• Users Online: 17
  • Home
  • Print this page
  • Email this page
Home About us Editorial board Ahead of print Current issue Search Archives Submit article Instructions Subscribe Contacts Login 


 
 Table of Contents  
ORIGINAL ARTICLE
Year : 2018  |  Volume : 18  |  Issue : 4  |  Page : 116-125

Predictive value of novel biomarkers for acute kidney injury in critically ill patients at Assiut University Hospitals


1 Department of Internal Medicine, Assiut University, Assuit, Egypt
2 Department of Anesthesia and Critical Care, Assiut University, Assuit, Egypt
3 Department of Biochemistry, Faculty of Medicine, Assiut University, Assuit, Egypt

Date of Submission01-Oct-2018
Date of Acceptance15-Nov-2018
Date of Web Publication17-Dec-2018

Correspondence Address:
Dr. Effat A.E Tony
Department of Internal Medicine, Faculty of Medicine, Assiut University, Assiut 71515
Egypt
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jesnt.jesnt_28_18

Rights and Permissions
  Abstract 


Introduction Acute kidney injury (AKI) is a clinical problem in critically ill patients, which is associated with adverse outcomes. There is a persistent need to find reliable biomarkers for the early diagnosis and prediction of AKI. Many genes are upregulated in the damaged kidneys, with the subsequent protein products appearing in the urine. Urinary liver-type fatty acid-binding protein (uL-FABP) and urinary kidney injury molecule-1 (uKIM-1) are among the promising upregulated biomarkers.
Aim To assess the ability of uL-FABP in comparison with kidney injury molecule-1 for early prediction of AKI in adult critically ill patients.
Patients and methods A cohort study was conducted enrolling 100 critically ill patients admitted to medical critical care units (CCUs) who had risk factors for developing AKI. Acute Physiology and Chronic Health Evaluation II score was calculated on admission. Serum creatinine was measured on admission and thereafter daily till the seventh day of CCU stay. Urine samples for uL-FABP and uKIM-1 assay were collected at the time of CCU admission, on day 3, and on day 5.
Results Among critically ill patients, 60% had AKI diagnosed mostly on the second (53.3%) and third (40%) day of CCU admission. There was a significant difference in Acute Physiology and Chronic Health Evaluation II score (P<0.001), and duration of CCU stay (P<0.01) between AKI and non-AKI groups. The mean baseline of uKIM-1 was significantly higher in patients with AKI (7.17±1.56 ng/ml) compared with those without AKI (3.01±0.85 ng/ml; P=0.01). A significant high baseline uL-FABP level in patients with AKI was 168.51±45.98 (P<0.001). The area under the receiver operating characteristic curves of uKIM-1 and uL-FABP levels at the time of admission for prediction of AKI in critically ill patients within the first 7 days of their stay were 0.95 and 0.78, respectively, with a better predictive performance of uKIM-1 than uL-FABP.
Conclusion UKIM-1 was a sensitive and specific biomarker (superior to uL-FABP) for the prediction of AKI in critically ill patients.

Keywords: acute kidney injury, critically ill patients, early prediction, kidney injury molecule-1, liver-type fatty acid-binding protein


How to cite this article:
Tony EA, Maghraby HH, Gayed SY, Sayed AA. Predictive value of novel biomarkers for acute kidney injury in critically ill patients at Assiut University Hospitals. J Egypt Soc Nephrol Transplant 2018;18:116-25

How to cite this URL:
Tony EA, Maghraby HH, Gayed SY, Sayed AA. Predictive value of novel biomarkers for acute kidney injury in critically ill patients at Assiut University Hospitals. J Egypt Soc Nephrol Transplant [serial online] 2018 [cited 2019 Mar 20];18:116-25. Available from: http://www.jesnt.eg.net/text.asp?2018/18/4/116/247707




  Introduction Top


Acute kidney injury (AKI) is an incessant clinical dilemma in critically ill patients in spite of the enhancements in revealing its pathophysiologic mechanisms and improvements of its supportive care [1],[2]. Among critically ill patients, AKI is independently accompanied by increased expenses of healthcare, increased duration of ICU stay, and increased risk of mortality, besides the enhanced deterioration in the direction of end-stage renal disease, particularly in the elderly [3],[4]. It was found that 5–50% of critically ill patients admitted in the ICU have an attack of AKI [5]. Moreover, renal replacement therapy (RRT) will be required in up to 4.9% of critically ill patients in the ICU [6]. In addition, AKI requiring RRT in the ICU has a high mortality of more than 50% [7]. This undesired fate of AKI could be explained by the relatively low sensitivity and specificity of serum creatinine (SCr), in addition to, the late recognition of AKI when the rise of SCr is utilized for its diagnosis [8]. Numerous genes are up-regulated in the event of kidney damage with the production of different proteins released in plasma and urine. Among the promising up-regulated biomarkers, urinary liver-type fatty acid-binding protein (uL-FABP) and urinary kidney injury molecule 1 (uKIM-1) excellently reflect renal tubular injury and have been shown as encouraging biomarkers in AKI [9],[10]. Therefore, this study was conducted to evaluate the value of uL-FABP compared with uKIM-1 in the early prediction of AKI occurrence in adult critically ill patients during their stay in medical ICUs, with the aim that their estimation in critically ill patients will become a standard of critical care medicine to decrease the rate of RRT, length of hospital stay, and/or mortality risk secondary to AKI early prediction.


