Using rare disease phenotype models to identify people at risk of COVID-19 related adverse outcome
Project led by Dr Honghan Wu at University College London, coordinated and supported by Health Data Research UK with funding from the National Institute for Health and Care Research (NIHR).
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People with underlying health conditions have greater risk of developing severe COVID-19, which often means poorer recovery. That is why governments and public health services have prioritised protection for these more clinically-vulnerable people, for example, with COVID-19 vaccination boosters.
However, the majority of those living with rare diseases – around 3.5 million people in the UK – are often overlooked. Rare diseases are often poorly recorded in patient records, leading to a challenge in identifying patients whose rare condition makes them clinically vulnerable. Furthermore, there are many people who are not diagnosed but share similar signs and symptoms (called phenotypes) to those who are diagnosed with a rare disease.
This project will tackle these challenges by bringing together a comprehensive set of knowledge about rare diseases and using the most up to date data science technologies to explore CVD-COVID-UK/COVID-IMPACT datasets. The aim is to create an accurate identification system for people living with rare diseases and those sharing similar phenotypes, who are clinically vulnerable.
Researchers also hope to uncover much-needed information on the added risks of severe COVID-19 in people who are clinically more vulnerable and come from disadvantaged socioeconomic backgrounds. This can then inform policy responses to provide better management and treatment for these most vulnerable groups who might have been overlooked. The team are well placed to derive quick actionable findings for the winter pressures as they have been working with CVD-COVID-UK/COVID-IMPACT on rare diseases since October 2021.