Neutrophil Gelatinase-associated Lipocalin at ICU Admission Predicts for Acute Kidney Injury in Adult Patients

Article (PDF Available)inAmerican Journal of Respiratory and Critical Care Medicine 183(7):907-14 · October 2010with71 Reads
DOI: 10.1164/rccm.200908-1214OC · Source: PubMed
Measured at intensive care unit admission (ICU), the predictive value of neutrophil gelatinase-associated lipocalin (NGAL) for severe acute kidney injury (AKI) is unclear. To assess the ability of plasma and urine NGAL to predict severe AKI in adult critically ill patients. Prospective-cohort study consisting of 632 consecutive patients. Samples were analyzed by Triage immunoassay for NGAL expression. The primary outcome measure was occurrence of AKI based on Risk-Injury-Failure (RIFLE) classification 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 ± 0.05 and 0.80 ± 0.04, respectively), RIFLE I (0.80 ± 0.06 and 0.85 ± 0.04, respectively), and RIFLE F (0.86 ± 0.06 and 0.88 ± 0.04, respectively) and comparable with those of admission estimated glomerular filtration rate (eGFR) (0.84 ± 0.04, 0.87 ± 0.04, and 0.92 ± 0.04, respectively). Plasma and urine NGAL significantly contributed to the accuracy of the "most efficient clinical model" with the best four variables including eGFR, improving the area under the curve for RIFLE F prediction to 0.96 ± 0.02 and 0.95 ± 0.01. Serial NGAL measurements did not provide additional information for the prediction of RIFLE F. NGAL measured at ICU admission predicts the development of severe AKI similarly to serum creatinine-derived eGFR. However, NGAL adds significant accuracy to this prediction in combination with eGFR alone or with other clinical parameters and has an interesting predictive value in patients with normal serum creatinine.
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Neutrophil Gelatinase-associated Lipocalin at ICU
Admission Predicts for Acute Kidney Injury
in Adult Patients
Hilde R. H. de Geus
, Jan Bakker
, Emmanuel M. E. H. Lesaffre
, and Jos L. M. L. le Noble
Department of Intensive Care and
Department of Biostatistics, Erasmus University Medical Center, Rotterdam, The Netherlands; and
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 occurrence of 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 60.05 and 0.80 60.04, respectively), RIFLE I
(0.80 60.06 and 0.85 60.04, respectively), and RIFLE F (0.86 60.06
and 0.88 60.04, respectively) and comparable with those of
admission estimated glomerular filtration rate (eGFR) (0.84 6
0.04, 0.87 60.04, and 0.92 60.04, respectively). Plasma and urine
NGAL significantly contributed to the accuracy of the ‘‘most efficient
clinical model’’ with the best four variables including eGFR, improv-
ing the area under the curve for RIFLE F prediction to 0.96 60.02 and
0.95 60.01. 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 significant accuracy to this prediction in combi-
nation with eGFR alone or with other clinical parameters and has an
interesting predictive value 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
increased risk of morbidity and mortality (1–4). In addition, when
AKI develops during hospital admission it results in accelerated
progression toward end-stage renal disease (ESRD), especially in
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
neutrophils (12). It is generally expressed at low concentrations in
various organs containing epithelial tissues, including the kidney.
When acute tubular damage occurs it is rapidly expressed at high
concentrations in both plasma and urine (7–9, 13).
The first clinical validation was performed in pediatric 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
parameters can be of additional value for the prediction of severe
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
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
intensive care.
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:
This article has an online supplement, which is accessible from this issue’s table of
contents at
Am J Respir Crit Care Med Vol 183. pp 907–914, 2011
Originally Published in Press as DOI: 10.1164/rccm.200908-1214OC on October 8, 2010
Internet address:
admitted patients between September 2007 and April 2008 were eligible
for enrollment. Exclusion criteria included age under 18 years, refusal of
consent, nephrectomy, chronic kidney disease (CKD), ESRD, and renal
transplantation. Deferred consent was used, and written informed
consent was obtained from all participants or their heath care proxy (20).
After admission, plasma and urine samples were collected (T 50) 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
variable was AKI occurring within 7 days after ICU admission according
to the Risk-Injury-Failure (RIFLE) classification (23). The RIFLE
classification is based on the rise in SCr compared with a baseline value.
Risk (RIFLE R) represents a 1.5–2 times increase, injury (RIFLE I) a 2–3
times increase, and failure (RIFLE F) a more than 3 times increase.
Statistical Analysis
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
characteristic (ROC) curves with their area under the curve (AUC) with
two times its standard error was calculated. Univariable and multivari-
able logistic regression analyses were used to assess the predictive value
of NGAL in combination with clinical parameters. Statistical significance
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
Pvalues are two-tailed, and Pvalues less than 0.05 were considered
statistically significant.
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.
Patient Characteristics
Of the 700 consecutive patients who were screened for inclusion
in the study, 68 (9.8%) were excluded because of refusal of
consent (n 56), nephrectomy (n 56), CKD, ESRD, kidney
transplantation (n 525), or missing admission data (n 531).
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 50 in 58.5%, T 524 in 24%, T 548 in 6.4%,
and T 572 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
cardiopulmonary resuscitation was performed, and in patients
with sepsis or multiorgan failure syndrome (P,0.0001)
(Table 1).
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 60.05 for RIFLE R and above, 0.80 60.06 for
RIFLE I and above, and 0.86 60.06 for RIFLE F. Similar
analysis for uNGAL revealed AUCs of 0.80 60.04 (RIFLE R),
0.85 60.04 (RIFLE I), and 0.88 60.04 (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 (P50.015 and P50.039).
