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Utility of Neutrophil Gelatinase-Associated Lipocalin To Predict Acute Kidney Injury in Patients with Sepsis

Acute Kidney Injury (AKI) is a common complication in critically ill adults.  Estimates are 30% to 60% of patients in the ICU will go on to develop AKI (Md Ralib, Nor & Pickering, 2017).  Immediate consequences of AKI are higher mortality rate, risk of requiring dialysis, increased length of stay and increased risk of another AKI.  Long term risks include development of chronic kidney disease and increased risk of cardiovascular and cerebrovascular events (Doyle and Forni, 2016). Research is ongoing into the viability of using new biomarkers to detect AKI faster than current clinical practice. The significance of early recognition of AKI is it could provide the opportunity to facilitate more timely intervention and thus mitigate associated morbidity.

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According to the KDIGO (Kidney Disease: Improving Global Outcomes) Acute Kidney Injury Work Group guidelines from 2012, AKI is currently defined by meeting any one of the following three criteria: 1) a rise in serum creatinine (SCr) greater than or equal to 0.3mg/dl within 48 hours; 2) a rise in SCr to greater than or equal to 1.5 baseline which is either known or assumed to have risen within the past 7 days; or 3) urine volume that is less than 0.5ml/kg/h for 6 hours. The current usage of SCr is as an indirect measure of kidney injury. Levels of SCr can be influenced by multiple factors which may impact the ability to provide an accurate and timely diagnosis (Parikh, Moledina, Coca, Thiessen-Philbrook & Garg, 2016). The concentration of SCr depends on numerous variables including hydration status, gender, protein intake and age (Shum et al., 2015). Other factors influencing SCr are the effects of medications and muscle metabolism.  Additionally, increases in SCr are not immediate in response to kidney injury and can take days to emerge, leading to delayed treatment (Hong, Lee, Park, Baek & Lee, 2013). This is problematic when managing patients as AKI is an independent risk factor for mortality.

The National Cancer Institute (n.d.), defines a biomarker as a molecule located in body fluids or tissues that is used as an indicator of normal or disease processes and can be used to determine the body’s response to treatment. The biomarker neutrophil gelatinase-associated lipocalin (NGAL) is being studied to determine if NGAL predicts AKI faster than SCr. The protein NGAL is produced by neutrophils which, in healthy patients, is secreted by various organs in small amounts. When kidneys are injured, the renal tubules produce large amounts of NGAL which is quickly detectable in blood and urine (Hong et al., 2013).

Sepsis is one of the main causes of ICU admission and is identified as a major contributing factor to AKI (Zhang et al. 2016). Sepsis related AKI is linked to increased morbidity and mortality (Md Ralib, Nor & Pickering, 2017).  NGAL levels in blood increase systemically in inflammatory conditions like sepsis (Vanmassenhove et al. 2015), confounding efforts to determine impending AKI. Conversely, urinary NGAL may not be influenced by systemic NGAL production (Mårtensson et al, 2010).  NGAL in blood has been examined in SIRS and sepsis indicating that it may be predictive of AKI, however the diagnostic cutoff level for AKI in sepsis may be higher than in other conditions (Hang et al., 2017). Despite the research done on NGAL there continues to be debate regarding NGAL’s ability to predict AKI in patients with sepsis due to lack of data (Zhang et al. 2016).

The use of the biomarker NGAL could impact change in practice if it is able to improve the ability to recognize early AKI potentially days before SCr. This would allow for earlier treatment and positively impact patient care by decreasing mortality, length of stay, long term morbidity and cost. While NGAL’s value in predicting AKI is being explored in numerous medical conditions, this paper will critique two papers published in 2015 that evaluated the ability of plasma and urine NGAL to predict AKI in adults with sepsis.

Dai et al. (2015) ran a two-year prospective observational cohort study to determine both the diagnostic and predictive value of NGAL, cystatin- C (Cys- C) and soluble triggering receptor expressed on myeloid -1 (sTREM-1) in sepsis associated AKI in a general ICU population. The study was conducted between March 2012 and March 2014 at the First Peoples’ Hospital of Chenzhou in China. The dependent and independent variables are respectively, the development of AKI and the values of blood & urinary NGAL, Cys-C and sTREM-1 at admission, 24, 48 and 72 hours.  A convenience sample of 112 inpatients with sepsis was obtained, 55 of which had AKI and 57 did not.

