Patient-specific, multiscale modelling of neointimal hyperplasia in lower-limb vein grafts using readily available clinical data.

The prediction of neointimal hyperplasia (NIH) growth, leading to vein graft failure in lower-limb peripheral arterial disease (PAD), is hindered by the multifactorial and multiscale mechanobiological mechanisms underlying the vascular remodelling process. Multiscale in silico models, linking patients' hemodynamics to NIH pathobiological mechanisms, can serve as a clinical support tool to monitor disease progression. Here, we propose a new computational pipeline for simulating NIH growth, carefully balancing model complexity/inclusion of mechanisms and readily available clinical data, and we use it to predict NIH growth for an entire vein graft. To this end, three different fittings to published in vitro data of time-averaged wall shear stress (TAWSS) vs nitric oxide (NO) production were tested for predicting long-term graft response (10-month follow-up) on a single patient. Additionally, the sensitivity of the model's predictions to different inflow boundary conditions (BCs) was assessed. The main findings indicate that: (i) a TAWSS-NO hyperbolic relationship best predicts long-term graft response; (ii) the model is insensitive to the inflow BCs if the waveform shape and the systolic acceleration time are comparable with the one acquired at the same time as the computed-tomography scan. This proof-of-concept study demonstrates the potential of using multiscale, computational techniques to predict NIH growth in lower-limb vein grafts, considering the routine clinical scenario of non-standardised data collection and sparse, incomplete datasets.