Details

Autor(en) / Beteiligte

• Ismail, Ahmed
• Doghish, Ahmed S
• Elkhatib, Walid F
• Magdy, Ahmed M
• Mahmoud, Eman E
• Ahmed, Mona I
• Khalil, Mahmoud A. F
• Tripathi, Vijay

Titel

Clinical and chest computed tomography features of patients suffering from mild and severe COVID-19 at Fayoum University Hospital in Egypt

Ist Teil von

• PloS one, 2022-07, Vol.17 (7), p.e0271271-e0271271

Ort / Verlag

San Francisco: Public Library of Science

Erscheinungsjahr

2022

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• Background In pandemic COVID-19 (coronavirus disease 2019), the prognosis of patients has been determined using clinical data and CT (computed tomography) scans, but it is still unclear whether chest CT characteristics are correlated to COVID-19 severity. Aim To explore the potential association between clinical data and 25-point CT score and investigate their predictive significance in COVID-19-positive patients at Fayoum University Hospital in Egypt. Methods This study was conducted on 252 Egyptian COVID-19 patients at Fayoum University Hospital in Egypt. The patients were classified into two groups: a mild group (174 patients) and a severe group (78 patients). The results of clinical laboratory data, and CT scans of severe and mild patients, were collected, analyzed, and compared. Results The severe group show high significance levels of CRP, alanine aminotransferase (ALT), aspartate aminotransferase (AST), creatinine, urea, ferritin, lactate dehydrogenase (LDH), neutrophil percent, and heart rate (HR) than the mild group. Lymphopenia, hypoalbuminemia, hypocalcemia, and decreased oxygen saturation (SpO2) were the most observed abnormalities in severe COVID-19 patients. Lymphopenia, low SpO2 and albumin levels, elevated serum LDH, ferritin, urea, and CRP levels were found to be significantly correlated with severity CT score (P<0.0001). Conclusion The clinical severity of COVID-19 and the CT score are highly correlated. Our findings indicate that the CT scoring system can help to predict COVID-19 disease outcomes and has a strong correlation with clinical laboratory testing.