Information collected on all patients included demographics and history of chronic illness as well as physiologic and clinical data. To diagnose AKI, urine output was collected every hour and SCr was collected on admission and every 12 hours. The authors state that white blood cell (WBC) count, C- reactive protein (CRP) and procalcitonin (PCT) were gathered to establish the severity of inflammation, although it is not stated when the inflammatory markers were collected. Blood and urine were processed using the same equipment, settings, techniques and storage methods. NGAL levels of both blood and urine were measured with predetermined ranges of 15 – 1,300 ng/ml and 0 – 4,000 ng/ml respectively. Investigators in the laboratory were blinded to clinical information for the duration of the study. In determining the ability of NGAL to predict AKI, the investigators used odds ratios and corresponding confidence intervals (CI’s) in blood and urine to asses the risk of developing AKI in sepsis. Analysis performed using receiver operating characteristic (ROC) and area under receiving operator characteristic (AUROC) ascertained the ability of NGAL to predict and diagnose AKI in sepsis. Statistical significance was determined to be a p value of < 0.05.

The study investigators used a multivariate analysis to determine the risk of AKI development in three models using blood and urine. The first model was the NGAL values of blood and urine, the second model was the NGAL values modified by SCr and the third model was modified by multiple variables including SCr. Results of all showed significant associations for AKI development with a p value of 0.001 or less.  The diagnostic value of NGAL in both blood and urine performed well with an AUROC respectively of 0.823 and 0.85 with a p value < 0.01. The predictive value of blood and urine NGAL also performed well with an AUROC of 0.830 and 0.879 also with a p value < 0.01.  The investigators conclude that plasma and urine NGAL are clinically useful in predicting and diagnosing AKI in adults with sepsis.

The authors acknowledge that while other studies have conflicting data showing that plasma NGAL is a poor predictor of AKI, those studies included patients with pre-existing kidney disease. This study asserts that because patients with pre-existing kidney disease were excluded, this study demonstrates the diagnostic value of plasma NGAL in adults with sepsis. While this may be true, more studies need to be conducted to determine this. Factors strengthening the internal validity and reliability of this study were that it was done on a single illness population, had strict exclusion criteria, had clearly described data collection and processing methods and clear explanation of data. Experimental mortality was present in determining the diagnostic and predictive value of NGAL. The AKI cohort in the diagnostic and predictive groups had a loss of 9 and 21 patients respectively. No analysis was presented to determine if the groups were still statistically similar.  Threats to external validity and generalizability include the study being done at a single center in China and on a small sample size.

A 17-month prospective observational cohort study was conducted by Si Nga et al. (2015) to determine if urinary NGAL (uNGAL) can predict AKI and death in septic patients admitted to the emergency room (ER). The study ran between January 2013 and May 2014 at the University Hospital in São Paolo, Brazil. Dependent variables were the development of AKI and death. Independent variables were SCr and uNGAL within 24 hours of admission (classified as NGAL1), between 24h and 48 h (NGAL2), and at AKI diagnosis (NGAL3). A convenience sample was gathered from all septic patients older than 18 who were admitted through the emergency room. One hundred and sixty-eight patients with sepsis were enrolled, 121 developed AKI and 47 did not. A control group of 20 healthy subjects between 30 and 50 years old were also enrolled. Selection criteria of the control group was not discussed.

Data was collected on demographics and comorbidities as well as clinical and physiologic data. Blood samples were collected once daily and urine was measured for NGAL and creatinine within the first 24 hours, between 24 and 48 hours and at the moment of AKI diagnosis. The samples were processed, frozen and NGAL was analyzed by the enzyme linked immunosorbent assay (ELISA). To determine diagnostic value of uNGAL, the investigators used median and interquartile ranges. Prediction of AKI and death using uNGAL were analyzed by calculating the AUROC. The analysis compared AKI patients with all non- AKI patients. Patients who survived were compared against those who did not.  Results were considered significant with a p value of 0.05 or less.

Results showed that uNGAL had diagnostic and predictive value in determining AKI. As a predictor of AKI using AUROC, uNGAL1 had a value of 0.73, uNGAL2 was 0.70, uNGAL1/uCr1 was 0.77 and uNGAL2/uCr2 was 0.84 showing that they were fair to good predictors of AKI. Out of the 168 patients included in the study, 87 of the patients already had AKI on admission. The study investigators did a sub analysis on the remaining 34 patients who developed AKI after hospitalization and the AUROC was between 0.81 and 0.89.  The authors determined that uNGAL is a sensitive but non-specific predictor of AKI in adults presenting to the emergency room with sepsis.

The authors attempted to control for confounding variables by limiting their study to five days, thereby reducing the effects of nephrotoxic medications and other complications. Strengths of this study were that it investigated a specific biomarker in a specific population and had a healthy control group. However, there were a number of other variables that pose a threat to internal validity. After admission to the ER patients were either admitted to the ICU, general wards or they stayed in the ER. The standard of care is different in each of these locations and introduces numerous variables. Selection bias is present as no statistical data is given regarding the homogeny or heterogeneity of the sample. The study did not have strict exclusion criteria as patients with mild to moderate chronic kidney disease, and those with existing AKI were included in the study. The results table presented 121 patients with AKI, however only 34 patients in the sample actually developed AKI after admission. Excluding those patients with AKI on admission would have increased internal validity. Information bias exists as there is no discussion of collection methods or equipment and minimal discussion of processing methods making reliability of instruments difficult to determine. This study is not generalizable as the sample size is small, it is not known if the patients with AKI and without AKI were similar, and was a single center study in Brazil.

