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  • Research article
  • Open Access
  • Open Peer Review

Serum and urine FGF23 and IGFBP-7 for the prediction of acute kidney injury in critically ill children

  • 1,
  • 2,
  • 1,
  • 3,
  • 3,
  • 1,
  • 2,
  • 2 and
  • 2, 3Email author
Contributed equally
BMC Pediatrics201818:192

https://doi.org/10.1186/s12887-018-1175-y

  • Received: 27 July 2017
  • Accepted: 11 June 2018
  • Published:
Open Peer Review reports

Abstract

Background

Fibroblast growth factor 23 (FGF23) and insulin-like growth factor binding protein 7 (IGFBP-7) are suggested to be biomarkers for predicting acute kidney injury (AKI). We compared them with proposed AKI biomarker of cystatin C (CysC), and aimed (1) to examine whether concentrations of these biomarkers vary with age, body weight, illness severity assessed by pediatric risk of mortality III score, and kidney function assessed by estimated glomerular filtration rate (eGFR), (2) to determine the association between these biomarkers and AKI, and (3) to evaluate whether these biomarkers could serve as early independent predictors of AKI in critically ill children.

Methods

This prospective single center study included 144 critically ill patients admitted to the pediatric intensive care unit (PICU) regardless of diagnosis. Serum and spot urine samples were collected during the first 24 h after PICU admission. AKI was diagnosed based on the AKI network (AKIN) criteria.

Results

Twenty-one patients developed AKI within 120 h of sample collection, including 11 with severe AKI defined as AKIN stages 2 and 3. Serum FGF23 levels were independently associated with eGFR after adjustment in a multivariate linear analysis (P < 0.001). Urinary IGFBP-7 (Adjusted OR = 2.94 per 1000 ng/mg increase, P = 0.035), serum CysC (Adjusted OR = 5.28, P = 0.005), and urinary CysC (Adjusted OR = 1.13 per 1000 ng/mg increase, P = 0.022) remained significantly associated with severe AKI after adjustment for body weight and illness severity, respectively. Urinary IGFBP-7 level was predictive of severe AKI and achieved the AUC of 0.79 (P = 0.001), but was not better than serum (AUC = 0.89, P < 0.001) and urinary (AUC = 0.88, P < 0.001) CysC in predicting severe AKI.

Conclusions

Serum FGF23 levels were inversely related to measures of eGFR. In contrast to serum and urinary FGF23 which are not associated with AKI in a general and heterogeneous PICU population, an increased urinary IGFBP-7 level was independently associated with the increased risk of severe AKI diagnosed within the next 5 days after sampling, but not superior to serum or urinary CysC in predicting severe AKI in critically ill children.

Keywords

  • Acute kidney injury
  • Critically ill children
  • Cystatin C
  • Fibroblast growth factor 23
  • Insulin-like growth factor binding protein 7
  • Pediatric risk of mortality III score

Background

Critically ill children are at a high risk of developing acute kidney injury (AKI), which is an independent risk factor associated with high mortality and morbidity [14]. Research in AKI has focused on identifying biomarkers for early diagnosis, which is crucial to initiate effective therapies [510]. Although potential biomarkers for predicting AKI have been identified during the last decade, strong evidence is still lacking to confirm that early biomarkers of AKI have beneficial effects on the clinical outcomes in a general intensive care unit (ICU) population, which leads to attempts to identify novel biomarkers that can predict the development of AKI at an earlier stage [5, 7, 11, 12]. Two of the emerging biomarkers of AKI are fibroblast growth factor 23 (FGF23) [1319] and insulin-like growth factor binding protein 7 (IGFBP-7) [2024].

FGF23, a circulating 26-kDa peptide produced by osteocytes, plays an important role in regulating phosphate and vitamin D homeostasis as a phosphate-regulating hormone [13]. Although it has been studied less extensively in AKI, a number of previous studies revealed that plasma FGF23 levels rise rapidly during AKI, suggesting that plasma FGF23 has the potential to diagnose AKI [1519]. In adult patients undergoing cardiac surgery [18] or in children undergoing cardiopulmonary bypass [19], plasma FGF23 was significantly higher and independently associated with adverse outcomes [18]. So far, two studies of FGF23 with small sample size have been carried out in adult ICU patients [14, 15]. Elevated level of FGF23 was reported in a cohort of 12 ICU patients with AKI compared with 8 control ICU patients without AKI [14]. Subsequently, a prospective observational study of 60 hospitalized adult patients, including 27 from ICU, showed that FGF23 level is elevated and associated with greater risk of death or need for renal replacement therapy [15]. Analysis of larger cohorts is necessary to see if these findings can be replicated in general ICU patients, and whether these findings can apply to critically ill children remains unclear.

IGFBP-7, also known as IGFBP-related protein 1 (IGFBP-rP1), is an additional member of the IGFBP family and involved with the phenomenon of G1 cell-cycle arrest [24]. Renal tubular cells can enter a short period of G1 cell-cycle arrest during the very early phases of cell injury, representing an early response to renal injury [25]. Indeed, urinary IGFBP-7 was identified by proteomics as an early prognostic marker of AKI severity [20]. IGFBP-7 and tissue inhibitor of metalloproteinases-2 (TIMP-2) were further validated in a large multicenter of ICU patients as a predictor of AKI defined by risk, injury, failure, loss, end-stage renal disease (RIFLE) criteria, suggesting that the urinary concentration of IGFBP7 multiplied by TIMP-2 is a novel prognostic urinary biomarker of AKI [23, 24]. However, whether IGFBP-7 alone is a new candidate predictive biomarker of AKI remains to be validated. Serum IGFBP-7 was reported to be associated with insulin resistance and diabetes [26] that may have direct renal effects, resulting in glomerular hyperfiltration and renal damage [27]. However, whether serum IGFBP-7 correlates with renal function, and whether there is a relationship between the serum IGFBP-7 concentration and urinary IGFBP-7 excretion remain elucidated.

In the present study, we assessed concentrations of both FGF23 and IGFBP-7 in serum and urine, and compared them with proposed biomarkers of AKI, serum and urinary cystatin C (CysC). We aimed (1) to examine whether concentrations of these biomarkers vary with age, body weight, and illness severity as assessed by the pediatric risk of mortality III (PRISM III) score, as well as with kidney function as assessed by estimated glomerular filtration rate (eGFR) in critically ill children, (2) to determine the association between these biomarkers and AKI, and (3) to evaluate whether serum and urinary FGF23 and IGFBP-7 could serve as early predictors of AKI, independently of potential confounders, in critically ill children.

