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10.1080/03007995.2021.1904862 http://scihub22266oqcxt.onion/10.1080/03007995.2021.1904862 33729889!8054492!33729889     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Curr+Med+Res+Opin 2021 ; 37 (6): 917-927 Nephropedia Template TP {{tp|p=33729889|t=2021. A risk score based on baseline risk factors for predicting mortality in COVID-19 patients |pdf=|usr=}}{{33729889}} gab.com Text A risk score based on baseline risk factors for predicting mortality in COVID-19 patients JO: Curr Med Res Opin http://www.kidney.de/pget.php?&tw=1&pmid=33729889 #FOAvip BACKGROUND: To develop a sensitive and clinically applicable risk assessment tool identifying coronavirus disease 2019 (COVID-19) patients with a high risk of mortality at hospital admission. This model would assist frontline clinicians in optimizing medical treatment with limited resources. METHODS: 6415 patients from seven hospitals in Wuhan city were assigned to the training and testing cohorts. A total of 6351 patients from another three hospitals in Wuhan, 2169 patients from outside of Wuhan, and 553 patients from Milan, Italy were assigned to three independent validation cohorts. A total of 64 candidate clinical variables at hospital admission were analyzed by random forest and least absolute shrinkage and selection operator (LASSO) analyses. RESULTS: Eight factors, namely, Oxygen saturation, blood Urea nitrogen, Respiratory rate, admission before the date the national Maximum number of daily new cases was reached, Age, Procalcitonin, C-reactive protein (CRP), and absolute Neutrophil counts, were identified as having significant associations with mortality in COVID-19 patients. A composite score based on these eight risk factors, termed the OURMAPCN-score, predicted the risk of mortality among the COVID-19 patients, with a C-statistic of 0.92 (95% confidence interval [CI] 0.90-0.93). The hazard ratio for all-cause mortality between patients with OURMAPCN-score >11 compared with those with scores </= 11 was 18.18 (95% CI 13.93-23.71; p < .0001). The predictive performance, specificity, and sensitivity of the score were validated in three independent cohorts. CONCLUSIONS: The OURMAPCN score is a risk assessment tool to determine the mortality rate in COVID-19 patients based on a limited number of baseline parameters. This tool can assist physicians in optimizing the clinical management of COVID-19 patients with limited hospital resources. Twit Text FOAVip A risk score based on baseline risk factors for predicting mortality in COVID-19 patients ????Curr Med Res Opin http://www.kidney.de/pget.php?&tw=1&pmid=33729889 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33729889 #FOAvip English Wikipedia <ref>{{Cite pmid|33729889}}</ref> A risk score based on baseline risk factors for predicting mortality in COVID-19 patients #MMPMID33729889 Chen Z; Chen J; Zhou J; Lei F; Zhou F; Qin JJ; Zhang XJ; Zhu L; Liu YM; Wang H; Chen MM; Zhao YC; Xie J; Shen L; Song X; Zhang X; Yang C; Liu W; Zhang X; Guo D; Yan Y; Liu M; Mao W; Liu L; Ye P; Xiao B; Luo P; Zhang Z; Lu Z; Wang J; Lu H; Xia X; Wang D; Liao X; Peng G; Liang L; Yang J; Chen G; Azzolini E; Aghemo A; Ciccarelli M; Condorelli G; Stefanini GG; Wei X; Zhang BH; Huang X; Xia J; Yuan Y; She ZG; Guo J; Wang Y; Zhang P; Li H Curr Med Res Opin 2021[Jun]; 37 (6): 917-927 PMID33729889show ga BACKGROUND: To develop a sensitive and clinically applicable risk assessment tool identifying coronavirus disease 2019 (COVID-19) patients with a high risk of mortality at hospital admission. This model would assist frontline clinicians in optimizing medical treatment with limited resources. METHODS: 6415 patients from seven hospitals in Wuhan city were assigned to the training and testing cohorts. A total of 6351 patients from another three hospitals in Wuhan, 2169 patients from outside of Wuhan, and 553 patients from Milan, Italy were assigned to three independent validation cohorts. A total of 64 candidate clinical variables at hospital admission were analyzed by random forest and least absolute shrinkage and selection operator (LASSO) analyses. RESULTS: Eight factors, namely, Oxygen saturation, blood Urea nitrogen, Respiratory rate, admission before the date the national Maximum number of daily new cases was reached, Age, Procalcitonin, C-reactive protein (CRP), and absolute Neutrophil counts, were identified as having significant associations with mortality in COVID-19 patients. A composite score based on these eight risk factors, termed the OURMAPCN-score, predicted the risk of mortality among the COVID-19 patients, with a C-statistic of 0.92 (95% confidence interval [CI] 0.90-0.93). The hazard ratio for all-cause mortality between patients with OURMAPCN-score >11 compared with those with scores |*COVID-19/epidemiology/mortality[MESH] |China[MESH] |Hospitalization/statistics & numerical data[MESH] |Humans[MESH] |Italy[MESH] |Risk Assessment/*methods[MESH]A risk score based on baseline risk factors for predicting mortality i(epmc).pdf DeepDyve Pubget Overpricing