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Clinical, laboratory and imaging predictors for critical illness and mortality in patients with COVID-19: protocol for a systematic review and meta-analysis
INTRODUCTION: With the threat of a worldwide pandemic of COVID-19, it is important to identify the prognostic factors for critical conditions among patients with non-critical COVID-19. Prognostic factors and models may assist front-line clinicians in rapid identification of high-risk patients, early...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7768616/ https://www.ncbi.nlm.nih.gov/pubmed/33361074 http://dx.doi.org/10.1136/bmjopen-2020-039813 |
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author | Lai, Xinxing Liu, Jian Zhang, Tianyi Feng, Luda Jiang, Ping Kang, Ligaoge Liu, Qiang Gao, Ying |
author_facet | Lai, Xinxing Liu, Jian Zhang, Tianyi Feng, Luda Jiang, Ping Kang, Ligaoge Liu, Qiang Gao, Ying |
author_sort | Lai, Xinxing |
collection | PubMed |
description | INTRODUCTION: With the threat of a worldwide pandemic of COVID-19, it is important to identify the prognostic factors for critical conditions among patients with non-critical COVID-19. Prognostic factors and models may assist front-line clinicians in rapid identification of high-risk patients, early management of modifiable factors, appropriate triaging and optimising the use of limited healthcare resources. We aim to systematically assess the clinical, laboratory and imaging predictors as well as prediction models for severe or critical illness and mortality in patients with COVID-19. METHODS AND ANALYSIS: All peer-reviewed and preprint primary articles with a longitudinal design that focused on prognostic factors or models for critical illness and mortality related to COVID-19 will be eligible for inclusion. A systematic search of 11 databases including PubMed, EMBASE, Web of Science, Cochrane Library, CNKI, VIP, Wanfang Data, SinoMed, bioRxiv, Arxiv and MedRxiv will be conducted. Study selection will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Data extraction will be performed using the modified version of the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist and quality will be evaluated using the Newcastle-Ottawa Scale and the Quality In Prognosis Studies tool. The association between prognostic factors and outcomes of interest will be synthesised and a meta-analysis will be conducted with three or more studies reporting a particular factor in a consistent manner. ETHICS AND DISSEMINATION: Ethical approval was not required for this systematic review. We will disseminate our findings through publication in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD 42020178798. |
format | Online Article Text |
id | pubmed-7768616 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-77686162020-12-28 Clinical, laboratory and imaging predictors for critical illness and mortality in patients with COVID-19: protocol for a systematic review and meta-analysis Lai, Xinxing Liu, Jian Zhang, Tianyi Feng, Luda Jiang, Ping Kang, Ligaoge Liu, Qiang Gao, Ying BMJ Open Infectious Diseases INTRODUCTION: With the threat of a worldwide pandemic of COVID-19, it is important to identify the prognostic factors for critical conditions among patients with non-critical COVID-19. Prognostic factors and models may assist front-line clinicians in rapid identification of high-risk patients, early management of modifiable factors, appropriate triaging and optimising the use of limited healthcare resources. We aim to systematically assess the clinical, laboratory and imaging predictors as well as prediction models for severe or critical illness and mortality in patients with COVID-19. METHODS AND ANALYSIS: All peer-reviewed and preprint primary articles with a longitudinal design that focused on prognostic factors or models for critical illness and mortality related to COVID-19 will be eligible for inclusion. A systematic search of 11 databases including PubMed, EMBASE, Web of Science, Cochrane Library, CNKI, VIP, Wanfang Data, SinoMed, bioRxiv, Arxiv and MedRxiv will be conducted. Study selection will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Data extraction will be performed using the modified version of the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist and quality will be evaluated using the Newcastle-Ottawa Scale and the Quality In Prognosis Studies tool. The association between prognostic factors and outcomes of interest will be synthesised and a meta-analysis will be conducted with three or more studies reporting a particular factor in a consistent manner. ETHICS AND DISSEMINATION: Ethical approval was not required for this systematic review. We will disseminate our findings through publication in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD 42020178798. BMJ Publishing Group 2020-12-24 /pmc/articles/PMC7768616/ /pubmed/33361074 http://dx.doi.org/10.1136/bmjopen-2020-039813 Text en © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/ http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. |
spellingShingle | Infectious Diseases Lai, Xinxing Liu, Jian Zhang, Tianyi Feng, Luda Jiang, Ping Kang, Ligaoge Liu, Qiang Gao, Ying Clinical, laboratory and imaging predictors for critical illness and mortality in patients with COVID-19: protocol for a systematic review and meta-analysis |
title | Clinical, laboratory and imaging predictors for critical illness and mortality in patients with COVID-19: protocol for a systematic review and meta-analysis |
title_full | Clinical, laboratory and imaging predictors for critical illness and mortality in patients with COVID-19: protocol for a systematic review and meta-analysis |
title_fullStr | Clinical, laboratory and imaging predictors for critical illness and mortality in patients with COVID-19: protocol for a systematic review and meta-analysis |
title_full_unstemmed | Clinical, laboratory and imaging predictors for critical illness and mortality in patients with COVID-19: protocol for a systematic review and meta-analysis |
title_short | Clinical, laboratory and imaging predictors for critical illness and mortality in patients with COVID-19: protocol for a systematic review and meta-analysis |
title_sort | clinical, laboratory and imaging predictors for critical illness and mortality in patients with covid-19: protocol for a systematic review and meta-analysis |
topic | Infectious Diseases |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7768616/ https://www.ncbi.nlm.nih.gov/pubmed/33361074 http://dx.doi.org/10.1136/bmjopen-2020-039813 |
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