The Accuracy of Urinary TIMP-2 and IGFBP7 for the Diagnosis of Cardiac Surgery-Associated Acute Kidney Injury: A Systematic Review and Meta-Analysis

Abstract

Background:

Tissue inhibitor of metalloproteinase 2 (TIMP-2) and insulin-like growth factor binding protein 7 (IGFBP7) are recent promising markers for identification of cardiac surgery-associated acute kidney injury (CSA-AKI). The aim of this study was systematically and quantitatively to evaluate the accuracy of urinary TIMP-2 and IGFBP7 for the diagnosis of CSA-AKI.

Methods:

Three databases including PubMed, ISI web of knowledge, and Embase were systematically searched from inception to March 2018. Two investigators conducted the processes of literature search study selection, data extraction, and quality evaluation independently. Meta-DiSc and STATA were used for all statistical analyses.

Results:

A total of 8 studies comprising 552 patients were included in this meta-analysis. Pooled sensitivity and specificity with corresponding 95% confidence intervals (CIs) were 0.79 (95% CI, 0.71-0.86, I ² = 74.2%) and 0.76 (95% CI, 0.72-0.80, I ² = 80.8%), respectively. Pooled positive likelihood ratio (LR), negative LR, and diagnostic odds ratio were 3.49 (95% CI, 2.44-5.00, I ² = 61.5%), 0.31(95% CI, 0.19-0.51, I ² = 51.8%), and 14.89 (95% CI, 7.31-30.32, I ² = 27.9%), respectively. The area under curve estimated by summary receiver operating characteristic was 0.868 (standard error [SE] 0.032) with a Q* value of 0.799 (SE 0.032). Sensitivity analysis demonstrated that one study notably affected the stability of pooled results. One of the subgroups investigated—AKI threshold—could account for partial heterogeneity.

Conclusion:

Urinary TIMP-2 and IGFBP7 is a helpful biomarker for early diagnosis of CSA-AKI. And, the potential of this biomarker with a broader spectrum of clinical settings may be the focus of future studies.

Keywords

TIMP-2 IGFBP7 cardiac surgery-associated acute kidney injury diagnosis

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References

Bernardi

Schmidlin

Schiferer

, et al. Impact of preoperative serum creatinine on short- and long-term mortality after cardiac surgery: a cohort study. Br J Anaesth. 2015;114(1):53–62.

Thakar

. Perioperative acute kidney injury. Adv Chronic Kidney Dis. 2013;20(1):67–75.

Wijeysundera

Karkouti

Dupuis

, et al. Derivation and validation of a simplified predictive index for renal replacement therapy after cardiac surgery. JAMA. 2007;297(16):1801–1809.

Murray

Mehta

Shaw

, et al. Potential use of biomarkers in acute kidney injury: report and summary of recommendations from the 10th Acute Dialysis Quality Initiative consensus conference. Kidney Int. 2014;85(3):513–521.

Katagiri

Doi

Honda

, et al. Combination of two urinary biomarkers predicts acute kidney injury after adult cardiac surgery. Ann Thorac Surg. 2012;93(2):577–583.

Parikh

Thiessen-Philbrook

Garg

, et al. Performance of kidney injury molecule-1 and liver fatty acid-binding protein and combined biomarkers of AKI after cardiac surgery. Clin J Am Soc Nephrol. 2013;8(7):1079–1088.

Susantitaphong

Perianayagam

Tighiouart

Kouznetsov

Liangos

Jaber

. Urinary alpha- and pi-glutathione S-transferases for early detection of acute kidney injury following cardiopulmonary bypass. Biomarkers. 2013;18(4):331–337.

Liang

Liu

Chen

, et al. Combination of urinary kidney injury molecule-1 and interleukin-18 as early biomarker for the diagnosis and progressive assessment of acute kidney injury following cardiopulmonary bypass surgery: a prospective nested case–control study. Biomarkers. 2010;15(4):332–339.

Liu

Che

Xue

, et al. Urinary L-FABP and its combination with urinary NGAL in early diagnosis of acute kidney injury after cardiac surgery in adult patients. Biomarkers. 2013;18(1):95–101.

10.

US Food and Drug Administration. FDA allows marketing of the first test to assess risk of developing acute kidney injury [Press release]; 2016. http://www.fda.gov/News%20Events/Newsroom/Press%20Announcements/ucm%20412910.htm. Accessed February 5, 2016.

11.

