Wall-Specific V-Flow Hemodynamics in Lower-Limb Peripheral Artery Disease: Diagnostic Nomogram and Periplaque Mapping

Lower limb peripheral artery disease (PAD) involves localized atherosclerosis with disturbed hemodynamics. V-flow imaging is a vector Doppler technique enabling real-time assessment of wall-specific hemodynamic parameters such as wall shear stress (WSS) and oscillatory shear index (OSI). This study included a case-control diagnostic evaluation and a prospective observational component for exploratory prognostic assessment.

The study enrolled 95 patients with PAD and 100 controls with plaque-free lower limb arteries. V-flow imaging measured WSSmax, WSSmean, and OSI across major lower limb arteries. Multivariate logistic regression and receiver operating characteristic analysis identified PAD predictors. A nomogram combining V-flow and clinical parameters was developed. V-flow analysis of 25 femoral plaques assessed upstream-downstream WSS/OSI variation. A total of 28 patients with Fontaine IV PAD were followed for 6 months to exploratorily assess preoperative V-flow parameters in relation to major adverse limb events.

Patients with PAD exhibited globally lower WSS and higher OSI in plaque-free segments compared with controls. Combined V-flow and clinical parameters showed moderate discriminative performance for PAD diagnosis (area under the curve, 0.87; sensitivity 80.1%; specificity 79.3%), with an optimism-corrected area under the curve of 0.86. Femoral plaques exhibited lower downstream WSS and higher OSI. Preoperative V-flow parameters were exploratorily assessed for 6-month major adverse limb events in 28 patients with Fontaine IV PAD (optimism-corrected area under the curve, 0.73); given the limited sample size, these findings were preliminary and require external validation.

V-flow imaging identified wall-specific hemodynamic disturbances that may precede morphological changes. These parameters may aid PAD diagnosis. The major adverse limb events analysis was secondary and exploratory due to the small sample size, and its predictive utility requires further validation.