Computational modeling identifies embolic stroke of undetermined source patients with potential arrhythmic substrate

Background: Late-gadolinium enhanced (LGE)-MRI has revealed atrial fibrotic remodeling in embolic stroke of undetermined source (ESUS) patients comparable to that observed in atrial fibrillation (AFib) patients. The absence of AFib in ESUS patients may be due to differences in the fibrotic substrate characteristics or the lack of triggers needed to initiate AFib. We used simulations in computational models reconstructed from LGE-MRI scans to study the role of atrial fibrosis as a pathophysiological link between AFib and ESUS. Methods: ESUS (per standard criteria) was verified by a neurologist. 45 ablation-naive AFib patients and 45 ESUS patients within three months of stroke underwent LGE-MRI for fibrosis assessment. Left atrial (LA) models were built from LGE-MRI scans. Fiber orientations were mapped into each LA model using universal atrial coordinates. Burst pacing from 15 known AFib trigger sites was used to test inducibility of arrhythmia sustained by reentry. Results: We observed sustained reentry in 23/45 (51%) ESUS and 28/45 (62%) AFib models. Overall, the fibrosis burden was significantly higher for patients in whom simulations showed inducibility (16.8 {+/-} 5.04% vs. 10.19 {+/-} 3.14%; P<0.0001); however, within the inducible and non-inducible sub-groups, there was no significant difference in fibrosis burden for ESUS vs. AFib patients (P=0.068 and P=0.58, respectively). This suggests that the presence of a pre-clinical substrate in ESUS is correlated with fibrosis burden, although exceptions to this supposition were not uncommon (i.e., inducible low-fibrosis and non-inducible high-fibrosis models). Conclusions: In this modeling study, pro-arrhythmic properties of fibrosis in ESUS and AFib are indistinguishable suggesting that some ESUS patients have a pre-clinical fibrotic substrate but do not have AFib due to a lack of suitable triggers.

[1]  N. Trayanova,et al.  Characterizing the arrhythmogenic substrate in personalized models of atrial fibrillation: sensitivity to mesh resolution and pacing protocol in AF models. , 2021, Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology.

[2]  N. Akoum,et al.  Fibrosis, atrial fibrillation and stroke: clinical updates and emerging mechanistic models , 2020, Heart.

[3]  Steven E. Williams,et al.  In silico Comparison of Left Atrial Ablation Techniques That Target the Anatomical, Structural, and Electrical Substrates of Atrial Fibrillation , 2020, Frontiers in Physiology.

[4]  Rheeda L. Ali,et al.  Preprocedure Application of Machine Learning and Mechanistic Simulations Predicts Likelihood of Paroxysmal Atrial Fibrillation Recurrence Following Pulmonary Vein Isolation , 2020, Circulation. Arrhythmia and electrophysiology.

[5]  N. Trayanova,et al.  Constructing a Human Atrial Fibre Atlas , 2020, Annals of Biomedical Engineering.

[6]  Rheeda L. Ali,et al.  Arrhythmogenic Propensity of the Fibrotic Substrate after AF Ablation: A Longitudinal Study using MRI-Based Atrial Models. , 2019, Cardiovascular research.

[7]  W. Longstreth,et al.  Embolic stroke of undetermined source correlates to atrial fibrosis without atrial fibrillation , 2019, Neurology.

[8]  Stefan L. Zimmerman,et al.  Computationally guided personalized targeted ablation of persistent atrial fibrillation , 2019, Nature Biomedical Engineering.

[9]  Steven Niederer,et al.  A technique for measuring anisotropy in atrial conduction to estimate conduction velocity and atrial fibre direction , 2019, Comput. Biol. Medicine.

[10]  Marianna Meo,et al.  Universal atrial coordinates applied to visualisation, registration and construction of patient specific meshes , 2018, Medical Image Anal..

[11]  N. Trayanova,et al.  Arrhythmogenic propensity of the fibrotic substrate after atrial fibrillation ablation : a longitudinal study using magnetic resonance imaging-based atrial models , 2019 .

[12]  O. Simonetti,et al.  Human Atrial Fibrillation Drivers Resolved With Integrated Functional and Structural Imaging to Benefit Clinical Mapping. , 2018, JACC. Clinical electrophysiology.

[13]  N. Trayanova,et al.  Arrhythmia dynamics in computational models of the atria following virtual ablation of re-entrant drivers. , 2018, Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology.

[14]  N. Trayanova,et al.  The Fibrotic Substrate in Persistent Atrial Fibrillation Patients: Comparison Between Predictions From Computational Modeling and Measurements From Focal Impulse and Rotor Mapping , 2018, Front. Physiol..

[15]  N. Trayanova,et al.  Comparing Reentrant Drivers Predicted by Image-Based Computational Modeling and Mapped by Electrocardiographic Imaging in Persistent Atrial Fibrillation , 2018, Front. Physiol..

[16]  N. Trayanova,et al.  Relationship Between Fibrosis Detected on Late Gadolinium-Enhanced Cardiac Magnetic Resonance and Re-Entrant Activity Assessed With Electrocardiographic Imaging in Human Persistent Atrial Fibrillation. , 2017, JACC. Clinical electrophysiology.

[17]  Olaf Dössel,et al.  Regional conduction velocity calculation from clinical multichannel electrograms in human atria , 2018, Comput. Biol. Medicine.

