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.

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.

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.