Supervisors

Suitable for

Abstract

Prerequisites: Computational Medicine (recommended)

 

Abstract

Hypertrophic cardiomyopathy (HCM) is characterised by complex interactions between electrical abnormalities, ventricular remodelling, and mechanical dysfunction. While the 12-lead ECG is widely available and cost-effective, its interpretation is often confounded by overlapping phenotypes. Myocardial strain, derived from echocardiography or cardiac MRI, captures regional mechanical impairment such as reduced deformation, mechanical dispersion, or dyssynchrony. Individually, both ECG and strain offer valuable insight, but their combined interpretation has not been systematically explored through mechanistic modelling.

This project will integrate ECG features and myocardial strain metrics to identify electro-mechanical markers associated with HCM severity and arrhythmic risk. Students will extract ECG descriptors (intervals, morphology, repolarisation features) and strain-based indices (global and regional strain, temporal dispersion). These will be analysed jointly and supported by mechanistic simulations of ventricular activation and repolarisation to explore plausible structural or ionic substrates underlying the observed patterns. Potential avenues include clustering electro-mechanical phenotypes, linking ECG–strain concordance to proposed mechanisms, or developing simple risk-stratification models.

The overarching aim is to establish mechanistically grounded electro-mechanical signatures that improve current non-invasive risk assessment in HCM.

• References

[1] Distinct ECG phenotypes identified in hypertrophic cardiomyopathy using machine learning associate with arrhythmic risk markers. https://doi.org/10.3389/fphys.2018.00213

[2] Electrocardiogram phenotypes in hypertrophic cardiomyopathy caused by distinct mechanisms: apico-basal repolarisation gradients vs. Purkinje-myocardial coupling abnormalities. https://doi.org/10.1093/europace/euy226