Department of Cardiology, Children's Hospital Capital Institute of Pediatrics, No.2, Yabao Rd, Chaoyang District, Beijing, 100020, China.
Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Ital J Pediatr. 2024 Sep 18;50(1):185. doi: 10.1186/s13052-024-01739-1.
Echocardiography-based ultrasomics analysis aids Kawasaki disease (KD) diagnosis but its role in predicting coronary artery lesions (CALs) progression remains unknown. We aimed to develop and validate a predictive model combining echocardiogram-based ultrasomics with clinical parameters for CALs progression in KD.
Total 371 KD patients with CALs at baseline were enrolled from a retrospective cohort (cohort 1, n = 316) and a prospective cohort (cohort 2, n = 55). CALs progression was defined by increased Z scores in any coronary artery branch at the 1-month follow-up. Patients in cohort 1 were split randomly into training and validation set 1 at the ratio of 6:4, while cohort 2 comprised validation set 2. Clinical parameters and ultrasomics features at baseline were analyzed and selected for models construction. Model performance was evaluated by area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC) and decision curve analysis (DCA) in the training and two validation sets.
At the 1-month follow-ups, 65 patients presented with CALs progression. Three clinical parameters and six ultrasomics features were selected to construct the model. The clinical-ultrasomics model exhibited a good predictive capability in the training, validation set 1 and set 2, achieving AUROCs of 0.83 (95% CI, 0.75-0.90), 0.84 (95% CI, 0.74-0.94), and 0.73 (95% CI, 0.40-0.86), respectively. Moreover, the AUPRC values and DCA of three model demonstrated that the clinical-ultrasomics model consistently outperformed both the clinical model and the ultrasomics model across all three sets, including the training set and the two validation sets.
Our study demonstrated the effective predictive capacity of a prediction model combining echocardiogram-based ultrasomics features and clinical parameters in predicting CALs progression in KD.
基于超声心动图的超声组学分析有助于川崎病（KD）的诊断，但它在预测冠状动脉病变（CAL）进展中的作用尚不清楚。我们旨在开发和验证一种预测模型，该模型将基于超声心动图的超声组学与临床参数相结合，用于预测 KD 患者的 CAL 进展。
共纳入了来自回顾性队列（队列 1，n=316）和前瞻性队列（队列 2，n=55）的 371 例基线有 CAL 的 KD 患者。CAL 进展定义为在 1 个月随访时任何冠状动脉分支的 Z 评分增加。队列 1 中的患者随机分为 6:4 的训练集和验证集 1，而队列 2 则由验证集 2组成。对基线时的临床参数和超声组学特征进行分析，并选择用于模型构建。在训练集和两个验证集中，通过接收者操作特征曲线下面积（AUROC）、精准召回曲线下面积（AUPRC）和决策曲线分析（DCA）评估模型性能。
在 1 个月的随访中，有 65 例患者出现 CAL 进展。选择了三个临床参数和六个超声组学特征来构建模型。临床-超声组学模型在训练集、验证集 1 和验证集 2中均具有良好的预测能力，AUROC 分别为 0.83（95%CI，0.75-0.90）、0.84（95%CI，0.74-0.94）和 0.73（95%CI，0.40-0.86）。此外，三个模型的 AUPRC 值和 DCA 表明，在所有三个集合（包括训练集和两个验证集）中，临床-超声组学模型在预测 CAL 进展方面始终优于临床模型和超声组学模型。
本研究表明，基于超声心动图的超声组学特征与临床参数相结合的预测模型能够有效预测 KD 患者的 CAL 进展。