Table 2 lists the calculated sensitivities at fixed specificities of
50%, 70%, and 90% (derived by visual inspection of the ROC
curves) with the corresponding cut-off concentrations of pNGAL
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 m
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 5498) at the time of ICU admission (i.e., excluding patients
with an eGFR ,60 ml/min/1.73 m
). 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 60.10 and 0.79 60.10 compared with 0.65 60.10 and
0.67 60.10 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
eGFR and clinical variables improved the prediction significantly
for pNGAL (P50.014) and almost significantly for uNGAL
Using a stepwise forward likelihood ratio logistic regression
NGAL, eGFR, diagnosis of sepsis, WBC count, and temperature
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 60.02 to
0.96 60.02 for pNGAL and from 0.94 60.02 to 0.95 60.01 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-
jects across these three risk groups when NGAL was added to the
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
variables for pNGAL was used and for two patients it became less
accurate. Among the subjects without RIFLE F, nine were
correctly reclassified in a lower risk category, whereas three were
incorrectly reclassified to be at higher risk. The same analysis was
performed for uNGAL (see online supplement). The generated
net reclassification improvement for pNGAL and uNGAL added
to the clinical prediction model was 8.5% (P50.087) and 2.3%
(P50.370), 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
stratified by RIFLE classification over time is shown in the online
supplement (see Figures E1 and E2). To determine if serial
sampling could be of additional value for the prediction of RIFLE
F pNGAL and uNGAL values and those of the other predictors at
T50 and T 524 (temperature, pH, bicarbonate, potassium,
BUN, WBC, CRP, and lactate) were used for multivariable
logistic regression analysis. In addition, age, BMI, diagnosis of
sepsis, eGFR MDRD T 50, the 24-hour urine production, the 24-
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 524 (P50.000) and CRP T 50(P50.024) for
the pNGAL model. Adding pNGAL T 524 changed the model’s
AUC from 0.63 60.04 to 0.91 60.03, underlining that pNGAL
T524 is a very strong predictor for RIFLE F. For urine, the
model showed NGAL T 524 (P50.001), temperature T 50
(P50.02), APACHE T 524 (P50.011), urine production T 5
24 (P50.009), pH T 524 (P50.005), and potassium T 50(P5
0.055) as most efficient predictors. Adding uNGAL T 524
changed the model’s AUC from 0.84 60.06 to 0.93 60.04.
Assessment of both pNGAL and uNGAL values’ difference
scores in the logistic regression analysis showed that the temporal
Variable Non-AKI (n5461) RIFLE R (n567) RIFLE I (n548) RIFLE F (n556)PValue
Age, yr 58 (43,68) 59 (45,70) 61.5 (53, 75) 62 (50, 68) NS
Male, n (%) 264 (57) 46 (69) 29 (60) 30 (54) NS
BMI, kg/m
24.5 (22.5, 27.2) 25.5 (22.5, 27.4) 25.5 (22.9, 28.6) 25.3 (22.1, 28.1) NS
Temperature 36.9 (36.2, 37.6) 37 (36.2, 37.6) 36.6 (35.8, 37.7) 36.9 (36.3, 37.8) NS
SCr, mg/dl 0.75 (0.61, 0.91) 1.10 (0.82, 1.39) 1.30 (0.82, 1.64) 2.09 (1.31, 2.86) ,0.0001
eGFR, ml/min/1.73 m
104 (84, 129) 70 (50, 97) 54 (41, 92) 32 (21, 50) ,0.0001
Plasma NGAL, ng/ml 153 (85, 233) 268 (145, 397) 353 (169, 531) 680 (332, 1195) ,0.0001
Urine NGAL, ng/ml 75 (37, 206) 323 (74, 963) 523 (199, 2640) 2,013 (564, 4124) ,0.0001
pH 7.39 (7.34, 7.44) 7.35 (7.29, 7.42) 7.33 (7.27, 7.41) 7.31 (7.26, 7.40) ,0.0001
, mmol/L 22 (20.1, 24.3) 21 (18.2, 23.7) 19.9 (16, 23.7) 18 (13.4, 20.9) ,0.0001
K, mmol/L 3.9 (3.6, 4.3) 4.1 (3.7, 4.6) 4.3 (3.6, 4.5) 4.3 (3.9, 4.9) ,0.0001
BUN, mmol/L 5.5 (4.2, 7.3) 8.6 (5.1, 12.1) 8.8 (5.8, 17.1) 14.1 (8.4, 26.6) ,0.0001
White blood cell count, 10
/ml 11.4 (8.4, 15.1) 10 (6.9, 14.8) 11.8 (6.9, 16.2) 11 (6.3, 17.6) NS
CRP, mmol/L 12 (3, 68) 72 (8, 158) 25 (6, 134) 118 (36, 198) ,0.0001
Lactate, mmol/L 1.5 (1, 2.4) 2.2 (1.4, 3.2) 2.3 (1.3, 4.6) 2.3 (1.2, 4.2) ,0.0001
Apache II score 16 (13, 22) 19 (15, 28) 24 (20, 29) 25 (22, 28) ,0.0001
SOFA score 4 (2, 6) 7 (4, 9) 8 (6, 11) 11 (8, 13) ,0.0001
UP, ml/kg/h 1.1 (0.8, 1.7) 1 (0.7, 1.4) 0.8 (0.6, 1.3) 0.5 (0.2, 0.9) ,0.0001
RRT, n (%) 0 (0) 0 (0) 0 (0) 28 (50) ,0.0001
ICU mortality, n (%) 49 (8) 10 (15) 9 (19) 26 (46) ,0.0001
Hospital mortality, n (%) 71 (11) 20 (30) 16 (33) 30 (54) ,0.0001
Diagnostic group, n (%)
Postoperative 166 (36) 15 (22) 6 (13) 5 (9) ,0.0001
Medical 99 (22) 13 (19) 15 (31) 11 (20) NS
Neurologic 88 (19) 5 (8) 1 (2) 1 (2) ,0.0001
Neurotrauma 27 (6) 2 (3) 0 (0) 1 (2) NS
Multitrauma 26 (6) 6 (9) 4 (8) 1 (2) NS
LTX 19 (4) 8 (12) 1 (2) 1 (2) NS
Sepsis 14 (3) 6 (9) 8 (17) 15 (27) ,0.0001
CPR 11 (2) 6 (9) 7 (15) 3 (5) ,0.0001
Hemorrhagic shock 9 (2) 4 (6) 3 (6) 3 (5) NS
MOF 1 (0) 2 (3) 3 (6) 15 (27) ,0.0001
