Biomarkers in organ failure

Trends in Anaesthesia and Critical Care 3 (2013) 97e104

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Trends in Anaesthesia and Critical Care journal homepage: www.elsevier.com/locate/tacc

REVIEW

Biomarkers in organ failure Eleonora Bonicolini a, Stefano Romagnoli b, *, Angelo Raffaele De Gaudio a, Flavia Petrini c a

Department of Health Sciences, Section of Anesthesiology and Intensive Care, University of Florence, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy Cardio-Thoracic and Vascular Anesthesia and Intensive Care, University of Florence, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy c Department of Anesthesia, Intensive Care and Emergency Medicine, University of Chieti-Pescara, Italy b

s u m m a r y Keywords: Biomarkers Heart failure Acute kidney injury Respiratory failure

Biomarkers are quantifiable indicators of normal biological processes, pathogenic processes or pharmacological responses to a therapeutic intervention. They have been recently introduced into clinical practice as a means for risk assessment, screening, diagnosis, staging, and prognosis. Some of them (cardiac and kidney biomarkers) have already found a precise role as the leading actor and actress in clinical medicine while others (pulmonary biomarkers) are still under evaluation in the different settings. Together with their invaluable properties, biomarkers have some important characteristics that should never be underestimated. A single biomarker rarely seems to have all the characteristics required to meet the clinical needs for a complete organ failure assessment, every biomarker seems to show different behaviour in different kinds of diseases, and several confounding factors have to be considered when interpreting biomarkers used for clinical assessment. The research on biomarkers is running fast and it is likely that a panel of biomarkers may be the best approach for achieving a complete patient assessment together with an adequate clinical evaluation and monitoring. Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction Despite the term “biomarker” (biological marker) having been introduced in 1989 as a Medical Subject Heading (MeSH) term, a standardized definition was given only in 2001 by the NIH working group, which defined a biomarker as “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes or pharmacological responses to a therapeutic intervention”.1 According to this definition, biomarkers should be considered to be recordable parameters from patients (e.g. blood pressure, heart rate, ECG), quantifiable imaging findings and quantitatively measurable molecules in biological samples (e.g. blood, urine, pleural or peritoneal fluid, tissue sample). The latter is most commonly referred to when discussing biomarkers.2 Depending on the information provided in the clinical setting, biomarkers have been classified as antecedent (used to assess the risk of developing illness), screening (to detect subclinical disease),

* Corresponding author. Department of Anesthesia and Critical Care, Azienda Ospedaliero-Universitaria Careggi, University of Florence, Largo Brambilla 3, 50134 Florence, Italy. Fax: þ39 055 794 7706. E-mail addresses: [email protected], stefano.romagnoli@unifi.it (S. Romagnoli). 2210-8440/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.tacc.2013.05.001

diagnostic (to confirm an overt disease), staging (to estimate disease severity) or prognostic (to predict and monitor the response to therapies and to foresee the course of the disease).1 Due to the extensive potential applications, not just in clinical but also in experimental settings such as surrogate end-points in early-phase drug trials, the development of biomarkers is an exciting and challenging research branch. Interests and investments in this field have grown but have not yet been proportionally followed by the introduction of novel biomarkers into clinical practice. Candidate biomarkers are numerous, particularly after the introduction of the “Omics” scientific approach. Using high-throughput methods, Omics sciences reveal patterns of expression of mRNA, proteins and metabolites (transcriptomic, proteomic and metabolomic science, respectively). Thus, the lack of biomarkers is not due to a shortage of candidates but is instead due to the long and difficult development path from candidate discovery to clinical application, which passes through qualification, verification and validation phases.3 The biomarker dosage is a quantitative test. In this kind of test, sensitivity and specificity vary relating to the chosen cut-off values achieving different levels of accuracy. Obviously, properties’ relative importance differs according to the reasons for measuring the biomarker, with a preference for sensible tests to rule out diagnosis and specific tests to confirm diagnosis. Indeed, the validation of a new biomarker for everyday clinical use requires several large independent studies

