1Universitätsklinikum Schleswig-Holstein Innere Medizin III mit den Schwerpunkten Kardiologie, Angiologie und internistische Intensivmedizin Kiel, Deutschland; 2Universitätsklinikum Schleswig-Holstein Klinik für angeborene Herzfehler und Kinderkardiologie Kiel, Deutschland
Background:
Mapping of the ventricular arrhythmias (VA) is often not possible due to the non-inducibility of the electrophysiological study (EPS). Since twelve-lead electrocardiograms (ECG) of the clinical ventricular tachycardia (VT) are also often not available, stored implantable cardioverter-defibrillator (ICD) electrograms (EGMs) of the clinical VT are often the only documentation. The pace mapping matching to the stored ICD-EGM template of the clinical VT can help to identify the VT exit site. Automatic objective computing of similarity scores between EGMs is impossible because ICD programmer devices do not have any interface allowing the export of digital EGMs. Therefore, images can only be accessed for similarity by subjective eyeballing. The MatcherNet Siamese neural network takes a pair of EGM images and predicts their similarity.
Purpose: This study aimed to assess the feasibility of the automatic ICD-EGMs matching by MatcherNet neural network for pace mapping of VT/premature ventricular contraction (PVC) exit site.
Methods:
Consecutive patients undergoing VT ablation or ablation of ventricular fibrillation, triggered by PVC were included in the study, if the clinical VT/PVC was not inducible during EPS but ICD-EGMs of clinical VT/PVC were stored in the device. Electro-anatomical mapping was performed to reveal substrates (low-voltage areas, abnormal potentials, DEEP-potentials, conduction crowding). Subsequently, the low-output pacing from various locations tagged on the 3D map of LV was performed, and the paced ICD-EGM images were stored from the ICD programmer device for offline analysis and calculation of the similarity. RF ablation was conducted based on the substrate map findings, including the area of the most remarkable similarity between paced ICD-EGMs and clinical ICD-EGM templates assessed by eyeballing.
The ICD-EGMs similarity scores (Manhattan similarity and Pearson correlation) were predicted by MatcherNet for each pacing site offline and transferred to the 3D map for the assessment of similarity distribution (Figure 1).
Results: We included 7 patients with VA episodes registered and stored by their ICD. The clinical characteristics are presented in Table1. In all patients, we were able to find the sites of highest ICD-EGM similarity to the clinical template with gradual decrementing of the similarity score at the increasingly distant pacing areas. The site of the highest similarity was collocated with VT substrate and ablation areas in all patients. Abrupt change of the similarity score at the adjacent pacing sites was detected in the 2 cases, indicating the possible protected VT isthmus. After ablation procedure only one patient with DCM experienced VA recurrence.
Conclusion: This technology opens new possibilities for the utilization of ICD-EGMs during the pace mapping of ventricular tachycardia, especially in the cases of non-inducibility during electrophysiological study.