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      Biomarkers for prediction of renal replacement therapy in acute kidney injury: a systematic review and meta-analysis

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          Abstract

          Purpose

          Acute kidney injury (AKI) frequently occurs in critically ill patients and often precipitates use of renal replacement therapy (RRT). However, the ideal circumstances for whether and when to start RRT remain unclear. We performed evidence synthesis of the available literature to evaluate the value of biomarkers to predict receipt of RRT for AKI.

          Methods

          We conducted a PRISMA-guided systematic review and meta-analysis including all trials evaluating biomarker performance for prediction of RRT in AKI. A systematic search was applied in MEDLINE, Embase, and CENTRAL databases from inception to September 2017. All studies reporting an area under the curve (AUC) for a biomarker to predict initiation of RRT were included.

          Results

          Sixty-three studies comprising 15,928 critically ill patients (median per study 122.5 [31–1439]) met eligibility. Forty-one studies evaluating 13 different biomarkers were included. Of these biomarkers, neutrophil gelatinase-associated lipocalin (NGAL) had the largest body of evidence. The pooled AUCs for urine and blood NGAL were 0.720 (95% CI 0.638–0.803) and 0.755 (0.706–0.803), respectively. Blood creatinine and cystatin C had pooled AUCs of 0.764 (0.732–0.796) and 0.768 (0.729–0.807), respectively. For urine biomarkers, interleukin-18, cystatin C, and the product of tissue inhibitor of metalloproteinase-2 and insulin growth factor binding protein-7 showed pooled AUCs of 0.668 (0.606–0.729), 0.722 (0.575–0.868), and 0.857 (0.789–0.925), respectively.

          Conclusion

          Though several biomarkers showed promise and reasonable prediction of RRT use for critically ill patients with AKI, the strength of evidence currently precludes their routine use to guide decision-making on when to initiate RRT.

          Electronic supplementary material

          The online version of this article (10.1007/s00134-018-5126-8) contains supplementary material, which is available to authorized users.

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          Most cited references86

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          The meaning and use of the area under a receiver operating characteristic (ROC) curve.

          A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect differences in the accuracy of diagnostic techniques.
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            Discovery and validation of cell cycle arrest biomarkers in human acute kidney injury

            Introduction Acute kidney injury (AKI) can evolve quickly and clinical measures of function often fail to detect AKI at a time when interventions are likely to provide benefit. Identifying early markers of kidney damage has been difficult due to the complex nature of human AKI, in which multiple etiologies exist. The objective of this study was to identify and validate novel biomarkers of AKI. Methods We performed two multicenter observational studies in critically ill patients at risk for AKI - discovery and validation. The top two markers from discovery were validated in a second study (Sapphire) and compared to a number of previously described biomarkers. In the discovery phase, we enrolled 522 adults in three distinct cohorts including patients with sepsis, shock, major surgery, and trauma and examined over 300 markers. In the Sapphire validation study, we enrolled 744 adult subjects with critical illness and without evidence of AKI at enrollment; the final analysis cohort was a heterogeneous sample of 728 critically ill patients. The primary endpoint was moderate to severe AKI (KDIGO stage 2 to 3) within 12 hours of sample collection. Results Moderate to severe AKI occurred in 14% of Sapphire subjects. The two top biomarkers from discovery were validated. Urine insulin-like growth factor-binding protein 7 (IGFBP7) and tissue inhibitor of metalloproteinases-2 (TIMP-2), both inducers of G1 cell cycle arrest, a key mechanism implicated in AKI, together demonstrated an AUC of 0.80 (0.76 and 0.79 alone). Urine [TIMP-2]·[IGFBP7] was significantly superior to all previously described markers of AKI (P 0.72. Furthermore, [TIMP-2]·[IGFBP7] significantly improved risk stratification when added to a nine-variable clinical model when analyzed using Cox proportional hazards model, generalized estimating equation, integrated discrimination improvement or net reclassification improvement. Finally, in sensitivity analyses [TIMP-2]·[IGFBP7] remained significant and superior to all other markers regardless of changes in reference creatinine method. Conclusions Two novel markers for AKI have been identified and validated in independent multicenter cohorts. Both markers are superior to existing markers, provide additional information over clinical variables and add mechanistic insight into AKI. Trial registration ClinicalTrials.gov number NCT01209169.
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              Early detection of acute renal failure by serum cystatin C.

