Patient-Specific Hemodynamic Simulation for Predicting Stroke Laterality in Cardiac Embolism.

Area of Science:

• Cardiovascular research
• Neurology
• Biomedical engineering

Background:

• Cardioembolic sources cause 20-30% of acute ischemic strokes (AIS), leading to significant morbidity.
• Conventional imaging methods confirm stroke etiology retrospectively but do not capture dynamic embolic transport.
• Predicting stroke laterality is crucial for understanding disease mechanisms and guiding treatment.

Purpose of the Study:

• To predict stroke laterality in patients with cardioembolic AIS.
• To integrate patient-specific computational fluid dynamics (CFD) simulations with Bayesian logistic regression.
• To analyze dynamic embolic transport behavior in the anterior circulation.

Main Methods:

• Eight patients with anterior circulation AIS of cardiac origin underwent high-resolution computed tomography angiography.
• Patient-specific CFD models were generated to simulate pulsatile blood flow and particle transport.
• Two embolic bias features (long-term and short-term) were derived and used in a Bayesian logistic regression model.

Main Results:

• The right internal carotid artery received more embolic particles than the left ICA.
• Distinct patterns of embolic bias (x1 and x2) were observed between right-sided and left-sided strokes.
• The combined CFD and Bayesian model demonstrated accurate prediction of stroke laterality in this cohort.

Conclusions:

• CFD-based embolic modeling coupled with Bayesian analysis accurately predicts stroke laterality in cardioembolic AIS.
• This integrated approach reveals patient-specific embolic transport dynamics.
• The findings highlight a novel method for understanding the mechanisms of cardioembolic stroke.