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Predicting Illness Severity and Short-Term Outcomes of COVID-19: A Retrospective Cohort Study in China
Among 417 COVID-19 patients in Shenzhen, demographic characteristics, clinical manifestations and baseline laboratory tests showed significant differences between mild-moderate cohort and severe-critical cohort.Based on these differences, a convenient mathematical model was established to predict th...
Autores principales: | , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7237911/ https://www.ncbi.nlm.nih.gov/pubmed/33554186 http://dx.doi.org/10.1016/j.xinn.2020.04.007 |
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author | Chen, Chuming Wang, Haihui Liang, Zhichao Peng, Ling Zhao, Fang Yang, Liuqing Cao, Mengli Wu, Weibo Jiang, Xiao Zhang, Peiyan Li, Yinfeng Chen, Li Feng, Shiyan Li, Jianming Meng, Lingxiang Wu, Huishan Wang, Fuxiang Liu, Quanying Liu, Yingxia |
author_facet | Chen, Chuming Wang, Haihui Liang, Zhichao Peng, Ling Zhao, Fang Yang, Liuqing Cao, Mengli Wu, Weibo Jiang, Xiao Zhang, Peiyan Li, Yinfeng Chen, Li Feng, Shiyan Li, Jianming Meng, Lingxiang Wu, Huishan Wang, Fuxiang Liu, Quanying Liu, Yingxia |
author_sort | Chen, Chuming |
collection | PubMed |
description | Among 417 COVID-19 patients in Shenzhen, demographic characteristics, clinical manifestations and baseline laboratory tests showed significant differences between mild-moderate cohort and severe-critical cohort.Based on these differences, a convenient mathematical model was established to predict the illness severity of COVID-19. The model includes four parameters: age, BMI, CD4(+) lymphocytes and IL-6 levels. The AUC of the model is 0.911.The high risk factors for developing to severe COVID-19 are: age ≥ 55 years, BMI > 27 kg / m(2), IL-6 ≥ 20 pg / ml, CD4(+) T cell ≤ 400 count / μ L.Among 249 discharged COVID-19 patients, those who recovered after 20 days had a lower count of platelet, a higher level of estimated glomerular filtration rate, and higher level of interleukin-6 and myoglobin than those who recovered within 20 days. |
format | Online Article Text |
id | pubmed-7237911 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-72379112020-05-20 Predicting Illness Severity and Short-Term Outcomes of COVID-19: A Retrospective Cohort Study in China Chen, Chuming Wang, Haihui Liang, Zhichao Peng, Ling Zhao, Fang Yang, Liuqing Cao, Mengli Wu, Weibo Jiang, Xiao Zhang, Peiyan Li, Yinfeng Chen, Li Feng, Shiyan Li, Jianming Meng, Lingxiang Wu, Huishan Wang, Fuxiang Liu, Quanying Liu, Yingxia Innovation (Camb) Report Among 417 COVID-19 patients in Shenzhen, demographic characteristics, clinical manifestations and baseline laboratory tests showed significant differences between mild-moderate cohort and severe-critical cohort.Based on these differences, a convenient mathematical model was established to predict the illness severity of COVID-19. The model includes four parameters: age, BMI, CD4(+) lymphocytes and IL-6 levels. The AUC of the model is 0.911.The high risk factors for developing to severe COVID-19 are: age ≥ 55 years, BMI > 27 kg / m(2), IL-6 ≥ 20 pg / ml, CD4(+) T cell ≤ 400 count / μ L.Among 249 discharged COVID-19 patients, those who recovered after 20 days had a lower count of platelet, a higher level of estimated glomerular filtration rate, and higher level of interleukin-6 and myoglobin than those who recovered within 20 days. Elsevier 2020-04-22 /pmc/articles/PMC7237911/ /pubmed/33554186 http://dx.doi.org/10.1016/j.xinn.2020.04.007 Text en © 2020 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Report Chen, Chuming Wang, Haihui Liang, Zhichao Peng, Ling Zhao, Fang Yang, Liuqing Cao, Mengli Wu, Weibo Jiang, Xiao Zhang, Peiyan Li, Yinfeng Chen, Li Feng, Shiyan Li, Jianming Meng, Lingxiang Wu, Huishan Wang, Fuxiang Liu, Quanying Liu, Yingxia Predicting Illness Severity and Short-Term Outcomes of COVID-19: A Retrospective Cohort Study in China |
title | Predicting Illness Severity and Short-Term Outcomes of COVID-19: A Retrospective Cohort Study in China |
title_full | Predicting Illness Severity and Short-Term Outcomes of COVID-19: A Retrospective Cohort Study in China |
title_fullStr | Predicting Illness Severity and Short-Term Outcomes of COVID-19: A Retrospective Cohort Study in China |
title_full_unstemmed | Predicting Illness Severity and Short-Term Outcomes of COVID-19: A Retrospective Cohort Study in China |
title_short | Predicting Illness Severity and Short-Term Outcomes of COVID-19: A Retrospective Cohort Study in China |
title_sort | predicting illness severity and short-term outcomes of covid-19: a retrospective cohort study in china |
topic | Report |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7237911/ https://www.ncbi.nlm.nih.gov/pubmed/33554186 http://dx.doi.org/10.1016/j.xinn.2020.04.007 |
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