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What did the project achieve?
We’ve created a new tool that could ultimately help doctors and parents make difficult decisions during the early care of very premature babies,” says Dr T’ng Chang Kwok of the University of Nottingham. “By identifying infants most at risk of dying or developing a severe lung condition, it has the potential to guide more personalised treatments – saving lives and preventing long-term complications.”
Babies born very early – before 32 weeks of pregnancy – are at high risk of dying or developing a lung condition called bronchopulmonary dysplasia (BPD). Doctors can prescribe medicines, such as steroids, to help prevent BPD. But these treatments can have serious side effects, such as cerebral palsy – and doctors must carefully balance the potential risks and benefits. Delaying treatment can reduce its effectiveness, while treating all babies early could expose some to avoidable harm.
This research aimed to develop a new tool to support decision-making by accurately predicting which babies are most likely to benefit from early preventative treatment. Using artificial intelligence (AI), Dr Kwok analysed data in electronic medical records from many thousands of very preterm babies, to look for patterns that can predict which infants are at greatest risk of dying or developing severe BPD.
“We’ve developed an online tool, co-designed with doctors and parents of premature babies, to help predict which infants are most at risk of dying – which has shown promising results in early testing,” says Dr Kwok. “We’re also testing a more complex tool to predict the risk of BPD using data from thousands of babies in neonatal units across England and Australasia.”
Dr Kwok has also created a free digital platform used by hundreds of clinicians and researchers worldwide to explore outcomes in 83,463 very premature babies born in England and Wales between 2010-2020.
“We hope this work will ultimately lead to new tools that can be integrated into routine neonatal care, helping doctors identify which very premature babies need early treatment while sparing others from unnecessary side effects,” says Dr Kwok. “By supporting more personalised treatment, this approach could improve survival, reduce long-term complications, and help parents feel informed and involved in decisions about their child’s treatment.”
In the future, this AI-based approach could be extended to develop new prediction tools for other illnesses linked to preterm birth, helping to improve care and long-term outcomes for these vulnerable babies.
This research was completed on
How are children’s lives affected now?
Babies born very prematurely can develop BPD because their lungs have not fully developed in the womb. They will usually need oxygen therapy via a ventilator to support their breathing and, while this treatment is life-saving, it can sometimes cause damage to their tiny lungs.
“While most affected babies get better as they grow older, some will develop breathing difficulties – such as coughing, wheezing or breathlessness – later in childhood,” says Dr Kwok, “Those who are most severely affected with BPD may need long-term oxygen therapy at home – and they may also experience learning difficulties.”
Doctors can prescribe different medicines, such as steroids, to help prevent BPD in preterm babies. But these treatments can have serious side effects, such as cerebral palsy – and doctors must balance the potential risks and benefits when making decisions.
“Delaying treatment can make it less effective – but the early treatment of all babies would mean some are exposed to unnecessary risks,” says Dr Kwok. “Predicting who is most likely to benefit from treatment and when would give each child the best chance of a successful outcome.”
How could this research help?
“We aim to develop a new AI-based tool that can support clinical decision-making for premature babies who are at high risk of dying or developing BPD,” says Dr Kwok.
The researchers will analyse data within electronic medical records collected from many thousands of very preterm babies – which are routinely recorded on an NHS system called BadgerNet.
“By accurately recognising complex patterns within these data, AI could identify which very preterm babies are likely to benefit from treatment – and pinpoint the best time to start,” says Dr Kwok. “
This new tool – called PRIOR-AI (PRemature Infant Outcome Risk – Artificial Intelligence) – would be made freely available and integrated into BadgerNet so that doctors across the UK can use it immediately to tailor treatment plans for very preterm babies.
If successful, PRIOR-AI could help support doctors when making difficult treatment decisions – helping save lives and reducing the risk of long-term complications for these vulnerable babies,” says Dr Kwok. “It will also help parents to understand their child’s condition, empowering shared decision-making.
References
- Office for National Statistics (England and Wales). Birth Characteristics, 2020 edition, Table 8: https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/livebirths/datasets/birthcharacteristicsinenglandandwales [website accessed 25 May 2022]
- Public Health Scotland, Births in Scottish hospitals: https://publichealthscotland.scot/publications/births-in-scottish-hospitals/births-in-scottish-hospitals-year-ending-31-march-2021/ [website accessed 28 June 2022]
- Northern Ireland Statistics and Research Agency, Registrar General Annual Report 2020. https://www.nisra.gov.uk/system/files/statistics/RG%20Annual%20Report%202020%20Accessible.pdf [website accessed 25 May 2022]
- Royal College of Paediatrics and Child Health, National Neonatal Audit Programme Annual Report on 2020 data: https://www.rcpch.ac.uk/resources/national-neonatal-audit-programme-annual-report-2020 [website accessed 25 May 2022]
Research Training Fellowships:
Each year, Action Medical Research awards these prestigious grants to help the brightest and best doctors and scientists develop their career in medical research.
Research table
Project details
| Project Leader | Dr T’ng C Kwok, MRCPCH BMBS BMedSci |
| Location | Academic Child Health, School of Medicine, University of Nottingham |
| Project Team |
Professor Don Sharkey, BMedSci BMBS PhD FRCPCH
Professor Carol Coupland, PhD Professor Jon Garibaldi, PhD |
| Other Locations | School of Computer Science, University of Nottingham |
| Grant Awarded | |
| Grant Amount | £195,383 |
| Start Date | |
| End Date | |
| Grant Code (GN number) | GN2941 |
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