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10.3390/jcm10122652 http://scihub22266oqcxt.onion/10.3390/jcm10122652 34208640!8233968!34208640     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=34208640&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Clin+Med 2021 ; 10 (12): ä Nephropedia Template TP {{tp|p=34208640|t=2021. Predictive Value of Comorbid Conditions for COVID-19 Mortality |pdf=|usr=}}{{34208640}} gab.com Text Predictive Value of Comorbid Conditions for COVID-19 Mortality JO: J Clin Med http://www.kidney.de/pget.php?&tw=1&pmid=34208640 #FOAvip In this paper, we aim at understanding the broad spectrum of factors influencing the survival of infected patients and the correlations between these factors to create a predictive probabilistic score for surviving the COVID-19 disease. Initially, 510 hospital admissions were counted in the study, out of which 310 patients did not survive. A prediction model was developed based on this data by using a Bayesian approach. Following the data collection process for the development study, the second cohort of patients totaling 541 was built to validate the risk matrix previously created. The final model has an area under the curve of 0.773 and predicts the mortality risk of SARS-CoV-2 infection based on nine disease groups while considering the gender and age of the patient as distinct risk groups. To ease medical workers' assessment of patients, we created a visual risk matrix based on a probabilistic model, ranging from a score of 1 (very low mortality risk) to 5 (very high mortality risk). Each score comprises a correlation between existing comorbid conditions, the number of comorbid conditions, gender, and age group category. This clinical model can be generalized in a hospital context and can be used to identify patients at high risk for whom immediate intervention might be required. Twit Text FOAVip Predictive Value of Comorbid Conditions for COVID-19 Mortality ????J Clin Med http://www.kidney.de/pget.php?&tw=1&pmid=34208640 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34208640 #FOAvip English Wikipedia <ref>{{Cite pmid|34208640}}</ref> Predictive Value of Comorbid Conditions for COVID-19 Mortality #MMPMID34208640 Marincu I; Bratosin F; Vidican I; Bostanaru AC; Frent S; Cerbu B; Turaiche M; Tirnea L; Timircan M J Clin Med 2021[Jun]; 10 (12): ä PMID34208640show ga In this paper, we aim at understanding the broad spectrum of factors influencing the survival of infected patients and the correlations between these factors to create a predictive probabilistic score for surviving the COVID-19 disease. Initially, 510 hospital admissions were counted in the study, out of which 310 patients did not survive. A prediction model was developed based on this data by using a Bayesian approach. Following the data collection process for the development study, the second cohort of patients totaling 541 was built to validate the risk matrix previously created. The final model has an area under the curve of 0.773 and predicts the mortality risk of SARS-CoV-2 infection based on nine disease groups while considering the gender and age of the patient as distinct risk groups. To ease medical workers' assessment of patients, we created a visual risk matrix based on a probabilistic model, ranging from a score of 1 (very low mortality risk) to 5 (very high mortality risk). Each score comprises a correlation between existing comorbid conditions, the number of comorbid conditions, gender, and age group category. This clinical model can be generalized in a hospital context and can be used to identify patients at high risk for whom immediate intervention might be required. äPredictive Value of Comorbid Conditions for COVID-19 Mortality (epmc).pdf DeepDyve Pubget Overpricing