Indian Journal of Critical Care Medicine

Register      Login



Volume / Issue

Online First

Related articles

VOLUME 21 , ISSUE 1 ( 2017 ) > List of Articles


Dynamic changes of plasma neutrophil gelatinase-associated lipocalin predicted mortality in critically ill patients with systemic inflammatory response syndrome

Suhaila Nanyan, Azrina Ralib, Mohd Mat Nor

Keywords : Mortality, neutrophil gelatinase-associated lipocalin, sepsis, systemic inflammatory response syndrome

Citation Information : Nanyan S, Ralib A, Mat Nor M. Dynamic changes of plasma neutrophil gelatinase-associated lipocalin predicted mortality in critically ill patients with systemic inflammatory response syndrome. Indian J Crit Care Med 2017; 21 (1):23-29.

DOI: 10.4103/0972-5229.198322

License: CC BY-ND 3.0

Published Online: 01-11-2017

Copyright Statement:  Copyright © 2017; The Author(s).


Background and Aims: About 50% of patients admitted to the Intensive Care Unit have systemic inflammatory response syndrome (SIRS), and about 10%-20% of them died. Early risk stratification is important to reduce mortality. Plasma neutrophil gelatinase-associated lipocalin (NGAL) is increased by inflammation and infection. Its ability to predict mortality in SIRS patients is of interest. We evaluated the ability of serial measurement of NGAL for the prediction of mortality in critically ill patients with SIRS. Materials and Methods: This is a secondary analysis of a single-center, prospective, observational study. Patients who fulfill the SIRS criteria were recruited in the study. Delta NGAL at 24 and 48 h (ΔNGAL-24 and ΔNGAL-48) was defined as 24 and 48 h NGAL minus day 1 NGAL; NGAL clearance (NGALc) was defined as percentage of ΔNGAL over day 1 NGAL. The primary outcome of the study is in-hospital mortality. Results: A total of 151 patients were analyzed, of which 53 (35%) died. Nonsurvivors were older (51 vs. 45, P = 0.03) and had higher Sequential Organ Failure Assessment (9 ± 7 vs. 7 ± 4, P = 0.02) and Simplified Acute Physiology Score II (47 ± 15 vs. 40 ± 15, P = 0.01) scores as compared to survivors. NGAL concentrations over 3 days were higher in nonsurvivors compared to survivors (repeated measures analysis of variance, P = 0.02). Day 1 NGAL, ΔNGAL-24, and NGALc-24 were not independently predictive of mortality. However, day 3 NGAL, ΔNGAL-48, and NGALc-48 were predictive after adjusted for age and severity of illness (odds ratio 9.1 [1.97-41.7]). Conclusions: NGAL dynamics over 48 h independently predicted mortality in critically ill patients with SIRS. This could assist clinicians in risk stratification of this group of high-risk patients.

