Late ventricular potential for risk prediction of sudden cardiac death risk: a valuable tool or an unnecessary step?

Background/Introduction

Signal-averaged electrocardiography (SA-ECG) is a high-resolution electrocardiography that can detect late ventricular potential, which known to be a noninvasive tool for risk stratification of sudden cardiac death (SCD) by predicting reentrant ventricular tachyarrhythmia. There is a paucity of data with SA-ECG on SCD survivors without structural heart disease, whereas majority of previous studies had been focused on post myocardial infarction survivors.

Purpose

This study assessed the clinical utility of SA-ECG as a risk stratification modality for lethal arrhythmic event in patients at risk of SCD without definite structural heart disease.

Methods

Total 629 patients who experienced or had potential risk of SCD were studied with SA-ECG. Among them, 48 patients who were found to have significant structural heart disease were excluded, except arrhythmogenic right ventricular cardiomyopathy. Major arrhythmic event (MAE) was defined as composite of all-cause death, aborted SCD, and sustained VT during any time either before visit of clinic or during follow up period. Syncope and non-sustained VT was defined as non-major arrhythmic event. SA-ECG was defined positive when fulfilling three or more criterion: (1) unfiltered QRS duration ≥114ms, (2) filtered QRS duration ≥114ms, (3) duration of terminal QRS <40uV exceeding 40ms, and (4) root mean square voltage in the terminal 40ms of ≤20ms.

Results

Among total 581 patients, 145 patients with positive SA-ECG showed higher incidence of MAE compared to patients with negative SA-ECG (21.4% vs. 6.7%, OR 3.816 [95% CI 2.208–6.597], p<0.001, Table). As the number of positive SA-ECG criteria increases, incidence of MAE tended to increase sequentially, which was markedly noted from 2 positive to 3 positive criteria (10.7% to 20.8%, p<0.001, Figure). In particular, patient with inherited arrhythmia showed higher rate of positive late potential compared to those with non-inherited arrhythmia (51.0% vs. 19.3%, p<0.001).

Conclusion

This study showed that at least 3 out of 4 diagnostic criteria in SA-ECG can independently predict lethal arrhythmic events and the positive late potential was associated with lethal arrhythmic event that leads to SCD, suggesting risk prediction for SCD using SA-ECG in patients even without structural heart disease including inherited arrhythmias.

Funding Acknowledgement

Type of funding sources: None.

Clinical role of genetic testing for overlapping between Brugada syndrome and arrhythmogenic right ventricular cardiomyopathy

Background/Introduction

Brugada syndrome (BrS) and arrhythmogenic right ventricular cardiomyopathy (ARVC) are inherited arrhythmias that may predispose to sudden cardiac arrest. Although its pathogenetic mechanisms differ, overlapping features between BrS and ARVC have been demonstrated previously. However, it remains to be determined whether genetic testing for ARVC-related gene is needed in patients with BrS.

Purpose

This study is aimed to analyze genetic profiles of BrS patients using next generation sequencing (NGS) based multigene panel including ARVC related genes.

Methods

Patients who were confirmed as BrS or clinically suspected as BrS with type 2 or 3 Brugada pattern electrocardiography were studied. Genetic testing using NGS panels (Illumina Inc., San Diego, CA, USA) included 30 genetic variants associated with inherited arrhythmia and genetic cardiomyopathy.

Results

Among the total 119 patients from BrS registry, 63 patients were confirmed as BrS and 56 patients were clinically suspected as BrS without fulfilling diagnostic criteria. One-hundred-fourteen patients (95.8%) were male, and mean age of onset was 43.6 year-old. Genetic variants were identified in 25 of 42 patients who received genetic testing. Six out of 25 patients (24.0%) showed ARVC-related genotypes (2 PKP2, 1 DSG2, 1 TMEM43, 1 JUP, and 1 DSP) (Figure 1 and Table 1). None of the patients showed structural or electrocardiographic features that fulfill diagnostic criteria of ARVC. It is notable that ARVC-related genotypes were mostly frequently accounted for BrS patients, following SCN5A and SCN10A.

Conclusion

In the clinic setting, ARVC-related genetic variants were identified in significant proportion of BrS patients, supporting that genetic testing of ARVC-overlapping is needed. This study suggests that follow-up including imaging study should be considered in BrS patients with ARVC-related genotypes to monitor disease progression as ARVC.

Funding Acknowledgement

Type of funding sources: None.

Artificial intelligence-predicted poor responders to catheter ablation for atrial fibrillation

Funding Acknowledgements

Type of funding sources: None.

Introduction

Although atrial fibrillation (AF) catheter ablation is effective for rhythm control, in some patients it is hard to maintain sinus rhythm in spite of repeated AF catheter ablation (AFCA) procedures and anti-arrhythmic drugs (AADs). We explored the pre-procedural predictors for poor responders to AFCA and tested whether artificial intelligence (AI) assists the prediction of poor responders in the independent cohort by determining the invasive parameters.

Methods

Among 1,214 patients who underwent AFCA and regular rhythm follow-up for 56.2 ± 33.8 months (59 ± 11 years, 73.5% male, 68.6% paroxysmal AF), we differentiated 92 poor responders defined as those with sustained AF despite repeat AFCAs, AADs, or electrical cardioversion. Using the Youden index, we identified advanced LA remodeling with lower LA voltage under 1.109mV. AI model, which was derived from development cohort using medical record, was applied to predict LA voltage <1.109mV in the independent cohort (n = 634, poor responders = 24) using a grad-cam score.

Results

The patients with lower LA voltage under 1.109mV showed significantly poorer rhythm outcomes (Log-rank p < 0.001). We determined invasive parameter LA voltage by using the multiple variables (age, female sex, AF type, CHA2DS2VASc score, LA dimension, E/em, hemoglobin, PR interval) and achieved relatively good prediction power of AI for LA voltage <1.109mV (AUC = 0.734, sensitivity 0.729, specificity 0.643) in the test cohort. In the independent cohort, the AI model showed good discrimination power for poor responders (AUC 0.751, p < 0.001) by estimating LA voltage, which is an invasive variable. The patients with predicted lower LA voltage (grad-cam score <0) showed poorer rhythm outcome after active rhythm control (Log-rank p < 0.001)

Conclusions

The patients with advanced atrial remodeling with low LA voltage, which can be predicted by an AI, showed significantly higher recurrence of AF after AFCA with AADs or cardioversion. AI may assist to select these poor responder patients before the AFCA procedure. Abstract Figure.