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10.1007/s00330-020-06973-9

http://scihub22266oqcxt.onion/10.1007/s00330-020-06973-9
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suck abstract from ncbi


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pmid32474631      Eur+Radiol 2020 ; 30 (11): 6139-6150
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  • Relationship between clinical types and radiological subgroups defined by latent class analysis in 2019 novel coronavirus pneumonia caused by SARS-CoV-2 #MMPMID32474631
  • Fang X; Li X; Bian Y; Ji X; Lu J
  • Eur Radiol 2020[Nov]; 30 (11): 6139-6150 PMID32474631show ga
  • OBJECTIVES: To investigate whether meaningful subgroups sharing the CT features of patients with COVID-19 pneumonia could be identified using latent class analysis (LCA) and explore the relationship between the LCA-derived subgroups and clinical types. METHODS: This retrospective review included 499 patients with confirmed COVID-19 pneumonia between February 11 and March 8, 2020. Subgroups sharing the CT features were identified using LCA. Univariate and multivariate logistic regression models were utilized to analyze the association between clinical types and the LCA-derived subgroups. RESULTS: Two radiological subgroups were identified using LCA. There were 228 subjects (45.69%) in class 1 and 271 subjects (54.31%) in class 2. The CT findings of class 1 were smaller pulmonary infection volume, more peripheral distribution, more GGO, more maximum lesion range
  • |*Betacoronavirus[MESH]
  • |*Latent Class Analysis[MESH]
  • |COVID-19[MESH]
  • |Coronavirus Infections/*diagnostic imaging/*pathology/physiopathology[MESH]
  • |Cross-Sectional Studies[MESH]
  • |Female[MESH]
  • |Humans[MESH]
  • |Lung/diagnostic imaging/pathology/physiopathology[MESH]
  • |Male[MESH]
  • |Middle Aged[MESH]
  • |Pandemics[MESH]
  • |Pneumonia, Viral/*diagnostic imaging/*pathology/physiopathology[MESH]
  • |Retrospective Studies[MESH]
  • |SARS-CoV-2[MESH]
  • |Severity of Illness Index[MESH]


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