  Patients and methods Top


Patients

The Institutional Review Board of Medical Ethics of Assiut University, Faculty of Medicine, approved the study. Written consents for required investigations and for getting a blood and urine samples from the patients participating in this study were obtained. The current prospective cohort study analyzed critically ill patients admitted to the medical critical care units (CCUs) at Assiut University Hospitals in the period between May 2016 and December 2017 for enrolment in the study. Critically ill patients were considered as those who experienced dysfunction or failure of one or more organs/system, and their survival depended on advanced instruments of monitoring and therapy [11].

Inclusion criteria

Presence of sepsis, shock, hypovolemia, heart failure, nephrotoxic drug exposure within the past week, liver cell failure, obstructive jaundice, neurological deficit, and hematological malignancies as risk factors of AKI.

Exclusion criteria

Age under 18 years, after trauma, after surgery, nephrectomy, history of chronic kidney disease, baseline or initially estimated GFR less than 60 ml/min/1.73 m2 AKI, anuria and oliguria at the time of CCU admission, death or discharge before completion of biomarkers sampling, and refusal of consent, in addition to history of diabetes mellitus, hypertension, autoimmune diseases, obstructive uropathy, renal transplantation, and RRT.

Procedures

SCr was measured on admission and thereafter daily at 8:00 a.m. till the seventh day of admission by using creatinine kit Randox RX Monza (catalogue no. CR 510), Dublin, Ireland. Baseline SCr was defined as the steady-state level 4 weeks before admission. If not available, the admission value was used as a surrogate baseline. Missing immediate admission samples were replaced by first collection values within 12 h after admission. The estimated glomerular filtration rate (eGFR) was calculated at the time of CCU admission using the modification of diet in renal disease study equation: GFR (ml/min/1.73 m2)=186×(Scr mg/dl)−1.154×(age)−0.203×(0.742 if female) [12]. Patients with on-admission eGFR less than 60 ml /min/1.73 m2 were excluded from the study. Urine samples were collected for L-FABP and KIM-1 assay within 12 h after CCU admission (day 1), then in the morning on day 3, and on day 5 of admission. After that urine samples were analyzed by enzyme-linked immunosorbent assay (ELISA) for the expression of biomarkers using KIM-1 ELISA kit catalog number: SG-10108 manufactured by SinoGeneClon Biotech Co. Ltd, HangZhou (China) and L-FABP ELISA kit catalog number: SG-10897 manufactured by SinoGeneClon Biotech Co. Ltd, respectively, according to the manufacturer’s instructions and expressed as their absolute values. For disease severity assessment, the Acute Physiology and Chronic Health Evaluation score (APACHE II) was calculated once within 24 h of admission [13]. Detailed history and careful clinical examination were done. Complete blood counts, liver functions and serum electrolytes, and daily hourly and cumulative urine output were done at the time of CCU admission. The initiation of RRT, hospital stay days, and hospital mortality were recorded.

The primary outcome variable was AKI occurring during the first week after hospital admission according to the Kidney Disease Improving Global Outcomes (KDIGO) classification of AKI [14] in comparison with the uL-FABP and uKIM-1 values on day 1 (at the time of CCU admission), on day 3, and on day 5 of CCU admission. The KDIGO classification was determined based on the worst of either SCr criteria or urine output criteria. KDIGO defines AKI as any of the following criteria:
  1. Criterion 1 includes an increase in SCr by 0.3 mg/dl or more within 48 h or
  2. Criterion 2 includes an increase in SCr to 1.5 times baseline or more within the last 7 days or
  3. Criterion 3 includes urine output less than 0.5 ml/kg/h for 6 h.


The KDIGO has also recommended a staging system for the severity of the AKI. Patients with AKI were classified as stages 1, 2, and 3, correspondingly: stage 1–increase in SCr of at least 0.3 mg/dl within 48 h or increase to more than or equal to 150–200% from baseline within 7 days or urine output less than 0.5 ml/kg/h for more than 6 h; stage 2–increase in SCr to more than 200–300% from baseline or urine output less than 0.5 ml/kg/h for more than 12 h; and stage 3–increase in SCr to more than 300% from baseline or SCr of at least 4.0 mg/dl or initiation of RRT or urine output less than 0.3 ml/kg/h for 24 h or anuria for 12 h. UL-FABP and uKIM-1 values on day 1, on day 3, and on day 5 of CCU admission were also correlated with AKI time of onset and severity.

Statistical analysis

Data were collected and analyzed using statistical package for the social sciences (SPSS, version 20, IBM, Armonk, New York, USA). Continuous data were expressed in the form of mean±SD or median (range), whereas nominal data were expressed in form of frequency (percentage). χ2-Test was used to compare the nominal data of different groups in the study, whereas student t-test was used to compare mean of different two groups and analysis of variance test for more than two groups. Multivariate regression analysis was used to determine the predictors of AKI. Receiver operating characteristic (ROC) curve, the area under the ROC curve [area under the curve (AUC)], sensitivities, and specificities were used to determine the diagnostic accuracy of KIM-1 and L-FABP in the early prediction of AKI. In all statistical analyses, 95% confidence intervals (CIs) were considered. For a biomarker to be considered predictive, it must have an AUC significantly more than 0.5. P value was significant if less than 0.05.


