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10.1016/j.ejrad.2020.109202 http://scihub22266oqcxt.onion/10.1016/j.ejrad.2020.109202 32745895!7388797!32745895     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 298.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Eur+J+Radiol 2020 ; 130 (ä): 109202 Nephropedia Template TP {{tp|p=32745895|t=2020. Role of computed tomography in predicting critical disease in patients with covid-19 pneumonia: A retrospective study using a semiautomatic quantitative method |pdf=|usr=}}{{32745895}} gab.com Text Role of computed tomography in predicting critical disease in patients with covid-19 pneumonia: A retrospective study using a semiautomatic quantitative method JO: Eur J Radiol http://www.kidney.de/pget.php?&tw=1&pmid=32745895 #FOAvip BACKGROUND: So far, only a few studies evaluated the correlation between CT features and clinical outcome in patients with COVID-19 pneumonia. PURPOSE: To evaluate CT ability in differentiating critically ill patients requiring invasive ventilation from patients with less severe disease. METHODS: We retrospectively collected data from patients admitted to our institution for COVID-19 pneumonia between March 5th-24th. Patients were considered critically ill or non-critically ill, depending on the need for mechanical ventilation. CT images from both groups were analyzed for the assessment of qualitative features and disease extension, using a quantitative semiautomatic method. We evaluated the differences between the two groups for clinical, laboratory and CT data. Analyses were conducted on a per-protocol basis. RESULTS: 189 patients were analyzed. PaO(2)/FIO(2) ratio and oxygen saturation (SaO(2)) were decreased in critically ill patients. At CT, mixed pattern (ground glass opacities (GGO) and consolidation) and GGO alone were more frequent respectively in critically ill and in non-critically ill patients (p?<?0.05). Lung volume involvement was significantly higher in critically ill patients (38.5 % vs. 5.8 %, p?<?0.05). A cut-off of 23.0 % of lung involvement showed 96 % sensitivity and 96 % specificity in distinguishing critically ill patients from patients with less severe disease. The fraction of involved lung was related to lactate dehydrogenase (LDH) levels, PaO(2)/FIO(2) ratio and SaO(2) (p?<?0.05). CONCLUSION: Lung disease extension, assessed using quantitative CT, has a significant relationship with clinical severity and may predict the need for invasive ventilation in patients with COVID-19. Twit Text FOAVip Role of computed tomography in predicting critical disease in patients with covid-19 pneumonia: A retrospective study using a semiautomatic quantitative method ????Eur J Radiol http://www.kidney.de/pget.php?&tw=1&pmid=32745895 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32745895 #FOAvip English Wikipedia <ref>{{Cite pmid|32745895}}</ref> Role of computed tomography in predicting critical disease in patients with covid-19 pneumonia: A retrospective study using a semiautomatic quantitative method #MMPMID32745895 Leonardi A; Scipione R; Alfieri G; Petrillo R; Dolciami M; Ciccarelli F; Perotti S; Cartocci G; Scala A; Imperiale C; Iafrate F; Francone M; Catalano C; Ricci P Eur J Radiol 2020[Sep]; 130 (ä): 109202 PMID32745895show ga BACKGROUND: So far, only a few studies evaluated the correlation between CT features and clinical outcome in patients with COVID-19 pneumonia. PURPOSE: To evaluate CT ability in differentiating critically ill patients requiring invasive ventilation from patients with less severe disease. METHODS: We retrospectively collected data from patients admitted to our institution for COVID-19 pneumonia between March 5th-24th. Patients were considered critically ill or non-critically ill, depending on the need for mechanical ventilation. CT images from both groups were analyzed for the assessment of qualitative features and disease extension, using a quantitative semiautomatic method. We evaluated the differences between the two groups for clinical, laboratory and CT data. Analyses were conducted on a per-protocol basis. RESULTS: 189 patients were analyzed. PaO(2)/FIO(2) ratio and oxygen saturation (SaO(2)) were decreased in critically ill patients. At CT, mixed pattern (ground glass opacities (GGO) and consolidation) and GGO alone were more frequent respectively in critically ill and in non-critically ill patients (p? |*Betacoronavirus[MESH] |Aged[MESH] |COVID-19[MESH] |Coronavirus Infections/*diagnostic imaging[MESH] |Critical Illness[MESH] |Evaluation Studies as Topic[MESH] |Female[MESH] |Humans[MESH] |Image Interpretation, Computer-Assisted/*methods[MESH] |Lung/diagnostic imaging[MESH] |Male[MESH] |Middle Aged[MESH] |Pandemics[MESH] |Pneumonia, Viral/*diagnostic imaging[MESH] |Research Design[MESH] |Retrospective Studies[MESH] |Risk Factors[MESH] |SARS-CoV-2[MESH] |Sensitivity and Specificity[MESH]Role of computed tomography in predicting critical disease in patients(epmc).pdf DeepDyve Pubget Overpricing