Machine learning risk prediction of mortality for patients undergoing surgery with perioperative SARS-CoV-2: The COVIDSurg mortality score

Gianluca Pellino; D. E. Smith; A. Morton; D. R.A. Cox; M. Y. Ahmed; K. Beyer; T. A. Nguyen; C. J. Busch; C. Hirche; C. Zografos; L. A. Smith; S. Martin; F. Bianco; M. Serra; S. Ferrero; M. V. Marino; G. Ferrari; S. Rossi; A. Rodríguez Gonzalez; M. P.J. Lopez; S. Lourenço; S. Ali; L. Cecchini; J. J. Garcia Gutierrez; A. Sanchez Lopez; E. M. Targarona Soler; S. Sanchez Cabús; V. Molina Santos; J. A. Gonzalez Lopez; R. Medrano Caviedes; A. Moral Duarte; E. Espin-Basany; A. Pérez Núñez; P. Gomez; J. Saavedra; M. Hernández-García; M. Garcia Alonso; P. Hernandez; J. M. García Pérez; M. I. Rodriguez Fernandez; J. Valdes-Hernandez; A. Vázquez-Fernández; M. T. Fernández Martín; S. Ergun; S. Ali; E. Martin; S. Fraser; L. A. Smith; S. Mukherjee; A. James; Y. Zhang; L. Onos; D. Chen; B. Kim; B. Nunez-Garcia

doi:10.1093/bjs/znab183

Machine learning risk prediction of mortality for patients undergoing surgery with perioperative SARS-CoV-2: The COVIDSurg mortality score

Gianluca Pellino, D. E. Smith, A. Morton, D. R.A. Cox, M. Y. Ahmed, K. Beyer, T. A. Nguyen, C. J. Busch, C. Hirche, C. Zografos, L. A. Smith, S. Martin, F. Bianco, M. Serra, S. Ferrero, M. V. Marino, G. Ferrari, S. Rossi, A. Rodríguez Gonzalez, M. P.J. LopezS. Lourenço, S. Ali, L. Cecchini, J. J. Garcia Gutierrez, A. Sanchez Lopez, E. M. Targarona Soler, S. Sanchez Cabús, V. Molina Santos, J. A. Gonzalez Lopez, R. Medrano Caviedes, A. Moral Duarte, E. Espin-Basany, A. Pérez Núñez, P. Gomez, J. Saavedra, M. Hernández-García, M. Garcia Alonso, P. Hernandez, J. M. García Pérez, M. I. Rodriguez Fernandez, J. Valdes-Hernandez, A. Vázquez-Fernández, M. T. Fernández Martín, S. Ergun, S. Ali, E. Martin, S. Fraser, L. A. Smith, S. Mukherjee, A. James, Y. Zhang, L. Onos, D. Chen, B. Kim, B. Nunez-Garcia

Research output: Contribution to journal › Article › Research › peer-review

25 Citations (Scopus)

Original language	English
Article number	znab183
Journal	British Journal of Surgery
Volume	19
Issue number	4
DOIs	https://doi.org/10.1093/bjs/znab183
Publication status	Published - 1 Jan 2021

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SDG 3 Good Health and Well-being

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10.1093/bjs/znab183

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Pellino, G., Smith, D. E., Morton, A., Cox, D. R. A., Ahmed, M. Y., Beyer, K., Nguyen, T. A., Busch, C. J., Hirche, C., Zografos, C., Smith, L. A., Martin, S., Bianco, F., Serra, M., Ferrero, S., Marino, M. V., Ferrari, G., Rossi, S., Rodríguez Gonzalez, A., ... Nunez-Garcia, B. (2021). Machine learning risk prediction of mortality for patients undergoing surgery with perioperative SARS-CoV-2: The COVIDSurg mortality score. British Journal of Surgery, 19(4), Article znab183. https://doi.org/10.1093/bjs/znab183

@article{de20beaac2a94dfbbd11c149f43b298f,

title = "Machine learning risk prediction of mortality for patients undergoing surgery with perioperative SARS-CoV-2: The COVIDSurg mortality score",

author = "Gianluca Pellino and Smith, \{D. E.\} and A. Morton and Cox, \{D. R.A.\} and Ahmed, \{M. Y.\} and K. Beyer and Nguyen, \{T. A.\} and Busch, \{C. J.\} and C. Hirche and C. Zografos and Smith, \{L. A.\} and S. Martin and F. Bianco and M. Serra and S. Ferrero and Marino, \{M. V.\} and G. Ferrari and S. Rossi and \{Rodr{\'i}guez Gonzalez\}, A. and Lopez, \{M. P.J.\} and S. Louren{\c c}o and S. Ali and L. Cecchini and \{Garcia Gutierrez\}, \{J. J.\} and \{Sanchez Lopez\}, A. and \{Targarona Soler\}, \{E. M.\} and \{Sanchez Cab{\'u}s\}, S. and \{Molina Santos\}, V. and \{Gonzalez Lopez\}, \{J. A.\} and \{Medrano Caviedes\}, R. and \{Moral Duarte\}, A. and E. Espin-Basany and \{P{\'e}rez N{\'u}{\~n}ez\}, A. and P. Gomez and J. Saavedra and M. Hern{\'a}ndez-Garc{\'i}a and \{Garcia Alonso\}, M. and P. Hernandez and \{Garc{\'i}a P{\'e}rez\}, \{J. M.\} and \{Rodriguez Fernandez\}, \{M. I.\} and J. Valdes-Hernandez and A. V{\'a}zquez-Fern{\'a}ndez and \{Fern{\'a}ndez Mart{\'i}n\}, \{M. T.\} and S. Ergun and S. Ali and E. Martin and S. Fraser and Smith, \{L. A.\} and S. Mukherjee and A. James and Y. Zhang and L. Onos and D. Chen and B. Kim and B. Nunez-Garcia",

