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



Bravo, Laura, Nepogodiev, Dmitri, Glasbey, James C, Li, Elizabeth, Simoes, Joana FF, Kamarajah, Sivesh K, Picciochi, Maria, Abbott, Tom EF, Ademuyiwa, Adesoji O, Arnaud, Alexis P
et al (show 4809 more authors) (2021) Y 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). pp. 1274-1292.

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Abstract

To support the global restart of elective surgery, data from an international prospective cohort study of 8492 patients (69 countries) was analysed using artificial intelligence (machine learning techniques) to develop a predictive score for mortality in surgical patients with SARS-CoV-2. We found that patient rather than operation factors were the best predictors and used these to create the COVIDsurg Mortality Score (https://covidsurgrisk.app). Our data demonstrates that it is safe to restart a wide range of surgical services for selected patients.

Item Type: Article
Uncontrolled Keywords: COVIDSurg Collaborative , Humans, Surgical Procedures, Operative, Models, Statistical, Risk Assessment, Cohort Studies, Datasets as Topic, Machine Learning, COVID-19, SARS-CoV-2
Divisions: Faculty of Health and Life Sciences
Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology
Depositing User: Symplectic Admin
Date Deposited: 04 Oct 2021 07:41
Last Modified: 18 Jan 2023 21:27
DOI: 10.1093/bjs/znab183
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3139142