  Results Top


Of the 270 adult critically ill patients who were screened for inclusion in the study, 170 were excluded because of the presence of AKI at the time of CCU admission (36 patients), the absence of risk factors for AKI (24 patients), presence of DM (41 patients), presence of hypertension (29 patients), presence of CKD (15 patients), patients with baseline eGFR less than 60 ml/min/1.73 m2 (12 patients), death or discharge from the hospital before completion of biomarkers sampling (11 patients), and refusal to participate (two patients). Thus, 100 patients were included in the analysis. Based on the criteria of KDIGO classification of AKI, 60 (60%) patients had AKI, whereas 40 patients did not develop AKI. Twelve (20%) patients were diagnosed based on criterion 1 and four (6.7%) patients on criterion 2, whereas most patients − 20 (33.3%) − were diagnosed based on criteria 1 and 2 together. Moreover, 12 (20%) patients were diagnosed based on criteria 1 and 3 together, and 12 (20%) patients were diagnosed based on the combined three criteria. According to KDIGO classification of AKI, 36 (60%) of them had stage 1 AKI, whereas 16 (26.7%) and eight (13.3%) patients had stage 2 and 3 AKI, respectively. It was noticed that most cases were diagnosed at second (53.3%) and third day (40%) of admission in contrast to 6.7% who were diagnosed beyond the third day of CCU admission.

Patient characteristics are shown in [Table 1]. The mean age of patients with AKI (65.20±11.23 years) was significantly higher than the mean age of patients without AKI (47.10±14.47 years) (P<0.001). Overall, 56 (93.3%) patients with AKI compared with 28 (70%) patients without AKI were 40 years old or more. The majority (60%) of both groups were males, with no significant difference. The mean of both systolic and diastolic blood pressures in patients with AKI (103±18 and 60±9.36 mmHg, respectively) were significantly lower than their means in patients without AKI (117±11.14 and 71.8±7.7 mmHg, respectively; P<0.001) for each. Regarding APACHE II score, patients with AKI had a highly significant more elevation in APACHE II score compared with patients without AKI (18.13±3.14 vs. 13.90±3.66; P<0.001). Interestingly, the most frequent risk factors among our patients with AKI in descending manner were hypovolemia in 32 (53.3%), sepsis in 28 (46.7%), and shock in 25 (41.7%), which were significantly higher in the patients with AKI compared with those without AKI (P<0.001). Patients with AKI had a highly significant more duration in CCU compared with patients without AKI (9.34±1.90 vs. 7.54±0.50 days; P<0.01). None of the patients had dialysis within their admission, with a nonsignificant difference in mortality. There were no significant differences regarding the place of admission between patients with AKI and those without AKI.
Table 1 Patient characteristics

Click here to view


Patient characteristics based on the stages of AKI are demonstrated in [Table 2].
Table 2 Patient characteristics based on the stages of acute kidney injury

Click here to view


The mean of baseline blood urea nitrogen was significantly higher in patients with AKI in comparison with those without AKI (31.9±5.98 vs. 16.1±4.43 mg/dl, P=0.03). However, the mean of SCr (0.90±0.07 vs. 0.74±0.19 mg/dl, P=0.06), urinary output (1095.33±520.12 vs. 1745±426.94 ml/24 h, P=0.05), and eGFR (108.22±20.01 vs. 102.23±23.05 ml/min/1.73 m2, P=0.06) had no significant differences between patients with AKI and those without AKI. Regarding the novel biomarkers, the mean level of baseline uKIM-1 was significantly higher in patients with AKI (7.17±1.56 ng/ml) in comparison with patients without AKI (3.01±0.85 ng/ml, P=0.01). Moreover, baseline uL-FABP was significantly higher in patients with AKI 168.51±45.98 versus 56.6±18.27 ng/ml in those without AKI (P<0.001).

[Table 3] showed that patients’ uKIM-1 and uL-FABP concentrations at the time of CCU admission were significantly related to the deterioration of KDIGO stage severity (P<0.05). The mean values of uL-FABP were higher in those patients with liver diseases [liver cell failure (174.11±32.43 ng/ml) and obstructive jaundice (160.22±34.01 ng/ml)] in comparison with other risk factors, whereas the mean values of uKIM-1 did not show any significant differences between different risk factors. Baseline renal function tests and novel biomarkers at the time of CCU admission based on the stages of AKI are summarized in [Table 3].
Table 3 Baseline renal function tests and novel biomarkers at the time of critical care unit admission based on the stages of acute kidney injury

Click here to view


Both uKIM-1 and uL-FABP showed increased levels as early as the time of CCU admission (7.17±1.56 and 168.51±45.98 ng/ml, respectively) in patients with AKI and continued to increase reaching to higher values on the third day of admission (9.91±1.99 and 198.32±33.11 ng/ml, respectively) and then their mean levels started to decline to reach 6.04±2.54 and 150.22±53.09 ng/ml, respectively, on the 5th day of admission, with a significant difference (P<0.05). In the patients without AKI, on the contrary, the mean levels of both uKIM-1 and uL-FABP remained nearly constant from the first day (4.83±1.01 and 56.6±18.27 mg/dl, respectively) to the third day (4.97±0.98 and 53.6±17.75 mg/dl, respectively) up to the fifth day (4.66±0.96 and 59.3±17.98 mg/dl, respectively) without any significant difference. Moreover, the mean level of SCr showed the peak (1.95±0.69 mg/dl) during the fourth day of CCU admission, whereas both uKIM-1 and uL-FABP reached to the peak mean values (9.98±0.67 and 194.8±22.01 ng/ml, respectively) in patients with AKI earlier (third day) than the mean of SCr.

