Neutrophil Gelatinase-associated Lipocalin at ICU
Admission Predicts for Acute Kidney Injury
in Adult Patients
Hilde R. H. de Geus1, Jan Bakker1, Emmanuel M. E. H. Lesaffre2,3, and Jos L. M. L. le Noble1
1Department of Intensive Care and2Department of Biostatistics, Erasmus University Medical Center, Rotterdam, The Netherlands; and3L-Biostat,
Catholic University of Leuven, Leuven, Belgium
Rationale: Measured at intensive care unit admission (ICU), the pre-
dictive value of neutrophil gelatinase-associated lipocalin (NGAL) for
severe acute kidney injury (AKI) is unclear.
Objectives: To assess the ability of plasma and urine NGAL to predict
severe AKI in adult critically ill patients.
Methods: Prospective-cohort study consisting of 632 consecutive
Measurements and Main Results: Samples were analyzed by Triage
immunoassay for NGAL expression. The primary outcome measure
was occurrenceof AKI based on Risk-Injury-Failure (RIFLE) classifica-
tion during the first week of ICU stay. A total of 171 (27%) patients
developed AKI. Of these 67, 48, and 56 were classified as RIFLE R, I,
and F, respectively. Plasma and urine NGAL values at ICU admission
were significantly related to AKI severity. The areas under the
receiver operating characteristic curves for plasma and urine NGAL
were for RIFLE R (0.77 6 0.05 and 0.80 6 0.04, respectively), RIFLE I
(0.80 6 0.06 and 0.85 6 0.04, respectively), and RIFLE F (0.86 6 0.06
and 0.88 6 0.04, respectively) and comparable with those of
admission estimated glomerular filtration rate (eGFR) (0.84 6
0.04, 0.87 6 0.04, and 0.92 6 0.04, respectively). Plasma and urine
clinical model’’ with the best four variables including eGFR, improv-
0.95 6 0.01. Serial NGAL measurements did not provide additional
information for the prediction of RIFLE F.
Conclusions: NGAL measured at ICU admission predicts the develop-
ment of severe AKI similarly to serum creatinine–derived eGFR.
However, NGAL adds significantaccuracytothispredictionin combi-
nation with eGFR alone or with other clinical parameters and has an
interesting predictivevalue in patients with normal serum creatinine.
Keywords: NGAL; AKI; ICU; eGFR
In critically ill patients, acute kidney injury (AKI) is indepen-
dently associated with increased costs of medical care and
AKI develops during hospital admission it results in accelerated
elderly patients (5). Recent observational studies have shown
a 14% incidence of dialysis dependency at the time of hospital
discharge among survivors of critical illness (3). Therefore, early
recognition of renal injury is important and may help prevent
further renal damage and functional impairment.
Recent experimental (6–8) and clinical (9–11) studies have
identified biomarkers that may serve as early indicators of AKI.
Of these, neutrophil gelatinase-associated lipocalin (NGAL)
seems to be the most promising. NGAL is a 25-kD protein that
is covalently bound to gelatinase and is secreted from human
When acute tubular damage occurs it is rapidly expressed at high
concentrations in both plasma and urine (7–9, 13).
Thefirstclinical validationwasperformedinpediatric cardiac
surgery patients (9). In this study NGAL measured 2 hours after
surgery was an excellent predictor of AKI, whereas serum
creatinine (SCr) did not start to rise until 24 to 72 hours after
surgery. However, in settings in which the initiation of renal
injury is unclear, such as in cases of sepsis, trauma, and acute and
critical illness, the predictive value of plasma NGAL (pNGAL)
and urine NGAL (uNGAL) is less certain (13–19). Furthermore,
whether NGAL on its own or in combination with clinical
AKI has yet to be determined.
We therefore conducted a prospective study in a large cohort
of adult patients in the intensive care unit (ICU) to assess the
predictive value of pNGAL and uNGAL levels at the time of
admission with regard to the development of severe AKI during
the early days of ICU treatment and their extended contribu-
tion in early diagnosis beyond estimated glomerular filtration
rate (eGFR). None of the results of this current study have been
previously reported in abstract form.
A detailed method session is given in the online supplement.
