Abstract

Objective: Kawasaki disease (KD) is characterized as an acute systemic vasculitis predominantly affecting young children, with coronary artery lesions (CALs) representing the most serious complication. Therapeutic resistance to intravenous immunoglobulin (IVIG) remains a significant clinical challenge. Consequently, numerous investigations have sought to identify predictive risk factors for IVIG resistance (IVIGR) and CAL development. Limited research has systematically evaluated the prognostic utility of electrocardiographic (ECG) parameters in KD outcome prediction. This study was therefore undertaken to assess the contributory value of ECG analysis in determining KD prognosis and therapeutic responses. Methods: This prospective cohort study enrolled 255 hospitalized children diagnosed with KD at West China Second University Hospital between July 2022 and December 2024. Initially, univariate analysis was performed to identify risk factors differentiating IVIGR from non-IVIGR patients and CAL from non-CAL patients. Statistically significant parameters were subsequently incorporated into machine learning analyses. Random forest algorithms were employed to construct predictive models based on the following: (1) complete blood count parameters, (2) biochemical and metabolic profiles, (3) electrocardiographic features, and (4) a comprehensive multimodal model integrating all parameters. These models generated feature importance scores, providing hierarchical rankings that quantified the relative contribution of each predictor to outcome prediction. Results: Univariate analysis demonstrated that alterations in hematological parameters, biochemical and metabolic profiles, and electrocardiographic features were significantly associated with therapeutic responses to IVIG and CAL development. Machine learning analysis revealed that ECG parameters individually contributed modest predictive weight for KD prognosis. However, the integration of ECG features into the comprehensive model substantially enhanced the discriminatory capacity, elevating the area under the curve (AUC) to 0.92 for CAL prediction. For IVIGR prediction, ECG-exclusive models demonstrated suboptimal performance in early disease management. Nevertheless, the multimodal integration of ECG with inflammatory and metabolic biomarkers achieved a comparable AUC of 0.92 for IVIGR prediction. Conclusions: This study establishes that ECG parameter alterations are significantly associated with CAL development and IVIGR in KD patients. Although ECG features demonstrate a limited independent predictive capacity compared to inflammatory and metabolic biomarkers, their integration into comprehensive predictive models substantially enhances discriminatory performance. These findings underscore the complementary value of electrocardiographic assessment in multimodal risk stratification strategies for KD management, supporting the clinical utility of ECG analysis as an adjunctive prognostic tool when combined with conventional laboratory parameters.

IPC Classification

Keywords