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calculating sensibility, specificity and diagnostic accuracy for different cut-off values, demonstrating discriminatory power between diseased and healthy patients (AUC-ROC curve > 0.5) (Table 1) and assessing possible outcome advantages due to different post-test diagnostic or therapeutic approaches.2 Desirable biomarker dosage should be acceptable to the patient, reproducible, easy to perform and interpret by the physician, rapidly available and low cost. It should have high sensitivity, specificity and accuracy for the syndrome it is intended to identify and should add clinical information that is able to modify disease course or patient outcome.4 For the purpose of this article we have shortlisted a group of biomarkers among the wide choice of them in the research field rather than in clinical practice. So we will focus on the most promising biomarkers for the three main organ failures: heart, kidney and lung failure. 2. Biomarkers in heart failure Biomarker research in heart failure (HF) has been developing rapidly over recent years. Even though the majority of data focused on the natriuretic peptides (NPs), a large number of different molecules are in the development of pipeline. In a recent review, candidate biomarkers in HF are classified according to the process in which they function.5 Thus, beyond the most famous NPs belonging to the myocyte stress biomarkers, there are a lot of inflammation, oxidative stress, myocyte injury-apoptosis, extracellular-matrix remodelling, neuro-hormones and extracardiac involvement markers under evaluation for an HF assessment role. A summary of the properties of HF biomarkers we have chosen to talk about is shown in Table 2. NPs are one of the most widely studied heart biomarkers for HF assessment. They are a family of three structurally related peptides: Atrial Natriuretic Peptide (ANP) and B-type Natriuretic Peptide (BNP), which are synthesized by ventricular and atrial myocytes and the C-type Natriuretic Peptide (CNP) produced by vascular endothelial cells.6 Briefly, ANP and BNP genes are rapidly upregulated mainly by myocytes mechanical stretch due to volume or pressure overload and also by injury or hypoxia regardless of mechanical stress.7e10 Thus a broad variety of cardiac abnormalities leads to their release, such as left ventricular systolic and diastolic dysfunction, myocardial structural changes, pulmonary artery hypertension, abnormal right ventricular function or size, valvular heart disease and arrhythmias. Such plasma peptides mediate natriuresis, vasodilation, renineangiotensinealdosterone and sympathetic systems inhibition, thus representing a compensatory mechanism that attempts to restore fluid homoeostasis and blood pressure.11 Lots of studies have been done to evaluate the possibility of considering ANP and BNP not only as mediators but also as biomarkers of HF.

Table 1 Discriminatory power of a test using area under the receiver operating characteristic curve (AUC-ROC). AUC-ROC

Performance

¼0.5 >0.5, 0.7 >0.7, 0.9 >0.9, <1 ¼1

Not informative Poor Moderateegood High Perfect

With permission to reproduce: Fischer JE, Bachmann LM, Jaeschke R. A readers’ guide to the interpretation of diagnostic test properties: clinical example of sepsis. Intensive Care Medicine. 2003 Jul; 29(7):1043e51.

Looking at BNP in particular, its N-terminal pro-hormone (NTproBNP) and, more recently, the mid-regional of ANP pro-hormone (MR-proANP) plasma measurements have been demonstrated to improve HF diagnostic accuracy, with respect to all clinical findings, in patients presenting with dyspnoea.12e15 Their receiver operating characteristic (ROC) analysis area under the curve (AUC) for HF diagnosis is almost the same (0.91, 0.90, 0.90, respectively).15 Studies performed in healthy persons (where there is no known cardiovascular disease or detectable structural heart disease) demonstrated that the levels of BNP and NT-proBNP tend to be affected by age and gender, with higher levels in older individuals and in women.16,17 Moreover, obesity18 and thyroid dysfunction19 have been demonstrated to influence BNP and NT-proBNP plasma levels. Furthermore, renal impairment (estimated Glomerular Filtration Rate; eGFR < 60 ml/min) may affect BNP and NT-proBNP level. Thus some authors suggest higher cut-off values to improve the specificity for HF diagnosis even though the NPs diagnostic accuracy appeared only lightly reduced as compared to that in patients with preserved renal function.20,21 However NPs values are particularly useful in an emergency setting to distinguish cardiac from pulmonary based dyspnoea: patients with acute HF have higher peptide levels than those with respiratory dyspnoea.22,23 According to the European Society of Cardiology guidelines for the diagnosis and treatment of acute and chronic heart failure, in patients with acute onset of symptoms, a BNP plasma level < 100 pg/ml or NT-proBNP < 300 pg/ml or MRproANP < 120 pg/ml makes an HF diagnosis unlikely.24 Together with NPs, circulating troponins (Tn) should be routinely performed in patients with HF. The clinically important Tn are troponin I (TnI) and troponin T (TnT).25 Both are components of the contractile apparatus of myocardial cells and are expressed almost exclusively in the heart. Elevations of these biomarkers in the blood reflect an injury leading to necrosis of myocardial cells and their testing has become the gold-standard for the diagnosis of acute myocardial infarction (AMI).26 Contemporary consensus guidelines recommend the Tn level measurement be integrated with clinical evaluation and ECG in the early assessment of patients with acute HF syndrome in order to rule out AMI as the precipitant.24 The second EuroHealth Failure Survey reported that 42% of new-onset acute HF is caused by acute coronary syndromes (ACS) mostly due to AMI.27 However many studies performed in patients without evident or clear ACS have demonstrated that measurable or elevated Tn values in acute HF may occur independently of AMI, especially after the introduction of high sensitive Tn assay.28e31 High levels of Tn do not indicate the underlying mechanism of myocardial injury/necrosis (ischaemic vs non-ischaemic).32 Moreover, a number of non-cardiac causes may increase Tn levels.32,33 A recent retrospective study performed using data from the Acute Decompensated Heart Failure National Registry (ADHERE) demonstrated that Tn admission status (positive or negative, defined through TnT cut-off of 0.1 hg/l or TnI cut-off of 1 mg/l) correlates with hospital resources utilization (Intensive Care Unit, ICU admission, median hospital/ICU length of stay and procedures) and in-hospital mortality (regardless of ischaemic or nonischaemic origin and inotropic agents or vasodilators administered). Examining the Tn level as a continuous variable, higher levels were associated with higher mortality.34 Many studies have shown that NPs may also have prognostic prediction capacity. The REDHOT trial enrolled patients presenting to the Emergency Department (ED) with shortness of breath and BNP levels above 100 pg/ml in order to evaluate the prognostic contribution of BNP levels, HF perceived severity using NYHA functional class and clinical decision-making (admission or discharge). Authors found that BNP level at the time of ED presentation was a strong predictor of 90-day outcomes (all-cause

