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10.1016/j.diii.2020.12.002 http://scihub22266oqcxt.onion/10.1016/j.diii.2020.12.002 33419693!7746121!33419693     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Diagn+Interv+Imaging 2021 ; 102 (2): 77-84 Nephropedia Template TP {{tp|p=33419693|t=2021. COVID-19: A qualitative chest CT model to identify severe form of the disease |pdf=|usr=}}{{33419693}} gab.com Text COVID-19: A qualitative chest CT model to identify severe form of the disease JO: Diagn Interv Imaging http://www.kidney.de/pget.php?&tw=1&pmid=33419693 #FOAvip PURPOSE: The purpose of this study was to identify clinical and chest computed tomography (CT) features associated with a severe form of coronavirus disease 2019 (COVID-19) and to propose a quick and easy to use model to identify patients at risk of a severe form. MATERIALS AND METHODS: A total of 158 patients with biologically confirmed COVID-19 who underwent a chest CT after the onset of the symptoms were included. There were 84 men and 74 women with a mean age of 68+/-14 (SD) years (range: 24-96years). There were 100 non-severe and 58 severe cases. Their clinical data were recorded and the first chest CT examination was reviewed using a computerized standardized report. Univariate and multivariate analyses were performed in order to identify the risk factors associated with disease severity. Two models were built: one was based only on qualitative CT features and the other one included a semi-quantitative total CT score to replace the variable representing the extent of the disease. Areas under the ROC curves (AUC) of the two models were compared with DeLong's method. RESULTS: Central involvement of lung parenchyma (P<0.001), area of consolidation (P<0.008), air bronchogram sign (P<0.001), bronchiectasis (P<0.001), traction bronchiectasis (P<0.011), pleural effusion (P<0.026), large involvement of either one of the upper lobes or of the middle lobe (P<0.001) and total CT score>/=15 (P<0.001) were more often observed in the severe group than in the non-severe group. No significant differences were found between the qualitative model (large involvement of either upper lobes or middle lobe [odd ratio (OR)=2.473], central involvement [OR=2.760], pleural effusion [OR=2.699]) and the semi-quantitative model (total CT score>/=15 [OR=3.342], central involvement [OR=2.344], pleural effusion [OR=2.754]) with AUC of 0.722 (95% CI: 0.638-0.806) vs. 0.739 (95% CI: 0.656-0.823), respectively (P=0.209). CONCLUSION: We have developed a new qualitative chest CT-based multivariate model that provides independent risk factors associated with severe form of COVID-19. Twit Text FOAVip COVID-19: A qualitative chest CT model to identify severe form of the disease ????Diagn Interv Imaging http://www.kidney.de/pget.php?&tw=1&pmid=33419693 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33419693 #FOAvip English Wikipedia <ref>{{Cite pmid|33419693}}</ref> COVID-19: A qualitative chest CT model to identify severe form of the disease #MMPMID33419693 Devie A; Kanagaratnam L; Perotin JM; Jolly D; Ravey JN; Djelouah M; Hoeffel C Diagn Interv Imaging 2021[Feb]; 102 (2): 77-84 PMID33419693show ga PURPOSE: The purpose of this study was to identify clinical and chest computed tomography (CT) features associated with a severe form of coronavirus disease 2019 (COVID-19) and to propose a quick and easy to use model to identify patients at risk of a severe form. MATERIALS AND METHODS: A total of 158 patients with biologically confirmed COVID-19 who underwent a chest CT after the onset of the symptoms were included. There were 84 men and 74 women with a mean age of 68+/-14 (SD) years (range: 24-96years). There were 100 non-severe and 58 severe cases. Their clinical data were recorded and the first chest CT examination was reviewed using a computerized standardized report. Univariate and multivariate analyses were performed in order to identify the risk factors associated with disease severity. Two models were built: one was based only on qualitative CT features and the other one included a semi-quantitative total CT score to replace the variable representing the extent of the disease. Areas under the ROC curves (AUC) of the two models were compared with DeLong's method. RESULTS: Central involvement of lung parenchyma (P<0.001), area of consolidation (P<0.008), air bronchogram sign (P<0.001), bronchiectasis (P<0.001), traction bronchiectasis (P<0.011), pleural effusion (P<0.026), large involvement of either one of the upper lobes or of the middle lobe (P<0.001) and total CT score>/=15 (P<0.001) were more often observed in the severe group than in the non-severe group. No significant differences were found between the qualitative model (large involvement of either upper lobes or middle lobe [odd ratio (OR)=2.473], central involvement [OR=2.760], pleural effusion [OR=2.699]) and the semi-quantitative model (total CT score>/=15 [OR=3.342], central involvement [OR=2.344], pleural effusion [OR=2.754]) with AUC of 0.722 (95% CI: 0.638-0.806) vs. 0.739 (95% CI: 0.656-0.823), respectively (P=0.209). CONCLUSION: We have developed a new qualitative chest CT-based multivariate model that provides independent risk factors associated with severe form of COVID-19. |*Computer Simulation[MESH] |*Tomography, X-Ray Computed[MESH] |Adult[MESH] |Aged[MESH] |Aged, 80 and over[MESH] |Bronchiectasis/diagnostic imaging[MESH] |COVID-19/*diagnostic imaging[MESH] |Female[MESH] |Humans[MESH] |Lung/*diagnostic imaging[MESH] |Male[MESH] |Middle Aged[MESH] |Multivariate Analysis[MESH] |ROC Curve[MESH] |Risk Factors[MESH]COVID-19: A qualitative chest CT model to identify severe form of the (epmc).pdf DeepDyve Pubget Overpricing