Developing a risk prediction model for sudden cardiac death in children with hypertrophic cardiomyopathy - Zhen, Zhen; Xu, Yeqiong; Chen, Xi; Na, Jia; Xiao, Yanyan; Yuan, Yue (Frontiers in Pediatrics, 2025)
ObjectiveThis study aimed to develop a predictive model for sudden cardiac death (SCD) in children with hypertrophic cardiomyopathy (HCM).MethodsThe retrospective study included children diagnosed with HCM who visited Beijing Children's Hospital, Capital Medical University between January 2006 and August 2022. Cox regression analysis was used to identify risk factors for SCD. A nomogram was constructed based on risk factors identified through multivariate analysis.ResultsA total of 184 children (115 boys and 69 girls) were included in the study. The median (IQR) age at the initial diagnosis was 4.54 (0.50–10.25) years. Of these, 141 children were diagnosed with primary HCM, while 43 had secondary HCM. The multivariate analysis showed that age <1 year [hazard ratio (HR), 95% confidence interval (CI): 6.232 (2.858–13.591)], female sex [HR: 2.547 (1.460–4.444)], a family history of HCM [HR: 2.622 (1.468–4.683)], pathological Q-waves [HR: 2.290 (1.285–4.082)], fragmented QRS waves [HR: 3.526 (1.786–6.963)], combined arrhythmias (HR: 2.218 [1.136–4.333]), increased interventricular septal thickness [HR: 1.055 (1.008–1.105)], and increased left ventricular posterior wall thickness [HR: 1.060 (1.026–1.096)] were significantly associated with SCD. The nomogram-based SCD prediction model demonstrated strong discriminatory ability, with areas under the curve (AUC) of 0.887 (95% CI: 0.829–0.945) at 1 year, 0.839 (95% CI: 0.777–0.902) at 2 years, 0.847 (95% CI: 0.782–0.912) at 3 years, 0.855 (95% CI: 0.791–0.919) at 4 years, 0.850 (95% CI: 0.789–0.911) at 5 years, and 0.845 (95% CI: 0.763–0.926) at 10 years. Predicted probabilities closely aligned with observed probabilities, indicating good calibration of the model.ConclusionA predictive model for SCD in children with HCM was developed, demonstrating strong internal consistency and reliability. External validation is recommended before clinical implementation.