Artificial intelligence-adjudicated spatiotemporal dispersion: A patient-unique fingerprint of persistent atrial fibrillation.

BACKGROUND: Spatiotemporal dispersion-guided ablation is a tailored approach for patients in persistent atrial fibrillation (PsAF). The characterization of dispersion extent and distribution and its association with common clinical descriptors of PsAF patients has not been studied. OBJECTIVES: Artificial intelligence-adjudicated dispersion extent and distribution (AI-DED) was obtained with a machine/deep learning classifier (VX1 Software, Volta Medical) in PsAF patients undergoing ablation. The purpose of this study was to test the hypothesis that AI-DED is unique to each patient and independent of common procedural and clinical parameters. METHODS: In a subanalysis of the Ev-AIFib study (NCT03434964), spatiotemporal dispersion maps were built with VX1 software in 78 consecutive persistent and long-standing PsAF patients. AI-DED was quantified using 2 distinct approaches (visual regional characterization or automated global quantification of AI-DED). RESULTS: AI-DED paired-subregion Euclidean distance measurements between 78 patients (average distance 5.07 ± 0.60; min 2.23; max 9.75) demonstrate that AI-DED is a patient-unique characteristic of PsAF. Importantly, both AF type and AF history do not correlate with AI-DED levels (R2 = 0.006, P = .53; and R2 = 0.03, P = .25, respectively). The most extensive AI-DED levels are not associated with poorer procedural (83%, 81%, and 83% of AF termination in low, medium, and high dispersion groups, respectively; P = .954) and long-term (88%, 75%, and 91% of freedom from AF/atrial tachycardia after multiple procedures; P = .517) outcomes. CONCLUSION: The atrial distribution and extent of multipolar electrogram spatiotemporal dispersion follow a nonrandom, albeit patient-unique, distribution in PsAF patients. AI-DED may represent a procedure-implementable fingerprint of the PsAF substrate.

Artificial intelligence software standardizes electrogram-based ablation outcome for persistent atrial fibrillation.

INTRODUCTION: Multiple groups have reported on the usefulness of ablating in atrial regions exhibiting abnormal electrograms during atrial fibrillation (AF). Still, previous studies have suggested that ablation outcomes are highly operator- and center-dependent. This study sought to evaluate a novel machine learning software algorithm named VX1 (Volta Medical), trained to adjudicate multipolar electrogram dispersion. METHODS: This study was a prospective, multicentric, nonrandomized study conducted to assess the feasibility of generating VX1 dispersion maps. In 85 patients, 8 centers, and 17 operators, we compared the acute and long-term outcomes after ablation in regions exhibiting dispersion between primary and satellite centers. We also compared outcomes to a control group in which dispersion-guided ablation was performed visually by trained operators. RESULTS: The study population included 29% of long-standing persistent AF. AF termination occurred in 92% and 83% of the patients in primary and satellite centers, respectively, p = 0.31. The average rate of freedom from documented AF, with or without antiarrhythmic drugs (AADs), was 86% after a single procedure, and 89% after an average of 1.3 procedures per patient (p = 0.4). The rate of freedom from any documented atrial arrhythmia, with or without AADs, was 54% and 73% after a single or an average of 1.3 procedures per patient, respectively (p < 0.001). No statistically significant differences between outcomes of the primary versus satellite centers were observed for one (p = 0.8) or multiple procedures (p = 0.4), or between outcomes of the entire study population versus the control group (p > 0.2). Interestingly, intraprocedural AF termination and type of recurrent arrhythmia (i.e., AF vs. AT) appear to be predictors of the subsequent clinical course. CONCLUSION: VX1, an expertise-based artificial intelligence software solution, allowed for robust center-to-center standardization of acute and long-term ablation outcomes after electrogram-based ablation.

Impact of pacemaker mode in patients with atrioventricular conduction disturbance after trans-catheter aortic valve implantation.

OBJECTIVES: This study aimed to assess the impact of pacemaker mode programming on clinical outcomes in patients with high-degree atrioventricular conduction disturbance (AVCD) after transcatheter aortic valve implantation (TAVI). BACKGROUND: Although high-degree AVCD after TAVI can receive pacemaker, recovery of the AVCD is often observed. Specific pacemaker algorithms (AAI-DDD mode switch) are available which favor spontaneous atrioventricular conduction. METHODS: Of 1,621 consecutive multi-center TAVI patients, 269 (16.4%) received pacemaker. We retrospectively included 91 patients with persistent high-degree AVCD at hospital discharge. Pacemaker dependency was defined as absence, inadequate intrinsic ventricular rhythm, or ventricular pacing time > 95% on pacemaker interrogation during follow-up. Comparison of heart failure hospitalization and death between conventional DDD (cDDD) and other modes was examined (AAI-DDD and VVI). RESULTS: During a mean follow-up duration of 13 months, the pacemaker dependency rate was 52.8%. Patients with cDDD mode (N = 36: 40.0%) had significantly more pacemaker dependency. Multivariate analysis showed that cDDD mode was independently associated with pacemaker dependency (odds ratio = 3.63, P = 0.03). Moreover, cDDD patients had a significant higher incidence of heart failure hospitalization (Hospitalization: cDDD vs. others = 45.4% vs. 18.2%, P = 0.03) and had a higher incidence of mortality (Death: cDDD vs. the others = 27.0% vs. 4.4%, P = 0.06). CONCLUSIONS: Up to half of patients implanted for high-degree AVCD after TAVI had conduction recovery. Patients with cDDD programming at hospital discharge had more pacemaker dependency and a worse cardiac prognosis. Thus, pacemaker mode should be systematically set to promote spontaneous atrioventricular conduction in patients with pacemaker implantation after TAVI.