Machine learning risk prediction of mortality for patients undergoing surgery with perioperative SARS-CoV-2: the COVIDSurg mortality score.

COVIDSurg, Collaborative and Bosanquet, David ORCID: https://orcid.org/0000-0003-2304-0489 2021. Machine learning risk prediction of mortality for patients undergoing surgery with perioperative SARS-CoV-2: the COVIDSurg mortality score. British Journal of Surgery 108 (11) , 1274–1292. 10.1093/bjs/znab183

PDF - Published Version Available under License Creative Commons Attribution. Download (499kB)

Abstract

Since the beginning of the COVID-19 pandemic tens of millions of operations have been cancelled1 as a result of excessive postoperative pulmonary complications (51.2 per cent) and mortality rates (23.8 per cent) in patients with perioperative SARS-CoV-2 infection2. There is an urgent need to restart surgery safely in order to minimize the impact of untreated non-communicable disease. As rates of SARS-CoV-2 infection in elective surgery patients range from 1–9 per cent3–8, vaccination is expected to take years to implement globally9 and preoperative screening is likely to lead to increasing numbers of SARS-CoV-2-positive patients, perioperative SARS-CoV-2 infection will remain a challenge for the foreseeable future. To inform consent and shared decision-making, a robust, globally applicable score is needed to predict individualized mortality risk for patients with perioperative SARS-CoV-2 infection. The authors aimed to develop and validate a machine learning-based risk score to predict postoperative mortality risk in patients with perioperative SARS-CoV-2 infection.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Medicine
Additional Information:	David Bosanquet is a member of COVIDSurg Collaborative
Publisher:	Oxford University Press
ISSN:	0007-1323
Date of First Compliant Deposit:	1 July 2024
Date of Acceptance:	26 April 2021
Last Modified:	01 Jul 2024 14:00
URI:	https://orca.cardiff.ac.uk/id/eprint/169958

Actions (repository staff only)

Edit Item