Author: Meiler, Stefanie; Schaible, Jan; Poschenrieder, Florian; Scharf, Gregor; Zeman, Florian; Rennert, Janine; Pregler, Benedikt; Kleine, Henning; Stroszczynski, Christian; Zorger, Niels; Hamer, Okka W.
Title: Can CT performed in the early disease phase predict outcome of patients with COVID 19 pneumonia? Analysis of a cohort of 64 patients from Germany Cord-id: eofo5fw7 Document date: 2020_8_28
ID: eofo5fw7
Snippet: PURPOSE: The aim of this study was to investigate if CT performed in the early disease phase can predict the course of COVID-19 pneumonia in a German cohort. METHOD: All patients with RT-PCR proven COVID-19 pneumonia and chest CT performed within 10 days of symptom onset between March 1 st and April 15th 2020 were retrospectively identified from two tertiary care hospitals. 12 CT features, their distribution in the lung and the global extent of opacifications were evaluated. For analysis of prog
Document: PURPOSE: The aim of this study was to investigate if CT performed in the early disease phase can predict the course of COVID-19 pneumonia in a German cohort. METHOD: All patients with RT-PCR proven COVID-19 pneumonia and chest CT performed within 10 days of symptom onset between March 1 st and April 15th 2020 were retrospectively identified from two tertiary care hospitals. 12 CT features, their distribution in the lung and the global extent of opacifications were evaluated. For analysis of prognosis two compound outcomes were defined: positive outcome was defined as either discharge or regular ward care; negative outcome was defined as need for mechanical ventilation, treatment on intensive care unit, extracorporeal membrane oxygenation or death. Follow-up was performed until June 19th. For statistical analysis uni- und multivariable logistic regression models were calculated. RESULTS: 64 patients were included in the study. By univariable analysis the following parameters predicted a negative outcome: consolidation (p = 0.034), crazy paving (p = 0.004), geographic shape of opacification (p = 0.022), dilatation of bronchi (p = 0.002), air bronchogram (p = 0.013), vessel enlargement (p = 0.014), pleural effusion (p = 0.05), bilateral disease (p = 0.004), involvement of the upper lobes (p = 0.004, p = 0.015) or the right middle lobe (p < 0.001) and severe extent of opacifications (p = 0.002). Multivariable analysis revealed crazy paving and severe extent of parenchymal involvement to be independently predictive for a poor outcome. CONCLUSIONS: Easy to assess CT features in the early phase of disease independently predicted an adverse outcome of patients with COVID-19 pneumonia.
Search related documents:
Co phrase search for related documents- acute ards respiratory distress syndrome and logistic regression model: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13
- acute ards respiratory distress syndrome and lung injury: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
- acute ards respiratory distress syndrome and lung parenchyma: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19
- acute ards respiratory distress syndrome and lung tissue: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
- logistic regression and lung injury: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19
- logistic regression and lung parenchyma: 1, 2, 3, 4, 5, 6, 7
- logistic regression and lung tissue: 1, 2, 3, 4, 5, 6, 7
- logistic regression model and lung parenchyma: 1
Co phrase search for related documents, hyperlinks ordered by date