Article ; Online: Lung abnormalities on computed tomography of Vietnamese patients with COVID-19 and the association with medical variables.
IJID regions
2024 Volume 10, Page(s) 183–190
Abstract: Objectives: Patients with COVID-19 may experience a lung injury without presenting clinical symptoms. Early detection of lung injury in patients with COVID-19 is required to enhance prediction and prevent severe progression.: Methods: Lung lesions in ...
Abstract | Objectives: Patients with COVID-19 may experience a lung injury without presenting clinical symptoms. Early detection of lung injury in patients with COVID-19 is required to enhance prediction and prevent severe progression. Methods: Lung lesions in patients with COVID-19 were defined using the Fleischner Society terminology. Chest computed tomography lesions and their correlation with demographic characteristics and medical variables were identified. Results: Patients with mild and moderate COVID-19 had up to 45% lung injuries, whereas critical patients had 55%. However, patients with mild and moderate COVID-19 typically had low-level lung injuries. Ground-glass (68.1%), consolidation (48.8%), opacity (36.3%), and nodular (6.9%) lung lesions were the most prevalent in patients with COVID-19. Patients with COVID-19 infected with the Delta variant had worse lung injury than those infected with the Alpha and Omicron. People vaccinated with ≥2 doses showed a lower risk of lung injury than those vaccinated with <1 dose. Patients <18 years old were less likely to have a lung injury than patients >18 years old. The treatment outcomes were unaffected by the severity of the lung injury. Conclusion: Patients with mild COVID-19 had a similar risk of lung injury as patients with severe COVID-19. Thus, using chest computed tomography to detect lung injury can enhance the treatment outcomes and reduce the patient's risk of pulmonary complications. |
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Language | English |
Publishing date | 2024-01-19 |
Publishing country | England |
Document type | Journal Article |
ISSN | 2772-7076 |
ISSN (online) | 2772-7076 |
DOI | 10.1016/j.ijregi.2024.01.006 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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