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Analysis of clinical features and early warning signs in patients with severe COVID-19: A retrospective cohort study

Coronavirus disease 2019 (COVID-19) was first identified in Wuhan, China, in December 2019. Although previous studies have described the clinical aspects of COVID-19, few studies have focused on the early detection of severe COVID-19. Therefore, this study aimed to identify the predictors of severe...

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Autores principales: Liu, Xinkui, Yue, Xinpei, Liu, Furong, Wei, Le, Chu, Yuntian, Bao, Honghong, Dong, Yichao, Cheng, Wenjie, Yang, Linpeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7319317/
https://www.ncbi.nlm.nih.gov/pubmed/32589691
http://dx.doi.org/10.1371/journal.pone.0235459
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author Liu, Xinkui
Yue, Xinpei
Liu, Furong
Wei, Le
Chu, Yuntian
Bao, Honghong
Dong, Yichao
Cheng, Wenjie
Yang, Linpeng
author_facet Liu, Xinkui
Yue, Xinpei
Liu, Furong
Wei, Le
Chu, Yuntian
Bao, Honghong
Dong, Yichao
Cheng, Wenjie
Yang, Linpeng
author_sort Liu, Xinkui
collection PubMed
description Coronavirus disease 2019 (COVID-19) was first identified in Wuhan, China, in December 2019. Although previous studies have described the clinical aspects of COVID-19, few studies have focused on the early detection of severe COVID-19. Therefore, this study aimed to identify the predictors of severe COVID-19 and to compare clinical features between patients with severe COVID-19 and those with less severe COVID-19. Patients admitted to designated hospital in the Henan Province of China who were either discharged or died prior to February 15, 2020 were enrolled retrospectively. Additionally, patients who underwent at least one of the following treatments were assigned to the severe group: continuous renal replacement therapy, high-flow oxygen absorption, noninvasive and invasive mechanical ventilation, or extracorporeal membrane oxygenation. The remaining patients were assigned to the non-severe group. Demographic information, initial symptoms, and first visit examination results were collected from the electronic medical records and compared between the groups. Multivariate logistic regression analysis was performed to determine the predictors of severe COVID-19. A receiver operating characteristic curve was used to identify a threshold for each predictor. Altogether,104 patients were enrolled in our study with 30 and 74 patients in the severe and non-severe groups, respectively. Multivariate logistic analysis indicated that patients aged ≥63 years (odds ratio = 41.0; 95% CI: 2.8, 592.4), with an absolute lymphocyte value of ≤1.02×10(9)/L (odds ratio = 6.1; 95% CI = 1.5, 25.2) and a C-reactive protein level of ≥65.08mg/L (odds ratio = 8.9; 95% CI = 1.0, 74.2) were at a higher risk of severe illness. Thus, our results could be helpful in the early detection of patients at risk for severe illness, enabling the implementation of effective interventions and likely lowering the morbidity of COVID-19 patients.
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spelling pubmed-73193172020-06-30 Analysis of clinical features and early warning signs in patients with severe COVID-19: A retrospective cohort study Liu, Xinkui Yue, Xinpei Liu, Furong Wei, Le Chu, Yuntian Bao, Honghong Dong, Yichao Cheng, Wenjie Yang, Linpeng PLoS One Research Article Coronavirus disease 2019 (COVID-19) was first identified in Wuhan, China, in December 2019. Although previous studies have described the clinical aspects of COVID-19, few studies have focused on the early detection of severe COVID-19. Therefore, this study aimed to identify the predictors of severe COVID-19 and to compare clinical features between patients with severe COVID-19 and those with less severe COVID-19. Patients admitted to designated hospital in the Henan Province of China who were either discharged or died prior to February 15, 2020 were enrolled retrospectively. Additionally, patients who underwent at least one of the following treatments were assigned to the severe group: continuous renal replacement therapy, high-flow oxygen absorption, noninvasive and invasive mechanical ventilation, or extracorporeal membrane oxygenation. The remaining patients were assigned to the non-severe group. Demographic information, initial symptoms, and first visit examination results were collected from the electronic medical records and compared between the groups. Multivariate logistic regression analysis was performed to determine the predictors of severe COVID-19. A receiver operating characteristic curve was used to identify a threshold for each predictor. Altogether,104 patients were enrolled in our study with 30 and 74 patients in the severe and non-severe groups, respectively. Multivariate logistic analysis indicated that patients aged ≥63 years (odds ratio = 41.0; 95% CI: 2.8, 592.4), with an absolute lymphocyte value of ≤1.02×10(9)/L (odds ratio = 6.1; 95% CI = 1.5, 25.2) and a C-reactive protein level of ≥65.08mg/L (odds ratio = 8.9; 95% CI = 1.0, 74.2) were at a higher risk of severe illness. Thus, our results could be helpful in the early detection of patients at risk for severe illness, enabling the implementation of effective interventions and likely lowering the morbidity of COVID-19 patients. Public Library of Science 2020-06-26 /pmc/articles/PMC7319317/ /pubmed/32589691 http://dx.doi.org/10.1371/journal.pone.0235459 Text en © 2020 Liu et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Liu, Xinkui
Yue, Xinpei
Liu, Furong
Wei, Le
Chu, Yuntian
Bao, Honghong
Dong, Yichao
Cheng, Wenjie
Yang, Linpeng
Analysis of clinical features and early warning signs in patients with severe COVID-19: A retrospective cohort study
title Analysis of clinical features and early warning signs in patients with severe COVID-19: A retrospective cohort study
title_full Analysis of clinical features and early warning signs in patients with severe COVID-19: A retrospective cohort study
title_fullStr Analysis of clinical features and early warning signs in patients with severe COVID-19: A retrospective cohort study
title_full_unstemmed Analysis of clinical features and early warning signs in patients with severe COVID-19: A retrospective cohort study
title_short Analysis of clinical features and early warning signs in patients with severe COVID-19: A retrospective cohort study
title_sort analysis of clinical features and early warning signs in patients with severe covid-19: a retrospective cohort study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7319317/
https://www.ncbi.nlm.nih.gov/pubmed/32589691
http://dx.doi.org/10.1371/journal.pone.0235459
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