It was noted that both uKIM-1 and uL-FABP levels at the time of CCU admission predict the development of AKI in adult critically ill patients within the first 7 days of their CCU stay, where uKIM-1 has 93.3% sensitivity and 88% specificity for AKI prediction at a cut-off point more than 4.4 ng/ml with AUC of 0.95, positive predictive value of 92%, and negative predictive value of 90% (P<0.001). Moreover, at a cut-off point more than 92 ng/dl, uL-FABP has 92% sensitivity and 45% specificity for prediction of AKI during the first 7 days of CCU admission with AUC of 0.78, positive predictive value of 71.4%, and negative predictive value of 78% (P<0.001; [Figure 1]).
Figure 1 Receiver operating characteristic curve analysis for the ability of urinary KIM-1 and urinry L-FABP at the time of CCU admission in prediction of AKI. AKI, acute kidney injury; CCU, critical care unit; KIM, kidney injury molecule; L-FABP, L-type fatty acid binding protein.

Click here to view


The test performance of uKIM-1 decreased as the severity of functional damage and KDIGO classification stages deteriorated, whereas uL-FABP showed a nearly steady performance as summarized in [Table 4]. Furthermore, the third day uKIM-1 and uL-FABP values did not provide additional accuracy in the prediction of AKI; on the contrary, they reported a lower AUC (0.58 and 0.56, respectively) than demonstrated for on admission values (0.95 and 0.78, respectively).
Table 4 Diagnostic accuracy of on admission urinary kidney injury molecule 1 and liver-type fatty acid-binding protein in prediction of different stages of acute kidney injury

Click here to view


The multivariate regression analysis for evaluation of the most efficient predictors of AKI showed that APACHE II score at the time of admission [odds ratio (OR): 3.02; 95% CI: 15.3–21.99; P=0.01] was the major independent predictor of AKI occurrence during the first 7 days of CCU admission followed by on-admission uKIM-1 (OR: 2.98; 95% CI: 2.44–6.09; P=0.01) and on-admission presence of risk factors for AKI (OR: 2.11; 95% CI: 1.34–7.99; P=0.02). The multivariate regression analysis of the other predictors is demonstrated in [Table 5].
Table 5 Multivariate regression analysis for prediction of acute kidney injury in studied patients

Click here to view



  Discussion Top


Our study shows that uKIM-1 and uL-FABP levels at the time of CCU admission can predict the development of AKI in adult critically ill patients within the first 7 days of their CCU stay, where uKIM-1 showed a better predictive performance with a nearly equal sensitivity and better specificity than uL-FABP, which showed, surprisingly, a humble specificity. The test performance of uKIM-1 decreased as the severity of functional damage and KDIGO classification stages deteriorate, whereas uL-FABP showed a nearly steady performance. Furthermore, using serial uKIM-1 and uL-FABP did not provide additional accuracy in the prediction of AKI. Finally, patients with liver diseases as risk factors of AKI have significantly higher uL-FABP values compared with other risk factors. Moreover, it was found that on-admission APACHE II score was the major independent predictor of AKI occurrence during the first 7 days of CCU admission.

KIM-1 characteristics made us believe that it might be an ideal biomarker of AKI owing to the absence of KIM-1 expression in the normal kidney, its marked upregulation and insertion into the apical membrane of the proximal tubule, its persistence in the epithelial cell until the cell has completely recovered, the rapid and robust cleavage of the ectodomain, and the room temperature stability of the ectodomain [15]. Presence of KIM-1 in the urine is highly specific for kidney injury as no other organs have been shown to express KIM-1 to a degree that would influence kidney excretion [16]. Moreover, uL-FABP is a newly emerging biomarker undetectable in healthy control urine, which is explained by efficient proximal tubular internalization via megalin-mediated endocytosis [17]. Under ischemic conditions, tubular L-FABP gene expression is induced and the proximal tubular re-absorption of L-FABP is reduced leading to its expression in urine [17].

In the current study, we found that uKIM-1 showed an increase in mean levels as early as the time of CCU admission in those patients with AKI (7.17±1.56 ng/ml), whereas its values remained within the accepted values in the patients without AKI (3.01±0.85 ng/ml) [18]. On the contrary, uL-FABP demonstrated high mean levels at the time of CCU admission as well in patients with AKI in comparison with those without (168.51±45.98 vs. 56.6±18.27 ng/ml, respectively). This finding could be explained by increased L-FABP levels in nonrenal insult situations. L-FABP is highly expressed in the liver (2–5% of cytosolic protein) as well as in the kidney, lung, pancreas, and intestine [19], and it is a key regulator of hepatic lipid metabolism by influencing the uptake, transport, mitochondrial oxidation, and esterification of fatty acids [20]. Moreover, Foucaud et al. [21] had reported that L-FABP is detectable in bile in the absence of cellular injury. Moreover, Akbal et al. [22] have recently shown that L-FABP in humans would be a new diagnostic marker for detecting non-alcoholic fatty liver disease. In addition, Shi et al. [23] and Ishimura et al. [24] suggested that L-FABP may also, like the A-FABP, function as an endocrine factor and is associated with obesity and insulin resistance. Most recently, Cakir et al. [25] have concluded that serum and urine L-FABP may be a new diagnostic marker for liver damage in patients with acute hepatitis. Moreover, it showed that L-FABP could be used for the diagnosis of liver damage in patients with acute hepatitis, chronic hepatitis, and also cirrhosis [25].

