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Published on: May 12, 2017
Body surface potential driven personalisation of electrophysiological digital twins in hypertrophic cardiomyopathy
Shambhavi Malik1, Ludovica Cicci1, Abdul Qayyum1
1National Heart and Lung Institute, Imperial College London, London, United Kingdom.
Insights
We developed a computational model to personalize hypertrophic cardiomyopathy (HCM) electrophysiology using body surface potential (BSP) data. This approach accurately captures patient-specific electrical properties, aiding in understanding arrhythmia risk.
Area of Science:
- Cardiovascular Electrophysiology
- Computational Biology
- Medical Imaging
Background:
- Hypertrophic cardiomyopathy (HCM) exhibits significant patient variability in ventricular electrophysiology, leading to arrhythmic risks not fully assessed by current methods.
- Electrocardiographic imaging (ECGI) offers high-density body surface potential (BSP) data but is primarily descriptive.
- Computational modeling provides a mechanistic approach to interpret BSP signals and understand underlying tissue properties.
Purpose of the Study:
- To develop and validate a workflow integrating multimodal imaging and Bayesian calibration for patient-specific electrophysiology (EP) modeling in HCM.
- To personalize EP models using high-density BSP data for a mechanistic understanding of arrhythmogenic substrate.
- To assess the generalizability of calibrated models beyond the calibration conditions.
Main Methods:
- Constructed patient-specific, anatomically detailed finite-element torso-heart models for 17 HCM patients using CT, CMR, and 252-electrode BSP recordings.
- Simulated ventricular depolarization and repolarization using a reaction-eikonal formulation and a detailed ionic model (ToR-ORd-dynCl).
- Employed emulator-based Bayesian history matching for EP parameter personalization, calibrating QRS and T-wave morphology.
Main Results:
- Calibrated models accurately reproduced clinical BSP morphology across 94% of electrodes (median PCC of 0.89).
- Bayesian calibration reduced uncertainty in EP parameters, yielding physiologically plausible conduction and repolarization properties.
- Models successfully generalized to right-ventricular pacing without retuning, reflecting observed clinical trends.
Conclusions:
- High-density BSP data can functionally personalize mechanistic EP models in HCM.
- The developed framework captures patient-specific EP properties and generalizes beyond calibration, supporting mechanistic investigation of arrhythmogenic substrates.
- This approach offers a powerful tool for understanding and potentially predicting arrhythmia risk in HCM patients.
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) [Formula: see text] 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.
