Combination of Urinary Biomarkers Improves Early Detection of Acute Kidney Injury in Patients With Heart Failure

Abstract

Background: Acute kidney injury (AKI) is associated with morality and repeated hospitalization, and is frequently encountered in patients with acute decompensated heart failure (ADHF). However, few effective tools exist for early AKI identification and risk stratification.

Methods and Results: This was a prospective observational study conducted in the coronary care unit (CCU) of a tertiary care university hospital. Patients with a diagnosis of ADHF and who were using diuretics were enrolled. Samples collected between December 2013 and February 2015 were tested for serum cystatin C (Cys-C), urinary neutrophil gelatinase-associated lipocalin, and kidney injury molecule-1 (KIM-1). Demographic, clinical, and laboratory data were evaluated. A total of 103 adult patients with a mean age of 68 years were investigated. AKI was diagnosed in 49 patients (47.6%). For predicting intrinsic AKI on the first day of CCU admission, a combination of Cys-C and urine KIM-1 yielded an excellent area under the receiver operating characteristic curve of 0.828, a sensitivity of 71.0%, and specificity of 43.0%, for an overall accuracy of 78%.

Conclusions: In this study, we found that combinations of the biomarker (Cys-C and KIM-1) were an effective clinical model for predicting AKI in patients with ADHF. The biomarker was also useful for differentiating subclinical AKI in patients with ADHF. (Circ J 2016; 80: 1017–1023)

Acute kidney injury (AKI) is a common, harmful, and potentially treatable complication responsible for increased medical expenditure, repeated admissions, and poor outcomes in patients with acute decompensated heart failure (ADHF). The incidence of AKI ranges from 10% to 30% depending on the definition of AKI, etiologies, and inpatient or outpatient setting.^1–3 Acute cardiorenal syndrome (type 1 CRS [CRS1]) refers to an acute cardiac dysfunction leading to worsening renal function.^4–6 Even minor alterations in serum creatinine (SCr) levels (>0.25 mg/dl) in patients with CRS1 are associated with increased mortality.^7,8 However, patients with heart failure generally receive daily angiotensin-converting enzyme inhibitors or angiotensin receptor blockers, which inhibit efferent renal arteriolar vasoconstriction, thus lowering glomerular filtration pressure. Patients with heart failure also receive daily diuretics to prevent sodium and fluid overload, which can cause proneness to dehydration during acute illness. Thus, these patients might be vulnerable to AKI during abrupt worsening of cardiac function.^9,10 The current diagnostic tool for AKI using serum creatinine (Cr) delayed the diagnosis 3 days after kidney insult. A tool for early identification is the most critical means of improving the prognosis for the condition. Novel AKI biomarkers can be divided into 2 broad classes: those involving functional changes (serum cystatin C [Cys-C]) or structure damage (urinary neutrophil gelatinase-associated lipocalin [NGAL] and kidney injury molecule-1 [KIM-1]) (Figure 1A).^11–14 Although prior research has revealed that NGAL, KIM-1, and Cys-C predict AKI in patients with ADHF separately, no study has examined whether combining markers of the 2 categories could improve diagnostic accuracy.^15–19 Thus, the present study investigated this hypothesis, compared the efficacy and accuracy of these markers, and explored the combined usage.

Figure 1.

(A) A concept of combining functional and damage markers for stratifying patients with acute kidney injury (AKI) into 4 categories. Cystatin C was used as the functional marker and neutrophil gelatinase-associated lipocalin (NGAL) and kidney injury molecule-1 (KIM-1) were used as the damage markers. The image was obtained from http://www.ADQI.org with permission. (B) The enrollment of study patients.

Methods

Study Design, Patient Information, and Data Collection

This cross-sectional study was performed in the coronary care unit (CCU) of a tertiary care referral center between December 2013 and February 2015. The study protocol was approved by the local institutional review board. The inclusion criteria were as follows: age ≥18 years, admission for ADHF, and having received intravenous diuretics therapy. Patients who were receiving dialysis, reported prior organ transplantation, or refused to join this study were excluded (Figure 1B). Heart failure was diagnosed according to the European Society of Cardiology criteria.