Definition of abbreviations: AKI 5acute lung injury; APACHE II 5Acute Physiology and Chronic Health Evaluation score at T 524; BMI 5body mass index; BUN 5
blood urea nitrogen; CPR 5cardiopulmonary resuscitation; CRP 5C-reactive protein; eGFR 5estimated glomerular filtration rate according to the Modification of Diet
in Renal Disease Study Equation (MDRD); ICU 5intensive care unit; HCO
5bicarbonate; K 5potassium; LTX 5liver transplant surgery; MOF 5multiorgan failure;
NGAL 5neutrophil gelatinase-associated lipocalin; NS 5nonsignificant; OR 5odds ratio; RC 5regression coefficient; RIFLE 5Risk-Injury-Failure; RRT 5renal
replacement therapy; SCr 5serum creatinine; SOFA 5sequential organ failure assessment score at T 524; UP 5urine 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
significant reduction in sample size with the diminished 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 514) who did not develop AKI,
uNGAL levels were significantly higher than those of patients
in the other diagnostic groups. The median NGAL value was
1,264.1 ng/ml (650.3, 4,124) (Figure 3). In the group of 14 patients
with septic non-AKI, one received renal drainage because of
obstructive hydronephrosis, one had a positive urine culture with
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 (P50.0005).
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 60.06 and AUC 0.89 6
0.04). However, SCr and eGFR reached similar performances
(respectively, AUC 0.90 60.05 and 0.91 60.05). Both pNGAL
and uNGAL have a minor role in predicting hospital mortality
with very modest performances (AUC 0.63 60.06 and AUC
0.64 60.06).
The present study shows that pNGAL and uNGAL levels at time
of ICU admission predict the development of severe AKI and the
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
available parameters improves the prediction significantly. Using
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
Figure 1. Admission plasma (A) and urine (B) neutrophil gelatinase-
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 Pvalues 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 5acute lung injury.
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 m
. AUC values 62SE
are presented parenthetically after the RIFLE classification. eGFR 5
estimated glomerular filtration rate; MDRD 5Modification of Diet in
Renal Disease Study Equation; NGAL 5neutrophil gelatinase-associ-
ated lipocalin; SENS 5sensitivity; SPEC 5specificity.
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
processes activating and repressing iron-responsive genes that are
vital to the regeneration processes that occur after damage is
inflicted to these cells. Under normal circumstances the estimated
half life of pNGAL is approximately 10 minutes, with urinary loss
less than 0.2% (28, 29). PNGAL and uNGAL concentrations
increase by 10- to 100-fold during the 2 hours that follow tubular
injury (7–9), whereas SCr does not start to rise until 24 to 72 hours
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.
First, in the current study NGAL measurement was performed
immediately after ICU admission and patients were monitored
for the occurrence of AKI for the next 7 days. The timing of
NGAL measurement in the previously mentioned studies ranged
from 48 hours after the initiation of mechanical ventilation (up to
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
Sensitivity Specificity
Cutoffs for pNGAL
168 ng/ml 0.91 0.50 0.15 0.98
245 ng/ml 0.82 0.70 0.21 0.98
417 ng/ml 0.70 0.90 0.40 0.97
Cutoffs for uNGAL
94 ng/ml 0.98 0.50 0.16 1.00
247 ng/ml 0.89 0.70 0.22 0.98
1,310 ng/ml 0.55 0.90 0.35 0.95
Definition of abbreviations: NGAL 5neutrophil gelatinase-associated lipocalin;
pNGAL 5plasma NGAL; uNGAL 5urine NGAL.
Plasma NGAL Urine NGAL
Variable OR (B) (SE) PValue OR (B) (SE) PValue
NGAL, ng/ml 1.83 0.6 (0.25) 0.017 1.49 0.40 (0.23) 0.088
eGFR, ml/min/1.73 m
0.95 20.05 (0.01) 0.000 0.94 20.06 (0.01) 0.000
Age, yr 0.98 20.02 (0.02) 0.240 0.97 20.03 (0.02) 0.131
BMI, kg/m
0.93 20.07 (0.06) 0.240 0.91 20.09 (0.06) 0.144
Temp, 8C 0.68 20.39 (0.16) 0.016 0.68 20.39 (0.16) 0.015
Sepsis 10.52 2.35 (0.70) 0.001 14.16 2.65 (0.66) 0.000
PH 2.07 0.73 (3.35) 0.828 3.79 1.33 (3.57) 0.709
, mmol/L 1.01 0.01 (0.06) 0.822 1.01 0.01 (0.06) 0.819
K, mmol/L 2.10 0.74 (0.34) 0.028 1.93 0.66 (0.35) 0.057
BUN, mmol/L 0.99 20.01 (0.04) 0.816 1.00 0.00 (0.04) 0.972
/ml 0.93 20.07 (0.03) 0.025 0.94 20.06 (0.03) 0.025
CRP, mmol/L 1.00 0.00 (0.00) 0.259 1.00 0.00 (0.00) 0.326
Lactate, mmol/L 0.87 20.14 (0.11) 0.224 0.88 20.13 (0.11) 0.237
Total 0.014 0.092
Definition of abbreviations:B5beta; BMI 5body mass index; BUN 5blood urea nitrogen; CRP 5C-reactive protein; eGFR 5
estimated glomerular filtration rate according to the Modification of Diet in Renal Disease Study Equation (MDRD); HCO
bicarbonate; K 5potassium; NGAL 5neutrophil gelatinase-associated lipocalin; OR 5odds ratio; RC 5regression coefficient;
Temp 5temperature; WBC 5white blood cell.
