Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1016/j.clinimag.2021.04.008 http://scihub22266oqcxt.onion/10.1016/j.clinimag.2021.04.008 33910141!8058052!33910141     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33910141&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 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 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 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Clin+Imaging 2021 ; 79 (ä): 96-101 Nephropedia Template TP {{tp|p=33910141|t=2021. Automated quantitative thin slice volumetric low dose CT analysis predicts disease severity in COVID-19 patients |pdf=|usr=}}{{33910141}} gab.com Text Automated quantitative thin slice volumetric low dose CT analysis predicts disease severity in COVID-19 patients JO: Clin Imaging http://www.kidney.de/pget.php?&tw=1&pmid=33910141 #FOAvip PURPOSE: This study aimed to identify predictive (bio-)markers for COVID-19 severity derived from automated quantitative thin slice low dose volumetric CT analysis, clinical chemistry and lung function testing. METHODS: Seventy-four COVID-19 patients admitted between March 16th and June 3rd 2020 to the Asklepios Lung Clinic Munich-Gauting, Germany, were included in the study. Patients were categorized in a non-severe group including patients hospitalized on general wards only and in a severe group including patients requiring intensive care treatment. Fully automated quantification of CT scans was performed via IMBIO CT Lung Texture analysis software. Predictive biomarkers were assessed with receiver-operator-curve and likelihood analysis. RESULTS: Fifty-five patients (44% female) presented with non-severe COVID-19 and 19 patients (32% female) with severe disease. Five fatalities were reported in the severe group. Accurate automated CT analysis was possible with 61 CTs (82%). Disease severity was linked to lower residual normal lung (72.5% vs 87%, p = 0.003), increased ground glass opacities (GGO) (8% vs 5%, p = 0.031) and increased reticular pattern (8% vs 2%, p = 0.025). Disease severity was associated with advanced age (76 vs 59 years, p = 0.001) and elevated serum C-reactive protein (CRP, 92.2 vs 36.3 mg/L, p < 0.001), lactate dehydrogenase (LDH, 485 vs 268 IU/L, p < 0.001) and oxygen supplementation (p < 0.001) upon admission. Predictive risk factors for the development of severe COVID-19 were oxygen supplementation, LDH >313 IU/L, CRP >71 mg/L, <70% normal lung texture, >12.5% GGO and >4.5% reticular pattern. CONCLUSION: Automated low dose CT analysis upon admission might be a useful tool to predict COVID-19 severity in patients. Twit Text FOAVip Automated quantitative thin slice volumetric low dose CT analysis predicts disease severity in COVID-19 patients ????Clin Imaging http://www.kidney.de/pget.php?&tw=1&pmid=33910141 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33910141 #FOAvip English Wikipedia <ref>{{Cite pmid|33910141}}</ref> Automated quantitative thin slice volumetric low dose CT analysis predicts disease severity in COVID-19 patients #MMPMID33910141 Stoleriu MG; Gerckens M; Obereisenbuchner F; Zaimova I; Hetrodt J; Mavi SC; Schmidt F; Schoenlebe AA; Heinig-Menhard K; Koch I; Jorres RA; Spiro J; Nowak L; Hatz R; Behr J; Gesierich W; Heiss-Neumann M; Dinkel J Clin Imaging 2021[Nov]; 79 (ä): 96-101 PMID33910141show ga PURPOSE: This study aimed to identify predictive (bio-)markers for COVID-19 severity derived from automated quantitative thin slice low dose volumetric CT analysis, clinical chemistry and lung function testing. METHODS: Seventy-four COVID-19 patients admitted between March 16th and June 3rd 2020 to the Asklepios Lung Clinic Munich-Gauting, Germany, were included in the study. Patients were categorized in a non-severe group including patients hospitalized on general wards only and in a severe group including patients requiring intensive care treatment. Fully automated quantification of CT scans was performed via IMBIO CT Lung Texture analysis software. Predictive biomarkers were assessed with receiver-operator-curve and likelihood analysis. RESULTS: Fifty-five patients (44% female) presented with non-severe COVID-19 and 19 patients (32% female) with severe disease. Five fatalities were reported in the severe group. Accurate automated CT analysis was possible with 61 CTs (82%). Disease severity was linked to lower residual normal lung (72.5% vs 87%, p = 0.003), increased ground glass opacities (GGO) (8% vs 5%, p = 0.031) and increased reticular pattern (8% vs 2%, p = 0.025). Disease severity was associated with advanced age (76 vs 59 years, p = 0.001) and elevated serum C-reactive protein (CRP, 92.2 vs 36.3 mg/L, p < 0.001), lactate dehydrogenase (LDH, 485 vs 268 IU/L, p < 0.001) and oxygen supplementation (p < 0.001) upon admission. Predictive risk factors for the development of severe COVID-19 were oxygen supplementation, LDH >313 IU/L, CRP >71 mg/L, <70% normal lung texture, >12.5% GGO and >4.5% reticular pattern. CONCLUSION: Automated low dose CT analysis upon admission might be a useful tool to predict COVID-19 severity in patients. |*COVID-19[MESH] |Cone-Beam Computed Tomography[MESH] |Female[MESH] |Humans[MESH] |Lung/diagnostic imaging[MESH] |Male[MESH] |Middle Aged[MESH] |Retrospective Studies[MESH] |SARS-CoV-2[MESH] |Severity of Illness Index[MESH]Automated quantitative thin slice volumetric low dose CT analysis pred(epmc).pdf DeepDyve Pubget Overpricing