Methods

Cohorts, setting, and data collection

All patients who were admitted to the pediatric ICU (PICU) regardless of diagnosis in the university-affiliated tertiary children hospital from May to August 2012 were considered for inclusion in the prospective study. The criteria for PICU admission in our hospital were adopted from guidelines for developing admission and discharge policies for the PICU, as described previously [28, 29], including both medical and surgical patients and age between 1 month and 16 years. The exclusion criteria were the presence of congenital abnormality of the kidney, discharge from PICU before sampling, and unexpected discharge from the PICU or withdrawal of therapy. The Institutional Review Board of the Children’s Hospital of Soochow University approved the study. Informed parental written consent was obtained at enrollment of each patient, and all clinical investigations were conducted according to the principles expressed in the Declaration of Helsinki.

Assessment of illness severity

The PRISM III score, based on age-related physiological parameters collected in the first 24 h after PICU admission, was used as a measure to assess illness severity of critically ill children [30].

Diagnosis of AKI

The diagnosis of AKI developed within 120 h of sample collection was based on the serum creatinine (Cr) level defined by the AKI network (AKIN) criteria [1, 31] without urine output criteria. For patients with elevated serum Cr ≥ 106.1 μmol/L at PICU admission, the lowest Cr value during hospitalization was considered as the baseline Cr, in accordance with previous studies [32, 33]. Severity of AKI was characterized by the AKIN criteria. AKIN stage 1 was defined as mild AKI, and AKIN stages 2 and 3 were defined as severe AKI.

Measurement of serum and urinary FGF23 and IGFBP-7

Non-fasting venous blood and spot urine were collected during the first 24 h after PICU admission and immediately aliquoted and stored at − 80 °C. Serum and urine were first centrifuged at 1500×g at 4 °C for 15 min and the supernatants were used for the measurement. The FGF23 level was quantified by the human enzyme-linked immunosorbent assay (ELISA) kit (SEA746Hu, Cloud-Clone Corp, USA), according to the manufacturer’s protocol. The minimum detectable level of FGF23 was < 6.7 pg/mL, and the coefficient of variation of intra-assay and inter-assay were less than 10 and 12% respectively, corresponding to that reported by the manufacturer. The FGF23 levels were detectable in all serum samples and in 118 (81.9%) urinary samples. For those samples with undetectable FGF23 levels (18.1%), the FGF23 value was assumed to have a concentration at 6.7 pg/mL equivalent to the detection limit of the assay to facilitate the calculation for urinary FGF23/urinary Cr ratios.

The human IGFBP-rp1/IGFBP-7 ELISA kit (DY1334–05, R&D Systems, USA) was used for the measurement. The samples were diluted 20-fold to 100-fold in Reagent Diluent to ensure that the enzymatic reaction was maintained within the linear range. The coefficient of variation of intra-assay and inter-assay were less than 10%. The level of IGFBP-7 was detectable in all samples.

Measurement of serum and urinary CysC and Cr

The levels of CysC and Cr from the aliquoted samples were measured on an automatic biochemical analyzer (Hitachi 7600, Japan), as described previously [6]. The CysC level was measured using latex enhanced immunoturbidimetry assay, and the detection limit for CysC was 0.01 mg/L. The coefficient of variation of intra-assay and inter-assay were ≤ 10%. The CysC levels were detectable in all serum samples and in 131 (91.0%) urinary samples. Urinary CysC values for those with undetectable CysC levels were assumed to have the concentration at 0.01 mg/L equivalent to the detection limit of the assay for calculation of the urinary CysC/urinary Cr ratio. The serum and urinary Cr levels were measured automatically using the sarcosine oxidase method on the automatic biochemical analyzer.

Estimated glomerular filtration rate

Estimated GFR was calculated according to the following formula published by Bouvet et al. [34]: eGFR (ml/min) = 63.2× [1.2/serum CysC (mg/L)]0.56x [1.09/serum Cr (mg/dL)]0.35x [weight (kg)/45]0.3x [age (years)/14]0.4. The results of Cr and CysC were obtained from the aliquoted serum samples.

Statistical analysis

Data analyses were performed using SPSS statistical software. We first checked assumptions of normality and homogeneity of variance. The Mann-Whitney U test was used to analyze differences between two groups, and the Kruskal-Wallis H test was used to analyze differences among three groups. The chi-square test or Fisher’s exact test were used to compare differences in categorical variables among groups. Spearman’s analysis was performed to examine correlations. Univariate and multivariate linear analyses were used to analyze the association of variables with eGFR. The data for continuous variables were log-transformed to meet the assumptions of homogeneity of variances. Univariate and multivariate logistic regression analyses were used to calculate odds ratio (OR) to assess the association of biomarkers with AKI, and to identify independent variables associated with AKI. Model fit was assessed by the Hosmer-Lemeshow goodness-of-fit test with P > 0.05, suggesting the absence of a biased fit. The area under-the-receiver-operating-characteristic curve (AUC) was calculated to assess the predictive strength, and the nonparametric method of Delong was performed to compare differences between AUCs. Optimal cut-off points to maximize both sensitivity and specificity were determined using Sigma Plot 10.0 software.

Results

Patient characteristics

The study involved 144 critically ill children. Of a total of 179 children were admitted to the PICU during the study period, 35 were excluded: 2 died and 5 were discharged from PICU before sampling, 3 had withdrawal of therapy, and 25 had a failure in collecting blood and urine samples during the first 24 h after PICU admission. The leading cause of PICU admission in the cohort was neurologic diseases (33.3%), followed by respiratory diseases (30.6%). Twenty-four (16.7%) patients were diagnosed with sepsis.

Of the 144 patients, 21 (14.6%) developed AKI within 120 h of sample collection. Ten patients fulfilled the AKIN criteria stage 1 defined as mild AKI: 5 on the first, 3 on the second, 1 on the third, and 1 on the fifth day after PICU admission. Eleven patients fulfilled the criteria of AKIN stages 2 and 3 defined as severe AKI, including 6 patients developed AKIN stage 2: 5 on the first and 1 on the third day after admission; and 5 patients developed AKIN stage 3: 2 on the first, 2 on the second, and 1 on the fourth day after admission.

