Leveraging real world data to characterise the long term impact of COVID-19

With the emergence of SARS-CoV-2, COVID-19 characterization has primarily focused on the assessment of the acute effects of infection in patients1. It is known that the virus targets epithelial cells, endothelial cells and alveolar macrophages causing symptoms attributable to the lungs, digestive tract, kidneys, heart, brain, and other organs. Viral presence is also being investigated in other tissues such as skeletal muscle, smooth muscle, bone and cartilage2.

Individual symptoms and disease severity vary widely among patients, with some developing mild infections and others experiencing acute-respiratory- distress-syndrome (ARDS), sepsis, and other life-threatening conditions3,4.

Following patient recovery, a wide range of outcomes are possible. Some patients experience residual symptoms, while others develop new symptoms long after initial infection5. A wide range of organ systems and tissues can be affected. Symptoms include fatigue, dyspnea, cardiac abnormalities, cognitive impairment, sleep disturbances, post-traumatic-stress-disorder, muscle pain, headache. The extent to which these symptoms persist, the effects on pre-existing conditions and response to therapies are not well understood. There is also a lack of evidence on the risk factors for developing long term conditions and complications following COVID-19.

The proposed project aims to characterize patient profiles and phenomes; symptom patterns, risk factors and complications associated with long term COVID-19; the occurrence of cardiovascular and thromboembolic complications and their health outcomes; and to study and describe the potential progression of pre-existing comorbidities such as heart failure, kidney disease, and response to treatments.

The project will utilize existing health data sources in the UK and internationally mapped to the Observational Medical Outcomes Partnership (OMOP) Common Data Model and a network cohort study will be proposed. Patients recovering from COVID-19 and controls will be identified and followed up for up to 3 years.

Results from this research will support better patient management and the potential re-assessment and development of therapies to address the medical and public health burden of long term of COVID-19 disease.

The proposed partnership will provide the Pharmacoepidemiology Research Group (i.e. The Academic Partner) with access to international data sources and in-house expertise in the curation and processing of such data. The candidate will benefit from exposure to industry-led initiatives and working processes. Bayer (The Industry Partner) will benefit from knowledge exchange, training opportunities, and state-of-the-art expertise in the analysis of real world data existing within Prof Prieto-Alhambra's group. The project's results will likely inform the future management of long term COVID patients, as well as the development of future therapies to treat its complications.