Plasma NGAL Urine NGAL
Variable OR (B) (SE) PValue OR (B) (SE) PValue
NGAL, ng/ml 1.71 0.54 (0.21) 0.010 1.42 0.36 (0.17) 0.039
eGFR, ml/min/1.73 m
0.95 20.05 (0.01) 0.000 0.95 20.06 (0.01) 0.000
Sepsis 9.94 2.30 (0.59) 0.000 9.15 2.21 (0.53) 0.000
WBC, 10
/ml 0.95 20.06 (0.03) 0.057 0.95 20.05 (0.02) 0.051
Temp, 8C 0.78 20.25 (0.13) 0.061
Total 0.000 0.000
Definition of abbreviations:B5beta; eGFR 5estimated glomerular filtration
rate according to the Modification of Diet in Renal Disease Study Equation
(MDRD); NGAL 5neutrophil gelatinase-associated lipocalin; OR 5odds ratio;
RC 5regression coefficient; Temp 5temperature; WBC 5white 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
the changes in SCr (31). Therefore the time at which NGAL levels
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 50.71; 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 55.5) 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-
ments do not provide additional information for the prediction of
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
the fact that AKI is an inflammatory disease (41). In patients with
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.
Figure 3. Admission plasma (A) and urine (B) neutrophil gelatinase-
associated lipocalin values for patients without acute kidney injury
stratified by diagnostic groups. CPR 5cardiopulmonary resuscitation;
NGAL 5neutrophil gelatinase-associated lipocalin.
Lipoproteins also have strong affinity for TLRs that trigger an
innate immune response. Therefore, it could be postulated that
these circulating ligands that are linked to tubular epithelial TLR
activation are responsible for the increased uNGAL concentra-
tions that we observed in patients who had sepsis but showed no
increases in their SCr 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
subject inclusion time (up to 48 h after ICU admission). Intensive
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.
1. Liangos O, Wald R, O’Bell JW, Price L, Pereira BJ, Jaber BL. Epi-
demiology and outcomes of acute renal failure in hospitalized pa-
tients: a national survey. Clin J Am Soc Nephrol 2006;1:43–51.
2. Metnitz PG, Krenn CG, Steltzer H, Lang T, Ploder J, Lenz K, Le Gall
JR, Druml W. Effect of acute renal failure requiring renal replace-
ment therapy on outcome in critically ill patients. Crit Care Med 2002;
3. Uchino S, Kellum JA, Bellomo R, Doig GS, Morimatsu H, Morgera S,
Schetz M, Tan I, Bouman C, Macedo E, et al.; Beginning and Ending
Supportive Therapy for the Kidney (BEST Kidney) Investigators.
Acute renal failure in critically ill patients: a multinational, multicen-
ter study. JAMA 2005;294:813–818.
4. Tan SS, Hakkaart-van Roijen L, Al MJ, Bouwmans CA, Hoogendoorn
ME, Spronk PE, Bakker J. A microcosting study of intensive care unit
stay in the Netherlands. J Intensive Care Med 2008;23:250–257.
5. Ishani A, Xue JL, Himmelfarb J, Eggers PW, Kimmel PL, Molitoris BA,
Collins AJ. Acute kidney injury increases risk of ESRD among elderly.
J Am Soc Nephrol 2009;20:223–228.
6. Brian Reeves W, Kwon O, Ramesh G. Netrin-1 and kidney injury. II.
Netrin-1 is an early biomarker of acute kidney injury. Am J Physiol
Renal Physiol 2008;294:F731–F738.
7. Mishra J, Ma Q, Prada A, Mitsnefes M, Zahedi K, Yang J, Barasch J,
Devarajan P. Identification of neutrophil gelatinase-associated lip-
ocalin as a novel early urinary biomarker for ischemic renal injury.
J Am Soc Nephrol 2003;14:2534–2543.
8. Mishra J, Mori K, Ma Q, Kelly C, Barasch J, Devarajan P. Neutrophil
gelatinase-associated lipocalin: a novel early urinary biomarker for
cisplatin nephrotoxicity. Am J Nephrol 2004;24:307–315.
9. Mishra J, Dent C, Tarabishi R, Mitsnefes MM, Ma Q, Kelly C, Ruff SM,
Zahedi K, Shao M, Bean J, et al. Neutrophil gelatinase-associated
lipocalin (NGAL) as a biomarker for acute renal injury after cardiac
surgery. Lancet 2005;365:1231–1238.
10. Parikh CR, Abrahan E, Ancukiewicz M, Edelstein CL. Urine IL-18 is an
early diagnostic marker for acute kidney injury and predicts mortality
in the intensive care unit. J Am Soc Nephrol 2005;16:3046–3052.
11. Portilla D, Dent C, Sugaya T, Nagothu KK, Kundi I, Moore P, Noiri E,
Devarajan P. Liver fatty acid-binding protein as a biomarker of acute
kidney injury after cardiac surgery. Kidney Int 2008;73:465–472.
12. Kjeldsen L, Johnsen AH, Sengeløv H, Borregaard N. Isolation and
primary structure of NGAL, a novel protein associated with human
neutrophil gelatinase. J Biol Chem 1993;268:10425–10432.
13. Nickolas TL, O’Rourke MJ, Yang J, Sise ME, Canetta PA, Barasch N,
Buchen C, Khan F, Mori K, Giglio J, et al. Sensitivity and specificity
of a single emergency department measurement of urinary neutrophil
gelatinase-associated lipocalin for diagnosing acute kidney injury.
