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Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Int+J+Infect+Dis 2020 ; 94 (ä): 81-87 Nephropedia Template TP
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Epidemiological, clinical characteristics of cases of SARS-CoV-2 infection with abnormal imaging findings #MMPMID32205284
Zhang X; Cai H; Hu J; Lian J; Gu J; Zhang S; Ye C; Lu Y; Jin C; Yu G; Jia H; Zhang Y; Sheng J; Li L; Yang Y
Int J Infect Dis 2020[May]; 94 (ä): 81-87 PMID32205284show ga
PURPOSE: To investigate the epidemiological and clinical characteristics of COVID-19 patients with abnormal imaging findings. METHODS: Patients confirmed with SARS-CoV-2 infection in Zhejiang province from January 17 to February 8 who had undergone CT or X-ray were enrolled. Epidemiological and clinical data were analyzed among those with abnormal or normal imaging findings. RESULTS: Excluding 72 patients with normal images, 230 of 573 patients showed abnormalities affecting more than two lung lobes. The median radiographic score was 2.0, and there was a negative correlation between that score and the oxygenation index (rho = -0.657, P < 0.001). Patients with abnormal images were older (46.65 +/- 13.82), with a higher rate of coexisting condition (28.8%), a lower rate of exposure history, and longer time between onset and confirmation (5 days) than non-pneumonia patients (all P < 0.05). A higher rate of fever, cough, expectoration and headache, a lower level of lymphocytes, albumin, and serum sodium levels and a higher total bilirubin, creatine kinase, lactate dehydrogenase, and C-reactive protein levels and a lower oxygenation index were observed in pneumonia patients (all P < 0.05). Muscle ache, shortness of breath, nausea and vomiting, lower lymphocytes levels, and higher serum creatinine and radiographic score at admission were predictive factors for the severe/critical subtype. CONCLUSION: Patients with abnormal images have more obvious clinical manifestations and laboratory changes. Combing clinical features and radiographic scores can effectively predict severe/critical types.