In our study, elevation of uKIM-1 and L-FABP levels, which suggests proximal tubular injury, was observed earlier than the elevation of SCr in the patients with AKI (which occurred in the second and third day of CCU admission). UKIM-1 and L-FABP levels were not only higher in the patients with AKI than in those without AKI at the time of CCU admission, but also, they reached their maximum values before SCr did so. Furthermore, both uKIM-1 and L-FABP sampled at the time of CCU admission performed well regarding AKI prediction during the first 7 days of CCU admission, with AUC of 0.95 and 0.78, respectively; however, the biomarker with the primary large AUC for predicting the onset of AKI was uKIM-1.

Moreover, it was found that baseline values of both uKIM-1 and uL-FABP were nearly equally sensitive as predictors of AKI (sensitivity=93.3 and 92%, respectively); however, uKIM-1 was more specific (88 vs. 45% for uL-FABP). Most of our enrolled patients have sepsis, shock, and hypovolemia, making them extremely vulnerable to liver damage as well in addition to the enrolled patients with already established liver cell failure and obstructive jaundice. The poor specificity of uL-FABP can be explained by its diagnostic role, which was reported by Cakir et al. [25], not only in renal insults but also in the detection of liver cell damage.

Our findings regarding uKIM-1 predictive value of AKI in critically ill patients are consistent with Boghdady et al. [26] and El-Naggar et al. [27] Boghdady et al. [26] showed that uKIM-1 can detect AKI as early as 6 h from its occurrence and before the elevation of conventional markers with a sensitivity of 100% and specificity of 97% at 95% CI. Moreover, El-Naggar et al. [27] showed that KIM-1 had become significantly elevated before the SCr with an excellent sensitivity and specificity of 90.9 and 95.24%, respectively. In addition, Vaidya et al. [28] have demonstrated that uKIM-1 was significantly higher in patients with AKI than in those without AKI with sensitivity and specificity of 80 and 99%, respectively, showing a nearly similar area under the receiver operating characteristic curve (AUROCC) (0.93) as in our results but with much higher specificity [28].

Regarding uL-FABP performance, Doi et al. [29] and Asada et al. [30] were in concordance with our findings. Doi et al. [29] demonstrated that the AUC of uL-FABP for AKI prediction 1 week after ICU entry was 0.75. More recently, Asada et al. [30] showed that AUC-ROC of uL-FABP for AKI prediction during the first 7 days of ICU admission was 0.779 at 95% CI.

On the contrary, Endre et al. [31], Kashani et al. [32], and Matsui et al. [9] were in contrast to our findings. Endre et al. [31] found that the predictive performance for KIM-1 corrected for urinary creatinine concentration yielded AUC of 0.55 for AKI prediction within 48 h after admission. In addition, Kashani et al. [32] showed uKIM-1 AUC of 0.72 for predicting moderate to severe AKI (KDIGO stage 2–3) within 12 h of sample collection. Moreover, Matsui et al. [9] demonstrated that uL-FABP had an AUC of 0.95, sensitivity of 86%, and specificity of 100% for early detection of AKI during ICU admission.

Several explanations exist for the observed variability of uKIM-1 and uL-FABP’s test performances. First, in the current study, uKIM-1 and uL-FABP measurements were performed immediately after ICU admission and patients were monitored for the occurrence of AKI for the next 7 days, whereas the monitored timing of AKI occurrence in Endre et al. [31] and Kashani et al. [32] ranged from 12 h after sampling to 48 h after ICU admission. Thus, the short duration for AKI monitoring (48 h), the reversibility of the early phases of AKI, and the effects of intensive resuscitation in the golden hours after ICU admittance will be of a great influence on uKIM-1 and uL-FABP test performance. Second, there was variability in the sample size. Third, Kashani et al. [32] and Vaidya et al. [28] enrolled patients with AKI at the time of ICU admission indicating that a degree of renal function loss had already occurred in most of these patients. Accordingly, test results generated in patients with established AKI should not be used for the comparison with those in a cohort of newly developing AKI. Fourth, AKI and its severity defined by RIFLE criteria 2002, AKIN criteria 2007, and KDIGO criteria 2012 are dependent on how baseline SCr values are determined and will contribute to different outcomes between studies. In our study, the admission values were used as a surrogate baseline. Missing immediate admission samples were replaced by first collection values within 12 h after admission. This undoubtedly has resulted in an underestimation of attained KDIGO stage 1 in some of these patients leading to variability in test performance. At last, Endre et al. [31] and Matsui et al. [9] have used uKIM-1 and uL-FABP measurements as the corrected values to urinary creatinine concentrations, whereas the absolute values of them were used in our study, with subsequent variability in results and test performance.

In our study, we found that uKIM-1 and uL-FABP mean values were progressively higher with the advancement of KDIGO stage in patients with AKI and much higher than those of the patients without AKI; however, their test performance for AKI prediction did not strictly parallel the KDIGO stage increase. Accordingly, AUC-ROC for uKIM-1 ranged from 0.96 (stage 1) to 0.79 (stage 2) to 0.65 (stage 3) showing a decline rather than a progressive increase, indicating that uKIM-1 is not a good predictor of the AKI severity. In comparison, AUC-ROC for uL-FABP ranged from 0.78 (stage 1) to 0.68 (stage 2) to 0.77 (stage 3), showing a nearly constant performance along the progressive KDIGO stages of AKI. Our results seem to be consistent with Nickolas et al. [33] who stated that progressive increases in AUC-ROC for the prediction of RIFLE-Risk, RIFLE-Injury, and RIFLE-Failure were absent or less pronounced for uKIM-1 and uL-FABP at the time of hospital admission. There are several possible explanations for this contradiction: first, the number of patients diagnosed as KDIGO stage 2 (n=16) and 3 (n=8) AKI is relatively low in comparison with stage 1 patients (n=36) and beyond the minimum sample size requirements for sensitivity and specificity analysis making test performance limited with a large error of estimation.

