COVID-19/mortality; Cohort Studies; Datasets as Topic; Humans; Machine Learning; SARS-CoV-2; Surgical Procedures, Operative/mortality; Models, Statistical; Risk Assessment; Surgery
Abstract :
[en] 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.
Disciplines :
Surgery
Author, co-author :
COVIDSurg Collaborative
Khosravi, Mohammadhossein ; Université de Liège - ULiège > GIGA > GIGA Consciousness - Coma Science Group
Language :
English
Title :
Machine learning risk prediction of mortality for patients undergoing surgery with perioperative SARS-CoV-2: the COVIDSurg mortality score.
NIHR - National Institute for Health Research Bowel Research UK AUGIS - Association of Upper Gastrointestinal Surgery of Great Britain and Ireland BASO - British Association of Surgical Oncology British Gynecological Cancer Society ESCP - European Society of Coloproctology Medtronic Urology Foundation Sarcoma UK Vascular Society of Great Britain and Ireland HDRUK - Health Data Research UK UKRI - UK Research and Innovation Wellcome Trust Yorkshire Cancer Research
Funding text :
This report was funded by a National Institute for Health Research (NIHR) Global Health Research Unit Grant (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, The Urology Foundation, Sarcoma UK, Vascular Society for Great Britain and Ireland, Yorkshire Cancer Research, and the MRC Health Data Research UK (HDRUK/CFC/01), an initiative funded by UK Research and Innovation, Department of Health and Social Care (England) and the devolved administrations, and leading medical research charities. L.B.M. and G.V.G. also acknowledge the Wellcome Trust 4-year studentship programme in mechanisms of inflammatory disease (MIDAS; 215182/Z/19/Z). The funders had no role in study design, data collection, analysis and interpretation, or writing of this report. The views expressed are those of the authors and not necessarily those of the National Health Service, the NIHR, or the UK Department of Health and Social Care.
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