A comparison of the demographic and clinical characteristics and outcomes among patients with non-AKI, mild AKI, and severe AKI is displayed in Table 1.
Table 1

Demographic and clinical characteristics grouped according to AKI status

Variable

Non-AKI

Mild AKI

Severe AKI

P

(n = 123)

(n = 10)

(n = 11)

Age, months

12 [4–48]

30.5 [11.25–98]

59 [4–98]

0.049&

Body weight, kg

10 [6.5–14]

14 [8.75–26.25]

20 [6.5–30]*

0.024&

Male, n

70 (56.9)

5 (50.0)

7 (63.6)

0.819

PRISM III score

3 [0.25–6.75]

7.5 [4.25–10.5]*

17 [8–20]*#

< 0.001

Arterial pHa

7.409 [7.363–7.468]

7.461 [7.392–7.481]

7.400 [7.203–7.497]

0.297

Blood bicarbonatea, mmol/L

20.0 [17.6–22.2]

17.1 [15.5–20.0]*

17.1 [8.1–19.6]*

0.020φ

Serum albumina, g/L

41.7 [38.5–44.4]

40.2 [34.9–46.9]

35.3 [26.7–43.8]*

0.026φ

Serum creatininea, μmol/L

24.6 [19.5–31.8]

44.3 [26.9–72.1]*

86.4 [77.3–140.0]*#

< 0.001φ

Blood urea nitrogena, μmol/L

3.30 [2.54–4.40]

6.34 [3.41–8.53]*

7.00 [5.84–13.44]*

< 0.001φ

Serum sodiuma, μmol/L

134.6 [132.3–136.6]

135.8 [133.2–140.3]

132.8 [130.3–133.7]*#

0.008ζ

Serum potassiuma, μmol/L

4.02 [3.57–4.56]

4.31 [3.77–4.47]

4.32 [3.83–5.60]

0.157

MODSb, n

3 (2.4)

2 (20.0)*

6 (54.5)*

< 0.001φ

Shock/DICb, n

11 (8.9)

2 (20.0)

5 (45.5)*

< 0.001ζ

MVc, n

45 (36.6)

6 (60.0)

10 (90.9)*

0.001ζ

Duration of MVc, hours

0 [0–44]

35 [0–123.5]

115 [12–134]*

0.001ζ

Prolonged MV (> 48 h)c, n

26 (21.1)

4 (40.0)

8 (72.7)*

0.002φ

Antibioticsc, n

116 (94.3)

10 (100)

11 (100)

0.322

Inotropec, n

23 (18.7)

1 (10.0)

8 (72.7)*#

0.001φ

Furosemidec, n

31 (25.2)

3 (30.0)

11 (100)*#

0.032φ

Steroidsc, n

45 (36.6)

3 (30.0)

5 (45.5)

0.757

PICU LOS, hours

66 [36–141]

77.5 [38.25–256]

152 [118–181]*

0.032ζ

Death, n

5 (4.1)

1 (10.0)

2 (18.2)

0.093

Values are median [interquartile range]. Numbers in parentheses denote percentages

AKI network stage 1 was defined as mild AKI, and AKIN stages 2 and 3 were defined as severe AKI. AKI acute kidney injury, DIC disseminated intravascular coagulation, LOS length of stay, MODS multiple organ dysfunction syndrome, MV mechanical ventilation, PICU pediatric intensive care unit, PRISM III pediatric risk of mortality III

aThe first available laboratory results during the first 24 h after PICU admission. bDeveloped during PICU stay. cAdministration during PICU stay

*P < 0.05, compared with non-AKI; #P < 0.05, compared with mild AKI. &P > 0.05, after adjustment for PRISM III score. ζP > 0.05, φP < 0.05, after adjustment for body weight and PRISM III score

Correlation of serum and urinary biomarkers with age, body weight, gender, sepsis, and illness severity

Spearman’s correlation analyses of biomarkers with age, body weight, gender, sepsis, and PRISM III score are displayed in Table 2. Multivariate linear regression analyses, including variables of age, body weight, gender, sepsis, and PRISM III score, were further performed. Serum levels of FGF23 (P = 0.010) and CysC (P = 0.003) remained independently associated with age. In addition, when we grouped the patients into two age categories: ≤3 years (n = 102) and > 3 years (n = 42), the negative correlation between age and serum FGF23 levels was only significant in patients aged ≤3 years (r = − 0.590, P < 0.001), but not in patients aged > 3 years (r = 0.064, P = 0.682). Moreover, the correlation of sepsis with serum FGF23 (P = 0.068), urinary IGFBP-7 (P = 0.350), and urinary CysC (P = 0.391), however, did not remain significant after adjustment for age, body weight and illness severity in a multivariate analysis.
Table 2

Correlation of biomarkers with age, body weight, gender, sepsis, and illness severity

Variable

Statistics

sFGF23 pg/mL

sIGFBP-7 ng/mL

sCysC mg/L

uFGF23 pg/mg uCr

uIGFBP-7 ng/mg uCr

uCysC ng/mg uCr

Age, months

r

−0.608

− 0.274

− 0.369

− 0.209

0.049

− 0.114

P

< 0.001*

0.001

< 0.001*

0.012

0.556

0.175

Body weight, kg

r

−0.598

− 0.253

− 0.346

−0.233

0.066

−0.102

P

< 0.001

0.002

< 0.001

0.005

0.433

0.224

Gender

Z

−0.051

−0.682

−0.077

−1.271

− 0.020

−0.444

P

0.959

0.495

0.939

0.204

0.984

0.657

Sepsis

Z

−2.144

−1.812

−.901

− 1.614

−2.037

−2.589

P

0.032

0.070

0.368

0.107

0.042

0.010

PRISM III score

r

−0.002

0.093

0.084

0.054

0.327

0.253

P

0.981

0.269

0.317

0.524

< 0.001*

0.002*

PRISM III pediatric risk of mortality III, r = Spearman’s correlation coefficient; Z: The Mann-Whitney U test

*P < 0.05, multivariate linear regression analysis, including variables of age, body weight, gender, and PRISM III score. Continuous variables were log-transformed in multivariate analysis

