Can Innovative Methods Improve Risk Modelling in Emergency Laparotomy
Research type
Research Study
Full title
Can clinical and biochemical variables be used innovatively to improve risk modelling of mortality in patients undergoing emergency abdominal surgery?
IRAS ID
279552
Contact name
Alexander Darbyshire
Contact email
Sponsor organisation
University of Portsmouth
Duration of Study in the UK
1 years, 0 months, 0 days
Research summary
Emergency abdominal surgery is a procedure performed for bowel emergencies, such as a bowel blockage or perforation. It involves a large cut to access the abdomen called a laparotomy. For many patients it is a high-risk procedure with a mortality rate of 10%. It has become routine practice in the NHS to use risk models to predict a patient’s risk of death prior to laparotomy. This information is used by clinicians to help discuss the risks of surgery with patients and plan their care before and after. This study will investigate and test innovative ways of using clinical data and blood tests to try and improve the accuracy of models used to predict the risk of death with emergency laparotomy. The results of this project will be used to guide development of a much larger multi-site study to develop a new risk prediction model for emergency laparotomy.
Since December 2013, patients having an emergency laparotomy at Queen Alexandra Hospital (QAH) are entered into the National Emergency Laparotomy Audit (NELA). This study will use the NELA database for QAH to identify patients and their electronic hospital records that are kept as part of routine patient care. This information will be extracted onto datasets and patient identifiable information replaced with a study ID (a process called pseudonymisation). Statistical analysis will then be used to identify the variables which are highly predictive of post-operative mortality. These variables will be combined with established risk models to see if they can improve their accuracy at predicting mortality. We will compare their performance to established risk models, using the dataset.
REC name
South Central - Berkshire B Research Ethics Committee
REC reference
20/SC/0156
Date of REC Opinion
20 Apr 2020
REC opinion
Favourable Opinion