Literature DB >> 26048891

Biomarker Enhanced Risk Prediction for Adverse Outcomes in Critically Ill Patients Receiving RRT.

Francis Pike1, Raghavan Murugan2, Christopher Keener1, Paul M Palevsky3, Anitha Vijayan4, Mark Unruh5, Kevin Finkel6, Xiaoyan Wen2, John A Kellum7.   

Abstract

BACKGROUND AND OBJECTIVES: Higher plasma concentrations of inflammatory and apoptosis markers in critically ill patients receiving RRT are associated with RRT dependence and death. This study objective was to examine whether plasma inflammatory (IL-6, -8, -10, and -18; macrophage migration inhibitory factor) and apoptosis (death receptor-5, tumor necrosis factor receptor I and II) biomarkers augment risk prediction of renal recovery and mortality compared with clinical models. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: The Biologic Markers of Recovery for the Kidney study (n=817) was a prospective, nested, observational cohort study conducted as an ancillary to the Veterans Affairs/National Institutes of Health Acute renal failure Trial Network study, a randomized trial of intensive versus less intensive RRT in critically ill patients with AKI conducted between November 2003 and July 2007 at 27 Veterans Affairs- and university-affiliated centers. Primary outcomes of interest were renal recovery and mortality at day 60.
RESULTS: A parsimonious clinical model consisting of only four variables (age, mean arterial pressure, mechanical ventilation, and bilirubin) predicted renal recovery (area under the receiver-operating characteristic curve [AUROC], 0.73; 95% confidence interval [95% CI], 0.68 to 0.78) and mortality (AUROC, 0.74; 95% CI, 0.69 to 0.78). By contrast, individual biomarkers were only modestly predictive of renal recovery (AUROC range, 0.55-0.63) and mortality (AUROC range, 0.54-0.68). Adding plasma IL-8 to a parsimonious model augmented prediction of recovery (AUROC, 0.76; 95% CI, 0.71 to 0.81; P=0.04) and mortality (AUROC, 0.78; 95% CI, 0.73 to 0.82; P<0.01) compared with the clinical model alone.
CONCLUSIONS: This study suggests that a simple four-variable clinical model with plasma IL-8 had predictive value for renal recovery and mortality. These findings require external validation but could easily be used by clinicians.
Copyright © 2015 by the American Society of Nephrology.

Entities:  

Keywords:  apoptosis; dialysis; mortality

Mesh:

Substances:

Year:  2015        PMID: 26048891      PMCID: PMC4527029          DOI: 10.2215/CJN.09911014

Source DB:  PubMed          Journal:  Clin J Am Soc Nephrol        ISSN: 1555-9041            Impact factor:   8.237


  15 in total

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3.  Index for rating diagnostic tests.

Authors:  W J YOUDEN
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4.  Risk modeling in acute renal failure requiring dialysis: the introduction of a new model.

Authors:  E P Paganini; W K Halstenberg; M Goormastic
Journal:  Clin Nephrol       Date:  1996-09       Impact factor: 0.975

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Authors:  E R DeLong; D M DeLong; D L Clarke-Pearson
Journal:  Biometrics       Date:  1988-09       Impact factor: 2.571

6.  APACHE II: a severity of disease classification system.

Authors:  W A Knaus; E A Draper; D P Wagner; J E Zimmerman
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Authors:  M E Charlson; P Pompei; K L Ales; C R MacKenzie
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8.  Improved performance of urinary biomarkers of acute kidney injury in the critically ill by stratification for injury duration and baseline renal function.

Authors:  Zoltán H Endre; John W Pickering; Robert J Walker; Prasad Devarajan; Charles L Edelstein; Joseph V Bonventre; Christopher M Frampton; Michael R Bennett; Qing Ma; Venkata S Sabbisetti; Vishal S Vaidya; Angela M Walcher; Geoffrey M Shaw; Seton J Henderson; Maryam Nejat; John B W Schollum; Peter M George
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9.  Model to predict mortality in critically ill adults with acute kidney injury.

Authors:  Sevag Demirjian; Glenn M Chertow; Jane Hongyuan Zhang; Theresa Z O'Connor; Joseph Vitale; Emil P Paganini; Paul M Palevsky
Journal:  Clin J Am Soc Nephrol       Date:  2011-09       Impact factor: 8.237

10.  Intensity of renal support in critically ill patients with acute kidney injury.

Authors:  Paul M Palevsky; Jane Hongyuan Zhang; Theresa Z O'Connor; Glenn M Chertow; Susan T Crowley; Devasmita Choudhury; Kevin Finkel; John A Kellum; Emil Paganini; Roland M H Schein; Mark W Smith; Kathleen M Swanson; B Taylor Thompson; Anitha Vijayan; Suzanne Watnick; Robert A Star; Peter Peduzzi
Journal:  N Engl J Med       Date:  2008-05-20       Impact factor: 91.245

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Review 2.  Biomarkers for the Early Detection and Prognosis of Acute Kidney Injury.

Authors:  Rakesh Malhotra; Edward D Siew
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4.  Development and validation of clinical prediction models for acute kidney injury recovery at hospital discharge in critically ill adults.

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5.  Discovery of Novel Proteomic Biomarkers for the Prediction of Kidney Recovery from Dialysis-Dependent AKI Patients.

Authors:  Jaclyn R Daniels; Jennie Z Ma; Zhijun Cao; Richard D Beger; Jinchun Sun; Laura Schnackenberg; Lisa Pence; Devasmita Choudhury; Paul M Palevsky; Didier Portilla; Li-Rong Yu
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Review 6.  Overview of Diagnostic Criteria and Epidemiology of Acute Kidney Injury and Acute Kidney Disease in the Critically Ill Patient.

Authors:  Bethany C Birkelo; Neesh Pannu; Edward D Siew
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7.  Long-Term Clinical Outcomes after Early Initiation of RRT in Critically Ill Patients with AKI.

Authors:  Melanie Meersch; Mira Küllmar; Christoph Schmidt; Joachim Gerss; Toni Weinhage; Andreas Margraf; Thomas Ermert; John A Kellum; Alexander Zarbock
Journal:  J Am Soc Nephrol       Date:  2017-12-01       Impact factor: 10.121

8.  Biomarkers of AKI Progression after Pediatric Cardiac Surgery.

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9.  Red Blood Cell Distribution Width and Neutrophil-to-Lymphocyte Ratio in Predicting Adverse Outcomes of Acute Kidney Injury in Hospitalized Patients.

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Review 10.  Prediction Models and Their External Validation Studies for Mortality of Patients with Acute Kidney Injury: A Systematic Review.

Authors:  Tetsu Ohnuma; Shigehiko Uchino
Journal:  PLoS One       Date:  2017-01-05       Impact factor: 3.240

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