The institutional review board of Erasmus University Medical Center,
Rotterdam, The Netherlands, approved the study. All consecutive
AT A GLANCE COMMENTARY
Scientific Knowledge on the Subject
There is discrepancy in the literature regarding the useful-
ness of neutrophil gelatinase-associated lipocalin (NGAL)
as a biomarker to predict acute kidney injury in patients in
What This Study Adds to the Field
This study suggests that NGAL could be of value alone and
in combination with estimated glomerular filtration rate
and other clinical variables. Moreover, this study suggests
that serial measurements are useful for predicting severe
acute kidney injury.
(Received in original form August 7, 2009; accepted in final form October 8, 2010)
Supported by Biosite Inc., San Diego, California, which provided biomarker
measurements and statistical support.
Correspondence and requests for reprints should be addressed to Hilde de Geus, M.D.,
Department of Intensive Care Medicine, Erasmus University Medical Center, Rotter-
dam, PO Box 2040, 3000 CA, The Netherlands. E-mail: firstname.lastname@example.org
This article has an online supplement, which is accessible from this issue’s table of
contents at www.atsjournals.org
Am J Respir Crit Care Med
Originally Published in Press as DOI: 10.1164/rccm.200908-1214OC on October 8, 2010
Internet address: www.atsjournals.org
Vol 183. pp 907–914, 2011
admittedpatients between September 2007 and April 2008 were eligible
forenrollment.Exclusioncriteriaincludedage under18 years,refusalof
consent, nephrectomy, chronic kidney disease (CKD), ESRD, and renal
transplantation. Deferred consent was used, and written informed
After admission, plasma and urine samples were collected (T 5 0) and
thereafter at 4, 8, 24, 36, 48, 60, and 72 hours. Missing admission (T 5
0) samples were replaced by first collection values at either 4 or 8 hours
after admission. pNGAL and uNGAL were measured on the Triage
NGAL Test point-of-care fluorescence immunoassay in a laboratory,
masked to patient clinical data (Biosite, Inc, San Diego, CA). The
Triage NGAL test has been validated against an NGAL ELISA assay
(see online supplement) (21).
SCr was measured at admission and thereafter daily at 6:00 A.M. The
eGFR was calculated using the Modification of Diet in Renal Disease
Study Equation (MDRD) (see online supplement) (22). Baseline SCr
was defined as the steady state level 4 weeks before admission. If not
available, the admission value was used as a surrogate baseline. Other
variables included age, sex, body mass index (BMI), temperature, pH,
bicarbonate, potassium, blood-urea-nitrogen (BUN) content, white
blood cell (WBC) count, C-reactive protein (CRP), and lactate. For dis-
ease severity assessment, the Acute Physiology and Chronic Health
Evaluation score (APACHE II) and the sequential organ failure as-
sessment score (SOFA) were used. Furthermore, the cumulative urine
output, initiation of renal replacement therapy (RRT), ICU days, ICU
mortality, and hospital mortality were recorded. The primary outcome
to the Risk-Injury-Failure (RIFLE) classification (23). The RIFLE
classification is based on the rise in SCr compared with a baseline value.
times increase, and failure (RIFLE F) a more than 3 times increase.
MATLAB version 7.5.0 and SPSS version 16.0 were used. The relation-
ships between AKI and NGAL levels were assessed using the Mann-
Whitney U test and the chi-square test. Continuous variables were
described by medians and interquartile ranges. Receiver operating
two times its standard error was calculated. Univariable and multivari-
able logistic regression analyses were used to assess the predictive value
was assessed by estimating the standard error of its coefficient and
conducting a Wald test of the null hypothesis. Stepwise forward likeli-
hood ratio regression was used to determine the model’s most efficient
predictors. Goodness of fit was assessed using the Hosmer-Lemeshow
test. The net reclassification improvement was calculated. All reported
P values are two-tailed, and P values less than 0.05 were considered
Role of Funding Source
Biosite Incorporated (San Diego, CA) provided biomarker measure-
ments and statistical support. They had no role in study design, data
collection, or writing of the manuscript. The first author had full access
to all data and had final responsibility to submit for publication.
Of the 700 consecutive patients who were screened for inclusion
in the study, 68 (9.8%) were excluded because of refusal of
consent (n 5 6), nephrectomy (n 5 6), CKD, ESRD, kidney
transplantation (n 5 25), or missing admission data (n 5 31).
Thus, 632 (90.2%) patients were included in the analysis.
Patient characteristics are shown in Table 1.
