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A prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in thousands of deaths in the world. Information about prediction model of prognosis of SARS-CoV-2 infection is scarce. We used machine learning for processing laboratory findings of 110 patients with SARS-CoV-2 pneumonia...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7441177/ https://www.ncbi.nlm.nih.gov/pubmed/32820210 http://dx.doi.org/10.1038/s41598-020-71114-7 |
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author | Wu, Gang Zhou, Shuchang Wang, Yujin Lv, Wenzhi Wang, Shili Wang, Ting Li, Xiaoming |
author_facet | Wu, Gang Zhou, Shuchang Wang, Yujin Lv, Wenzhi Wang, Shili Wang, Ting Li, Xiaoming |
author_sort | Wu, Gang |
collection | PubMed |
description | The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in thousands of deaths in the world. Information about prediction model of prognosis of SARS-CoV-2 infection is scarce. We used machine learning for processing laboratory findings of 110 patients with SARS-CoV-2 pneumonia (including 51 non-survivors and 59 discharged patients). The maximum relevance minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator logistic regression model were used for selection of laboratory features. Seven laboratory features selected in the model were: prothrombin activity, urea, white blood cell, interleukin-2 receptor, indirect bilirubin, myoglobin, and fibrinogen degradation products. The signature constructed using the seven features had 98% [93%, 100%] sensitivity and 91% [84%, 99%] specificity in predicting outcome of SARS-CoV-2 pneumonia. Thus it is feasible to establish an accurate prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings. |
format | Online Article Text |
id | pubmed-7441177 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-74411772020-08-21 A prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings Wu, Gang Zhou, Shuchang Wang, Yujin Lv, Wenzhi Wang, Shili Wang, Ting Li, Xiaoming Sci Rep Article The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in thousands of deaths in the world. Information about prediction model of prognosis of SARS-CoV-2 infection is scarce. We used machine learning for processing laboratory findings of 110 patients with SARS-CoV-2 pneumonia (including 51 non-survivors and 59 discharged patients). The maximum relevance minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator logistic regression model were used for selection of laboratory features. Seven laboratory features selected in the model were: prothrombin activity, urea, white blood cell, interleukin-2 receptor, indirect bilirubin, myoglobin, and fibrinogen degradation products. The signature constructed using the seven features had 98% [93%, 100%] sensitivity and 91% [84%, 99%] specificity in predicting outcome of SARS-CoV-2 pneumonia. Thus it is feasible to establish an accurate prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings. Nature Publishing Group UK 2020-08-20 /pmc/articles/PMC7441177/ /pubmed/32820210 http://dx.doi.org/10.1038/s41598-020-71114-7 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Wu, Gang Zhou, Shuchang Wang, Yujin Lv, Wenzhi Wang, Shili Wang, Ting Li, Xiaoming A prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings |
title | A prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings |
title_full | A prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings |
title_fullStr | A prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings |
title_full_unstemmed | A prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings |
title_short | A prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings |
title_sort | prediction model of outcome of sars-cov-2 pneumonia based on laboratory findings |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7441177/ https://www.ncbi.nlm.nih.gov/pubmed/32820210 http://dx.doi.org/10.1038/s41598-020-71114-7 |
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