              Acute renal failure (ARF) is associated with high mortality. Presently, no specific therapy for ARF exists. Therefore, early detection of ARF is critical to prevent its progression. However, serum creatinine, the standard marker to detect ARF, demonstrates major limitations. We prospectively evaluated whether serum cystatin C detected ARF earlier than serum creatinine. In 85 patients at high risk to develop ARF, serum creatinine and cystatin C were determined daily. ARF was defined according to the Risk of renal dysfunction, Injury to the kidney, Failure of kidney function, Loss of kidney function, and ESRD (RIFLE) classification when creatinine increased by >/=50% (R-criteria), by >/=100% (I-criteria), or by >/=200% (F-criteria). In analogy, ARF was detected when cystatin C increased by >/=50%, by >/=100%, or by >/=200%. Forty-four patients developed ARF and 41 served as controls. In ARF by R-, I-, and F-criteria, the increase of cystatin C significantly preceded that of creatinine. Specifically, serum cystatin C increased already by >/=50% 1.5 +/- 0.6 days earlier compared to creatinine. Serum cystatin C demonstrated a high diagnostic value to detect ARF as indicated by area under the curve of the ROC analysis of 0.82 and 0.97 on the two days before the R-criteria was fulfilled by creatinine. Cystatin C detected ARF according to the R-criteria with a sensitivity of 55% and 82% on these days, respectively. Cystatin C also performed excellently, detecting ARF defined by the I- and F-criteria two days prior to creatinine, and moderately well predicting renal replacement therapy in the further course of ARF. Additionally, low T(3)- or T(3)/T(4) syndrome, glucocorticoid deficiency and excess did not affect cystatin C levels, adding to its usefulness in critically ill patients with ARF. Serum cystatin C is a useful detection marker of ARF, and may detect ARF one to two days earlier than creatinine.
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                Author and article information

                Contributors
                +43 512 504 24180 , michael.joannidis@i-med.ac.at
                Journal
                Intensive Care Med
                Intensive Care Med
                Intensive Care Medicine
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                0342-4642
                1432-1238
                14 March 2018
                14 March 2018
                2018
                : 44
                : 3
                : 323-336
                Affiliations
                [1 ]ISNI 0000 0000 8853 2677, GRID grid.5361.1, Division of Intensive Care and Emergency Medicine, Department of Internal Medicine, , Medical University Innsbruck, ; Anichstrasse 35, 6020 Innsbruck, Austria
                [2 ]ISNI 0000 0000 8853 2677, GRID grid.5361.1, Department of Medical Statistics, Informatics and Health Economics, , Medical University Innsbruck, ; Innsbruck, Austria
                [3 ]GRID grid.17089.37, Department of Critical Care Medicine, Faculty of Medicine and Dentistry, , University of Alberta, ; Edmonton, Canada
                [4 ]ISNI 0000 0000 9734 7019, GRID grid.41719.3a, UMIT-The Health and Life Sciences University, ; Hall, Austria
                Author information
                http://orcid.org/0000-0002-6996-0881
                Article
                5126
                10.1007/s00134-018-5126-8
                5861176
                29541790
                0212c098-106b-4ae8-8548-448cd6309613
                © The Author(s) 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 13 December 2017
                : 2 March 2018
                Categories
                Systematic Review
                Custom metadata
                © Springer-Verlag GmbH Germany, part of Springer Nature and ESICM 2018

                Emergency medicine & Trauma
                acute kidney injury,renal replacement therapy,biomarkers,prediction,meta-analysis

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