Dynamic biomarker trajectories predicting long COVID complications

University of Warwick

About the Project

Acute COVID-19 affects multiple organ systems, including the lungs, digestive tract, kidneys, heart, and brain. The long-term clinical consequences of COVID-19 are still poorly understood and are collectively termed post-acute sequelae of SARS-CoV-2 infection, known as long COVID. Long COVID, have severely affected recovery from the COVID-19 pandemic for patients and society alike. Long COVID is characterised by evolving, heterogeneous symptoms, making it challenging to derive an unambiguous definition. Those affected might have reduced functional capacity and cognitive and physical limitations, ultimately resulting in reduced autonomy. COVID-19 survivors even after 2 years post- infection exhibit problems with fatigue, muscle weakness, sleep difficulties, dizziness, headache, endocrine and metabolic disturbance, lung fibrosis and myalgia. There is also increasing evidence for brain-related injuries in patients with COVID-19. Even mild COVID-19 is linked to brain damage, which might provide insight into neurological or cognitive symptoms such as difficulty concentrating, trouble sleeping, memory loss, or confusion associated with long COVID.

There are significant knowledge gaps in our understanding of the disease process, who is most at risk and the type and variety of disease phenotypes associated with long COVID-19. There is a plethora of studies focusing on the patient previous history and responses during the acute infection stage in order to uncover clues that can be used to predict duration and severity. Increasingly, studies use big data to determine prevalence, symptoms, or risk factors for long COVID, and provide insights into the characteristics of patients with long COVID as well as classifications of distinct groupings of symptoms reported by patients. Many of these efforts intend for their long COVID patient classifier algorithm to be used for clinical trial recruitment to enable the discovery of treatments.

This multi-discipline PhD project aims to analyse longitudinal real-world clinical and laboratory data from UHCW NHS Trust to help predict the likelihood of long COVID disease progression to severe disease endpoints. It will exploit the potential of artificial intelligence in delineating disease trajectories and phenotypes of long COVID-19 patients and explore possible novel uses of emerging biomarkers. The availability of an extensive biobank of samples from COVID patients will allow investigations of biomarkers as novel classifiers of disease progression.

The student will be supervised by academics with complimentary expertise from Warwick Medical School and School of Engineering, NHS pathologists and diagnostic industry leaders from Siemens Healthineers. 


Dimitris grammatopoulos

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