Personalized Risk Prediction of Stroke and Silent Brain Infarction Using Computational Fluid Dynamics
Our objective is to establish the feasibility of a computational fluid dynamics (CFD)-based, left atrial (LA) regional blood flow analysis technology to predict stroke and abnormal brain MRI findings in patients with and without a history of atrial fibrillation (Afib). The proposed research will 1) identify novel biomarkers to predict stroke and silent brain infarction (SBI) in patients with and without a history of Afib, whereas the current guidelines allow risk stratification only in individuals with Afib; and 2) provide evidence for studies to evaluate prophylactic oral anticoagulation or LAA closure to reduce the risk of stroke, SBI, and cognitive decline.
Hiroshi Ashikaga, MD, PhD
We are currently applying the CFD technology to the Johns Hopkins Cardiac CT registry. After the retrospective analysis is completed we plan to conduct a prospective randomized trial test whether the CFD technology predicts stroke, SBI, and cognitive decline, and whether oral anticoagulation improves these outcomes.
“MRI May Help Gauge Stroke Risk in Those With Irregular Heartbeat” (U.S News & World Report)
“Motion-tracking MRI gives new insight into stroke risk” (Auntminnie.com)
“This visualization of a human heart could help doctors predict stroke risk” (Science Magazine)
For more information:
Laboratory of Unconventional Electrophysiology