In determining the predictive value of biomarkers for intrinsic AKI, the primary outcome was the development of AKI within 7 days of admission. According to the Kidney Disease Improving Global Outcomes (KDIGO) Clinical Practice Guidelines for Acute Kidney Injury, AKI was confirmed under either of the following conditions: SCr levels ≥0.3 mg/dl within 48 h or ≥ a 1.5-times increase in SCr levels from baseline within 7 days.^20,21

The following data were collected prospectively: demographic characteristics, etiologies, routine biochemistry tests, and hospital outcomes. Overall, 107 patients provided informed consent, but 4 of them were excluded from analysis because AKI developed within the first 24 h or the patients had a second kidney insult (contrast exposure) after sample collection. Thus, 103 patients were enrolled in this study.

Sampling and Quantifying Biomarkers

Urinary samples were collected in sterile non-heparinized tubes immediately after admission and then centrifuged at 5,000×g for 30 min at 4℃ to remove cells and debris. Blood samples collected in non-heparinized tubes immediately after admission were centrifuged at 2,000 rpm for 10 min. The samples were then stored at −80℃ for further processing. Urinary neutrophil NGAL, KIM-1, and Cys-C levels were measured in duplicate by using commercially available enzyme-linked immunosorbent assay (ELISA) kits according to manufacturer instructions (DLCN20, DKM100 and DSCTC0; R&D Systems, Minneapolis, MN, USA). B-type natriuretic peptide (BNP) levels were measured using a commercial immunometric assay method. When measurements exhibited a >5% variance, a third analysis was performed to ensure a variance of ≤5%.

Statistical Analysis

Continuous variables were summarized as the mean and standard error (SE), unless otherwise stated. The Kolmogorov-Smirnov test was used to determine the normal distribution of each variable. The continuous variables of the different stages of AKI groups were compared using repeated measures analysis of variance, which was conducted using the Tukey honestly significant difference test for post-hoc analysis. In addition, the χ² test was used to determine the trends and assess the categorical data.

The Hosmer-Lemeshow goodness-of-fit test was used for calibration when evaluating the number of observed and predicted AKI cases in the risk groups for the entire range of probabilities. Discrimination was assessed using the area under the receiver operating characteristic curve (AUC), which was compared through a non-parametric approach. The AUC analysis calculated cut-off values, sensitivity, specificity, and overall accuracy. Subsequently, cut-off points were calculated by acquiring the optimal Youden index, which is defined as sensitivity+specificity−1, where sensitivity and specificity are calculated as proportions. The Youden index has minimal and maximal values of −1 and +1, respectively, and a value of +1 is considered optimal for an algorithm. All statistical tests were 2-tailed, and P<0.05 was considered statistically significant.

Results

Study Population Characteristics

Overall, 103 patients (71 men and 32 women) with a mean age of 68 years were investigated. AKI was diagnosed in 49 patients (47.6%), 11 (10.7%) received dialysis within 7 days of admission and 14 (13.6%) died during hospitalization. The patients in the AKI group had a higher rate of renal replacement therapy (22.4% vs. 0%; P<0.001) and a 4-fold higher rate of in-hospital mortality (22.4% vs. 5.6%; P=0.012). Table 1 presents the characteristics of these patients, including age, sex, hematological parameters, and biomarker levels. Table 1 also presents a summary of the patient characteristics of the AKI and non-AKI groups. The AKI group patients were older and exhibited significantly higher levels of C-reactive protein (CRP) and BNP than the non-AKI group patients. In addition, the AKI group patients exhibited lower coma scale scores and albumin and hemoglobin levels than the non-AKI group patients. The baseline Cr of the groups exhibited no substantial difference (1.7±0.2 vs. 1.6±0.2; P=0.388), but peak Cr was significantly higher in the group with AKI within 7 days of admission (3.3±0.3 vs. 1.7±0.3; P<0.001). In the clinical outcomes, renal replacement therapy and in-hospital mortality were significantly higher in the group with AKI (Table 2). All biomarkers were significantly higher in the AKI group than in the non-AKI group, even after adjustment for urine creatinine (Figure 2). Table 3 lists the different etiologies for ADHF at admission. Among all the patients, poor fluid control was the primary etiology, followed by acute coronary syndrome (ACS), arrhythmia, and cardiogenic shock. There were no substantial differences between groups.

