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Background <p>Peripheral artery disease (PAD) is a leading cause of limb loss and morbidity worldwide, with chronic limb-threatening ischemia (CLTI) representing its most severe presentation. Although image-guided endovascular interventions are routinely performed, clinicians currently lack tools that provide real-time, patient-specific predictions of hemodynamic outcomes to guide revascularization decisions. Existing computational fluid dynamics (CFD) approaches can recover pre-operative hemodynamics but are typically too slow or insufficiently integrated into clinical workflows to support interactive, intraoperative planning.</p> Methods <p>We extend HarVI (HARVEY Virtual Intervention), a previously established digital twin framework, to the peripheral circulation and evaluate its use for real-time prediction of postoperative blood flow in patients with superficial femoral artery (SFA) lesions. HarVI integrates one-dimensional CFD with machine learning to enable rapid assessment of patient-specific revascularization strategies. Key components include: (1) automated boundary condition tuning using patient-averaged and optimization-based approaches; (2) simulation of a wide range of endovascular interventions via a machine-learned surrogate model; and (3) validation of predicted postoperative hemodynamics against clinical duplex ultrasound measurements. Performance was evaluated retrospectively in a cohort of seven patients with SFA disease.</p> Results <p>HarVI accurately predicted postoperative peak systolic velocities and reproduced full 1D CFD results across a synthetic revascularization landscape. Surrogate model predictions closely matched high-fidelity simulations while enabling rapid exploration of intervention scenarios, supporting near–real-time evaluation of treatment options.</p> Conclusions <p>These results establish HarVI as a promising digital twin platform for real-time, patient-specific intervention planning in PAD. By enabling rapid, data-driven prediction of postoperative hemodynamics, HarVI opens the door to interactive intraoperative decision support with the potential to improve revascularization outcomes in patients with CLTI.</p>

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Real-Time Peripheral Revascularization Planning in Chronic Limb Threatening Ischemia Using HarVI: A Digital Twin Approach

  • 期刊论文

  • 发表日期：

    2026年3月9日

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  • Cyrus Tanade,
  • Christopher W. Jensen,
  • Guinevere Ferreira,
  • Amanda Randles

摘要

Background

Peripheral artery disease (PAD) is a leading cause of limb loss and morbidity worldwide, with chronic limb-threatening ischemia (CLTI) representing its most severe presentation. Although image-guided endovascular interventions are routinely performed, clinicians currently lack tools that provide real-time, patient-specific predictions of hemodynamic outcomes to guide revascularization decisions. Existing computational fluid dynamics (CFD) approaches can recover pre-operative hemodynamics but are typically too slow or insufficiently integrated into clinical workflows to support interactive, intraoperative planning.

Methods

We extend HarVI (HARVEY Virtual Intervention), a previously established digital twin framework, to the peripheral circulation and evaluate its use for real-time prediction of postoperative blood flow in patients with superficial femoral artery (SFA) lesions. HarVI integrates one-dimensional CFD with machine learning to enable rapid assessment of patient-specific revascularization strategies. Key components include: (1) automated boundary condition tuning using patient-averaged and optimization-based approaches; (2) simulation of a wide range of endovascular interventions via a machine-learned surrogate model; and (3) validation of predicted postoperative hemodynamics against clinical duplex ultrasound measurements. Performance was evaluated retrospectively in a cohort of seven patients with SFA disease.

Results

HarVI accurately predicted postoperative peak systolic velocities and reproduced full 1D CFD results across a synthetic revascularization landscape. Surrogate model predictions closely matched high-fidelity simulations while enabling rapid exploration of intervention scenarios, supporting near–real-time evaluation of treatment options.

Conclusions

These results establish HarVI as a promising digital twin platform for real-time, patient-specific intervention planning in PAD. By enabling rapid, data-driven prediction of postoperative hemodynamics, HarVI opens the door to interactive intraoperative decision support with the potential to improve revascularization outcomes in patients with CLTI.