- Email: [email protected]
Novel urinary biomarkers and the early detection of acute kidney injury after open cardiac surgeries
Journal of Critical Care 40 (2017) 171–177
Contents lists available at ScienceDirect
Journal of Critical Care journal homepage: www.jccjournal.org
Novel urinary biomarkers and the early detection of acute kidney injury after open cardiac surgeries Said M. Elmedany, MD a,1, Salah S. Naga, MD b,2, Rania Elsharkawy, MD c,3, Rabab S. Mahrous, MD a,⁎,4, Ahmed I. Elnaggar a,5 a b c
Department of Anaesthesia and Surgical Intensive Care, Alexandria University, Egypt Department of Internal Medicine and Nephrology, Alexandria University, Egypt Department of Clinical Pathology, Medical Research Institute, Alexandria University, Egypt
1. Introduction Open heart surgery can be viewed as one of the greatest medical advances of the 20th century. It has been estimated that about 397,000 patient undergone cardiac surgeries in the United States in 2010 and N 80% of routine cardiac surgical procedures are performed using cardiopulmonary bypass (CPB) [1]. CPB is complicated by an increased incidence of acute kidney injury (AKI) with incidence rate of (1–30%), which is associated with increased risk of infection, diminished quality of life, delayed discharge and significant morbidity and mortality [2-5]. Despite the deleterious impact of AKI-CPB on outcome, its pathophysiology remains incompletely understood. It is probable that the pathophysiologic changes associated with CPB are accentuated as the duration of CPB increases, which subsequently increases the risk of developing AKICPB [6]. Recently, standardized clinical definitions of AKI have been implemented through the use of the RIFLE (Risk, Injury, Failure, Loss, and end stage renal disease (ESRD)), AKIN (Acute Kidney Injury Network) criteria [7,8], and later the Kidney Disease Improving Global Outcomes (KDIGO) work group began by defining AKI by harmonizing the prior RIFLE and AKIN criteria [9]. However, these criteria are still very much dependent on delayed serum creatinine elevations, the current gold standard for the diagnosis of AKI. Furthermore, as a functional marker of glomerular filtration, serum creatinine is not ideally suited to diagnose AKI caused by renal tubular injury, rather than reversible prerenal azotemia [7]. This unfavorable outcome might be tied to the late detection of AKI when the elevation of serum creatinine (SCr) is used where also the change in sCr does not discriminate the time and type of renal insult nor the site and extent of glomerular or tubular injury. Many genes are up-regulated in the damaged kidney with the corresponding protein ⁎ Corresponding author at: 33 Bahaa Eldin Elghatwary St., Smouha, Alexandria, Egypt. E-mail address: [email protected] (R.S. Mahrous). 1 Contribution: The senior author who lead the team of work. 2 Contribution: Clinical diagnosis of the patients of acute renal injury. 3 Contribution: Performing the laboratory investigations to detect the biomarkers. 4 Contribution: The senior anesthetist who anaesthetize the patients and monitor them throughout the operation, collect the samples and write the manuscript. 5 Contribution: The second anesthetist who help in anaesthesia, collect the samples, monitor and follow up the patients and write the manuscript.
http://dx.doi.org/10.1016/j.jcrc.2017.03.029 0883-9441/© 2017 Elsevier Inc. All rights reserved.
products appearing in plasma and urine. Some of these are candidate markers for more timely diagnosis of AKI [10]. Therefore, development of new AKI biomarkers has been emphasized to introduce more sensitive and accurate renal biomarkers to clinical use. Early detection of AKI is deemed important to develop therapeutic concepts to treat or at least ameliorate a renal insult. Within the last years various biomarkers reflective of ischaemic tubular injury have been developed to accomplish this task, among them Neutrophil-gelatinase-associated lipocalin (NGAL), Kidney-injury molecule-1 (KIM-1), Interleukin-18 (IL-18), and L-Fatty-acid-binding-protein (LFABP) [11]. However, most of these novel AKI markers have been derived from animal experiments inducing renal ischemia-reperfusion injury and cardiac surgery with cardiopulmonary bypass (CPB) may be regarded as a prototype clinical scenario of systemic ischaemia-reperfusion injury [12]. NGAL is a glycoprotein consisting of a polypeptide chain of 178 amino acids covalently bound to gelatinase which is synthesized and secreted when acute kidney injury with hypoxic conditions causes a disruption of the endothelial cells of the renal proximal tubules, resulting in increased uNGAL [4]. KIM-1 is a type I transmembrane glycoprotein that is associated with proximal tubule cell injury. Presence of KIM-1 in the urine is highly specific for kidney injury as it's undetected in normal urine. No other organ has been shown to express KIM-1 to a degree that would influence kidney excretion. It has been shown to be much more sensitive than creatinine as a marker for AKI [13]. This study was designed to study the role of urinary NGAL and KIM-1 as biomarkers for early detection of AKI in patients undergoing coronary artery bypass graft (CABG) under CPB, so as to find new tools for early diagnosis and assessment of severity of AKI and to correlate between uNGAL, uKIM-1, complete urine analysis, the other conventional markers of AKI (serum creatinine), and the clinical measurements. 2. Patients and methods This study was carried out on 45 adult patients, of both sexes Cleveland clinic score of low to intermediate grade (0–5) admitted to Alexandria Main University Hospital, department of cardiothoracic surgery scheduled for elective CABG using CPB. Patients with history of previous cardiac surgery, pre-existing renal impairment or with history of recent perioperative exposure to
172
S.M. Elmedany et al. / Journal of Critical Care 40 (2017) 171–177
Table 1 Demographic, preoperative and surgical related data.
Demographic data Sex Male, n (%) Female, n (%) Age (years)
Non-AKI (n = 34)
AKI (n = 11)
30 (88.2%) 4 (11.8%) 55.6 ± 7.9
7 (63.6%) 4 (36.4) 60.5 ± 6.6
0.064
Preoperative assessment by Cleveland clinic score 0, n 6 1, n 6 2, n 16 3, n 4 4, n 2 Mean ± SD 1.7 ± 1.0 Surgical related data Duration of anaesthesia (hr) Duration of surgery (hr) CPB time (min) Aortic cross-clamp (min) Blood requirement (units) Weaning of ventilator (hr) Chest tube drainage (ml/day) Adrenaline Dose (μg/kg/min) Duration (hr) Dopamine Dose (μg/kg/min) Duration (hr) POAF Neurological complications
Significance (p value)
0.206
0 0 2 3 6 3.4 ± 0.8
0.001⁎
3.9 ± 0.7 3.0 ± 0.7 74.2 ± 16.7 48.2 ± 14.8 1.6 ± 0.7 4.1 ± 1.0 518.8 ± 123.6
4.2 ± 0.8 3.4 ± 0.9 90.5 ± 16.2 60.9 ± 8.1 3.0 ± 0.6 3.9 ± 1.1 653.6 ± 79.2
0.143 0.129 0.010⁎ 0.017⁎ 0.001⁎
0.07 ± 0.02 6.27 ± 1.24
0.09 ± 0.01 6.29 ± 1.8
0.101 0.983
4.53 ± 2.03 8.0 ± 2.79 6 (17.6%) 7 (20.6%)
4.66 ± 0.81 15.64 ± 3.32 5 (45.5%) 4 (36.4%)
0.699 0.001⁎ 0.062! 0.213
0.605 0.002⁎
* Statistically significant at p ≤ 0.05.
nephrotoxic drugs, extremes of age (b18 or N75 years), off pump cardiac surgery, ejection fraction of b35%, emergency CABG or combined cardiac surgery, or neoplasms as they increase NGAL levels were excluded from the study. After approval of the local Medical Ethics committee of the Faculty of Medicine and taking a written informed consent, patients were screened for complete urine analysis, conventional renal function tests and novel urinary markers by blinded investigators. After formal pre-anaesthetic assessment of every patients as regard cardiac, coagulation profile, renal function, and pulmonary systems all preoperative cardiac medications was continued until the morning of surgery with the exception of drugs which have renal effects like ACE inhibitors drugs. On arrival to the operating room, peripheral and central venous cannulation and radial artery cannulation was done under local anaesthesia and IV sedation in the form of 2 mg midazolam and 4 mg morphine sulphate after applying standard monitoring to the patient. Induction and maintenance of anaesthesia was standardized in all patients in the form of midazolam (0.05 mg/kg), fentanyl (5 μg/kg), sevoflurane (6–7%), and rocuronium (0.9 mg/kg) to facilitate tracheal intubation. The lungs was ventilated at normocapnia (monitored by end tidal CO2 at 35 mmHg) with sevoflurane (1–2%) in an air-oxygen
mixture. Additional bolus doses of fentanyl and rocuronium was injected if necessary. CPB was managed to maintain mean arterial pressure (MAP) between 50 and 80 mmHg with tepid hypothermia (32°–34 °C) and keeping haematocrit above 20% with the addition of fresh packed RBCs as needed. Weaning from CPB and reperfusion of the heart was performed according to the patient's general condition and cross-clamp time. There was no fixed postoperative treatment regimen for either pharmaceutical or mechanical support. Weaning and extubation from the ventilator was done after haemodynamic (defined as a MAP of 60–90 mmHg, heart rate (HR) between 60 and 90 bpm, a central venous pressure (CVP) between 10 and 15 mmHg) and ventilatory stability. Analgesia was given in the form of morphine sulphate infusion (2–3 mg/h) in the first 48 h and paracetamol 1 g/6h. Measurements included assessment of the AKIN stages after ICU admission and every 6 h for the first 72 h postoperative; urinary examination for NGAL, KIM-1 (after induction, 2, 6, 12, and 24 h after termination of CPB), and urinary sediment microscopic examination [14] (preoperative, 2, 12, 24, and 48 h after termination of CPB); haematological measurements (sCr, and blood urea) just before anaesthesia and every day for 3 days after the end of surgery; and clinical measurements including (duration of anaesthesia and surgery, CPB and aortic cross clamping times, total amount of packed RBCs given during surgery, weaning from the ventilator postoperatively, and intra and postoperative complications). 2.1. Statistical analysis Data were analyzed by using SPSSR software (Statistical package for social science for personal computers) using “t” test, ANOVA test and chi-square X2 test, data were expressed as mean ± SD and P b 0.05 considered significant. 3. Results In this study 11 (24.4%) out of 45 patients developed AKI diagnosed by AKIN criteria of serum creatinine rise without finding any patient requiring renal replacement therapy. Demographics and surgical core data in the different groups are presented in Table 1.The demographic results didn't show any significant difference between both groups as regard age or gender with more females in AKI group (36.4% vs 11.8% in non-AKI group) in the AKI group. On comparing the CCS between the two groups it was found to be higher in the AKI patients. On assessing the AKIN stages in both groups it was significantly higher in the AKI group than the non-AKI group (starting from 24 h after surgery till third postoperative day) either due to increased serum creatinine in some patients or decreased urine output in others. As regard the CPB time and aortic cross-clamp time were significantly higher in the AKI group with mean values of (90.5 ± 16.2 vs 74.2 ± 16.2 min) and (60.9 ± 8.1 vs 48.2 ± 14.8 min) respectively. It was noticed that the amount of postoperative bleeding and perioperative
Table 2 Haematological measurements of biomarkers for acute kidney injury. Parameter Serum creatinine (mg/dl)
Estimated GFR (ml/min)
Urea (mg/dl)
Data are given as Mean ± SD. ⁎ Statistically significant at p ≤ 0.05.