AKI occurred in 171 patients (27%). Of those patients, 67
developed RIFLE R, 48 patients developed RIFLE I, and 56
patients developed RIFLE F. The time to reach a SCr increase
of more than 50% compared with baseline for the first time (5
RIFLE R) was T 5 0 in 58.5%, T 5 24 in 24%, T 5 48 in 6.4%,
and T 5 72 in 5.8% of the patients. Thus, 94.7% of the patients
reached ‘‘first AKI’’ within 72 hours after ICU admission.
Twenty-eight (50%) of the patients with AKI in the RIFLE F
class received RRT (4.4% of the overall patient cohort).
Baseline characteristics in all RIFLE classes were com-
pared with subjects who did not develop AKI. There were no
differences with respect to age, sex, or BMI. Patients with AKI
had higher APACHE II and SOFA scores than patients
without AKI (Table 1). Furthermore, there were positive
correlations between the severity of kidney injury and length
of stay, ICU mortality, and hospital mortality (Table 1). The
incidence of AKI was higher in patients admitted after
cardiopulmonary resuscitation was performed, and in patients
with sepsis or multiorgan failure syndrome (P , 0.0001)
Association between NGAL and AKI Development
Patients’ pNGAL and uNGAL concentrations at the time of
ICU admission were significantly related to their RIFLE scores
(P , 0.0001) (Table 1, Figure 1). The pNGAL test performance
for predicting the severity of AKI in the entire cohort showed
an AUC of 0.77 6 0.05 for RIFLE R and above, 0.80 6 0.06 for
RIFLE I and above, and 0.86 6 0.06 for RIFLE F. Similar
analysis for uNGAL revealed AUCs of 0.80 6 0.04 (RIFLE R),
0.85 6 0.04 (RIFLE I), and 0.88 6 0.04 (RIFLE F) (Figures 2A
and 2B). The differences between the plasma and urine AUCs
were not significant. The AUC and ROC curves for eGFR
predicting AKI stratified for RIFLE stage are shown in Figure
2C. Comparing the performance of eGFR with pNGAL and
uNGAL showed that only pNGAL predicting R and above or I
and above were significantly different compared with the
corresponding AUCs of eGFR (P 5 0.015 and P 5 0.039).
Table 2 lists the calculated sensitivities at fixed specificities of
50%, 70%, and 90% (derived by visual inspection of the ROC
and uNGAL for the prediction of RIFLE F.
Association between NGAL and AKI Development in Patients
with eGFR Greater Than 60 ml/min/1.73 m2
To determine the potential additional contribution of NGAL as
a biomarker predicting AKI before SCr has started to rise and
consequently eGFR has started to decline a subset analyses was
performed in patients with apparently normal renal function
(n 5 498) at the time of ICU admission (i.e., excluding patients
with an eGFR ,60 ml/min/1.73 m2). ROC analysis demon-
strated that in patients who did not show any increase in SCr yet
at ICU admission, pNGAL and uNGAL had diagnostic supe-
riority over SCr and eGFR for predicting severe AKI (RIFLE
I and F). The AUCs for pNGAL and uNGAL were respectively
0.75 6 0.10 and 0.79 6 0.10 compared with 0.65 6 0.10 and
0.67 6 0.10 for SCr and eGFR, respectively (Figure 2D).
Relative Contribution of NGAL to the Most Efficient Clinical
Prediction Model at Admission for Prediction of RIFLE F
Adding pNGAL and uNGAL to eGFR in a multivariable
logistic regression model improved the prediction significantly
(P , 0.001). To determine the added contribution of NGAL to
eGFR and other available clinical variables at ICU admission
for predicting the occurrence of RIFLE F within the first week
of patients’ ICU stay, additional logistic regression analysis was
performed (Table 3). The available clinical predictors included
age, BMI, temperature, diagnosis of sepsis, pH, bicarbonate, po-
tassium, BUN, WBC count, CRP, and lactate. Adding NGAL to
908 AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINEVOL 183 2011
eGFR and clinical variables improved the prediction significantly
for pNGAL (P 5 0.014) and almost significantly for uNGAL
(P 5 0.092).
Using a stepwise forward likelihood ratio logistic regression
NGAL,eGFR, diagnosis of sepsis, WBC count, andtemperature
on admission made the most efficient clinical model for the
prediction of RIFLE F for plasma out of the available variables
in this study. For urine the most efficient model comprised
NGAL, eGFR, diagnosis of sepsis, and WBC count (Table 4).
