Year: 2021
Type: Article
Title: Machine learning risk prediction of mortality for patients undergoing surgery with perioperative SARS-CoV-2: the COVIDSurg mortality score
Author: COVIDSurg Collaborative
Author: T. Risteski
Author: V. Cvetanovska Naunova
Author: L. Jovcheski
Author: E. Lazova
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 learningbased risk score to predict postoperative mortality risk in patients with perioperative SARS-CoV-2 infection.
Publisher: Oxford University Press
Relation: British Journal of Surgery
Identifier: oai:repository.ukim.mk:20.500.12188/16686
Identifier: http://hdl.handle.net/20.500.12188/16686Identifier: https://doi.org/10.1093/bjs/znab183