AKI Non-AKI P AKI Non-AKI P AKI Non-AKI P
preoperative
24 h after surgery
48 h after surgery
72 h after surgery
1.15 ± 0.33 1.01 ± 0.23 0.216 117.32 ± 22.38 125.63 ± 23.03 0.174 39.2 ± 9.7 36.4 ± 7.8 0.745
1.20 ± 0.22 0.99 ± 0.24 0.066 98.76 ± 20.19 134.94 ± 27.67 0.001⁎ 34.1 ± 8.1 31.7 ± 7.2 0.056
1.49 ± 0.41 1.04 ± 0.29 0.004⁎
1.60 ± 1.03 1.11 ± 0.49 0.036⁎ 52.48 ± 15.59 121.6 ± 26.02 0.001⁎ 45.9 ± 20.5 40.4 ± 15.7 0.001⁎
71.46 ± 18.1 121.9 ± 24.2 0.001⁎ 36.7 ± 10.7 34.3 ± 7.9 0.001⁎
S.M. Elmedany et al. / Journal of Critical Care 40 (2017) 171–177
Fig. 1. Comparison between the two studied groups according to urinary sediment analysis.
requirement for blood transfusion were higher in the AKI patients than non-AKI group (653.6 ± 79.2 vs 518.8 ± 123.6 ml/day) and (3.0 ± 0.6 vs 1.6 ± 0.7 packed RBC units) respectively. During postoperative observation of the patients in the ICU, postoperative atrial fibrillation occurred in 11 patients (24.4%). Reopening for exploration of increased bleeding or pericardial tamponade was done in two patients in each group without any evident source of bleeding. On blood analysis for urea and creatinine (Table 2) it was found that serum creatinine started to rise from the second postoperative day and was higher in the AKI group (Fig. 1) with mean value of (1.56 ± 0.28 vs 0.85 ± 0.14) and estimated GFR was significantly lower in the AKI patients after the first postoperative day with a mean of (98.76 ± 20.19 vs 134.94 ± 27.67) and these changes continued till the third postoperative day. The time course of urinary levels of renal biomarkers in both groups are presented in Tables 3 and 4, showing that all biomarkers increased significantly after surgery in comparison with baseline values. Microscopic urine analysis showed the urine sediment score (USS) were significantly higher in the AKI patients and this appeared as early as just 2 h after termination of CPB (Fig. 1) and continued to be higher in this group till the 2nd postoperative day with area under the curve (AUC) range (0.636–0.888) (Fig. 2). Urinary NGAL was observed to be
173
significantly higher in AKI group at 2 and 6 h after CPB with AUC of (0.710 and 0.700) respectively and corresponding predicted cutoff values of (82.75 and 92.84) ng/ml respectively (Fig. 3). Urinary KIM-1 was higher at the end of the first postoperative day than before surgery with values significantly higher in AKI patients at 12 and 24 h intervals after CPB with corresponding AUC of (0.725 and 0.703) respectively which were significant giving a predicted cutoff values of (8.4 and 9.95) ng/ml respectively (Fig. 4). Table 4 presents the AUC of the various urinary biomarker with the combination of both urinary NGAL and KIM-1 yielding a higher AUC of 0.801 (Fig. 5) denoting an increased diagnostic power of this combination. On adding USS to the previous combination it was noticed that the AUC increased significantly (Fig. 6) to be 0.906 with a 95% CI (0.812–1.001) giving a near perfect combination for accurate prediction of CSA-AKI. On running Pearson's correlation test to determine the correlation between each biomarker at its best AUC (2-h NGAL, 12-h KIM-1, and 24-h USS) and sUrea 48 h after CPB. uKIM-1 showed a significant positive relationship in both AKI group (p = 0.012) and Non AKI group (p = 0.006). Similarly, USS showed a statistically highly significant relationship in both AKI and Non AKI groups (p = 0.000). The same correlation was performed between the three parameters and sCr 48 h after CPB and resulted in a significant positive relationship between the three parameters with sCr on the second postoperative day in AKI group, (p = 0.039, 0.003 and 0.024 respectively). While in Non AKI group uNGAL and USS showed a significant relationship, (p = 0.013 and 0.009 respectively). The same correlation was performed between the three parameters and sCr 48 hr after CPB and resulted in a significant positive relationship between the three parameters with sCr on the second postoperative day in AKI group, (p = 0.039, 0.003 and 0.024 respectively). While in Non AKI group uNGAL and USS showed a significant relationship, (p = 0.013 and 0.009 respectively), (Table 5). Moreover, Pearson's correlation test was used to determine the relationship between the three main tested parameters (uNGAL, uKIM-1, and urinary sediment score) at their best AUCs (2 h, 12 h, and 24 h) respectively and with sCr 48 h after CPB and the clinical measurements (duration of anaesthesia, duration of surgery, CPB time (min), AXC time (min), transfused fresh blood (units), weaning from ventilator (hours), bleeding (ml/day). UNGAL showed a significant positive relationship only with both CPB time and postoperative bleeding (r = 0.294 and 0.300) respectively. For the 12 h uKIM-1, the relationship was generally negative weak and non-significant except with weaning from the ventilator where it was significant negative relationship (r = −0.323). On the contrary, USS measured 24 h after CPB had moderate to strong positive relationship with all the variables and these relations were significant. On using logistic regression analysis to assess the independent predictors for the development of AKI, univariable analysis (Table 6) found that USS 24 h after CPB, uNGAL 6 h post-CPB, uKIM-1 24 h postCPB, sCr 48 post-CPB, and aortic cross clamping time were all significantly associated with AKI. Then, multivariable binary logistic
Table 3 Urinary biomarkers measurement for acute kidney injury. Parameter NGAL Mean ± SD
KIM-1 Mean ± SD
Urine sediment score 1 & 2, n
AKI Non-AKI P AKI Non-AKI P AKI Non-AKI P
T1
T2
T3
T4
T5
T6
8.7 ± 2.7 7.4 ± 2.4 0.123 2.2 ± 0.4 2.1 ± 0.4 0.290 0 0 –
129.1 ± 96 79.2 ± 56.3 0.039⁎
142.7 ± 98.6 93.5 ± 61 0.048⁎
3.1 ± 0.4 2.9 ± 0.5 0.118 3 0 0.002⁎
6.1 ± 0.1 6.1 ± 0.4 0.534
99.1 ± 70.2 70.9 ± 49.3 0.147 8.6 ± 0.4 8.1 ± 0.7 0.023⁎ 9 2 0.001⁎
79.1 ± 70.4 50.0 ± 46.7 0.124 9.9 ± 0.1 9.5 ± 0.3 0.002⁎ 11 10 0.001⁎
9 6 0.001⁎
T1, T2, T3, T4, T5, and T6 refer to timing of urine samples collection of preoperative, 2 h, 6 h, 12 h, 24 h, and 48 h after termination of CPB. ⁎ Statistically significant at p ≤ 0.05.
174
S.M. Elmedany et al. / Journal of Critical Care 40 (2017) 171–177
Table 4 AUC & ROC of urinary biomarkers at different time intervals and the coordinates of the curves. Timing
AUC
Sig.
95% CI
Optimal cutoff point
Lower bound
Upper bound
Value (ng/ml)
Sensitivity
Specificity
uNGAL After induction 2 h after CPB 6 h after CPB 12 h after CPB 24 h after CPB
0.636 0.710 0.70 0.599 0.623
0.178 0.174 0.170 0.328 0.224
0.444 0.419 0.426 0.401 0.430
0.829 0.856 0.852 0.797 0.816
7.45 82.75 92.84 69.315 40.81
70% 72% 71% 63.6% 63.6%
68% 69% 71% 58.8% 58.8%
uKIM-1 After induction 2 h after CPB 6 h after CPB 12 h after CPB 24 h after CPB
0.612 0.631 0.484 0.725 0.703
0.267 0.196 0.874 0.027⁎ 0.045⁎
0.418 0.452 0.310 0.560 0.540
0.807 0.810 0.658 0.890 0.867
2.1 2.95 6.05 8.4 9.95
65.4% 54.5% 63.6% 81.8% 63.6%
59.2% 58.8% 52.9% 76.5% 70.6%
Urinary sediment score After induction 2 h after CPB 12 h after CPB 24 h after CPB 48 h after CPB
0.500 0.636 0.888 0.880 0.829
1.000 0.178 0.000⁎ 0.000⁎ 0.001⁎
0.302 0.426 0.746 0.783 0.677
0.698 0.847 1.029 0.977 0.981
AUC & ROC for combination of urinary biomarkers and the coordinates of the curves uNGAL& uKIM-1 0.801 0.004⁎ 0.640 uNGAL, uKIM-1, & USS 0.906 0.000⁎ 0.812
943 1.001
* Statistically significant at p ≤ 0.05.
regression analysis (Table 7) which revealed that the most powerful independent predictors of AKI were the urine sediment score examined 24 h after termination of CPB (RR, 4.752); uNGAL measured 6 h after CPB (RR, 1.020), and aortic cross-clamp time (RR, 1.087). 4. Discussion The primary goal of cardiac surgery is not just a minimally acceptable outcome where the patient survives without life-threatening complications or persistent clinically manifest organ dysfunctions or simply
Fig. 2. AUC of USS 24 h after CPB.
hospital survival; but a healthy, productive long-term survivor [15]. Despite many years of research, AKI remains an important and life threatening complication in patients undergoing cardiac surgery, and with respect to high incidence of this complication in this specific population has even got a sub-designation: cardiac surgery associated-AKI (CSAAKI) [11]. The pathophysiological features of CSA-AKI are complex and multifactorial including numerous factors: exogenous toxins, endogenous toxins, metabolic factors, ischaemia–reperfusion injury, micro-embolization, neuro-hormonal activation, inflammation, oxidative stress, and haemodynamic factors. These mechanisms of injury are likely to be active at different times with different intensities, are interrelated and probably synergistic. Because many pathways are involved, it is not surprising that combinations of biomarkers with different properties may prove most predictive [16]. Accordingly, a biomarker that can detect
Fig. 3. Comparison between the two studied groups according to uNGAL for AKI.
