Related Experiment Video
Updated: Dec 4, 2025

A Fibrin-Enriched and tPA-Sensitive Photothrombotic Stroke Model
Published on: June 4, 2021
Fibrosis, atrial fibrillation and stroke: clinical updates and emerging mechanistic models
Patrick M Boyle1, Juan Carlos Del Álamo2, Nazem Akoum3
1Bioengineering, University of Washington, Seattle, Washington, USA.
Insights
Atrial fibrosis is a key factor in stroke risk for patients with atrial fibrillation (AF) and embolic stroke of undetermined source (ESUS). Computational models integrating fibrosis and blood flow dynamics offer personalized treatment strategies.
Area of Science:
- Cardiology
- Biomedical Engineering
- Computational Biology
Background:
- Current stroke risk assessment in atrial fibrillation (AF) relies on clinical factors, but underlying mechanisms, including atrial fibrosis, are poorly understood.
- Embolic stroke of undetermined source (ESUS) presents challenges, as AF is often absent despite monitoring, highlighting the need for alternative risk stratification.
- Atrial fibrosis is increasingly recognized as a unifying substrate for both arrhythmia and thrombus formation, impacting stroke risk.
Purpose of the Study:
- To review current clinical knowledge on stroke risk in AF and ESUS.
- To explore the role of atrial fibrosis as a unifying mechanism in thromboembolism.
- To present computational modeling frameworks for understanding and predicting stroke risk.
Main Methods:
- Review of clinical data on stroke risk factors and atrial fibrillation.
- Analysis of computational models for AF initiation, maintenance, and fluid dynamics in the left atrium.
- Integration of multiscale modeling incorporating fibrotic, morphological, electrical, and mechanical remodeling.
Main Results:
- Atrial fibrosis serves as a substrate for both arrhythmias and thrombus formation.
- Computational models reveal potential arrhythmia vulnerability in ESUS patients.
- Fluid dynamics models enhance understanding of thrombus formation influenced by left atrial appendage morphology and blood flow.
Conclusions:
- Multiscale modeling integrating structural and electrical remodeling offers mechanistic insights into thromboembolism.
- Computational models integrating fibrosis and blood flow dynamics hold promise for personalized stroke risk assessment and treatment planning.
- A future paradigm integrating simulations into personalized treatment plans for stroke prevention is envisioned.
Abstract:
The current paradigm of stroke risk assessment and mitigation in patients with atrial fibrillation (AF) is centred around clinical risk factors which, in the presence of AF, lead to thrombus formation. The mechanisms by which these clinical risk factors lead to thromboembolism, including any role played by atrial fibrosis, are not understood. In patients who had embolic stroke of undetermined source (ESUS), the problem is compounded by the absence of AF in a majority of patients despite long-term monitoring. Atrial fibrosis has emerged as a unifying mechanism that independently provides a substrate for arrhythmia and thrombus formation. Fibrosis-based computational models of AF initiation and maintenance promise to identify therapeutic targets in catheter ablation. In ESUS, fibrosis is also increasingly recognised as a major risk factor, but the underlying mechanism of this correlation is unclear. Simulations have uncovered potential vulnerability to arrhythmia induction in patients who had ESUS. Likewise, computational models of fluid dynamics representing blood flow in the left atrium and left atrium appendage have improved our understanding of thrombus formation, in particular left atrium appendage shapes and blood flow changes influenced by atrial remodelling. Multiscale modelling of blood flow dynamics based on structural fibrotic and morphological changes with associated cellular and tissue electrical remodelling leading to electromechanical abnormalities holds tremendous promise in providing a mechanistic understanding of the clinical problem of thromboembolisation. We present a review of clinical knowledge alongside computational modelling frameworks and conclude with a vision of a future paradigm integrating simulations in formulating personalised treatment plans for each patient.

