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

Overview

1. Laboratory of Mathematics for Biology and Medicine, Department of Mathematics, University of Trento, Italy
2. Department of Mathematics and Scientific Computing, NAWI Graz, University of Graz, Austria
3. Gottfried Schatz Research Center, Division of Medical Physics and Biophysics, Medical University of Graz, Austria
4. BioTechMed-Graz, Austria
5. Department of Mechanical and Metallurgical Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Chile
6. Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Chile
7. Millennium Institute for Intelligent Healthcare Engineering, iHEALTH, Chile
8. Euler Institute, Università della Svizzera italiana, Switzerland

Abstract

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.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

The paper says that its authors' code is available on request: it was not published with the paper, so there is nothing to verify.

Code availability

The electrophysiological model and Bayesian optimization framework used for Purkinje network inference are available from previous work [20].

The constrained Bayesian optimization (CBO) framework and the coupling pipeline between electrophysiology and circulation models can be made available upon reasonable request to the corresponding author.

The CircAdapt implementation used in this study is based on a C++ version of the model developed at the Medical University of Graz and is not publicly available. A proof-of-concept implementation of the pipeline, using an external CircAdapt interface, can be provided upon reasonable request.

A simplified example demonstrating the main steps of the pipeline can be shared for reproducibility purposes.

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

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Data

No dataset and no data link were found in the paper.

Data availability

The clinical data used in this study consist of patient-specific ECG and CMR measurements and are subject to ethical and privacy restrictions; therefore, they are not publicly available.

Access to anonymized data may be considered upon reasonable request and subject to approval by the relevant institutional review board.

To facilitate reproducibility, a proof-of-concept dataset based on synthetic data can be provided upon reasonable request.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, journal, dates, 5 authors, 7 keywords, 1 funder, 54 references.

Cite

This paper

Bergantin, E., Caforio, F., Costabal, F. S., Augustin, C. M., & Pezzuto, S. (2026). Probabilistic Calibration of a Closed-loop Cardiac Electromechanical Model with Application to Cardiac Resynchronization Therapy. Research Square (preprint). https://doi.org/10.21203/rs.3.rs-9303831/v1

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