Ann Intern Med 2008;148:810–819.
14. Wheeler DS, Devarajan P, Ma Q, Harmon K, Monaco M, Cvijanovich
N, Wong HR. Serum neutrophil gelatinase-associated lipocalin (NGAL)
as a marker of acute kidney injury in critically ill children with septic
shock. Crit Care Med 2008;36:1297–1303.
15. Makris K, Markou N, Evodia E, Dimopoulou E, Drakopoulos I,
Ntetsika K, Rizos D, Baltopoulos G, Haliassos A. Urinary neutrophil
gelatinase-associated lipocalin (NGAL) as an early marker of acute
kidney injury in critically ill multiple trauma patients. Clin Chem Lab
Med 2009;47:79–82.
16. Zappitelli M, Washburn KK, Arikan AA, Loftis L, Ma Q, Devarajan P,
Parikh CR, Goldstein SL. Urine neutrophil gelatinase-associated
lipocalin is an early marker of acute kidney injury in critically ill
children: a prospective cohort study. Crit Care 2007;11:R84.
17. Siew ED, Ware LB, Gebretsadik T, Shintani A, Moons KG, Wicker-
sham N, Bossert F, Ikizler TA. Urine neutrophil gelatinase-associated
lipocalin moderately predicts acute kidney injury in critically ill
adults. J Am Soc Nephrol 2009;20:1823–1832.
18. Constantin JM, Futier E, Perbet S, Roszyk L, Lautrette A, Gillart T,
Guerin R, Jabaudon M, Souweine B, Bazin JE, et al. Plasma
neutrophil gelatinase-associated lipocalin is an early marker of acute
kidney injury in adult critically ill patients: a prospective study. J Crit
Care 2010;25:176.e1–6.
19. Cruz DN, de Cal M, Garzotto F, Perazella MA, Lentini P, Corradi V,
Piccinni P, Ronco C. Plasma neutrophil gelatinase-associated lip-
ocalin is an early biomarker for acute kidney injury in an adult ICU
population. Intensive Care Med 2010;36:444–451.
20. Jansen TC, Kompanje EJ, Bakker J. Deferred proxy consent in
emergency critical care research: ethically valid and practically
feasible. Crit Care Med 2009;37(Suppl. 1):S65–S68.
21. Dent CL, Ma Q, Dastrala S, Bennett M, Mitsnefes MM, Barasch J,
Devarajan P. 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.
22. Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more
accurate method to estimate glomerular filtration rate from serum
creatinine: a new prediction equation. Modification of Diet in Renal
Disease Study Group. Ann Intern Med 1999;130:461–470.
23. Bellomo R, Ronco C, Kellum JA, Mehta RL, Palevsky P. Acute renal
failure: definition, outcome measures, animal models, fluid therapy
and information technology needs: the Second International Consen-
sus Conference of the Acute Dialysis Quality Initiative (ADQI)
Group. Crit Care 2004;8:R204–R212.
24. Yang J, Goetz D, Li JY, Wang W, Mori K, Setlik D, Du T, Erdjument-
Bromage H, Tempst P, Strong R, et al. An iron delivery pathway
mediated by a lipocalin. Mol Cell 2002;10:1045–1056.
25. Gwira JA, Wei F, Ishiibe S, Ueland JM, Barasch J, Cantley LG.
Expression of neutrophil gelatinase-associated lipocalin regulates
epithelial morphogenesis in vitro. J Biol Chem 2005;280:7875–7882.
26. Schmidt-Ott KM, Mori K, Kalandadze A, Li JY, Paragas N, Nicholas T,
Devarajan P, Barasch J. Neutrophil gelatinase-associated lipocalin-
mediated iron traffic in kidney epithelia. Curr Opin Nephrol Hypertens
27. Schmidt-Ott KM, Mori K, Li JY, Kalandadze A, Cohen DJ, Devarajan
P, Barasch J. Dual action of neutrophil gelatinase-associated lip-
ocalin. J Am Soc Nephrol 2007;18:407–413.
28. Axelsson L, Bergenfeldt M, Ohlsson K. Studies of the release and
turnover of a human neutrophil lipocalin. Scand J Clin Lab Invest
29. Mori K, Lee HT, Rapoport D, Drexler IR, Foster K, Yang J, Schmidt-
Ott KM, Chen X, Li JY, Weiss S, et al. Endocytic delivery of
lipocalin-siderophore-iron complex rescues the kidney from ische-
mia-reperfusion injury. J Clin Invest 2005;115:610–621.
30. Haase-Fielitz A, Bellomo R, Devarajan P, Story D, Matalanis G,
Dragun D, Haase M. Novel and conventional serum biomarkers
de Geus, Bakker, Lesaffre, et al.: NGAL, a Biomarker for AKI in Adult Critically Ill Patients 913
predicting acute kidney injury in adult cardiac surgery: a prospective
cohort study. Crit Care Med 2009;37:553–560.
31. Molitoris BA. Transitioning to therapy in ischemic acute renal failure.
J Am Soc Nephrol 2003;14:265–267.
32. Haase-Fielitz A, Bellomo R, Devarajan P, Bennett M, Story D,
Matalanis G, Frei U, Dragun D, Haase M. The predictive perfor-
mance of plasma neutrophil gelatinase-associated lipocalin (NGAL)
increases with grade of acute kidney injury. Nephrol Dial Transplant
33. Ronco C. N-GAL: diagnosing AKI as soon as possible. Crit Care 2007;
34. Hoglen NC, Chen LS, Fisher CD, Hirakawa BP, Groessl T, Contreras
PC. Characterization of IDN-6556 (3-[2-(2-tert-butyl-phenylaminoox-
alyl)-amino]-propionylamino]-4-oxo-5-(2,3, 5,6-tetrafluoro-phenoxy)-
pentanoic acid): a liver-targeted caspase inhibitor. J Pharmacol Exp
Ther 2004;309:634–640.
