A computational study of the connection between coronary revascularization and cardio-cerebral hemodynamics.

Background And Objective: Some patients experience life-threatening strokes during coronary revascularization. Despite its clinical importance, few numerical studies have investigated the impact of coronary revascularization on cardio-cerebral hemodynamics. This study aims to address this gap by evaluating the effects of eliminating coronary stenosis on both coronary and cerebral blood flow using patient-specific simulations.

Methods: A patient-specific cardio-cerebral arterial network with a 70% stenosis in the left main coronary artery was reconstructed, and computational fluid dynamics were employed to evaluate the effects of eliminating coronary stenosis. The three-dimensional time-dependent incompressible Navier-Stokes equations were discretized using a stabilized P-P Galerkin finite element method and an implicit second-order backward differentiation formula. A regional blood flow distribution model, coupled with a lumped Windkessel model, was applied at the outlet boundaries. The 3D pulsatile blood flow was solved using a parallel solver based on a scalable Newton-Krylov-Schwarz algorithm, enabling fast and efficient simulations.

Results: Coronary revascularization significantly improved myocardial blood flow, increasing the coronary fractional flow reserve from 0.742 to 0.904, indicating enhanced myocardial perfusion. However, cerebral hemodynamics were negatively affected, with a 2.49% reduction in blood flow through the main cerebral artery, suggesting an elevated risk of cerebral ischemia. The proposed computational framework demonstrated good parallel scalability across thousands of processor cores.

Conclusions: This study highlights the dual impact of coronary revascularization, improving myocardial perfusion while potentially elevating the risk of cerebral ischemic complications. The efficient computational approach provides a valuable tool for evaluating cardio-cerebral hemodynamics in patient-specific settings, making it suitable for complex and time-intensive simulations.