CVD-COVID-UK aims to understand the relationship between COVID-19 and cardiovascular diseases such as heart attack, heart failure, stroke, and blood clots in the lungs through analyses of de-identified, linked, nationally collated healthcare datasets across the four nations of the UK.

The CVD-COVID-UK project, led by Professor Cathie Sudlow, Director of the BHF Data Science Centre, is one of the six National Flagship Projects approved by the NIHR-BHF Cardiovascular Partnership.


Cardiovascular disease impact on susceptibility to and poor outcomes from COVID-19

Patients with cardiovascular disease are at increased risk of developing COVID-19 and of poor outcomes of COVID-19, such as admission to hospital or intensive care, or of dying. This could be due to cardiovascular conditions themselves (e.g., heart disease, stroke), their risk factors (e.g., age, raised blood pressure), medications, or combinations of these. Understanding which patients are affected and why will help in developing strategies to reduce this risk.

Impact of infection with coronavirus on patients with cardiovascular disease

The direct impacts include immediate complications (e.g., acute heart injury, stroke) and potentially increased risk of heart attack, stroke and other cardiovascular events in the longer term, through inflammation, blood clotting risk or other factors. However, the nature and extent of these direct effects are not well understood.

Impact of the COVID-19 pandemic on the treatment and care of cardiovascular disease patients

The response by the government and health services to the COVID-19 pandemic also has indirect impacts on the presentation, diagnosis, management and outcomes of cardiovascular diseases. The numbers of people attending hospital with heart attack and stroke declined dramatically in the lead up to and after the announcement of lockdown (as demonstrated by the 4C Initiative). Further, patients were more often arriving too late for beneficial acute treatments (e.g., clot busting drugs) and after potentially preventable complications had developed. To inform government and NHS policy, we urgently need a deeper understanding of these unintended consequences.


We aim to answer three broad questions (see the protocol for more detail):

  1. What are the effects of cardiovascular diseases, their risk factors and medications on susceptibility to and poor outcomes (including admission to hospital, requirement for intensive care and death) from COVID-19 disease?
  2. What is the direct impact of SARS-CoV-2 infection on acute cardiovascular complications as well as on medium and longer-term cardiovascular risk?
  3. What is the indirect impact of the COVID-19 pandemic and the government and NHS response to it on the presentation, diagnosis, management and outcomes of cardiovascular diseases?

Researchers will access routinely collected datasets across the whole population of the UK within secure trusted research environments provided by NHS Digital in England, the National Data Safe Haven in Scotland, the SAIL Databank in Wales and the Honest Broker Service in Northern Ireland.


The following projects have been formally approved by the CVD-COVID-UK Approvals & Oversight Board:

Investigating the effects of angiotensin converting enzyme inhibitors and angiotensin receptor blockers on COVID-19 outcomes (Lead: Jonathan Sterne, University of Bristol)

ACE inhibitors and angiotensin receptor blockers are drugs that are commonly used to lower high blood pressure. These drugs may affect the ability of the coronavirus to enter cells of the body and cause COVID-19. We plan to investigate whether use of these drugs affects people’s chance of becoming infected or unwell with COVID-19.

We will conduct carefully planned analyses of data from millions of people’s healthcare records. These records provide information about people’s health and prescribed medications, as well as on which people became infected or unwell with COVID-19.

People who take drugs to lower their blood pressure are more likely to have other risk factors for becoming infected or unwell with COVID-19. Our analysis methods will account for these risk factors, to work out whether any change in risk is due to the blood pressure lowering drugs themselves.

Our results will help to inform the way that blood pressure lowering drugs are used in the future, particularly while the pandemic continues.

SARS-CoV-2 infection and risk of venous thromboembolism and arterial thrombotic events (Lead: William Whiteley, University of Edinburgh)

Coronavirus infection (‘COVID-19’) might increase a person’s chance of having a stroke, heart attack or clot in the deep veins or lungs (‘blood vessel diseases’).

During the COVID-19 pandemic, some doctors have looked after patients with COVID-19 who also had unusual strokes, clots or heart complaints. This suggests there could be a link between COVID-19 and blood vessel diseases. But no individual doctor will see enough patients to find out if COVID-19 really does increase the risk of blood vessel diseases.

To understand more, we will use healthcare records to study every person alive in England, Scotland and Wales at the beginning of the pandemic in 2020. We will find out how many people had a stroke, heart attack, heart condition or other disease of the blood vessels over the following year.

We will compare the number of people with COVID-19 infection who developed a blood vessel disease with the number of people without COVID-19 infection who developed a blood vessel disease. Different types of people might have different risks, so we will also examine people of different ages, ethnicities and medical history.

The result of this research will be an estimate of how much COVID-19 increases the risk of different blood vessel diseases. This information is needed so that people with COVID-19 know whether they need to worry about blood vessel diseases as they recover. If this research shows there is an increased risk, then treatments might be needed to reduce this.

