Imaging and biophysical modelling of thrombogenic mechanisms in atrial fibrillation and stroke.
Level 5 - mechanism / opinion, no new human data
Narrative review of imaging and biophysical modeling mechanisms without systematic synthesis or primary human data.
PubMed 36733827 · doi:10.3389/fcvm.2022.1074562
What was done
This narrative review summarizes developments in multi-modality cardiac imaging (cardiac computed tomography, magnetic resonance imaging, and echocardiography) and in-silico biophysical modeling (computational fluid dynamics and reaction-diffusion-convection equations) for evaluating thrombogenic mechanisms under Virchow's triad in patients with atrial fibrillation.
What was found
The abstract reports no numerical data or quantitative outcomes. It observes that while clinical imaging assesses blood flow velocities and left atrial fibrosis, it cannot directly capture coagulation dynamics, whereas computational tools simulate flow stasis, coagulation cascades, and endothelial stress metrics. Combining these modalities with machine learning is proposed to advance personalized stroke risk assessment.
Why it matters
Current stroke risk stratification in atrial fibrillation relies primarily on empirical clinical risk scores; combining imaging with biophysical simulation offers a path toward mechanistic, patient-specific risk estimation.
Limits
As a narrative review, it contains no primary patient data, statistical analyses, or systematic search methodology. The integrated computational and machine learning approaches discussed remain investigational and lack formal clinical validation frameworks.
Cited by
- supports Atrial fibrillation forms clots in the upper chambers of the heart, which can then travel into the lower chambers and be pumped to the brain.