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. 2018 Mar;44(3):323-336.
doi: 10.1007/s00134-018-5126-8. Epub 2018 Mar 14.

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

Sebastian J Klein  1 , Anna K Brandtner  1 , Georg F Lehner  1 , Hanno Ulmer  2 , Sean M Bagshaw  3 , Christian J Wiedermann  4 , Michael Joannidis  5
Affiliations

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

Sebastian J Klein et al. Intensive Care Med. 2018 Mar.

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.

Keywords: Acute kidney injury; Biomarkers; Meta-analysis; Prediction; Renal replacement therapy.

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Conflict of interest statement

SJK, AKB, GFL, and HU report no conflict of interest. SMB declares receiving honoraria as a speaker and consultant and research support from Baxter Healthcare Corp. He was a co-investigator in renal biomarker studies. CJW declares receiving honoraria as a speaker for Kedrion, CSL Behring, Baxter, and Grifols and as a consultant for CSL Behring and Grifols. MJ has received honoraria or research support from Baxter Healthcare Corp, AM-Pharma, CLS Behring, Fresenius, and Astute Medical. He was a co-investigator in renal biomarker studies.

Figures

Fig. 1
Fig. 1
Forest plots of urinary NGAL predicting RRT. a Urinary concentration of NGAL. b Urinary NGAL normalized to urinary creatinine. AUC area under the curve, RE Model random effects model, CI confidence interval, RRT renal replacement therapy (i.e., number of patients having received RRT)
Fig. 2
Fig. 2
Urinary cystatin C normalized to urinary creatinine. AUC area under the curve, RE Model random effects model, CI confidence interval, RRT renal replacement therapy (i.e., number of patients having received RRT)
Fig. 3
Fig. 3
Forest plot of urinary TIMP-2 × IGFBP-7 predicting RRT. AUC area under the curve, RE Model random effects model, CI confidence interval, RRT renal replacement therapy (i.e., number of patients having received RRT)
Fig. 4
Fig. 4
Forest plot of plasma, serum, and whole blood NGAL predicting RRT. AUC area under the curve, RE Model random effects model, CI confidence interval, RRT renal replacement therapy (i.e., number of patients having received RRT)
Fig. 5
Fig. 5
Forest plot of plasma and serum cystatin C predicting RRT. AUC area under the curve, RE Model random effects model, CI confidence interval, RRT renal replacement therapy (i.e., number of patients having received RRT)
Fig. 6
Fig. 6
Forest plot of plasma and serum creatinine predicting RRT. AUC area under the curve, RE Model random effects model, CI confidence interval, RRT renal replacement therapy (i.e., number of patients having received RRT)
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Comment in

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