AI predicts hospital patients most at risk of COVID-19
Ashleigh Myall, one of our AMR PhD students, has developed a machine learning tool to help predict patients’ risk of contracting COVID-19 after admission to hospital.
There is a need to develop predictive models to identify patients who are most at risk of contracting COVID-19 and intervene to mitigate against poor patient outcomes. We can do this alongside the usual measures to minimise outbreaks and further transmissions.Ashleigh Myall
Developed alongside researchers at Imperial College London and the Infection Prevention and Control (IPC) unit at Imperial College Healthcare NHS Trust, the tool was able to predict patients at high risk of developing COVID-19 with 87 per cent accuracy in a Lancet Digital Health study.
Identifying patients most at risk can help with reducing onward transmission to other patients and staff. The framework may also be used to identify the risk of other infectious diseases in the future, not just COVID-19.
Contracting COVID-19 whilst in hospital
Throughout the COVID-19 pandemic, some patients developed the infection during their hospital stay.
COVID-19 infections that have developed after admission to hospital account for 12-15% of all COVID-19 cases in healthcare settings, and reached 16.2% at the peaks of the pandemic.
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of all COVID-19 cases in healthcare settings have developed after admission to hospital
Meet the researchers
Ashleigh, lead author of the study, is an applied mathematics PhD student on our National PhD Training Programme in AMR Research. He is part of the ASPIRES consortium, which is aiming to improve the use of antibiotics in surgical settings.
Specifically, Ashleigh focuses on how pathogens (disease-causing bugs) spread in nosocomial infection (these are infections which are picked up by patients when they are in the hospital).
The new COVID-19 study was co-authored by Professor Mauricio Barahona and Professor Alison Holmes.
This framework could be used as part of a range of surveillance tools to enhance infection, prevention and control strategies, especially during the winter months when COVID-19 infections spread more easily.Ash Myall
A machine learning framework
Traditionally, predictions of infections in healthcare settings have focused on age, gender, comorbidities and patients’ lengths of stay, but have not taken into account patient contacts, locations, or patient flow through the hospital.
The researchers used routine hospital and patient data from the first two COVID-19 waves. They were able to train the tool to identify risk factors such as contact with infectious patients, where beds were situated, and how patients were moved, alongside the traditional categories of age and gender.
The tool analyses these risk factors and then gives a predicted risk score between 0 and 1.
of hospital COVID-19 cases studied were predicted using this tool
The team will be carrying out further work to extend the framework to include the omicron strain, other infectious diseases and understand how it could be integrated into healthcare settings.
Sid Mookerjee, co-author of the study and operational lead for antimicrobial stewardship, surveillance and epidemiology at Imperial College Healthcare NHS Trust, said the tool could "help hospital managers and clinicians design safe and effective patient care pathways and bed management, helping deliver world class care.”
The study is published in Lancet Digital Health.
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