Automated Detection of Complex Fractionated Atrial Electrograms In Substrate-Based Atrial Fibrillation Ablation: Better Discrimination with a New Setting of CARTO® Algorithm.

Background and purpose: Up until recently complex fractionated atrial electrogram (CFAE) ablation has been considered as time consuming and its achievement as challenging, especially for non experimented operators. Moreover, results of substrate ablation based on CFAE detection in atrial fibrillation (AF) are very disparate, mainly because of the operator's subjective electrogram visual analysis and the difficult distinction between CFAEs really involved in AF perpetuation from other CFAE. Automatic detection provided by 3D mapping system (CARTO® algorithm) can be helpful but is not selective enough, drawing too wide CFAE areas. We sought to demonstrate a better selectivity of a new CFAE algorithm setting in order to better discriminate CFAEs really involved in AF perpetuation from other CFAE. Methods and subjects: A population of 32 patients (60.4±12.7 years) with paroxysmal (n=3) AF (PAF), persistent (n=16) AF (PeAF) or long-standing persistent (n=13) AF (LSPeAF), and AF history =56±65 months, underwent CFAE ablation based on visual analysis. Before ablation, left atrium CFAE mapping was performed on CARTO® shortest complex interval (SCI) algorithm and reanalyzed after ablation with the two different settings: nominal (SCI 60-120ms/0.05-0.15mV) vs. customized setting (SCI 30-40ms/0,04-0.15mV). CFAE areas automatically detected by both settings (CFAE-CARTO® areas) were respectively measured. The decision to ablate CFAE was only based upon the operator's electrogram visual analysis taken as reference because of high AF termination rate (93.7%) due to operator's CFAE selection experience. These ablation points drawn reference-CFAE areas involved in AF perpetuation (ablation point=60mm2) allowing to compare the selectivity of the two previous automatic maps.
Results: With the customized CARTO® SCI setting, we observed a significant reduction of CFAE areas detected by CARTO® (CFAE-CARTO® areas) and of the ablated CFAE surface inside non-CFAE CARTO® areas, (30.6±20.5cm2 vs. 68.8±24.5cm2, p<0.0001, and 1.86±1.82% vs. 3±3%, p=0.003). Furthermore the proportion of ablated areas/detected CFAE-CARTO® areas were higher with customized setting (38.2±19.6% vs. 20.4±17.5%, p=0.008). Conclusions: This new customized CFAE algorithm setting is significantly more selective than the nominal one and allows an automated detection of CFAE really involved in AF perpetuation truer to an efficient experienced operator's electrogram visual analysis.