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Table 2 Biomarkers in heart failure. Type of Biomarker

NPs12e24,29,31,35,36,44,54e59

Myocyte mechanical stress

Molecule

BNP

Diagnosis Aim of the dosage

Cut-off values

Admission level

Changed values

Rule-out HF

<100 pg/mla

- Hospital readmission - Mortality

<300 pg/mla

- Length of hospital stay - ICU admission - 90-day cardiac event/hospital readmission - Mortalityb - Mortalityc

<120 pg/mla At least one value >99th percentile of URL and typical acute rise/fallf

- Mortality - Length of hospital stay - ICU admission - Procedures - Mortalityg

NT-proBNP

Tn24e26,28e34,37

Myocyte injury, apoptosis, necrosis

sST231,40,44,45

Myocardial Remodelling Neurohormone

MR-proADM15,46-54

MR-proANP TnI TnT

Risk stratification and outcome prediction of

Rule-out AMI as the precipitant of acute HF syndrome

sSt2

- Mortalityh

MR-proADM

- Mortalityi

- Hospital readmission - Mortality Higher risk of death if rising compared to stable or decreasing values

Therapy

Major confounders

Guided by their levelsd

- Age ([) - Gender (F > M) - Obesity (Y)e - Thyroid dysfunction ([) - Renal impairment ([)

- Rheumatology diseases ([) - Endocrinology diseases ([)

Abbreviations: NPs ¼ Natriuretic Peptides; BNP ¼ B-type Natriuretic Peptide; NT-proBNP ¼ amino-terminal fragment of pro-B-type natriuretic peptide; MR-proANP ¼ midregional pro-atrial natriuretic peptide; HF ¼ heart failure; ICU ¼ intensive care unit; F ¼ female; M ¼ male; Tn ¼ troponins; TnI ¼ troponin I; TnT ¼ troponin T; AMI ¼ acute myocardial infarction; URL ¼ upper reference limit; sST2 ¼ soluble form of interleukin-1 receptor-like-1; MR-proADM ¼ mid-regional pro-adrenomedullin. a For acute onset of symptoms.24 b In-hospital mortality37; post-discharged mortality.15,29,35,54 Better performance for prediction of cardiovascular than all-cause mortality15 and of long rather than short term mortality.15,54 c Post-discharged mortality.14,15,29,31,36,44,54 Better performance than BNP; for prediction of cardiovascular than all-cause mortality15 and for long rather than short term mortality.15,54 d In addition to standard management reduces adjusted mortality.57,58 Patients <75 years old and patients with systolic dysfunction seem to be more responsive.59 e MR-proANP levels are less affected by obesity with respect of that BNP and NT-proBNP.15 f Use the most sensitive assay with optimal precision expressed by coefficient of variation (CV) < 10% at the URL; assays with 10% < CV < 20% at the URL make identification of changing values more difficult; assays with CV > 20% at the URL should not be used. g In hospital mortality regardless of ischaemic/non-ischaemic reasons for their release and therapies (inotropic or vasodilator agents) performed34; post-discharged allcause mortality.14,29e31 h Post-discharged all-cause mortality.31,44,45 i Better performance than NPs; better prediction of cardiovascular rather than all-cause mortality15 and for short than long term mortality.15,54