S.M. Elmedany et al. / Journal of Critical Care 40 (2017) 171–177
175
Fig. 6. AUC for combined uNGAL, uKIM-1, and urinary sediment score.
Fig. 4. Comparison between the two studied groups according to uKIM-1 for AKI.
AKI early may facilitate intervention within this narrow window of reversibility. Ideally, such a biomarker would identify injury as it occurs intraoperatively or at least within a few hours after surgery. Recent research efforts have identified multiple proteins that may provide the basis for early diagnosis of AKI, among these biomarkers, NGAL, KIM1, and L-FABP [10]. The current study found that 11 out of 45 patients developed AKI diagnosed by AKIN criteria of serum creatinine rise where age and sex were not significantly different between both groups with more females in the AKI group as evidenced by others who found that female gender to be an important factor for AKI after CABG [2]. Preoperative CCS was significantly higher in the AKI patients. Supporting this, Kristovic et al. [17] who studied 1056 cardiac surgery patients to determine the
Fig. 5. AUC for combined uNGAL and uKIM-1.
independent predictors of AKI found that the model made by Thakar (CCS) was having the highest predictive value in discrimination of patients with risk for all AKI stages. The present study showed that CPB and aortic cross-clamp times were significantly longer in the AKI group which is found by other researchers [3,6] but Schopka and colleges [18] in their prospective study on 1428 CABG patients found that there was no significant difference between AKI and non AKI groups and related the risk for incidence of AKI to patient risk factors only like presence of atherosclerosis, preoperative reduction of cardiac output or hypotension. Anaemia and RBC transfusion could cause AKI either by harming the kidney directly or by increasing patients' susceptibility to concomitant renal insults [19]. Thus, red blood cell transfusions, by promoting a proinflammatory state, impairing tissue oxygen delivery, and exacerbating tissue oxidative stress, can be an important initiator of the ‘extension phase’ of kidney injury. This risk is likely influenced by the number of units transfused, the clinical setting, and the patients' susceptibilities which is similar to the current results [20]. Microscopic assessment of urine sediments by low power field for counting the granular casts and by high power field for counting the renal tubular epithelial cells was done to calculate the urine sediment score (USS) which was found to be significantly higher in AKI patients just 2 h after CPB till second postoperative day. Similarly, Schinstock et al. [21] on assessing urinalysis and uNGAL for early detection of AKI found that USS were significantly high in patients with AKI as defined by AKIN criteria for 2 days assessment and concluded that microscopic urinalysis was very specific for AKI. Urine microscopy also has many of the ideal biomarker qualities; however, urine sediment examination has some obstacles that undermine its ability to be considered an ideal biomarker. Diminishing physician competence in performance of this test is one such barrier this is due in part to the growth of automated urinalysis machines used by central laboratories [22]. Urinary NGAL was observed to be significantly higher in AKI group at 2 and 6 h after CPB termination with AUC of (0.710 and 0.700) and corresponding predicted cutoff values of (82.75 and 92.84) ng/ml respectively which was similar to previous findings of Paarmann et al. [3] Liu and colleges [4] who prospectively followed 109 adult patients undergoing open heart surgery to investigate the value of urinary L-FABP, uNGAL, and their combination in predicting the occurrence and severity
176
S.M. Elmedany et al. / Journal of Critical Care 40 (2017) 171–177
Table 5 Correlation between Urea with Urinary NGAL, Urinary sediment score and Urinary KIM-1.
Urinary NGAL 2 h after CPB Urinary KIM-1 12 h after CPB Urinary sediment score 24 h post CPB off
r p r p r p
Urea (mg/dl) 2nd day post-op
Serum creatinine 2nd day post-op
AKI
Non AKI
AKI
Non AKI
−0.435 0.158 0.693⁎ 0.012 0.938⁎
0.335 0.057 −0.470⁎ 0.006 0.603⁎
−0.601⁎ 0.039 0.772⁎ 0.003 0.645⁎
0.426⁎ 0.013 −0.253 0.156 0.446⁎
0.000
0.000
0.024
0.009
⁎ Statistically significant at p ≤ 0.05.
of CSA-AKI and found a significant high uNGAL measured zero and 2 h postoperatively with AUC of 0.866 and 0.871 respectively and the predicted cutoff points were 131.12 ng/mg Ucr and 33.73 ng/mg Ucr. On the other hand, Bignami and colleges [23] during their study of uNGAL time course during cardiac surgery on 19 patients divided in low and high risk patients according to preoperative criteria, found that uNGAL didn't show any increase after CBP with the highest reading after induction of anaesthesia (12.20 ng/ml) in comparison to the increasing readings of uNGAL in the high risk patients. This may be attributed to utilizing NGAL ELISA test in high risk patients which is not recommended by the manufacturing companies and comparing it with low risk patients. Also they used RIFLE criteria to identify AKI which depend on increase in plasma creatinine over one week without any patient in the low risk group developing AKI. Also, Schley et al. [24] when conducted a prospective study on 110 unselected patients undergoing cardiac surgery with CPB and assessed the performance of N15 plasma and urinary biomarkers as regard detection of AKI as defined by AKIN criteria and they found that plasma biomarkers (4-h NGAL AUC 0.83) had superior discriminative power to urinary biomarkers (4-h NGAL AUC 0.61) for the early detection of AKI and the discriminative power of urinary biomarkers increased when normalized to urinary creatinine but still less than plasma AUC values which may be related to the inclusion of unselected patients whatever the cardiac or renal condition. Studies reporting poorer diagnostic performance of NGAL have typically included patients with a wide spectrum of baseline renal function, and it is unknown whether this impacts the diagnostic performance of NGAL. The additional comorbidities typical of an adult cardiac surgical population may also introduce potential confounding variables, thus increasing the etiologic heterogeneity of AKI in this population [25]. Urinary KIM-1 was higher at the end of the first postoperative day than before surgery with values significantly higher in AKI patients at 12 and 24 hour intervals after CPB termination with corresponding
Table 6 Univariate analysis of different variables for prediction of AKI. B
USS 24 h after CPB NGAL 2 h after CPB NGAL 6 h after CPB KIM-1 12 h post-CPB KIM-1 24 h post-CPB Creat. After 48 h Surgery duration CPB time AXC time Age Female
1.72 0.009 0.019 0.380 1.681 0.846 0.567 0.014 1.710 0.057 1.399
Sig.
0.06⁎ 0.571 0.047⁎ 0.293 0.050⁎ 0.043⁎ 0.530 0.796 0.038⁎ 0.584 0.502
Exp (B)
5.859 1.200 1.600 1.463 5.369 2.330 1.764 1.014 6.200 1.058 4.052
95.0% C.I. for EXP(B) Lower
Upper
1.654 0.960 1.000 0.720 1.200 1.600 0.300 0.914 2.400 0.864 0.068
12.789 1.022 1.051 2.971 25.900 94.597 10.354 1.124 10.800 1.296 240.542
* Statistically significant at p ≤ 0.05. B: regression coefficient, Exp (B): odds ratio, C.I.: confidence interval. P ≤ 0.05 (significant). AXC: aortic cross-clamp, USS: urine sediment score, CPB: cardiopulmonary bypass.
Table 7 Multivariable analysis of different variables as regard incidence of AKI.
USS 24 h after CPB NGAL 6 h postop. AXC time
B
Sig.
1.65 0.020 0.083
0.007⁎ 0.033⁎ 0.020⁎
Exp (B)
4.752 1.020 1.087
95.0% C.I. for Exp (B) Lower
Upper
1.804 1.002 1.013
12.439 1.038 1.166
* Statistically significant at p ≤ 0.05. B: regression coefficient, Exp (B): odds ratio, C.I.: confidence interval. P ≤ 0.05 (significant). AXC: aortic cross-clamp, USS: urine sediment score, CPB: cardiopulmonary bypass.
