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How to use biomarkers efficiently in acute kidney injury

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co m m e nta r y
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see original article on page 1119
How to use biomarkers efficiently
in acute kidney injury
Norbert H. Lameire1, Raymond C. Vanholder1 and Wim A. Van Biesen1
We discuss the performance of novel biomarkers in acute kidney injury
(AKI). Comparison of the areas under the receiver operating
characteristic curves of several biomarkers with some clinical and/or
routine biochemical outcome parameters reveals that none of the
biomarkers has demonstrated a clear additional value beyond the
traditional approach in clinical decision making in patients with AKI.
Unscrutinized use of these biomarkers may distract from adequate
clinical evaluation and carries the risk of worse instead of better
patient outcome.
Kidney International (2011) 79, 1047–1050. doi:10.1038/ki.2011.21
“When you search in the stars for
what lies before your feet you risk to
stumbling over the cobblestones.”
freely translated from
Schopenhauer
(Source: Internet quotes)
Urinary biomarkers in the field of acute
kidney injury (AKI) are a hot topic. A
PubMed search using the terms �biological
marker, urine’ and �kidney injury, acute’
revealed 158 papers on humans published
in the past 24 months. The large majority,
as well as their accompanying editorials,
start or end with a statement that �urinary
biomarker X’ is very promising for clinical
management of AKI and detection of
renal damage before a fall in glomerular
1Renal Division, University Hospital, Ghent
University, Ghent, Belgium
Correspondence: Norbert H. Lameire, Renal
Division, Department of Medicine, University
Hospital, Ghent University, De Pintelaan 185, 9000
Ghent, Belgium. E-mail: norbert.lameire@ugent.be
Kidney International (2011) 79
filtration rate is noticeable from a rise in
serum creatinine (SCr) and/or urinary
output. Do the data really support this
optimistic view, and can unrestricted
implementation of biomarkers in clinical
practice be recommended at present?
The paper by Endre et al.1 in this issue of
Kidney International does not support this
view. In patients admitted to the intensive
care unit (ICU), the areas under the
receiver operating characteristic curves
(AUCs) of biomarkers were compared for
diagnosis and prediction of AKI, need of
renal replacement therapy (RRT), and/or
mortality. The overall performance of the
six biomarkers was poor, taking into
account that an AUC of 0.5 reflects the
diagnostic accuracy of random allocation.
Figure 1 and Table 1 show the AUC
values of different biomarkers, clinical
scores, and routine biochemical parameters (RIFLE classification or Screa) as
retrieved from human studies on AKI. A
huge variation in the values is obvious.
What are the possible explanations of
the inconsistent results obtained with
these biomarkers? Some studies2,3 show
AUC values greater than 0.9 but, although
truly positive, suffer from problems of
generalizability, as they include homogeneous populations with a well-defined
single injury to the kidney and with little
or no additional comorbidity. In more
heterogeneous populations such as adult
cardiac surgery and, certainly, general
ICU patients, the performance of biomarkers to detect AKI declines rapidly, with
much lower AUC values.1,4,5 The poor
performance of urinary biomarkers in
more general clinical conditions has
recently been confirmed by Metzger
et al.6 and could be overcome only by use
of a panel of 20 urinary peptides. The
novelty of the paper by Endre et al.1 is
their attempt to evaluate whether specific
biomarkers might perform better in specific clinical conditions—for example, in
patients with versus without preexisting
kidney disease, or with differing (presumed) timing of the kidney injury.
Although this strategy indeed improved
the results, enthusiasm is tempered by
some more in-depth considerations.
First, these new �categorized criteria’ are
�predictions of the past’ and need to be
validated in different populations. Experience teaches that diagnostic accuracy
plummets at such validation procedures.
In addition, all the biomarkers in the
EARLYARF trial have been determined
in nearly ideal research environments.
