Improving NEWS2 predictive accuracy
Research type
Research Study
Full title
Improving the ability of the National Early Warning Score (NEWS2) system to predict critical outcomes through additional patient data or amendments to the scoring process
IRAS ID
344472
Contact name
Chris Plummer
Contact email
Sponsor organisation
The Newcastle upon Tyne Hospitals NHS Foundation Trust Newcastle Joint Research Office
Clinicaltrials.gov Identifier
11002, R&D
Duration of Study in the UK
1 years, 0 months, 0 days
Research summary
In 2012, the Royal College of Physicians developed a National Early Warning Score (NEWS) tool to help healthcare professionals monitor patients and identify any deterioration in their status early enough to allow effective intervention to improve outcomes. This system was refined in 2017 as NEWS2, and has been widely adopted throughout the NHS. Previous studies have found that NEWS2 performs well in predicting patients who are likely to die in the next 24 hours, but that its ability to predict other important clinical outcomes over longer time periods is limited. Significantly, the system was not developed with data from older patients, and therefore is less good at predicting outcomes in this growing population. This is
particularly important as older adults are especially vulnerable to sudden physiological changes. In an ageing population, a tool that better predicts outcomes early enough for effective interventions, and can learn from continually accumulating data, would change clinical practice and significantly improve patient safety. Healthcare professionals would have the opportunity to prioritise evidence-based treatment to those most likely to benefit.In this study, we will use approximately 7 years of historical, anonymised patient data from the Newcastle upon Tyne Hospitals NHS Foundation Trust to explore ways in which a new alerting system could improve on NEWS2. We will use a large number of routinely collected patient variables which are linked with patient outcomes, and use these linked data to determine which variables, and with which algorithm, best predicts outcomes. We will then test the models developed on another set of patient data to determine their positive and negative predictive accuracy. After this study, we intend to test the best models on data from other NHS Trusts, to determine how well they perform in different patient populations and different healthcare environments.
REC name
Wales REC 4
REC reference
25/WA/0060
Date of REC Opinion
1 Apr 2025
REC opinion
Further Information Favourable Opinion