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...
- Autores:
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2020
- Institución:
- Universidad de Bogotá Jorge Tadeo Lozano
- Repositorio:
- Expeditio: repositorio UTadeo
- Idioma:
- eng
- OAI Identifier:
- oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/14032
- Acceso en línea:
- https://doi.org/10.1016/j.jrid.2020.07.003
http://hdl.handle.net/20.500.12010/14032
- Palabra clave:
- COVID-19
Chest CT features
Pneumonia
Severe type
Critical type
Síndrome respiratorio agudo grave
COVID-19
SARS-CoV-2
Coronavirus
- Rights
- License
- Abierto (Texto Completo)
Summary: | 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 2e3 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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