AUC of (0.725 and 0.703) which were significant giving a predicted cutoff values of (8.4 and 9.95) ng/ml respectively. In agreement with this, Torregrosa et al. [26] who assessed the usefulness of 3 urinary biomarkers (KIM-1, NGAL, AND L-FABP) for early detection of AKI in patients after coronary angiography in comparison to cardiac surgery patients measured 12 h after intervention in 193 patients and found a significantly higher levels of all of them in AKI groups of both types of patients(angiography and cardia surgery) than non-AKI patients with AUC values of 0.716(95% CI 0.556–0.875) uKIM-1 in cardiac surgery group 12 h after surgery. Also, Krawczeski et al. [16] who investigated the temporal relationship of biomarkers in 220 paediatric cardiac patients found that uNGAL significantly increased in AKI patients at 2 h after CPB initiation and uKIM-1 increased at 12 h and these elevations correlated with the AKI severity and clinical outcomes and improved AKI prediction above a clinical model with AUC value of 0.90 and 0.91 for 2-h and 6-h uNGAL and value of 0.79 and 0.84 for 12-h and 24-h uKIM-1. On using combination of both uNGAL and uKIM-1, the predictive performance was found to be increased with high significance and furthermore on addition of the 24-hr USS to this combination the AUC was again increased with very sensitive AUC of 0.906 with a 95% CI (0.812– 1.001). This was similar to the finding of Krawczeski et al. [16] and Liu et al. [4] who concluded that by using biomarker combinations there was an enhanced prediction of AKI. Each biomarker at its best AUC, (2-h NGAL, 12-h KIM-1, and 24-h USS) showed a positive significant correlation with serum creatinine level on the second postoperative day in AKI group, this finding is in favor of the effectiveness of these urinary biomarkers in early prediction of AKI. On studying the association between urine biomarkers and the clinical characteristics by using Spearman correlation test for each biomarker at its earliest elevation (2-h NGAL, 12-h KIM-1, and 24-h USS) it was found that; USS had a positive relationship with most of the clinical parameters in both groups. UKIM-1 was found to have a negative relationship with most of the clinical parameters especially in the non-AKI group more than the AKI group supporting the protective role of it in acute kidney injury early in its course. But uNGAL correlated poorly with the majority of the clinical measurements in both groups except with bleeding and CPB time there was a significant weak positive relationship. Comparably, Cai et al. [27] showed that urinary NGAL levels were positively correlated to CPB time in 59 patients undergoing cardiac surgery and Bagshaw et al. [28] urine sediment examination correlated well with urine NGAL level (correlation coefficient, 0.41; P = 0.012) and was complementary in predicting worsening AKI (as regard death and dialysis requirement) in intensive care unit patients. The importance of determining the consecutive sequence of the biomarkers is underscored by the fact that the course of experimental AKI proceeds in 4 phases: initiation, extension, maintenance, and recovery [29]. The initiation phase is the period during which initial exposure to the ischemic insult occurs and generation of reactive oxygen molecules and labile iron is initiated. Vasodilator, adenosine triphosphate donor, antioxidant, and iron chelation therapies may be especially
S.M. Elmedany et al. / Journal of Critical Care 40 (2017) 171–177
effective during this phase, and the appearance of the earliest noninvasive biomarkers such as NGAL may be used to trigger such therapies. Prolongation of ischemia followed by reperfusion accompanies the extension phase. Tubules undergo reperfusion-mediated cell death, and the injured endothelial and epithelial cells amplify the inflammatory cascades. This phase probably represents a window of opportunity for early diagnosis with intermediate biomarkers such as L-FABP and IL18 and active therapeutic intervention with anti-apoptotic and anti-inflammatory strategies. During the maintenance phase, both cell injury and regeneration occur simultaneously. Measures such as growth factors and stem cells that accelerate the endogenous regeneration processes, initiated by later biomarkers with high specificity such as KIM1, may be most effective during this phase [16]. 5. Conclusion From the present study, we concluded that distinct from traditional markers of function such as creatinine, the rapid response (within few hours) enables NGAL to potentially identify injured kidney much earlier than was previously possible, KIM-1 has been proven to be more specific to ischaemic renal injury with early response in the first postoperative day and its combination with the sensitive NGAL may enable more accurate prediction of CSA-AKI. Although urinary microscopic examination has been neglected for many years by physicians, it was found to be a near ideal renal biomarker with very high sensitivity and specificity and injury site informative. Financial disclosures None. Conflicts of interest None. References [1] Go AS, Mozaffarian D, Roger VL, Benjamin EJ, Berry JD, Borden WB, et al. Executive summary: heart disease and stroke statistics—2013 update: a report from the American Heart Association. Circulation 2013;127:143–52. [2] Kaya M, Kyaruzi M, Guler S, Bakir I, Yeniterzi M. The incidence and predictors of transient acute kidney injury in patients with pre-operative normal kidney functions undergoing coronary artery bypass graft surgery. Cardiovasc Surg 2015;3: 54–60. [3] Paarmann H, Charitos EL, Beichar EA, Heize H, Schon J, Berggreen A, et al. Duration of cardiopulmonary bypass is an important confounder when using biomarkers for early diagnosis of acute kidney injury in cardiac surgical patients. Appl Cardiopulm Pathophysiol 2013;17:284–97. [4] Liu S, Che M, Xue S, Xie B, Zhu M, Lu R, et al. Urinary L-FABP and its combination with urinary NGAL in early diagnosis of acute kidney injury after cardiac surgery in adult patients. Biomarkers 2013;18:95–101. [5] Brown JR, Kramer RS, MacKenzie TA, Coca SG, Sint K, Parikh CR. Determinants of acute kidney injury duration after cardiac surgery: an externally validated tool. Ann Thorac Surg 2012;93:570–6. [6] Kumar AB, Suneja M, Bayman EO, Weide GD, Tarasi M. Association between postoperative acute kidney injury and duration of cardiopulmonary bypass: a meta-analysis. J Cardiothorac Vasc Anesth 2012;26:64–9.
177
[7] 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 Consensus Conference of the Acute Dialysis Quality Initiative (ADQI) Group. Crit Care 2004;8:204–12. [8] Mehta RL, Kellum JA, Shah SV, Molitoris BA, Ronco C, Warnock DG, et al. Acute Kidney Injury Network: report of an initiative to improve outcomes in acute kidney injury. Crit Care 2007;11:R31. [9] Lopes JA. Acute kidney injury: definition and epidemiology. Port J Nephrol Hypert 2013;27:15–22. [10] De Geus HR, Betjes MG, Bakker J. Biomarkers for the prediction of acute kidney injury: a narrative review on current status and future challenges. Clin Kidney J 2012;5: 102–8. [11] Matsui K, Kamijo-Ikemori A, Sugaya T, Yasuda T, Kimura K. Usefulness of urinary biomarkers in early detection of acute kidney injury after cardiac surgery in adults. Circ J 2012;76:213–20. [12] Schiffl H, Lang SM. Update on biomarkers of acute kidney injury: moving closer to clinical impact? Mol Diagn Ther 2012;16:199–207. [13] Ji X, Ma LL, Lu J, Zhang SD, Huang Y, Hou XF, et al. Expression of kidney injury molecule-1 in renal ischemic postconditioning and its protective effect against renal ischemia-reperfusion injury in rats. Beijing Da Xue Xue Bao 2012;44:511–7. [14] Perazella MA, Coca SG, Hall IE, Lyanam U, Koraishy M, Parikh CR. Urine microscopy is associated with severity and worsening of acute kidney injury in hospitalized patients. Clin J Am Soc Nephrol 2010;5:402–8. [15] Murphy GS, Hessel EA, Groom RC. Optimal perfusion during CPB: an evidence based approach. Anesth Analg 2009;108:1394–417. [16] Krawczeski CD, Goldstein SL, Woo JG, Wang Y, Piyaphanee N, Ma Q, et al. Temporal relationship and predictive value of urinary acute kidney injury biomarkers after pediatric cardiopulmonary bypass. J Am Coll Cardiol 2011;58:2301–9. [17] Kristovic D, Horvatic I, Husedzinovic I, Sutlic Z, Rudez I, Baric D, et al. Cardiac surgery-associated acute kidney injury: risk factors analysis and comparison of prediction models. Interact Cardiovasc Thorac Surg 2015:1–8. [18] Schopka S, Diez C, Camboni D, Floerchinger B, Schmid C, Hilker M. Impact of cardiopulmonary bypass on acute kidney injury following coronary artery bypass grafting: a matched pair analysis. J Cardiothorac Surg 2014;9:20. [19] Karkouti K, Grocott HP, Hall RI, Jessen ME, Kruger C, Lerner AB, et al. Interrelationship of preoperative anaemia, intraoperative anaemia, and red blood cell transfusion as potentially risk factors for acute kidney injury in cardiac sugery: a historical multicentre cohort study. Can J Anaesth 2015;62:377–84. [20] Karkouti K. Transfusion and risk of acute kidney injury in cardiac surgery. Br J Anaesth 2012;109(Suppl. 1):i29–38. [21] Schinstock CA, Semret MH, Wagner SJ, Borland TM, Bryant SC, Kashani K, et al. Urinalysis is more specific and urinary neutrophil gelatinase-associated lipocalin is more sensitive for early detection of acute kidney injury. Nephrol Dial Transplant 2013;28:1175–85. [22] Zhao F, Jin Y, Chen X, Xie X. Clinical application of UF-1000i in combination with AX4280 for the screening test ability of urinary formed elements. J Clin Pathol 2013;66: 229–31. [23] Bignami E, Frati E, Meroni R, Simonini M, Di Prima AL, Manunta P, et al. Urinary neutrophil gelatinase-associated lipocalin time course during cardiac surgery. Ann Card Anaesth 2015;18:39–44. [24] Schley G, Köberle C, Manuilova E, Rutz S, Forster C, Weyand M, et al. Comparison of plasma and urine biomarker performance in acute kidney injury. PLoS One 2015;10. [25] McIlroy DR, Wagener G, Lee HT. Neutrophil gelatinase-associated lipocalin and acute kidney injury after cardiac surgery: the effect of baseline renal function on diagnostic performance. Clin J Am Soc Nephrol 2010;5:211–9. [26] Torregrosa I, Montoliu C, Urios A, Andrés-Costa MJ, Giménez-Garzó C, Juan I, et al. Urinary KIM-1, NGAL and L-FABP for the diagnosis of AKI in patients with acute coronary syndrome or heart failure undergoing coronary angiography. Heart Vessel 2014:1–9. [27] Cai L, Borowiec J, Xu S, Han W, Venge P. Assays of urine levels of HNL/NGAL in patients undergoing cardiac surgery and the impact of antibody configuration on their clinical performances. Clin Chim Acta 2009;403:121–5. [28] Bagshaw SM, Haase M, Haase-Fielitz A, Bennett M, Devarajan P, Bellomo R. A prospective evaluation of urine microscopy in septic and non-septic acute kidney injury. Nephrol Dial Transplant 2012;27:582–8. [29] Devarajan P. Update on mechanisms of ischemic acute kidney injury. J Am Soc Nephrol 2006;17:1503–20.
