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

Probabilistic Calibration of a Closed-loop Cardiac Electromechanical Model with Application to Cardiac Resynchronization Therapy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Probabilistic Calibration of a Closed-loop Cardiac Electromechanical Model with Application to Cardiac Resynchronization Therapy Ester Bergantin, Federica Caforio, Francisco Sahli Costabal, Christoph M. Augustin, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9303831/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version 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. Health sciences/Cardiology Biological sciences/Computational biology and bioinformatics Physical sciences/Engineering Physical sciences/Mathematics and computing Patient-specific modeling Electrophysiology Cardiac resynchronization therapy Ven-tricular dyssynchrony Eikonal model Bayesian optimization Uncertainty quantification Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 12 May, 2026 Reviews received at journal 12 May, 2026 Reviews received at journal 30 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviewers invited by journal 08 Apr, 2026 Editor assigned by journal 05 Apr, 2026 Submission checks completed at journal 04 Apr, 2026 First submitted to journal 02 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9303831","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":623120104,"identity":"cfc7ff60-8c4c-4aaa-8fd4-ee28f31f014f","order_by":0,"name":"Ester Bergantin","email":"","orcid":"","institution":"University of Trento","correspondingAuthor":false,"prefix":"","firstName":"Ester","middleName":"","lastName":"Bergantin","suffix":""},{"id":623120105,"identity":"f0447f2d-0894-4e28-bedf-26e10f104225","order_by":1,"name":"Federica Caforio","email":"","orcid":"","institution":"University of Graz","correspondingAuthor":false,"prefix":"","firstName":"Federica","middleName":"","lastName":"Caforio","suffix":""},{"id":623120106,"identity":"7c8b78a8-8a08-4e05-b5f6-6c2b78d87299","order_by":2,"name":"Francisco Sahli Costabal","email":"","orcid":"","institution":"Pontificia Universidad Católica de Chile","correspondingAuthor":false,"prefix":"","firstName":"Francisco","middleName":"Sahli","lastName":"Costabal","suffix":""},{"id":623120107,"identity":"0a10668f-21c7-447f-90b3-f675259ac938","order_by":3,"name":"Christoph M. 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