Machine learning risk prediction of mortality for patients undergoing surgery with perioperative SARS-CoV-2: the COVIDSurg mortality score
Journal
British Journal of Surgery
Date Issued
2021-11-11
Author(s)
COVIDSurg Collaborative
V. Cvetanovska Naunova
L. Jovcheski
E. Lazova
DOI
https://doi.org/10.1093/bjs/znab183
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.
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.
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