Many studies have investigated the ability of NGAL to predict AKI across a variety of illnesses (Haase, Bellomo, Devarajan, Schlattmann & Haase-Fielitz, 2009). However, it remains unclear if NGAL has utility in the prediction of AKI in adults with sepsis (Kim et al. 2016). Research to date has been mostly a few observational studies on small sample sizes (Kim et al. 2016) which presents threats to internal and external validity. Both Dai et al (2015) and Si Nga et al. (2015) attempt to address this gap in the literature. Both looked at the septic population specifically and NGAL’s relationship to AKI.  Efforts were made to address problems with internal and external validity with Dai et al (2015) being more successful than Si Nga et al. (2015).

While both studies explored NGALs predictive ability in AKI, Dai et. al. investigated both blood and urine NGAL while Si Nga et al. investigated only urine NGAL. Neither study completed a power analysis to determine sample size, which increases the risk of a type one error. The two studies reported statistically significant p values and AUROC values were considered good, leading both to conclude that NGAL is a clinically useful biomarker in predicting AKI. However, Dai et al. had stricter exclusion criteria, better control of internal validity and gave descriptive statistics of the sample groups. Si Nga et al. attempted to control internal threats by limiting the study to five days to control for treatment related confounders. However, many threats were introduced due to the study’s lack of strict exclusion criteria and lack of consistent setting. Due to the small sample sizes and being single center study’s, both have a lack of generalizability necessitating the need for further research.

Due to its consequences, earlier recognition of AKI is imperative in order to decrease the short and long term morbidity, mortality and cost of this condition.  Future investigation that includes large multi-center studies are warranted if NGAL is to be clinically useful in septic patients with impending AKI. Those studies need to carefully control for confounding factors to decrease internal threat. As with the two studies discussed here, research has been suggestive, but not conclusive on the ability of blood and urine NGAL to predict AKI in septic patients.

Quantitative Research Literature Review Table

Citation Aims Design & Methodology Sample & Setting Variables Measurement & Analysis Findings
Dai, 2015, Critical Care, vol 19 (223) Purpose: To determine the diagnostic and predictive value of the biomarkers NGAL, CYS-C and sTREM -1 for sepsis-associated AKI in a general ICU population.

Hypothesis: The Biomarkers NGAL, CYS-C and sTREM-1 will diagnose and predict sepsis associated AKI faster than current practice.

Design: Two-year prospective observational cohort in the general ICU in a Chinese hospital to determine the usefulness of NGAL, CYS-C and sTREM- 1 on predicting AKI in adults with sepsis.


Data collected on age, gender, etiological factors and underlying disease. SOFA and APACHE II scores.  sCr collected on admission and every 12 hours, urine output recorded every 2 hours. Baseline WBC, CRP, and PCT.

n = 112 patients with sepsis. Non-AKI n= 57. AKI n=55

Age: Non- AKI =51.0 +/- 15.6. AKI: 49.8 +/- 15.4.

Male: Non-AKI- 35 Male AKI- 27 Female Non-AKI- 22 Female AKI- 28

Recruitment procedures: Convenience

Inclusion: Consecutive adult patients with sepsis ≥18 years old.

Exclusion: Pts who- have pre-existing AKI or CKD, anuria, cancer, undergone high-dose steroid treatment, AIDS, not given consent or declined treatment, been exposed to radiocontrast/ nephrotoxic drugs. Pts who had a renal transplant, require RRT and who participated in other studies.

Independent: The biomarkers NGAL, CYS-C and sTREM-1in adults with sepsis

Dependent: Development of AKI in adults with sepsis

Other variables controlled for:  Age, MAP, APACHE II, SOFA, sCr, WBC, PCT, urine and plasma for NGAL, CYS-C and sTREM-1 and survival rates

Continuous variables with normal distributions are reported as mean ± standard deviations. Means between two groups analyzed using Students t test. Continuous variables not normally distributed stated as median values and interquartile range and compared using the Mann-Whitney U test. Qualitative variables stated as number (percentage) and compared using chi-square or Fishers exact test. Log-rank test calculated between group differences. Odds ratios and Confidence Intervals were analyzed using generalized estimating equations. Variables combined into three models 1. no moderator 2. sCr as moderator 3. MAP, APACHE II, SOFA, PCT and sCr.  ROC used to determine biomarker ability to detect AKI. AUROC was used to evaluate how well the model could determine AKI from non-AKI sepsis patients.  P values < 0.05 considered statistically significant. Predictive value of plasma and urine NGAL in development of AKI:

Plasma NGAL- AUROC 0.830, 95% CI 0.741 to 0.919. P <0.01.   Urine NGAL- AUROC 0.879, 95% CI 0.793 to 0.948. P < 0.01 Both indicate good accuracy.

