What is the clinical evidence from this study?

Study design: Observational. Population: Persistent Atrial Fibrillation (n=12). Intervention: Image-based computational modeling (RDsim) vs. Electrocardiographic imaging (RDECGI). Primary outcome: Regional agreement between macroscopic locations of reentrant drivers identified by simulations and ECGI (modified Cohen's κ0 = 0.11).

April 19, 2018Open Access

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

PBPatrick M. BoyleUniversity of Washington JHJoe B. HakimHarvard–MIT Division of Health Sciences and Technology SZSohail ZahidJohns Hopkins University

Key Result

Image-based computational modeling and electrocardiographic imaging showed fair regional agreement (modified Cohen's κ0 = 0.11) in identifying reentrant drivers, suggesting that human persistent atrial fibrillation is at least partially fibrosis-mediated.

Study Design

Type

Observational (n=12)

Blinding

Blinded assessors

Multicenter

Structured PICO

Does patient-specific computational modeling derived from LGE-MRI agree with electrocardiographic imaging in identifying reentrant drivers in patients with persistent atrial fibrillation?

Population

12 patients with persistent atrial fibrillation and fibrotic remodeling who underwent pre-ablation electrocardiographic imaging and had patient-specific computational models derived from LGE-MRI.

Exposure

Patient-specific computational modeling derived from late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) to predict reentrant drivers (RDsim)

Comparator

Electrocardiographic imaging (ECGI) mapping of reentrant drivers (RDECGI)

Outcome

Extent of agreement between macroscopic locations of reentrant drivers identified by simulations (RDsim) and ECGI (RDECGI)surrogate

Reentrant drivers in human persistent AF are at least partially fibrosis-mediated, and combining computational simulations with ECGI could potentially improve ablation outcomes.

Main Result

Effect estimate: modified Cohen's κ0 = 0.11

Limitations

ECGI does not establish a ground truth representation for the dynamics of electrical activation and is limited to reconstructing activity on the epicardial surface.
LGE-MRI cannot identify regions of fibrotic remodeling in certain parts of the heart, such as the LA roof.
Computational models assume average human AF electrophysiology at the cell- and tissue-scale, introducing uncertainty in the dynamics of RD localization.
Small sample size limits the ability to achieve statistical significance for clinical outcomes.

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 (RDsim/RDECGI) and analyze implications for ablation. We considered 12 PsAF patients who underwent RDECGI ablation. For the same cohort, we simulated AF and identified RDsim sites in patient-specific models with geometry and fibrosis distribution from pre-ablation LGE-MRI. RDsim- and RDECGI-harboring regions were compared, and the extent of agreement between macroscopic locations of RDs identified by simulations and ECGI was assessed. Effects of ablating RDECGI/RDsim were analyzed. RDsim were predicted in 28 atrial regions (median inter-quartile range (IQR) = 3.0 1.0; 3.0 per model). ECGI detected 42 RDECGI-harboring regions (4.0 2.0; 5.0 per patient). The number of regions with RDsim and RDECGI 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 RDsim- and RDECGI. 19 regions were found to harbor both RDsim and RDECGI, 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 RDECGI 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 RDECGI (i.e., targets in regions that also harbored RDsim) trended towards a higher rate of positive response compared to ablation of other RDECGI 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.

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