NIHRDataInsights/HRCSData huggingface dataset:
Title: Cardiac Positioning System (CPS) - An automated navigation system to guide catheter ablation therapy
Funder: Medical Research Council

Abstract: Background Reentrant waves can form around myocardial infarction scars, potentially resulting in VT. The reentrant pathway can be interrupted by radiofrequency ablation lesions, terminating and preventing VT. Pace mapping is performed to locate the exit site for ablation. Prediction of scar location is guided by an ECG of the clinical VT. Pacing is performed within the scar, and template and paced ECGs are compared. Good correlation indicates pacing from the exit site. The procedure is lengthy, difficult and VT recurrence rates are high. Objectives Regions of scar will be more accurately identified and human error mitigated, in guiding the catheter to the initial pacing site. Coordinates of scar location will be specified with an error boundary extending to cover 12% of the heart (average scar size). Pace mapping accuracy will be increased; given 40 is the average number of pace maps generated per patient, I aim to locate the target by sampling less than 10 maps: an improvement of 75%. Methods A machine learning algorithm will use clinical ECG, patient geometry and medication data for input, with corresponding location of successful ablation, derived from an MRI patient model, as output data. Given input data from newly presenting patients, the algorithm will predict the scar location by tagging the model and registering it with the electroanatomical map. Difference graphs calculated by comparing template and paced ECGs will provide input data for a machine learning algorithm, with corresponding location of successful ablation as output data. Given input data from new patients, the machine learning algorithm will indicate the ablation target. Research Outputs A software product will be produced and integrated into the CARTO mapping system for clinical use to improve ablation therapy success rates. IPR will be obtained and product information disseminated to clinicians via journals and conferences such as the European Heart Rhythm Association.


Research activity codes: 5.5 RADIOTHERAPY AND OTHER NON-INVASIVE THERAPIES;8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: CARDIOVASCULAR
Title: Vascular constructs using human pluripotent stem cells in the therapy of peripheral arterial disease
Funder: Medical Research Council

Abstract: We will generate endothelial cell constructs from hESC that closely resemble native vascular grafts. Our hypothesis is that culturing hESC-EC on decellularised vascular scaffold can produce durable and functional vascular grafts for peripheral artery disease. 1. Vessels will be decellularised resulting in a biocompatible scaffold that has similar mechanical characteristics compared to native tissues. Human arteries will be collected from the NHS Blood and Transplant Tissue Services. Human ESC-EC will be used to reseed decellularised scaffold which constructs have beneficial immunologic properties and function as the original vessels. 2. We will develop a robust process for generation of grafts in a bioreactor; seeding, delivery volume and surface size will be optimised. Long-term stability of endothelial phenotype will be characterised in 2D/3D cultures with cells from early and late time points. Cells and whole construct will be cryopreserved, which can provide a ready supply of allogeneic cell sources as required. 3. We will assess cell adhesion to extracellular matrices, viability/apoptosis, proliferation, migration by 3D confocal high content imaging. We will assess gene expression and secreted angiogenic factors in each 2D/3D cultures and in control arterial endothelial cells. The majority of these methods have been validated, are in routine use in 2D at Imperial College, will be adapted to 3D assays in most cases. 4. We will obtain key information on the functional ability of optimised grafts in a randomised blind controlled experimental trial in outbred pigs in Bristol. For this, hESC-EC cultures will be introduced in vivo to interface with host vasculature. We will generate batches of hESC-EC constructs that we can transfer to Bristol to perform in vivo engraftment for safety/durability assessment during 1 and 3 months follow-up.


Research activity codes: 5.2 CELLULAR AND GENE THERAPIES
Health categories: CARDIOVASCULAR
Title: 'Unlocking therapeutic innovation in heart failure through genomic data science
Funder: Medical Research Council

