To investigate the computed tomography (CT) characteristics and differential diagnosis of high altitude pulmonary edema (HAPE) and COVID-19, CT findings of 52 cases of HAPE confirmed in Medical Station of Sanshili Barracks, PLA 950 Hospital from May 1, 2020 to May 30, 2020 were collected retrospectively. The size, number, location, distribution, density and morphology of the pulmonary lesions of these CT data were analyzed and compared with some already existed COVID-19 CT images which come from two files, “Radiological diagnosis of COVID-19: expert recommendation from the Chinese Society of Radiology (First edition)” and “A rapid advice guideline for the diagnosis and treatment of 2019 novel corona-virus (2019-nCoV) infected pneumonia (standard version)”. The simple or multiple ground-glass opacity (GGO) lesions are located both in the HAPE and COVID-19 at the early stage, but only the thickening of interlobular septa, called “crazy paving pattern” belongs to COVID-19. At the next period, some increased cloudy shadows are located in HAPE, while lesions of COVID-19 are more likely to develop parallel to the direction of the pleura, and some of the lesions show the bronchial inflation. At the most serious stage, both the shadows in HAPE and COVID-19 become white, but the lesions of HAPE in the right lung are more serious than that of left lung. In summary, some cloudy shadows are the feature of HAPE CT image, and “crazy paving pattern” and “pleural parallel sign” belong to the COVID-19 CT, which can be used for differential diagnosis.
Citation: LI Wenzhe, LI Kai, ZHANG Nan, CHEN Gaofeng, LI Wenjun, TANG Jun, YUAN Fang. Differential diagnosis of high altitude pulmonary edema and COVID-19 with computed tomography feature. Journal of Biomedical Engineering, 2020, 37(6): 1031-1036. doi: 10.7507/1001-5515.202007043 Copy
-
Previous Article
Effects of wearing a mask on oxygenation of subjects with spontaneous breathing during supplementary oxygen through facemask -
Next Article
Study on classification and identification of depressed patients and healthy people among adolescents based on optimization of brain characteristics of network