The BHF Centre is funding a number of peer-reviewed research proposals and pump-priming pilot projects, with the objectives of promoting nascent collaboration and supporting the research leaders of the future.

COVID-19 projects

Collaborative research on cardiovascular medicine and COVID-19 using high-resolution clinical data
Investigator: Jamil Mayet

COVID-19 Human Population SusceptibiliTY COHORT Network (COVIDITY-COHORT)
Investigators: Ioanna Tsoulaki and Paul Elliott

PHOSP-COVID - Post-Covid-19 follow up cohort
Investigators: Edwin Chilvers and Luke Howard

Developing a screen to inhibit endothelial pro-thrombotic switch during severe COVID19 infection
Investigator: Anna Randi

Studies of Ang(1-7) and analogues to reduce the vascular complications of COVID-19
Investigators: David Owen, Katerina Pollock and Joseph Boyle

Imaging ACE2 dimerization – target for Sars-Cov2
Investigator: Paul French

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2020

Detecting and monitoring heart failure with handheld ultrasound
Investigator: Peter Weinberg

The project concerns the development of new methods for diagnosing and monitoring heart failure based on ultrasound monitoring of pulse waves in arteries. At present, definitive diagnosis requires an echocardiogram and takes place in a specialist centre. Echocardiograms are difficult to interpret; prolonged training is required and even then there is subjectivity about the results. The methods we are developing could be used by non-specialists, and in the community as well as in the clinic. They return absolute quantitative results and do not require interpretation of images.

Development of the Imperial Implantable Electro-Mechanical Coupling Loop Recorder
Investigators: Daniel Keene and Zac Whinnett

Implantable Cardioverter Defibrillators (ICDs) save lives. Unfortunately though, ~40% of the shocks they deliver are unnecessary. This is because ICD decision-making algorithms are limited, relying solely upon detected electrogram signals to diagnose potential ventricular arrhythmias.
These shocks are distressing, increase mortality, and may limit more systematic device utilisation. We have developed and patented a novel ICD decision-making algorithm, which for the first time combines synchronised electrogram with perfusion signals to create a personalised and reliable indicator of whether a shock is truly required or not. This project will build and test a proof-of-concept device which can implement our new method.

Finite element analysis of enlarged thoracic aortas: using aortic wall strain to predict aortic growth and failure with a machine learning approach
Investigator: Thanos Athanasiou

This project focuses on thoracic aortic aneurysms, which silently afflict patients who have a risk of sudden death from type A aortic dissection. In a previous project, we conducted comprehensive computational fluid dynamic (CFD) assessment of TAA (using data from 4D flow MRI), allowing us to generate detailed wall shear stress (WSS) assessment. In the current project, we are matching 4D flow MRI data and material properties from explanted aneurysm tissue to build a finite element model to map out aortic wall stress. This will be incorporated into a machine learning algorithm to a) rapidly predict TAA wall stress from routine imaging and b) predict aneurysm expansion and prognosis from serial imaging. This will provide a more accurate prediction of TAA expansion and rupture/dissection compared to current standards.

Phenotyping cardiovascular disease, disease traits and death with metabolomics, linked population-based data and machine learning
Investigator: Majid Ezzati

As acute myocardial infarction (AMI) events are shifted to older ages and survival from AMI improves, multi-morbidity is increasingly common in hospitalised AMI patients. The aim of this project to understand the patterns of multi-morbidity in AMI patients in different parts of the country, and evaluate how it contributes to geographical variations in survival and mortality from AMI.

Analysis of molecular signatures of cardiovascular disease by a point of care bioanalytical platform that combines paper-based assay technology with a mobile phone readout
Investigator: Molly Stevens

The objective of this project is to develop and validate a highly sensitive nanoparticle-based  point-of-care diagnostic platform to detect molecular signatures associated with cardiovascular disease. The simultaneous detection of biomarkers will be read through a smartphone camera and processed by machine learning algorithms to facilitate clinical decision-making and patient stratification.

Integrating imaging and genetics to identify fundamental determinants of cardiovascular form and function in health and disease
Investigator: James Ware