Comparing Reentrant Drivers Predicted by Image-Based Computational Modeling and Mapped by Electrocardiographic Imaging in Persistent Atrial Fibrillation

Abstract

Electrocardiographic mapping (ECGI) detects reentrant drivers (RDs) that perpetuate arrhythmia in persistent AF (PsAF). Patient-specific computational models derived from late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) identify all latent sites in the fibrotic substrate that could potentially sustain RDs, not just those manifested during mapped AF. The objective of this study was to compare RDs from simulations and ECGI (RD_sim/RD_ECGI) and analyze implications for ablation. We considered 12 PsAF patients who underwent RD_ECGI ablation. For the same cohort, we simulated AF and identified RD_sim sites in patient-specific models with geometry and fibrosis distribution from pre-ablation LGE-MRI. RD_sim- and RD_ECGI-harboring regions were compared, and the extent of agreement between macroscopic locations of RDs identified by simulations and ECGI was assessed. Effects of ablating RD_ECGI/RD_sim were analyzed. RD_sim were predicted in 28 atrial regions (median [inter-quartile range (IQR)] = 3.0 [1.0; 3.0] per model). ECGI detected 42 RD_ECGI-harboring regions (4.0 [2.0; 5.0] per patient). The number of regions with RD_sim and RD_ECGI per individual was not significantly correlated (R = 0.46, P = ns). The overall rate of regional agreement was fair (modified Cohen's κ₀ statistic = 0.11), as expected, based on the different mechanistic underpinning of RD_sim- and RD_ECGI. nineteen regions were found to harbor both RD_sim and RD_ECGI, suggesting that a subset of clinically observed RDs was fibrosis-mediated. The most frequent source of differences (23/32 regions) between the two modalities was the presence of RD_ECGI perpetuated by mechanisms other than the fibrotic substrate. In 6/12 patients, there was at least one region where a latent RD was observed in simulations but was not manifested during clinical mapping. Ablation of fibrosis-mediated RD_ECGI (i.e., targets in regions that also harbored RD_sim) trended toward a higher rate of positive response compared to ablation of other RD_ECGI targets (57 vs. 41%, P = ns). Our analysis suggests that RDs in human PsAF are at least partially fibrosis-mediated. Substrate-based ablation combining simulations with ECGI could improve outcomes.

Keywords: ablation; atrial fibrillation; computational modeling; electrocardiographic mapping; fibrotic remodeling; reentrant drivers.

Graphical Abstract

This study presents a comparison between reentrant driver (RD)-harboring regions identified by electrocardiographic imaging (ECGI), conducted prior to catheter ablation in persistent atrial fibrillation (PsAF) patients, and via simulations conducted in patient-specific computational models reconstructed from late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) scans. The finding of atrial regions in which both ECGI and simulations detected RDs (purple) suggests that PsAF is at least partially driven by fibrosis-mediated mechanisms. Simulations also identify “latent” RDs (red)—regions within the fibrotic substrate where an RD could persist, but never manifested during clinical mapping. Conversely, RD-harboring regions identified by ECGI but not in simulations (blue) indicate that some clinically mapped AF episodes were perpetuated by mechanisms other than the fibrotic substrate. Our retrospective analysis suggests that substrate-based ablation combining simulations with ECGI could improve outcomes.

Figure 1

Workflow for comparison of RD_sim and RD_ECGI locations. Torso electrodes recorded 15 s of pre-ablation AF in PsAF patients (A), and unipolar electrograms were reconstructed (B). Phase maps (C) were analyzed to construct RD-phase singularity histograms (D). Each patient underwent LGE-MRI (E), which was used to reconstruct 3D atrial models (F). Programmed electrical stimulation induced in-silico AF (G), and fibrosis-driven RD-phase singularity trajectories in simulations were determined (H). RDs from ECGI and simulations were compared. Panel (A) is reused with permission from Cochet et al. (2018).

Figure 2

Schematic defining region classification. RD_ECGI and RD_sim were classified into atrial regions as follows: (1/2) left/right PVs; (3) posterior LA; (4/5) superior/inferior RA; (6) anterior LA; and (7) inter-atrial groove. RD_ECGI- and RD_sim-harboring regions in this schematic are for illustrative purposes only and are not related to any particular patient/model.

Figure 3

Summary of RD-related findings for all patients and corresponding models. Table shows fibrosis burden, number and distribution of RD_sim- and RD_ECGI-harboring atrial regions, and acute clinical outcome of ECGI-driven ablation, i.e., continued AF or termination to sinus rhythm (SR) or atrial tachycardia (AT). Cell pairs are color-coded using the same scheme as Figure 2.

Figure 4

Relationship between the numbers of RD_sim-harboring regions predicted by each model and RD_ECGI-harboring regions observed during clinically mapped AF in the corresponding patients. Solid and dotted lines indicate best fit for linear regression and 95% confidence intervals, respectively.

Figure 5

Spatial co-localization of RD_sim (left) and RD_ECGI (right). (A) (patient 7): Matching RD_sim and RD_ECGI sites in the left PV region. (B) (patient 3): Matching RD_sim and RD_ECGI sites in the inter-atrial groove region.

Figure 6

Inter-rater agreement between RD_sim and RD_ECGI regions in patient-derived models. Cells color-coded via same classification scheme used in Figure 3.

Figure 7

ECGI+/Sim+, ECGI+/Sim−, ECGI−/Sim+, and ECGI−/Sim− region examples. RD trajectories in regions of interest for each panel are highlighted by dashed yellow circles. (A) (patient 1): matching RD sites in both left PV and posterior LA regions. (B) (patient 2): RD_ECGI site in posterior LA region was not observed during simulations. (C) (patient 4): RD_sim sites in inferior RA and inter-atrial groove were not observed during clinical mapping via ECGI. (D) (patient 11): Superior RA and anterior LA regions were free of RD activity in both simulations and ECGI. See Figure 5 for legend.

Figure 8

Regional distribution of ECGI+/Sim+, ECGI+/Sim-, ECGI-/Sim+ and ECGI-/Sim- regions. Each percentage value in each atrial region represents the proportion of all examples of the corresponding region classification (ECGI+/Sim+, etc.) corresponded to that region.

Figure 9

Outcome of RD_ECGI ablation in regions with and without RD_sim. (A,B) Rates of AF termination and positive response to ablation in ECGI+/Sim− vs. ECGI+/Sim+ regions. (C) AF cycle length prolongation (median + upper/lower quartile lines) in response to ablation of RD_ECGI targets in ECGI+/Sim+ vs. ECGI+/Sim− regions.

Figure 10

Number of RD_sim-harboring regions in five patient-derived models before and after ablation of RD_sim targets. The first round of simulated ablation targeted sites within ECGI+/Sim+ regions only; the second additionally targeted RDs in ECGI−/Sim+ regions.