Vijayan

Faubel

Askenazi

, et al. American Society of Nephrology Acute Kidney Injury Advisory Group. Clinical use of the urine biomarker [TIMP-2] × [IGFBP7] for acute kidney injury risk assessment. Am J K idney Dis. 2016;68:19–28.

12.

Price

Safi

rstein RL

Megyesi

The cell cycle and acute kidney injury. Kidney Int. 2009;76:604–613.

13.

Johnson

ACM

Zager

. Mechanisms underlying increased TIMP2 and IGFBP7 urinary excretion in experimental AKI. J Am Soc Nephrol. 2018;29(8):2157–2167.

14.

Kashani

Al-Khafaji

Ardiles

, et al. Discovery and validation of cell cycle arrest biomarkers in human acute kidney injury. Crit Care. 2013;17(1):R25.

15.

Bihorac

Chawla

Shaw

, et al. Validation of cell-cycle arrest biomarkers for acute kidney injury using clinical adjudication. Am J Respir Crit Care Med. 2014;189(8):932–939.

16.

Hoste

McCullough

Kashani

, et al. Derivation and validation of cutoffs for clinical use of cell cycle arrest biomarkers. Nephrol Dial Transplant. 2014;29(11):2054–2061.

17.

Dusse

Edayadiyil-Dudasova

Thielmann

, et al. Early prediction of acute kidney injury after transapical and transaortic aortic valve implantation with urinary G1 cell cycle arrest biomarkers. BMC Anesthesiol. 2016;16:76.

18.

Stroup

Berlin

Morton

, et al. Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group. JAMA. 2000;283(15):2008–2012.

19.

Liberati

Altman

Tetzlaff

, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. J Clin Epidemiol. 2009;62(10):e1–e34.

20.

Khwaja

KDIGO clinical practice guidelines for acute kidney injury. Nephron Clin Pract. 2012;120(4):c179–c184.

21.

Whiting

Rutjes

Westwood

, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155(8):529–536.

22.

Zamora

Abraira

Muriel

Khan

Coomarasamy

. Meta-DiSc: a software for meta-analysis of test accuracy data. BMC Med Res Methodol. 2006;6:31.

23.

Thompson

Sharp

. Explaining heterogeneity in meta-analysis: a comparison of methods. Stat Med. 1999;18(20):2693–2708.

24.

Lijmer

Bossuyt

Heisterkamp

. Exploring sources of heterogeneity in systematic reviews of diagnostic tests. Stat Med. 2002;21(11):1525–1537.

25.

Meersch

Schmidt

Van Aken

, et al. Urinary TIMP-2 and IGFBP7 as early biomarkers of acute kidney injury and renal recovery following cardiac surgery. PLoS One. 2014;9(3):e93460.

26.

Wetz

Richardt

Wand

, et al.

Quantification of urinary TIMP-2 and IGFBP-7: an adequate diagnostic test to predict acute kidney injury after cardiac surgery?

Crit Care. 2015;19:3.

27.

Pilarczyk

Edayadiyil-Dudasova

Wendt

, et al. Urinary [TIMP-2]*[IGFBP7] for early prediction of acute kidney injury after coronary artery bypass surgery. Ann Intensive Care. 2015;5(1):50.

28.

Oezkur

Magyar

Thomas

, et al. TIMP-2*IGFBP7 (Nephrocheck(R)) measurements at intensive care unit admission after cardiac surgery are predictive for acute kidney injury within 48 hours. Kidney Blood Press Res. 2017;42(3):456–467.

29.

Wang

Zou

Jin

, et al. Urinary TIMP-2 and IGFBP7 for the prediction of acute kidney injury following cardiac surgery. BMC Nephrol. 2017;18(1):177.

30.

Finge

Bertran

Roger

, et al. Interest of urinary [TIMP-2] × [IGFBP-7] for predicting the occurrence of acute kidney injury after cardiac surgery: a gray zone approach. Anesth Analg. 2017;125(3):762–769.

31.

Mayer

Bolliger

Scholz

, et al. Urine biomarkers of tubular renal cell damage for the prediction of acute kidney injury after cardiac surgery – a pilot study. J Cardiothorac Vasc Anesth. 2017;31(6):2072–2079.

32.

Ostermann

Joannidis

. Acute kidney injury 2016: diagnosis and diagnostic workup. Critical Care. 2016;20(1):299.

33.

Ortega-Loubon

Fernandez-Molina

Carrascal-Hinojal

Fulquet-Carreras

. Cardiac surgery-associated acute kidney injury. Ann Cardiac Anaesthes. 2016;19(4):687–698.

34.

