Machine learning risk prediction of mortality for patients undergoing surgery with perioperative SARS-CoV-2: the COVIDSurg mortality score
Articolo
Data di Pubblicazione:
2021
Citazione:
Machine learning risk prediction of mortality for patients undergoing surgery with perioperative SARS-CoV-2: the COVIDSurg mortality score / Candiani, M.; Bonavina, G.; Ottolina, J.; Valsecchi, L.; Mortini, P.; Gagliardi, F.; Piloni, M.; Medone, M.; Negri, G.; Bandiera, A.; De Nardi, P.; Sileri, P.; Carlucci, M.; Pelaggi, D.; Rosati, R.; Vignali, A.; Parise, P.; Elmore, U.; Gallo, G.. - In: BRITISH JOURNAL OF SURGERY. - ISSN 0007-1323. - 108:11(2021), pp. 1274-1292. [10.1093/bjs/znab183]
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.
Tipologia CRIS:
1.1 Articolo in rivista
Elenco autori:
Candiani, M.; Bonavina, G.; Ottolina, J.; Valsecchi, L.; Mortini, P.; Gagliardi, F.; Piloni, M.; Medone, M.; Negri, G.; Bandiera, A.; De Nardi, P.; Sileri, P.; Carlucci, M.; Pelaggi, D.; Rosati, R.; Vignali, A.; Parise, P.; Elmore, U.; Gallo, G.
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