Direct and indirect effects of the coronavirus (COVID-19) pandemic in individuals with cardiovascular disease (Lead: Ami Banerjee, UCL)

Coronavirus (COVID-19) directly impacts individuals who become infected with the virus. It can also influence people’s healthcare decisions (such as deciding not to attend medical appointments for fear of infection). In addition, hospitals have sometimes had to prioritise treatment of COVID-19 patients over routine healthcare.

Access to data from healthcare records will help to improve our understanding of what puts certain people at an increased level of risk. This includes people with cardiovascular disease, including heart disease and stroke.

This research project has three main priorities.

  1. For people with cardiovascular disease, we will estimate by how much becoming infected or unwell with COVID-19 increases their risk of dying within one year of infection. This will involve analysing routine healthcare data from multiple sources, including from general practices, hospitals and national death registers.
  2. We will study how the treatment of patients with cardiovascular disease has been affected during the pandemic.
  3. We will develop and improve models that predict the risk of dying at one year among people with cardiovascular disease who have and who have not contracted COVID-19.

Our research will help to shape health policy, help patients and clinicians to make more informed decisions, and improve health outcomes.

COVID and Cardiovascular Disease Risk Prediction (Lead: Angela Wood, University of Cambridge)

Cardiovascular disease (CVD), comprising mostly heart attacks and strokes, is one of the UK’s leading causes of death and disability. It is far better and cheaper to prevent CVD than to treat patients after they get sick. Consequently, doctors aim to identify patients at high risk of future CVD and offer healthy lifestyle advice and medication, such as statins.

However, studies have found that such advice is poorly communicated to patients. This has resulted in low numbers of patients choosing to make healthy lifestyle changes and take medication, especially amongst patients living in more deprived areas. It is important to improve the way CVD risk is communicated to patients across the whole of society so that more people benefit from advice and medication, and also to reduce inequalities in CVD.

Currently, doctors use risk prediction calculators to help decide whether a patient is at high risk of future CVD. The most commonly used risk prediction calculators in England were developed using data from 2004-2016 from 2.3 million patients in England. More current data is now available from over 56 million patients in England, as well as 10 million patients from Wales, Scotland and Northern Ireland. These datasets also include information about whether a patient has had COVID-19 and any related complications. Patients with COVID-19 complications may be at higher risk of future CVD than patients without COVID-19 complications. Therefore, COVID-19 information may be useful information for doctors in helping to identify the right patients at high risk of CVD.

We plan to develop new risk prediction tools to assess and help communicate a patients’ future risk of CVD. Our aims are (i) to better identify patients at high risk of CVD in the UK and (ii) to improve communication of CVD risk to patients across the whole of society. Ultimately, this should reduce CVD in the UK.

Data Management and Analysis methods (Lead: Angela Wood, University of Cambridge)

Since the COVID-19 pandemic, there has been rapid progress made towards the availability and accessibility of national healthcare data for research. Consequently, for the first time we are analysing data from over 65 million patients across the UK to help us understand more about COVID-19. Our analyses have the advantages of being able to study individuals with and without different health-related problems across all age groups, ethnicities, geographies and socioeconomic settings. Our results will be directly relevant to everyone living in the UK.

However, there are a number of challenges and limitations to using routinely collected healthcare data for research. The problems mainly arise because electronic health records are designed for clinical purposes, and do not necessary provide an accurate picture of the true health status on all patients at all times. If we do not address these problems properly in the analysis, then we will get biased results and make incorrect conclusions.

We aim to identify and provide solutions to address the challenges and limitations in the analysis of population-wide healthcare data. Ultimately, we want to ensure results arising from population-data healthcare data are accurately reported.

CVD-COVID-UK Consortium Membership, Principles and Privacy Statement

An inclusive, open and transparent consortium has been established to work on the CVD-COVID-UK project. Membership comprises data custodians, data scientists with methodological and analytical expertise (statisticians, epidemiologists, health informaticians, bioinformaticians, computer scientists and others) and clinicians (including cardiologists, stroke physicians, vascular surgeons and others), all of whom have signed up to an agreed set of principles.

The CVD-COVID-UK programme privacy statement explains how the partners involved in the CVD-COVID-UK consortium collect, store, manage and protect your personal data. It outlines the types of data that are accessed and how we use them.


To appropriately acknowledge our funders and recognise the use of patient data in our research outputs and publications, please use one of the following acknowledgements:

This work was funded by the BHF as part of the BHF Data Science Centre led by HDR UK (BHF Grant no. SP/19/3/34678). This work uses data provided by patients and collected by the NHS as part of their care and support.


This work is carried out with the support of the BHF Data Science Centre led by HDR UK (BHF Grant no. SP/19/3/34678). This work uses data provided by patients and collected by the NHS as part of their care and support.

Find out more about the BHF Data Science Centre