mortality, cardiac events or readmission), differently from NYHA functional class and the decision to admit or discharge patients. Among admitted patients, those with BNP level below 200 pg/ml had a better 90-day combined event rate (HF visits or admissions or all-cause mortality) than those with higher values (9% and 29% respectively) regardless of NYHA functional class.35 Also, NT-proBNP level at time of hospital admission for acute HF has been demonstrated to strongly predict short- and long-term mortality.14,36 Thus NPs or Tn levels may have a role in the prognosis evaluation and may help the physicians in performing the triage of patients with signs and symptoms of HF. They may have an even more notable role if sampled at the same time. More recently, Fonarow et al. examined the admission BNP level and Tn status for capacity to predict in-hospital mortality in the ADHERE population. According to the previously reported studies, the authors found an increased in-hospital mortality among patients admitted with a higher BNP level than 840 pg/ml (adjusted odds ratio, OR 1.60, p < 0.0001) or Tn positive status (adjusted OR 1.85, p < 0.0001). Furthermore, when BNP was above 840 pg/ml and Tn status was positive, an additive prognostic effect was found with an adjusted OR of 3.00 for in-hospital mortality in comparison to those patients with BNP < 840 pg/ml and Tn negative status. Moreover BNP level and Tn status also predicted the length of hospital stay (from a median of 4.1 days when none were elevated to 5.4 days when both were elevated) and ICU admission (from 14.1% of patients without

biomarkers elevation to 32.6% of patients with both elevated biomarkers).37 Some other promising novel biomarkers have been evaluated for prognostic prediction accuracy. The plasma values of the soluble form of the interleukin-33 receptor (sST2) are proved to predict remodelling following AMI and to correlate with echocardiographic ventricular structure.38,39 SST2 belongs to the group of reflecting remodelling biomarkers because it is thought to bind IL-33, neutralizing its antihypertrophic, antifibrotic and antiapoptotic effects.40 Its elevation has been observed in patients after AMI,41,42 in chronic HF43 and acute decompensated HF44,45 identifying patients with short- and long-term higher risk of death on follow-up. Recently, Pascual-Figal et al. evaluated the prognostic accuracy of NT-proBNP, TnT and sST2 in 107 consecutive patients admitted for acutely decompensated HF. The authors found that sST2 achieved comparable AUC-ROC for the occurrence of all-cause mortality to that of NT-proBNP (0.72 vs 0.71), while TnT had a lower AUC-ROC (0.66). Although each biomarker provided independent prognostic information, a multibiomarker score gave additive information: the presence of none, one, two, or three elevated biomarkers was associated with a notable rising incremental risk of death during the first year (from 0 when none were elevated to 53% when all were elevated).31 A promising biomarker, still under evaluation, is the midregional of the adrenomedullin precursor (MR-proADM). Adrenomedullin (ADM) is a 52-amino acid peptide, expressed in a wide

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Table 3 Biomarkers in acute kidney injury. Biological sample

Positive result

Aim of the dosage

Cut-off values

Cr64,79,80

Plasma Urined

48e72 h after renal insult

AKI diagnoses

RIFLE or AKIN criteria

NGAL65,67e75

Plasma Urineb

1e2 h after renal insult

-

Prediction of AKIa - Differentiate between intrinsic and pre-renal AKI or other diagnoses

-

CyC71,72,77e80

Serumd Urine

2 days before Cr

Prediction of AKIc

- Duration of AKI - Need for RRT - Short-term mortality

IL-1872,83,84

Urine

4e6 h after renal insult

EO85

Plasma

ILGFBP788 TIMP-288

Urine

Diagnose ATN and delayed graft function in transplant recipients Risk stratification for AKI before renal injury happenede

Predicted outcome

Duration/severity of AKI Step-up RIFLE class Need for RRT Short-term mortality

- Need for RRT - Short-term mortality

Major confounders -

Age Sex Race Liver disease Muscle mass Fluid status Age Sex CKD Malignancies Inflammation Sepsis Age Sex Race Non-renal elimination Inflammation Steroid use Thyroid dysfunction Inflammation

- ICU stay - Need for RRT - In-hospital mortality

Prediction of AKI RIFLE-Injury or RIFLE-Failure within 12 h after sample collection in ICU heterogeneous patientsf

