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10.3390/jcm9103066 http://scihub22266oqcxt.onion/10.3390/jcm9103066 32977606!7598151!32977606     free     free     free 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 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Clin+Med 2020 ; 9 (10): ä Nephropedia Template TP {{tp|p=32977606|t=2020. The PANDEMYC Score An Easily Applicable and Interpretable Model for Predicting Mortality Associated With COVID-19 |pdf=|usr=}}{{32977606}} gab.com Text The PANDEMYC Score An Easily Applicable and Interpretable Model for Predicting Mortality Associated With COVID-19 JO: J Clin Med http://www.kidney.de/pget.php?&tw=1&pmid=32977606 #FOAvip This study aimed to build an easily applicable prognostic model based on routine clinical, radiological, and laboratory data available at admission, to predict mortality in coronavirus 19 disease (COVID-19) hospitalized patients. METHODS: We retrospectively collected clinical information from 1968 patients admitted to a hospital. We built a predictive score based on a logistic regression model in which explicative variables were discretized using classification trees that facilitated the identification of the optimal sections in order to predict inpatient mortality in patients admitted with COVID-19. These sections were translated into a score indicating the probability of a patient's death, thus making the results easy to interpret. RESULTS: Median age was 67 years, 1104 patients (56.4%) were male, and 325 (16.5%) died during hospitalization. Our final model identified nine key features: age, oxygen saturation, smoking, serum creatinine, lymphocytes, hemoglobin, platelets, C-reactive protein, and sodium at admission. The discrimination of the model was excellent in the training, validation, and test samples (AUC: 0.865, 0.808, and 0.883, respectively). We constructed a prognostic scale to determine the probability of death associated with each score. CONCLUSIONS: We designed an easily applicable predictive model for early identification of patients at high risk of death due to COVID-19 during hospitalization. Twit Text FOAVip The PANDEMYC Score An Easily Applicable and Interpretable Model for Predicting Mortality Associated With COVID-19 ????J Clin Med http://www.kidney.de/pget.php?&tw=1&pmid=32977606 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32977606 #FOAvip English Wikipedia <ref>{{Cite pmid|32977606}}</ref> The PANDEMYC Score An Easily Applicable and Interpretable Model for Predicting Mortality Associated With COVID-19 #MMPMID32977606 Torres-Macho J; Ryan P; Valencia J; Perez-Butragueno M; Jimenez E; Fontan-Vela M; Izquierdo-Garcia E; Fernandez-Jimenez I; Alvaro-Alonso E; Lazaro A; Alvarado M; Notario H; Resino S; Velez-Serrano D; Meca A J Clin Med 2020[Sep]; 9 (10): ä PMID32977606show ga This study aimed to build an easily applicable prognostic model based on routine clinical, radiological, and laboratory data available at admission, to predict mortality in coronavirus 19 disease (COVID-19) hospitalized patients. METHODS: We retrospectively collected clinical information from 1968 patients admitted to a hospital. We built a predictive score based on a logistic regression model in which explicative variables were discretized using classification trees that facilitated the identification of the optimal sections in order to predict inpatient mortality in patients admitted with COVID-19. These sections were translated into a score indicating the probability of a patient's death, thus making the results easy to interpret. RESULTS: Median age was 67 years, 1104 patients (56.4%) were male, and 325 (16.5%) died during hospitalization. Our final model identified nine key features: age, oxygen saturation, smoking, serum creatinine, lymphocytes, hemoglobin, platelets, C-reactive protein, and sodium at admission. The discrimination of the model was excellent in the training, validation, and test samples (AUC: 0.865, 0.808, and 0.883, respectively). We constructed a prognostic scale to determine the probability of death associated with each score. CONCLUSIONS: We designed an easily applicable predictive model for early identification of patients at high risk of death due to COVID-19 during hospitalization. äThe PANDEMYC Score An Easily Applicable and Interpretable Model for P(epmc).pdf DeepDyve Pubget Overpricing