Collaborative: Age- Sex- and Ethnicity-Specific Cardiac Electrophysiology Phenotyping to Reduce Inequality in Atrial Fibrillation Care

MRC’s Collaborative awards provide students with experience of collaborative research with a non academic partner, enabling the student to spend a period of time with the non-academic partner (usually no less than three months over the lifetime of the PhD).

Background

Atrial fibrillation (AF) affects approximately 1.5 million people in the UK, yet significant inequalities exist in access to potentially curative treatments. Real-world data indicates ablation rates are lower in women compared to men, and decline sharply with age. Concurrently, emerging evidence suggests women exhibit different electrophysiological characteristics than men. Similar differences may exist based on other important factors including age and ethnicity. However, current clinical thresholds for defining abnormal electrophysiological characteristics are not sex-, age- or ethnicity-specific. This may lead to the misclassification of healthy atria as diseased, or conversely, the underestimation of risk, contributing to inequitable treatment pathways. The OpenEP|NET programme provides a unique infrastructure to address these questions, comprising a national cohort of arrhythmia patients with standardised electrophysiology data stored in a secure Trustworthy Research Environment (TRE).

Aims

This PhD project aims to investigate whether sex-, age- and ethnicity-specific electrophysiological phenotypes can improve patient stratification and reduce health inequalities in AF care. 

The PhD candidate will be supported to refine the aims of the proposal based on their interests and developing expertise. Initial aims will include: 

1. Evaluate demographic variation in electrophysiological features and their association with treatment outcomes.

Utilise the OpenEP|NET cohort to assess how features such as electrogram voltage and conduction velocity vary by age, sex and ethnicity. Evaluate their associations with outcomes following AF treatment. Where the data support it, compare the performance of common thresholds with subgroup-specific thresholds.

2. Develop and validate computational methods for robust electrogram phenotyping.

Use AI and other appropriate computational approaches to extract, summarise or classify electrogram features. Determine whether these methods identify clinically meaningful electrophysiological patterns not captured by conventional analysis. Assess performance across age, sex and ethnicity groups, including whether data limitations affect reliability or introduce unequal error.

3. Assess the clinical value and equity implications of the resulting phenotypes.

Test whether electrophysiological phenotypes and, where justified, demographic-specific thresholds, improve prediction of treatment outcomes beyond existing approaches. Explore whether phenotype and outcome differences are associated with variation in treatment pathways or access, using linked healthcare data, and inform the development of potential decision-support tools.

Training Outcomes

This project delivers integrated training aligned with PM:FLoHE strategic themes. The candidate will master quantitative methods, analysing large-scale electrophysiology datasets using OpenEP/MATLAB/Python/R within a secure Trustworthy Research Environment. Clinical precision medicine training covers atrial fibrillation pathophysiology and translational research principles, facilitated by clinical supervision and exposure to the interdisciplinary research team. The student will be supported to attend relevant international training opportunities including the Cardiac Arrhyhthmia Digital Twin summer school at the Alan Turing Institute (on which the lead supervisor teaches) and to apply for training via the 2-week Summer School in Computational Physiology (Simula, Oslo).

Professional development will include presenting at national conferences, mentoring undergraduates, and publishing 3-4 peer-reviewed papers. Training will align with DLA themes including Methodological Research and Health Inequalities, balancing the 180 mandatory programme credits with bespoke project skills. The student will join the PM:FLoHE cohort for interdisciplinary networking, enhancing leadership capabilities in precision medicine.

Discussions are underway to establish a collaborative partnership. A successful collaboration would enable a minimum 12-week industry placement focused on commercial strategy and implementation science. The student will investigate barriers to adopting novel phenotyping tools across diverse demographics, ensuring innovations reduce rather than widen health inequalities. This aligns with the PM:FLoHE intersectoral training theme, providing insight into translating precision medicine into equitable clinical practice.