Title: Precision medicine to improve the outcome of patients with heart failure with preserved ejection fraction
Funder: Department of Health and Social Care

Abstract: Research questionsIs it possible to reclassify heart failure with preserved ejection (HFpEF) into distinct diagnoses and do these distinct groups respond differentially to treatments? Is it possible to identify phenotypic and genetic factors that could be used to predict who will develop HFpEF?BackgroundHeart failure occurring in the presence of a normal, or preserved, left ventricular ejection fraction (HFpEF) is 'the single largest unmet need in cardiovascular medicine'.Rather than being a single diagnosis, HFpEF represents a heterogeneous syndrome involving a range of pathophysiological mechanisms, clinical factors and outcomes. However, to-date, HFpEF has been considered as a single disease entity. This 'one-size-fits-all' approach is a major reason why we do not know who will develop HFpEF, why there are no treatments, and why quality of life and prognosis remain so poor.Aims and objectivesI aim to: Reclassify HFpEF into distinct diagnoses based on disease mechanisms, clinical factors and outcome, and evaluate whether the distinct groups respond differentially to treatments. Identify phenotypic and genetic factors that could be used to predict who will develop HFpEF. Establish and lead a UK National HFpEF registry that will be a platform for collaborative UK clinical and translational HFpEF research.MethodsSingle-centre cohortI will apply machine learning techniques to a prospective cohort of 1,500 deeply phenotyped patients with HFpEF in order to derive and validate subgroups of HFpEF and evaluate differences in treatment response. I will compare the subgroups with matched patients without HFpEF in order to identify phenotypic complexes that distinguish HFpEF and investigate the role of genetics in HFpEF development.National HFpEF registryUsing Part 1 as a demonstrator, I will establish and lead a National HFpEF registry. The registry will provide deep phenotyping linked to outcomes at scale, be a platform for trials matching drug mechanism of action with HFpEF subgroup/anticipated treatment response, and provide simplified UK-wide access for industry. The NIHR-BHF Cardiovascular Partnership have agreed to make this a national priority study, and the BHF Data Science Centre will adopt it as a vanguard project. More than ten UK centres have agreed to participate.NIHR Health Informatics Collaborative (HIC)In parallel to Parts 1 and 2, I will apply similar machine learning techniques to routinely collected electronic health record data held in the NIHR Cardiovascular HIC, which provides less detailed patient characterisation but much greater numbers (>250,000 patients).Timelines for deliveryFourteen milestones including: National registry operational month 19, HIC analysis complete month 22, recruitment to single-centre cohort complete month 24.Anticipated impact and disseminationThis fellowship is an exciting opportunity to deliver personalised care to patients with HFpEF that will provide the basis for tailored therapies, improved risk stratification and potentially preventative interventions. It will open new avenues of clinical and biological research, provide an industry platform and reduce strain on NHS and Government budgets.Dissemination will be via my Patient Advisory Group, lay written and video summaries, websites, patient group presentations, high-impact open-access publications, conference presentations, a dedicated symposium and media outlets.

Lay abstract: BackgroundHeart failure (HF) means that the heart is no longer able to pump blood around the body properly, causing patients to develop breathlessness, swollen feet and ankles, and extreme tiredness. Around a million people in the UK have HF, and many more are at risk of developing it. HF is the most common reason why older people are admitted to hospital, and 2 to 3 of every 10 people diagnosed die within a year. As such, HF is one of the most important health problems facing the UK.In about half of patients with HF, the forward pumping function of the heart is normal. This is called heart failure with preserved ejection fraction, or HFpEF. Patients with HFpEF have traditionally been treated as a single group, but it is becoming clear that HFpEF includes patients with a range of different underlying heart and blood vessel abnormalities, and a variety of potentially contributing conditions. The 'one size fits all' approach has prevented us from developing any effective therapies for HFpEF, or even being able to predict who will develop it.AimsTo reclassify HFpEF into more distinct diagnoses and evaluate whether patients in each of the distinct groups respond differently to treatments. To identify clinical features and genetic factors that could be used to predict who will develop HFpEF.Design and methodsPart 1I will apply artificial intelligence techniques to a group of 1,500 highly characterised patients with HFpEF in order to reclassify the condition into more distinct diagnoses. I will compare patients in each of the distinct groups to see if they respond differently to treatments and compare them with similar individuals without HFpEF to identify features that distinguish HFpEF. I will also investigate the role of genetics in its development.Part 2Using Part 1 as an example, I will develop and lead a National HFpEF registry. The aim is that many hospitals across the UK will contribute so that the registry will contain detailed health information about thousands of patients with HFpEF. This will allow researchers from across the country, and beyond, to work together to gain a better understanding of why people develop HFpEF, develop better tests for it and identify and test new treatments. The NIHR-British Heart Foundation (BHF) Cardiovascular Partnership have agreed to make this a national priority study, and the BHF Data Science Centre will adopt it as a vanguard project. More than ten UK centres have agreed to participate.Part 3In parallel to Parts 1 and 2, I will apply artificial intelligence techniques to health data that is collected routinely as part of patient care, which is held in the NIHR Health Informatics Collaborative Cardiovascular theme. Patient characterisation will be less detailed than in Parts 1 and 2, but study size will be much larger (250,000 patients).Patient and public involvementI have a well-established Patient Advisory Group comprising patients with HF, that I set up with a patient involvement bursary and INVOLVE. The proposed research has been identified as a priority. Members helped write this summary. The Group will continue to meet annually. Members will help develop information resources and be actively involved in management and dissemination.DisseminationVideo and written summaries of the results, made in conjunction with patients involved with the research, will be put onto a website that will be built for the studies. Results will also be published in scientific journals, presented at scientific meetings and patients involved with the research will take part in presentations to patient groups.

Research activity codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health categories: CARDIOVASCULAR