Are people with chronic lung diseases at a higher risk of cardiovascular complications after having COVID-19 than people who don’t have lung diseases?
The successfully awarded research project through a funding call by Health Data Research UK and the Alan Turing Institute is led by Jennifer Quint (Imperial College London). The research project will work to use national data to answer this key COVID-19 research question.
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We are aiming to investigate if people with COVID-19 and pre-existing lung disease (for example asthma or chronic obstructive pulmonary disease) are at a higher risk of heart attacks, strokes, or blood clots in the lungs after COVID-19 compared with people with COVID-19 without pre-existing lung disease.
When people get COVID-19, how sick they get and what complications they have varies hugely. An international research prioritisation exercise was undertaken by members of this team in collaboration with Asthma UK-British Lung Foundation. We asked people with underlying lung diseases what should the priorities be for further research and investigation. The research question for this research was then developed from this consultation.
We will collaborate with researchers to use UK-wide de-identified patient data to answer this question. These data sit in secure and safe research environments with several data sources linked together, including primary care, hospital data, death registry data, COVID-19 testing and vaccination data.
This research is of public benefit as we will answer important questions we know patients and the public want answered. We will add value to these data, sharing expertise and data on lung disease. Findings will help better understand complications following COVID-19 in people with lung disease, if we should be treating them differently or even if some treatments taken for lung disease protect from some COVID-19 complications. Our work could be used to develop disease specific risk models’ clinicians can use to predict an individual’s risk, communicate the risk effectively or design a personalised follow-up schedule which reassures the patient.