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10.1016/j.ajem.2020.08.090 http://scihub22266oqcxt.onion/10.1016/j.ajem.2020.08.090 33046291!7467869!33046291     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 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 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       Am+J+Emerg+Med 2021 ; 45 (ä): 345-351 Nephropedia Template TP {{tp|p=33046291|t=2021. Identifying and quantifying robust risk factors for mortality in critically ill patients with COVID-19 using quantile regression |pdf=|usr=}}{{33046291}} gab.com Text Identifying and quantifying robust risk factors for mortality in critically ill patients with COVID-19 using quantile regression JO: Am J Emerg Med http://www.kidney.de/pget.php?&tw=1&pmid=33046291 #FOAvip OBJECTIVE: Many laboratory indicators form a skewed distribution with outliers in critically ill patients with COVID-19, for which robust methods are needed to precisely determine and quantify fatality risk factors. METHOD: A total of 192 critically ill patients (142 were discharged and 50 died in the hospital) with COVID-19 were included in the sample. Quantile regression was used to determine discrepant laboratory indexes between survivors and non-survivors and quantile shift (QS) was used to quantify the difference. Logistic regression was then used to calculate the odds ratio (OR) and the predictive power of death for each risk indicator. RESULTS: After adjusting for multiple comparisons and controlling numerous confounders, quantile regression revealed that the laboratory indexes of non-survivors were significantly higher in C-reactive protein (CRP; QS = 0.835, p < .001), white blood cell counts (WBC; QS = 0.743, p < .001), glutamic oxaloacetic transaminase (AST; QS = 0.735, p < .001), blood glucose (BG; QS = 0.608, p = .059), fibrin degradation product (FDP; QS = 0.730, p = .080), and partial pressure of carbon dioxide (PCO(2)), and lower in oxygen saturation (SO(2); QS = 0.312, p < .001), calcium (Ca(2+); QS = 0.306, p = .073), and pH. Most of these indexes were associated with an increased fatality risk, and predictive for the probability of death. Especially, CRP is the most prominent index with and odds ratio of 205.97 and predictive accuracy of 93.2%. CONCLUSION: Laboratory indexes provided reliable information on mortality in critically ill patients with COVID-19, which might help improve clinical prediction and treatment at an early stage. Twit Text FOAVip Identifying and quantifying robust risk factors for mortality in critically ill patients with COVID-19 using quantile regression ????Am J Emerg Med http://www.kidney.de/pget.php?&tw=1&pmid=33046291 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33046291 #FOAvip English Wikipedia <ref>{{Cite pmid|33046291}}</ref> Identifying and quantifying robust risk factors for mortality in critically ill patients with COVID-19 using quantile regression #MMPMID33046291 Linli Z; Chen Y; Tian G; Guo S; Fei Y Am J Emerg Med 2021[Jul]; 45 (ä): 345-351 PMID33046291show ga OBJECTIVE: Many laboratory indicators form a skewed distribution with outliers in critically ill patients with COVID-19, for which robust methods are needed to precisely determine and quantify fatality risk factors. METHOD: A total of 192 critically ill patients (142 were discharged and 50 died in the hospital) with COVID-19 were included in the sample. Quantile regression was used to determine discrepant laboratory indexes between survivors and non-survivors and quantile shift (QS) was used to quantify the difference. Logistic regression was then used to calculate the odds ratio (OR) and the predictive power of death for each risk indicator. RESULTS: After adjusting for multiple comparisons and controlling numerous confounders, quantile regression revealed that the laboratory indexes of non-survivors were significantly higher in C-reactive protein (CRP; QS = 0.835, p < .001), white blood cell counts (WBC; QS = 0.743, p < .001), glutamic oxaloacetic transaminase (AST; QS = 0.735, p < .001), blood glucose (BG; QS = 0.608, p = .059), fibrin degradation product (FDP; QS = 0.730, p = .080), and partial pressure of carbon dioxide (PCO(2)), and lower in oxygen saturation (SO(2); QS = 0.312, p < .001), calcium (Ca(2+); QS = 0.306, p = .073), and pH. Most of these indexes were associated with an increased fatality risk, and predictive for the probability of death. Especially, CRP is the most prominent index with and odds ratio of 205.97 and predictive accuracy of 93.2%. CONCLUSION: Laboratory indexes provided reliable information on mortality in critically ill patients with COVID-19, which might help improve clinical prediction and treatment at an early stage. |Aged[MESH] |COVID-19/*epidemiology[MESH] |China/epidemiology[MESH] |Comorbidity[MESH] |Critical Illness/*mortality[MESH] |Female[MESH] |Humans[MESH] |Male[MESH] |Middle Aged[MESH] |Retrospective Studies[MESH] |Risk Assessment/*methods[MESH] |Risk Factors[MESH] |SARS-CoV-2[MESH]Identifying and quantifying robust risk factors for mortality in criti(epmc).pdf DeepDyve Pubget Overpricing