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

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    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 5, 2025
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    A new computational tool predicts blood flow in chronic limb-threatening ischemia (CLTI) patients before surgery. This tool helps surgeons choose the best revascularization strategy to improve limb perfusion and outcomes.

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    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.