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.
Why the study?
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 (PsAF) who underwent RDECGI ablation
Comparison
Patient-specific computational modeling derived… vs Electrocardiographic imaging mapping of…
Design
Cohort
Authors
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Limited agreement between methods warrants caution against clinical adoption; hypothesis-generating for fibrosis-mediated drivers in persistent AF.
Observational (n=12)
Blinded assessors
No
Does patient-specific computational modeling derived from LGE-MRI agree with electrocardiographic imaging in identifying reentrant drivers in patients with persistent atrial fibrillation?
Effect estimate: modified Cohen's κ0 = 0.11
Reentrant drivers in human persistent AF are at least partially fibrosis-mediated, and combining computational simulations with ECGI could potentially improve ablation outcomes.
Boyle et al. (2018) conducted an observational in Persistent Atrial Fibrillation (n=12). Image-based computational modeling (RDsim) vs. Electrocardiographic imaging (RDECGI) was evaluated on Regional agreement between macroscopic locations of reentrant drivers identified by simulations and ECGI (modified Cohen's κ0 = 0.11). 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.
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