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Clinical characteristics and prognostic nomograms of 12555 non-severe COVID-19 cases with Omicron infection in Shanghai

BACKGROUND: Omicron variant of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has rapidly become a global threat to public health. Numerous asymptomatic and mild cases had been admitted in shelter hospitals to quickly win the fight against Omicron pandemic in Shanghai. However, lit...

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Autores principales: Yin, Chun, Hu, Bo, Li, Kunyan, Liu, Xian, Wang, Shuili, He, Rulin, Ding, Haibing, Jin, Mingpeng, Chen, Cheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10504722/
https://www.ncbi.nlm.nih.gov/pubmed/37716953
http://dx.doi.org/10.1186/s12879-023-08582-5
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author Yin, Chun
Hu, Bo
Li, Kunyan
Liu, Xian
Wang, Shuili
He, Rulin
Ding, Haibing
Jin, Mingpeng
Chen, Cheng
author_facet Yin, Chun
Hu, Bo
Li, Kunyan
Liu, Xian
Wang, Shuili
He, Rulin
Ding, Haibing
Jin, Mingpeng
Chen, Cheng
author_sort Yin, Chun
collection PubMed
description BACKGROUND: Omicron variant of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has rapidly become a global threat to public health. Numerous asymptomatic and mild cases had been admitted in shelter hospitals to quickly win the fight against Omicron pandemic in Shanghai. However, little is known about influencing factors for deterioration and length of stay (LOS) in hospitals among these non-severe cases. METHODS: This study included 12,555 non-severe cases with COVID-19 in largest shelter hospital of Shanghai, aiming to explore prognostic factors and build effective models for prediction of LOS. RESULTS: Data showed that 75.0% of participants were initially asymptomatic. In addition, 94.6% were discharged within 10 days, only 0.3% with deterioration in hospitals. The multivariate analysis indicated that less comorbidities (OR = 1.792, P = 0.012) and booster vaccination (OR = 0.255, P = 0.015) was associated with the decreased risk of deterioration. Moreover, age (HR = 0.991, P < 0.001), number of symptoms (HR = 0.969, P = 0.005), time from diagnosis to admission (HR = 1.013, P = 0.001) and Cycle threshold (CT) values of N gene (HR = 1.081, P < 0.001) were significant factors associated with LOS. Based on these factors, a concise nomogram model for predicting patients discharged within 3 days or more than 10 days was built in the development cohort. In validation cohort, 0.75 and 0.73 of Areas under the curve (AUC) in nomograms, similar with AUC in models of simple machine learning, showed good performance in estimating LOS. CONCLUSION: Collectively, this study not only provides important evidence to deeply understand clinical characteristics and risk factors of short-term prognosis in Shanghai Omicron outbreaks, but also offers a concise and effective nomogram model to predict LOS. Our findings will play critical roles in screening high-risk groups, providing advice on duration of quarantine and helping decision-makers with better preparation in outbreak of COVID-19. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-023-08582-5.
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spelling pubmed-105047222023-09-17 Clinical characteristics and prognostic nomograms of 12555 non-severe COVID-19 cases with Omicron infection in Shanghai Yin, Chun Hu, Bo Li, Kunyan Liu, Xian Wang, Shuili He, Rulin Ding, Haibing Jin, Mingpeng Chen, Cheng BMC Infect Dis Research BACKGROUND: Omicron variant of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has rapidly become a global threat to public health. Numerous asymptomatic and mild cases had been admitted in shelter hospitals to quickly win the fight against Omicron pandemic in Shanghai. However, little is known about influencing factors for deterioration and length of stay (LOS) in hospitals among these non-severe cases. METHODS: This study included 12,555 non-severe cases with COVID-19 in largest shelter hospital of Shanghai, aiming to explore prognostic factors and build effective models for prediction of LOS. RESULTS: Data showed that 75.0% of participants were initially asymptomatic. In addition, 94.6% were discharged within 10 days, only 0.3% with deterioration in hospitals. The multivariate analysis indicated that less comorbidities (OR = 1.792, P = 0.012) and booster vaccination (OR = 0.255, P = 0.015) was associated with the decreased risk of deterioration. Moreover, age (HR = 0.991, P < 0.001), number of symptoms (HR = 0.969, P = 0.005), time from diagnosis to admission (HR = 1.013, P = 0.001) and Cycle threshold (CT) values of N gene (HR = 1.081, P < 0.001) were significant factors associated with LOS. Based on these factors, a concise nomogram model for predicting patients discharged within 3 days or more than 10 days was built in the development cohort. In validation cohort, 0.75 and 0.73 of Areas under the curve (AUC) in nomograms, similar with AUC in models of simple machine learning, showed good performance in estimating LOS. CONCLUSION: Collectively, this study not only provides important evidence to deeply understand clinical characteristics and risk factors of short-term prognosis in Shanghai Omicron outbreaks, but also offers a concise and effective nomogram model to predict LOS. Our findings will play critical roles in screening high-risk groups, providing advice on duration of quarantine and helping decision-makers with better preparation in outbreak of COVID-19. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-023-08582-5. BioMed Central 2023-09-16 /pmc/articles/PMC10504722/ /pubmed/37716953 http://dx.doi.org/10.1186/s12879-023-08582-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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
Yin, Chun
Hu, Bo
Li, Kunyan
Liu, Xian
Wang, Shuili
He, Rulin
Ding, Haibing
Jin, Mingpeng
Chen, Cheng
Clinical characteristics and prognostic nomograms of 12555 non-severe COVID-19 cases with Omicron infection in Shanghai
title Clinical characteristics and prognostic nomograms of 12555 non-severe COVID-19 cases with Omicron infection in Shanghai
title_full Clinical characteristics and prognostic nomograms of 12555 non-severe COVID-19 cases with Omicron infection in Shanghai
title_fullStr Clinical characteristics and prognostic nomograms of 12555 non-severe COVID-19 cases with Omicron infection in Shanghai
title_full_unstemmed Clinical characteristics and prognostic nomograms of 12555 non-severe COVID-19 cases with Omicron infection in Shanghai
title_short Clinical characteristics and prognostic nomograms of 12555 non-severe COVID-19 cases with Omicron infection in Shanghai
title_sort clinical characteristics and prognostic nomograms of 12555 non-severe covid-19 cases with omicron infection in shanghai
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10504722/
https://www.ncbi.nlm.nih.gov/pubmed/37716953
http://dx.doi.org/10.1186/s12879-023-08582-5
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