TY  - JOUR
A1  - Bravo, Laura
A1  - Nepogodiev, Dmitri
A1  - Glasbey, James C.
A1  - Li, Elizabeth
A1  - Simoes, Joana F. F.
A1  - Kamarajah, Sivesh K.
A1  - Picciochi, Maria
A1  - Abbott, Tom E. F.
A1  - Ademuyiwa, Adesoji O.
A1  - Arnaud, Alexis P.
A1  - Agarwal, Arnav
A1  - Brar, Amanpreet
A1  - Elhadi, Muhammed
A1  - Mazingi, Dennis
A1  - Cardoso, Victor Roth
A1  - Lawday, Samuel
A1  - Sayyed, Raza
A1  - Omar, Omar M.
A1  - Ramos de la Madina, Antonio
A1  - Slater, Luke
A1  - Venn, Mary
A1  - Gkoutos, Georgios
A1  - Bhangu, Aneel
T1  - Machine learning risk prediction of mortality for patients undergoing surgery with perioperative SARS-CoV-2: the COVIDSurg mortality score
T2  - British Journal of Surgery
N2  - 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.
Y1  - 2021
UR  - https://opus.bibliothek.uni-augsburg.de/opus4/frontdoor/index/index/docId/123921
UR  - https://nbn-resolving.org/urn:nbn:de:bvb:384-opus4-1239217
SN  - 0007-1323
SN  - 1365-2168
N1  - Published on behalf of the CVIDSurg Collaborative. Please see the publisher's website for further details.
VL  - 108
IS  - 11
SP  - 1274
EP  - 1292
PB  - Oxford University Press (OUP)
ER  -