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10.1080/09537104.2020.1760230 http://scihub22266oqcxt.onion/10.1080/09537104.2020.1760230 32367765!7212543!32367765     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Platelets 2020 ; 31 (5): 674-679 Nephropedia Template TP {{tp|p=32367765|t=2020. Prediction of severe illness due to COVID-19 based on an analysis of initial Fibrinogen to Albumin Ratio and Platelet count |pdf=|usr=}}{{32367765}} gab.com Text Prediction of severe illness due to COVID-19 based on an analysis of initial Fibrinogen to Albumin Ratio and Platelet count JO: Platelets http://www.kidney.de/pget.php?&tw=1&pmid=32367765 #FOAvip Concomitant coagulation disorder can occur in severe patients withCOVID-19, but in-depth studies are limited. This study aimed to describe the parameters of coagulation function of patients with COVID-19 and reveal the risk factors of developing severe disease. This study retrospectively analyzed 113patients with SARS-CoV-2 infection in Taizhou Public Health Center. Clinical characteristics and indexes of coagulation function were collected. A multivariate Cox analysis was performed to identify potential biomarkers for predicting disease progression. Based on the results of multivariate Cox analysis, a Nomogram was built and the predictive accuracy was evaluated through the calibration curve, decision curve, clinical impact curve, and Kaplan-Meier analysis. Sensitivity, specificity, predictive values were calculated to assess the clinical value. The data showed that Fibrinogen, FAR, and D-dimer were higher in the severe patients, while PLTcount, Alb were much lower. Multivariate Cox analysis revealed that FAR and PLT count were independent risk factors for disease progression. The optimal cutoff values for FAR and PLT count were 0.0883 and 135*10(9)/L, respectively. The C-index [0.712 (95% CI = 0.610-0.814)], decision curve, clinical impact curve showed that Nomogram could be used to predict the disease progression. In addition, the Kaplan-Meier analysis revealed that potential risk decreased in patients with FAR<0.0883 and PLT count>135*10(9)/L.The model showed a good negative predictive value [(0.9474 (95%CI = 0.845-0.986)].This study revealed that FAR and PLT count were independent risk factors for severe illness and the severity of COVID-19 might be excluded when FAR<0.0883 and PLT count>135*10(9)/L. Twit Text FOAVip Prediction of severe illness due to COVID-19 based on an analysis of initial Fibrinogen to Albumin Ratio and Platelet count ????Platelets http://www.kidney.de/pget.php?&tw=1&pmid=32367765 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32367765 #FOAvip English Wikipedia <ref>{{Cite pmid|32367765}}</ref> Prediction of severe illness due to COVID-19 based on an analysis of initial Fibrinogen to Albumin Ratio and Platelet count #MMPMID32367765 Bi X; Su Z; Yan H; Du J; Wang J; Chen L; Peng M; Chen S; Shen B; Li J Platelets 2020[Jul]; 31 (5): 674-679 PMID32367765show ga Concomitant coagulation disorder can occur in severe patients withCOVID-19, but in-depth studies are limited. This study aimed to describe the parameters of coagulation function of patients with COVID-19 and reveal the risk factors of developing severe disease. This study retrospectively analyzed 113patients with SARS-CoV-2 infection in Taizhou Public Health Center. Clinical characteristics and indexes of coagulation function were collected. A multivariate Cox analysis was performed to identify potential biomarkers for predicting disease progression. Based on the results of multivariate Cox analysis, a Nomogram was built and the predictive accuracy was evaluated through the calibration curve, decision curve, clinical impact curve, and Kaplan-Meier analysis. Sensitivity, specificity, predictive values were calculated to assess the clinical value. The data showed that Fibrinogen, FAR, and D-dimer were higher in the severe patients, while PLTcount, Alb were much lower. Multivariate Cox analysis revealed that FAR and PLT count were independent risk factors for disease progression. The optimal cutoff values for FAR and PLT count were 0.0883 and 135*10(9)/L, respectively. The C-index [0.712 (95% CI = 0.610-0.814)], decision curve, clinical impact curve showed that Nomogram could be used to predict the disease progression. In addition, the Kaplan-Meier analysis revealed that potential risk decreased in patients with FAR<0.0883 and PLT count>135*10(9)/L.The model showed a good negative predictive value [(0.9474 (95%CI = 0.845-0.986)].This study revealed that FAR and PLT count were independent risk factors for severe illness and the severity of COVID-19 might be excluded when FAR<0.0883 and PLT count>135*10(9)/L. |*Betacoronavirus[MESH] |*Nomograms[MESH] |*Pandemics[MESH] |*Platelet Count[MESH] |Adult[MESH] |Area Under Curve[MESH] |Biomarkers/blood[MESH] |Blood Coagulation Tests[MESH] |COVID-19[MESH] |China/epidemiology[MESH] |Coronavirus Infections/*blood/epidemiology[MESH] |Disease Progression[MESH] |Female[MESH] |Fibrin Fibrinogen Degradation Products/analysis[MESH] |Fibrinogen/*analysis[MESH] |Humans[MESH] |Kaplan-Meier Estimate[MESH] |Male[MESH] |Middle Aged[MESH] |Pneumonia, Viral/*blood/epidemiology[MESH] |Predictive Value of Tests[MESH] |Prognosis[MESH] |Proportional Hazards Models[MESH] |ROC Curve[MESH] |Retrospective Studies[MESH] |Risk Factors[MESH] |SARS-CoV-2[MESH] |Serum Albumin, Human/*analysis[MESH]Prediction of severe illness due to COVID-19 based on an analysis of i(epmc).pdf DeepDyve Pubget Overpricing