Table 1. Demographic Data and Clinical Characteristics Upon Admission of Non-AKI and AKI Groups

	All patients (n=103)	AKI (n=49)	Non-AKI (n=54)	P value
Demographic data
Age (years)	68±1	69±2	67±2	0.448
Sex, male (%)	71 (68.9)	31 (63.3)	40 (74.1)	0.236
DM (%)	53 (51.5)	27 (55.1)	26 (48.1)	0.481
Clinical parameter
Mean arterial pressure (mmHg)	83±2	84±3	83±2	0.734
Glasgow coma scale (points)	13±0	12±0	13±0	0.030
Albumin (g/L)	3.4±0.1	3.2±0.1	3.6±0.1	<0.001
Hemoglobin (g/dl)	11.6±0.2	10.6±0.3	12.4±0.3	<0.001
Serum sodium (mmol/L)	138±1	139±1	138±1	0.127
CRP (mg/L)	7.3±0.9	9.7±1.5	5.0±0.9	0.011
Troponin I (ng/ml)	0.48±0.12	0.41±0.15	0.57±0.17	0.483
BNP (pg/ml )	1,123±111	1,455±180	821±120	0.005
Ejection fraction (%)	49±2	51±3	47±3	0.333
Biomarkers
Serum cystatin C (mg/L)	2.4±0.1	3.0±0.2	1.8±0.1	<0.001
Urine NGAL (ng/ml)	239.9±32.1	390.6±14.4	103.2±19.4	<0.001
Urine KIM-1 (ng/ml)	1.8±0.1	2.4±0.1	1.4±0.9	<0.001
Urine NGAL/Cr ratio (ng/mg)	471±70	751±124	228±53	<0.001
Urine KIM-1/Cr ratio (ng/mg)	2.83±0.17	3.59±0.26	2.13±0.20	<0.001
Urine Cr (mg/dl)	81.1±5.1	76.8±6.7	84.9±7.6	0.437

Results are presented as mean±SD and percentage. AKI, acute kidney injury; AST, aspartate aminotransferase; BNP, B-type natriuretic peptide; Cr, creatinine; CRP, C-reactive protein; DM, diabetes mellitus; KIM-1, kidney injury molecule-1; NGAL, neutrophil gelatinase-associated lipocalin.

Table 2. Renal Function and Clinical Outcomes Upon Admission of Non-AKI and AKI Groups

	All patients (n=103)	AKI (n=49)	Non-AKI (n=54)	P value
Clinical parameter
Serum Cr baseline (mg/dl)	1.6±0.1	1.7±0.2	1.6±0.2	0.388
Serum Cr peak (mg/dl)	2.4±0.2	3.3±0.3	1.7±0.3	<0.001
Outcomes
RRT within 7 days	11 (10.7)	11 (22.4)	0	<0.001
In-hospital mortality	14 (13.6)	11 (22.4)	3 (5.6)	0.012

Results are presented as mean±SD and percentage. RRT, renal replacement therapy. Other abbreviations as in Table 1.

Figure 2.

Comparisons of expression in different biomarkers according to acute kidney injury (AKI).

Table 3. Principle Etiology for ADHF at Admission

	All patients (n=103)	AKI (n=49)	Non-AKI (n=54)	P value
Poor fluid control	53 (51.5)	30 (61.2)	22 (42.6)	0.059
Acute coronary syndrome	18 (17.5)	7 (14.3)	11 (20.4)	0.417
Arrhythmia	15 (14.6)	5 (10.2)	10 (18.5)	0.273
Cardiogenic shock	12 (11.7)	6 (12.2)	6 (11.1)	1.000
Others	5 (11.7)	1 (2.0)	4 (7.4)	0.366

Results are presented as percentage. ADHF, acute decompensated heart failure; AKI, acute kidney injury.

Discriminatory Power of Biomarkers in Predicting AKI

The accuracy of blood Cys-C, urinary NGAL, and KIM-1 in predicting AKI was assessed through receiver operating characteristic curve analysis (Table 4, Figure S1A–C). When single molecular was used, urinary NGAL exhibited the highest AUC of 0.813. No significant change in AUC was observed for urinary NGAL or KIM-1 after normalization by using urine creatinine. The Youden index was used to determine the optimal cut-off value for predicting AKI. As shown in Table 5, NGAL exhibited the highest Youden index, with a sensitivity of 92.0% and specificity of 57.0% for a threshold value of 42.54 ng/ml. When the functional and structural markers were combined, both Cys-C+NGAL and Cys-C+KIM-1 exhibited improved AKI discriminatory power (AUCs of 0.825±0.040 vs. 0.813±0.041 and 0.825±0.040 vs. 0.757±0.047, respectively). The overall correctness and Youden index were also improved by combining these markers. In addition, significant differences were observed in the cumulative event rates (P<0.05) between the different cut-off of biomarkers at the 7-day follow up (Figure S2A–G).

