作者
Rita Murri, Jacopo Lenkowicz, Carlotta Masciocchi, Chiara Iacomini, Massimo Fantoni, Andrea Damiani, Antonio Marchetti, Paolo Domenico Angelo Sergi, Giovanni Arcuri, Alfredo Cesario, Stefano Patarnello, Massimo Antonelli, Rocco Bellantone, Roberto Bernabei, Stefania Boccia, Paolo Calabresi, Andrea Cambieri, Roberto Cauda, Cesare Colosimo, Filippo Crea, Ruggero De Maria, Valerio De Stefano, Francesco Franceschi, Antonio Gasbarrini, Ornella Parolini, Luca Richeldi, Maurizio Sanguinetti, Andrea Urbani, Maurizio Zega, Giovanni Scambia, Vincenzo Valentini
发表日期
2021/10/27
期刊
Scientific Reports
卷号
11
期号
1
页码范围
21136
出版商
Nature Publishing Group UK
简介
The COVID-19 pandemic is impressively challenging the healthcare system. Several prognostic models have been validated but few of them are implemented in daily practice. The objective of the study was to validate a machine-learning risk prediction model using easy-to-obtain parameters to help to identify patients with COVID-19 who are at higher risk of death. The training cohort included all patients admitted to Fondazione Policlinico Gemelli with COVID-19 from March 5, 2020, to November 5, 2020. Afterward, the model was tested on all patients admitted to the same hospital with COVID-19 from November 6, 2020, to February 5, 2021. The primary outcome was in-hospital case-fatality risk. The out-of-sample performance of the model was estimated from the training set in terms of Area under the Receiving Operator Curve (AUROC) and classification matrix statistics by averaging the results of fivefold cross …
学术搜索中的文章
R Murri, J Lenkowicz, C Masciocchi, C Iacomini… - Scientific Reports, 2021