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10.3389/fpubh.2020.00461 http://scihub22266oqcxt.onion/10.3389/fpubh.2020.00461 32850612!7432145!32850612     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Front+Public+Health 2020 ; 8 (ä): 461 Nephropedia Template TP {{tp|p=32850612|t=2020. A Predicting Nomogram for Mortality in Patients With COVID-19 |pdf=|usr=}}{{32850612}} gab.com Text A Predicting Nomogram for Mortality in Patients With COVID-19 JO: Front Public Health http://www.kidney.de/pget.php?&tw=1&pmid=32850612 #FOAvip Background: The global COVID-19 epidemic remains severe, with the cumulative global death toll reaching more than 207,170 as of May 2, 2020 (1). Purpose: Our research objective is to establish a reliable nomogram to predict mortality in COVID-19 patients. The nomogram can help us distinguish between patients who are at high risk of death and need close attention. Patients and Methods: For the single-center retrospective study, we collected 21 cases of patients who died in the critical illness area of the Optical Valley Branch of Tongji Hospital, Huazhong University of Science and Technology, from February 9 to March 10. Additionally, we selected 99 patients discharged during this period for analysis. The nomogram was constructed to predict the mortality for COVID-19 patients using the primary group of 120 patients and was validated using an independent cohort of 84 patients. We used multivariable logistic regression analysis to construct the prediction model. The nomogram was evaluated for calibration, differentiation, and clinical usefulness. Results: The predictors included in the nomogram were c-reactive protein, PaO(2)/FiO(2), and cTnI. The areas under the curves of the nomogram were 0.988 (95% CI: 0.972-1.000) and 0.956 (95% CI, 0.874-1.000) in the primary and validation groups, respectively. Decision curve analysis suggests that the nomogram may have clinical usefulness. Conclusion: This study provides a nomogram containing c-reactive protein, PaO(2)/FiO(2), and cTnI that can be conveniently used to predict individual mortality in COVID-19 patients. Next, we will collect as many cases as possible from multiple centers to build a more reliable nomogram to predict mortality for COVID-19 patients. Twit Text FOAVip A Predicting Nomogram for Mortality in Patients With COVID-19 ????Front Public Health http://www.kidney.de/pget.php?&tw=1&pmid=32850612 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32850612 #FOAvip English Wikipedia <ref>{{Cite pmid|32850612}}</ref> A Predicting Nomogram for Mortality in Patients With COVID-19 #MMPMID32850612 Pan D; Cheng D; Cao Y; Hu C; Zou F; Yu W; Xu T Front Public Health 2020[]; 8 (ä): 461 PMID32850612show ga Background: The global COVID-19 epidemic remains severe, with the cumulative global death toll reaching more than 207,170 as of May 2, 2020 (1). Purpose: Our research objective is to establish a reliable nomogram to predict mortality in COVID-19 patients. The nomogram can help us distinguish between patients who are at high risk of death and need close attention. Patients and Methods: For the single-center retrospective study, we collected 21 cases of patients who died in the critical illness area of the Optical Valley Branch of Tongji Hospital, Huazhong University of Science and Technology, from February 9 to March 10. Additionally, we selected 99 patients discharged during this period for analysis. The nomogram was constructed to predict the mortality for COVID-19 patients using the primary group of 120 patients and was validated using an independent cohort of 84 patients. We used multivariable logistic regression analysis to construct the prediction model. The nomogram was evaluated for calibration, differentiation, and clinical usefulness. Results: The predictors included in the nomogram were c-reactive protein, PaO(2)/FiO(2), and cTnI. The areas under the curves of the nomogram were 0.988 (95% CI: 0.972-1.000) and 0.956 (95% CI, 0.874-1.000) in the primary and validation groups, respectively. Decision curve analysis suggests that the nomogram may have clinical usefulness. Conclusion: This study provides a nomogram containing c-reactive protein, PaO(2)/FiO(2), and cTnI that can be conveniently used to predict individual mortality in COVID-19 patients. Next, we will collect as many cases as possible from multiple centers to build a more reliable nomogram to predict mortality for COVID-19 patients. |*COVID-19/mortality[MESH] |*Nomograms[MESH] |Aged[MESH] |China/epidemiology[MESH] |Female[MESH] |Humans[MESH] |Male[MESH] |Middle Aged[MESH]A Predicting Nomogram for Mortality in Patients With COVID-19 (epmc).pdf DeepDyve Pubget Overpricing