Detecting clinical deterioration using machine learning
Research type
Research Study
Full title
Detecting clinical deterioration in respiratory hospital patients using machine learning
IRAS ID
271822
Contact name
Sherif Gonem
Contact email
Sponsor organisation
University of Nottingham
Duration of Study in the UK
2 years, 3 months, 30 days
Research summary
Early warning scores are used in NHS hospitals to detect when patients are deteriorating and might need extra medical treatment. Currently used scoring systems are good at detecting sepsis and internal bleeding but sometimes they can fail to pick up breathing problems. They also don't work very well in some patients with long-term breathing problems because the score can be high all the time, even when patients are stable. This can result in doctors being called to see patients who don't need any extra treatment which can distract them from those who really need help.
This project has been funded by the Medical Research Council and aims to develop a better scoring system for monitoring people with breathing problems in hospital. Our goal is that the new scoring system will be better at picking up when people with breathing problems are getting worse and that it will not give too many false alarms.
First we will take a close look at how early warning scores are working in patients with breathing problems. We will check how many times doctors are being called to see this group of patients each day and what extra treatments are being given as a result. This will help us understand what needs to change in our new scoring system.
Next we will gather information about 22,000 patients who were admitted to hospital with breathing problems and examine 1000 cases in detail. We will look at how their vital signs got worse before they developed breathing complications in hospital. We will team up with computer scientists and statisticians to develop a new scoring system which is better than the one we use currently.
REC name
East Midlands - Nottingham 1 Research Ethics Committee
REC reference
20/EM/0064
Date of REC Opinion
18 Mar 2020
REC opinion
Favourable Opinion