Second, the decreased test performance for uKIM-1 with the declining AUROCC for stage 2 and 3 AKI owing to further decrease in the specificity of uKIM-1 in the more advanced AKI stages could be explained by the possibility that its upregulation and increased values may be reflecting a regenerative healing process rather than a further tubular injury. This is consistent with Ichimura et al. [34] who stated that KIM-1 has been recently shown to be a phosphatidylserine receptor that confers a phagocytic phenotype on epithelial cells. Notably, Yang et al. [35] reported that the expression of KIM-1 in renal tubules mediates epithelial cell phagocytosis of apoptotic cells, which protects the kidney after acute injury by downregulating innate immunity and inflammation. Moreover, Zhang et al. [36] and Ing et al. [37] have reported that KIM-1 has a possible role for renal repair process and is markedly upregulated not only in tubular injury but also during regenerating renal proximal tubule epithelial cells following ischemic and toxic renal injury; however, the precise mechanism of KIM‐1 and its shed ectodomain on restoration of tubular integrity after injury is not fully understood.Regarding uL-FABP, the non-progressively increased test performance of uL-FABP in the prediction of the more advanced stages of AKI could be explained by its humble specificity and the possibility that its upregulation and increased values may be reflecting a liver damage rather than a renal insult. Notably, our study demonstrated the presence of obstructive jaundice only in patients with stage 3 AKI. Moreover, deterioration of liver function was significantly more in those patients with stage 3 AKI in comparison with stages 1 and 2 AKI. Foucaud et al. [21] found that L-FABP is detectable in bile in the absence of cellular injury. Hence, its serum level is logically assumed to be increased with obstructive jaundice. In addition, Kamijo et al. [38] demonstrated that L-FABP was initially identified in the liver, and Cakir et al. [25] reported that hepatic dysfunction was shown to increase serum L-FABP levels, so false positivity of uL-FABP as a predictor for AKI in patients with liver dysfunction was assumed.

In the current study, using serial uKIM-1 and uL-FABP measurements did not provide additional accuracy in the prediction of AKI. On the contrary, third-day uKIM-1 and uL-FABP had an AUROCC of 0.58 and 0.56, respectively, in comparison with 0.95 and 0.78 for on-admission uKIM-1and uL-FABP, respectively. This failure to get a steady or better performance could be explained by the significant reduction in sample size as the newly established AKI cases decreased with the progression of time leaving only four newly established cases of AKI beyond the third day of admission, which is beyond the minimum sample size requirements for sensitivity and specificity analysis, the matter that prevents us from doing a diagnostic test performance for fifth day urinary biomarkers in the prediction of AKI.

The multivariate regression analysis done in our study demonstrated that APACHE II score was the most prominent independent predictor followed by uKIM-1 and the presence of risk factors for AKI. Moreover, our study showed that APACHE II score was significantly higher in patients with AKI. Our finding was supported by Mohamed et al. [39] and Doi et al. [29] who showed that APACHE II score was significantly lower in patients without AKI in comparison with patients with AKI. These results augment the preference of APACHE II score use over the other scoring systems for disease severity evaluation [40].

It is reasonable that a number of limitations might have influenced the results obtained. First, this is a single-center study with a relatively small sample size. Our results need to be validated in larger multicenter cohorts of critically ill patients in all tertiary care units all over Egypt. Second, the majority of our enrolled patients did not have any registered medical records, so our study included patients with unknown baseline creatinine where admission values were used as a surrogate baseline − if not available − immediate admission samples will be replaced by first collection values within 12 h after admission which may produce misclassification of early AKI status. Third, APACHE II scores were determined for all patients at the time of CCU enrollment, but were not calculated for subsequent hospital days.

Fourth, in this study, we relied on a single SCr evaluation every day rather than evaluation several times per day, that is, every 6 h especially during the first 48 h after CCU admission, which can lead to missing of SCr changes in between the fixed time-point daily estimations, hence underestimation of AKI. Moreover, wide gaps of SCr detection can give a faulty declaration of the maximum values and time of the peaked level. Finally, despite the high cost, it was better to check the urinary biomarkers every 8 h during the first 2 days after CCU admission followed by daily estimation till the end of the post-admission evaluation period. Long gaps between biomarkers evaluation can lead to inaccurate judgment about the precise biomarker fluctuations before and after AKI occurrence. In addition, maximum values, time of peaked levels, and test performance of serial measurements of urinary biomarkers can be biased.

Our data also have implications for future research. Effect of the use of novel biomarkers on long-term follow-up and outcomes of AKI need to be included in future studies with a collaboration of multicenter cohorts. No doubt that an ideal biomarker would provide more detailed information about the type, intensity, and location of the renal injury. The upcoming studies should focus upon the role of novel biomarkers in the differentiation between a transient and persistent AKI, in addition to their benefits regarding discrimination between prerenal disease (decreased renal perfusion) from acute tubular necrosis as the cause of AKI in addition to their predictive performance. This study has gone some way toward changing our understanding of the predictive value of uL-FABP for AKI in the presence of hepatic affection. We suggest that further studies should be undertaken to explore the validity of uL-FABP use in AKI prediction within the context of liver diseases. In addition, future studies should target the protective, repairing, and may be the therapeutic role of KIM-1 in AKI.