Association of serum and urinary biomarkers with eGFR

Univariate and multivariate linear analyses were used to analyze the association of biomarkers with kidney function as assessed by eGFR. Serum levels of FGF23 (P < 0.001), IGFBP-7 (P = 0.003), and CysC (P < 0.001) and urinary levels of FGF23 (P = 0.001) and CysC (P = 0.022) were associated with eGFR in the univariate linear regression analysis in Table 3. To identify whether these biomarkers were independently associated with eGFR, the multivariate linear analysis was further conducted. The association of eGFR with serum FGF23 (P = 0.040) and urinary CysC (P = 0.001) remained significant in the multivariate analysis after adjustment for age and body weight, as shown in Table 3.
Table 3

Association of variables with eGFR

Variable

Univariate regression

Multivariate regression

B coefficient (SE)

P

B coefficient (SE)

P

Age, months

0.524 (0.025)

< 0.001

  

Body weight, kg

1.129 (0.067)

< 0.001

  

Gender

−0.063 (0.062)

0.317

  

PRISM III score

0.000 (0.006)

0.959

  

MV

−0.033 (0.063)

0.595

  

Duration of MV, hours

0.000 (0.000)

0.302

  

sFGF23, pg/mL

−0.842 (0.108)

< 0.001

−0.156 (0.075)a

0.040

sIGFBP-7, ng/mL

−0.657(0.214)

0.003

−0.111 (0.113)a

0.327

sCysC, mg/L

−1.062 (0.113)

< 0.001

−0.702 (0.048)a

< 0.001

uFGF23, pg/mg uCr

−0.169 (0.051)

0.001

−0.050 (0.027)a

0.061

uIGFBP-7, ng/mg uCr

−0.013 (0.065)

0.843

  

uCysC, ng/mg uCr

−0.097 (0.042)

0.022

−0.067 (0.020)a

0.001

eGFR estimated glomerular filtration rate, MV mechanical ventilation, PRISM III pediatric risk of mortality III. eGFR was calculated based on age, body weight, and serum levels of creatinine and cystatin C

aAfter adjustment for age and body weight. All continuous variables were log-transformed

Association of serum and urinary biomarkers with severe AKI

Comparisons of serum and urinary levels of FGF23, IGFBP-7, and CysC among patients with non-AKI, mild AKI, and severe AKI are shown in Table 4 and Fig. 1. Since there was no significant difference in serum and urinary levels of FGF23, IGFBP-7, and CysC between patients with mild AKI and without AKI (P > 0.05), univariate and multivariate logistic analyses were used to analyze the association of biomarkers with severe AKI in Table 5.
Table 4

Serum and urinary FGF23, IGFBP-7 and CysC levels grouped according to AKI status

Biomarker

Non-AKI

Mild AKI

Severe AKI

P

(n = 123)

(n = 10)

(n = 11)

sFGF23, pg/mL

79.33 [49.88–115.84]

59.97 [50.25–81.57]

92.33 [49.98–107.50]

0.372

sIGFBP-7, ng/mL

107.92 [87.47–125.02]

108.17 [83.65–135.71]

125.26 [103.07–148.35]

0.255

sCysC, mg/L

0.60 [0.47–0.78]

0.73 [0.54–0.96]

1.10 [1.06–1.72]*#

< 0.001

uFGF23, pg/mg uCr

74.40 [39.20–225.8]

47.14 [28.82–130.6]

172.93 [114.37–448.25]*#

0.033

uIGFBP-7, ng/mg uCr

291.57 [135.60–539.04]

244.33 [87.51–478.73]

653.50 [301.94–2072.06]*#

0.005

uCysC, ng/mg uCr

183.17 [94.62–494.96]

122.38 [80.27–332.97]

6559.79 [1224.42–30,414.64]*#

< 0.001

Values are median [interquartile range]

AKI network stage 1 was defined as mild AKI, and AKIN stages 2 and 3 were defined as severe AKI

*P < 0.05, compared with non-AKI; #P < 0.05, compared with mild AKI

Fig. 1
Fig. 1

Comparison of the levels of biomarkers among critically ill children with non-AKI, mild AKI, and severe AKI. a serum level of FGF23, b serum level of IGFBP-7; c serum level of CysC, d urinary level of FGF23, e urinary level of IGFBP-7, f urinary level of CysC. AKI network stage 1 was defined as mild AKI. AKI network stages 2 and 3 were defined as severe AKI. Each circle represents an individual patient; the horizontal lines indicate geometric means with 95% confidence interval. Probability values: the Mann-Whitney U test. The P value for comparison between non-AKI (n = 123) and severe AKI (n = 11), and for comparison between mild (n = 10) and severe (n = 11) AKI

Table 5

Association of variables with severe AKI

Variable

OR

95% CI

P

AOR

95% CI

P

Age, months

1.01

1.00–1.03

0.026

1.01d

0.99–1.02

0.567

Body weight, kg

1.09

1.03–1.16

0.003

1.03d

0.96–1.12

0.428

Gender

0.74

0.21–2.65

0.642

   

PRISM III score

1.36

1.18–1.55

< 0.001

1.32e

1.15–1.53

< 0.001

MV

16.08

2.00–129.36

0.009

5.03f

0.50–50.56

0.170

Duration of MV, hours

1.00

1.00–1.00

0.494

   

Sepsis

3.23

0.87–12.05

0.081

   

eGFR, mL/min

0.98

0.96–1.01

0.138

   

sFGF23, pg/mL

1.00

0.99–1.01

0.730

   

sIGFBP-7, ng/mL

1.01

0.99–1.02

0.096

   

sCysC, mg/L

6.67

1.84–24.18

0.004

5.28f, g

1.64–16.99

0.005

uFGF23, pg/mg uCr

1.15a

0.47–2.82

0.761

   

uIGFBP-7, ng/mg uCr

4.37b

1.82–10.49

0.001

2.94b, f, g

1.08–8.01

0.035

uCysC, ng/mg uCr

1.21c

1.10–1.34

< 0.001

1.13c, f, g

1.02–1.25

0.022

AKI, acute kidney injury; AOR, Adjusted OR; CI, confidence interval; eGFR, estimated glomerular filtration rate; MV, mechanical ventilation; OR, odds ratio; PRISM III, pediatric risk of mortality III

Severe AKI was defined as AKI network stages 2 and 3

aOdds ratio represents the increase in risk per 1000 pg/mg increase in uFGF23/uCr. bOdds ratio represents the increase in risk per 1000 ng/mg increase in uIGFBP-7/uCr. cOdds ratio represents the increase in risk per 1000 ng/mg increase in uCysC/uCr

dAfter adjustment for PRISM III score. eAfter adjustment for age and body weight. fAfter adjustment for body weight and PRISM III score. gP < 0.05, after adjustment for body weight, sepsis, and PRISM III score

The association of serum CysC (P = 0.005), urinary IGFBP-7 (P = 0.035), and urinary CysC (P = 0.022) with severe AKI remained significant after controlling for body weight and illness severity as assessed by PRISM III score (Table 5).

