Machine learning risk prediction of mortality for patients undergoing surgery with perioperative SARS-CoV-2: the COVIDSurg mortality score
| Author | dc.contributor.author | COVIDSurg Collaborative | |
| Admission date | dc.date.accessioned | 2022-05-23T22:05:50Z | |
| Available date | dc.date.available | 2022-05-23T22:05:50Z | |
| Publication date | dc.date.issued | 2021 | |
| Cita de ítem | dc.identifier.citation | BJS, 2021, 108, 1274–1292 | es_ES |
| Identifier | dc.identifier.other | 10.1093/bjs/znab183 | |
| Identifier | dc.identifier.uri | https://repositorio.uchile.cl/handle/2250/185676 | |
| Abstract | dc.description.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. | es_ES |
| Patrocinador | dc.description.sponsorship | National Institute for Health Research (NIHR) NIHR 16.136.79 Association of Coloproctology of Great Britain and Ireland Bowel & Cancer Research Bowel Research UK Association of Upper Gastrointestinal Surgeons British Association of Surgical Oncology British Gynaecological Cancer Society European Society of Coloproctology Medtronic NIHR Academy Urology Foundation Sarcoma UK Vascular Society for Great Britain and Ireland Yorkshire Cancer Research MRC Health Data Research UK by UK Research and Innovation, Department of Health and Social Care (England) HDRUK/CFC/01 Wellcome Trust European Commission 215182/Z/19/Z | es_ES |
| Lenguage | dc.language.iso | en | es_ES |
| Publisher | dc.publisher | Oxford Univ Press, England | es_ES |
| Type of license | dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | * |
| Link to License | dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
| Source | dc.source | British Journal of Surgery | es_ES |
| Título | dc.title | Machine learning risk prediction of mortality for patients undergoing surgery with perioperative SARS-CoV-2: the COVIDSurg mortality score | es_ES |
| Document type | dc.type | Artículo de revista | es_ES |
| dc.description.version | dc.description.version | Versión publicada - versión final del editor | es_ES |
| dcterms.accessRights | dcterms.accessRights | Acceso abierto | es_ES |
| Cataloguer | uchile.catalogador | cfr | es_ES |
| Indexation | uchile.index | Artículo de publícación WoS | es_ES |
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Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States