Table 4. Comparison of Calibration and Discrimination of Biomarkers on the First Day of Coronary Care Unit Admission in Predicting Intrinsic AKI

	Calibration			Discrimination
	Goodnessof- fit (χ2)	df	P value	AUC±SE	95% CI	P value
Serum cystatin C (mg/L)	19.188	1	<0.001	0.742±0.049	0.646–0.838	<0.001
Urine NGAL (ng/ml)	29.707	1	<0.001	0.813±0.041	0.732–0.894	<0.001
Urine KIM-1 (ng/ml)	21.673	1	<0.001	0.757±0.047	0.664–0.849	<0.001
Urine NGAL/Cr ratio (ng/mg)	19.836	1	<0.001	0.770±0.046	0.679–0.861	<0.001
Urine KIM-1/Cr ratio (ng/mg)	19.588	1	<0.001	0.753±0.048	0.659–0.847	<0.001
Cystatin C+NGAL	35.199	2	<0.001	0.825±0.040	0.746–0.904	<0.001
Cystatin C+KIM-1	36.149	2	<0.001	0.828±0.040	0.748–0.907	<0.001

AUC, areas under curve; CI, confidence interval; df, degree of freedom; SE, standard error. Other abbreviations as in Table 1.

Table 5. Prediction of Intrinsic AKI After Coronary Care Unit Admission

Predictive factor	Cut-off point†	Youden index	Sensitivity (%)	Specificity (%)	Overall accuracy (%)
Serum cystatin C (mg/L)	2.7	0.44	0.55	0.56	0.73
Urine NGAL (ng/ml)	42.54	0.49	0.92	0.57	0.74
Urine KIM-1 (ng/ml)	1.62	0.44	0.80	0.44	0.71
Urine NGAL/Cr ratio (ng/mg)	125.36	0.44	0.78	0.67	0.72
Urine KIM-1/Cr ratio (ng/mg)	3.28	0.43	0.63	0.80	0.69
Cystatin C+NGAL	0.40†	0.52	0.78	0.74	0.75
Cystatin C+KIM-1	0.55†	0.57	0.71	0.43	0.78

Ather abbreviations as in Tables 1,4. ^†Cut-off points of the biomarker panels were the predicted probability generated from the multiple logistic regression model using both and as independent variables.

Four Biomarker Quadrants

Table 6 lists the categories of the different biomarker combinations illustrated in Figure 1A. The upper-right quadrant was defined as subclinical AKI, representing patients who manifested kidney damage without evidence of functional change. We found that patients with Cys-C below the cut-off level but NGAL exceeding 42.54 ng/ml or KIM-1 exceeding 1.62 ng/ml were still at risk of AKI during hospital course (48.7% and 51.4%, respectively). The clinical symptoms of AKI in these cases were obscure. In addition, some patients diagnosed with AKI belonged to the category of loss of function without exhibiting kidney damage. Twenty-five percent of patients had Cys-C exceeding the cut-off level but with normal NGAL. Furthermore, 63.6% of the patients who had an elevated Cys-C level were further diagnosed as having AKI but had normal KIM-1 levels.

Table 6. Combination of Kidney Functional and Damage Markers Simultaneously Provides a Simple Method for Stratifying Patients With AKI

	Damage marker
	NGAL		KIM-1
	<42.54 ng/ml	≥42.54 ng/ml	<1.62 ng/ml	≥1.62 ng/ml
Functional marker
Cystatin C <2.71 mg/L	3/31 (9.7%)	19/39 (48.7%)	4/35 (11.4%)	18/35 (51.4%)
Cystatin C ≥2.71 mg/L	1/4 (25%)	26/29 (89.7%)	7/11 (63.6%)	20/22 (90.9%)

Results are presented as percentage. Abbreviations as in Tables 1,5.

