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Significance of clinical phenomes of patients with COVID‐19 infection: A learning from 3795 patients in 80 reports
A new coronavirus SARS‐CoV‐2 has caused outbreaks in multiple countries and the number of cases is rapidly increasing through human‐to‐human transmission. Clinical phenomes of patients with SARS‐CoV‐2 infection are critical in distinguishing it from other respiratory infections. The extent and chara...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7240842/ https://www.ncbi.nlm.nih.gov/pubmed/32508041 http://dx.doi.org/10.1002/ctm2.17 |
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author | Zhang, Linlin Wang, Diane C. Huang, Qihong Wang, Xiangdong |
author_facet | Zhang, Linlin Wang, Diane C. Huang, Qihong Wang, Xiangdong |
author_sort | Zhang, Linlin |
collection | PubMed |
description | A new coronavirus SARS‐CoV‐2 has caused outbreaks in multiple countries and the number of cases is rapidly increasing through human‐to‐human transmission. Clinical phenomes of patients with SARS‐CoV‐2 infection are critical in distinguishing it from other respiratory infections. The extent and characteristics of those phenomes varied depending on the severities of the infection, for example, beginning with fever or a mild cough, progressed with signs of pneumonia, and worsened with severe or even fatal respiratory difficulty in acute respiratory distress syndrome. We summarized clinical phenomes of 3795 patients with COVID‐19 based on 80 published reports from the onset of outbreak to March 2020 to emphasize the importance and specificity of those phenomes in diagnosis and treatment of infection, and evaluate the impact on medical services. The data show that the incidence of male patients was higher than that of females and the level of C‐reaction protein was increased as well as most patients' imaging included ground‐glass opacity. Clinical phenomes of SARS‐CoV‐2 infection were compared with those of SARS‐CoV and MERS‐CoV infections. There is an urgent need to develop an artificial intelligence‐based machine learning capacity to analyze and integrate radiomics‐ or imaging‐based, patient‐based, clinician‐based, and molecular measurements‐based data to fight the outbreak of COVID‐19 and enable more efficient responses to unknown infections in future. |
format | Online Article Text |
id | pubmed-7240842 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-72408422020-06-01 Significance of clinical phenomes of patients with COVID‐19 infection: A learning from 3795 patients in 80 reports Zhang, Linlin Wang, Diane C. Huang, Qihong Wang, Xiangdong Clin Transl Med Short Communication A new coronavirus SARS‐CoV‐2 has caused outbreaks in multiple countries and the number of cases is rapidly increasing through human‐to‐human transmission. Clinical phenomes of patients with SARS‐CoV‐2 infection are critical in distinguishing it from other respiratory infections. The extent and characteristics of those phenomes varied depending on the severities of the infection, for example, beginning with fever or a mild cough, progressed with signs of pneumonia, and worsened with severe or even fatal respiratory difficulty in acute respiratory distress syndrome. We summarized clinical phenomes of 3795 patients with COVID‐19 based on 80 published reports from the onset of outbreak to March 2020 to emphasize the importance and specificity of those phenomes in diagnosis and treatment of infection, and evaluate the impact on medical services. The data show that the incidence of male patients was higher than that of females and the level of C‐reaction protein was increased as well as most patients' imaging included ground‐glass opacity. Clinical phenomes of SARS‐CoV‐2 infection were compared with those of SARS‐CoV and MERS‐CoV infections. There is an urgent need to develop an artificial intelligence‐based machine learning capacity to analyze and integrate radiomics‐ or imaging‐based, patient‐based, clinician‐based, and molecular measurements‐based data to fight the outbreak of COVID‐19 and enable more efficient responses to unknown infections in future. John Wiley and Sons Inc. 2020-04-04 /pmc/articles/PMC7240842/ /pubmed/32508041 http://dx.doi.org/10.1002/ctm2.17 Text en © 2020 The Authors. Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Short Communication Zhang, Linlin Wang, Diane C. Huang, Qihong Wang, Xiangdong Significance of clinical phenomes of patients with COVID‐19 infection: A learning from 3795 patients in 80 reports |
title | Significance of clinical phenomes of patients with COVID‐19 infection: A learning from 3795 patients in 80 reports |
title_full | Significance of clinical phenomes of patients with COVID‐19 infection: A learning from 3795 patients in 80 reports |
title_fullStr | Significance of clinical phenomes of patients with COVID‐19 infection: A learning from 3795 patients in 80 reports |
title_full_unstemmed | Significance of clinical phenomes of patients with COVID‐19 infection: A learning from 3795 patients in 80 reports |
title_short | Significance of clinical phenomes of patients with COVID‐19 infection: A learning from 3795 patients in 80 reports |
title_sort | significance of clinical phenomes of patients with covid‐19 infection: a learning from 3795 patients in 80 reports |
topic | Short Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7240842/ https://www.ncbi.nlm.nih.gov/pubmed/32508041 http://dx.doi.org/10.1002/ctm2.17 |
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