The switch to commercial kits will further add to the diagnostic inaccuracy,
along with the variability of the cutoff
values of different markers as reported in
many studies.7 Second, although the
stratification criteria applied by Endre
et al.1—underlying kidney function and
timing of insult—seem to make sense,
the time to insult had to be estimated for
a substantial part of the population, and
in clinical practice this information is
often lacking. The majority of deaths after
AKI occur in patients in whom the time
of the renal insult is unknown.8 Moreover, not knowing the time of insult is
most problematic in patients with smoldering disease, such as sepsis—exactly
the patient population that would benefit most from a robust AKI marker. In
1047
com m enta r y
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Figure 1 | Areas under the receiver operating characteristic curves as reported in different
studies. Studies were identified from a PubMed search using the medical subject heading (MeSH)
terms �biological marker, urine’ and �kidney injury, acute’ with the limits �human’ and �last two years.’
Studies were limited to those reporting data on urinary biomarkers, except for the study by
Shapiro et al.2 The numbers along the y axis correspond to the study numbers in Table 1. Pink bars:
Areas under the receiver operating characteristic curves of clinical or basic biochemistry criteria. Blue
bars: Studies with neutrophil gelatinase-associated lipocalin (NGAL). Green bars: Studies with other
novel biomarkers. 1–28: Studies with acute kidney injury as end point; 29–33: studies with need for
renal replacement therapy or worsening renal function as end point; 34–37: studies with mortality
as end point.
addition, the performance of neutrophil
gelatinase-associated lipocalin (NGAL),
for instance, was best 12 – 36 h after
injury,1 far from the promised �early diagnosis’; it is unclear whether this is earlier
than with other, more readily available
markers such as the RIFLE or Acute Kidney Injury Network criteria, based on
urinary output9 or increase of SCr. In
view of the increasing incidence of acuteon-chronic kidney disease, the poor performance of biomarkers, at least NGAL,
in patients with preexisting CKD seems
problematic.4 This apparently leads to the
sobering conclusion that biomarkers
seem to perform best in those patients
and circumstances in which the need for
them is the least.
There are more fundamental issues
that need to be addressed in the discussion on the place of biomarkers in AKI.
1048
First, a biomarker is useful only if it adds
value beyond the currently available
diagnostic armamentarium. It is at
present unclear whether a biomarker
performs better than the clinical expertise of the treating physician or basic
markers such as SCr or urinary output.
Although Koyner et al.10 were unable to
predict AKI using urinary NGAL and
cystatin C, a very high predictive power
was found for need of RRT. One wonders, however, whether clinical evaluation would not predict this outcome
equally well. Alterations in urine flow
appeared to be a sensitive and early
marker of renal dysfunction in ICU
patients,9 while, for example, extracorporeal perfusion time or clinical
appraisal predicted AKI in patients after
coronary bypass with the same performance as plasma interleukin-18. 11 In
patients presenting with signs of incipient sepsis at the emergency department,
baseline SCr had an AUC of 0.72 for predicting AKI.12 These data are remarkably
comparable to those of Nickolas et al.13
In their study, the sensitivity of baseline
SCr and urinary NGAL was comparable,
but NGAL had a higher specificity. As
only 30 of 661 patients developed AKI, a
flowchart with use of SCr as first line and
of NGAL for confirmation as second line
seems a more optimal and more costefficient approach than unscrutinized
screening in everyone. According to Han
et al. , 14 change in SCr was at least as
accurate as NGAL and kidney injury
molecule-1 (KIM-1) in determining
AKI, and Siew et al.5 found that NGAL
added only in a limited way to clinical
prediction in discriminating AKI in a
general ICU population. If biomarkers
ever play a role in clinical practice, they
very likely should become part of a decision tree preceded by thorough clinical
appraisal and simple biochemical markers. Blind unscrutinized use of biomarkers may distract from clinical evaluation
and carries a risk of worse instead of better outcomes. Studies evaluating the performance of biomarkers should thus
compare their performance with that of
clinical appraisal.
Finally, Endre et al.1 point to the high
negative predictive power of biomarkers
for AKI with need of RRT: an astonishing
97%. However, as only 19 of 528 patients
did develop the need for RRT, the question may be raised of how many of those
19 patients would have escaped dialysis if
a �super-performing’ biomarker had made
the diagnosis earlier than occurred in
the current setting of �normal clinical
practice.’