Ability of serum and urinary biomarkers to predict severe AKI

The predictive ability of serum and urinary CysC and urinary IGFBP-7 levels for severe AKI is shown in Table 6. Serum CysC displayed the highest AUC of 0.89 (P < 0.001), which was similar to the result obtained based on the PRISM III score (AUC = 0.92, P < 0.001), for predicting severe AKI in critically ill children, followed by urinary CysC (AUC = 0.88, P < 0.001).
Table 6

Predictive characteristics of biomarkers for severe AKI

Variable

AUC

95% CI

P

Optimal cut-off value

Sensitivity (%)

Specificity (%)

PRISM III score

0.92

0.84–0.99

< 0.001

7.5

90.9

77.4

sCysC, mg/L

0.89

0.82–0.97

< 0.001

0.81

90.9

78.2

uCysC, ng/mg uCr

0.88

0.76–0.99

< 0.001

1145.0

81.8

86.5

uIGFBP-7, ng/mg uCr

0.79

0.66–0.92

0.001

563.4

72.7

79.0

uIGFBP-7, combined with sCysC

0.89

0.79–0.99

< 0.001

   

uIGFBP-7, combined with uCysC

0.88

0.79–0.98

< 0.001

   

uIGFBP-7, combined with sCysC and uCysC

0.90

0.81–1.00

< 0.001

   

Severe AKI was defined as AKI network stages 2 and 3

AKI acute kidney injury, AUC the area under the ROC curve, CI confidence interval, PRISM III pediatric risk of mortality III

Urinary IGFBP-7 level was predictive of severe AKI and achieved the AUC of 0.79 (P = 0.001), but was not better than serum CysC and urinary CysC, in predicting severe AKI. However, the difference between the two AUCs of either urinary IGFBP-7 (AUC = 0.79) and serum CysC (AUC = 0.89) (P = 0.103) or urinary IGFBP-7 and urinary CysC (AUC = 0.88) (P = 0.225) did not reach statistically significant. In addition, combining urinary IGFBP-7 with serum and urinary CysC improved the predictive performance, which was superior to urinary IGFBP-7 alone (P = 0.029), but not significantly better than serum CysC alone (P = 0.689). ROC curves for the ability of serum CysC, urinary IGFBP-7, urinary CysC, and PRISM III score to predict severe AKI in critically ill children are shown in Fig. 2.
Fig. 2
Fig. 2

ROC curves for the ability of urinary IGFBP-7, serum and urinary cystatin C, and PRISM III score to predict severe AKI in critically ill children. AKI network stages 2 and 3 were defined as severe AKI. AKI, acute kidney injury; AUC, the area under the ROC curve; PRISM III, pediatric risk of mortality III; ROC, receiver operating characteristic. The P value for comparison between the AUCs of urinary IGFBP-7 and serum cystatin C was 0.103 and for comparison between the AUCs of urinary IGFBP-7 and urinary cystatin C was 0.225

Discussion

Our results demonstrated that serum FGF23 level was inversely related to measures of eGFR, and an increased urinary level of IGFBP-7 was associated with the increased risk of severe AKI diagnosed within the next 5 days after sampling. However, urinary IGFBP-7 was not superior to serum or urinary CysC in predicting severe AKI in critically ill children.

Previous findings indicate that variables, such as age, gender, and illness severity, may interfere with CysC and other traditional renal biomarkers [6, 35]. We found that both serum CysC and FGF23 levels were independently associated with age. Serum CysC concentration has been reported to be gradually declined with increasing age in younger children less than 3 years old, which reflects renal maturation [35]. Similarly, the decreased serum FGF23 level with increasing age during the first 3 years of age as seen in the present study may also reflect renal maturation. This result is consistent with a previous finding that FGF23 concentration was elevated at birth and higher than reported in adults [36]. Moreover, the FGF23 is a circulating peptide produced by osteocytes. Previous studies have shown that there is a relationship between FGF23 and bone formation [37, 38], suggesting that the negative correlation between serum FGF23 level and age might be related to osteogenesis and skeletal maturation. However, the decreased serum FGF23 level with increasing age was only seen in younger children less than 3 years old. Data on 1,25-dihydroxyvitamin D and parathyroid hormone (PTH) levels were not available in the study, and thus the association between FGF23 and PTH could not be studied. Further studies are necessary to identify whether the association of serum FGF23 with age is in relation to osteogenesis and skeletal maturation.

Significant correlations between biomarkers and measures of kidney function assessed by eGFR were identified in the present study. Previous studies have suggested that eGFR based on both serum Cr and CysC levels is more accurate than equations based on either [34, 39]. Therefore, we calculated eGFR based on both serum Cr and CysC, and demonstrated that the association of eGFR with serum FGF23 levels persisted even after adjustment for age and body weight, indicating that serum FGF23 levels have an inverse relationship to kidney function. This result is in line with a previous study conducted in adult patients with preserved renal function, where higher plasma FGF23 concentration was associated with lower estimated GFR [40]. Our data highlight the need to determine whether serum FGF23 is a potential marker for monitoring kidney dysfunction in critically ill children in large multicenter studies.

