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Prediction of the Rehabilitation Duration and Risk Management for Mild-Moderate COVID-19
OBJECTIVES: More than 80% of coronavirus disease 2019 (COVID-19) cases are mild or moderate. In this study, a risk model was developed for predicting rehabilitation duration (the time from hospital admission to discharge) of the mild-moderate COVID-19 cases and was used to conduct refined risk manag...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cambridge University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7369334/ https://www.ncbi.nlm.nih.gov/pubmed/32576328 http://dx.doi.org/10.1017/dmp.2020.214 |
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author | Zheng, Qiong-Na Xu, Mei-Yan Zheng, Yong-Le Wang, Xiu-Ying Zhao, Hui |
author_facet | Zheng, Qiong-Na Xu, Mei-Yan Zheng, Yong-Le Wang, Xiu-Ying Zhao, Hui |
author_sort | Zheng, Qiong-Na |
collection | PubMed |
description | OBJECTIVES: More than 80% of coronavirus disease 2019 (COVID-19) cases are mild or moderate. In this study, a risk model was developed for predicting rehabilitation duration (the time from hospital admission to discharge) of the mild-moderate COVID-19 cases and was used to conduct refined risk management for different risk populations. METHODS: A total of 90 consecutive patients with mild-moderate COVID-19 were enrolled. Large-scale datasets were extracted from clinical practices. Through the multivariable linear regression analysis, the model was based on significant risk factors and was developed for predicting the rehabilitation duration of mild-moderate cases of COVID-19. To assess the local epidemic situation, risk management was conducted by weighing the risk of populations at different risk. RESULTS: Ten risk factors from 44 high-dimensional clinical datasets were significantly correlated to rehabilitation duration (P < 0.05). Among these factors, 5 risk predictors were incorporated into a risk model. Individual rehabilitation durations were effectively calculated. Weighing the local epidemic situation, threshold probability was classified for low risk, intermediate risk, and high risk. Using this classification, risk management was based on a treatment flowchart tailored for clinical decision-making. CONCLUSIONS: The proposed novel model is a useful tool for individualized risk management of mild-moderate COVID-19 cases, and it may readily facilitate dynamic clinical decision-making for different risk populations. |
format | Online Article Text |
id | pubmed-7369334 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Cambridge University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-73693342020-07-20 Prediction of the Rehabilitation Duration and Risk Management for Mild-Moderate COVID-19 Zheng, Qiong-Na Xu, Mei-Yan Zheng, Yong-Le Wang, Xiu-Ying Zhao, Hui Disaster Med Public Health Prep Original Research OBJECTIVES: More than 80% of coronavirus disease 2019 (COVID-19) cases are mild or moderate. In this study, a risk model was developed for predicting rehabilitation duration (the time from hospital admission to discharge) of the mild-moderate COVID-19 cases and was used to conduct refined risk management for different risk populations. METHODS: A total of 90 consecutive patients with mild-moderate COVID-19 were enrolled. Large-scale datasets were extracted from clinical practices. Through the multivariable linear regression analysis, the model was based on significant risk factors and was developed for predicting the rehabilitation duration of mild-moderate cases of COVID-19. To assess the local epidemic situation, risk management was conducted by weighing the risk of populations at different risk. RESULTS: Ten risk factors from 44 high-dimensional clinical datasets were significantly correlated to rehabilitation duration (P < 0.05). Among these factors, 5 risk predictors were incorporated into a risk model. Individual rehabilitation durations were effectively calculated. Weighing the local epidemic situation, threshold probability was classified for low risk, intermediate risk, and high risk. Using this classification, risk management was based on a treatment flowchart tailored for clinical decision-making. CONCLUSIONS: The proposed novel model is a useful tool for individualized risk management of mild-moderate COVID-19 cases, and it may readily facilitate dynamic clinical decision-making for different risk populations. Cambridge University Press 2020-06-24 /pmc/articles/PMC7369334/ /pubmed/32576328 http://dx.doi.org/10.1017/dmp.2020.214 Text en © Society for Disaster Medicine and Public Health, Inc. 2020 http://creativecommons.org/licenses/by/4.0/ This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Research Zheng, Qiong-Na Xu, Mei-Yan Zheng, Yong-Le Wang, Xiu-Ying Zhao, Hui Prediction of the Rehabilitation Duration and Risk Management for Mild-Moderate COVID-19 |
title | Prediction of the Rehabilitation Duration and Risk Management for Mild-Moderate COVID-19 |
title_full | Prediction of the Rehabilitation Duration and Risk Management for Mild-Moderate COVID-19 |
title_fullStr | Prediction of the Rehabilitation Duration and Risk Management for Mild-Moderate COVID-19 |
title_full_unstemmed | Prediction of the Rehabilitation Duration and Risk Management for Mild-Moderate COVID-19 |
title_short | Prediction of the Rehabilitation Duration and Risk Management for Mild-Moderate COVID-19 |
title_sort | prediction of the rehabilitation duration and risk management for mild-moderate covid-19 |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7369334/ https://www.ncbi.nlm.nih.gov/pubmed/32576328 http://dx.doi.org/10.1017/dmp.2020.214 |
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