In conclusion, biomarkers can be of use
in the unraveling of biochemical and biological processes during AKI. However,
the optimism about their use in the
approach of clinical AKI seems at present
to be not warranted. We believe that first
of all a careful clinical appraisal is still the
mainstay of diagnosis and therapy. So far,
in our opinion, none of the biomarkers
has demonstrated a clear additional value
in the clinical decision process. Their
unlimited use risks distracting from
important clinical evaluation, resulting in
Kidney International (2011) 79
co m m e nta r y
Table 1 | AUC values of different biomarkers compared with clinical scores and routine biochemical parameters in clinical AKI
Reference (first author)
Marker
Shapiro12
Shapiro12
No. of patients/no.
of events
Patients
AUC
Plasma NGAL
Sepsis
0.82
SCr
Sepsis
0.72
661/24
McIlroy4
NGAL
CPB < 60 ml/min
0.55
142/35
McIlroy4
NGAL
CPB > 60 ml/min
0.34
122/29
Siew5
IL-18
Mixed ICU
0.62
451/86
Siew5
NGAL
Mixed ICU
0.71
451/86
Siew5
NGAL + IL-18
Mixed ICU
0.71
451/86
Martensson15
NGAL
Sepsis
0.86
45/18
Liangos16
KIM-1
CPB
0.78
103/13
Liangos16
NAG
CPB
0.62
103/13
Liangos16
NGAL
CPB
0.5
103/13
Liangos16
IL-18
CPB
0.66
103/13
Liangos16
CPB
0.62
103/13
Liangos16
Cystatin C
CPB
0.5
103/13
Liangos16
CCF score
CPB
0.83
103/13
Liangos16
ECB time
CPB
0.67
103/13
Han14
KIM-1
CPB
0.68
100/36
Han14
NAG
CPB
0.61
100/36
Han14
NGAL
CPB
0.59
100/36
Han14
1-MG
661/24
CPB
0.67
100/36
Tuladhar17
NGAL
SCr baseline
CPB
0.96
50/9
Makris2
NGAL
Polytrauma without comorbidity
0.97
31/11
Makris2
SCr baseline
Polytrauma without comorbidity
0.79
31/11
Metzger6
NGAL
Mixed ICU
0.54
30/16
Metzger6
IL-18
Mixed ICU
0.57
30/16
Metzger6
KIM-1
Mixed ICU
0.71
30/16
Metzger6
AKI marker pattern
Mixed ICU
0.82
30/16
Ferguson18
L-FABP
AKI patients vs. general ICU
0.82
105/92
Yang19
NGAL
Hospitalized patients with AKI
0.88
100/34
Bagshaw20
NGAL
AKI patients
0.7
80/13
Bagshaw20
RIFLE
AKI patients
0.95
80/13
Bagshaw20
NGAL
AKI patients
0.7
80/20
Bagshaw20
RIFLE
AKI patients
1
80/20
Bagshaw20
NGAL
AKI patients
0.62
80/25
Bagshaw20
RIFLE
AKI patients
0.70
80/25
Yang19
NGAL
Hospitalized patients with AKI
0.80
100/12
Doi21
L-FABP
Septic shock with AKI
0.994
145/68
Abbreviations: AKI, acute kidney injury; AUC, area under the receiver operating characteristic curve; CCF score, Cleveland Clinic Foundation score; CPB, cardiopulmonary
bypass; ECB, extracorporeal bypass; ICU, intensive care unit; IL-18, interleukin-18; KIM-1, kidney injury molecule-1; L-FABP: L-type fatty acid-binding protein; 1-MG,
1-microglobulin; NAG, N-acetyl glucosamine; NGAL, neutrophil gelatinase-associated lipocalin; SCr, serum creatinine.
worse instead of better outcome for the
patient, and, in the best case, a waste of
money. We hope that in future studies,
the performance of biomarkers will be
evaluated on top of clinical and basic bioKidney International (2011) 79
chemical parameters and will be coupled
to an intervention. Those who believe
they should look at the stars to guide their
steps, risk stumbling over simple cobblestones before their feet on the ground.
DISCLOSURE
All the authors declared no competing interests.
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