To our knowledge, this study is the first to examine the relationships between serum and urinary IGFBP-7 and FGF23 levels with AKI in critically ill children. Of note, our observation of FGF23 levels in critically ill children with AKI is not consistent with previous research [16, 18, 19], and furthermore FGF23 levels in both urine and serum are not useful for the prediction of AKI in critically ill children. The most likely explanation for this discrepancy between our data and previous data could be that we evaluated the predictive accuracy of FGF23 in a general and heterogeneous PICU population rather than in a specific clinical setting, such as in patients undergone cardiac surgery [16, 18, 19] or in randomly selected ICU patients [14, 15]. Given the heterogeneity and dynamic nature of AKI, the predictive performance is dependent strongly on the underlying conditions. The poor results derived from a mixed heterogeneous PICU might be related to the low specificity of FGF23 for AKI. Indeed, upregulation of FGF23 was reported in patients with hypertension, advanced diabetic nephropathy, and cardiovascular disease [41] or in patients with end stage liver disease [42]. Our data support the concept that the usefulness of biomarkers should be addressed differently for different clinical settings [7]. In addition, the level of FGF23 was substantially influenced by age and body weight, which might be considered as disadvantages in the clinical utility of FGF23 as an AKI biomarker in PICU population. The age did not remain significantly associated with severe AKI after adjustment for illness severity in the present study, suggesting that the positive correlation of age with AKI might be due to the higher prevalence of severe underlying diseases in older children, rather than due to a direct effect of age.

One of our major findings was a significant association of urinary IGFBP-7 with severe AKI in critically ill children, which is in line with the previous report from Aregger et al. [20], where urinary IGFBP-7 was identified by proteomics as an early prognostic marker of AKI severity. We verified the use of urinary IGFBP-7 and evaluated the impact of urinary IGFBP-7 on predicting severe AKI in a general PICU population, independent of the severity of illness. It is well accepted that a desirable biomarker should be characterized by a high accuracy and unaffected by potential confounders. The odds ratio for urinary IGFBP-7 to predict severe AKI occurrence remained significant after adjustment for body weight and severity of illness, as assessed by PRISM III score, demonstrating that urinary IGFBP-7 was independently associated with increased risk for severe AKI in critically ill children.

Our study provides the first evidence of a significant association of urinary IGFBP-7 with severe AKI in critically ill children; however, urinaryIGFBP-7 level is not superior to serum or urinary CysC in predicting severe AKI. Since multiple pathways are involved in the development and progression of AKI, a single biomarker may be unlikely to provide the required predictive accuracy in general PICU population, and a panel of biomarkers for accurately predicting AKI might be necessary. Nevertheless, despite the biological diversity, the combination of urinary IGFBP-7 and serum or urinary CysC did not substantially improve the prediction of severe AKI in critically ill children.

The ROC curve analysis in the present study showed that serum CysC appeared to play a greater role in predicting severe AKI, which is in agreement with previous studies where serum CysC has been reported to be associated with an increased risk of AKI in various pediatric cohorts [8, 9]. Notably, although two studies have shown that serum CysC is an early and accurate biomarker for AKI in general critically ill children [8, 9], we are the first to demonstrate that serum CysC was independently associated with AKI, even after adjustment for body weight and illness severity as assessed by PRISM III score. Our results strongly indicate that serum CysC could serve as an independent biomarker to predict severe AKI in critically ill children.

This present study has some limitations. Firstly, we utilized elevated serum Cr levels as a reference standard to define AKI. Although serum Cr remains a widely used marker for evaluating kidney function in PICU, its disadvantage has been well discussed and recognized. Secondly, although the use of urine output criteria for AKI diagnosis has not been well validated [43], it has been suggested that patients meeting both serum Cr and urine output criteria for AKI have worse outcomes compared with patients who manifest AKI predominantly by one criterion [44]. The diagnosis and staging of AKI based only on serum Cr without urine output criteria may have under estimated incidence and grade of AKI. Thirdly, previous studies have indicated that AKI incidence is best estimated by choosing the lowest Cr value within the first week in the ICU as baseline Cr, suggesting that any reasonable estimate based on Cr measures is likely to be better than an estimate that takes into account only age, gender, and race [32]. However, the use of the lowest Cr value during hospitalization as the baseline Cr for patients with elevated serum Cr (≥106.1 μmol/L) at PICU admission has not been validated in critically ill children. Fourthly, the lack of serial measurements of these biomarkers during PICU stay might reduce the likelihood of observing the difference between AKI and non-AKI groups. Fifthly, although the urinary levels of IGFBP-7 and CysC were affected by sepsis; urinary IGFBP-7 and CysC were independently associated with increased risk for severe AKI, even after adjustment for the presence of sepsis. The present study was not powered to specifically detect differences in these biomarkers between septic children with versus without AKI. Finally, the relatively small sample size limited the power to perform logistic regression between these biomarkers and mortality.

Conclusions

Our results have shown that serum FGF23 levels are inversely related to measures of eGFR, irrespective of illness severity, suggesting that the elevated serum FGF23 level may reflect a decline in kidney function independently. In contrast to serum and urinary FGF23 which are not associated with AKI in a general and heterogeneous PICU population, an increased urinary level of IGFBP-7 was independently associated with increased risk of severe AKI diagnosed within the next 5 days after sampling. However, urinary IGFBP-7 was not superior to serum or urinary CysC in predicting severe AKI in critically ill children. Further investigation is needed to explore the role of FGF23 and IGFBP-7 for prediction of AKI in various pediatric cohorts.

Notes

Abbreviations

AKI: 

Acute kidney injury

AKIN: 

AKI network

AOR: 

Adjusted odds ratio

CI: 

Confidence interval

Cr: 

Creatinine

CysC: 

Cystatin C

eGFR: 

Estimated glomerular filtration rate

FGF23: 

Fibroblast growth factor 23

IGFBP-7: 

Insulin-like growth factor binding protein 7

IQR: 

Interquartile range

LOS: 

Length of stay

MV: 

Mechanical ventilation

OR: 

Odds ratio

PICU: 

Pediatric intensive care unit

PRISM III score: 

Pediatric risk of mortality III

PTH: 

Parathyroid hormone

Declarations

Acknowledgements

We thank the staff in biochemistry laboratory for technical assistance.

Funding

This work was supported by grants from the National Natural Science Foundation of China (81370773, 81741054, 81571551, and 81501840), JiangSu province’s science and technology support Program (Social Development BE2016675), Natural Science Foundation of Jiangsu province (BK20171217, BK20151206), Key talent of women’s and children’s health of JiangSu province (FRC201738), SuZhou clinical key disease diagnosis and treatment technology foundation (LCZX201611). The funders had no role in study design, data collection, preparation of the manuscript, and decision to publish.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Authors’ contributions

ZB was responsible for collecting data and samples, participated in data analysis. FF participated in data analysis and helped to draft the manuscript. ZX participated in collecting data and samples. CL carried out the human enzyme-linked immunosorbent assay (ELISA) and participated in data collection. XW carried out ELISA and participated in data collection. JC participated in data analysis. JP participated in data analysis and interpretation. JW participated in the design of the study and coordination. YL had primary responsibility for study design, performing the experiments, data analysis, interpretation of data, and writing of the manuscript. All authors read and approved the final manuscript.