Contents lists available at ScienceDirect
Journal of Critical Care journal homepage: www.jccjournal.org
Novel urinary biomarkers and the early detection of acute kidney injury after open cardiac surgeries Said M. Elmedany, MD a,1, Salah S. Naga, MD b,2, Rania Elsharkawy, MD c,3, Rabab S. Mahrous, MD a,⁎,4, Ahmed I. Elnaggar a,5 a b c
Department of Anaesthesia and Surgical Intensive Care, Alexandria University, Egypt Department of Internal Medicine and Nephrology, Alexandria University, Egypt Department of Clinical Pathology, Medical Research Institute, Alexandria University, Egypt
1. Introduction Open heart surgery can be viewed as one of the greatest medical advances of the 20th century. It has been estimated that about 397,000 patient undergone cardiac surgeries in the United States in 2010 and N 80% of routine cardiac surgical procedures are performed using cardiopulmonary bypass (CPB) [1]. CPB is complicated by an increased incidence of acute kidney injury (AKI) with incidence rate of (1–30%), which is associated with increased risk of infection, diminished quality of life, delayed discharge and significant morbidity and mortality [2-5]. Despite the deleterious impact of AKI-CPB on outcome, its pathophysiology remains incompletely understood. It is probable that the pathophysiologic changes associated with CPB are accentuated as the duration of CPB increases, which subsequently increases the risk of developing AKICPB [6]. Recently, standardized clinical definitions of AKI have been implemented through the use of the RIFLE (Risk, Injury, Failure, Loss, and end stage renal disease (ESRD)), AKIN (Acute Kidney Injury Network) criteria [7,8], and later the Kidney Disease Improving Global Outcomes (KDIGO) work group began by defining AKI by harmonizing the prior RIFLE and AKIN criteria [9]. However, these criteria are still very much dependent on delayed serum creatinine elevations, the current gold standard for the diagnosis of AKI. Furthermore, as a functional marker of glomerular filtration, serum creatinine is not ideally suited to diagnose AKI caused by renal tubular injury, rather than reversible prerenal azotemia [7]. This unfavorable outcome might be tied to the late detection of AKI when the elevation of serum creatinine (SCr) is used where also the change in sCr does not discriminate the time and type of renal insult nor the site and extent of glomerular or tubular injury. Many genes are up-regulated in the damaged kidney with the corresponding protein ⁎ Corresponding author at: 33 Bahaa Eldin Elghatwary St., Smouha, Alexandria, Egypt. E-mail address: [email protected] (R.S. Mahrous). 1 Contribution: The senior author who lead the team of work. 2 Contribution: Clinical diagnosis of the patients of acute renal injury. 3 Contribution: Performing the laboratory investigations to detect the biomarkers. 4 Contribution: The senior anesthetist who anaesthetize the patients and monitor them throughout the operation, collect the samples and write the manuscript. 5 Contribution: The second anesthetist who help in anaesthesia, collect the samples, monitor and follow up the patients and write the manuscript.
http://dx.doi.org/10.1016/j.jcrc.2017.03.029 0883-9441/© 2017 Elsevier Inc. All rights reserved.
products appearing in plasma and urine. Some of these are candidate markers for more timely diagnosis of AKI [10]. Therefore, development of new AKI biomarkers has been emphasized to introduce more sensitive and accurate renal biomarkers to clinical use. Early detection of AKI is deemed important to develop therapeutic concepts to treat or at least ameliorate a renal insult. Within the last years various biomarkers reflective of ischaemic tubular injury have been developed to accomplish this task, among them Neutrophil-gelatinase-associated lipocalin (NGAL), Kidney-injury molecule-1 (KIM-1), Interleukin-18 (IL-18), and L-Fatty-acid-binding-protein (LFABP) [11]. However, most of these novel AKI markers have been derived from animal experiments inducing renal ischemia-reperfusion injury and cardiac surgery with cardiopulmonary bypass (CPB) may be regarded as a prototype clinical scenario of systemic ischaemia-reperfusion injury [12]. NGAL is a glycoprotein consisting of a polypeptide chain of 178 amino acids covalently bound to gelatinase which is synthesized and secreted when acute kidney injury with hypoxic conditions causes a disruption of the endothelial cells of the renal proximal tubules, resulting in increased uNGAL [4]. KIM-1 is a type I transmembrane glycoprotein that is associated with proximal tubule cell injury. Presence of KIM-1 in the urine is highly specific for kidney injury as it's undetected in normal urine. No other organ has been shown to express KIM-1 to a degree that would influence kidney excretion. It has been shown to be much more sensitive than creatinine as a marker for AKI [13]. This study was designed to study the role of urinary NGAL and KIM-1 as biomarkers for early detection of AKI in patients undergoing coronary artery bypass graft (CABG) under CPB, so as to find new tools for early diagnosis and assessment of severity of AKI and to correlate between uNGAL, uKIM-1, complete urine analysis, the other conventional markers of AKI (serum creatinine), and the clinical measurements. 2. Patients and methods This study was carried out on 45 adult patients, of both sexes Cleveland clinic score of low to intermediate grade (0–5) admitted to Alexandria Main University Hospital, department of cardiothoracic surgery scheduled for elective CABG using CPB. Patients with history of previous cardiac surgery, pre-existing renal impairment or with history of recent perioperative exposure to
172
S.M. Elmedany et al. / Journal of Critical Care 40 (2017) 171–177
Table 1 Demographic, preoperative and surgical related data.
Demographic data Sex Male, n (%) Female, n (%) Age (years)
Non-AKI (n = 34)
AKI (n = 11)
30 (88.2%) 4 (11.8%) 55.6 ± 7.9
7 (63.6%) 4 (36.4) 60.5 ± 6.6
0.064
Preoperative assessment by Cleveland clinic score 0, n 6 1, n 6 2, n 16 3, n 4 4, n 2 Mean ± SD 1.7 ± 1.0 Surgical related data Duration of anaesthesia (hr) Duration of surgery (hr) CPB time (min) Aortic cross-clamp (min) Blood requirement (units) Weaning of ventilator (hr) Chest tube drainage (ml/day) Adrenaline Dose (μg/kg/min) Duration (hr) Dopamine Dose (μg/kg/min) Duration (hr) POAF Neurological complications
Significance (p value)
0.206
0 0 2 3 6 3.4 ± 0.8
0.001⁎
3.9 ± 0.7 3.0 ± 0.7 74.2 ± 16.7 48.2 ± 14.8 1.6 ± 0.7 4.1 ± 1.0 518.8 ± 123.6
4.2 ± 0.8 3.4 ± 0.9 90.5 ± 16.2 60.9 ± 8.1 3.0 ± 0.6 3.9 ± 1.1 653.6 ± 79.2
0.143 0.129 0.010⁎ 0.017⁎ 0.001⁎
0.07 ± 0.02 6.27 ± 1.24
0.09 ± 0.01 6.29 ± 1.8
0.101 0.983
4.53 ± 2.03 8.0 ± 2.79 6 (17.6%) 7 (20.6%)
4.66 ± 0.81 15.64 ± 3.32 5 (45.5%) 4 (36.4%)
0.699 0.001⁎ 0.062! 0.213
0.605 0.002⁎
* Statistically significant at p ≤ 0.05.
nephrotoxic drugs, extremes of age (b18 or N75 years), off pump cardiac surgery, ejection fraction of b35%, emergency CABG or combined cardiac surgery, or neoplasms as they increase NGAL levels were excluded from the study. After approval of the local Medical Ethics committee of the Faculty of Medicine and taking a written informed consent, patients were screened for complete urine analysis, conventional renal function tests and novel urinary markers by blinded investigators. After formal pre-anaesthetic assessment of every patients as regard cardiac, coagulation profile, renal function, and pulmonary systems all preoperative cardiac medications was continued until the morning of surgery with the exception of drugs which have renal effects like ACE inhibitors drugs. On arrival to the operating room, peripheral and central venous cannulation and radial artery cannulation was done under local anaesthesia and IV sedation in the form of 2 mg midazolam and 4 mg morphine sulphate after applying standard monitoring to the patient. Induction and maintenance of anaesthesia was standardized in all patients in the form of midazolam (0.05 mg/kg), fentanyl (5 μg/kg), sevoflurane (6–7%), and rocuronium (0.9 mg/kg) to facilitate tracheal intubation. The lungs was ventilated at normocapnia (monitored by end tidal CO2 at 35 mmHg) with sevoflurane (1–2%) in an air-oxygen
mixture. Additional bolus doses of fentanyl and rocuronium was injected if necessary. CPB was managed to maintain mean arterial pressure (MAP) between 50 and 80 mmHg with tepid hypothermia (32°–34 °C) and keeping haematocrit above 20% with the addition of fresh packed RBCs as needed. Weaning from CPB and reperfusion of the heart was performed according to the patient's general condition and cross-clamp time. There was no fixed postoperative treatment regimen for either pharmaceutical or mechanical support. Weaning and extubation from the ventilator was done after haemodynamic (defined as a MAP of 60–90 mmHg, heart rate (HR) between 60 and 90 bpm, a central venous pressure (CVP) between 10 and 15 mmHg) and ventilatory stability. Analgesia was given in the form of morphine sulphate infusion (2–3 mg/h) in the first 48 h and paracetamol 1 g/6h. Measurements included assessment of the AKIN stages after ICU admission and every 6 h for the first 72 h postoperative; urinary examination for NGAL, KIM-1 (after induction, 2, 6, 12, and 24 h after termination of CPB), and urinary sediment microscopic examination [14] (preoperative, 2, 12, 24, and 48 h after termination of CPB); haematological measurements (sCr, and blood urea) just before anaesthesia and every day for 3 days after the end of surgery; and clinical measurements including (duration of anaesthesia and surgery, CPB and aortic cross clamping times, total amount of packed RBCs given during surgery, weaning from the ventilator postoperatively, and intra and postoperative complications). 2.1. Statistical analysis Data were analyzed by using SPSSR software (Statistical package for social science for personal computers) using “t” test, ANOVA test and chi-square X2 test, data were expressed as mean ± SD and P b 0.05 considered significant. 3. Results In this study 11 (24.4%) out of 45 patients developed AKI diagnosed by AKIN criteria of serum creatinine rise without finding any patient requiring renal replacement therapy. Demographics and surgical core data in the different groups are presented in Table 1.The demographic results didn't show any significant difference between both groups as regard age or gender with more females in AKI group (36.4% vs 11.8% in non-AKI group) in the AKI group. On comparing the CCS between the two groups it was found to be higher in the AKI patients. On assessing the AKIN stages in both groups it was significantly higher in the AKI group than the non-AKI group (starting from 24 h after surgery till third postoperative day) either due to increased serum creatinine in some patients or decreased urine output in others. As regard the CPB time and aortic cross-clamp time were significantly higher in the AKI group with mean values of (90.5 ± 16.2 vs 74.2 ± 16.2 min) and (60.9 ± 8.1 vs 48.2 ± 14.8 min) respectively. It was noticed that the amount of postoperative bleeding and perioperative
Table 2 Haematological measurements of biomarkers for acute kidney injury. Parameter Serum creatinine (mg/dl)
Estimated GFR (ml/min)
Urea (mg/dl)
Data are given as Mean ± SD. ⁎ Statistically significant at p ≤ 0.05.
