, Zhang Yong
Department of Cardiology, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430016, China.
Department of Rheumatology, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430016, China.
Comput Methods Programs Biomed. 2025 Mar;260:108588. doi: 10.1016/j.cmpb.2025.108588. Epub 2025 Jan 6.
Predicting potential risk factors for the occurrence of coronary artery lesions (CAL) in children with Kawasaki disease (KD) is critical for subsequent treatment. The aim of our study was to establish and validate a nomograph-based model for identifying children with KD at risk for CAL.
Hospitalized children with KD attending Wuhan Children's Hospital from Jan 2011 to Dec 2023 were included in the study and were grouped into a training set (4793 cases) and a validation set (2054 cases) using a simple random sampling method in a 7:3 ratio. The analysis was performed using RStudio software, which first used LASSO regression analysis to screen for the best predictors, and then analyzed the screened predictors using logistic regression analysis to derive independent predictors and construct a nomogram model to predict CAL risk. The receiver operating characteristic (ROC) and calibration curves were employed to evaluate the discrimination and calibration of the model. Finally, decision curve analysis (DCA) was utilized to validate the clinical applicability of the models assessed in the data.
Of the 6847 eligible children with KD included, 845 (12 %) were ultimately diagnosed with CAL, of whom 619 were boys (73 %) with a median age of 1.81 (0.74, 3.51) years. Six significant independent predictors were identified, including sex, intravenous immunoglobulin nonresponse, peripheral blood hemoglobin, platelet distribution width, platelet count, and serum albumin. Our model has acceptable discriminative power, with areas under the curve at 0.671 and 0.703 in the training and validation sets, respectively. DCA analysis showed that the prediction model had great clinical utility when the threshold probability interval was between 0.1 and 0.5.
We constructed and internally validated a nomograph-based predictive model based on six variables consisting of sex, intravenous immunoglobulin nonresponse, peripheral blood hemoglobin, platelet distribution width, platelet count, and serum albumin, which may be useful for earlier identification of children with KD who may have CAL.
预测川崎病(KD)患儿发生冠状动脉病变(CAL)的潜在危险因素对后续治疗至关重要。本研究的目的是建立并验证一种基于列线图的模型,用于识别有CAL风险的KD患儿。
纳入2011年1月至2023年12月在武汉儿童医院住院的KD患儿,采用简单随机抽样方法按7:3的比例分为训练集(4793例)和验证集(2054例)。使用RStudio软件进行分析,首先采用LASSO回归分析筛选最佳预测因子,然后对筛选出的预测因子进行逻辑回归分析,得出独立预测因子并构建预测CAL风险的列线图模型。采用受试者工作特征(ROC)曲线和校准曲线评估模型的区分度和校准度。最后,利用决策曲线分析(DCA)验证数据中评估模型的临床适用性。
在纳入的6847例符合条件的KD患儿中,845例(12%)最终被诊断为CAL,其中619例为男孩(73%),中位年龄为1.81(0.74,3.51)岁。确定了6个显著的独立预测因子,包括性别、静脉注射免疫球蛋白无反应、外周血血红蛋白、血小板分布宽度、血小板计数和血清白蛋白。我们的模型具有可接受的区分能力,训练集和验证集的曲线下面积分别为0.671和0.703。DCA分析表明,当阈值概率区间在0.1至0.5之间时,预测模型具有很大的临床实用性。
我们构建并内部验证了一种基于列线图的预测模型,该模型基于性别、静脉注射免疫球蛋白无反应、外周血血红蛋白、血小板分布宽度、血小板计数和血清白蛋白这6个变量,可能有助于早期识别可能患有CAL的KD患儿。