Changes in atrial fibrillation cycle length (AF-CL) are broadly used as a ‘ground truth’ to assess the effect of substrate modification during AF ablation. This work sought to optimize thresholds for changes in coronary sinus CL (CS-CL) after local ablation using different atrial electrogram (AEG)-derived markers. 834 AEGs were collected from 11 patients undergoing persAF ablation. CS-CL was measured before and after each ablation point. Five AEG-derived markers were tested as classifiers for CS-CL changes: ICL (Biosense Webster), CFE-Mean (St. Jude Medical), Wave Similarity, Shannon Entropy and AEG-CL. The area under the receiver operating characteristic (AUROC) curve was used to assess the quality of classification for each marker. Maximum AUROC was found at threshold values between 9 and 14 ms in all markers, except for Shannon Entropy. The average AUROC of the five markers reached a maximum of 0.60 at a threshold value of 10 ms. The 10 ms threshold is suggested as a starting setpoint for future studies seeking to identify AF ablation targets based on an objective 'ground truth'.
The outcomes of ablation targeting either reentry activations or fractionated activity during persistent atrial fibrillation (AF) therapy remain suboptimal due to, among others, the intricate underlying AF dynamics. In the present work, we sought to investigate such AF dynamics in a heterogeneous simulation setup using recurrence quantification analysis (RQA). AF was simulated in a spherical model of the left atrium, from which 412 unipolar atrial electrograms (AEGs) were extracted (2 s duration; 5 mm spacing). The phase was calculated using the Hilbert transform, followed by the identification of points of singularity (PS). Three regions were defined according to the occurrence of PSs: 1) no rotors; 2) transient rotors and; 3) long-standing rotors. Bipolar AEGs (1114) were calculated from pairs of unipolar nodes and bandpass filtered (30-300 Hz). The CARTO criterion (Biosense Webster) was used for AEGs classification (normal vs. fractionated). RQA attributes were calculated from the filtered bipolar AEGs: determinism (DET); recurrence rate (RR); laminarity (LAM). Sample entropy (SampEn) and dominant frequency (DF) were also calculated from the AEGs. Regions with longstanding rotors have shown significantly lower RQA attributes and SampEn when compared to the other regions, suggesting a higher irregular behaviour (P≤0.01 for all cases). Normal and fractionated AEGs were found in all regions (respectively; Region 1: 387 vs. 15; Region 2: 221 vs. 13; Region 3: 415 vs. 63). Region 1 vs. Region 3 have shown significant differences in normal AEGs (P≤0.0001 for all RQA attributes and SampEn), and significant differences in fractionated AEGs for LAM, RR and SampEn (P=0.0071, P=0.0221 and P=0.0086, respectively). Our results suggest the co-existence of normal and fractionated AEGs within long-standing rotors. RQA has unveiled distinct dynamic patterns–irrespective of AEGs classification–related to regularity structures and their nonstationary behaviour in a rigorous deterministic context.