COVIDSurg Collaborative Co-authors;
(2021)
Machine learning risk prediction of mortality for patients undergoing surgery with perioperative SARS-CoV-2: the COVIDSurg mortality score.
British Journal of Surgery
, Article znab183. 10.1093/bjs/znab183.
(In press).
Preview |
Text
znab183.pdf - Published Version Download (553kB) | Preview |
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. In order 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.
Archive Staff Only
 |
View Item |
