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Predicting progression to severe COVID-19 using the PAINT score

OBJECTIVES: One of the major challenges in treating patients with coronavirus disease 2019 (COVID-19) is predicting the severity of disease. We aimed to develop a new score for predicting progression from mild/moderate to severe COVID-19. METHODS: A total of 239 hospitalized patients with COVID-19 f...

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Autores principales: Wang, Ming, Wu, Dongbo, Liu, Chang-Hai, Li, Yan, Hu, Jianghong, Wang, Wei, Jiang, Wei, Zhang, Qifan, Huang, Zhixin, Bai, Lang, Tang, Hong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9134988/
https://www.ncbi.nlm.nih.gov/pubmed/35619076
http://dx.doi.org/10.1186/s12879-022-07466-4
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author Wang, Ming
Wu, Dongbo
Liu, Chang-Hai
Li, Yan
Hu, Jianghong
Wang, Wei
Jiang, Wei
Zhang, Qifan
Huang, Zhixin
Bai, Lang
Tang, Hong
author_facet Wang, Ming
Wu, Dongbo
Liu, Chang-Hai
Li, Yan
Hu, Jianghong
Wang, Wei
Jiang, Wei
Zhang, Qifan
Huang, Zhixin
Bai, Lang
Tang, Hong
author_sort Wang, Ming
collection PubMed
description OBJECTIVES: One of the major challenges in treating patients with coronavirus disease 2019 (COVID-19) is predicting the severity of disease. We aimed to develop a new score for predicting progression from mild/moderate to severe COVID-19. METHODS: A total of 239 hospitalized patients with COVID-19 from two medical centers in China between February 6 and April 6, 2020 were retrospectively included. The prognostic abilities of variables, including clinical data and laboratory findings from the electronic medical records of each hospital, were analysed using the Cox proportional hazards model and Kaplan–Meier methods. A prognostic score was developed to predict progression from mild/moderate to severe COVID-19. RESULTS: Among the 239 patients, 216 (90.38%) patients had mild/moderate disease, and 23 (9.62%) progressed to severe disease. After adjusting for multiple confounding factors, pulmonary disease, age > 75, IgM, CD16(+)/CD56(+) NK cells and aspartate aminotransferase were independent predictors of progression to severe COVID-19. Based on these five factors, a new predictive score (the ‘PAINT score’) was established and showed a high predictive value (C-index = 0.91, 0.902 ± 0.021, p < 0.001). The PAINT score was validated using a nomogram, bootstrap analysis, calibration curves, decision curves and clinical impact curves, all of which confirmed its high predictive value. CONCLUSIONS: The PAINT score for progression from mild/moderate to severe COVID-19 may be helpful in identifying patients at high risk of progression. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-022-07466-4.
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spelling pubmed-91349882022-05-26 Predicting progression to severe COVID-19 using the PAINT score Wang, Ming Wu, Dongbo Liu, Chang-Hai Li, Yan Hu, Jianghong Wang, Wei Jiang, Wei Zhang, Qifan Huang, Zhixin Bai, Lang Tang, Hong BMC Infect Dis Research OBJECTIVES: One of the major challenges in treating patients with coronavirus disease 2019 (COVID-19) is predicting the severity of disease. We aimed to develop a new score for predicting progression from mild/moderate to severe COVID-19. METHODS: A total of 239 hospitalized patients with COVID-19 from two medical centers in China between February 6 and April 6, 2020 were retrospectively included. The prognostic abilities of variables, including clinical data and laboratory findings from the electronic medical records of each hospital, were analysed using the Cox proportional hazards model and Kaplan–Meier methods. A prognostic score was developed to predict progression from mild/moderate to severe COVID-19. RESULTS: Among the 239 patients, 216 (90.38%) patients had mild/moderate disease, and 23 (9.62%) progressed to severe disease. After adjusting for multiple confounding factors, pulmonary disease, age > 75, IgM, CD16(+)/CD56(+) NK cells and aspartate aminotransferase were independent predictors of progression to severe COVID-19. Based on these five factors, a new predictive score (the ‘PAINT score’) was established and showed a high predictive value (C-index = 0.91, 0.902 ± 0.021, p < 0.001). The PAINT score was validated using a nomogram, bootstrap analysis, calibration curves, decision curves and clinical impact curves, all of which confirmed its high predictive value. CONCLUSIONS: The PAINT score for progression from mild/moderate to severe COVID-19 may be helpful in identifying patients at high risk of progression. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-022-07466-4. BioMed Central 2022-05-26 /pmc/articles/PMC9134988/ /pubmed/35619076 http://dx.doi.org/10.1186/s12879-022-07466-4 Text en © The Author(s) 2022 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
Wang, Ming
Wu, Dongbo
Liu, Chang-Hai
Li, Yan
Hu, Jianghong
Wang, Wei
Jiang, Wei
Zhang, Qifan
Huang, Zhixin
Bai, Lang
Tang, Hong
Predicting progression to severe COVID-19 using the PAINT score
title Predicting progression to severe COVID-19 using the PAINT score
title_full Predicting progression to severe COVID-19 using the PAINT score
title_fullStr Predicting progression to severe COVID-19 using the PAINT score
title_full_unstemmed Predicting progression to severe COVID-19 using the PAINT score
title_short Predicting progression to severe COVID-19 using the PAINT score
title_sort predicting progression to severe covid-19 using the paint score
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9134988/
https://www.ncbi.nlm.nih.gov/pubmed/35619076
http://dx.doi.org/10.1186/s12879-022-07466-4
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