Probabilistic Calibration of a Closed-loop Cardiac Electromechanical Model with Application to Cardiac Resynchronization Therapy

Cardiac conduction disorders such as left bundle branch block (LBBB) induce ventricular dyssynchrony and increase the risk of heart failure. Cardiac resynchronization therapy (CRT) improves cardiac function in approximately 70% of patients; however, optimizing patient selection and pacing strategy remains challenging. We present a patient-specific computational framework that integrates physics-based modeling with Bayesian personalization to simulate cardiac electromechanical function and acute response to CRT. Patient-specific anatomies are reconstructed from cardiac MRI, and electrical activation is modeled using a three-dimensional Eikonal formulation with probabilistic inference of the Purkinje network from ECG data via Bayesian Optimization. This approach enables uncertainty quantification by identifying multiple activation patterns consistent with clinical observations. Electrical activation is coupled to a closed-loop cardiovascular model (CircAdapt), calibrated using Constrained Bayesian Optimization to match MRI-derived volumetric measurements. The framework enables the simulation of multiple pacing strategies and the non-invasive estimation of their acute hemodynamic effects, while propagating electrophysiological uncertainty to mechanical outputs. Relying exclusively on non-invasive data and maintaining low computational cost, the proposed framework provides a scalable approach toward uncertainty-aware cardiac digital twins for personalized CRT planning.