Recurrence of ventricular tachycardia (VT) after ablation in patients with previous myocardial infarction is associated with adverse prognosis. However, the impact of the timing of VT recurrence on outcomes is unclear. We analyzed data from a multicenter collaborative database of patients who underwent catheter ablation for infarct-related VT. Multivariable Cox regression analyses investigated the effect of the timing of VT recurrence on the composite outcome of death or heart transplantation using VT recurrence as a time-varying covariate. A total of 1412 patients were included (92% men; age: 66.7±10.7 years), and 605 patients (42.8%) had a recurrence after median 116 days (188 [31.1%] within 1 month, 239 [39.5%] between 1 and 12 months, and 178 [29.4%] after 12 months). At median follow-up of 670 days, 375 patients (26.6%) experienced death or heart transplantation. The median time from recurrence to death or heart transplantation was 65 and 198.5 days in patients with recurrence ≤30 days and >30 days post ablation, respectively. The adjusted hazard ratio (95% confidence interval) for the effect of VT recurrence occurring immediately post ablation on death or heart transplantation was 3.45 (2.33-5.11) in reference to no recurrence. However, the magnitude of this effect decreased statistically significantly (P<0.001) as recurrence occurred later in the follow-up period. The respective risk estimates for VT recurrence at 30 days, 6 months, 1 year, and 2 years were 3.36 (2.29-4.93), 2.94 (2.09-4.14), 2.50 (1.85-3.37), and 1.81 (1.37-2.40). VT recurrence post ablation is associated with a mortality risk that is highest soon after the ablation and decreases gradually thereafter.

Background

Artificial intelligence (AI) applied to 12-lead electrocardiographs (ECGs) can detect hypertrophic cardiomyopathy (HCM).

Objectives

The purpose of this study was to determine if AI-enhanced ECG (AI-ECG) can track longitudinal therapeutic response and changes in cardiac structure, function, or hemodynamics in obstructive HCM during mavacamten treatment.

Methods

We applied 2 independently developed AI-ECG algorithms (University of California-San Francisco and Mayo Clinic) to serial ECGs (n = 216) from the phase 2 PIONEER-OLE trial of mavacamten for symptomatic obstructive HCM (n = 13 patients, mean age 57.8 years, 69.2% male). Control ECGs from 2,600 age- and sex-matched individuals without HCM were obtained. AI-ECG output was correlated longitudinally to echocardiographic and laboratory metrics of mavacamten treatment response.

Results

In the validation cohorts, both algorithms exhibited similar performance for HCM diagnosis, and exhibited mean HCM score decreases during mavacamten treatment: patient-level score reduction ranged from approximately 0.80 to 0.45 for Mayo and 0.70 to 0.35 for USCF algorithms; 11 of 13 patients demonstrated absolute score reduction from start to end of follow-up for both algorithms. HCM scores were significantly associated with other HCM-relevant parameters, including left ventricular outflow tract gradient at rest, postexercise, and with Valsalva, and NT-proBNP level, independent of age and sex (all P < 0.01). For both algorithms, the strongest longitudinal correlation was between AI-ECG HCM score and left ventricular outflow tract gradient postexercise (slope estimate: University of California-San Francisco 0.70 [95% CI: 0.45-0.96], P < 0.0001; Mayo 0.40 [95% CI: 0.11-0.68], P = 0.007).

Conclusions

AI-ECG analysis longitudinally correlated with changes in echocardiographic and laboratory markers during mavacamten treatment in obstructive HCM. These results provide early evidence for a potential paradigm for monitoring HCM therapeutic response.