The Digital Twin in Heart Failure

Lead Supervisor
Dr Pablo Lamata
Wellcome Trust Senior Research Fellow and Reader in Computational Cardiology
Dept. of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, FoLSM, King’s College London

Dr Gerry Carr-White
Consultant Cardiologist, Guys’ and St Thomas’ NHS Foundation Trust

Project Details

Cardiovascular healthcare is traditionally delivered by a range of sometimes not well coordinated units. The front-line detection of the general practitioner and the specialised services in the hospital, such as cardiology and radiology, represent, islands of care‚ that the patient visits. There are indeed bridges built between them, and a huge drive within our NHS to improve the coordination amongst them and their flow of data.

Heart failure (HF) is a condition that will require numerous visits through these healthcare units, and thus generates a wealth of data that ends up scattered in different information systems. It is a complex condition caused by many aetiologies. The vision is that a better management of this condition is possible by focusing on the patient journey, where the system is able to ensure the right patients get the right treatment or technology at the right time, in the right place. We at King’s Health Partners (KHP) are leading this change with our Value Based Healthcare Programme, where Dr Carr-White is the lead on the cardiovascular care pathway with a specific focus on heart failure ([Burnhope19]).

An enabler technology towards this vision is the construction of the digital twin of the heart of each patient. In health care the digital twin denotes the vision of a comprehensive, virtual tool that integrates coherently and dynamically the clinical data acquired over time for an individual using mechanistic and statistical models. We at KCL are championing this vision [Corral20], with a wide experience in the personalization of mechanistic models to the clinical data (e.g. imaging) available.  This ambitious project will be divided into five specific steps towards it, five objectives:

  1. To design the digital twin template. 
  2. To populate a database of digital twins in HF with preserved ejection fraction (HFpEF) and aortic stenosis (AS)
  3. To investigate the feasibility of inferring the longitudinal progression of (1) diastolic filling performance in HFpEF and (2) valvulopathy in AS through the capabilities of statistical induction or mechanistic deduction of the digital twin. 
  4. Building in our initial experience and data [Webb18a], to describe the pathways into HFpEF by a cluster analysis of the digital twins with two specific aims: (1) to investigate the mechanistic links with their aetiology, and (2) to investigate the potential risk predictive value by linking with outcome metrics/events retrieved from the EHR. 
  5. To investigate the feasibility of defining the optimal time for valve surgery in AS from the longitudinal evolution of the digital twin. 

This project would suit a student with experience in data science, electronic health record, informatics/computer science, bioengineering or applied mathematics, and with an interest in clinical healthcare applications of machine learning. 

[Burnhope19] Burnhope E, Carr-White G. Economic impact of introducing TYRX amongst patients with heart failure and reduced ejection fraction undergoing implanted cardiac device procedures: a retrospective model based cost analysis. J Med Econ. 2019 May;22(5):464-470. doi: 10.1080/13696998.2019.1581621

[Corral20] Corral J, Lamata P. The digital twin to enable the vision of precision cardiology. European Heart Journal, ehaa159,

[Webb18a] Webb J, Carr-White G. Is heart failure with mid range ejection fraction (HFmrEF) a distinct clinical entity or an overlap group? Int J Cardiol Heart Vasc. 2018 Sep 6;21:1-6. doi: 10.1016/j.ijcha.2018.06.001. eCollection 2018 Dec.


The student will get the regulatory clearance to access Guy’ and St Thomas Trust (GSTT) electronic health records (EHR), together with the linked EHR from primary and secondary care. Building these links will be enabled through our London Medical Imaging and AI Centre for Value Based Healthcare, and through the leading role of Dr. Carr-White within KHP‚Äôs Value Based Healthcare Programme. Our GSTT manages around 900 HF patients every year.


Heart failure; Digital Twin