Scientific basis / background: Stablishing the causes (i.e. aetiology) of cardiovascular conditions such as hypertension is key for their effective and efficient management. We at KCL have generated initial promising evidence that the heart remodels its 3D anatomy in unique ways depending on the cause of increased afterload.
Aim: To propose new computational-based biomarkers of the heart that informs the aetiology of prognosis in hypertension.
Techniques and skills: The PhD will develop and apply computational and modelling technologies. Starting from medical images (magnetic resonance, computer tomography, echocardiography) and from functional data (electrocardiogram, wearable information), a computational replica of the individual heart of patients will be developed– this is their digital twin. The main opportunity is to build the synergies between mechanistic and statistical inference (see figure). The ability to cope with limited data (thus making sound population inferences), and to minimise the impact of noise and model assumptions, are two of the main challenges to be addressed in this field.
Specific objectives (for each of the 3 years):
To further develop computational techniques that build the digital twin, the computational replica of the heart of a patient, capturing their anatomy, electrophysiological and mechanical status.
To apply the computational techniques to a retrospective cohort of 100 patients with diverse grade of severity of hypertension.
To analyse the relationships between the computational parameters and the known diagnosis and prognosis of the patients, in order to (1) unveil novel mechanistic insights and (2) propose new diagnostic and prognostic markers.