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Deep learning prediction of likelihood of ICU admission and mortality in COVID-19 patients using clinical variables

BACKGROUND: This study aimed to develop a deep-learning model and a risk-score system using clinical variables to predict intensive care unit (ICU) admission and in-hospital mortality in COVID-19 patients. METHODS: This retrospective study consisted of 5,766 persons-under-investigation for COVID-19...

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Autores principales: Li, Xiaoran, Ge, Peilin, Zhu, Jocelyn, Li, Haifang, Graham, James, Singer, Adam, Richman, Paul S., Duong, Tim Q.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7651477/
https://www.ncbi.nlm.nih.gov/pubmed/33194455
http://dx.doi.org/10.7717/peerj.10337
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author Li, Xiaoran
Ge, Peilin
Zhu, Jocelyn
Li, Haifang
Graham, James
Singer, Adam
Richman, Paul S.
Duong, Tim Q.
author_facet Li, Xiaoran
Ge, Peilin
Zhu, Jocelyn
Li, Haifang
Graham, James
Singer, Adam
Richman, Paul S.
Duong, Tim Q.
author_sort Li, Xiaoran
collection PubMed
description BACKGROUND: This study aimed to develop a deep-learning model and a risk-score system using clinical variables to predict intensive care unit (ICU) admission and in-hospital mortality in COVID-19 patients. METHODS: This retrospective study consisted of 5,766 persons-under-investigation for COVID-19 between 7 February 2020 and 4 May 2020. Demographics, chronic comorbidities, vital signs, symptoms and laboratory tests at admission were collected. A deep neural network model and a risk-score system were constructed to predict ICU admission and in-hospital mortality. Prediction performance used the receiver operating characteristic area under the curve (AUC). RESULTS: The top ICU predictors were procalcitonin, lactate dehydrogenase, C-reactive protein, ferritin and oxygen saturation. The top mortality predictors were age, lactate dehydrogenase, procalcitonin, cardiac troponin, C-reactive protein and oxygen saturation. Age and troponin were unique top predictors for mortality but not ICU admission. The deep-learning model predicted ICU admission and mortality with an AUC of 0.780 (95% CI [0.760–0.785]) and 0.844 (95% CI [0.839–0.848]), respectively. The corresponding risk scores yielded an AUC of 0.728 (95% CI [0.726–0.729]) and 0.848 (95% CI [0.847–0.849]), respectively. CONCLUSIONS: Deep learning and the resultant risk score have the potential to provide frontline physicians with quantitative tools to stratify patients more effectively in time-sensitive and resource-constrained circumstances.
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spelling pubmed-76514772020-11-12 Deep learning prediction of likelihood of ICU admission and mortality in COVID-19 patients using clinical variables Li, Xiaoran Ge, Peilin Zhu, Jocelyn Li, Haifang Graham, James Singer, Adam Richman, Paul S. Duong, Tim Q. PeerJ Bioinformatics BACKGROUND: This study aimed to develop a deep-learning model and a risk-score system using clinical variables to predict intensive care unit (ICU) admission and in-hospital mortality in COVID-19 patients. METHODS: This retrospective study consisted of 5,766 persons-under-investigation for COVID-19 between 7 February 2020 and 4 May 2020. Demographics, chronic comorbidities, vital signs, symptoms and laboratory tests at admission were collected. A deep neural network model and a risk-score system were constructed to predict ICU admission and in-hospital mortality. Prediction performance used the receiver operating characteristic area under the curve (AUC). RESULTS: The top ICU predictors were procalcitonin, lactate dehydrogenase, C-reactive protein, ferritin and oxygen saturation. The top mortality predictors were age, lactate dehydrogenase, procalcitonin, cardiac troponin, C-reactive protein and oxygen saturation. Age and troponin were unique top predictors for mortality but not ICU admission. The deep-learning model predicted ICU admission and mortality with an AUC of 0.780 (95% CI [0.760–0.785]) and 0.844 (95% CI [0.839–0.848]), respectively. The corresponding risk scores yielded an AUC of 0.728 (95% CI [0.726–0.729]) and 0.848 (95% CI [0.847–0.849]), respectively. CONCLUSIONS: Deep learning and the resultant risk score have the potential to provide frontline physicians with quantitative tools to stratify patients more effectively in time-sensitive and resource-constrained circumstances. PeerJ Inc. 2020-11-06 /pmc/articles/PMC7651477/ /pubmed/33194455 http://dx.doi.org/10.7717/peerj.10337 Text en © 2020 Li et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Li, Xiaoran
Ge, Peilin
Zhu, Jocelyn
Li, Haifang
Graham, James
Singer, Adam
Richman, Paul S.
Duong, Tim Q.
Deep learning prediction of likelihood of ICU admission and mortality in COVID-19 patients using clinical variables
title Deep learning prediction of likelihood of ICU admission and mortality in COVID-19 patients using clinical variables
title_full Deep learning prediction of likelihood of ICU admission and mortality in COVID-19 patients using clinical variables
title_fullStr Deep learning prediction of likelihood of ICU admission and mortality in COVID-19 patients using clinical variables
title_full_unstemmed Deep learning prediction of likelihood of ICU admission and mortality in COVID-19 patients using clinical variables
title_short Deep learning prediction of likelihood of ICU admission and mortality in COVID-19 patients using clinical variables
title_sort deep learning prediction of likelihood of icu admission and mortality in covid-19 patients using clinical variables
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7651477/
https://www.ncbi.nlm.nih.gov/pubmed/33194455
http://dx.doi.org/10.7717/peerj.10337
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