Progression prediction of coronary artery lesions by echocardiography-based ultrasomics analysis in Kawasaki disease

Abstract

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. Early identification of patients at risk of progression of coronary artery lesions remains vital for improving the prognosis of patients with Kawasaki disease. Whether ultrasomics help predict the progression of coronary artery lesions in Kawasaki disease remains unclear. The present study included 371 patients with Kawasaki disease and analyzed 1484 echocardiographic images. An integrated model combining echocardiogram-based ultrasomics features and clinical parameters was established and validated, demonstrating satisfactory performance in predicting the progression of coronary artery lesions in Kawasaki disease.