Body surface potential driven personalisation of electrophysiological digital twins in hypertrophic cardiomyopathy

Shambhavi Malik, Ludovica Cicci, Abdul Qayyum, Rahul Ghelani, Ji-jian Chow, Jagdeep Singh Mohal, Zachary I Whinnett, Amanda Varnava, Gernot Plank, Prapa Kanagaratnam and Steven A Niederer

PLOS Computational Biology, 2026, vol. 22, issue 7, 1-26

Abstract: Background: Hypertrophic cardiomyopathy (HCM) is associated with marked inter-patient heterogeneity in ventricular electrophysiology, contributing to arrhythmic risk that is insufficiently captured by current clinical methods. Electrocardiographic imaging (ECGI) provides high-density body surface potential (BSP) measurements but remains largely descriptive. Computational modelling offers a mechanistic framework to interpret BSP signals in terms of underlying tissue-level properties. Methods and findings: We developed a BSP-driven workflow to construct patient-specific electrophysiology (EP) models of HCM by integrating multimodal clinical imaging with Bayesian model calibration. Anatomically detailed torso-heart finite-element models were generated for 17 HCM patients using thoracic computed tomography (CT), cardiac magnetic resonance imaging (CMR), and 252-electrode BSP recordings. Ventricular depolarisation and repolarisation were simulated using a reaction-eikonal (RE) formulation coupled to a biophysically detailed ToR-ORd-dynCl ionic model. Emulator-based Bayesian history matching (HM) was used to personalise EP parameters, with staged calibration of QRS and T-wave morphology informed by targeted sensitivity analysis. The calibrated cohort reproduced clinical BSP morphology with Pearson correlation coefficient (PCC) ≥0.6 for a median of 94.0% (IQR: 91.6 to 96.8%) of electrodes, achieving a median PCC of 0.89 (IQR: 0.80 to 0.94) across the full 252-electrode vest. Calibration substantially reduced uncertainty in the high-dimensional EP parameter space while yielding physiologically plausible conduction and repolarisation properties. Models calibrated exclusively to sinus rhythm robustly generalised to right-ventricular (RV) apical pacing without parameter retuning, reproducing clinically observed pacing-induced trends in depolarisation and repolarisation. Exploratory analysis revealed biologically consistent associations between inferred EP parameters and patient demographics. Conclusions: This study demonstrates that high-density BSP data can be used to functionally personalise mechanistic EP in HCM. The framework captures intrinsic patient-specific EP properties and generalises beyond the calibration condition, supporting its use for mechanistic investigation of arrhythmogenic substrate. Author summary: HCM shows significant variability in how electrical signals propagate through the heart, contributing to differences in arrhythmia risk between patients. While BSPs can non-invasively measure cardiac electrical activity, these signals are difficult to interpret in terms of the underlying myocardial properties. We developed a computational framework that combines BSP signals with patient-specific anatomical models to create personalised EP digital twins. We used high-resolution medical imaging and Bayesian calibration techniques to adjust model parameters and reproduce clinically measured BSPs. We tested whether models calibrated to sinus rhythm could generalise to a different electrical activation pattern by simulating RV pacing without further tuning. Calibrated models were able to reproduce patient-specific BSP signals with good accuracy and captured key electrical changes observed during pacing. The inferred model parameters were physiologically plausible and showed consistent relationships with patient characteristics such as age and body size. These findings show that BSP-driven digital twins can capture intrinsic electrical properties of individual HCM hearts rather than simply fitting surface signals. This approach provides a foundation for mechanistic studies of arrhythmogenic substrate and future in silico investigations of disease progression and therapy.

Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014555 (text/html)
https://journals.plos.org/ploscompbiol/article/fil ... 14555&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1014555

DOI: 10.1371/journal.pcbi.1014555

Access Statistics for this article

More articles in PLOS Computational Biology from Public Library of Science
Bibliographic data for series maintained by ploscompbiol ().