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10.1016/j.tmaid.2020.101782 http://scihub22266oqcxt.onion/10.1016/j.tmaid.2020.101782 32526372!7836898!32526372     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Travel+Med+Infect+Dis 2020 ; 36 (ä): 101782 Nephropedia Template TP {{tp|p=32526372|t=2020. Establishing a model for predicting the outcome of COVID-19 based on combination of laboratory tests |pdf=|usr=}}{{32526372}} gab.com Text Establishing a model for predicting the outcome of COVID-19 based on combination of laboratory tests JO: Travel Med Infect Dis http://www.kidney.de/pget.php?&tw=1&pmid=32526372 #FOAvip INTRODUCTION: There are currently no satisfactory methods for predicting the outcome of Coronavirus Disease-2019 (COVID-19). The aim of this study is to establish a model for predicting the prognosis of the disease. METHODS: The laboratory results were collected from 54 deceased COVID-19 patients on admission and before death. Another 54 recovered COVID-19 patients were enrolled as control cases. RESULTS: Many laboratory indicators, such as neutrophils, AST, gamma-GT, ALP, LDH, NT-proBNP, Hs-cTnT, PT, APTT, D-dimer, IL-2R, IL-6, IL-8, IL-10, TNF-alpha, CRP, ferritin and procalcitonin, were all significantly increased in deceased patients compared with recovered patients on admission. In contrast, other indicators such as lymphocytes, platelets, total protein and albumin were significantly decreased in deceased patients on admission. Some indicators such as neutrophils and procalcitonin, others such as lymphocytes and platelets, continuously increased or decreased from admission to death in deceased patients respectively. Using these indicators alone had moderate performance in differentiating between recovered and deceased COVID-19 patients. A model based on combination of four indicators (P = 1/[1 + e(-(-2.658+0.587xneutrophils - 2.087xlymphocytes - 0.01xplatelets+0.004xIL-2R))]) showed good performance in predicting the death of COVID-19 patients. When cutoff value of 0.572 was used, the sensitivity and specificity of the prediction model were 90.74% and 94.44%, respectively. CONCLUSIONS: Using the current indicators alone is of modest value in differentiating between recovered and deceased COVID-19 patients. A prediction model based on combination of neutrophils, lymphocytes, platelets and IL-2R shows good performance in predicting the outcome of COVID-19. Twit Text FOAVip Establishing a model for predicting the outcome of COVID-19 based on combination of laboratory tests ????Travel Med Infect Dis http://www.kidney.de/pget.php?&tw=1&pmid=32526372 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32526372 #FOAvip English Wikipedia <ref>{{Cite pmid|32526372}}</ref> Establishing a model for predicting the outcome of COVID-19 based on combination of laboratory tests #MMPMID32526372 Wang F; Hou H; Wang T; Luo Y; Tang G; Wu S; Zhou H; Sun Z Travel Med Infect Dis 2020[Jul]; 36 (ä): 101782 PMID32526372show ga INTRODUCTION: There are currently no satisfactory methods for predicting the outcome of Coronavirus Disease-2019 (COVID-19). The aim of this study is to establish a model for predicting the prognosis of the disease. METHODS: The laboratory results were collected from 54 deceased COVID-19 patients on admission and before death. Another 54 recovered COVID-19 patients were enrolled as control cases. RESULTS: Many laboratory indicators, such as neutrophils, AST, gamma-GT, ALP, LDH, NT-proBNP, Hs-cTnT, PT, APTT, D-dimer, IL-2R, IL-6, IL-8, IL-10, TNF-alpha, CRP, ferritin and procalcitonin, were all significantly increased in deceased patients compared with recovered patients on admission. In contrast, other indicators such as lymphocytes, platelets, total protein and albumin were significantly decreased in deceased patients on admission. Some indicators such as neutrophils and procalcitonin, others such as lymphocytes and platelets, continuously increased or decreased from admission to death in deceased patients respectively. Using these indicators alone had moderate performance in differentiating between recovered and deceased COVID-19 patients. A model based on combination of four indicators (P = 1/[1 + e(-(-2.658+0.587xneutrophils - 2.087xlymphocytes - 0.01xplatelets+0.004xIL-2R))]) showed good performance in predicting the death of COVID-19 patients. When cutoff value of 0.572 was used, the sensitivity and specificity of the prediction model were 90.74% and 94.44%, respectively. CONCLUSIONS: Using the current indicators alone is of modest value in differentiating between recovered and deceased COVID-19 patients. A prediction model based on combination of neutrophils, lymphocytes, platelets and IL-2R shows good performance in predicting the outcome of COVID-19. |Aged[MESH] |Aged, 80 and over[MESH] |Alkaline Phosphatase/metabolism[MESH] |Aspartate Aminotransferases/metabolism[MESH] |Betacoronavirus[MESH] |C-Reactive Protein/metabolism[MESH] |COVID-19[MESH] |Case-Control Studies[MESH] |Coronavirus Infections/blood/metabolism/*mortality[MESH] |Female[MESH] |Ferritins/metabolism[MESH] |Fibrin Fibrinogen Degradation Products/metabolism[MESH] |Humans[MESH] |Interleukin-10/metabolism[MESH] |Interleukin-6/metabolism[MESH] |Interleukin-8/metabolism[MESH] |L-Lactate Dehydrogenase/metabolism[MESH] |Leukocyte Count[MESH] |Lymphocyte Count[MESH] |Male[MESH] |Middle Aged[MESH] |Models, Theoretical[MESH] |Natriuretic Peptide, Brain/metabolism[MESH] |Neutrophils[MESH] |Pandemics[MESH] |Partial Thromboplastin Time[MESH] |Peptide Fragments/metabolism[MESH] |Pneumonia, Viral/blood/metabolism/*mortality[MESH] |Procalcitonin/metabolism[MESH] |Prognosis[MESH] |Prothrombin Time[MESH] |ROC Curve[MESH] |Receptors, Interleukin-2/metabolism[MESH] |SARS-CoV-2[MESH] |Troponin T/metabolism[MESH] |Tumor Necrosis Factor-alpha/metabolism[MESH]Establishing a model for predicting the outcome of COVID-19 based on c(epmc).pdf DeepDyve Pubget Overpricing