Adding NGAL changed the model’s AUCs from 0.95 6 0.02 to
0.96 6 0.02 for pNGAL and from 0.94 6 0.02 to 0.95 6 0.01 for
uNGAL. Furthermore, we assessed the ability of pNGAL and
uNGAL to ‘‘reclassify’’ the degree of risk for RIFLE F within 7
days as assessed by the model. Subjects were categorized into
prespecified ‘‘low-risk,’’ ‘‘medium-risk,’’ and ‘‘high-risk’’ groups
using cut-offs of less than 30%, 30–60%, and greater than 60%,
respectively. We compared the proportions of reclassified sub-
clinical model for plasma and urine (see online supplement for
detailed reclassification table). For five patients with RIFLE F
reclassification was more accurate when the model with all four
accurate. Among the subjects without RIFLE F, nine were
correctlyreclassified ina lower risk category,whereas three were
performed for uNGAL (see online supplement). The generated
to the clinical prediction model was 8.5% (P 5 0.087) and 2.3%
(P 5 0.370), respectively.
Relative Contribution of Serial NGAL Measurements
to the Most Efficient Clinical Prediction Model for
Prediction of RIFLE F
The progression of mean pNGAL and uNGAL concentrations
supplement (see Figures E1 and E2). To determine if serial
T 5 0 and T 5 24 (temperature, pH, bicarbonate, potassium,
BUN, WBC, CRP, and lactate) were used for multivariable
logistic regression analysis. In addition, age, BMI, diagnosis of
hour cumulative fluid balance, APACHE II, and SOFA score
were added. All subjects with established RIFLE F or missing
data in the first 24 hours were excluded, leaving 429 patients for
the plasma and 411 for the urine analysis. With stepwise forward
likelihood ratio logistic regression the most efficient predictors
were pNGAL T 5 24 (P 5 0.000) and CRP T 5 0 (P 5 0.024) for
AUC from 0.63 6 0.04 to 0.91 6 0.03, underlining that pNGAL
T 5 24 is a very strong predictor for RIFLE F. For urine, the
model showed NGAL T 5 24 (P 5 0.001), temperature T 5 0
(P 5 0.02), APACHE T 5 24 (P 5 0.011), urine production T 5
24 (P 5 0.009), pH T 5 24 (P 5 0.005), and potassium T 5 0 (P5
0.055) as most efficient predictors. Adding uNGAL T 5 24
changed the model’s AUC from 0.84 6 0.06 to 0.93 6 0.04.
Assessment of both pNGAL and uNGAL values’ difference
TABLE 1. PATIENTS’ CHARACTERISTICS AND CLINICAL OUTCOME
VariableNon-AKI (n 5 461) RIFLE R (n 5 67) RIFLE I (n 5 48) RIFLE F (n 5 56)P Value
Male, n (%)
eGFR, ml/min/1.73 m2
Plasma NGAL, ng/ml
Urine NGAL, ng/ml
White blood cell count, 109/ml
Apache II score
RRT, n (%)
ICU mortality, n (%)
Hospital mortality, n (%)
Diagnostic group, n (%)
24.5 (22.5, 27.2)
36.9 (36.2, 37.6)
0.75 (0.61, 0.91)
104 (84, 129)
153 (85, 233)
75 (37, 206)
7.39 (7.34, 7.44)
22 (20.1, 24.3)
3.9 (3.6, 4.3)
5.5 (4.2, 7.3)
11.4 (8.4, 15.1)
12 (3, 68)
1.5 (1, 2.4)
16 (13, 22)
4 (2, 6)
1.1 (0.8, 1.7)
25.5 (22.5, 27.4)
37 (36.2, 37.6)
1.10 (0.82, 1.39)
70 (50, 97)
268 (145, 397)
323 (74, 963)
7.35 (7.29, 7.42)
21 (18.2, 23.7)
4.1 (3.7, 4.6)
8.6 (5.1, 12.1)
10 (6.9, 14.8)
72 (8, 158)
2.2 (1.4, 3.2)
19 (15, 28)
7 (4, 9)
1 (0.7, 1.4)
61.5 (53, 75)
25.5 (22.9, 28.6)
36.6 (35.8, 37.7)
1.30 (0.82, 1.64)
54 (41, 92)
353 (169, 531)
523 (199, 2640)
7.33 (7.27, 7.41)
19.9 (16, 23.7)
4.3 (3.6, 4.5)
8.8 (5.8, 17.1)
11.8 (6.9, 16.2)
25 (6, 134)
2.3 (1.3, 4.6)
24 (20, 29)
8 (6, 11)
0.8 (0.6, 1.3)
62 (50, 68)
25.3 (22.1, 28.1)
36.9 (36.3, 37.8)
2.09 (1.31, 2.86)
32 (21, 50)
680 (332, 1195)
2,013 (564, 4124)
7.31 (7.26, 7.40)
18 (13.4, 20.9)
4.3 (3.9, 4.9)
14.1 (8.4, 26.6)
11 (6.3, 17.6)
118 (36, 198)
2.3 (1.2, 4.2)
25 (22, 28)
11 (8, 13)
0.5 (0.2, 0.9)
Definition of abbreviations: AKI 5 acute lung injury; APACHE II 5 Acute Physiology and Chronic Health Evaluation score at T 5 24; BMI 5 body mass index; BUN 5
