Impact of artificial intelligence arrhythmia mapping on time to first ablation, procedure duration, and fluoroscopy use.

INTRODUCTION: Artificial intelligence (AI) ECG arrhythmia mapping provides arrhythmia source localization using 12-lead ECG data; whether this information impacts procedural efficiency is unknown. We performed a retrospective, case-control study to evaluate the hypothesis that AI ECG mapping may reduce time to ablation, procedural duration, and fluoroscopy. MATERIALS AND METHODS: Cases in which system output was used were retrospectively enrolled according to IRB-approved protocols at each site. Matched control cases were enrolled in reverse chronological order beginning on the last day for which the technology was unavailable. Controls were matched based upon physician, institution, arrhythmia, and a predetermined complexity rating. Procedural metrics, fluoroscopy data, and clinical outcomes were assessed from time-stamped medical records. RESULTS: The study group consisted of 28 patients (age 65 ± 11 years, 46% female, left atrial dimension 4.1 ± 0.9 cm, LVEF 50 ± 18%) and was similar to 28 controls. The most common arrhythmia types were atrial fibrillation (n = 10), premature ventricular complexes (n = 8), and ventricular tachycardia (n = 6). Use of the system was associated with a 19.0% reduction in time to ablation (133 ± 48 vs. 165 ± 49 min, p = 0.02), a 22.6% reduction in procedure duration (233 ± 51 vs. 301 ± 83 min, p < 0.001), and a 43.7% reduction in fluoroscopy (18.7 ± 13.3 vs. 33.2 ± 18.0 min, p < 0.001) versus controls. At 6 months follow-up, arrhythmia-free survival was 73.5% in the study group and 63.3% in the control group (p = 0.56). CONCLUSION: Use of forward-solution AI ECG mapping is associated with reductions in time to first ablation, procedure duration, and fluoroscopy without an adverse impact on procedure outcomes or complications.

Electrical Substrate Ablation for Refractory Ventricular Fibrillation: Results of the AVATAR Study.

[Figure: see text].

Spatiotemporal Progression of Early Human Ventricular Fibrillation.

Objectives: The objective of this study was to evaluate the spatio-temporal organization and progression of human ventricular fibrillation (VF) in the left (LV) and right (RV) ventricles. Background: Studies suggest that localized sources contribute to VF maintenance, but the evolution of VF episodes has not been quantified. Methods: Synchrony between electrograms recorded from 25 patients with induced VF is computed and used to define the Asynchronous Index (ASI), indicating regions which are out-of-step with surrounding tissue. Computer simulations show that ASI can identify the location of VF-maintaining sources, where larger values of ASImax correlate with more stable sources. Results: Automated synchrony analysis shows elevated values of ASI in a majority of self-terminating episodes (LV: 8/9, RV: 7/8) and sustained episodes (LV: 11/11, RV: 12/12). The locations of ASImax in sustained episodes co-localize with rotor cores when rotational activity is simultaneously present in phase maps (LV: 8/8, RV: 5/7, p<.05). The distribution of ASImax differentiates self-terminating from sustained episodes (mean ASImax = 0.60±0.14 and 0.70±0.16, respectively; p=0.01). Across sustained episodes the LV exhibits an increase in ASImax with time. Conclusions: Quantitative analysis identifies localized asynchronous regions that correlate with sources in VF, with sustained episodes evolving to exhibit more stable activation in the LV. This successive increase in stability indicates a stabilizing agent may be responsible for perpetuating fibrillation in a "migrate-and-capture" mechanism in the LV.

Rotors exhibit greater surface ECG variation during ventricular fibrillation than focal sources due to wavebreak, secondary rotors, and meander.

INTRODUCTION: Ventricular fibrillation is a common life-threatening arrhythmia. The ECG of VF appears chaotic but may allow identification of sustaining mechanisms to guide therapy. HYPOTHESIS: We hypothesized that rotors and focal sources manifest distinct features on the ECG, and computational modeling may identify mechanisms of such features. METHODS: VF induction was attempted in 31 patients referred for ventricular arrhythmia ablation. Simultaneous surface ECG and intracardiac electrograms were recorded using biventricular basket catheters. Endocardial phase maps were used to mechanistically classify each VF cycle as rotor or focally driven. ECGs were analyzed from patients demonstrating both mechanisms in the primary analysis and from all patients with induced VF in the secondary analysis. The ECG voltage variation during each mechanism was compared. Biventricular computer simulations of VF driven by focal sources or rotors were created and resulting ECGs of each VF mechanism were compared. RESULTS: Rotor-based VF exhibited greater voltage variation than focal source-based VF in both the primary analysis (n = 8, 110 ± 24% vs. 55 ± 32%, P = 0.02) and the secondary analysis (n = 18, 103 ± 30% vs. 67 ± 34%, P = 0.009). Computational VF simulations also revealed greater voltage variation in rotors compared to focal sources (110 ± 19% vs. 33 ± 16%, P = 0.001), and demonstrated that this variation was due to wavebreak, secondary rotor initiation, and rotor meander. CONCLUSION: Clinical and computational studies reveal that quantitative criteria of ECG voltage variation differ significantly between VF-sustaining rotors and focal sources, and provide insight into the mechanisms of such variation. Future studies should prospectively evaluate if these criteria can separate clinical VF mechanisms and guide therapy.