AKI Non-AKI P AKI Non-AKI P AKI Non-AKI P
preoperative
24 h after surgery
48 h after surgery
72 h after surgery
1.15 ± 0.33 1.01 ± 0.23 0.216 117.32 ± 22.38 125.63 ± 23.03 0.174 39.2 ± 9.7 36.4 ± 7.8 0.745
1.20 ± 0.22 0.99 ± 0.24 0.066 98.76 ± 20.19 134.94 ± 27.67 0.001⁎ 34.1 ± 8.1 31.7 ± 7.2 0.056
1.49 ± 0.41 1.04 ± 0.29 0.004⁎
1.60 ± 1.03 1.11 ± 0.49 0.036⁎ 52.48 ± 15.59 121.6 ± 26.02 0.001⁎ 45.9 ± 20.5 40.4 ± 15.7 0.001⁎
71.46 ± 18.1 121.9 ± 24.2 0.001⁎ 36.7 ± 10.7 34.3 ± 7.9 0.001⁎
S.M. Elmedany et al. / Journal of Critical Care 40 (2017) 171–177
Fig. 1. Comparison between the two studied groups according to urinary sediment analysis.
requirement for blood transfusion were higher in the AKI patients than non-AKI group (653.6 ± 79.2 vs 518.8 ± 123.6 ml/day) and (3.0 ± 0.6 vs 1.6 ± 0.7 packed RBC units) respectively. During postoperative observation of the patients in the ICU, postoperative atrial fibrillation occurred in 11 patients (24.4%). Reopening for exploration of increased bleeding or pericardial tamponade was done in two patients in each group without any evident source of bleeding. On blood analysis for urea and creatinine (Table 2) it was found that serum creatinine started to rise from the second postoperative day and was higher in the AKI group (Fig. 1) with mean value of (1.56 ± 0.28 vs 0.85 ± 0.14) and estimated GFR was significantly lower in the AKI patients after the first postoperative day with a mean of (98.76 ± 20.19 vs 134.94 ± 27.67) and these changes continued till the third postoperative day. The time course of urinary levels of renal biomarkers in both groups are presented in Tables 3 and 4, showing that all biomarkers increased significantly after surgery in comparison with baseline values. Microscopic urine analysis showed the urine sediment score (USS) were significantly higher in the AKI patients and this appeared as early as just 2 h after termination of CPB (Fig. 1) and continued to be higher in this group till the 2nd postoperative day with area under the curve (AUC) range (0.636–0.888) (Fig. 2). Urinary NGAL was observed to be
173
significantly higher in AKI group at 2 and 6 h after CPB with AUC of (0.710 and 0.700) respectively and corresponding predicted cutoff values of (82.75 and 92.84) ng/ml respectively (Fig. 3). Urinary KIM-1 was higher at the end of the first postoperative day than before surgery with values significantly higher in AKI patients at 12 and 24 h intervals after CPB with corresponding AUC of (0.725 and 0.703) respectively which were significant giving a predicted cutoff values of (8.4 and 9.95) ng/ml respectively (Fig. 4). Table 4 presents the AUC of the various urinary biomarker with the combination of both urinary NGAL and KIM-1 yielding a higher AUC of 0.801 (Fig. 5) denoting an increased diagnostic power of this combination. On adding USS to the previous combination it was noticed that the AUC increased significantly (Fig. 6) to be 0.906 with a 95% CI (0.812–1.001) giving a near perfect combination for accurate prediction of CSA-AKI. On running Pearson's correlation test to determine the correlation between each biomarker at its best AUC (2-h NGAL, 12-h KIM-1, and 24-h USS) and sUrea 48 h after CPB. uKIM-1 showed a significant positive relationship in both AKI group (p = 0.012) and Non AKI group (p = 0.006). Similarly, USS showed a statistically highly significant relationship in both AKI and Non AKI groups (p = 0.000). The same correlation was performed between the three parameters and sCr 48 h after CPB and resulted in a significant positive relationship between the three parameters with sCr on the second postoperative day in AKI group, (p = 0.039, 0.003 and 0.024 respectively). While in Non AKI group uNGAL and USS showed a significant relationship, (p = 0.013 and 0.009 respectively). The same correlation was performed between the three parameters and sCr 48 hr after CPB and resulted in a significant positive relationship between the three parameters with sCr on the second postoperative day in AKI group, (p = 0.039, 0.003 and 0.024 respectively). While in Non AKI group uNGAL and USS showed a significant relationship, (p = 0.013 and 0.009 respectively), (Table 5). Moreover, Pearson's correlation test was used to determine the relationship between the three main tested parameters (uNGAL, uKIM-1, and urinary sediment score) at their best AUCs (2 h, 12 h, and 24 h) respectively and with sCr 48 h after CPB and the clinical measurements (duration of anaesthesia, duration of surgery, CPB time (min), AXC time (min), transfused fresh blood (units), weaning from ventilator (hours), bleeding (ml/day). UNGAL showed a significant positive relationship only with both CPB time and postoperative bleeding (r = 0.294 and 0.300) respectively. For the 12 h uKIM-1, the relationship was generally negative weak and non-significant except with weaning from the ventilator where it was significant negative relationship (r = −0.323). On the contrary, USS measured 24 h after CPB had moderate to strong positive relationship with all the variables and these relations were significant. On using logistic regression analysis to assess the independent predictors for the development of AKI, univariable analysis (Table 6) found that USS 24 h after CPB, uNGAL 6 h post-CPB, uKIM-1 24 h postCPB, sCr 48 post-CPB, and aortic cross clamping time were all significantly associated with AKI. Then, multivariable binary logistic
Table 3 Urinary biomarkers measurement for acute kidney injury. Parameter NGAL Mean ± SD
KIM-1 Mean ± SD
Urine sediment score 1 & 2, n
AKI Non-AKI P AKI Non-AKI P AKI Non-AKI P
T1
T2
T3
T4
T5
T6
8.7 ± 2.7 7.4 ± 2.4 0.123 2.2 ± 0.4 2.1 ± 0.4 0.290 0 0 –
129.1 ± 96 79.2 ± 56.3 0.039⁎
142.7 ± 98.6 93.5 ± 61 0.048⁎
3.1 ± 0.4 2.9 ± 0.5 0.118 3 0 0.002⁎
6.1 ± 0.1 6.1 ± 0.4 0.534
99.1 ± 70.2 70.9 ± 49.3 0.147 8.6 ± 0.4 8.1 ± 0.7 0.023⁎ 9 2 0.001⁎
79.1 ± 70.4 50.0 ± 46.7 0.124 9.9 ± 0.1 9.5 ± 0.3 0.002⁎ 11 10 0.001⁎
9 6 0.001⁎
T1, T2, T3, T4, T5, and T6 refer to timing of urine samples collection of preoperative, 2 h, 6 h, 12 h, 24 h, and 48 h after termination of CPB. ⁎ Statistically significant at p ≤ 0.05.
174
S.M. Elmedany et al. / Journal of Critical Care 40 (2017) 171–177
Table 4 AUC & ROC of urinary biomarkers at different time intervals and the coordinates of the curves. Timing
AUC
Sig.
95% CI
Optimal cutoff point
Lower bound
Upper bound
Value (ng/ml)
Sensitivity
Specificity
uNGAL After induction 2 h after CPB 6 h after CPB 12 h after CPB 24 h after CPB
0.636 0.710 0.70 0.599 0.623
0.178 0.174 0.170 0.328 0.224
0.444 0.419 0.426 0.401 0.430
0.829 0.856 0.852 0.797 0.816
7.45 82.75 92.84 69.315 40.81
70% 72% 71% 63.6% 63.6%
68% 69% 71% 58.8% 58.8%
uKIM-1 After induction 2 h after CPB 6 h after CPB 12 h after CPB 24 h after CPB
0.612 0.631 0.484 0.725 0.703
0.267 0.196 0.874 0.027⁎ 0.045⁎
0.418 0.452 0.310 0.560 0.540
0.807 0.810 0.658 0.890 0.867
2.1 2.95 6.05 8.4 9.95
65.4% 54.5% 63.6% 81.8% 63.6%
59.2% 58.8% 52.9% 76.5% 70.6%
Urinary sediment score After induction 2 h after CPB 12 h after CPB 24 h after CPB 48 h after CPB
0.500 0.636 0.888 0.880 0.829
1.000 0.178 0.000⁎ 0.000⁎ 0.001⁎
0.302 0.426 0.746 0.783 0.677
0.698 0.847 1.029 0.977 0.981
AUC & ROC for combination of urinary biomarkers and the coordinates of the curves uNGAL& uKIM-1 0.801 0.004⁎ 0.640 uNGAL, uKIM-1, & USS 0.906 0.000⁎ 0.812
943 1.001
* Statistically significant at p ≤ 0.05.
regression analysis (Table 7) which revealed that the most powerful independent predictors of AKI were the urine sediment score examined 24 h after termination of CPB (RR, 4.752); uNGAL measured 6 h after CPB (RR, 1.020), and aortic cross-clamp time (RR, 1.087). 4. Discussion The primary goal of cardiac surgery is not just a minimally acceptable outcome where the patient survives without life-threatening complications or persistent clinically manifest organ dysfunctions or simply
Fig. 2. AUC of USS 24 h after CPB.
hospital survival; but a healthy, productive long-term survivor [15]. Despite many years of research, AKI remains an important and life threatening complication in patients undergoing cardiac surgery, and with respect to high incidence of this complication in this specific population has even got a sub-designation: cardiac surgery associated-AKI (CSAAKI) [11]. The pathophysiological features of CSA-AKI are complex and multifactorial including numerous factors: exogenous toxins, endogenous toxins, metabolic factors, ischaemia–reperfusion injury, micro-embolization, neuro-hormonal activation, inflammation, oxidative stress, and haemodynamic factors. These mechanisms of injury are likely to be active at different times with different intensities, are interrelated and probably synergistic. Because many pathways are involved, it is not surprising that combinations of biomarkers with different properties may prove most predictive [16]. Accordingly, a biomarker that can detect
Fig. 3. Comparison between the two studied groups according to uNGAL for AKI.