Ethics approval and consent to participate

The Institutional Review Board of the Children’s Hospital of Soochow University approved the study. Informed parental written consent was obtained at enrollment of each patient, and all clinical investigations were conducted according to the principles expressed in the Declaration of Helsinki. Additionally, our manuscript adheres to STROBE guidelines for reporting observational studies.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Pediatric Intensive Care Unit, Children’s Hospital of Soochow University, Suzhou, JiangSu province, China
(2)
Institute of Pediatric Research, Children’s Hospital of Soochow University, Suzhou, JiangSu province, China
(3)
Department of nephrology, Institute of pediatric research, Children’s Hospital of Soochow University, Suzhou, JiangSu province, China

References

  1. Singbartl K, Kellum JA. AKI in the ICU: definition, epidemiology, risk stratification, and outcomes. Kidney Int. 2012;81:819–25.View ArticlePubMedGoogle Scholar
  2. Alkandari O, Eddington KA, Hyder A, Gauvin F, Ducruet T, Gottesman R, et al. Acute kidney injury is an independent risk factor for pediatric intensive care unit mortality, longer length of stay and prolonged mechanical ventilation in critically ill children: a two-center retrospective cohort study. Crit Care. 2011;15:R146.View ArticlePubMedPubMed CentralGoogle Scholar
  3. Sanchez-Pinto LN, Goldstein SL, Schneider JB, Khemani RG. Association between progression and improvement of acute kidney injury and mortality in critically ill children. Pediatr Crit Care Med. 2015;16:703–10.View ArticlePubMedGoogle Scholar
  4. Volpon LC, Sugo EK, Consulin JC, Tavares TL, Aragon DC, Carlotti AP. Epidemiology and outcome of acute kidney injury according to pediatric risk, injury, failure, loss, end-stage renal disease and kidney disease: Improving Global Outcomes Criteria in Critically Ill Children-A Prospective Study. Pediatr Crit Care Med. 2016;17:e229–38.View ArticlePubMedGoogle Scholar
  5. Coca SG, Yalavarthy R, Concato J, Parikh CR. Biomarkers for the diagnosis and risk stratification of acute kidney injury: a systematic review. Kidney Int. 2008;73:1008–16.View ArticlePubMedGoogle Scholar
  6. Li Y, Fu C, Zhou X, Xiao Z, Zhu X, Jin M, et al. Urine interleukin-18 and cystatin-C as biomarkers of acute kidney injury in critically ill neonates. Pediatr Nephrol. 2012;27:851–60.View ArticlePubMedPubMed CentralGoogle Scholar
  7. Vanmassenhove J, Vanholder R, Nagler E, Van Biesen W. Urinary and serum biomarkers for the diagnosis of acute kidney injury: an in-depth review of the literature. Nephrol Dial Transplant. 2013;28:254–73.View ArticlePubMedGoogle Scholar
  8. Ataei N, Bazargani B, Ameli S, Madani A, Javadilarijani F, Moghtaderi M, et al. Early detection of acute kidney injury by serum cystatin C in critically ill children. Pediatr Nephrol. 2014;29:133–8.View ArticlePubMedGoogle Scholar
  9. Volpon LC, Sugo EK, Carlotti AP. Diagnostic and prognostic value of serum cystatin C in critically ill children with acute kidney injury. Pediatr Crit Care Med. 2015;16:e125–31.View ArticlePubMedGoogle Scholar
  10. Sellmer A, Bech BH, Bjerre JV, Schmidt MR, Hjortdal VE, Esberg G, et al. Urinary neutrophil gelatinase-associated Lipocalin in the evaluation of patent ductus arteriosus and AKI in very preterm neonates: a cohort study. BMC Pediatr. 2017;17:7.View ArticlePubMedPubMed CentralGoogle Scholar
  11. Ronco C. Acute kidney injury: from clinical to molecular diagnosis. Crit Care. 2016;20:201.View ArticlePubMedPubMed CentralGoogle Scholar
  12. Lameire NH, Vanholder RC, Van Biesen WA. How to use biomarkers efficiently in acute kidney injury. Kidney Int. 2011;79:1047–50.View ArticlePubMedGoogle Scholar
  13. Kovesdy CP, Quarles LD. FGF23 from bench to bedside. Am J Physiol Renal Physiol. 2016;310:F1168–74.View ArticlePubMedGoogle Scholar
  14. Zhang M, Hsu R, Hsu CY, Kordesch K, Nicasio E, Cortez A, et al. FGF-23 and PTH levels in patients with acute kidney injury: a cross-sectional case series study. Ann Intensive Care. 2011;1:21.View ArticlePubMedPubMed CentralGoogle Scholar
  15. Leaf DE, Wolf M, Waikar SS, Chase H, Christov M, Cremers S, et al. FGF-23 levels in patients with AKI and risk of adverse outcomes. Clin J Am Soc Nephrol. 2012;7:1217–23.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Christov M, Waikar SS, Pereira RC, Havasi A, Leaf DE, Goltzman D, et al. Plasma FGF23 levels increase rapidly after acute kidney injury. Kidney Int. 2013;84:776–85.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Neyra JA, Moe OW, Hu MC. Fibroblast growth factor 23 and acute kidney injury. Pediatr Nephrol. 2015;30:1909–18.View ArticlePubMedGoogle Scholar
  18. Leaf DE, Christov M, Juppner H, Siew E, Ikizler TA, Bian A, et al. Fibroblast growth factor 23 levels are elevated and associated with severe acute kidney injury and death following cardiac surgery. Kidney Int. 2016;89:939–48.View ArticlePubMedPubMed CentralGoogle Scholar
  19. Ali FN, Hassinger A, Price H, Langman CB. Preoperative plasma FGF23 levels predict acute kidney injury in children: results of a pilot study. Pediatr Nephrol. 2013;28:959–62.View ArticlePubMedGoogle Scholar
  20. Aregger F, Uehlinger DE, Witowski J, Brunisholz RA, Hunziker P, Frey FJ, et al. Identification of IGFBP-7 by urinary proteomics as a novel prognostic marker in early acute kidney injury. Kidney Int. 2014;85:909–19.View ArticlePubMedGoogle Scholar
