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

dc.contributor.author

COVIDSurg Collaborative

dc.date.accessioned

2022-03-20T02:01:48Z

dc.date.available

2022-03-20T02:01:48Z

dc.date.issued

2021-11

dc.date.updated

2022-03-20T02:01:48Z

dc.description.abstract

<jats:p>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.</jats:p>

dc.identifier

6316029

dc.identifier.issn

0007-1323

dc.identifier.issn

1365-2168

dc.identifier.uri

https://hdl.handle.net/10161/24549

dc.language

eng

dc.publisher

Oxford University Press (OUP)

dc.relation.ispartof

The British journal of surgery

dc.relation.isversionof

10.1093/bjs/znab183

dc.subject

COVIDSurg Collaborative

dc.subject

Humans

dc.subject

Surgical Procedures, Operative

dc.subject

Models, Statistical

dc.subject

Risk Assessment

dc.subject

Cohort Studies

dc.subject

Datasets as Topic

dc.subject

Machine Learning

dc.subject

COVID-19

dc.subject

SARS-CoV-2

dc.title

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

dc.type

Journal article

pubs.begin-page

1274

pubs.end-page

1292

pubs.issue

11

pubs.organisational-group

Duke

pubs.organisational-group

School of Medicine

pubs.organisational-group

Staff

pubs.organisational-group

Clinical Science Departments

pubs.organisational-group

Orthopaedics

pubs.organisational-group

Surgery

pubs.publication-status

Published

pubs.volume

108

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