35. Doi K, Suzuki Y, Nakao A, Fujita T, Noiri E. Radical scavenger
edaravone developed for clinical use ameliorates ischemia/reperfu-
sion injury in rat kidney. Kidney Int 2004;65:1714–1723.
36. Mishra J, Mori K, Ma Q, Kelly C, Yang J, Mitsnefes M, Barasch J,
Devarajan P. Amelioration of ischemic acute renal injury by neutrophil
gelatinase-associated lipocalin. J Am Soc Nephrol 2004;15:3073–3082.
37. Jerkic
´M, Miloradovic
´Z, Jovovic
´D, Mihailovic
´N, Elena JV,
´D, Grujic
´-Adanja G, Rodrı
´guez-Barbero A, Markovic
Lipkovski J, Vojvodic
´SB, et al. Relative roles of endothelin-1 and
angiotensin II in experimental post-ischaemic acute renal failure.
Nephrol Dial Transplant 2004;19:83–94.
38. Gong H, Wang W, Kwon TH, Jonassen T, Li C, Ring T, FrøkiAEr J,
Nielsen S. EPO and alpha-MSH prevent ischemia/reperfusion-
induced down-regulation of AQPs and sodium transporters in rat
kidney. Kidney Int 2004;66:683–695.
39. Gueler F, Rong S, Park JK, Fiebeler A, Menne J, Elger M, Mueller DN,
Hampich F, Dechend R, Kunter U, et al. Postischemic acute renal
failure is reduced by short-term statin treatment in a rat model. JAm
Soc Nephrol 2002;13:2288–2298.
40. Waikar SS, Betensky RA, Bonventre JV. Creatinine as the gold standard
for kidney injury biomarker studies? Nephrol Dial Transplant 2009;
41. Bonventre JV, Zuk A. Ischemic acute renal failure: an inflammatory
disease? Kidney Int 2004;66:480–485.
42. Aliprantis AO, Yang RB, Mark MR, Suggett S, Devaux B, Radolf JD,
Klimpel GR, Godowski P, Zychlinsky A. Cell activation and apo-
ptosis by bacterial lipoproteins through toll-like receptor-2. Science
43. Cowland JB, Sørensen OE, Sehested M, Borregaard N. Neutrophil
gelatinase-associated lipocalin is up-regulated in human epithelial cells
by IL-1 beta, but not by TNF-alpha. JImmunol2003;171:6630–6639.
44. Flo TH, Smith KD, Sato S, Rodriguez DJ, Holmes MA, Strong RK,
Akira S, Aderem A. Lipocalin 2 mediates an innate immune response
to bacterial infection by sequestrating iron. Nature 2004;432:917–921.
45. Martensson, J, Bell M, Oldner A, Xu S, Venge P, Martling CR.
Neutrophil gelatinase-associated lipocalin in adult septic patients
with and without acute kidney injury. Intensive Care Med 2010;36:
    • To our knowledge, the present study demonstrates for the first time that a panel of sCysC plus uNAG yields greater predictive abilities for AKI in an adult general ICU cohort. Several potential serum and urine biomarkers of kidney injury have been identified, such as neutrophil gelatinase-associated lipocalin (NGAL) [35, 36], kidney injury molecule 1 [37], interleukin 18 [38], NAG [14], CysC [39], urinary albumin [40] , tissue inhibitor of metalloproteinase 2, and insulin-like growth factor-binding protein 7 [41]. Among them, sCysC, uNAG, and uACR are clinically available in China and other countries.
    [Show abstract] [Hide abstract] ABSTRACT: Background Although serum cystatin C (sCysC), urinary N-acetyl-β-d-glucosaminidase (uNAG), and urinary albumin/creatinine ratio (uACR) are clinically available, their optimal combination for acute kidney injury (AKI) detection and prognosis prediction remains unclear. We aimed to assess the discriminative abilities of these biomarkers and their possible combinations for AKI detection and intensive care unit (ICU) mortality prediction in critically ill adults. MethodsA multicenter, prospective observational study was conducted in mixed medical-surgical ICUs at three tertiary care hospitals. One thousand eighty-four adult critically ill patients admitted to the ICUs were studied. We assessed the use of individual biomarkers (sCysC, uNAG, and uACR) measured at ICU admission and their combinations with regard to AKI detection and prognosis prediction. ResultsAUC-ROCs for sCysC, uNAG, and uACR were calculated for total AKI (0.738, 0.650, and 0.683, respectively), severe AKI (0.839, 0.706, and 0.771, respectively), and ICU mortality (0.727, 0.793, and 0.777, respectively). The panel of sCysC plus uNAG detected total and severe AKI with significantly higher accuracy than either individual biomarkers or the other two panels (uNAG plus uACR or sCysC plus uACR). For detecting total AKI, severe AKI, and ICU mortality at ICU admission, this panel yielded AUC-ROCs of 0.756, 0.863, and 0.811, respectively; positive predictive values of 0.71, 0.31, and 0.17, respectively; and negative predictive values of 0.81, 0.97, and 0.98, respectively. Moreover, this panel significantly contributed to the accuracy of the clinical models for AKI detection and ICU mortality prediction, as measured by the AUC-ROC, continuous net reclassification index, and incremental discrimination improvement index. The comparable performance of this panel was further confirmed with bootstrap internal validation. Conclusions The combination of a functional marker (sCysC) and a tubular damage marker (uNAG) revealed significantly superior discriminative performance for AKI detection and yielded additional prognostic information on ICU mortality.
