Harnessing 12-lead ECG and MRI data to personalise repolarisation profiles in cardiac digital twin models for enhanced virtual drug testing
Year: 2024
Type: article
Abstract: Cardiac digital twins are computational tools capturing key functional and anatomical characteristics of patient hearts for investigating disease phenotypes and predicting responses to therapy. When paired with large-scale computational resources and large clinical datasets, digital twin technology can enable virtual clinical trials on virtual cohorts to fast-track therapy development. Here, we present an open-source automated pipeline for personalising ventricular electrophysiological function based on routinely acquired magnetic resonance imaging (MRI) data and the standard 12-lead electrocardiogram (ECG). Using MRI-based anatomical models, a sequential Monte-Carlo approximate Bayesian computational inference method is extended to infer electrical activation and repolarisation characteristics from the ECG. Fast simulations are conducted with a reaction-Eikonal model, including the Purkinje network and biophysically-detailed subcellular ionic current dynamics for repolarisation. For each patient, parameter uncertainty is represented by inferring an envelope of plausible ventricular models rather than a single one, which means that parameter uncertainty can be propagated to therapy evaluation. Furthermore, we have developed techniques for translating from reaction-Eikonal to monodomain simulations, which allows more realistic simulations of cardiac electrophysiology. The pipeline is demonstrated in three healthy subjects, where our inferred pseudo-diffusion reaction-Eikonal models reproduced the patient's ECG with a median Pearson's correlation coefficient of 0.9, and then translated to monodomain simulations with a median correlation coefficient of 0.84 across all subjects. We then demonstrate our digital twins for virtual evaluation of Dofetilide with uncertainty quantification. These evaluations using our cardiac digital twins reproduced dose-dependent QTc and T peak to T end prolongations that are in keeping with large population drug response data. The methodologies for cardiac digital twinning presented here are a step towards personalised virtual therapy testing and can be scaled to generate virtual populations for clinical trials to fast-track therapy evaluation. The tools developed for this paper are open-source, documented, and made publicly available.
Source: Medical Image Analysis
Institutions: University of Oxford, Queensland University of Technology
Language: English
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,970
Funders: U.S. Department of Energy, Wellcome Trust, European Commission, Queensland University of Technology, Partnership for Advanced Computing in Europe AISBL, Centro Svizzero di Calcolo Scientifico, National Supercomputing Centre Singapore, Office of Science, Engineering and Physical Sciences Research Council, Australian Research Council, Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers, Argonne National Laboratory, HORIZON EUROPE European Innovation Council
Awards: G102163711, G1062445980, G107546177, G1313983767, G1664757510, G2287611682, G262962599, G3075337988, G4082297326, G434337789, A Centre of Excellence in Computational Biomedicine, A Centre of Excellence in Computational Biomedicine, Human in silico clinical trials in post myocardial infarction: mechanistic investigations into phenotypic electromechanical variability and response to treatment, G498139845, G5904151224, G6256534168, G6719690533, G6848031779, G6918803902, Human in silico clinical trials in post myocardial infarction: mechanistic investigations into phenotypic electromechanical variability and response to treatment, CompBioMedX: Computational Biomedicine at the Exascale, G9000287530, G969889393, G993149817
OpenAlex ID: W4403532250