Atrial Fibrosis, Ischaemic Stroke and Atrial Fibrillation

Atrial fibrosis is an important component of the arrhythmic substrate in AF. Evidence suggests that atrial fibrosis also plays a role in increasing the risk of stroke in patients with the arrhythmia. Patients with embolic stroke of undetermined source (ESUS), who are suspected to have AF but are rarely shown to have it, frequently demonstrate evidence of atrial fibrosis; measured using late-gadolinium enhancement MRI, this manifests as atrial remodelling encompassing structural, functional and electrical properties. In this review, the authors discuss the available evidence linking atrial disease, including fibrosis, with the risk of ischaemic stroke in AF, as well as in the ESUS population, in whom it has been linked to recurrent stroke and new-onset AF. They also discuss the implications of this association on future research that may elucidate the mechanism of stroke and stroke prevention strategies in the AF and ESUS populations

Association of atrial tissue fibrosis identified by delayed enhancement MRI and atrial fibrillation catheter ablation: the DECAAF study

IMPORTANCE: Left atrial fibrosis is prominent in patients with atrial fibrillation (AF). Extensive atrial tissue fibrosis identified by delayed enhancement magnetic resonance imaging (MRI) has been associated with poor outcomes of AF catheter ablation. OBJECTIVE: To characterize the feasibility of atrial tissue fibrosis estimation by delayed enhancement MRI and its association with subsequent AF ablation outcome. DESIGN, SETTING, AND PARTICIPANTS: Multicenter, prospective, observational cohort study of patients diagnosed with paroxysmal and persistent AF (undergoing their first catheter ablation) conducted between August 2010 and August 2011 at 15 centers in the United States, Europe, and Australia. Delayed enhancement MRI images were obtained up to 30 days before ablation. MAIN OUTCOMES AND MEASURES: Fibrosis quantification was performed at a core laboratory blinded to the participating center, ablation approach, and procedure outcome. Fibrosis blinded to the treating physicians was categorized as stage 1 (<10% of the atrial wall), 2 (≥10%-<20%), 3 (≥20%-<30%), and 4 (≥30%). Patients were followed up for recurrent arrhythmia per current guidelines using electrocardiography or ambulatory monitor recording and results were analyzed at a core laboratory. Cumulative incidence of recurrence was estimated by stage at days 325 and 475 after a 90-day blanking period (standard time allowed for arrhythmias related to ablation-induced inflammation to subside) and the risk of recurrence was estimated (adjusting for 10 demographic and clinical covariates). RESULTS: Atrial tissue fibrosis estimation by delayed enhancement MRI was successfully quantified in 272 of 329 enrolled patients (57 patients [17%] were excluded due to poor MRI quality). There were 260 patients who were followed up after the blanking period (mean [SD] age of 59.1 [10.7] years, 31.5% female, 64.6% with paroxysmal AF). For recurrent arrhythmia, the unadjusted overall hazard ratio per 1% increase in left atrial fibrosis was 1.06 (95% CI, 1.03-1.08; P < .001). Estimated unadjusted cumulative incidence of recurrent arrhythmia by day 325 for stage 1 fibrosis was 15.3% (95% CI, 7.6%-29.6%); stage 2, 32.6% (95% CI, 24.3%-42.9%); stage 3, 45.9% (95% CI, 35.5%-57.5%); and stage 4, 51.1% (95% CI, 32.8%-72.2%) and by day 475 was 15.3% (95% CI, 7.6%-29.6%), 35.8% (95% CI, 26.2%-47.6%), 45.9% (95% CI, 35.6%-57.5%), and 69.4% (95% CI, 48.6%-87.7%), respectively. Similar results were obtained after covariate adjustment. The addition of fibrosis to a recurrence prediction model that includes traditional clinical covariates resulted in an improved predictive accuracy with the C statistic increasing from 0.65 to 0.69 (risk difference of 0.05; 95% CI, 0.01-0.09). CONCLUSIONS AND RELEVANCE: Among patients with AF undergoing catheter ablation, atrial tissue fibrosis estimated by delayed enhancement MRI was independently associated with likelihood of recurrent arrhythmia. The clinical implications of this association warrant further investigation

Explainable Machine Learning to Predict Anchored Reentry Substrate Created by Persistent Atrial Fibrillation Ablation in Computational Models

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

Machine Learning and the Conundrum of Stroke Risk Prediction

Stroke is a leading cause of death worldwide. With escalating healthcare costs, early non-invasive stroke risk stratification is vital. The current paradigm of stroke risk assessment and mitigation is focused on clinical risk factors and comorbidities. Standard algorithms predict risk using regression-based statistical associations, which, while useful and easy to use, have moderate predictive accuracy. This review summarises recent efforts to deploy machine learning (ML) to predict stroke risk and enrich the understanding of the mechanisms underlying stroke. The surveyed body of literature includes studies comparing ML algorithms with conventional statistical models for predicting cardiovascular disease and, in particular, different stroke subtypes. Another avenue of research explored is ML as a means of enriching multiscale computational modelling, which holds great promise for revealing thrombogenesis mechanisms. Overall, ML offers a new approach to stroke risk stratification that accounts for subtle physiologic variants between patients, potentially leading to more reliable and personalised predictions than standard regression-based statistical associations