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Development and Validation of a Predictive Model for Severe COVID-19: A Case-Control Study in China

Background: Predicting the risk of progression to severe coronavirus disease 2019 (COVID-19) could facilitate personalized diagnosis and treatment options, thus optimizing the use of medical resources. Methods: In this prospective study, 206 patients with COVID-19 were enrolled from regional medical...

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Autores principales: Meng, Zirui, Wang, Minjin, Zhao, Zhenzhen, Zhou, Yongzhao, Wu, Ying, Guo, Shuo, Li, Mengjiao, Zhou, Yanbing, Yang, Shuyu, Li, Weimin, Ying, Binwu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8185163/
https://www.ncbi.nlm.nih.gov/pubmed/34113636
http://dx.doi.org/10.3389/fmed.2021.663145
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author Meng, Zirui
Wang, Minjin
Zhao, Zhenzhen
Zhou, Yongzhao
Wu, Ying
Guo, Shuo
Li, Mengjiao
Zhou, Yanbing
Yang, Shuyu
Li, Weimin
Ying, Binwu
author_facet Meng, Zirui
Wang, Minjin
Zhao, Zhenzhen
Zhou, Yongzhao
Wu, Ying
Guo, Shuo
Li, Mengjiao
Zhou, Yanbing
Yang, Shuyu
Li, Weimin
Ying, Binwu
author_sort Meng, Zirui
collection PubMed
description Background: Predicting the risk of progression to severe coronavirus disease 2019 (COVID-19) could facilitate personalized diagnosis and treatment options, thus optimizing the use of medical resources. Methods: In this prospective study, 206 patients with COVID-19 were enrolled from regional medical institutions between December 20, 2019, and April 10, 2020. We collated a range of data to derive and validate a predictive model for COVID-19 progression, including demographics, clinical characteristics, laboratory findings, and cytokine levels. Variation analysis, along with the least absolute shrinkage and selection operator (LASSO) and Boruta algorithms, was used for modeling. The performance of the derived models was evaluated by specificity, sensitivity, area under the receiver operating characteristic (ROC) curve (AUC), Akaike information criterion (AIC), calibration plots, decision curve analysis (DCA), and Hosmer–Lemeshow test. Results: We used the LASSO algorithm and logistic regression to develop a model that can accurately predict the risk of progression to severe COVID-19. The model incorporated alanine aminotransferase (ALT), interleukin (IL)-6, expectoration, fatigue, lymphocyte ratio (LYMR), aspartate transaminase (AST), and creatinine (CREA). The model yielded a satisfactory predictive performance with an AUC of 0.9104 and 0.8792 in the derivation and validation cohorts, respectively. The final model was then used to create a nomogram that was packaged into an open-source and predictive calculator for clinical use. The model is freely available online at https://severeconid-19predction.shinyapps.io/SHINY/. Conclusion: In this study, we developed an open-source and free predictive calculator for COVID-19 progression based on ALT, IL-6, expectoration, fatigue, LYMR, AST, and CREA. The validated model can effectively predict progression to severe COVID-19, thus providing an efficient option for early and personalized management and the allocation of appropriate medical resources.
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spelling pubmed-81851632021-06-09 Development and Validation of a Predictive Model for Severe COVID-19: A Case-Control Study in China Meng, Zirui Wang, Minjin Zhao, Zhenzhen Zhou, Yongzhao Wu, Ying Guo, Shuo Li, Mengjiao Zhou, Yanbing Yang, Shuyu Li, Weimin Ying, Binwu Front Med (Lausanne) Medicine Background: Predicting the risk of progression to severe coronavirus disease 2019 (COVID-19) could facilitate personalized diagnosis and treatment options, thus optimizing the use of medical resources. Methods: In this prospective study, 206 patients with COVID-19 were enrolled from regional medical institutions between December 20, 2019, and April 10, 2020. We collated a range of data to derive and validate a predictive model for COVID-19 progression, including demographics, clinical characteristics, laboratory findings, and cytokine levels. Variation analysis, along with the least absolute shrinkage and selection operator (LASSO) and Boruta algorithms, was used for modeling. The performance of the derived models was evaluated by specificity, sensitivity, area under the receiver operating characteristic (ROC) curve (AUC), Akaike information criterion (AIC), calibration plots, decision curve analysis (DCA), and Hosmer–Lemeshow test. Results: We used the LASSO algorithm and logistic regression to develop a model that can accurately predict the risk of progression to severe COVID-19. The model incorporated alanine aminotransferase (ALT), interleukin (IL)-6, expectoration, fatigue, lymphocyte ratio (LYMR), aspartate transaminase (AST), and creatinine (CREA). The model yielded a satisfactory predictive performance with an AUC of 0.9104 and 0.8792 in the derivation and validation cohorts, respectively. The final model was then used to create a nomogram that was packaged into an open-source and predictive calculator for clinical use. The model is freely available online at https://severeconid-19predction.shinyapps.io/SHINY/. Conclusion: In this study, we developed an open-source and free predictive calculator for COVID-19 progression based on ALT, IL-6, expectoration, fatigue, LYMR, AST, and CREA. The validated model can effectively predict progression to severe COVID-19, thus providing an efficient option for early and personalized management and the allocation of appropriate medical resources. Frontiers Media S.A. 2021-05-25 /pmc/articles/PMC8185163/ /pubmed/34113636 http://dx.doi.org/10.3389/fmed.2021.663145 Text en Copyright © 2021 Meng, Wang, Zhao, Zhou, Wu, Guo, Li, Zhou, Yang, Li and Ying. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Medicine
Meng, Zirui
Wang, Minjin
Zhao, Zhenzhen
Zhou, Yongzhao
Wu, Ying
Guo, Shuo
Li, Mengjiao
Zhou, Yanbing
Yang, Shuyu
Li, Weimin
Ying, Binwu
Development and Validation of a Predictive Model for Severe COVID-19: A Case-Control Study in China
title Development and Validation of a Predictive Model for Severe COVID-19: A Case-Control Study in China
title_full Development and Validation of a Predictive Model for Severe COVID-19: A Case-Control Study in China
title_fullStr Development and Validation of a Predictive Model for Severe COVID-19: A Case-Control Study in China
title_full_unstemmed Development and Validation of a Predictive Model for Severe COVID-19: A Case-Control Study in China
title_short Development and Validation of a Predictive Model for Severe COVID-19: A Case-Control Study in China
title_sort development and validation of a predictive model for severe covid-19: a case-control study in china
topic Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8185163/
https://www.ncbi.nlm.nih.gov/pubmed/34113636
http://dx.doi.org/10.3389/fmed.2021.663145
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