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oai:docta.ucm.es:20.500.14352/1284532025-12-05T00:50:16Zcom_20.500.14352_14col_20.500.14352_15

Machine learning risk prediction of mortality for patients undergoing surgery with perioperative SARS-CoV-2: the COVIDSurg mortality score Mateo Sierra, Olga 616.98:578.834 Ciencias Biomédicas Enfermedades infecciosas 32 Ciencias Médicas 3205.05 Enfermedades Infecciosas La autoría del artículo es del grupo de Investigación: COVIDSurg Collaborative, al que pertenece Olga Mateo Sierra. 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. National Institute for Health Research (EEUU) Association of Coloproctology of Great Britain and Ireland Depto. de Cirugía Fac. de Medicina TRUE pub 2025-12-04T13:07:00Z 2025-12-04T13:07:00Z 2021-11 journal article VoR https://hdl.handle.net/20.500.14352/128453 0007-1323 10.1093/bjs/znab183 1365-2168 eng COVIDSurg Collaborative. Machine learning risk prediction of mortality for patients undergoing surgery with perioperative SARS-CoV-2: the COVIDSurg mortality score. Br J Surg. 2021 Nov 11;108(11):1274-1292. doi: 10.1093/bjs/znab183 Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ open access application/pdf Oxford University Press