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Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/140078
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dc.contributor.authorDajti, I.-
dc.contributor.authorValenzuela, J.I.-
dc.contributor.authorBoccalatte, L.A.-
dc.contributor.authorGemelli, N.A.-
dc.contributor.authorSmith, D.E.-
dc.contributor.authorDudi-Venkata, N.N.-
dc.contributor.authorKroon, H.M.-
dc.contributor.authorSammour, T.-
dc.contributor.authorRoberts, M.-
dc.contributor.authorMitchell, D.-
dc.contributor.authorLah, K.-
dc.contributor.authorPearce, A.-
dc.contributor.authorMorton, A.-
dc.contributor.authorDawson, A.C.-
dc.contributor.authorDrane, A.-
dc.contributor.authorSharpin, C.-
dc.contributor.authorNataraja, R.M.-
dc.contributor.authorPacilli, M.-
dc.contributor.authorCox, D.R.A.-
dc.contributor.authorMuralidharan, V.-
dc.contributor.authoret al.-
dc.date.issued2021-
dc.identifier.citationBritish Journal of Surgery, 2021; 108(4):1274-1292-
dc.identifier.issn0007-1323-
dc.identifier.issn1365-2168-
dc.identifier.urihttps://hdl.handle.net/2440/140078-
dc.description.abstract<jats:p>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.</jats:p>-
dc.description.statementofresponsibilityLaura Bravo ... COVIDSurg Collaborative : (Royal Adelaide Hospital, N. N. Dudi-Venkata, H. M. Kroon, T. Sammour) ... et al..-
dc.language.isoen-
dc.publisherOxford University Press (OUP)-
dc.rights© The Author(s) 2021. Published by Oxford University Press on behalf of BJS Society Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.-
dc.source.urihttp://dx.doi.org/10.1093/bjs/znab183-
dc.subjectGeneral surgery-
dc.subject.meshHumans-
dc.subject.meshSurgical Procedures, Operative-
dc.subject.meshModels, Statistical-
dc.subject.meshRisk Assessment-
dc.subject.meshCohort Studies-
dc.subject.meshDatasets as Topic-
dc.subject.meshMachine Learning-
dc.subject.meshCOVID-19-
dc.subject.meshSARS-CoV-2-
dc.titleMachine learning risk prediction of mortality for patients undergoing surgery with perioperative SARS-CoV-2: the COVIDSurg mortality score-
dc.typeJournal article-
dc.identifier.doi10.1093/bjs/znab183-
pubs.publication-statusPublished-
dc.identifier.orcidDudi-Venkata, N.N. [0000-0002-9775-3599]-
dc.identifier.orcidKroon, H.M. [0000-0002-8923-7527]-
dc.identifier.orcidSammour, T. [0000-0002-4918-8871]-
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