blood urea nitrogen; CPR 5 cardiopulmonary resuscitation; CRP 5 C-reactive protein; eGFR 5 estimated glomerular filtration rate according to the Modification of Diet
in Renal Disease Study Equation (MDRD); ICU 5 intensive care unit; HCO325 bicarbonate; K 5 potassium; LTX 5 liver transplant surgery; MOF 5 multiorgan failure;
NGAL 5 neutrophil gelatinase-associated lipocalin; NS 5 nonsignificant; OR 5 odds ratio; RC 5 regression coefficient; RIFLE 5 Risk-Injury-Failure; RRT 5 renal
replacement therapy; SCr 5 serum creatinine; SOFA 5 sequential organ failure assessment score at T 5 24; UP 5 urine production first 24 hours after admission.
de Geus, Bakker, Lesaffre, et al.: NGAL, a Biomarker for AKI in Adult Critically Ill Patients 909
changes were not relevant, pointing out that the NGAL value
measured closer to the end point ‘‘RIFLE F’’ was the strongest
Analyzing further contribution of serial measurements over
the succeeding time points was not possible because of the
significantreduction insamplesize withthediminished availabil-
ity of equal measurements. Furthermore, because the difference
in serial measurements in the first 24 hours did not add to the
prediction of RIFLE F, it is not expected that the results will be
different when analyzing subsequent time points.
Association of NGAL and Sepsis in Patients without AKI
In patients with sepsis (n 5 14) who did not develop AKI,
uNGAL levels were significantly higher than those of patients
in the other diagnostic groups. The median NGAL value was
with septic non-AKI, one received renal drainage because of
obstructive hydronephrosis, onehad a positive urine culturewith
Acinetobacter species, one had a positive WBC and nitrite count
in the urine sediment without a positive culture already under
antibiotic treatment, and one patient had a proved renal abscess
with Escherichia coli. After we adjusted the uNGAL analysis
removing those patients and patients who died within 48 hours
after admission to the ICU, uNGAL levels were still significantly
higher among patients with a diagnosis of sepsis than among
patients in the other diagnostic groups (P 5 0.0005).
Association between NGAL and RRT or Mortality
In the entire cohort both NGAL plasma and urine values were
predictive of RRT initiation within the first week of ICU
admission (respectively, AUC 0.88 6 0.06 and AUC 0.89 6
0.04). However, SCr and eGFR reached similar performances
(respectively, AUC 0.90 6 0.05 and 0.91 6 0.05). Both pNGAL
and uNGAL have a minor role in predicting hospital mortality
with very modest performances (AUC 0.63 6 0.06 and AUC
0.64 6 0.06).
initiation of RRT in critically ill patients within the first 7 days of
their ICU stay. Furthermore, adding NGAL values to a model
with eGFR alone or to the most efficient clinical model with
serial NGAL measurements did not provide additional accuracy
in the prediction of RIFLE F. Finally, patients with sepsis but no
AKI have significantly higher urinary NGAL values compared
with other patients without AKI.
NGAL fulfills a central role in regulating epithelial neo-
genesis, and in iron chelation and delivery after ischemic or toxic
insults to the renal tubular epithelium (24, 25). After kidney
injury, NGAL is rapidly expressed on the apical epithelial
membranes of the distal nephron. NGAL is excreted in the urine
through exocytosis and has local bacteriostatic and proapoptotic
effects (26, 27). PNGAL is easily filtered by the glomerulus and
associated lipocalin (NGAL) concentrations stratified by Risk-Injury-
Failure (RIFLE) classification. An exploratory Mann-Whitney U test of
adjacent categories, including nonacute kidney injury versus R, R versus
I, and I versus F, resulted in P values of , 0.0001, 0.10, and 0.0005,
respectively, for plasma NGAL and , 0.0001, 0.028, and 0.001,
respectively, for urine NGAL. AKI 5 acute lung injury.