  Conclusion Top


UKIM-1 is a reliable, sensitive, and specific biomarker for the prediction of AKI among critically ill patients with an obvious superior test performance and specificity to uL-FABP.

Acknowledgements

This study was supported by Assiut University Hospitals.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Bagshaw SM, George C, Bellomo R. Changes in the incidence and outcome for early acute kidney injury in a cohort of Australian intensive care units. Crit Care 2007; 11:R68.  Back to cited text no. 1
    
2.
Hoste EA, Kellum JA. Acute kidney injury: epidemiology and diagnostic criteria. Curr Opin Crit Care 2006; 12:531–537.  Back to cited text no. 2
    
3.
Tan SS, Hakkaart-van Roijen L, Al MJ, Bouwmans CA, Hoogendoorn ME, Spronk PE et al. A microcosting study of intensive care unit stay in the Netherlands. J Intensive Care Med 2008; 23:250–257.  Back to cited text no. 3
    
4.
Ishani A, Xue JL, Himmelfarb J, Eggers PW, Kimmel PL, Molitoris BA et al. Acute kidney injury increases risk of ESRD among elderly. J Am Soc Nephrol 2009; 20:223–228.  Back to cited text no. 4
    
5.
Wang Y, Fang Y, Teng J, Ding X. Acute kidney injuryepidemiology: from recognition to intervention. Contrib Nephrol 2016; 187:1–8.  Back to cited text no. 5
    
6.
Edelstein CL. Biomarkers in acute kidney injury. Second ed. The Colorado, USA: Elsevier Inc.; 2017.  Back to cited text no. 6
    
7.
Kes P, Jukić NB. Acute kidney injury in the intensive care unit. Bosn J Basic Med Sci 2010; 10(Suppl 1):S8–S12.  Back to cited text no. 7
    
8.
de Geus HRH, 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; 5:102–108.  Back to cited text no. 8
    
9.
Matsui K, Kamijo-Ikemori A, Hara M, Sugaya T, Kodama T, Fujitani S et al. Clinical significance of tubular and podocyte biomarkers in acute kidney injury. Clin Exp Nephrol 2011; 15:220–225.  Back to cited text no. 9
    
10.
Teo SH, Endre ZH. Biomarkers in acute kidney injury (AKI). Best Pract Res Clin Anaesthesiol 2017; 31:331–344.  Back to cited text no. 10
    
11.
Waydhas C. Intrahospital transport of critically ill patients. Crit Care 1999; 3:R83–R89.  Back to cited text no. 11
    
12.
Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann Intern Med 1999; 130:461–470.  Back to cited text no. 12
    
13.
Pilz G, Gurniak T, Bujdoso O, Werdan K. A basic program for calculation of APACHE II and Elebute scores and sepsis evaluation in intensive care medicine. Comput Biol Med 1991; 21:143–159.  Back to cited text no. 13
    
14.
National Guideline C. KDIGO clinical practice guideline for acute kidney injury. KDIGO guidlies; 2012.  Back to cited text no. 14
    
15.
Waikar SS, Bonventre JV. Biomarkers for the diagnosis of acute kidney injury. Curr Opin Nephrol Hypertens 2007; 16:557–564.  Back to cited text no. 15
    
16.
Vaidya VS, Ferguson MA, Bonventre JV. Biomarkers of acute kidney injury. Annu Rev Pharmacol Toxicol 2008; 48:463–493.  Back to cited text no. 16
    
17.
Oyama Y, Takeda T, Hama H, Tanuma A, Iino N, Sato K et al. Evidence for megalin-mediated proximal tubular uptake of L-FABP, a carrier of potentially nephrotoxic molecules. Lab Invest 2005; 85:522–531.  Back to cited text no. 17
    
18.
Pennemans V, Rigo JM, Faes C, Reynders C, Penders J, Swennen Q. Establishment of reference values for novel urinary biomarkers for renal damage in the healthy population: Are age and gender an issue? Clin Chem Lab Med 2013; 51:1795–1802.  Back to cited text no. 18
    
19.
Furuhashi M, Hotamisligil GS. Fatty acid-binding proteins: role in metabolic diseases and potential as drug targets. Nat Rev Drug Discov 2008; 7:489–503.  Back to cited text no. 19
    
20.
Atshaves BP, Martin GG, Hostetler HA, McIntosh AL, Kier AB, Schroeder F. Liver fatty acid-binding protein and obesity. J Nutr Biochem 2010; 21:1015–1032.  Back to cited text no. 20
    
21.
Foucaud L, Grillasca J, Niot I, Domingo N, Lafont H, Planells R et al. Output of liver fatty acid-binding protein (L-FABP) in bile. Biochim Biophys Acta 1999; 1436:593–599.  Back to cited text no. 21
    
22.
Akbal E, Kocak E, Akyurek O, Koklu S, Batgi H, Senes M. Liver fatty acid-binding protein as a diagnostic marker for non-alcoholic fatty liver disease. Wien Klin Wochenschr 2016; 128:48–52.  Back to cited text no. 22
    
