Continuous tracking of aortic aneurysm diameter with peripheral pulse waves: a computational framework combining sequential Markov chain Monte Carlo with Kalman filtering

OBJECTIVE: Abdominal aortic aneurysms (AAA) affect more than 1% of adults over 50 and carry significant mortality risk. Current surveillance relies on intermittent imaging (ultrasound or MRI) at 6--24 month intervals, which may miss rapid growth acceleration between visits. We investigate the feasibility of continuous aneurysm diameter tracking using peripheral pulse waves, like those detected by photoplethysmography (PPG) devices. Approach. We use a simplified one-dimensional (1D) hemodynamic model that simulates pulse wave propagation from the heart to the pedal digital artery. We first demonstrate diameter estimation when the hemodynamic model parameters defining systemic circulation are known within bounds for an individual, aggregating thousands of observations over hours or days. We then address the more challenging scenario where systemic parameters are only known to be within wider population-level bounds, using a sequential Monte Carlo approach that combines an ensemble Markov chain Monte Carlo (MCMC) sampler with Kalman filtering to marginalise over unknown parameters while tracking the aneurysm diameter. Both approaches are evaluated through 12-month tracking simulations of virtual patients with constant and accelerating aneurysm growth. Main results. While single-observation diameter estimation is fundamentally limited by noise and confounding variables, aggregating 1,600 measurements under baseline noise conditions reduces diameter uncertainty to 0.8 mm when patient-specific hemodynamic parameters are known within bounds. In this setting, tracking simulations across eight virtual patients achieve average root-mean-square error (RMSE) of ∼0.3 mm. When systemic parameters are known only within population-level bounds, joint Bayesian estimation over the full parameter space achieves a median RMSE of 0.65 mm (1.4±0.3 mm, mean±standard error) across 50 virtual patients, remaining within clinically relevant ranges despite the underlying parameters being only partially identifiable. Significance. These computational results suggest that peripheral pulse wave monitoring through PPG sensors could complement traditional imaging for AAA, potentially enabling earlier detection of growth and more timely intervention.