    Full-text · Article · Dec 2017
    • Third, although some novel biomarkers have been recently introduced as potentially better predictors of AKI, their applicability as markers of severity remain limited in clinical practice, especially in emergency departments and ICUs. Numerous studies have explored the utilization of early biomarkers of AKI, such as NGAL and the recent combination of tissue inhibitor of metalloproteinase-2 and urine insulin-like growth factor-binding protein 7, in critically ill patients during the last decade [53][54][55][56][57][58][59]. However, for critically ill patients with AKI, biomarkers that reflect their ever-changing states are needed, and there is limited time to wait for test results.
    [Show abstract] [Hide abstract] ABSTRACT: Background Continuous renal replacement therapy (CRRT) is essential in the management of critically ill patients with acute kidney injury (AKI). However, the optimal timing for initiating CRRT remains controversial, especially in elderly patients. Therefore, we investigated the outcomes of early CRRT initiation in elderly patients with AKI. MethodsA total of 607 patients ≥65 years of age who started CRRT due to AKI between August 2009 and December 2013 were prospectively enrolled. They were divided into two groups based on the median 6-hour urine output immediately before CRRT initiation. Propensity score matching was used to compare the overall survival rate, CRRT duration, and hospitalization duration. ResultsThe median age of both groups was 73.0 years, and 60 % of the patients were male. The most common cause of AKI was sepsis. In the early CRRT group, the mean arterial pressure was higher, but the prothrombin time and total bilirubin, aspartate aminotransferase, and alanine aminotransferase levels were lower. The overall cumulative survival rate was higher in the early CRRT group (log-rank P < 0.01). Late CRRT initiation was associated with a higher mortality rate than early initiation after adjusting for age, sex, the Charlson comorbidity index, systolic arterial pressure, prothrombin time, the total bilirubin, aspartate aminotransferase, and alanine aminotransferase levels, cumulative fluid balance and diuretic use (hazard ratio, 1.35; 95 % confidence interval 1.06, 1.71, P = 0.02). Following propensity score matching, patient survival was significantly better in the early CRRT group than in the late CRRT group (P < 0.01). The total duration of hospitalization from the start of CRRT was shorter among the survivors when CRRT was started earlier (26.7 versus 39.1 days, P = 0.04). ConclusionA better prognosis can be expected if CRRT is applied early in the course of AKI in critically ill, elderly patients.
    Full-text · Article · Dec 2016
    • Fourth, existing biomarkers, such as neutrophil gelatinase-associated lipocalin, have had mixed results in identifying patients with AKI. In the cohorts studied, the KDIGO stage of AKI influenced the effectiveness of biomarkers to predict the development of AKI with lower test performance characteristics in patients with less severe AKI[29][30][31]. Because the trajectory of SCr identifies patients with increased risk of poor clinical outcomes, it is possible that identifying AKI subphenotypes may improve biomarker performance.
    [Show abstract] [Hide abstract] ABSTRACT: Background Acute kidney injury (AKI) is common among intensive care unit (ICU) patients. AKI is highly heterogeneous, with variable links to poor outcomes. Current approaches to classify AKI severity and identify patients at highest risk for poor outcomes focus on the maximum change in serum creatinine (SCr) values. However, these scores are hampered by the need for a reliable baseline SCr value and the absence of a component differentiating transient from persistent rises in SCr. We hypothesized that identification of resolving or nonresolving AKI subphenotypes based on the early trajectory of SCr values in the ICU would better differentiate patients at risk of hospital mortality. Methods We performed a secondary analysis of two prospective studies of ICU patients admitted to a trauma ICU (group 1; n = 1914) or general medical-surgical ICUs (group 2; n = 1867). In group 1, we tested definitions for resolving and nonresolving AKI subphenotypes and selected the definitions resulting in subphenotypes with the greatest separation in risk of death relative to non-AKI controls. We applied this definition to group 2 and tested whether the subphenotypes were independently associated with hospital mortality after adjustment for AKI severity. ResultsAKI occurred in 46% and 69% of patients in groups 1 and 2, respectively. In group 1, a resolving AKI subphenotype (defined as a decrease in SCr of 0.3 mg/dl or 25% from maximum in the first 72 h of study enrollment) was associated with a low risk of death. A nonresolving AKI subphenotype (defined as all AKI cases not meeting the “resolving” definition) was associated with a high risk of death. In group 2, the resolving AKI subphenotype was not associated with increased mortality (relative risk [RR] 0.86, 95% CI 0.63–1.17), whereas the nonresolving AKI subphenotype was associated with higher mortality (RR 1.68, 95% CI 1.15–2.44) even after adjustment for AKI severity stage. Conclusions The trajectory of SCr levels identifies AKI subphenotypes with different risks for death, even among AKI cases of similar severity. These AKI subphenotypes might better define the patients at risk for poor outcomes who might benefit from novel interventions.
    Full-text · Article · Dec 2016
    • As renal stress and damage to the kidneys precede the observed decline in GFR [14], diagnostic AKI biomarker research in the last decade has focused on detection of these early signals151617. Studies have shown that urinary biomarkers like neutrophil gelatinaseassociated lipocalin (NGAL)1819202122, and recently the panel tissue inhibitor of metalloproteinases 2 (TIMP-2) and insulin-like growth factor-binding protein 7 (IGFBP7) [21, 23, 24], can detect AKI in critically ill patients earlier than SCr or UO, even when using the most sensitive KDIGO criteria. In addition, these biomarkers may also allow detection of other outcomes such as progression of AKI, use of RRT, development of CKD, and long-term mortality [25, 26] .