23.
Shi J, Zhang Y, Gu W, Cui B, Xu M, Yan Q et al. Serum liver fatty acid binding protein levels correlate positively with obesity and insulin resistance in Chinese young adults. PLoS ONE 2012; 7:e48777.  Back to cited text no. 23
    
24.
Ishimura S, Furuhashi M, Watanabe Y, Hoshina K, Fuseya T, Mita T et al. Circulating levels of fatty acid-binding protein family and metabolic phenotype in the general population. PLoS ONE 2013; 8:e81318.  Back to cited text no. 24
    
25.
Cakir OO, Toker A, Ataseven H, Demir A, Polat H. The importance of liver-fatty acid binding protein in diagnosis of liver damage in patients with acute hepatitis. J Clin Diagn Res 2017; 11:OC17–OC21.  Back to cited text no. 25
    
26.
Boghdady I, El Naggar M, Emara M, EL-Shazly R, Mahmoud K. Kidney injury molecule-1 as an early marker for acute kidney injury in critically ill patients. Menoufia Med J 2013; 26:98–104.  Back to cited text no. 26
  [Full text]  
27.
El Naggar GF, El Srogy HA, Fathy SM. Kidney injury molecule 1 (KIM-1): an early novel biomarker for acute kidney injury (AKI) in critically − ill patients. Life Sci J 2012; 9:3937–3943.  Back to cited text no. 27
    
28.
Vaidya VS, Waikar SS, Ferguson MA, Collings FB, Sunderland K, Gioules C et al. Urinary biomarkers for sensitive and specific detection of acute kidney injury in humans. Clin Transl Sci 2008; 1:200–208.  Back to cited text no. 28
    
29.
Doi K, Negishi K, Ishizu T, Katagiri D, Fujita T, Matsubara T et al. Evaluation of new acute kidney injury biomarkers in a mixed intensive care unit. Crit Care Med 2011; 39:2464–2469.  Back to cited text no. 29
    
30.
Asada T, Isshiki R, Hayase N, Sumida M, Inokuchi R, Noiri E et al. Impact of clinical context on acute kidney injury biomarker performances: differences between neutrophil gelatinase-associated lipocalin and L-type fatty acid-binding protein. Sci Rep 2016; 6:33077.  Back to cited text no. 30
    
31.
Endre ZH, Pickering JW, Walker RJ, Devarajan P, Edelstein CL, Bonventre JV et al. Improved performance of urinary biomarkers of acute kidney injury in the critically ill by stratification for injury duration and baseline renal function. Kidney Int 2011; 79:1119–1130.  Back to cited text no. 31
    
32.
Kashani K, Al-Khafaji A, Ardiles T, Artigas A, Bagshaw SM, Bell M et al. Discovery and validation of cell cycle arrest biomarkers in human acute kidney injury. Crit Care 2013; 17:R25.  Back to cited text no. 32
    
33.
Nickolas TL, Schmidt-Ott KM, Canetta P, Forster C, Singer E, Sise M et al. Diagnostic and prognostic stratification in the emergency department using urinary biomarkers of nephron damage: a multicenter prospective cohort study. J Am Coll Cardiol 2012; 59:246–255.  Back to cited text no. 33
    
34.
Ichimura T, Asseldonk EJ, Humphreys BD, Gunaratnam L, Duffield JS, Bonventre JV. Kidney injury molecule-1 is a phosphatidylserine receptor that confers a phagocytic phenotype on epithelial cells. J Clin Invest 2008; 118:1657–1668.  Back to cited text no. 34
    
35.
Yang L, Brooks CR, Xiao S, Sabbisetti V, Yeung MY, Hsiao LL et al. KIM-1-mediated phagocytosis reduces acute injury to the kidney. J Clin Invest 2015; 125:1620–1636.  Back to cited text no. 35
    
36.
Zhang Z, Cai CX. Kidney injury molecule-1 (KIM-1) mediates renal epithelial cell repair via ERK MAPK signaling pathway. Mol Cell Biochem 2016; 416:109–116.  Back to cited text no. 36
    
37.
Ing LA, Tang SC, Lai KN, Leung JC. Kidney injury molecule-1: more than just an injury marker of tubular epithelial cells? J Cell Physiol 2013; 228:917–924.  Back to cited text no. 37
    
38.
Kamijo-Ikemori A, Ichikawa D, Matsui K, Yokoyama T, Sugaya T, Kimura K. Urinary L-type fatty acid binding protein (L-FABP) as a new urinary biomarker promulgated by the Ministry of Health, Labour and Welfare in Japan. Rinsho Byori 2013; 61:635–640.  Back to cited text no. 38
    
39.
Mohamed H, Mukhtar A, Mostafa S, Wageh S, Eladawy A, Zaghlol A et al. Epidemiology of acute kidney injury in surgical intensive care at University Hospital in Egypt. A prospective observational study. Egypt J Anaesth 2013; 29:413–417.  Back to cited text no. 39
    
40.
Bouch DC, Thompson JP. Severity scoring systems in the critically ill. Cont Educ Anaesth Crit Care Pain 2008; 8:181–185.  Back to cited text no. 40
    


    Figures

  [Figure 1]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]



 

Top
 
 
  Search
 
Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

 
  In this article
   Abstract
  Introduction
  Patients and methods
  Results
  Discussion
  Conclusion
   References
   Article Figures
   Article Tables

 Article Access Statistics
    Viewed250    
    Printed58    
    Emailed0    
    PDF Downloaded64    
    Comments [Add]    

Recommend this journal


[TAG2]
[TAG3]
[TAG4]