Abbreviations: Cr ¼ creatinine; AKI ¼ acute kidney injury; NGAL ¼ Neutrophil Gelatinase-Associated Lipocalin; RRT ¼ renal replacement therapy; CKD ¼ chronic kidney disease; CyC ¼ cystatin C; IL-18 ¼ interleukin-18; ATN ¼ acute tubular necrosis; EO ¼ endogenous ouabain; ICU ¼ intensive care unit; ILGFBP7 ¼ insulin-like growth factorbinding protein 7; TIMP-2 ¼ tissue inhibitor of metalloproteinase-2. a Better performance in paediatric than in adult patients.67,69 Progressively better performance in critically ill patients, post-cardiac surgery and contrast induced AKI69 and for prediction of RIFLE-Risk, RIFLE-Injury or RIFLE-Failure.71 b Less affected by sepsis.73,74 c Better performance of serum CyC than urinary CyC.77 d Every one alone or together could be used to estimate the Glomerular Filtration Rate.79,80 e Plasma ouabain seems to be a biomarker and a player of AKI. The coming availability of its inhibitor (Rostafuroxin, currently on phase II drug trial) could open to targeted therapy. f Each molecule performed better than the known biomarkers (e.g. NGAL, CyC, IL-18) and they performed even better when used together.

range of tissues such as brain, lungs, heart, kidneys, gastrointestinal organs, endothelial cells, vascular smooth muscle cells, fibroblasts and adipocytes.46,47 A number of stimuli are able to induce ADM mRNA expression (shear stress, stretch, hypoxia, oxidative stress)48e50 or to increase ADM production and secretion (angiotensin II, norepinephrine, endothelin-1, bradykinin, corticosteroids and thyroid hormone).51 ADM produces lots of haemodynamic effects (decrease in preload, afterload, and total peripheral resistance) through arterial and venous dilatation and seems to inhibit cell growth and hypertrophy.52,53 MR-proADM has been evaluated both in acute and chronic HF as a prognostic marker. The BACH trial, carried out in patients admitted to the ED with acute-onset dyspnoea, demonstrated that when dyspnoea was due to HF then MRproADM, assessed at time of admission, was superior to BNP and NT-proBNP in predicting 30-day mortality (AUC-ROC for all-cause mortality 0.739 vs 0.555 vs 0.641, respectively; AUC-ROC for cardiovascular mortality 0.790 vs 0.584 vs 0.651, respectively). The ability of MR-proADM progressively decreased from short- to longterm predictions. Indeed its AUC-ROC for predicting 90-day allcause or cardiovascular mortality decreased to 0.674 and 0.740 respectively, similar to that of the NT-proBNP (0.664 and 0.724, respectively).15 In a subgroup analysis of the BACH trial, Peacock et al. demonstrated that MR-proADM proved to be a better predictor for short-term mortality (14-day mortality) in comparison with NPs (AUC-ROC: BNP 0.48, NT-proBNP 0.59, MR-proADM 0.74).54

To summarize, MR-proADM could have a prognostic value and may play an important role in planning the most appropriate destination for patients admitted with HF. On the contrary, NPs were demonstrated to be less accurate at the onset of HF but served as a useful predictor for long-term outcomes.15,54 However, BNP or NT-proBNP initial values are important in order to track the clinical course of patients undergoing therapies. Moreover, they may have a role in the titration of HF therapy. Michtalik et al. evaluated the role of NT-proBNP level acute changes during the first hospitalization for acute HF in 217 consecutive patients. A reduction of less than 50% in NT-proBNP concentration, from the admission to the discharge, was associated with a 57% greater risk of readmission/ death within one year, regardless of age, gender, race, admission creatinine, admission NT-proBNP, comorbidity, length of stay and left ventricular ejection fraction.55 Farmakis et al. found similar results in 69 patients admitted with acutely decompensated chronic HF treated with levosimendan. Event-free survival was longer in patients with a 58% or more reduction in BNP concentration (median, 135 vs 43 days).56 Several randomized controlled trials evaluated event-free survival and mortality in patients in which therapies were titrated according to the BNP or NT-proBNP levels. This analysis gave conflicting results, but pooled analyses of both positive and negative studies indicated an adjusted mortality reduction (hazard ratio 0.76e0.69) when biomarker-guided care was given in addition to standard management.57,58 However, 75-yr old patients or younger