    [Show abstract] [Hide abstract] ABSTRACT: Acute kidney injury (AKI) occurs frequently and adversely affects patient and kidney outcomes, especially when its severity increases from stage 1 to stages 2 or 3. Early interventions may counteract such deterioration, but this requires early detection. Our aim was to evaluate whether the novel renal damage biomarker urinary chitinase 3-like protein 1 (UCHI3L1) can detect AKI stage ≥2 more early than serum creatinine and urine output, using the respective Kidney Disease | Improving Global Outcomes (KDIGO) criteria for definition and classification of AKI, and compare this to urinary neutrophil gelatinase-associated lipocalin (UNGAL). This was a translational single-center, prospective cohort study at the 22-bed surgical and 14-bed medical intensive care units (ICU) of Ghent University Hospital. We enrolled 181 severely ill adult patients who did not yet have AKI stage ≥2 based on the KDIGO criteria at time of enrollment. The concentration of creatinine (serum, urine) and CHI3L1 (serum, urine) was measured at least daily, and urine output hourly, in the period from enrollment till ICU discharge with a maximum of 7 ICU-days. The concentration of UNGAL was measured at enrollment. The primary endpoint was the development of AKI stage ≥2 within 12 h after enrollment. After enrollment, 21 (12 %) patients developed AKI stage ≥2 within the next 7 days, with 6 (3 %) of them reaching this condition within the first 12 h. The enrollment concentration of UCHI3L1 predicted the occurrence of AKI stage ≥2 within the next 12 h with a good AUC-ROC of 0.792 (95 % CI: 0.726–0.849). This performance was similar to that of UNGAL (AUC-ROC of 0.748 (95 % CI: 0.678–0.810)). Also, the samples collected in the 24-h time frame preceding diagnosis of the 1 st episode of AKI stage ≥2 had a 2.0 times higher (95 % CI: 1.3–3.1) estimated marginal mean of UCHI3L1 than controls. We further found that increasing UCHI3L1 concentrations were associated with increasing AKI severity. In this pilot study we found that UCHI3L1 was a good biomarker for prediction of AKI stage ≥2 in adult ICU patients.
    Full-text · Article · Dec 2016
    • Most of these markers can be easily measured in urine and plasma and their concentration increases with duration and severity of acute tubular injury [154, 155, 160– 163]. These biomarkers have demonstrated benefits in regard to early detection, need for RRT, recovery of kidney function, and prediction of prognosis of AKI in a variety of clinical settings [37, 44, 105, 113, 150, 157, 159,163164165166167168169170171172173174175176177178179. Based on the incidence and clinical sequelae of EAKI, the utility of novel biomarkers in this cohort may be beneficial, particularly with regard to delineating major renal insult, and should be investigated.
    [Show abstract] [Hide abstract] ABSTRACT: Extracorporeal membrane oxygenation (ECMO) is a modified cardiopulmonary bypass (CPB) circuit capable of providing prolonged cardiorespiratory support. Recent advancement in ECMO technology has resulted in increased utilisation and clinical application. It can be used as a bridge-to-recovery, bridge-to-bridge, bridge-to-transplant, or bridge-to-decision. ECMO can restitute physiology in critically ill patients, which may minimise the risk of progressive multiorgan dysfunction. Alternatively, iatrogenic complications of ECMO clearly contribute to worse outcomes. These factors affect the risk : benefit ratio of ECMO which ultimately influence commencement/timing of ECMO. The complex interplay of pre-ECMO, ECMO, and post-ECMO pathophysiological processes are responsible for the substantial increased incidence of ECMO-associated acute kidney injury (EAKI). The development of EAKI significantly contributes to morbidity and mortality; however, there is a lack of evidence defining a potential benefit or causative link between ECMO and AKI. This area warrants investigation as further research will delineate the mechanisms involved and subsequent strategies to minimise the risk of EAKI. This review summarizes the current literature of ECMO and AKI, considers the possible benefits and risks of ECMO on renal function, outlines the related pathophysiology, highlights relevant investigative tools, and ultimately suggests an approach for future research into this under investigated area of critical care.
    Full-text · Article · Feb 2016
    • Prediction of AKI in malaria could theoretically become more accurate with the use of markers of structural kidney injury, such as the novel biomarkers Neutrophil Gelatinase-Associated Lipocalin (NGAL) and Kidney Injury Molecule-1 (KIM-1). Recent studies, performed in a broad range of experimental and clinical settings including cardiac surgery, kidney transplantation, contrastinduced AKI and critically ill patients, show that the use of these markers may improve risk assessment303132333435. A study in adult malaria patients in Bangladesh showed that NGAL was not superior to creatinine to predict the requirement of RRT, but patients in this study generally presented severely ill and more than half of them already had a decreased eGFR at admission [36] .
    [Show abstract] [Hide abstract] ABSTRACT: Background: Acute kidney injury (AKI) is a known complication of malaria, and is reported to occur in up to 40 % of adult patients with a severe Plasmodium falciparum infection in endemic regions. To gain insight in the incidence and risk factors of AKI in imported P. falciparum malaria, a retrospective analysis was performed on a large cohort of mostly non-immune patients with imported P. falciparum malaria. Aiming to include not only severe but also milder forms of renal failure, the KDIGO criteria were used to define AKI. Methods: Clinical and laboratory data from 485 consecutive cases of imported P. falciparum malaria were extracted from the Rotterdam Malaria Cohort database. Acute kidney injury (AKI) was defined using the KDIGO criteria. Univariate and multivariate logistic regression analyses were used to identify risk factors for AKI. Results: AKI was seen in 39 (8 %) of all patients and in 23 (38 %) of the 61 patients with severe malaria. Eight patients eventually needed renal replacement therapy (RRT); seven of them already had AKI at presentation. Higher age, higher leucocyte count and thrombocytopaenia were independently-associated with AKI but their positive predictive values were relatively poor. Conclusion: AKI was found to be a common complication in adults with imported P. falciparum necessitating RRT in only a small minority of patients. The use of the KDIGO staging allows early recognition of a decline in renal function.
    Full-text · Article · Dec 2015
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