Leveraging Computational Fluid Dynamics for Next-Generation Preoperative Planning in Vascular Surgery

Area of Science:

• Vascular Surgery
• Biomedical Engineering
• Medical Imaging

Background:

• Chronic limb-threatening ischemia (CLTI) leads to significant disability and mortality globally.
• Current surgical revascularization for CLTI has high rates of limb loss and early death.
• Predicting improved distal blood flow after intervention remains a major challenge in CLTI treatment.

Purpose of the Study:

• To introduce and validate an angiography-based computational tool for preoperative assessment of CLTI revascularization strategies.
• To enable surgeons to select interventions that maximize distal perfusion.
• To improve patient outcomes by enhancing the accuracy of surgical planning.

Main Methods:

• Development of a computational model utilizing patient-specific angiography data.
• Preoperative assessment of revascularization strategies using the computational tool.
• Pilot study involving patients undergoing femoral artery angioplasty for CLTI.

Main Results:

• The computational tool accurately predicts distal arterial flow in CLTI patients.
• Demonstration of the tool's efficacy in a pilot study.
• Successful application in guiding surgical strategy selection for femoral artery angioplasty.

Conclusions:

• Angiography-based computational modeling is a promising approach for predicting distal blood flow in CLTI.
• This tool can aid surgeons in optimizing revascularization strategies.
• Improved prediction of blood flow may lead to reduced limb loss and better patient outcomes in CLTI.