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Predictors of mortality in patients with coronavirus disease 2019: a systematic review and meta-analysis

BACKGROUND: Coronavirus disease 2019 (COVID-19) is associated with a high mortality rate, especially in patients with severe illness. We conducted a systematic review and meta-analysis to assess the potential predictors of mortality in patients with COVID-19. METHODS: PubMed, EMBASE, the Cochrane Li...

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Autores principales: Shi, Changcheng, Wang, Limin, Ye, Jian, Gu, Zhichun, Wang, Shuying, Xia, Junbo, Xie, Yaping, Li, Qingyu, Xu, Renjie, Lin, Nengming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8264491/
https://www.ncbi.nlm.nih.gov/pubmed/34238232
http://dx.doi.org/10.1186/s12879-021-06369-0
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author Shi, Changcheng
Wang, Limin
Ye, Jian
Gu, Zhichun
Wang, Shuying
Xia, Junbo
Xie, Yaping
Li, Qingyu
Xu, Renjie
Lin, Nengming
author_facet Shi, Changcheng
Wang, Limin
Ye, Jian
Gu, Zhichun
Wang, Shuying
Xia, Junbo
Xie, Yaping
Li, Qingyu
Xu, Renjie
Lin, Nengming
author_sort Shi, Changcheng
collection PubMed
description BACKGROUND: Coronavirus disease 2019 (COVID-19) is associated with a high mortality rate, especially in patients with severe illness. We conducted a systematic review and meta-analysis to assess the potential predictors of mortality in patients with COVID-19. METHODS: PubMed, EMBASE, the Cochrane Library, and three electronic Chinese databases were searched from December 1, 2019 to April 29, 2020. Eligible studies reporting potential predictors of mortality in patients with COVID-19 were identified. Unadjusted prognostic effect estimates were pooled using the random-effects model if data from at least two studies were available. Adjusted prognostic effect estimates were presented by qualitative analysis. RESULTS: Thirty-six observational studies were identified, of which 27 were included in the meta-analysis. A total of 106 potential risk factors were tested, and the following important predictors were associated with mortality: advanced age, male sex, current smoking status, preexisting comorbidities (especially chronic kidney, respiratory, and cardio-cerebrovascular diseases), symptoms of dyspnea, complications during hospitalization, corticosteroid therapy and a severe condition. Additionally, a series of abnormal laboratory biomarkers of hematologic parameters, hepatorenal function, inflammation, coagulation, and cardiovascular injury were also associated with fatal outcome. CONCLUSION: We identified predictors of mortality in patients with COVID-19. These findings could help healthcare providers take appropriate measures and improve clinical outcomes in such patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-021-06369-0.
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spelling pubmed-82644912021-07-08 Predictors of mortality in patients with coronavirus disease 2019: a systematic review and meta-analysis Shi, Changcheng Wang, Limin Ye, Jian Gu, Zhichun Wang, Shuying Xia, Junbo Xie, Yaping Li, Qingyu Xu, Renjie Lin, Nengming BMC Infect Dis Research Article BACKGROUND: Coronavirus disease 2019 (COVID-19) is associated with a high mortality rate, especially in patients with severe illness. We conducted a systematic review and meta-analysis to assess the potential predictors of mortality in patients with COVID-19. METHODS: PubMed, EMBASE, the Cochrane Library, and three electronic Chinese databases were searched from December 1, 2019 to April 29, 2020. Eligible studies reporting potential predictors of mortality in patients with COVID-19 were identified. Unadjusted prognostic effect estimates were pooled using the random-effects model if data from at least two studies were available. Adjusted prognostic effect estimates were presented by qualitative analysis. RESULTS: Thirty-six observational studies were identified, of which 27 were included in the meta-analysis. A total of 106 potential risk factors were tested, and the following important predictors were associated with mortality: advanced age, male sex, current smoking status, preexisting comorbidities (especially chronic kidney, respiratory, and cardio-cerebrovascular diseases), symptoms of dyspnea, complications during hospitalization, corticosteroid therapy and a severe condition. Additionally, a series of abnormal laboratory biomarkers of hematologic parameters, hepatorenal function, inflammation, coagulation, and cardiovascular injury were also associated with fatal outcome. CONCLUSION: We identified predictors of mortality in patients with COVID-19. These findings could help healthcare providers take appropriate measures and improve clinical outcomes in such patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-021-06369-0. BioMed Central 2021-07-08 /pmc/articles/PMC8264491/ /pubmed/34238232 http://dx.doi.org/10.1186/s12879-021-06369-0 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Shi, Changcheng
Wang, Limin
Ye, Jian
Gu, Zhichun
Wang, Shuying
Xia, Junbo
Xie, Yaping
Li, Qingyu
Xu, Renjie
Lin, Nengming
Predictors of mortality in patients with coronavirus disease 2019: a systematic review and meta-analysis
title Predictors of mortality in patients with coronavirus disease 2019: a systematic review and meta-analysis
title_full Predictors of mortality in patients with coronavirus disease 2019: a systematic review and meta-analysis
title_fullStr Predictors of mortality in patients with coronavirus disease 2019: a systematic review and meta-analysis
title_full_unstemmed Predictors of mortality in patients with coronavirus disease 2019: a systematic review and meta-analysis
title_short Predictors of mortality in patients with coronavirus disease 2019: a systematic review and meta-analysis
title_sort predictors of mortality in patients with coronavirus disease 2019: a systematic review and meta-analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8264491/
https://www.ncbi.nlm.nih.gov/pubmed/34238232
http://dx.doi.org/10.1186/s12879-021-06369-0
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