PDF Share
  1. Brun-Buisson C. The epidemiology of the systemic inflammatory response. Intensive Care Med 2000;26 Suppl 1:S64-74.
  2. Pittet D, Rangel-Frausto S, Li N, Tarara D, Costigan M, Rempe L, et al. Systemic inflammatory response syndrome, sepsis, severe sepsis and septic shock: Incidence, morbidities and outcomes in surgical ICU patients. Intensive Care Med 1995;21:302-9.
  3. Qiu H, Du B, Liu D. Clinical study of systemic inflammatory response syndrome and multiple organ dysfunction syndrome in critically patients. Zhonghua Wai Ke Za Zhi 1997;35:402-5.
  4. Soni SS, Cruz D, Bobek I, Chionh CY, Nalesso F, Lentini P, et al. NGAL: A biomarker of acute kidney injury and other systemic conditions. Int Urol Nephrol 2009;42:141-50.
  5. Bolignano D, Donato V, Coppolino G, Campo S, Buemi A, Lacquaniti A, et al. Neutrophil gelatinase-associated lipocalin (NGAL) as a marker of kidney damage. Am J Kidney Dis 2008;52:595-605.
  6. Schmidt-Ott KM, Mori K, Li JY, Kalandadze A, Cohen DJ, Devarajan P, et al. Dual action of neutrophil gelatinase-associated lipocalin. J Am Soc Nephrol 2007;18:407-13.
  7. Clerico A, Galli C, Fortunato A, Ronco C. Neutrophil gelatinase-associated lipocalin (NGAL) as biomarker of acute kidney injury: A review of the laboratory characteristics and clinical evidences. Clin Chem Lab Med 2012;50:1505-17.
  8. Devarajan P. Emerging biomarkers of acute kidney injury. Contrib Nephrol 2007;156:203-12.
  9. Shapiro NI, Trzeciak S, Hollander JE, Birkhahn R, Otero R, Osborn TM, et al. A prospective, multicenter derivation of a biomarker panel to assess risk of organ dysfunction, shock, and death in emergency department patients with suspected sepsis. Crit Care Med 2009;37:96-104.
  10. Bagshaw SM, Bennett M, Haase M, Haase-Fielitz A, Egi M, Morimatsu H, et al. Plasma and urine neutrophil gelatinase-associated lipocalin in septic versus non-septic acute kidney injury in critical illness. Intensive Care Med 2010;36:452-61.
  11. Mårtensson J, Bell M, Xu S, Bottai M, Ravn B, Venge P, et al. Association of plasma neutrophil gelatinase-associated lipocalin (NGAL) with sepsis and acute kidney dysfunction. Biomarkers 2013;18:349-56.
  12. de Geus HR, Bakker J, Lesaffre EM, le Noble JL. Neutrophil gelatinase-associated lipocalin at ICU admission predicts for acute kidney injury in adult patients. Am J Respir Crit Care Med 2011;183:907-14.
  13. Shapiro NI, Trzeciak S, Hollander JE, Birkhahn R, Otero R, Osborn TM, et al. The diagnostic accuracy of plasma neutrophil gelatinase-associated lipocalin in the prediction of acute kidney injury in emergency department patients with suspected sepsis. Ann Emerg Med 2010;56:52-9.e1.
  14. Ralib AM, Pickering JW, Shaw GM, Than MP, George PM, Endre ZH, et al. The clinical utility window for acute kidney injury biomarkers in the critically ill. Crit Care 2014;18:601.
  15. Md Ralib A, Mat Nor MB, Pickering JW. Plasma neutrophil gelatinase associated lipocalin diagnosed acute kidney injury in patients with systemic inflammatory disease and sepsis. Nephrology (Carlton) 2016 Apr 8. doi: 10.1111/nep.12796 [Epub ahead of print].
  16. Pickering JW, Frampton CM, Endre ZH. Evaluation of trial outcomes in acute kidney injury by creatinine modeling. Clin J Am Soc Nephrol 2009;4:1705-15.
  17. Ralib AM, Pickering JW, Shaw GM, Devarajan P, Edelstein CL, Bonventre JV, et al. Test characteristics of urinary biomarkers depend on quantitation method in acute kidney injury. J Am Soc Nephrol 2012;23:322-33.
  18. Mat Nor MB, Md Ralib A. Procalcitonin clearance for early prediction of survival in critically ill patients with severe sepsis. Crit Care Res Pract 2014;2014:819034.
  19. Levy MM, Fink MP, Marshall JC, Abraham E, Angus D, Cook D, et al. 2001 SCCM/ESICM/ACCP/ATS/SIS international sepsis definitions conference. Crit Care Med 2003;31:1250-6.
  20. Dent CL, Ma Q, Dastrala S, Bennett M, Mitsnefes MM, Barasch J, et al. Plasma neutrophil gelatinase-associated lipocalin predicts acute kidney injury, morbidity and mortality after pediatric cardiac surgery: A prospective uncontrolled cohort study. Crit Care 2007;11:R127.
  21. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982;143:29-36.
  22. Fluss R, Faraggi D, Reiser B. Estimation of the Youden Index and its associated cutoff point. Biom J 2005;47:458-72.
  23. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics 1988;44:837-45.
  24. Rangel-Frausto MS, Pittet D, Costigan M, Hwang T, Davis CS, Wenzel RP. The natural history of the systemic inflammatory response syndrome (SIRS). A prospective study. JAMA 1995;273:117-23.
  25. Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation 2007;115:928-35.
  26. Bewick V, Cheek L, Ball J. Statistics review 13: Receiver operating characteristic curves. Crit Care 2004;8:508-12.
PDF Share
PDF Share

© Jaypee Brothers Medical Publishers (P) LTD.