Early prediction of in-hospital death of COVID-19 patients: a machine-learning model based on age, blood analyses, and chest x-ray score

An early-warning model to predict in-hospital mortality on admission of COVID-19 patients at an emergency department (ED) was developed and validated using a machine-learning model. In total, 2782 patients were enrolled between March 2020 and December 2020, including 2106 patients (first wave) and 6...

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Autores principales:	Emirena Garrafa, Marika Vezzoli, Marco Ravanelli, Davide Farina, Andrea Borghesi, Stefano Calza, Roberto Maroldi
Formato:	article
Lenguaje:	EN
Publicado:	eLife Sciences Publications Ltd 2021
Materias:	biomarker COVID-19 random forests VIM SMOTE PDP Medicine R Science Q Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/de7bc0bd7a0641b2ab166f48a4955499
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spelling	oai:doaj.org-article:de7bc0bd7a0641b2ab166f48a49554992021-12-01T12:14:11ZEarly prediction of in-hospital death of COVID-19 patients: a machine-learning model based on age, blood analyses, and chest x-ray score10.7554/eLife.706402050-084Xe70640https://doaj.org/article/de7bc0bd7a0641b2ab166f48a49554992021-10-01T00:00:00Zhttps://elifesciences.org/articles/70640https://doaj.org/toc/2050-084XAn early-warning model to predict in-hospital mortality on admission of COVID-19 patients at an emergency department (ED) was developed and validated using a machine-learning model. In total, 2782 patients were enrolled between March 2020 and December 2020, including 2106 patients (first wave) and 676 patients (second wave) in the COVID-19 outbreak in Italy. The first-wave patients were divided into two groups with 1474 patients used to train the model, and 632 to validate it. The 676 patients in the second wave were used to test the model. Age, 17 blood analytes, and Brescia chest X-ray score were the variables processed using a random forests classification algorithm to build and validate the model. Receiver operating characteristic (ROC) analysis was used to assess the model performances. A web-based death-risk calculator was implemented and integrated within the Laboratory Information System of the hospital. The final score was constructed by age (the most powerful predictor), blood analytes (the strongest predictors were lactate dehydrogenase, D-dimer, neutrophil/lymphocyte ratio, C-reactive protein, lymphocyte %, ferritin std, and monocyte %), and Brescia chest X-ray score (https://bdbiomed.shinyapps.io/covid19score/). The areas under the ROC curve obtained for the three groups (training, validating, and testing) were 0.98, 0.83, and 0.78, respectively. The model predicts in-hospital mortality on the basis of data that can be obtained in a short time, directly at the ED on admission. It functions as a web-based calculator, providing a risk score which is easy to interpret. It can be used in the triage process to support the decision on patient allocation.Emirena GarrafaMarika VezzoliMarco RavanelliDavide FarinaAndrea BorghesiStefano CalzaRoberto MaroldieLife Sciences Publications LtdarticlebiomarkerCOVID-19random forestsVIMSMOTEPDPMedicineRScienceQBiology (General)QH301-705.5ENeLife, Vol 10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	biomarker COVID-19 random forests VIM SMOTE PDP Medicine R Science Q Biology (General) QH301-705.5
spellingShingle	biomarker COVID-19 random forests VIM SMOTE PDP Medicine R Science Q Biology (General) QH301-705.5 Emirena Garrafa Marika Vezzoli Marco Ravanelli Davide Farina Andrea Borghesi Stefano Calza Roberto Maroldi Early prediction of in-hospital death of COVID-19 patients: a machine-learning model based on age, blood analyses, and chest x-ray score
description	An early-warning model to predict in-hospital mortality on admission of COVID-19 patients at an emergency department (ED) was developed and validated using a machine-learning model. In total, 2782 patients were enrolled between March 2020 and December 2020, including 2106 patients (first wave) and 676 patients (second wave) in the COVID-19 outbreak in Italy. The first-wave patients were divided into two groups with 1474 patients used to train the model, and 632 to validate it. The 676 patients in the second wave were used to test the model. Age, 17 blood analytes, and Brescia chest X-ray score were the variables processed using a random forests classification algorithm to build and validate the model. Receiver operating characteristic (ROC) analysis was used to assess the model performances. A web-based death-risk calculator was implemented and integrated within the Laboratory Information System of the hospital. The final score was constructed by age (the most powerful predictor), blood analytes (the strongest predictors were lactate dehydrogenase, D-dimer, neutrophil/lymphocyte ratio, C-reactive protein, lymphocyte %, ferritin std, and monocyte %), and Brescia chest X-ray score (https://bdbiomed.shinyapps.io/covid19score/). The areas under the ROC curve obtained for the three groups (training, validating, and testing) were 0.98, 0.83, and 0.78, respectively. The model predicts in-hospital mortality on the basis of data that can be obtained in a short time, directly at the ED on admission. It functions as a web-based calculator, providing a risk score which is easy to interpret. It can be used in the triage process to support the decision on patient allocation.
format	article
author	Emirena Garrafa Marika Vezzoli Marco Ravanelli Davide Farina Andrea Borghesi Stefano Calza Roberto Maroldi
author_facet	Emirena Garrafa Marika Vezzoli Marco Ravanelli Davide Farina Andrea Borghesi Stefano Calza Roberto Maroldi
author_sort	Emirena Garrafa
title	Early prediction of in-hospital death of COVID-19 patients: a machine-learning model based on age, blood analyses, and chest x-ray score
title_short	Early prediction of in-hospital death of COVID-19 patients: a machine-learning model based on age, blood analyses, and chest x-ray score
title_full	Early prediction of in-hospital death of COVID-19 patients: a machine-learning model based on age, blood analyses, and chest x-ray score
title_fullStr	Early prediction of in-hospital death of COVID-19 patients: a machine-learning model based on age, blood analyses, and chest x-ray score
title_full_unstemmed	Early prediction of in-hospital death of COVID-19 patients: a machine-learning model based on age, blood analyses, and chest x-ray score
title_sort	early prediction of in-hospital death of covid-19 patients: a machine-learning model based on age, blood analyses, and chest x-ray score
publisher	eLife Sciences Publications Ltd
publishDate	2021
url	https://doaj.org/article/de7bc0bd7a0641b2ab166f48a4955499
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Early prediction of in-hospital death of COVID-19 patients: a machine-learning model based on age, blood analyses, and chest x-ray score

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