Admission plasma (A) and urine (B) neutrophil gelatinase-
Figure 2. Receiver operating characteristic curve analysis for the ability
of admission plasma (A) and urine (B) neutrophil gelatinase-associated
lipocalin, estimated glomerular filtration rate (C) to predict acute
kidney injury, stratified by Risk-Injury-Failure (RIFLE) classification. (D)
Both plasma and urine neutrophil gelatinase-associated lipocalin’s
predictive properties for RIFLE I or worse in patients with estimated
glomerular filtration rate above 60 ml/min/1.73 m2. AUC values 6 2 SE
are presented parenthetically after the RIFLE classification. eGFR 5
estimated glomerular filtration rate; MDRD 5 Modification of Diet in
Renal Disease Study Equation; NGAL 5 neutrophil gelatinase-associ-
ated lipocalin; SENS 5 sensitivity; SPEC 5 specificity.
910 AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINEVOL 1832011
reabsorbed in the apical membranes of the proximal tubules.
Reabsorbtion is mediated by megalin-cubulin dependent endo-
cytosis with a very high affinity. The delivered iron is needed in
vital to the regeneration processes that occur after damage is
less than 0.2% (28, 29). PNGAL and uNGAL concentrations
increase by 10- to 100-fold during the 2 hours that follow tubular
after the initial renal insult (9, 16, 30).
Because we are interested in the possible prevention of
(further) kidney injury in patients who are critically ill, AKI was
assessed only during the first week of each patient’s ICU stay to
link the condition of the patient at the time of admission and the
initial resuscitation efforts to the development of AKI.
In this study, we found that pNGAL and uNGAL measured at
the time of admission were good predictors of AKI. The test per-
formance of both pNGAL and uNGAL increased as the severity
of the functional damage to the kidney’s increased; the AUCs
ranged from 0.77 (RIFLE R) to 0.86 (RIFLE F) for pNGAL and
from 0.80 (RIFLE R) to 0.88 (RIFLE F) for uNGAL.
Previous studies in pediatric patients in the ICU with sepsis
and septic shock (14) and in a group of adult critically ill
patients (17) have studied the predictive accuracy of pNGAL
and uNGAL reporting AUCs of 0.68 and 0.64 for sustained
AKI. Both Zappitelli and coworkers (16) (pediatric population)
and Cruz and coworkers (19) (adult population) observed
AUC’s for prediction of RIFLE R or worse AKI by NGAL
that were comparable with those observed in the present study.
Constantin and coworkers (18) and Nickolas and coworkers
(13) reported very high AUCs for the ability of pNGAL and
uNGAL to predict AKI in critically ill adult and emergency
department patients (0.92 and 0.95, respectively). Several
explanations exist for the observed variability of NGAL’s test
performance in these studies, in which the timing of renal insult
was not strictly identified.
immediately after ICU admission and patients were monitored
for the occurrence of AKI for the next 7 days. The timing of
3 d after admission) to within 24 hours of ICU admission to the
first possible moment on ICU admission. With the rapid changes
in pNGAL and uNGAL concentrations, the slow changes in SCr
concentrations, the reversibility of the early phases in the
continuum of AKI, and the effects of intensive resuscitation in
the golden hours after ICU admittance, timing of measurement
TABLE 2. NEUTROPHIL GELATINASE-ASSOCIATED LIPOCALIN
TEST CHARACTERISTICS AT DIFFERENT CUTOFF VALUES FOR
THE PREDICTION OF RISK-INJURY-FAILURE F
Cutoffs for pNGAL
Cutoffs for uNGAL
Definition of abbreviations: NGAL 5 neutrophil gelatinase-associated lipocalin;
pNGAL 5 plasma NGAL; uNGAL 5 urine NGAL.
TABLE 3. MULTIVARIABLE LOGISTIC REGRESSION FOR THE PREDICTION OF RIFLE F COMBINING
NGAL WITH EGFR AND OTHER CLINICAL PREDICTORS
Plasma NGALUrine NGAL
Variable OR(B)(SE)P ValueOR(B)(SE)P Value
eGFR, ml/min/1.73 m2
Definition of abbreviations: B 5 beta; BMI 5 body mass index; BUN 5 blood urea nitrogen; CRP 5 C-reactive protein; eGFR 5
estimated glomerular filtration rate according to the Modification of Diet in Renal Disease Study Equation (MDRD); HCO325
bicarbonate; K 5 potassium; NGAL 5 neutrophil gelatinase-associated lipocalin; OR 5 odds ratio; RC 5 regression coefficient;
Temp 5 temperature; WBC 5 white blood cell.
