Personalized haemodynamic modelling for non-invasive quantification of arterial stiffness in the human vasculature

Abstract Arterial stiffness is a pivotal predictor of cardiovascular events, yet its in vivo characterization remains elusive because arterial wall mechanics cannot be directly measured. Conventional inverse approaches require high-resolution imaging to resolve wall motion, restricting their clinical applicability. Here, we present a non-invasive computational framework that infers regional arterial stiffness solely from routinely acquired pressure and blood flow velocity waveforms by leveraging personalized one-dimensional haemodynamic models of the human arterial network. Patient-specific models were constructed for 21 individuals with hypertension using stroke volume, arterial diameters, brachial pressures and carotid flow profiles. Regional stiffness parameters were identified through optimization to reconcile simulated and measured haemodynamics, and validated against brachial-ankle pulse wave velocity. The inferred stiffness demonstrated strong concordance with clinical measurements (r = 0.81; standard error = 279 cm s)–1. Longitudinal evaluation in 13 patients further revealed therapy-dependent remodelling, with calcium channel blockers selectively reducing pathological stiffening while preserving compliant vessels. Together, these results establish model-based inversion of standard haemodynamic data as a practical, scalable approach for quantifying arterial stiffness, enabling individualized cardiovascular risk assessment and treatment monitoring.