2University of Kara, Faculty of Health Sciences, Anesthesiology and Intensive Care, Kara, Togo
3University of Lomé, Faculty of Health Sciences, Lomé, Togo
4University of Abomey-Calavi, Faculty of Health Sciences, Cotonou, Benin
5University of Ottawa, Faculty of Medicine, Ottawa, Canada DOI : 10.37678/dcybd.2024.3477 Background: Lung injuries in COVID-19 are often associated with severity scores. This study aimed to describe the relationship between the clinical categorization of patients used in a low-resource setting and the severity of chest Computed Tomography (CT) scan features.
Methods: A retrospective, descriptive, and analytical study was conducted in the intensive care unit (ICU) at the National COVID-19 Reference Hospital. Patients were classified into moderate and severe clinical forms, based on the World Health Organization (WHO) definition of clinical syndromes associated with COVID-19. CT scans were categorized into moderate (≤ 50%) or severe (> 50%) grades, depending on the extent of lung injuries. The chi-square test or Fisher's exact test and logistic regression were analyses performed using R software.
Results: One hundred and thirty-three patients, with a mean age of 57.9 ± 15.6 years and a sex ratio of 1.2, were included in the study. They had comorbidities in 84.2% and presented with a moderate (41.3%) and severe clinical form (58.7%). Lung lesions were of moderate (45.1%) and severe (54.9%) grade. Clinical severity was associated with the extent of lung lesions on CT scans (p<0.001). Diabetes (p=0.01), low blood pressure (p=0.04), SpO2 <85% (p=0.04), and respiratory distress (p=0.02) were associated with severe clinical forms. Obesity (p=0.01), SpO2 <85% (p=0.04), and respiratory distress (p=0.02) were associated with severe CT scan.
Conclusions: The clinical severity of COVID-19 patients was associated with pulmonary CT scan severity. This clinical categorization could be useful in low-resource settings to guide the management of patients with COVID-19.
Keywords : COVID-19, chest Computed Tomography scan, clinical categorization, respiratory distress, intensive care