Cargando…
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...
Autores principales: | , , , , , , , , |
---|---|
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 |
_version_ | 1785106788305076224 |
---|---|
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. |
format | Online Article Text |
id | pubmed-10504722 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT yinchun clinicalcharacteristicsandprognosticnomogramsof12555nonseverecovid19caseswithomicroninfectioninshanghai AT hubo clinicalcharacteristicsandprognosticnomogramsof12555nonseverecovid19caseswithomicroninfectioninshanghai AT likunyan clinicalcharacteristicsandprognosticnomogramsof12555nonseverecovid19caseswithomicroninfectioninshanghai AT liuxian clinicalcharacteristicsandprognosticnomogramsof12555nonseverecovid19caseswithomicroninfectioninshanghai AT wangshuili clinicalcharacteristicsandprognosticnomogramsof12555nonseverecovid19caseswithomicroninfectioninshanghai AT herulin clinicalcharacteristicsandprognosticnomogramsof12555nonseverecovid19caseswithomicroninfectioninshanghai AT dinghaibing clinicalcharacteristicsandprognosticnomogramsof12555nonseverecovid19caseswithomicroninfectioninshanghai AT jinmingpeng clinicalcharacteristicsandprognosticnomogramsof12555nonseverecovid19caseswithomicroninfectioninshanghai AT chencheng clinicalcharacteristicsandprognosticnomogramsof12555nonseverecovid19caseswithomicroninfectioninshanghai |