Abstract: Objective 1. Define the contribution of common genetic variation to risk of HF and clinically established sub-phenotypes We will update the existing HERMES analysis to incorporate imputation to the Haplotype Reference Consortium panel. Analysis will be limited to European ancestry participants. For case-control analysis, we will compare all HF (prevalent + incident) with non-HF and perform stratified analysis of subtypes defined by comorbidity. For ejection fraction stratified analysis, we will use randomly selected proportional controls. We will include sex and age as covariates and results will be combined using METAL. Objective 2. Identify and characterise new HF sub-phenotypes using clinical, EHR and genomic data. We will select clinical characteristics a priori based on past publications and expert judgment. We will test a range of methods for feature selection and dimensionality reduction to identify the most predictive and will use principal components and factor analysis to further reduce dimensionality. To cluster participants, we will use machine learning algorithms including connectivity models, centroid models, and expectation-maximisation. To investigate the genetic heterogeneity of disease clusters we will use BUHMBOX and other approaches and, where powered, GWA. Objective 3. Discover and validate therapeutic targets for heart failure subgroups through genetic causal inference analysis. To investigate a potential causal role of serum proteins and other candidate drug targets, we will derive tool variants from expression quantitative trait loci (QTL), protein QTL from multiplexed aptamer- and affinity-based assays and downstream intermediate phenotypes. We will select variants in cis- with the cognate protein for each LD block using cluster analysis. We will perform Mendelian randomisation analysis using a range of regression models and will characterise effect mediators using multi-omic datasets enriched for imaging and EHR.


Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS;5.1 PHARMACEUTICALS
Health categories: CARDIOVASCULAR
Title: A translational data integration platform for the stratification of patients based on clinical, laboratory and magnetic resonances imaging
Funder: Medical Research Council

Abstract: The UKB and BBR cohorts collectively represent the full spectrum between health and cardiovascular disease. Both cohorts will be analysed using parallel tranSMART data warehouse infrastructures, enabling a comparison between healthy and diseased subjects and integration of high level findings from both cohorts. Thus, we will establish a foundation for translational cardiovascular research in the UK with a detailed data provenance schema and common analytical pipeline. To achieve this, Unified Medical Language System (UMLS) coding standards will be used to standardize EHR data into official nomenclature. In order to analyze and potentially integrate multiple datasets between tranSMART instances, extensive data curation will be necessary, prior experience from the eTRIKS and AETIONOMY IMI projects will mitigate the risks associated with this process. The data will be made available to other applications, via a flexible tranSMART API, including a data constructor feeding a machine learning software layer called Ada. Using Ada's powerful machine-learning algorithms we will stratify patients and by adapting the BrainMesh package for heart data, we will investigate the MR images collected by UKB and BBR. Docker instances will help to create reproducible and portable data warehouse instrances, while Git versioning will provide clarity in versioning. Software and datasets will be made available in public repositories where appropriate or via Zenodo and referenced by a top-level unique Digital Object Identifiers. Application of FAIR (Findable, Accessible, Interoperable and Reproducible) principles, within the broader scope of each resource access conditions will guarantee the sustainable long-term use of these tools and datasets for future researchers. Allowing us to create a critical mass of highly skilled scientists dedicated to health data research.


Research activity codes: 2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY)
Health categories: CARDIOVASCULAR
Title: Identification of Metabolic Phenotypes and Systemic Biochemical Reaction Networks Associated with Human Blood Pressure
Funder: Medical Research Council

Abstract: I aim to evaluate the relationship between the urinary metabolic profile and blood pressure, including the linkage with adiposity, salt intake and associated dietary patterns and map these in a holistic systems framework. Objectives: A) To uncover the metabolic signature of blood pressure by means of a Metabolome-Wide Association Study with control for confounding variables using 1H Nuclear Magnetic Resonance (NMR) spectroscopy and Direct Injection Mass Spectrometry (DIMS) data. B) To identify novel associations between the intake of dietary nutrients and excretion of urinary metabolites to pinpoint dietary patterns associated with cardiometabolic risk. C) To investigate the interdependence between cation (calcium, magnesium, potassium and sodium) and metabolite excretions with respect to renal function and (re-)absorption, and homeostatic regulation. D) To map the pathways perturbed by high blood pressure, sodium consumption and dietary patterns, and assess the overlap with the metabolic and systemic signature of adiposity and other risk factors to reveal the integrated systems network of blood pressure. Methodology: I propose to use the Metabolome-Wide Association Study paradigm on 24-hour urine samples measured with 1H NMR spectroscopy and DIMS, and enhance this by coupling multivariate statistical methods with the analysis of integrated systems metabolic reaction networks. Outcomes: This work will improve the understanding of the molecular mechanisms underlying high blood pressure and define new potential starting points for the development of appropriate and comprehensive preventive and treatment strategies to address the increasing problem of blood pressure worldwide.


Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: CARDIOVASCULAR
Title: An integrated genetic and proteomic approach to understanding cardiovascular disease aetiology
Funder: Medical Research Council

Abstract: To gain insight into cardiovascular disease aetiology, I will use Mendelian randomisation (MR). MR exploits the natural randomisation of alleles during meiosis to disentangle causation from confounding. By providing evidence that a molecular trait is causal, MR can help identify drug targets, thus reducing the high attrition rate in pharmaceutical pipelines. A major bottleneck limiting use of MR is lack of suitable genetic instruments. To address this, we will use data from INTERVAL to perform GWAS of plasma proteins. Proteins are ideal for MR since they are under proximal genetic control and can be targeted pharmacologically. We recently performed GWAS of 3000 plasma proteins measured using the SOMAscan assay in 3301 individuals. We will extend this to 10000 individuals (providing major power gains, particularly for rarer variants) using a 5000-plex SOMAscan assay. I also co-lead the SCALLOP consortium meta-analysis of GWASs of plasma proteins measured using Olink immunoassays. A second strand of work involves -omic studies in patients with vasculitis. Prof Mason (Imperial) and I have established a multi-national collaboration studying proteomic signatures in Takayasu arteritis (TA), a vasculitis of large arteries resulting in vascular stenosis or aneurysm. Some patients have aggressive disease whereas others have a more indolent course. Prospectively identifying these subtypes would allow for tailored treatments, but current measures of disease activity inadequately predict outcome. We propose to discover novel plasma biomarkers for TA activity and prognosis through a wide-angle proteomic approach coupled with machine learning. Our results will provide a basis for stratified medicine, a key theme of HDR UK, and elucidate the pathogenic pathways underlying TA (and the role of inflammation in vascular injury more braodly), thus providing a foundation for novel therapeutic strategies. Assay/infrastructure costs associated with the TA project are separately funded.


Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: CARDIOVASCULAR
Title: Predicting Atrial Fibrillation Mechanisms Through Deep Learning
Funder: Medical Research Council

Abstract: Persistent atrial fibrillation (AF) patients are a heterogeneous population: some patients require multiple procedures, with more extensive ablation strategies; while for others, isolation of the pulmonary veins using ablation (PVI) is sufficient. Identifying persistent AF patients where PVI will be a sufficient treatment remains a clinical challenge, which if solved could lead to improved safety, better patient selection, as well as decreased time and cost for procedures. Biophysical simulations personalised to cardiac imaging and electrical data may offer substantial insights into the mechanisms underlying AF, but run too slowly to be used during clinical procedures. My objective is to develop a combined biophysical simulation and deep learning network pipeline that accurately quantifies the likelihood of success of PVI for an individual patient quickly enough for use during a clinical procedure, to guide ablation therapy. Methodology: We will simulate a virtual patient cohort covering the range of observed electrical and anatomical properties. These biophysical simulations will use the cardiac monodomain equation and the Courtemanche-Ramirez-Nattel atrial cell model, solved on meshes constructed from MRI images, with different fibrosis distributions, and repolarisation and conduction properties. The deep learning convolutional neural network will be trained to large quantities of post-processed data from biophysical simulations to ensure that the network captures the physics and physiology of the system. The training will then be augmented with the complexity and reality of clinical data. Finally, the deep learning pipeline will be tested in a retrospective study. We hope this study will provide insights into the mechanisms sustaining AF. We hope that different research and clinical centres will contribute to and make use of the trained network to predict patient-specific AF mechanisms and PVI ablation outcomes.


Research activity codes: 2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY);4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: CARDIOVASCULAR
Title: Evaluation of the clinical efficacy of texture analysis in myocardial quantification
Funder: Medical Research Scotland

Abstract: Traditional image analysis within clinical imaging has focussed largely on the visual inspection and analysis of images of the heart. However a wealth of information is present in each and every medical image that is simply beyond the reading of the human eye. One advance in medical image analysis is that of texture analysis which measures the non-uniformity of signal within body tissues. This has been most widely explored in the field of cancer imaging where tumour non-uniformity has been shown to provide diagnostic information on the malignancy of a tumour, its response to treatment and its long term prognosis. However texture analysis has been poorly analysed in the field of cardiac imaging. Three pre-existing well characterised research cohorts involving over 3,500 participants will be used to assess this new image analysis technique in a large group with minimal cost. The goal of this research project is to evaluate this novel image analysis technique to determine how the heart muscle texture changes in health, in disease, and in response to drug therapy. It will also assess if this characterisation of the heart muscle improves on the information provided by current image analysis. Potential benefits of this involve the development and validation of a novel image analysis technique that can be applied in routinely acquired clinical images thus expanding the use and diagnostic information that can be extracted from these without any extra burden to the patient and minimal extra cost to the health service.


