Identification and personalised risk prediction for severe COVID-19 in patients with rare disorders impacting cardiovascular health
We know individuals with underlying health conditions have greater risk of developing severe COVID-19 and ending up with poorer outcomes. That is why governments and public health services have been providing dedicated and prioritised protections for these more clinically vulnerable people – for example, via recommending shielding or being prioritised to have COVID-19 vaccinations.
However, the majority of those living with rare diseases – around 5.8% of the UK population, or 3.7 million people - are often overlooked. Rare diseases are often poorly recorded in clinical data leading to a challenge in identifying patients whose rare condition makes them clinically vulnerable. We don’t know the most effective way to personalise and manage treatments for patients with rare diseases who contracted COVID-19. Furthermore, there are many people who are not diagnosed but share similar clinical presentations (so-called phenotypes) to those diagnosed rare-disease patients. We know some of them are likely to share similar vulnerabilities. However, we don’t know how to identify them currently.
In this project, we aim to tackle these challenges by bringing together a comprehensive set of knowledge about rare diseases, and applying the most up to date data science technologies to use such knowledge and resources on CVD-COVID-UK datasets.
In this way, we hope to develop a more accurate identification system for people living with rare diseases who are clinically vulnerable. We will also provide the much needed information on the risk of severe COVID-19 in people with rare diseases, hopefully leading to an improvement in their care by providing evidence on treatments that may work better for them. Furthermore, we will analyse the compound risks of severe COVID-19 in people bearing clinical risks and disadvantaged socioeconomic backgrounds, aiming to inform policy responses for providing better management and treatment for these most vulnerable groups who might previously have been overlooked.
We are matched with a data analyst from the department of health and social care. This will enable us a speedy dissemination of our work to the policy makers realising swift and actionable suggestions. We will also disseminate our findings to charities and societies of rare diseases in the UK and beyond to maximise the impact of our work.
The issues stated above will be addressed in outputs from a number of related sub-projects. Follow the links below to view repositories containing the protocol, data curation and analysis code, and phenotyping algorithms and codelists for each sub-project:
This project has been approved by the CVD-COVID-UK/COVID-IMPACT Approvals & Oversight Board (Project ID: CCU019).