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and patients with systolic dysfunction appeared to be more responsive to biomarker titrated therapy than those older than 75yr or with HF with preserved ejection fraction.59 Furthermore, due to the large spectrum of cardiac abnormalities that can induce BNP or NT-proBNP increase, the same drug therapy, aimed at reducing volume overload with loop diuretics, might not fit for all, especially during compensated HF phases. Many clinical trials are evaluating the potential role of different HF pathophysiology-related biomarkers within a multimarker-based treatment strategy that could, in the future, lead to better individualized care for these patients.60 For example McMurray et al. demonstrated that only in patients with C-reactive protein level above 2 mg/l, rosuvastatin treatment was associated with better outcomes.61 Likewise in the Randomized Aldactone Evaluation Study, it was found that the benefit from spironolactone was associated with higher levels of collagen synthesis markers.62 3. Biomarker in acute kidney injury Acute kidney injury (AKI) is a highly prevalent, tricky to diagnose and arduous to treat syndrome with significant morbidity and mortality in the critical care setting. Nowadays, supportive therapies (dialysis and haemofiltration) are its main management strategy but in-hospital mortality exceeds 50% without appreciable improvement over recent decades.63 Although both RIFLE and AKIN criteria employ the serum creatinine (sCr) level to define AKI, current efforts are focused on identifying markers able to detect even earlier AKI stages than those identified through that traditional biomarker.64 Several limitations are known to affect sCr levels as an early marker of kidney injury and they may imply an unacceptable delay in diagnosis and therapy. A detectable increase in sCr level occurs up to 48e72 h after a renal injury has occurred and indicates only the renal filter capacity. That, in turn, has a large functional reserve (sCr elevation appears after more than 50% of kidney function is lost). Moreover, lack of sensitivity affects sCr levels in patients with liver disease, low muscle mass or altered fluid status. Finally, an increase in sCr can be observed in pre-renal, intra-renal or postrenal AKI. Considering all the above, sCr should not be regarded as an early and always reliable measure of renal function, particularly in acute settings. The critical care setting needs an early biomarker that allows clinicians to correctly stratify patient risk, to diagnose AKI early and to set a targeted therapy quickly. A summary of properties of AKI biomarkers we have chosen to talk about is shown in Table 3. The most studied and promising AKI biomarker is the Neutrophil Gelatinase-Associated Lipocalin (NGAL). It is a 25 kDa irontransporting protein involved both in innate immunity and in cellular proliferation and recovery. NGAL is a ubiquitous epithelial protein and its mRNA is up-regulated in neutrophils and epithelial cells by ischaemia and cytokines.65 It is freely filtered and almost completely reabsorbed in the proximal tubule. It was shown, first in experimental models and then in the clinical setting, that NGAL can be sampled in plasma (pNGAL) and urine (uNGAL) 1e2 h after a renal insult.66e68 Mishra et al., for the first time examined (uNGAL and pNGAL) values for early prediction of AKI (defines as sCr elevation >50% from baseline) in 71 paediatric patients undergoing cardiopulmonary bypass (CPB). In this study, by means of a multiple stepwise regression analysis, the authors demonstrated that only the uNGAL, measured after 2 h of CPB, was the strongest independent predictor of AKI with an AUC-ROC of 0.998.67 A number of studies, performed in adult and paediatric patients, showed inhomogeneous NGAL performances. A meta-analysis of 19