Research activity codes: 2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY);4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: CARDIOVASCULAR
Title: Role of CDK9 in zebrafish heart repair and regeneration following resolution of inflammation
Funder: Medical Research Scotland

Abstract: The zebrafish larva is an important model organism for investigation of complex in vivo physiological and pathological processes, specifically human disease. In particular tissue regeneration; neonatal mammals have lost the capacity for cardiac regeneration by 7 days of age. In contrast, zebrafish can regenerate all organs and tissues, regardless of age. Newly hatched zebrafish (embryos or larvae) up to 5 days old are NOT considered sentient beings and do NOT feel pain or suffer as defined by veterinary surgeons and the scientific community. As a consequence, they are NOT a protected organism under UK Government and EU legislation. However, zebrafish larvae possess many functional biological responses in order to survive at this early stage of life. In particular, they are capable of defending themselves from infection and protecting themselves from trauma and unwanted tissue damage. These responses, called an inflammatory response, if unchecked or uncontrolled, can lead to acute and chronic inflammatory diseases in animals and humans. In the heart they can lead to scarring and abnormal physiological reponses that in turn can result in heart failure. We hypothesise that when the inflammatory response starts to abate (a process termed inflammation resolution) a new chain of events is set in motion that aids recovery, repair of tissues and, especially in the fish, permits tissue regeneration (a process that occurs in certain mammalian tissues such as the liver but not in many other tissues and organs). In this study we will visualise, quantify and manipulate the inflammatory response especially inflammation resolution in the heart in order to promote tissue repair and regeneration. For this we will investigate the role of key white blood cells, i.e. neutrophils and macrophages cells involved in the inflammatory response and analyse the processes and pathways that our laboratory has shown to be important for inflammation resolution. We strongly believe that using zebrafish larvae to study inflammation resolution will lead to a greater understanding of tissue repair and regeneration that will have a major impact in biological and medical research and contribute to translational medicine.


Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS
Health categories: CARDIOVASCULAR
Title: Plasma exchange and glucocorticoids in anti-neutrophil cytoplasm antibody associated systemic vasculitis: a randomized controlled trial. PEXIVAS
Funder: Department of Health and Social Care

Abstract: ANCA associated systemic vasculitis is a multi-system autoimmune disease, of unknown cause, which causes vital organ failure and premature death. Therapy with high dose glucocorticoids and cyclophosphamide achieves disease control but contributes to the early mortality and chronic disease damage associated with the disease. Several studies have examined whether plasma exchange improves outcomes when applied in addition to standard therapy to patients with severe presentations. A meta-analysis has concluded that although benefits on renal function have been observed a convincing effect on the composite end-point of death and end stage renal failure remains to be confirmed. Two international vasculitis networks, the European Vasculitis study group (EUVAS) and the Vasculitis Clinical Research Consortium (VCRC, USA) have combined to develop the current project which aims to recruit 500 patients with severe ANCA vasculitis, and examine whether plasma exchange reduces the frequency of death and end stage renal disease. In view of the likely contribution of high dose glucocorticoids to severe adverse events, this study will also examine, in a factorial design, a reduced dose glucocorticoid regimen in comparison to a standard dose regimen. Patients will be recruited from Australia/New Zealand, Canada, the European Union and the USA over five years and will be followed for a minimum of two years.

Lay abstract: Severe, antineutrophil cytoplasm antibody associated vasculitis (AAV) is an uncommon disease of the immune system diagnosed in approximately 1430 per 100,000 people in U.K. each year. ANCA associated vasculitis is important because it carries a poor prognosis with up to 50% of patients dying or developing kidney failure within 5 years. Two major problems hinder the treatment of ANCA associated vasculitis: 1) a lack of treatment strategies to bring the disease under control quickly before it causes major organ damage, and 2) a high degree of treatment related toxicity. We propose a randomised controlled trial to examine these problems in patients with severe ANCA associated vasculitis. Patients will be randomly assigned to receive plasma exchange (a method of rapidly removing harmful antibodies to promptly gain control of ANCA associated vasculitis) or to not receive plasma exchange. Patients will also be randomly assigned to receive either a standard dose of steroids or a lowdose scheme which is predicted to reduce treatment related toxicity. By addressing these problems we hope to significantly improve patient survival and reduce the frequency of kidney failure in patients with ANCA associated vasculitis.

Research activity codes: 6.1 PHARMACEUTICALS;7.3 MANAGEMENT AND DECISION MAKING
Health categories: CARDIOVASCULAR