Clinical Profile and CT-Chest Patterns of 56 Patients with Covid-19 Pneumonia

Olla A M Ibrahim, Enas M Alhaen, Walid A S Mohmmed, Wadyan M A Salh
2020 AL-MUKHTAR JOURNAL OF SCIENCES  
Since the COVID-19 pandemic was announced, the concern of radiologists and physicians regarding its diagnosis has been profoundly raised. The primary reference for confirming COVID-19 pneumonia relies on reverse transcriptase-polymerase chain reaction (RT-PCR) testing, where the subject of availability, false negative-rates, practice dependency, and time-consumption, made computed tomography (CT) more superior in Covid-19 pneumonia diagnosis, which was the situation in Al-Baida-city\Libya. The
more » ... irst local case in Al-Baida-city was reported on 30 July 2020, followed by a dramatic surge in the number of cases, which necessitated the recognition of main clinical features and CT-patterns of COVID-19 to facilitate rapid diagnosis. The aim of study: Describe the clinical features and the CT-chest patterns of COVID-19 pneumonia among the studied population. A descriptive case series study was conducted in the central hospital of Al-Baida city /Libya from 11 August to 21 September 2020, which involved 56 patients (31 females and 25 males). Patients' ages ranged from 28-88 years (62.79± 11.3). Non-contrast CT-chest was performed on all patients. The main patients' complaints were fever 94.6%, dyspnea 89.3%, and cough 85.7%. The most common CT pattern among the studied cases was ground-glass opacities found in 100% of patients, followed by vascular thickening 88%, consolidative lesion 71.4%, crazy-paving pattern 57%, vacuolar sign 57.1%, architecture distortion 40%, halo sign 34%, reverse halo sign 34.5%, and traction-bronchiectasis 16% of the studied cases. Finally, recognition of CT-chest patterns of Covid-19 pneumonia plays a significant role in early detection, and therefore isolation and management of the disease. The findings of this study can be used as a baseline for further research in the future.
doi:10.54172/mjsc.v35i4.334 fatcat:uysfku5knrcxfd5ibzsepbkzv4