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studies covering more than 2500 patients demonstrated that uNGAL and pNGAL had similar diagnostic and prognostic values. The overall standardized AUC-ROC of NGAL to predict AKI was 0.815 with a better performance in paediatric than in adult patients (AUC-ROC 0.930 vs 0.782). The best AKI predictive ability was found for contrast-induced nephropathy, post-cardiac surgery patients and ICU heterogeneous patients (AUC-ROC 0.894, 0.775 and 0.728, respectively).69 More recently, a study performed in 145 hospitalized patients with AKI showed that uNGAL levels were able to effectively differentiate between intrinsic (acute tubular necrosis or kidney structural damage) and pre-renal AKI with an AUC-ROC 0.87.70 Moreover Nickolas et al., evaluating the relationship between the uNGAL and the duration/severity of AKI (RIFLE criteria), found that uNGAL levels were significantly higher in patients with sustained AKI (duration  72 h) compared with patients with transient AKI (<72 h). Furthermore, uNGAL values progressively and significantly increased proportionally with RIFLE severity class. Indeed the AUC-ROC for uNGAL progressively increased for the prediction of RIFLE-Risk (0.72), RIFLE-Injury (0.8) and RIFLE-Failure (0.83).71 A recent prospective observational trial including more than 500 ICU patients showed that uNGAL, cystatin C (CyC) and interleukin-18 (IL-18) were able to predict death and need for RRT at 7 days (AUC-ROCs above 0.70).72 Among the major limitations of NGAL as a predictor biomarker for AKI are the influence of patient age, gender (women have higher levels than men) and some diseases such as infections, chronic kidney disease, malignancies and chronic obstructive pulmonary disease.65 Major concerns arise around sepsis since it has been demonstrated to be a confounding factor that itself contributes to the development of AKI. In order to better understand the influence of sepsis on NGAL dosage, Mårtensson et al. enrolled 45 critically ill patients and pointed out a stepwise increase in median peak level of pNGAL and uNGAL in case of SIRS, severe sepsis and septic shock without AKI (pNGAL: 111 ng/ml, 116 ng/ml and 134 ng/ml respectively; uNGAL: 24.4 ng/ml creatinine, 47.7 ng/ml creatinine and 63.5 ng/mg creatinine respectively). The median peak levels of pNGAL and uNGAL increased further in case of septic shock and concomitant AKI. However, significant differences in median peak levels between septic shock patients with and without AKI were described for uNGAL (319 vs 63.5 ng/mg creatinine, p < 0.05) and not for pNGAL (216 vs 134 ng/ml, p ¼ 0.06). Thus, good predictions of AKI within the next 12 h were found for uNGAL and pNGAL compared to all non-AKI patients (AUC-ROC 0.86 and 0.85, respectively) and only for uNGAL compared to non-AKI patients with septic shock (AUC-ROC 0.86). The capacity to predict AKI by uNGAL levels appeared less affected by the presence of septic shock. Authors speculated about the possibility that the pNGAL concentration due to AKI might be clouded by NGAL secreted by the sepsis activated neutrophils.73 Similar results were found in a recent observational trial on 11 paediatric septic patients. Unlike the uNGAL level, the pNGAL concentration was significantly different in septic patients compared to controls and furthermore its median level was not able to discriminate between septic patients with and without AKI.74 However Bagshaw et al., investigating 83 critically ill adult patients with AKI, demonstrated significantly higher uNGAL and pNGAL peak levels in case of sepsis-based AKI than non-septic AKI. The AUC-ROC for discriminating septic vs non-septic AKI was 0.77 for pNGAL (cut-off 280 ng/ml) and 0.7 for uNGAL (cut-off 150 ng/mg creatinine). Interestingly authors found a correlation between the white blood cell count in sepsis-based AKI patients and peak levels of pNGAL, but not of uNGAL.75 More and larger studies are needed to completely understand sepsis as a confounding factor for NGAL performance as an AKI biomarker. A noteworthy kidney biomarker is CyC. It is a 13 kDa cysteine proteinase inhibitor protein produced at constant rate by all

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nucleated cells. It is freely filtered by the glomerulus and fully catabolized by the proximal tubular epithelium. Thus, the measurable urinary CyC (uCyC) may suggest tubular injury.76 A recent meta-analysis including more than 3000 patients from different settings (post-cardiac surgery, paediatric and critically ill patients) found that uCyC measured 24 h after a renal insult or at ICU admission has only moderate AKI prediction performance (AUC-ROC of 0.67). A much better performance was found for serum CyC (sCyC) with an AUC-ROC of 0.87.77 A study performed in critically ill patients at risk of AKI demonstrated sCyC elevation about two days before sCr fulfilled the risk-criteria.78 SCyC has been extensively evaluated also as a GFR gauge in patients with chronic kidney disease and appeared less affected by age, gender and muscle mass compared to sCr. However, GFR estimation in patients with very low GFR (GFR < 15 ml/min/1.73 m2) was better performed with sCr than sCyC, probably due to its non-renal elimination.79,80 Among early kidney biomarkers, interleukin 18 (IL-18) has been extensively studied. Between 4 and 6 h after a renal injury, it has been found that the proximal tubular epithelial cells increase IL-18 production and higher levels can be sampled in the urine.81 IL-18 is a pro-inflammatory cytokine synthesized by a lot of cells in inflammatory states.82 Numerous studies evaluated urinary IL-18 as a predictive or diagnostic marker of AKI, giving different performances depending on the population studied. Good performance was found in transplant recipients with an AUC-ROC for diagnosis of acute tubular necrosis and delayed graft function of 0.95.83 Instead, Siew et al. found an AUC-ROC of only 0.62 for AKI prediction within 24 h in a cohort of 451 ICU heterogeneous patients. Although IL-18 concentration was not predictive of AKI, it was associated with poor clinical outcome (28-day death or need for dialysis). It seems that IL-18 reflected the inflammatory patient state more than kidney damage.84 Further studies are needed to clarify the role of IL-18 in patients with AKI and inflammatory states. Recently, two studies introduced three new candidate biomarkers for AKI. Bignami et al. performed a clinical study to evaluate endogenous ouabain (EO) as a biomarker of AKI (first part) and an experimental study (second and third parts) to understand the meaning of high level of ouabain on kidney function.85 Ouabain is an adrenal stress hormone that modulates the Naþ/Kþ ATPase.86 In the first part they evaluated the plasma EO level in 626 cardiac surgery patients before (bEO) and after surgery (aEO). They found that bEO levels were highly correlated to aEO and sCr. Moreover with each incrementing bEO tertile, patients had an increasing prevalence of postoperative AKI, ICU stay, need for RRT and inhospital mortality. After adjustments for covariates (age, basal eGFR, EF, hypertension, diabetes, surgery type and reintervention), the third tertile remained associated with AKI conversely to the clinical scores (EUROSCORE and ACEF). The AUC-ROC for diagnosis of severe AKI was 0.75. Furthermore, they elaborated a clinical model to predict AKI with an AUC-ROC of 0.79 and found that through adding pEO, a significant increase of predictive ability could be achieved (AUC-ROC from 0.79 to 0.85, p < 0.01). In the second and third parts of the study they used a rat model and incubating podocyte primary cell cultures respectively to demonstrate that high levels of circulating ouabain have harmful effects on kidneys (reduce Cr clearance, increase urinary protein excretion and reduce podocyte nephrin expression). According to this study, plasma EO seemed to be both a biomarker for and a player in AKI. The coming availability of an EO inhibitor (Rostafuroxin), currently on phase II drug trial, could provide an opportunity for targeted therapy in order to minimize the risk of AKI.87 Another two AKI biomarkers were recently discovered and evaluated: the insulin-like growth factor binding protein 7

