Assessment of ventricular conduction in Brugada patients by morphological characterization of QRS complexes

Background Brugada syndrome (BrS) is an inherited cardiac disorder associated with the risk of malignant arrhythmias, cardiogenic syncope, and sudden cardiac arrest. It is characterized by delayed and heterogeneous electrical activation, predominantly involving the right ventricular outflow tract. Diagnosis relies on the presence of a characteristic type-I electrocardiographic (ECG) pattern, either spontaneous or drug-induced. However, asymptomatic cases may remain underdiagnosed due to the marked temporal variability of ECG-pattern manifestations. Therefore, improved non-invasive tools for diagnosis and risk stratification in BrS remain a clinical need. Purpose We hypothesize that quantification of spatiotemporal variability in ventricular conduction could improve diagnostic assessment and risk stratification in BrS. The study therefore aims to characterize QRS-complex spatiotemporal dynamics from 24-hour, high-precordial, 12-lead Holter ECG recordings. Methods Delayed ventricular activation was quantified using a lead-specific propagation progression time (PPT), defined as the time point within the QRS complex at which 30%, 50%, 70% of cumulative QRS energy is reached. PPT values were computed every 30 minutes. Temporal dynamic of PPT was assessed computing the 24-hour medians and standard deviations. The methodology was evaluated in a cohort of 162 subjects, including 118 BrS patients and 44 controls. In addition, the resting (~ 60 bpm) QRS morphology was modelled using Hermite polynomials fitting and clustered with k-means based on principal component projections of Hermite coefficients and width parameter to identify ECG phenotypes in BrS population (see Figure 1). Results BrS patients showed significantly prolonged PPT values across all leads versus controls. The greatest between-group difference occurred at 70% of QRS energy (median PPT in lead V22IC: 74 [63–87] ms vs 60 [56–66] ms; p < 0.001). Increased intra-subject temporal variability of PPT at 70% QRS energy discriminated controls from drug-induced type-I BrS (AUC > 0.75 in leads V22IC and V13IC). Clustering identified two BrS subgroups. Cluster 2 exhibited slowed depolarization with significantly higher τ70 values and more positive QRS complexes than Cluster 1 (see median QRS and PPT table in figure 2), without differences in clinical risk markers (see Figure 1). Nevertheless, a high percentage of patients with spontaneous type-I pattern is present in Cluster 2 (see Cluster characterization in Figure 2). Conclusion PPT analysis provides a non-invasive measure of ventricular conduction abnormalities in BrS and can improve diagnostic discrimination. While resting QRS morphology clustering did not stratify risk, it may inform subgroup-specific computational modelling. Variability-based PPT metrics reflecting QRS-morphology changes warrant further evaluation as potential markers for diagnostic and risk stratification.