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

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.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.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.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.

Duke Scholars

Author Amanda Randles Biomedical Engineering