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Semi-quantitative analysis for the dynamic chest CT imaging features from onset to recovery in severe and critical COVID-19

OBJECTIVE: To investigate in the CT manifestations of severe and critical coronavirus disease 2019 (COVID-19) patients. METHODS: Medical data was collected for 2 severe patients and 4 critical COVID-19 patients from onset to their recovery. Three or four CT scans for each patient were taken. The sem...

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Autores principales: Liu, Ruxiu, Lei, Chaoqi, Yao, Shunyu, Shi, Shan, Li, Jun, Hu, Dongpeng, Liao, Xiang, Wang, Zhi, Fang, Jiliang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Beijing You'an Hospital affiliated to Capital Medical University. Production and hosting by Elsevier B.V. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7392192/
https://www.ncbi.nlm.nih.gov/pubmed/32838008
http://dx.doi.org/10.1016/j.jrid.2020.07.003
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author Liu, Ruxiu
Lei, Chaoqi
Yao, Shunyu
Shi, Shan
Li, Jun
Hu, Dongpeng
Liao, Xiang
Wang, Zhi
Fang, Jiliang
author_facet Liu, Ruxiu
Lei, Chaoqi
Yao, Shunyu
Shi, Shan
Li, Jun
Hu, Dongpeng
Liao, Xiang
Wang, Zhi
Fang, Jiliang
author_sort Liu, Ruxiu
collection PubMed
description OBJECTIVE: To investigate in the CT manifestations of severe and critical coronavirus disease 2019 (COVID-19) patients. METHODS: Medical data was collected for 2 severe patients and 4 critical COVID-19 patients from onset to their recovery. Three or four CT scans for each patient were taken. The semi-quantitative analysis method was introduced for lesion and its distribution area. RESULTS: The ground-glass opacities (GGO) and mixed GGO with consolidation were found as the most frequent features. Consolidation followed, and the appearance of stripes which showed an increasing trend before the patient was discharged. Consolidation was associated with clinical severity and disease progression, and the rapid change of the lesion in a short period of time was also a notable feature within 2–3 weeks. After being discharged, the efficacy of treatment could be demonstrated by a follow up CT scan. The distribution of lesion also showed dynamic progress in the follow up CT scan. CONCLUSION: CT scans in the whole course provided the entire inflammation information to assess clinical severity, disease progression and the treatment efficacy for COVID-19.
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spelling pubmed-73921922020-07-31 Semi-quantitative analysis for the dynamic chest CT imaging features from onset to recovery in severe and critical COVID-19 Liu, Ruxiu Lei, Chaoqi Yao, Shunyu Shi, Shan Li, Jun Hu, Dongpeng Liao, Xiang Wang, Zhi Fang, Jiliang Radiol Infect Dis Research Article OBJECTIVE: To investigate in the CT manifestations of severe and critical coronavirus disease 2019 (COVID-19) patients. METHODS: Medical data was collected for 2 severe patients and 4 critical COVID-19 patients from onset to their recovery. Three or four CT scans for each patient were taken. The semi-quantitative analysis method was introduced for lesion and its distribution area. RESULTS: The ground-glass opacities (GGO) and mixed GGO with consolidation were found as the most frequent features. Consolidation followed, and the appearance of stripes which showed an increasing trend before the patient was discharged. Consolidation was associated with clinical severity and disease progression, and the rapid change of the lesion in a short period of time was also a notable feature within 2–3 weeks. After being discharged, the efficacy of treatment could be demonstrated by a follow up CT scan. The distribution of lesion also showed dynamic progress in the follow up CT scan. CONCLUSION: CT scans in the whole course provided the entire inflammation information to assess clinical severity, disease progression and the treatment efficacy for COVID-19. Beijing You'an Hospital affiliated to Capital Medical University. Production and hosting by Elsevier B.V. 2020-09 2020-07-30 /pmc/articles/PMC7392192/ /pubmed/32838008 http://dx.doi.org/10.1016/j.jrid.2020.07.003 Text en © 2021 Beijing You'an Hospital affiliated to Capital Medical University. Production and hosting by Elsevier B.V. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Research Article
Liu, Ruxiu
Lei, Chaoqi
Yao, Shunyu
Shi, Shan
Li, Jun
Hu, Dongpeng
Liao, Xiang
Wang, Zhi
Fang, Jiliang
Semi-quantitative analysis for the dynamic chest CT imaging features from onset to recovery in severe and critical COVID-19
title Semi-quantitative analysis for the dynamic chest CT imaging features from onset to recovery in severe and critical COVID-19
title_full Semi-quantitative analysis for the dynamic chest CT imaging features from onset to recovery in severe and critical COVID-19
title_fullStr Semi-quantitative analysis for the dynamic chest CT imaging features from onset to recovery in severe and critical COVID-19
title_full_unstemmed Semi-quantitative analysis for the dynamic chest CT imaging features from onset to recovery in severe and critical COVID-19
title_short Semi-quantitative analysis for the dynamic chest CT imaging features from onset to recovery in severe and critical COVID-19
title_sort semi-quantitative analysis for the dynamic chest ct imaging features from onset to recovery in severe and critical covid-19
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7392192/
https://www.ncbi.nlm.nih.gov/pubmed/32838008
http://dx.doi.org/10.1016/j.jrid.2020.07.003
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