[18]  Natalia A Trayanova,et al.  Sensitivity of reentrant driver localization to electrophysiological parameter variability in image-based computational models of persistent atrial fibrillation sustained by a fibrotic substrate. , 2017, Chaos.

[19]  C. Israel,et al.  Detection of atrial fibrillation in patients with embolic stroke of undetermined source by prolonged monitoring with implantable loop recorders , 2017, Thrombosis and Haemostasis.

[20]  F. Marchlinski,et al.  Techniques for the provocation, localization, and ablation of non-pulmonary vein triggers for atrial fibrillation. , 2017, Heart rhythm.

[21]  T. Hirano [Embolic Stroke of Undetermined Source]. , 2017, Brain and nerve = Shinkei kenkyu no shinpo.

[22]  Min Ju You,et al.  Varieties of reentrant dynamics. , 2017, Chaos.

[23]  S. Connolly,et al.  Embolic Stroke of Undetermined Source: A Systematic Review and Clinical Update , 2017, Stroke.

[24]  Rémi Dubois,et al.  Modelling methodology of atrial fibrosis affects rotor dynamics and electrograms. , 2016, Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology.

[25]  Markus Bär,et al.  Reentry and Ectopic Pacemakers Emerge in a Three-Dimensional Model for a Slab of Cardiac Tissue with Diffuse Microfibrosis near the Percolation Threshold , 2016, PloS one.

[26]  Natalia A Trayanova,et al.  Feasibility of using patient-specific models and the "minimum cut" algorithm to predict optimal ablation targets for left atrial flutter. , 2016, Heart rhythm.

[27]  N. Trayanova,et al.  Patient-derived models link re-entrant driver localization in atrial fibrillation to fibrosis spatial pattern. , 2016, Cardiovascular research.

[28]  R. Passman,et al.  Uncovering Atrial Fibrillation Beyond Short-Term Monitoring in Cryptogenic Stroke Patients: Three-Year Results From the Cryptogenic Stroke and Underlying Atrial Fibrillation Trial , 2016, Circulation. Arrhythmia and electrophysiology.

[29]  Marcelo Lobosco,et al.  Simulation of Ectopic Pacemakers in the Heart: Multiple Ectopic Beats Generated by Reentry inside Fibrotic Regions , 2015, BioMed research international.

[30]  Yves Coudière,et al.  A bilayer model of human atria: mathematical background, construction, and assessment. , 2014, Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology.

[31]  Ashok J. Shah,et al.  Driver Domains in Persistent Atrial Fibrillation , 2014, Circulation.

[32]  Stanley Nattel,et al.  The clinical profile and pathophysiology of atrial fibrillation: relationships among clinical features, epidemiology, and mechanisms. , 2014, Circulation research.

[33]  Nazem Akoum,et al.  Association of atrial tissue fibrosis identified by delayed enhancement MRI and atrial fibrillation catheter ablation: the DECAAF study. , 2014, JAMA.

[34]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[35]  Maxime Sermesant,et al.  Inverse relationship between fractionated electrograms and atrial fibrosis in persistent atrial fibrillation: combined magnetic resonance imaging and high-density mapping. , 2013, Journal of the American College of Cardiology.

[36]  Natalia A Trayanova,et al.  Mechanisms of Human Atrial Fibrillation Initiation: Clinical and Computational Studies of Repolarization Restitution and Activation Latency , 2012, Circulation. Arrhythmia and electrophysiology.

[37]  Eric Kerfoot,et al.  Verification of cardiac tissue electrophysiology simulators using an N-version benchmark , 2011, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[38]  Christophe Geuzaine,et al.  Gmsh: A 3‐D finite element mesh generator with built‐in pre‐ and post‐processing facilities , 2009 .

[39]  G Plank,et al.  Solvers for the cardiac bidomain equations. , 2008, Progress in biophysics and molecular biology.

[40]  S. Landas,et al.  Spatial Distribution of Fibrosis Governs Fibrillation Wave Dynamics in the Posterior Left Atrium During Heart Failure , 2007, Circulation research.

[41]  Alexander V Panfilov,et al.  Is heart size a factor in ventricular fibrillation? Or how close are rabbit and human hearts? , 2006, Heart rhythm.

[42]  P. Platonov,et al.  Deterioration of interatrial conduction in patients with paroxysmal atrial fibrillation: electroanatomic mapping of the right atrium and coronary sinus. , 2004, Heart rhythm.

[43]  G Plank,et al.  Computational tools for modeling electrical activity in cardiac tissue. , 2003, Journal of electrocardiology.

[44]  K W Hewett,et al.  The rate and anisotropy of impulse propagation in the postnatal terminal crest are correlated with remodeling of Cx43 gap junction pattern. , 2000, Cardiovascular research.

[45]  M. Courtemanche,et al.  Ionic mechanisms underlying human atrial action potential properties: insights from a mathematical model. , 1998, The American journal of physiology.

[46]  M. Scheinman,et al.  Acceleration of typical atrial flutter due to double-wave reentry induced by programmed electrical stimulation. , 1998, Circulation.

[47]  M. Courtemanche,et al.  Ionic mechanisms underlying human atrial action potential properties: insights from a mathematical model. , 1998, American journal of physiology. Heart and circulatory physiology.

[48]  M. Allessie,et al.  High-density mapping of electrically induced atrial fibrillation in humans. , 1994, Circulation.