Diagnostic value of plasma and urine NGAL:

Plasma NGAL- AUROC 0.823, 95% CI 0.730-0.916. P <0.01. Urine NGAL- AUROC 0.855, 95% CI 0.777-0.933

AKI risk:  plasma NGAL

Model 1.           p<0.001

OR   CI (95%)


Model 2.          <p0.001

1.012 (1.009-1.014)

Model 3.           p<0.001


AKI risk:   urine NGAL

Model 1.          p <0.001


Model 2.          p <0.001

1.029 (1.014-1.045)

Model 3.          p = 0.001

1.027 (1.011-1.044)

NGAL– neutrophil gelatinase-associated lipocalin CYS-C– cystatin-c   sTREM-1 – soluble triggering receptor expressed on myeloid cells -1 AKI– Acute Kidney Injury ICU– Intensive Care Unit SOFA-Sequential Organ Failure Assessment APACHE II- Acute Physiology and Chronic Health Evaluation II sCr-serum creatinine WBC– white blood cell, CRP- C-reactive protein PCT– pro calcitonin CKD– chronic kidney disease RRT– renal replacement therapy MAP– mean arterial pressure ROC– receiver operating characteristic AUROC– area under the receiver operating characteristic curves

Quantitative Research Literature Review Table

Citation Aims Design & Methodology Sample & Setting Variables Measurement & Analysis Findings
Si Nga, 2015, BioMed Research International, 2015, 413751-8 What was the purpose of the study?

To determine the efficacy of urinary NGAL as predictor of AKI and death in septic patients admitted to the emergency room.
Hypothesis is that urinary NGAL is a predictor of AKI and death in septic patients admitted to the emergency department.

This was a prospective observational cohort study involving all septic patients admitted into a University hospital from January 2013 to May 2014.


Data was gathered on samples of blood collected once daily for five days or earlier (discharge or death). Urine NGAL and sCr within the 1st 24 hours of admission, between 34- 48 hours and at the moment of AKI diagnosis. Medical record and data recording to collect age, gender, patient history, history, APACHE II, diagnosis, disposition (ICU vs ward vs ED) and development of AKI.

n = 188

Healthy control= 20

Non-AKI = 47

AKI= 121

Age: 68 ± 15.4

AKI admission= 87

During hospitalization = 34

What recruitment procedures were used? Convenience

Inclusion Criteria: Patients ≥ 18 years old who met the sepsis criteria according to the “Survival Sepsis Campaign 2012”

Exclusion criteria:  Patients with CKD ≥ stage 4 and patients who had kidney transplants.  


Urine was analyzed for uNGAL and sCr within the first 24 hours after admission (uNGAL1), between 24h and 48 h (uNGAL2), and at moment of AKI diagnosis (uNGAL3).

DependentDevelopment of AKI, death

Continuous variables with normal distribution were stated as ± Standard Deviation. Non-normal distribution states as median and interquartile range. Categorical variables described as percentage.  Continuous variables with parametric data was analyzed with the Students t test and Kruskal-Wallis test for non-normal data. Diagnostic characteristics of urinary NGAL prediction of AKI used AUCROC analysis. uNGAL1 (ng/mL)

AKI: uNGAL1- 3.86 (2.6-9.5) Non-AKI uNGAL1 -3.5(0.8-5) p= 0.003.

uNGAL2 (ng/ml)

AKI– 3.03 (0.65-4.33) Non-AKI uNGAL2- 2.76 (2.3-7.83) p=0.009

uNGAL1/uCr (ng/mg)

AKI: uNGAL1- 75.08 (37-165) Non-AKI uNGAL1- 53.31 (17.79-102.2)  p < 0.0001

uNGAL2/uCr (ng/mg) AKI– 77.2 (29.49-160.6) Non-AKI uNGAL2- 60.29 (17.56-85.64)  p = 0.002.

AUCROC             p

uNGAL 1              0.83   =   0.03

uNGAL 2              0.81   =   0.01

uNGAL1/uCr1      0.87   =   0.02

uNGAL2/uCr2      0.89 = 0.0001

NGAL– neutrophil gelatinase-associated lipocalin AKI– Acute Kidney Injury sCr– serum creatinine APACHE II– Acute Physiology and Chronic Health Evaluation II ED– emergency department CKD– chronic kidney disease AUCROC– Area Under the Receiver Operating Characteristic Curves

uNGAL– urinary neutrophil gelatinase-associated lipocalin uCr– Urine Creatinine


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