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10.17305/bjbms.2020.5466 http://scihub22266oqcxt.onion/10.17305/bjbms.2020.5466 33577443!8554695!33577443     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 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       Bosn+J+Basic+Med+Sci 2021 ; 21 (6): 739-745 Nephropedia Template TP {{tp|p=33577443|t=2021. Predictive performance of CT for adverse outcomes among COVID-19 suspected patients: a two-center retrospective study |pdf=|usr=}}{{33577443}} gab.com Text Predictive performance of CT for adverse outcomes among COVID-19 suspected patients: a two-center retrospective study JO: Bosn J Basic Med Sci http://www.kidney.de/pget.php?&tw=1&pmid=33577443 #FOAvip The aim of the study was to compare the performance of various computed tomography (CT) reporting tools, including zonal CT visual score (ZCVS), the number of involved lobes, and Radiological Society of North America (RSNA) categorization in predicting adverse outcomes among patients hospitalized due to the lower respiratory symptoms during the coronavirus disease 2019 (COVID-19) pandemic. A total of 405 patients admitted with severe respiratory symptoms who underwent a chest CT were enrolled. The primary adverse outcome was intensive care unit (ICU) admission of patients. Predictive performances of reporting tools were compared using the area under the receiver operating characteristic curves (AUC ROC). Among the 405 patients, 39 (9.63%) required ICU support during their hospital stay. At least two or more observers reported a typical and indeterminate COVID-19 pneumonia CT pattern according to RSNA categorization in 70% (285/405) of patients. Among these, 63% (179/285) had a positive polymerase chain reaction (PCR test for the SARS-CoV-2 virus. The median number of lobes involved according to CT was higher in patients who required ICU support (median interquartile range [IQR], 5[3; 5] vs. 3[0; 5]). The median ZCVS score was higher among the patients that subsequently required ICU support (median [IQR], 4[0; 12] vs. 13[5.75; 24]). The bootstrap comparisons of AUC ROC showed significant differences between reporting tools, and the ZCVS was found to be superior (AUC ROC, 71-75%). The ZCVS score at the first admission showed a linear and significant association with adverse outcomes among patients with the lower respiratory tract symptoms during the COVID-19 pandemic. Twit Text FOAVip Predictive performance of CT for adverse outcomes among COVID-19 suspected patients: a two-center retrospective study ????Bosn J Basic Med Sci http://www.kidney.de/pget.php?&tw=1&pmid=33577443 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33577443 #FOAvip English Wikipedia <ref>{{Cite pmid|33577443}}</ref> Predictive performance of CT for adverse outcomes among COVID-19 suspected patients: a two-center retrospective study #MMPMID33577443 Baysal B; Dogan MB; Gulbay M; Sorkun M; Koksal M; Bastug A; Kazancioglu S; Ozbay BO; Icten S; Arslan F; Cag Y; Bodur H; Vahaboglu H Bosn J Basic Med Sci 2021[Dec]; 21 (6): 739-745 PMID33577443show ga The aim of the study was to compare the performance of various computed tomography (CT) reporting tools, including zonal CT visual score (ZCVS), the number of involved lobes, and Radiological Society of North America (RSNA) categorization in predicting adverse outcomes among patients hospitalized due to the lower respiratory symptoms during the coronavirus disease 2019 (COVID-19) pandemic. A total of 405 patients admitted with severe respiratory symptoms who underwent a chest CT were enrolled. The primary adverse outcome was intensive care unit (ICU) admission of patients. Predictive performances of reporting tools were compared using the area under the receiver operating characteristic curves (AUC ROC). Among the 405 patients, 39 (9.63%) required ICU support during their hospital stay. At least two or more observers reported a typical and indeterminate COVID-19 pneumonia CT pattern according to RSNA categorization in 70% (285/405) of patients. Among these, 63% (179/285) had a positive polymerase chain reaction (PCR test for the SARS-CoV-2 virus. The median number of lobes involved according to CT was higher in patients who required ICU support (median interquartile range [IQR], 5[3; 5] vs. 3[0; 5]). The median ZCVS score was higher among the patients that subsequently required ICU support (median [IQR], 4[0; 12] vs. 13[5.75; 24]). The bootstrap comparisons of AUC ROC showed significant differences between reporting tools, and the ZCVS was found to be superior (AUC ROC, 71-75%). The ZCVS score at the first admission showed a linear and significant association with adverse outcomes among patients with the lower respiratory tract symptoms during the COVID-19 pandemic. |*Tomography, X-Ray Computed[MESH] |Adult[MESH] |Aged[MESH] |COVID-19/*complications/*diagnostic imaging/mortality[MESH] |Critical Care[MESH] |Female[MESH] |Hospitalization[MESH] |Humans[MESH] |Male[MESH] |Middle Aged[MESH] |Predictive Value of Tests[MESH] |Prognosis[MESH] |ROC Curve[MESH] |Retrospective Studies[MESH]Predictive performance of CT for adverse outcomes among COVID-19 suspe(epmc).pdf DeepDyve Pubget Overpricing