note = "Funding Information: 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.",

year = "2021",

month = jan,

day = "1",

doi = "10.1093/bjs/znab183",

language = "English",

volume = "19",

journal = "British Journal of Surgery",

issn = "0007-1323",

number = "4",

}

Pellino, G, Smith, DE, Morton, A, Cox, DRA, Ahmed, MY, Beyer, K, Nguyen, TA, Busch, CJ, Hirche, C, Zografos, C, Smith, LA, Martin, S, Bianco, F, Serra, M, Ferrero, S, Marino, MV, Ferrari, G, Rossi, S, Rodríguez Gonzalez, A, Lopez, MPJ, Lourenço, S, Ali, S, Cecchini, L, Garcia Gutierrez, JJ, Sanchez Lopez, A, Targarona Soler, EM, Sanchez Cabús, S , Molina Santos, V , Gonzalez Lopez, JA , Medrano Caviedes, R , Moral Duarte, A , Espin-Basany, E, Pérez Núñez, A, Gomez, P, Saavedra, J, Hernández-García, M, Garcia Alonso, M, Hernandez, P, García Pérez, JM, Rodriguez Fernandez, MI, Valdes-Hernandez, J, Vázquez-Fernández, A, Fernández Martín, MT, Ergun, S, Ali, S, Martin, E, Fraser, S, Smith, LA, Mukherjee, S, James, A, Zhang, Y, Onos, L, Chen, D, Kim, B & Nunez-Garcia, B 2021, 'Machine learning risk prediction of mortality for patients undergoing surgery with perioperative SARS-CoV-2: The COVIDSurg mortality score', British Journal of Surgery, vol. 19, no. 4, znab183. https://doi.org/10.1093/bjs/znab183

TY - JOUR

T1 - Machine learning risk prediction of mortality for patients undergoing surgery with perioperative SARS-CoV-2

T2 - The COVIDSurg mortality score

AU - Pellino, Gianluca

AU - Smith, D. E.

AU - Morton, A.

AU - Cox, D. R.A.

AU - Ahmed, M. Y.

AU - Beyer, K.

AU - Nguyen, T. A.

AU - Busch, C. J.

AU - Hirche, C.

AU - Zografos, C.

AU - Smith, L. A.

AU - Martin, S.

AU - Bianco, F.

AU - Serra, M.

AU - Ferrero, S.

AU - Marino, M. V.

AU - Ferrari, G.

AU - Rossi, S.

AU - Rodríguez Gonzalez, A.

AU - Lopez, M. P.J.

AU - Lourenço, S.

AU - Ali, S.

AU - Cecchini, L.

AU - Garcia Gutierrez, J. J.

AU - Sanchez Lopez, A.

AU - Targarona Soler, E. M.

AU - Sanchez Cabús, S.

AU - Molina Santos, V.

AU - Gonzalez Lopez, J. A.

AU - Medrano Caviedes, R.

AU - Moral Duarte, A.

AU - Espin-Basany, E.

AU - Pérez Núñez, A.

AU - Gomez, P.

AU - Saavedra, J.

AU - Hernández-García, M.

AU - Garcia Alonso, M.

AU - Hernandez, P.

AU - García Pérez, J. M.

AU - Rodriguez Fernandez, M. I.

AU - Valdes-Hernandez, J.

AU - Vázquez-Fernández, A.

AU - Fernández Martín, M. T.

AU - Ergun, S.

AU - Ali, S.

AU - Martin, E.

AU - Fraser, S.

AU - Smith, L. A.

AU - Mukherjee, S.

AU - James, A.

AU - Zhang, Y.

AU - Onos, L.

AU - Chen, D.

AU - Kim, B.

AU - Nunez-Garcia, B.

N1 - Funding Information: 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.

PY - 2021/1/1

Y1 - 2021/1/1

UR - https://www.scopus.com/pages/publications/85113431484

U2 - 10.1093/bjs/znab183

DO - 10.1093/bjs/znab183

M3 - Article

C2 - 34227657

AN - SCOPUS:85113431484

SN - 0007-1323

VL - 19

JO - British Journal of Surgery

JF - British Journal of Surgery

IS - 4

M1 - znab183

ER -

Machine learning risk prediction of mortality for patients undergoing surgery with perioperative SARS-CoV-2: The COVIDSurg mortality score

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