2Department of Cardiology, Tunceli State Hospital, Tunceli, Türkiye
3Department of Cardiology, Mehmet Akif Ersoy Thoracic and Cardiovascular Surgery Training and Research Hospital, İstanbul, Türkiye
Abstract
Background: Hypertrophic cardiomyopathy (HCM) is characterized by asymmetric left ventricular hypertrophy and myocardial fibrosis, which significantly increases the risk of sudden cardiac death (SCD). Existing risk stratification models are limited in predicting SCD risk in patients within the “gray zone”—those with intermediate risk. This study investigates the prognostic utility of the Index of Cardiac Electrophysiological Balance (ICEB) and its corrected variant (ICEBc) in predicting ventricular arrhythmias (VAs) in HCM. To evaluate the predictive value of ICEB and ICEBc for Life-Threatening Arrhythmias (LTA) and non-sustained ventricular tachycardia (NSVT) in HCM and compare their performance with traditional repolarization parameters and the European Society of Cardiology (ESC) SCD Risk Score.
Methods: A retrospective observational study was conducted at a single center, including 127 HCM patients categorized into 3 groups: LTA (n = 45), NSVT (n = 29), and control (n = 53). Electrocardiographic parameters, including ICEB, ICEBc, Tp-e interval, Tp-e/QTc ratio, and QRS-T angle were measured. Multiple logistic regression and receiver operating characteristic (ROC) curve analyses were performed to identify independent predictors of VAs.
Results: The ICEB and ICEBc were significantly lower in LTA and NSVT groups compared to the control group (P < .001), indicating increased arrhythmogenic risk. The ROC curve analysis showed that ICEB and ICEBc had superior predictive power for LTA and NSVT compared to traditional markers and the ESC SCD Risk Score, with the highest area under the curve (AUC) for the Base + ICEB Model (AUC = 0.79).
Conclusion: The ICEB and ICEBc are robust markers of repolarization heterogeneity and effective predictors of VAs in HCM patients. Their integration into existing risk stratification models could enhance predictive accuracy, particularly for gray zone patients.