  21. Konvalinka A. Urine proteomics for acute kidney injury prognosis: another player and the long road ahead. Kidney Int. 2014;85:735–8.View ArticlePubMedGoogle Scholar
  22. Lameire N, Vanmassenhove J, Van Biesen W, Vanholder R. The cell cycle biomarkers: promising research, but do not oversell them. Clin Kidney J. 2016;9:353–8.View ArticlePubMedPubMed CentralGoogle Scholar
  23. Wetz AJ, Richardt EM, Wand S, Kunze N, Schotola H, Quintel M, et al. Quantification of urinary TIMP-2 and IGFBP-7: an adequate diagnostic test to predict acute kidney injury after cardiac surgery? Crit Care. 2015;19:3.View ArticlePubMedPubMed CentralGoogle Scholar
  24. 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.View ArticlePubMedPubMed CentralGoogle Scholar
  25. Price PM, Safirstein RL, Megyesi J. The cell cycle and acute kidney injury. Kidney Int. 2009;76:604–13.View ArticlePubMedPubMed CentralGoogle Scholar
  26. Liu Y, Wu M, Ling J, Cai L, Zhang D, Gu HF, et al. Serum IGFBP7 levels associate with insulin resistance and the risk of metabolic syndrome in a Chinese population. Sci Rep. 2015;5:10227.View ArticlePubMedPubMed CentralGoogle Scholar
  27. Tucker BJ, Anderson CM, Thies RS, Collins RC, Blantz RC. Glomerular hemodynamic alterations during acute hyperinsulinemia in normal and diabetic rats. Kidney Int. 1992;42:1160–8.View ArticlePubMedGoogle Scholar
  28. Guidelines for developing admission and discharge policies for the pediatric intensive care unit. American Academy of Pediatrics. Committee on hospital care and section of critical care. Society of Critical Care Medicine. Pediatric section admission criteria task force. Pediatrics. 1999;103:840–2.View ArticleGoogle Scholar
  29. Bai Z, Zhu X, Li M, Hua J, Li Y, Pan J, et al. Effectiveness of predicting in-hospital mortality in critically ill children by assessing blood lactate levels at admission. BMC Pediatr. 2014;14:83.View ArticlePubMedPubMed CentralGoogle Scholar
  30. Pollack MM, Patel KM, Ruttimann UE. PRISM III: an updated pediatric risk of mortality score. Crit Care Med. 1996;24:743–52.View ArticlePubMedGoogle Scholar
  31. 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.View ArticlePubMedPubMed CentralGoogle Scholar
  32. Pickering JW, Endre ZH. Back-calculating baseline creatinine with MDRD misclassifies acute kidney injury in the intensive care unit. Clin J Am Soc Nephrol. 2010;5:1165–73.View ArticlePubMedPubMed CentralGoogle Scholar
  33. Li Y, Wang J, Bai Z, Chen J, Wang X, Pan J, et al. Early fluid overload is associated with acute kidney injury and PICU mortality in critically ill children. Eur J Pediatr. 2016;175:39–48.View ArticlePubMedGoogle Scholar
  34. Bouvet Y, Bouissou F, Coulais Y, Seronie-Vivien S, Tafani M, Decramer S, et al. GFR is better estimated by considering both serum cystatin C and creatinine levels. Pediatr Nephrol. 2006;21:1299–306.View ArticlePubMedGoogle Scholar
  35. Finney H, Newman DJ, Thakkar H, Fell JM, Price CP. Reference ranges for plasma cystatin C and creatinine measurements in premature infants, neonates, and older children. Arch Dis Child. 2000;82:71–5.View ArticlePubMedPubMed CentralGoogle Scholar
  36. Fatani T, Binjab A, Weiler H, Sharma A, Rodd C. Persistent elevation of fibroblast growth factor 23 concentrations in healthy appropriate-for-gestational-age preterm infants. J Pediatr Endocrinol Metab. 2015;28:825–32.View ArticlePubMedGoogle Scholar
  37. Lima F, El-Husseini A, Monier-Faugere MC, David V, Mawad H, Quarles D, et al. FGF-23 serum levels and bone histomorphometric results in adult patients with chronic kidney disease on dialysis. Clin Nephrol. 2014;82:287–95.View ArticlePubMedPubMed CentralGoogle Scholar
  38. Samadfam R, Richard C, Nguyen-Yamamoto L, Bolivar I, Goltzman D. Bone formation regulates circulating concentrations of fibroblast growth factor 23. Endocrinology. 2009;150:4835–45.View ArticlePubMedGoogle Scholar
  39. Deng F, Finer G, Haymond S, Brooks E, Langman CB. Applicability of estimating glomerular filtration rate equations in pediatric patients: comparison with a measured glomerular filtration rate by iohexol clearance. Transl Res. 2015;165:437–45.View ArticlePubMedGoogle Scholar
  40. Dhayat NA, Ackermann D, Pruijm M, Ponte B, Ehret G, Guessous I, et al. Fibroblast growth factor 23 and markers of mineral metabolism in individuals with preserved renal function. Kidney Int. 2016;90:648–57.View ArticlePubMedGoogle Scholar
  41. Scialla JJ, Wolf M. Roles of phosphate and fibroblast growth factor 23 in cardiovascular disease. Nat Rev Nephrol. 2014;10:268–78.View ArticlePubMedGoogle Scholar
  42. Prie D, Forand A, Francoz C, Elie C, Cohen I, Courbebaisse M, et al. Plasma fibroblast growth factor 23 concentration is increased and predicts mortality in patients on the liver-transplant waiting list. PLoS One. 2013;8:e66182.View ArticlePubMedPubMed CentralGoogle Scholar
  43. Md Ralib A, Pickering JW, Shaw GM, Endre ZH. The urine output definition of acute kidney injury is too liberal. Crit Care. 2013;17:R112.View ArticlePubMedPubMed CentralGoogle Scholar
  44. Kellum JA. Diagnostic criteria for acute kidney injury: Present and Future. Crit Care Clin. 2015;31:621–32.View ArticlePubMedPubMed CentralGoogle Scholar

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