TABLE 4. STEPWISE FORWARD LIKELIHOOD RATIO LOGISTIC
REGRESSION FOR DETERMINATION OF MOST EFFICIENT
CLINICAL MODEL FOR THE PREDICTION OF RISK-INJURY-FAILURE F
Plasma NGAL Urine NGAL
Variable OR (B)(SE)P Value OR (B) (SE)P Value
eGFR, ml/min/1.73 m20.95 20.05 (0.01) 0.000 0.95 20.06 (0.01) 0.000
Sepsis9.94 2.30 (0.59) 0.000 9.15
WBC, 109/ml0.95 20.06 (0.03) 0.057 0.95 20.05 (0.02) 0.051
Temp, 8C 0.78 20.25 (0.13) 0.061
1.710.54 (0.21) 0.010 1.420.36 (0.17) 0.039
2.21 (0.53) 0.000
Definition of abbreviations: B 5 beta; eGFR 5 estimated glomerular filtration
rate according to the Modification of Diet in Renal Disease Study Equation
(MDRD); NGAL 5 neutrophil gelatinase-associated lipocalin; OR 5 odds ratio;
RC 5 regression coefficient; Temp 5 temperature; WBC 5 white blood cell count.
de Geus, Bakker, Lesaffre, et al.: NGAL, a Biomarker for AKI in Adult Critically Ill Patients 911
has effects on the NGAL concentrations measured in relation to
are measured clearly influences their test performance.
Second, the number of patients with AKI in a given study
and their RIFLE class distribution also influences test results
(32). Because of the large sample size in this study and the fairly
equal patient distribution between RIFLE categories, we were
able to analyze the ability of NGAL to predict more severe AKI
end points, such as RIFLE F. In contrast, Wheeler and co-
workers (14) used very unusual criteria for AKI, making it
impossible to compare their results with those of other studies.
The AKI cohort in the study performed by Siew and coworkers
(17) was comprised of patients with less severe stages of AKI
(median uNGAL 127 ng/ml interquartile range [IQR]: 32–623
and median SCr 1.5 mg/dl IQR: 1–2.2 at enrollment) resulting in
low performance characteristics of NGAL (AUC 5 0.71; 95%
confidence interval, 0.63–0.78). Nickolas and coworkers (13)
reported that NGAL was an excellent predictor of AKI (AUC 5
0.95; 95% confidence interval, 0.88–1) in an emergency depart-
ment setting. However, the mean SCr and fractional sodium
excretion of this entire AKI subgroup at the time of study
inclusion were 5.6 mg/dl (SD 5 5.5) and 6.9% (SD 9.1), re-
spectively, indicating that severe loss of renal function had already
occurred in most of these patients. Accordingly, test results
generated in patients with established AKI should not be used
for the comparison with those in a cohort of developing AKI.
Third, AKI and its severity defined by RIFLE are dependent
on how baseline SCr values are determined and will contribute
to different outcomes between studies. In our study the first
available SCr value was used as a surrogate baseline when
a patient’s historical data were not available. This undoubtedly
has resulted in an underestimation of attained RIFLE stage in
some of these patients. Furthermore, with the artificial defini-
tion of AKI using three set severity stages the issue of timing
may simply be definitional.
This study adds to the current literature because it showed
that NGAL significantly improves the diagnostic accuracy for
severe AKI adding it to MDRD eGFR calculated at ICU
admission, even in patients having an apparently normal eGFR
at admission. Especially in these patients this could be of value
because their AKI is not yet reflected in an increase in SCr.
Patients in the ICU are typically diagnosed with AKI several
days after the onset of their illness or injury, resulting in a delay
in the discontinuation or dose adjustment of nephrotoxic
medications or continued use of procedures that could cause
further renal damage. Whether NGAL levels have the potential
to influence clinical decision making in the ICU should be the
topic for further randomized studies that should be performed
before using NGAL measurements in clinical practice.