S.M. Elmedany et al. / Journal of Critical Care 40 (2017) 171–177
175
Fig. 6. AUC for combined uNGAL, uKIM-1, and urinary sediment score.
Fig. 4. Comparison between the two studied groups according to uKIM-1 for AKI.
AKI early may facilitate intervention within this narrow window of reversibility. Ideally, such a biomarker would identify injury as it occurs intraoperatively or at least within a few hours after surgery. Recent research efforts have identified multiple proteins that may provide the basis for early diagnosis of AKI, among these biomarkers, NGAL, KIM1, and L-FABP [10]. The current study found that 11 out of 45 patients developed AKI diagnosed by AKIN criteria of serum creatinine rise where age and sex were not significantly different between both groups with more females in the AKI group as evidenced by others who found that female gender to be an important factor for AKI after CABG [2]. Preoperative CCS was significantly higher in the AKI patients. Supporting this, Kristovic et al. [17] who studied 1056 cardiac surgery patients to determine the
Fig. 5. AUC for combined uNGAL and uKIM-1.
independent predictors of AKI found that the model made by Thakar (CCS) was having the highest predictive value in discrimination of patients with risk for all AKI stages. The present study showed that CPB and aortic cross-clamp times were significantly longer in the AKI group which is found by other researchers [3,6] but Schopka and colleges [18] in their prospective study on 1428 CABG patients found that there was no significant difference between AKI and non AKI groups and related the risk for incidence of AKI to patient risk factors only like presence of atherosclerosis, preoperative reduction of cardiac output or hypotension. Anaemia and RBC transfusion could cause AKI either by harming the kidney directly or by increasing patients' susceptibility to concomitant renal insults [19]. Thus, red blood cell transfusions, by promoting a proinflammatory state, impairing tissue oxygen delivery, and exacerbating tissue oxidative stress, can be an important initiator of the ‘extension phase’ of kidney injury. This risk is likely influenced by the number of units transfused, the clinical setting, and the patients' susceptibilities which is similar to the current results [20]. Microscopic assessment of urine sediments by low power field for counting the granular casts and by high power field for counting the renal tubular epithelial cells was done to calculate the urine sediment score (USS) which was found to be significantly higher in AKI patients just 2 h after CPB till second postoperative day. Similarly, Schinstock et al. [21] on assessing urinalysis and uNGAL for early detection of AKI found that USS were significantly high in patients with AKI as defined by AKIN criteria for 2 days assessment and concluded that microscopic urinalysis was very specific for AKI. Urine microscopy also has many of the ideal biomarker qualities; however, urine sediment examination has some obstacles that undermine its ability to be considered an ideal biomarker. Diminishing physician competence in performance of this test is one such barrier this is due in part to the growth of automated urinalysis machines used by central laboratories [22]. Urinary NGAL was observed to be significantly higher in AKI group at 2 and 6 h after CPB termination with AUC of (0.710 and 0.700) and corresponding predicted cutoff values of (82.75 and 92.84) ng/ml respectively which was similar to previous findings of Paarmann et al. [3] Liu and colleges [4] who prospectively followed 109 adult patients undergoing open heart surgery to investigate the value of urinary L-FABP, uNGAL, and their combination in predicting the occurrence and severity
176
S.M. Elmedany et al. / Journal of Critical Care 40 (2017) 171–177
Table 5 Correlation between Urea with Urinary NGAL, Urinary sediment score and Urinary KIM-1.
Urinary NGAL 2 h after CPB Urinary KIM-1 12 h after CPB Urinary sediment score 24 h post CPB off
r p r p r p
Urea (mg/dl) 2nd day post-op
Serum creatinine 2nd day post-op
AKI
Non AKI
AKI
Non AKI
−0.435 0.158 0.693⁎ 0.012 0.938⁎
0.335 0.057 −0.470⁎ 0.006 0.603⁎
−0.601⁎ 0.039 0.772⁎ 0.003 0.645⁎
0.426⁎ 0.013 −0.253 0.156 0.446⁎
0.000
0.000
0.024
0.009
⁎ Statistically significant at p ≤ 0.05.
of CSA-AKI and found a significant high uNGAL measured zero and 2 h postoperatively with AUC of 0.866 and 0.871 respectively and the predicted cutoff points were 131.12 ng/mg Ucr and 33.73 ng/mg Ucr. On the other hand, Bignami and colleges [23] during their study of uNGAL time course during cardiac surgery on 19 patients divided in low and high risk patients according to preoperative criteria, found that uNGAL didn't show any increase after CBP with the highest reading after induction of anaesthesia (12.20 ng/ml) in comparison to the increasing readings of uNGAL in the high risk patients. This may be attributed to utilizing NGAL ELISA test in high risk patients which is not recommended by the manufacturing companies and comparing it with low risk patients. Also they used RIFLE criteria to identify AKI which depend on increase in plasma creatinine over one week without any patient in the low risk group developing AKI. Also, Schley et al. [24] when conducted a prospective study on 110 unselected patients undergoing cardiac surgery with CPB and assessed the performance of N15 plasma and urinary biomarkers as regard detection of AKI as defined by AKIN criteria and they found that plasma biomarkers (4-h NGAL AUC 0.83) had superior discriminative power to urinary biomarkers (4-h NGAL AUC 0.61) for the early detection of AKI and the discriminative power of urinary biomarkers increased when normalized to urinary creatinine but still less than plasma AUC values which may be related to the inclusion of unselected patients whatever the cardiac or renal condition. Studies reporting poorer diagnostic performance of NGAL have typically included patients with a wide spectrum of baseline renal function, and it is unknown whether this impacts the diagnostic performance of NGAL. The additional comorbidities typical of an adult cardiac surgical population may also introduce potential confounding variables, thus increasing the etiologic heterogeneity of AKI in this population [25]. Urinary KIM-1 was higher at the end of the first postoperative day than before surgery with values significantly higher in AKI patients at 12 and 24 hour intervals after CPB termination with corresponding
Table 6 Univariate analysis of different variables for prediction of AKI. B
USS 24 h after CPB NGAL 2 h after CPB NGAL 6 h after CPB KIM-1 12 h post-CPB KIM-1 24 h post-CPB Creat. After 48 h Surgery duration CPB time AXC time Age Female
1.72 0.009 0.019 0.380 1.681 0.846 0.567 0.014 1.710 0.057 1.399
Sig.
0.06⁎ 0.571 0.047⁎ 0.293 0.050⁎ 0.043⁎ 0.530 0.796 0.038⁎ 0.584 0.502
Exp (B)
5.859 1.200 1.600 1.463 5.369 2.330 1.764 1.014 6.200 1.058 4.052
95.0% C.I. for EXP(B) Lower
Upper
1.654 0.960 1.000 0.720 1.200 1.600 0.300 0.914 2.400 0.864 0.068
12.789 1.022 1.051 2.971 25.900 94.597 10.354 1.124 10.800 1.296 240.542
* Statistically significant at p ≤ 0.05. B: regression coefficient, Exp (B): odds ratio, C.I.: confidence interval. P ≤ 0.05 (significant). AXC: aortic cross-clamp, USS: urine sediment score, CPB: cardiopulmonary bypass.
Table 7 Multivariable analysis of different variables as regard incidence of AKI.
USS 24 h after CPB NGAL 6 h postop. AXC time
B
Sig.
1.65 0.020 0.083
0.007⁎ 0.033⁎ 0.020⁎
Exp (B)
4.752 1.020 1.087
95.0% C.I. for Exp (B) Lower
Upper
1.804 1.002 1.013
12.439 1.038 1.166
* Statistically significant at p ≤ 0.05. B: regression coefficient, Exp (B): odds ratio, C.I.: confidence interval. P ≤ 0.05 (significant). AXC: aortic cross-clamp, USS: urine sediment score, CPB: cardiopulmonary bypass.
