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10.1002/jmv.26713 http://scihub22266oqcxt.onion/10.1002/jmv.26713 33289142!ä!33289142 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Med+Virol 2021 ; 93 (4): 2332-2339 Nephropedia Template TP {{tp|p=33289142|t=2021. Development and validation of a nomogram using on admission routine laboratory parameters to predict in-hospital survival of patients with COVID-19 |pdf=|usr=}}{{33289142}} gab.com Text Development and validation of a nomogram using on admission routine laboratory parameters to predict in-hospital survival of patients with COVID-19 JO: J Med Virol http://www.kidney.de/pget.php?&tw=1&pmid=33289142 #FOAvip To develop and validate a nomogram using on admission data to predict in-hospital survival probabilities of coronavirus disease 2019 (COVID-19) patients. We analyzed 855 COVID-19 patients with 52 variables. The least absolute shrinkage and selection operator regression and multivariate Cox analyses were used to screen significant factors associated with in-hospital mortality. A nomogram was established based on the variables identified by Cox regression. The performance of the model was evaluated by C-index and calibration plots. Decision curve analysis was conducted to determine the clinical utility of the nomogram. Six variables, including neutrophil (hazard ratio [HR], 1.088; 95% confidence interval [CI], [1.0004-1.147]; p < .001), C-reactive protein (HR, 1.007; 95% CI, [1.0026-1.011]; p = .002), IL-6 (HR, 1.001; 95% CI, [1.0003-1.002]; p = .005), d-dimer (HR, 1.034; 95% CI, [1.0111-1.057]; p = .003), prothrombin time (HR 1.086, 95% CI [1.0369-1.139], p < .001), and myoglobin (HR, 1.001; 95% CI, [1.0007-1.002]; p < .001), were identified and applied to develop a nomogram. The nomogram predicted 14-day and 28-day survival probabilities with reasonable accuracy, as assessed by the C-index (0.912) and calibration plots. Decision curve analysis showed relatively wide ranges of threshold probability, suggesting a high clinical value of the nomogram. Neutrophil, C-reactive protein, IL-6, d-dimer, prothrombin time, and myoglobin levels were significantly correlated with in-hospital mortality of COVID-19 patients. Demonstrating satisfactory discrimination and calibration, this model could predict patient outcomes as early as on admission and might serve as a useful triage tool for clinical decision making. Twit Text FOAVip Development and validation of a nomogram using on admission routine laboratory parameters to predict in-hospital survival of patients with COVID-19 ????J Med Virol http://www.kidney.de/pget.php?&tw=1&pmid=33289142 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33289142 #FOAvip English Wikipedia <ref>{{Cite pmid|33289142}}</ref> Development and validation of a nomogram using on admission routine laboratory parameters to predict in-hospital survival of patients with COVID-19 #MMPMID33289142 Chen H; Chen R; Yang H; Wang J; Hou Y; Hu W; Yu J; Li H J Med Virol 2021[Apr]; 93 (4): 2332-2339 PMID33289142show ga To develop and validate a nomogram using on admission data to predict in-hospital survival probabilities of coronavirus disease 2019 (COVID-19) patients. We analyzed 855 COVID-19 patients with 52 variables. The least absolute shrinkage and selection operator regression and multivariate Cox analyses were used to screen significant factors associated with in-hospital mortality. A nomogram was established based on the variables identified by Cox regression. The performance of the model was evaluated by C-index and calibration plots. Decision curve analysis was conducted to determine the clinical utility of the nomogram. Six variables, including neutrophil (hazard ratio [HR], 1.088; 95% confidence interval [CI], [1.0004-1.147]; p < .001), C-reactive protein (HR, 1.007; 95% CI, [1.0026-1.011]; p = .002), IL-6 (HR, 1.001; 95% CI, [1.0003-1.002]; p = .005), d-dimer (HR, 1.034; 95% CI, [1.0111-1.057]; p = .003), prothrombin time (HR 1.086, 95% CI [1.0369-1.139], p < .001), and myoglobin (HR, 1.001; 95% CI, [1.0007-1.002]; p < .001), were identified and applied to develop a nomogram. The nomogram predicted 14-day and 28-day survival probabilities with reasonable accuracy, as assessed by the C-index (0.912) and calibration plots. Decision curve analysis showed relatively wide ranges of threshold probability, suggesting a high clinical value of the nomogram. Neutrophil, C-reactive protein, IL-6, d-dimer, prothrombin time, and myoglobin levels were significantly correlated with in-hospital mortality of COVID-19 patients. Demonstrating satisfactory discrimination and calibration, this model could predict patient outcomes as early as on admission and might serve as a useful triage tool for clinical decision making. |*Nomograms[MESH] |Aged[MESH] |Aged, 80 and over[MESH] |COVID-19/metabolism/*mortality[MESH] |China/epidemiology[MESH] |Female[MESH] |Fibrin Fibrinogen Degradation Products/metabolism[MESH] |Hospital Mortality[MESH] |Hospitalization[MESH] |Hospitals/statistics & numerical data[MESH] |Humans[MESH] |Male[MESH] |Middle Aged[MESH] |Proportional Hazards Models[MESH] |Retrospective Studies[MESH] |Risk Assessment[MESH] |Risk Factors[MESH]Development and validation of a nomogram using on admission routine la(epmc).pdf DeepDyve Pubget Overpricing