Related Experiment Video
Updated: May 30, 2025
Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
Published on: July 20, 2022
MRI-based modelling of left atrial flow and coagulation to predict risk of thrombogenesis in atrial fibrillation
Ahmed Qureshi1, Paolo Melidoro1, Maximilian Balmus1
1School of Biomedical Engineering and Imaging Sciences, King's College London, UK.
Insights
Atrial fibrillation (AF) patients need better stroke risk assessment. This study uses computational modeling to create a new score, revealing risks missed by current clinical tools, improving personalized prevention.
Area of Science:
- Cardiovascular Research
- Medical Imaging
- Computational Biology
Background:
- Atrial fibrillation (AF) affects millions globally, increasing stroke risk.
- Current clinical scores inadequately capture thrombotic mechanisms in AF patients.
- Left atrial appendage (LAA) thrombi are primary sources of AF-related strokes.
Purpose of the Study:
- To develop an advanced risk stratification score for thrombus formation in AF patients.
- To integrate biophysical computational modeling with clinical data for personalized stroke risk assessment.
- To address limitations of existing scores like CHA₂DS₂-VASc in mechanistic insights.
Main Methods:
- Utilized high-resolution Cine MRI to create patient-specific 3D models of the left atrium.
- Employed computational fluid dynamics (CFD) to simulate blood flow.
- Coupled CFD with reaction-diffusion-convection equations for clotting proteins to assess thrombogenesis.
- Developed a novel risk score combining clinical and modeling data.
Main Results:
- Generated patient-specific thrombus formation risk scores categorized as low (A), moderate (B), and high (C).
- Identified significant thrombotic risks in some patients with low CHA₂DS₂-VASc scores (e.g., score of 0).
- Demonstrated the ability to characterize all aspects of Virchow's triad for mechanistic risk assessment.
Conclusions:
- The novel integrated score offers a more comprehensive and mechanistic assessment of thrombotic risk in AF.
- This approach enhances personalized stroke prevention strategies by accounting for individual pathophysiology.
- Biophysical modeling provides crucial insights beyond traditional clinical scoring systems.
Abstract:
Atrial fibrillation (AF), impacting nearly 50 million individuals globally, is a major contributor to ischaemic strokes, predominantly originating from the left atrial appendage (LAA). Current clinical scores like CHA₂DS₂-VASc, while useful, provide limited insight into the pro-thrombotic mechanisms of Virchow's triad-blood stasis, endothelial damage, and hypercoagulability. This study leverages biophysical computational modelling to deepen our understanding of thrombogenesis in AF patients. Utilising high temporal resolution Cine magnetic resonance imaging (MRI), a 3D patient-specific modelling pipeline for simulating patient-specific flow in the left atrium was developed. This computational fluid dynamics (CFD) approach was coupled with reaction-diffusion-convection equations for key clotting proteins, leading to an innovative risk stratification score that combines clinical and modelling data. This approach categorises thrombogenic risk into low (A), moderate (B), and high (C) levels. Applied to a cohort of nine patients, pre- and post-catheter ablation therapy, this approach generates novel risk scores of thrombus formation, which are based of mechanistic characterisation of all aspects of the Virchow's triad. Currently, thrombogenesis mechanisms are not factored in widespread clinical risks scores based on demographic characteristics and co-morbidities. Notably, some patients with a CHA₂DS₂-VASc score of 0 (lowest clinical risk) exhibited much higher risks once the individual pathophysiology was accounted for. This discrepancy highlights the limitations of the CHA₂DS₂-VASc score in providing detailed mechanistic insights into patient-specific thrombogenic risk. This work introduces a comprehensive method for assessing thrombus formation risks in AF patients, emphasising the value of integrating biophysical modelling with clinical scores to enhance personalised stroke prevention strategies.
