Why the study?
Existing post-infarct VT ablation targeting strategies rely on invasive EP mapping or surface ECGs, but clinical arrhythmias are not always recorded on 12-lead ECGs; using implanted device EGMs may aid ablation planning.
Does a computational deep learning platform utilizing surface ECGs and intracardiac EGMs accurately localize post-infarct VT exit sites?
Does a computational deep learning platform utilizing surface ECGs and intracardiac EGMs accurately localize post-infarct VT exit sites?
A novel deep learning platform can accurately localize post-infarct VT exit sites non-invasively using surface ECGs and implanted device EGMs, potentially enhancing ablation planning.
May support non-invasive post-infarct VT localization; hypothesis-generating in animal simulations and requires human validation.
AIMS: Existing strategies that identify post-infarct ventricular tachycardia (VT) ablation target either employ invasive electrophysiological (EP) mapping or non-invasive modalities utilizing the electrocardiogram (ECG). Their success relies on localizing sites critical to the maintenance of the clinical arrhythmia, not always recorded on the 12-lead ECG. Targeting the clinical VT by utilizing electrograms (EGM) recordings stored in implanted devices may aid ablation planning, enhancing safety and speed and potentially reducing the need of VT induction. In this context, we aim to develop a non-invasive computational-deep learning (DL) platform to localize VT exit sites from surface ECGs and implanted device intracardiac EGMs. METHODS AND RESULTS: A library of ECGs and EGMs from simulated paced beats and representative post-infarct VTs was generated across five torso models. Traces were used to train DL algorithms to localize VT sites of earliest systolic activation; first tested on simulated data and then on a clinically induced VT to show applicability of our platform in clinical settings. Localization performance was estimated via localization errors (LEs) against known VT exit sites from simulations or clinical ablation targets. Surface ECGs successfully localized post-infarct VTs from simulated data with mean LE = 9.61 ± 2.61 mm across torsos. VT localization was successfully achieved from implanted device intracardiac EGMs with mean LE = 13.10 ± 2.36 mm. Finally, the clinically induced VT localization was in agreement with the clinical ablation volume. CONCLUSION: The proposed framework may be utilized for direct localization of post-infarct VTs from surface ECGs and/or implanted device EGMs, or in conjunction with efficient, patient-specific modelling, enhancing safety and speed of ablation planning.
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Monaci et al. (2022) studied this question.
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