(ILGFBP7) and the tissue inhibitor metalloproteinase-2 (TIMP-2), two molecules involved in cell cycle arrest after damage. In the first phase of the study (discovery phase), Kashani et al. examined 340 markers in 522 ICU heterogeneous patients in order to identify the best performing in the predictive development of AKI RIFLE I or F within 12e36 h. In the second phase (Sapphire phase), the two biomarkers identified (ILGFBP7 and TIMP-2) were validated in 728 ICU patients without moderate or severe AKI (KDIGO stage 2 or 3). ILGFBP7 and TIMP-2 performed better than the known biomarker (urinary and plasma NGAL, pCyC, uKIM-1, urinary IL-18, urinary L-FABP) with an AUC-ROC for developing AKI within 12 h of 0.76 and 0.79 respectively. They performed even better when used together (AUC-ROC 0.8).88 4. Biomarkers in acute respiratory distress syndrome Although research on the biomarkers of Acute Respiratory Distress Syndrome (ARDS) has revealed new and important information about the pathophysiology of this syndrome, none is suitable for routine clinical use under diagnostic and prognostic points of view.89,90 Recently Ware et al. evaluated patient outcome prediction ability for a panel of six clinical predictors (Acute Physiology And Chronic Health Evaluation III, organ failures, age, underlying cause, alveolar-arterial oxygen gradient, plateau pressure) and eight biomarkers (von Willebrand factor antigen, surfactant protein D, tumour necrosis factor receptor-1, interleukin-6, interleukin-8, intercellular adhesion molecule-1, protein C, plasminogen activator inhibitor-1) in 528 patients enrolled in the NHLBI ARDS clinical trial.91 The authors found that when only clinical predictors were considered, the AUC-ROC was 0.815 and when only biomarkers were used, the AUC-ROC was 0.756. The combination of clinical and biological markers significantly increased the AUC-ROC to 0.85. Among the studied biomarkers, the plasma interleukin 8, an inflammatory biomarker and the surfactant protein D, a marker of alveolar type II cell injury, were found to be most efficient by increasing the AOU-ROC to 0.834 when added to the clinical predictors. 5. Conclusions Biomarkers are important but can also sometimes be challenging issues in the critically ill patient. Notwithstanding that the usefulness of biomarkers is certain, but as research into new ones continues some concerns emerge. Firstly, single biomarkers rarely seem to have all the characteristics required to meet the clinical needs for a complete organ failure assessment (screening, diagnosis, risk stratification, prognosis and response to therapy). Secondly, every biomarker seems to show different behaviour in different kinds of diseases: more predictable performances are described in homogeneous patients such as those who have undergone cardiac surgery while, in heterogeneous patient populations, biomarkers give more complex information that should be specifically contextualized. Thirdly, several confounding factors have to be considered when interpreting biomarkers used for clinical assessment. In conclusion, every organ failure is part of a complex network of relationships with other organs and, therefore, the use of a single biomarker has a limited value in the global patient evaluation. A panel of biomarkers seems to be a possible solution for achieving a complete patient assessment together with an adequate clinical evaluation and monitoring. The contribution of the newest “Omics” sciences may have a role in adding novel and more accurate biomarkers.

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Conflict of interest statement All the authors declare no conflict of interests.

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