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Early predictors and screening tool developing for severe patients with COVID-19
BACKGROUND: Coronavirus disease 2019 (COVID-19) is a declared global pandemic, causing a lot of death. How to quickly screen risk population for severe patients is essential for decreasing the mortality. Many of the predictors might not be available in all hospitals, so it is necessary to develop a...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8496134/ https://www.ncbi.nlm.nih.gov/pubmed/34620102 http://dx.doi.org/10.1186/s12879-021-06662-y |
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author | Fang, Le Xie, Huashan Liu, Lingyun Lu, Shijun Lv, Fangfang Zhou, Jiancang Xu, Yue Ge, Huiqing Yu, Min Liu, Limin |
author_facet | Fang, Le Xie, Huashan Liu, Lingyun Lu, Shijun Lv, Fangfang Zhou, Jiancang Xu, Yue Ge, Huiqing Yu, Min Liu, Limin |
author_sort | Fang, Le |
collection | PubMed |
description | BACKGROUND: Coronavirus disease 2019 (COVID-19) is a declared global pandemic, causing a lot of death. How to quickly screen risk population for severe patients is essential for decreasing the mortality. Many of the predictors might not be available in all hospitals, so it is necessary to develop a simpler screening tool with predictors which can be easily obtained for wide wise. METHODS: This retrospective study included all the 813 confirmed cases diagnosed with COVID-19 before March 2nd, 2020 in a city of Hubei Province in China. Data of the COVID-19 patients including clinical and epidemiological features were collected through Chinese Disease Control and Prevention Information System. Predictors were selected by logistic regression, and then categorized to four different level risk factors. A screening tool for severe patient with COVID-19 was developed and tested by ROC curve. RESULTS: Seven early predictors for severe patients with COVID-19 were selected, including chronic kidney disease (OR 14.7), age above 60 (OR 5.6), lymphocyte count less than < 0.8 × 10(9) per L (OR 2.5), Neutrophil to Lymphocyte Ratio larger than 4.7 (OR 2.2), high fever with temperature ≥ 38.5℃ (OR 2.2), male (OR 2.2), cardiovascular related diseases (OR 2.0). The Area Under the ROC Curve of the screening tool developed by above seven predictors was 0.798 (95% CI 0.747–0.849), and its best cut-off value is > 4.5, with sensitivity 72.0% and specificity 75.3%. CONCLUSIONS: This newly developed screening tool can be a good choice for early prediction and alert for severe case especially in the condition of overload health service. |
format | Online Article Text |
id | pubmed-8496134 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-84961342021-10-08 Early predictors and screening tool developing for severe patients with COVID-19 Fang, Le Xie, Huashan Liu, Lingyun Lu, Shijun Lv, Fangfang Zhou, Jiancang Xu, Yue Ge, Huiqing Yu, Min Liu, Limin BMC Infect Dis Research Article BACKGROUND: Coronavirus disease 2019 (COVID-19) is a declared global pandemic, causing a lot of death. How to quickly screen risk population for severe patients is essential for decreasing the mortality. Many of the predictors might not be available in all hospitals, so it is necessary to develop a simpler screening tool with predictors which can be easily obtained for wide wise. METHODS: This retrospective study included all the 813 confirmed cases diagnosed with COVID-19 before March 2nd, 2020 in a city of Hubei Province in China. Data of the COVID-19 patients including clinical and epidemiological features were collected through Chinese Disease Control and Prevention Information System. Predictors were selected by logistic regression, and then categorized to four different level risk factors. A screening tool for severe patient with COVID-19 was developed and tested by ROC curve. RESULTS: Seven early predictors for severe patients with COVID-19 were selected, including chronic kidney disease (OR 14.7), age above 60 (OR 5.6), lymphocyte count less than < 0.8 × 10(9) per L (OR 2.5), Neutrophil to Lymphocyte Ratio larger than 4.7 (OR 2.2), high fever with temperature ≥ 38.5℃ (OR 2.2), male (OR 2.2), cardiovascular related diseases (OR 2.0). The Area Under the ROC Curve of the screening tool developed by above seven predictors was 0.798 (95% CI 0.747–0.849), and its best cut-off value is > 4.5, with sensitivity 72.0% and specificity 75.3%. CONCLUSIONS: This newly developed screening tool can be a good choice for early prediction and alert for severe case especially in the condition of overload health service. BioMed Central 2021-10-07 /pmc/articles/PMC8496134/ /pubmed/34620102 http://dx.doi.org/10.1186/s12879-021-06662-y 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 Fang, Le Xie, Huashan Liu, Lingyun Lu, Shijun Lv, Fangfang Zhou, Jiancang Xu, Yue Ge, Huiqing Yu, Min Liu, Limin Early predictors and screening tool developing for severe patients with COVID-19 |
title | Early predictors and screening tool developing for severe patients with COVID-19 |
title_full | Early predictors and screening tool developing for severe patients with COVID-19 |
title_fullStr | Early predictors and screening tool developing for severe patients with COVID-19 |
title_full_unstemmed | Early predictors and screening tool developing for severe patients with COVID-19 |
title_short | Early predictors and screening tool developing for severe patients with COVID-19 |
title_sort | early predictors and screening tool developing for severe patients with covid-19 |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8496134/ https://www.ncbi.nlm.nih.gov/pubmed/34620102 http://dx.doi.org/10.1186/s12879-021-06662-y |
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