Key result
A random forest classifier accurately identified nonconductive tissue regions serving as substrate for anchored reentry-driven recurrent arrhythmia with an area under the curve of 0.91±0.03.
Why the study?
Postablation arrhythmia recurrence occurs in ~40% of persistent AF patients, but the emergent interaction between ablation-induced scar and native fibrosis is poorly understood.
Does a random forest machine learning classifier accurately predict anchored reentry substrate created by persistent atrial fibrillation ablation in computational models?
Does a random forest machine learning classifier accurately predict anchored reentry substrate created by persistent atrial fibrillation ablation in computational models?
Effect estimate: AUC 0.91
Explainable machine learning combined with computational simulations can accurately identify nonconductive tissue patterns prone to serving as arrhythmogenic substrate for anchored reentry after persistent atrial fibrillation ablation.
May guide substrate mapping in computational AF models; hypothesis-generating and requires clinical validation.
Background Postablation arrhythmia recurrence occurs in ~40% of patients with persistent atrial fibrillation. Fibrotic remodeling exacerbates arrhythmic activity in persistent atrial fibrillation and can play a key role in reentrant arrhythmia, but emergent interaction between nonconductive ablation-induced scar and native fibrosis (ie, residual fibrosis) is poorly understood. Methods and Results We conducted computational simulations in pre- and postablation left atrial models reconstructed from late gadolinium enhanced magnetic resonance imaging scans to test the hypothesis that ablation in patients with persistent atrial fibrillation creates new substrate conducive to recurrent arrhythmia mediated by anchored reentry. We trained a random forest machine learning classifier to accurately pinpoint specific nonconductive tissue regions (ie, areas of ablation-delivered scar or vein/valve boundaries) with the capacity to serve as substrate for anchored reentry-driven recurrent arrhythmia (area under the curve: 0.91±0.03). Our analysis suggests there is a distinctive nonconductive tissue pattern prone to serving as arrhythmogenic substrate in postablation models, defined by a specific size and proximity to residual fibrosis. Conclusions Overall, this suggests persistent atrial fibrillation ablation transforms substrate that favors functional reentry (ie, rotors meandering in excitable tissue) into an arrhythmogenic milieu more conducive to anchored reentry. Our work also indicates that explainable machine learning and computational simulations can be combined to effectively probe mechanisms of recurrent arrhythmia.
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Bifulco et al. (2023) studied Persistent atrial fibrillation. Random forest machine learning classifier was evaluated on Identification of nonconductive tissue regions serving as substrate for anchored reentry-driven recurrent arrhythmia (AUC 0.91). A random forest classifier accurately identified nonconductive tissue regions serving as substrate for anchored reentry-driven recurrent arrhythmia with an area under the curve of 0.91±0.03.
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