Uchino

Kellum

Bellomo

, et al. Acute renal failure in critically ill patients: a multinational, multicenter study. JAMA. 2005;294(7):813–818.

35.

Kim

Jang

Huh

, et al. Incidence, risk factors, and prediction of acute kidney injury after off-pump coronary artery bypass grafting. Ren Fail. 2011;33(3):316–322.

36.

Meersch

Schmidt

Hoffmeier

, et al. Prevention of cardiac surgery-associated AKI by implementing the KDIGO guidelines in high risk patients identified by biomarkers: the PrevAKI randomized controlled trial. Intensive Care Med. 2017;43(11):1551–1561.

37.

Lameire

Hoste

. Reflections on the definition, classification, and diagnostic evaluation of acute renal failure. Curr Opin Crit Care. 2004;10(6):468–475.

38.

Devarajan

. Update on mechanisms of ischemic acute kidney injury. J Am Soc Nephrol. 2006;17(6):1503–1520.

39.

Boonstra

Post

. Molecular events associated with reactive oxygen species and cell cycle progression in mammalian cells. Gene. 2004;337:1–13.

40.

Rodier

Campisi

Bhaumik

. Two faces of p53: aging and tumor suppression. Nucleic Acids Res. 2007;35(22):7475–7484.

41.

O’Neal

Shaw

Billings

IV . Acute kidney injury following cardiac surgery: current understanding and future directions. Crit Care. 2016;20(1):187.

42.

Katz

Ronco

. Acute kidney stress – a useful term based on evolution in the understanding of acute kidney injury. Crit Care. 2016;20:23.

43.

Fischer

Bachmann

Jaeschke

. A readers’ guide to the interpretation of diagnostic test properties: clinical example of sepsis. Intensive Care Med. 2003;29(7):1043–1051.

44.

McMahon

Koyner

: Risk stratification for acute kidney injury: are biomarkers enough? Adv Chronic Kidney Dis. 2016;23(3):167–178.

45.

Song

Liu

Niu

Lin

. Diagnostic prediction of urinary [TIMP-2] × [IGFBP7] for acute kidney injury: a meta-analysis exploring detection time and cutoff levels. Oncotarget. 2017;8(59):100631–100639.

46.

Gong

Tian

Liao

. Diagnostic value of urine tissue inhibitor of metalloproteinase-2 and insulin-like growth factor-binding protein 7 for acute kidney injury: a meta-analysis. PLoS One. 2017;12(1):e0170214.

47.

Jia

Huang

Zheng

. Prognostic value of cell cycle arrest biomarkers in patients at high risk for acute kidney injury: a systematic review and meta-analysis. Nephrology (Carlton). 2017;22(11):831–837.

48.

Liu

Mao

, et al. The diagnostic accuracy of urinary [TIMP-2].[IGFBP7] for acute kidney injury in adults: a PRISMA-compliant meta-analysis. Medicine. 2017;96(27):e7484.

49.

Jia

Huang

Zheng

: Diagnostic value of urinary tissue inhibitor of metalloproteinase-2 and insulin-like growth factor binding protein 7 for acute kidney injury: a meta-analysis. Crit Care. 2017;21(1):77.

50.

Tangri

Komenda

, et al. Urinary, plasma, and serum biomarkers’ utility for predicting acute kidney injury associated with cardiac surgery in adults: a meta-analysis. Am J Kidney Dis. 2015;66(6):993–1005.

51.

Prazella

Coca

Hall

Iyanam

Koraishy

Parikh

. Urine microscopy is associated with severity and worsening of acute kidney injury in hospitalized patients. Clin J Am Soc Nephrol. 2010;5(3):402–408.

52.

Kimmel

Shi

Wasser

Biegger

Alscher

Schanz

. Urinary [TIMP-2].[IGFBP7] – novel biomarkers to predict acute kidney injury. Am J Nephrol. 2016;43(5):375–382.

53.

Perazella

. The urine sediment as a biomarker of kidney disease. Am J Kidney Dis. 2015;66(5):748–755.

54.

Vandenberghe

De Loor

Hoste

. Diagnosis of cardiac surgery-associated acute kidney injury from functional to damage biomarkers. Curr Opin Anaesthesiol. 2017;30(1):66–75.

55.

Beitland

Waldum-Grevbo

Nakstad

, et al. Urine biomarkers give early prediction of acute kidney injury and outcome after out-of-hospital cardiac arrest. Crit Care. 2016;20(1):314.

56.

Najafi

: Serum creatinine role in predicting outcome after cardiac surgery beyond acute kidney injury. World J Cardiol. 2014;6(9):1006–1021.