AUC of (0.725 and 0.703) which were significant giving a predicted cutoff values of (8.4 and 9.95) ng/ml respectively. In agreement with this, Torregrosa et al. [26] who assessed the usefulness of 3 urinary biomarkers (KIM-1, NGAL, AND L-FABP) for early detection of AKI in patients after coronary angiography in comparison to cardiac surgery patients measured 12 h after intervention in 193 patients and found a significantly higher levels of all of them in AKI groups of both types of patients(angiography and cardia surgery) than non-AKI patients with AUC values of 0.716(95% CI 0.556–0.875) uKIM-1 in cardiac surgery group 12 h after surgery. Also, Krawczeski et al. [16] who investigated the temporal relationship of biomarkers in 220 paediatric cardiac patients found that uNGAL significantly increased in AKI patients at 2 h after CPB initiation and uKIM-1 increased at 12 h and these elevations correlated with the AKI severity and clinical outcomes and improved AKI prediction above a clinical model with AUC value of 0.90 and 0.91 for 2-h and 6-h uNGAL and value of 0.79 and 0.84 for 12-h and 24-h uKIM-1. On using combination of both uNGAL and uKIM-1, the predictive performance was found to be increased with high significance and furthermore on addition of the 24-hr USS to this combination the AUC was again increased with very sensitive AUC of 0.906 with a 95% CI (0.812– 1.001). This was similar to the finding of Krawczeski et al. [16] and Liu et al. [4] who concluded that by using biomarker combinations there was an enhanced prediction of AKI. Each biomarker at its best AUC, (2-h NGAL, 12-h KIM-1, and 24-h USS) showed a positive significant correlation with serum creatinine level on the second postoperative day in AKI group, this finding is in favor of the effectiveness of these urinary biomarkers in early prediction of AKI. On studying the association between urine biomarkers and the clinical characteristics by using Spearman correlation test for each biomarker at its earliest elevation (2-h NGAL, 12-h KIM-1, and 24-h USS) it was found that; USS had a positive relationship with most of the clinical parameters in both groups. UKIM-1 was found to have a negative relationship with most of the clinical parameters especially in the non-AKI group more than the AKI group supporting the protective role of it in acute kidney injury early in its course. But uNGAL correlated poorly with the majority of the clinical measurements in both groups except with bleeding and CPB time there was a significant weak positive relationship. Comparably, Cai et al. [27] showed that urinary NGAL levels were positively correlated to CPB time in 59 patients undergoing cardiac surgery and Bagshaw et al. [28] urine sediment examination correlated well with urine NGAL level (correlation coefficient, 0.41; P = 0.012) and was complementary in predicting worsening AKI (as regard death and dialysis requirement) in intensive care unit patients. The importance of determining the consecutive sequence of the biomarkers is underscored by the fact that the course of experimental AKI proceeds in 4 phases: initiation, extension, maintenance, and recovery [29]. The initiation phase is the period during which initial exposure to the ischemic insult occurs and generation of reactive oxygen molecules and labile iron is initiated. Vasodilator, adenosine triphosphate donor, antioxidant, and iron chelation therapies may be especially
S.M. Elmedany et al. / Journal of Critical Care 40 (2017) 171–177
effective during this phase, and the appearance of the earliest noninvasive biomarkers such as NGAL may be used to trigger such therapies. Prolongation of ischemia followed by reperfusion accompanies the extension phase. Tubules undergo reperfusion-mediated cell death, and the injured endothelial and epithelial cells amplify the inflammatory cascades. This phase probably represents a window of opportunity for early diagnosis with intermediate biomarkers such as L-FABP and IL18 and active therapeutic intervention with anti-apoptotic and anti-inflammatory strategies. During the maintenance phase, both cell injury and regeneration occur simultaneously. Measures such as growth factors and stem cells that accelerate the endogenous regeneration processes, initiated by later biomarkers with high specificity such as KIM1, may be most effective during this phase [16]. 5. Conclusion From the present study, we concluded that distinct from traditional markers of function such as creatinine, the rapid response (within few hours) enables NGAL to potentially identify injured kidney much earlier than was previously possible, KIM-1 has been proven to be more specific to ischaemic renal injury with early response in the first postoperative day and its combination with the sensitive NGAL may enable more accurate prediction of CSA-AKI. Although urinary microscopic examination has been neglected for many years by physicians, it was found to be a near ideal renal biomarker with very high sensitivity and specificity and injury site informative. Financial disclosures None. Conflicts of interest None. References [1] Go AS, Mozaffarian D, Roger VL, Benjamin EJ, Berry JD, Borden WB, et al. Executive summary: heart disease and stroke statistics—2013 update: a report from the American Heart Association. Circulation 2013;127:143–52. [2] Kaya M, Kyaruzi M, Guler S, Bakir I, Yeniterzi M. The incidence and predictors of transient acute kidney injury in patients with pre-operative normal kidney functions undergoing coronary artery bypass graft surgery. Cardiovasc Surg 2015;3: 54–60. [3] Paarmann H, Charitos EL, Beichar EA, Heize H, Schon J, Berggreen A, et al. Duration of cardiopulmonary bypass is an important confounder when using biomarkers for early diagnosis of acute kidney injury in cardiac surgical patients. Appl Cardiopulm Pathophysiol 2013;17:284–97. [4] Liu S, Che M, Xue S, Xie B, Zhu M, Lu R, et al. Urinary L-FABP and its combination with urinary NGAL in early diagnosis of acute kidney injury after cardiac surgery in adult patients. Biomarkers 2013;18:95–101. [5] Brown JR, Kramer RS, MacKenzie TA, Coca SG, Sint K, Parikh CR. Determinants of acute kidney injury duration after cardiac surgery: an externally validated tool. Ann Thorac Surg 2012;93:570–6. [6] Kumar AB, Suneja M, Bayman EO, Weide GD, Tarasi M. Association between postoperative acute kidney injury and duration of cardiopulmonary bypass: a meta-analysis. J Cardiothorac Vasc Anesth 2012;26:64–9.
177
[7] 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 Consensus Conference of the Acute Dialysis Quality Initiative (ADQI) Group. Crit Care 2004;8:204–12. [8] Mehta RL, Kellum JA, Shah SV, Molitoris BA, Ronco C, Warnock DG, et al. Acute Kidney Injury Network: report of an initiative to improve outcomes in acute kidney injury. Crit Care 2007;11:R31. [9] Lopes JA. Acute kidney injury: definition and epidemiology. Port J Nephrol Hypert 2013;27:15–22. [10] De Geus HR, Betjes MG, Bakker J. Biomarkers for the prediction of acute kidney injury: a narrative review on current status and future challenges. Clin Kidney J 2012;5: 102–8. [11] Matsui K, Kamijo-Ikemori A, Sugaya T, Yasuda T, Kimura K. Usefulness of urinary biomarkers in early detection of acute kidney injury after cardiac surgery in adults. Circ J 2012;76:213–20. [12] Schiffl H, Lang SM. Update on biomarkers of acute kidney injury: moving closer to clinical impact? Mol Diagn Ther 2012;16:199–207. [13] Ji X, Ma LL, Lu J, Zhang SD, Huang Y, Hou XF, et al. Expression of kidney injury molecule-1 in renal ischemic postconditioning and its protective effect against renal ischemia-reperfusion injury in rats. Beijing Da Xue Xue Bao 2012;44:511–7. [14] Perazella MA, Coca SG, Hall IE, Lyanam U, Koraishy M, Parikh CR. Urine microscopy is associated with severity and worsening of acute kidney injury in hospitalized patients. Clin J Am Soc Nephrol 2010;5:402–8. [15] Murphy GS, Hessel EA, Groom RC. Optimal perfusion during CPB: an evidence based approach. Anesth Analg 2009;108:1394–417. [16] Krawczeski CD, Goldstein SL, Woo JG, Wang Y, Piyaphanee N, Ma Q, et al. Temporal relationship and predictive value of urinary acute kidney injury biomarkers after pediatric cardiopulmonary bypass. J Am Coll Cardiol 2011;58:2301–9. [17] Kristovic D, Horvatic I, Husedzinovic I, Sutlic Z, Rudez I, Baric D, et al. Cardiac surgery-associated acute kidney injury: risk factors analysis and comparison of prediction models. Interact Cardiovasc Thorac Surg 2015:1–8. [18] Schopka S, Diez C, Camboni D, Floerchinger B, Schmid C, Hilker M. Impact of cardiopulmonary bypass on acute kidney injury following coronary artery bypass grafting: a matched pair analysis. J Cardiothorac Surg 2014;9:20. [19] Karkouti K, Grocott HP, Hall RI, Jessen ME, Kruger C, Lerner AB, et al. Interrelationship of preoperative anaemia, intraoperative anaemia, and red blood cell transfusion as potentially risk factors for acute kidney injury in cardiac sugery: a historical multicentre cohort study. Can J Anaesth 2015;62:377–84. [20] Karkouti K. Transfusion and risk of acute kidney injury in cardiac surgery. Br J Anaesth 2012;109(Suppl. 1):i29–38. [21] Schinstock CA, Semret MH, Wagner SJ, Borland TM, Bryant SC, Kashani K, et al. Urinalysis is more specific and urinary neutrophil gelatinase-associated lipocalin is more sensitive for early detection of acute kidney injury. Nephrol Dial Transplant 2013;28:1175–85. [22] Zhao F, Jin Y, Chen X, Xie X. Clinical application of UF-1000i in combination with AX4280 for the screening test ability of urinary formed elements. J Clin Pathol 2013;66: 229–31. [23] Bignami E, Frati E, Meroni R, Simonini M, Di Prima AL, Manunta P, et al. Urinary neutrophil gelatinase-associated lipocalin time course during cardiac surgery. Ann Card Anaesth 2015;18:39–44. [24] Schley G, Köberle C, Manuilova E, Rutz S, Forster C, Weyand M, et al. Comparison of plasma and urine biomarker performance in acute kidney injury. PLoS One 2015;10. [25] McIlroy DR, Wagener G, Lee HT. Neutrophil gelatinase-associated lipocalin and acute kidney injury after cardiac surgery: the effect of baseline renal function on diagnostic performance. Clin J Am Soc Nephrol 2010;5:211–9. [26] Torregrosa I, Montoliu C, Urios A, Andrés-Costa MJ, Giménez-Garzó C, Juan I, et al. Urinary KIM-1, NGAL and L-FABP for the diagnosis of AKI in patients with acute coronary syndrome or heart failure undergoing coronary angiography. Heart Vessel 2014:1–9. [27] Cai L, Borowiec J, Xu S, Han W, Venge P. Assays of urine levels of HNL/NGAL in patients undergoing cardiac surgery and the impact of antibody configuration on their clinical performances. Clin Chim Acta 2009;403:121–5. [28] Bagshaw SM, Haase M, Haase-Fielitz A, Bennett M, Devarajan P, Bellomo R. A prospective evaluation of urine microscopy in septic and non-septic acute kidney injury. Nephrol Dial Transplant 2012;27:582–8. [29] Devarajan P. Update on mechanisms of ischemic acute kidney injury. J Am Soc Nephrol 2006;17:1503–20.