These studies may include applying more intensive resusci-
tation, avoiding nephrotoxic drugs, or implementation of a more
timely initiation of RRT in patients with elevated NGAL levels
(33). In addition, recent animal studies examining interventions
to reverse AKI have been promising, implying that it may be
possible to reverse AKI in humans if it is treated early (29, 34–
39). Second, this study adds to current knowledge because we
defined a most efficient clinical model in the prediction of AKI
using available data at the time of ICU admission, improving
the predictive accuracy for RIFLE F significantly with NGAL
above eGFR and clinical predictors. The predictive accuracy of
eGFR on its own was roughly comparable with that of pNGAL
or uNGAL. However, we should take into account that SCr is
used to define the end point RIFLE F and is likewise used to
calculate eGFR, which is incorporation bias. Therefore, it is
somewhat biased to compare NGAL’s performance with the
ability of SCr to predict itself. Furthermore, in this study, the
point of first AKI was satisfied in many patients at the time of
ICU admission (58.5%). As such, it is to be expected that in many
of the AKI cases, SCr would already be elevated at the time of
admission. AKI that was present at the time of ICU admission was
determined by retrospective collection of baseline SCr values from
the patient records before admission. However, in clinical practice,
a prior baseline SCr is more often not available at ICU admission
and as such it is not possible to correctly determine the end point
of AKI compared with CKD. Furthermore, we should also take
into account that NGAL is a direct injury marker that is un-
fortunately compared with a gold standard AKI diagnosis that is
based on a functional marker (SCr), which has major imperfections
on its own (40). In this context it is indispensable to emphasize the
importance of (injury) biomarker combinations to achieve more
accurate predictions irrespective of SCr.
Third, we showed that temporal changes in NGAL measure-
RIFLE F. And finally, we found that patients with sepsis without
AKI had markedly increased uNGAL concentrations, whereas
there were no significant differences between groups with regard
to the pNGAL values. A possible explanation for our results lies
in the two-compartment model theory of NGAL (which applies
to an animal model under relatively normal conditions) (27) and
AKI, human Toll-like receptor 2 (TLR2) stimulates tubular
epithelial apoptosis (42) and NGAL expression (43). Bacterial
pathogens produce lipoproteins and activate cytokine networks
by inducing the expression of multiple proinflammatory genes.
associated lipocalin values for patients without acute kidney injury
stratified by diagnostic groups. CPR 5 cardiopulmonary resuscitation;
NGAL 5 neutrophil gelatinase-associated lipocalin.
Admission plasma (A) and urine (B) neutrophil gelatinase-
912 AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINEVOL 1832011
Lipoproteins also have strong affinity for TLRs that trigger an
innate immune response. Therefore, it could be postulated that
activation are responsible for the increased uNGAL concentra-
tions that we observed in patients who had sepsis but showed no
increases in theirSCr levels (44). However, a very recent study in
patients with sepsis, septic shock, and systemic inflammatory
response syndrome has reported contradictory findings (45). A
possible explanation for this difference is the variability of the
resuscitation and the administration of antibiotics may have
already occurred before study inclusion, therefore most likely
inducing rapid changes of uNGAL values.
In conclusion, the present study shows that both pNGAL
and uNGAL levels at ICU admission are good predictors of
severe AKI and significantly add to the prediction of AKI using
eGFR and to a model with clinical parameters. Because the
study population reflects a mixed group of diagnoses that are
present in most ICUs these findings could have major clinical
implications regarding optimization of therapy in patients at
risk for AKI. Our findings could also facilitate studies of the
effectiveness of early therapeutic and supportive interventions
in patients with established AKI.
Author Disclosure: H.R.H.D.G. received $1,001–$5,000 from Inverness Medical in
lecture fees. J.B. received up to $1,000 from Hutchinson Technology in advisory
board fees; up to $1,000 from GlaxoSmithKline and up to $1,000 from
Hutchinson Technology in lecture fees; and $10,001–$50,000 from Pulsion in
industry-sponsored grants. E.M.E.H.L. received $5,001–$10,000 from Novartis
and $5,001–$10,000 from Boheringer for consultancy statistics. J.L.M.L.L.N.
received up to $1,000 from Biosite/Inverness in nonpromotional lecture fees.
Acknowledgment: The authors thank Ken Kupfer, Brian Noland, Kristina Little,
and Gillian Parker from Inverness Medical Innovations, Inc., for their support of
this project. They express appreciation to the nurse coordinator, Wil Mol, for her
contribution and to the patients and their families for their participation.
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