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10.1093/ckj/sfw107 http://scihub22266oqcxt.onion/10.1093/ckj/sfw107 C5466088!5466088!28616211     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=28616211&cmd=llinks): Failed to open stream: HTTP request failed! 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Acute kidney injury in patients with severe sepsis or septic shock: a comparison between the ?Risk, Injury, Failure, Loss of kidney function, End-stage kidney disease? (RIFLE), Acute Kidney Injury Network (AKIN) and Kidney Disease: Improving Global Outcomes (KDIGO) classifications |pdf=|usr=}}{{28616211}} gab.com Text Acute kidney injury in patients with severe sepsis or septic shock: a comparison between the Risk, Injury, Failure, Loss of kidney function, End-stage kidney disease (RIFLE), Acute Kidney Injury Network (AKIN) and Kidney Disease: Improving Global Outcomes (KDIGO) classifications JO: Clin Kidney J http://www.kidney.de/pget.php?&tw=1&pmid=28616211 #FOAvip Purpose: Using the Risk, Injury, Failure, Loss of kidney function, End-stage kidney disease (RIFLE), Acute Kidney Injury Network (AKIN) and Kidney Disease: Improving Global Outcomes (KDIGO) systems, the incidence of acute kidney injury (AKI) and their ability to predict in-hospital mortality in severe sepsis or septic shock was compared. Materials and methods: We performed a retrospective analysis of 457 critically ill patients with severe sepsis or septic shock hospitalized between January 2008 and December 2014. Multivariate logistic regression was employed to evaluate the association between the RIFLE, AKIN and KDIGO systems with in-hospital mortality. Model fit was assessed by the goodness-of-fit test and discrimination by the area under the receiver operating characteristic (AUROC) curve. Statistical significance was defined as P < 0.05. Results: RIFLE (84.2%) and KDIGO (87.5%) identified more patients with AKI than AKIN (72.8%) (P < 0.001). AKI defined by AKIN and KDIGO was associated with in-hospital mortality {AKIN: adjusted odds ratio [OR] 2.3[95% confidence interval (CI) 1.3?4], P = 0.006; KDIGO: adjusted OR 2.7[95% CI 1.2?6.2], P = 0.021} while AKI defined by RIFLE was not [adjusted OR 2.0 (95% CI 1?4), P = 0.063]. The AUROC curve for in-hospital mortality was similar between the three classifications (RIFLE 0.652, P < 0.001; AKIN 0.686, P < 0.001; KDIGO 0.658, P < 0.001). Conclusions: RIFLE and KDIGO diagnosed more patients with AKI than AKIN, but the prediction ability for in-hospital mortality was similar between the three systems. Twit Text FOAVip Acute kidney injury in patients with severe sepsis or septic shock: a comparison between the Risk, Injury, Failure, Loss of kidney function, End-stage kidney disease (RIFLE), Acute Kidney Injury Network (AKIN) and Kidney Disease: Improving Global Outcomes (KDIGO) classifications ????Clin Kidney J http://www.kidney.de/pget.php?&tw=1&pmid=28616211 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28616211 #FOAvip English Wikipedia <ref>{{Cite pmid|28616211}}</ref> Acute kidney injury in patients with severe sepsis or septic shock: a comparison between the ?Risk, Injury, Failure, Loss of kidney function, End-stage kidney disease? (RIFLE), Acute Kidney Injury Network (AKIN) and Kidney Disease: Improving Global Outcomes (KDIGO) classifications #MMPMID28616211 Pereira M; Rodrigues N; Godinho I; Gameiro J; Neves M; Gouveia J; Costa e Silva Z; Lopes JA Clin Kidney J 2017[Jun]; 10 (3): 332-40 PMID28616211show ga Purpose: Using the Risk, Injury, Failure, Loss of kidney function, End-stage kidney disease (RIFLE), Acute Kidney Injury Network (AKIN) and Kidney Disease: Improving Global Outcomes (KDIGO) systems, the incidence of acute kidney injury (AKI) and their ability to predict in-hospital mortality in severe sepsis or septic shock was compared. Materials and methods: We performed a retrospective analysis of 457 critically ill patients with severe sepsis or septic shock hospitalized between January 2008 and December 2014. Multivariate logistic regression was employed to evaluate the association between the RIFLE, AKIN and KDIGO systems with in-hospital mortality. Model fit was assessed by the goodness-of-fit test and discrimination by the area under the receiver operating characteristic (AUROC) curve. Statistical significance was defined as P < 0.05. Results: RIFLE (84.2%) and KDIGO (87.5%) identified more patients with AKI than AKIN (72.8%) (P < 0.001). AKI defined by AKIN and KDIGO was associated with in-hospital mortality {AKIN: adjusted odds ratio [OR] 2.3[95% confidence interval (CI) 1.3?4], P = 0.006; KDIGO: adjusted OR 2.7[95% CI 1.2?6.2], P = 0.021} while AKI defined by RIFLE was not [adjusted OR 2.0 (95% CI 1?4), P = 0.063]. The AUROC curve for in-hospital mortality was similar between the three classifications (RIFLE 0.652, P < 0.001; AKIN 0.686, P < 0.001; KDIGO 0.658, P < 0.001). Conclusions: RIFLE and KDIGO diagnosed more patients with AKI than AKIN, but the prediction ability for in-hospital mortality was similar between the three systems. �Acute kidney injury in patients with severe sepsis or septic shock: a (epmc).pdf DeepDyve Pubget Overpricing