[Correlation between clinical classification of COVID-19 and imaging characteristics of MSCT volume scanning of the lungs].


Journal

Nan fang yi ke da xue xue bao = Journal of Southern Medical University
ISSN: 1673-4254
Titre abrégé: Nan Fang Yi Ke Da Xue Xue Bao
Pays: China
ID NLM: 101266132

Informations de publication

Date de publication:
30 Mar 2020
Historique:
entrez: 8 5 2020
pubmed: 8 5 2020
medline: 12 5 2020
Statut: ppublish

Résumé

To investigate the correlation between the clinical classification of coronavirus disease 2019 (COVID-19) and the imaging characteristics of multislice spiral computed tomography (MSCT) volume scanning of the lungs. The clinical data and thoracic MSCT volume scanning data were analyzed retrospectively for 102 patients with COVID-19 diagnosed and hospitalized in the First Affiliated Hospital of Bengbu Medical College between January 18 and February 26, 2020. According to the Fifth Edition of the Diagnosis and Treatment Guidelines by the National Health Commission, the patients were divided into common type, severe type and critical type. The imaging characteristics including the lung sides of the lesions, lung segment involved, lesion distribution, and lesion number and density were compared among the patients with different clinical types of COVID-19. Seventy-seven of the patients had common type, 18 had severe type and 7 had critical type of COVID-19. The main clinical manifestations included fever, cough and fatigue. Severe and critical types were more frequently seen in elderly patients, who were more prone to show such symptoms as asthenia, breathing difficulty and dyspnea. Two patients presented with no obvious abnormality in the first CT examinations; in the remaining 100 patients, 89.0% had bilateral lung lesions, 16.0% had diffuse lesions, involving a mean of 6.56±4.22 lung segments. Compared with the patients with the common type, the severe and critical patients had a significantly greater number of lung segments involved ( MSCT volume scanning not only allows early diagnosis of COVID-19 but also provides evidence for evaluating the severity of COVID-19 to assist in the clinical treatment of the patients.

Identifiants

pubmed: 32376573
doi: 10.12122/j.issn.1673-4254.2020.03.04
pmc: PMC7167319
doi:

Types de publication

Journal Article

Langues

chi

Sous-ensembles de citation

IM

Pagination

321-326

Références

Lancet. 2020 Feb 15;395(10223):497-506
pubmed: 31986264
Sci China Life Sci. 2020 Mar;63(3):457-460
pubmed: 32009228

Auteurs

Fei Guo (F)

Department of Radiology, First Affiliated Hospital, Bengbu Medical College, Bengbu 230004, China.
Department of Medical Imaging Diagnosis, School of Medical Imaging, Bengbu Medical College, Bengbu 230004, China.

Lin Zhu (L)

Department of Radiology, First Affiliated Hospital, Bengbu Medical College, Bengbu 230004, China.

Hong Xu (H)

Department of Medical Imaging Diagnosis, School of Medical Imaging, Bengbu Medical College, Bengbu 230004, China.

Lei Qin (L)

Department of Radiology, First Affiliated Hospital, Bengbu Medical College, Bengbu 230004, China.
Department of Medical Imaging Diagnosis, School of Medical Imaging, Bengbu Medical College, Bengbu 230004, China.

Xiaohan Liang (X)

Department of Radiology, First Affiliated Hospital, Bengbu Medical College, Bengbu 230004, China.
Department of Medical Imaging Diagnosis, School of Medical Imaging, Bengbu Medical College, Bengbu 230004, China.

Xuefei Deng (X)

School of Basic Medical Sciences, Anhui Medical University, Hefei 230032, China.

Articles similaires

[Redispensing of expensive oral anticancer medicines: a practical application].

Lisanne N van Merendonk, Kübra Akgöl, Bastiaan Nuijen
1.00
Humans Antineoplastic Agents Administration, Oral Drug Costs Counterfeit Drugs

Smoking Cessation and Incident Cardiovascular Disease.

Jun Hwan Cho, Seung Yong Shin, Hoseob Kim et al.
1.00
Humans Male Smoking Cessation Cardiovascular Diseases Female
Humans United States Aged Cross-Sectional Studies Medicare Part C
1.00
Humans Yoga Low Back Pain Female Male

Classifications MeSH