Discussion

Patients with ADHF typically exhibit complex syndromes with numerous pathways that affect renal function and involve renal hypoperfusion, kidney congestion, renin-angiotension-aldosterone system activation, sympathetic tone activation, inflammatory cytokines, nonosmotic vasopressin release, reactive oxygen species, septicemia, complement system activation, and drug toxicity, which result in mitochondrial dysfunction, tubule cell damage, endothelial cell injury, immune system activation, and microvascular obstruction in the kidney. Prolonged injury can induce cell cycle arrest, cell death, prolonged inflammation, microvascular loss, and pericyte-myofibroblast transition, leading to chronic hypoxia, sodium retention, and hypertension and then progress to end-stage kidney disease.^22,23 Worsening renal function in CRS1 has been consistently found to be an independent risk factor for in-hospital and 1-year mortality in patients with acute heart failure and ST-segment elevation myocardial infarction.^8,24–26 In the present study, the AKI group patients exhibited anemia, hypoalbuminemia, and higher CRP, which reflect the severity of heart failure, water retention, inflammation, and malnutrition, and are known risk factors of AKI.^5,27–29 The patients with higher BNP means, worse heart function, or undergoing kidney ischemic injury were also demonstrated in prior research.^30,31

Studies using biomarkers for the early detection of AKI in patients with heart failure have been conducted mainly in ward settings with a prevalence of AKI from 11.8% to 17.0%.^15,16 In contrast to previous research, we performed a prospective cohort in a CCU setting, thus we enrolled more patients with a critical illness, with a prevalence of AKI of 47.6%. The GALLANT trial conducted by Maisel et al examined the discriminatory power of plasma NGAL and BNP in predicting re-admission and all-cause mortality. The AUC of NGAL for events was 0.731. Patients with a NGAL ≤100 ng/ml had favorable outcomes regardless of whether BNP exceeded 330 pg/ml, indicating that body fluid removal without causing acute tubule injury benefits patient survival.¹⁷ Bettencourt et al investigated AKI detection in a general ward by studying plasma NGAL and observed an AUC=0.93, with a sensitivity of 100% and specificity of 86.7% when the cut-off was 170 ng/ml.¹⁵ However, the number of patients with AKI (n=14) in the study was low. Two investigations by Tang et al explored the association between urinary NGAL and different aspects of renal dysfunction. Higher urine NGAL was correlated with impaired natriuresis, water excretion, and glomerular filtration rate, and urinary NGAL and KIM-1 exhibited AUCs of 0.615 and 0.658, respectively, in predicting AKI. However, these studies also examined only a limited number of AKI cases (n=14).^16,18 In our research, the urinary NGAL, KIM-1, and serum Cys-C having higher AUCs (0.813, 0.757, and 0.742) than those in the aforementioned research may be because of the high-risk population in our study. After combining the functional and structural markers, both Cys-C+NGAL and Cys-C+KIM-1 improved the discriminatory power for detecting AKI (AUCs of 0.825±0.040 vs. 0.813±0.041 and 0.825±0.040 vs. 0.757±0.047, respectively). According to a review of the literature, the present study is the first to enroll NGAL, KIM-1, and Cys-C, and to assess the combination of these biomarkers by using a larger sample size. Tolvaptan, a selective arginine vasopressin receptor blocker, is used as an adjuvant therapy with loop diuretics to induce free water diuresis in ADHF.³² Further investigations might use these early markers as a guide for Tovaptan use in patients without distal tubule injury. Patients with both ADHF and ACS were at high risk of AKI after emergent percutaneous coronary intervention.^33,34 Early detection of AKI might be beneficial for risk-evaluation strategies. Therapy shall be individualized, tailored to each patient as per guideline recommendations.³⁵ Furthermore, compared to Cr, present biomarkers reflect the renal dysfunction rapidly and may be more suitable for clinical trials that focus on acute-illness patients.³⁶

Neutrophil gelatinase-associated lipocalin is a 25-kDa protein of the lipocalin family that is produced by neutrophil and also expressed in the kidney, liver, and epithelial cells during acute pathologic states such as inflammation, infection, intoxication, ischemia, and neoplastic transformation.³⁷ In AKI, both upregulated NGAL expression in the distal nephron and impaired proximal tubular NGAL reabsorption contribute to increased urinary and plasma NGAL levels.³⁸ KIM-1 is a type I cell membrane glycoprotein that acts as a phosphatidylserine receptor, which involves the apoptotic and necrotic pathways of damaged proximal tubule cells.³⁹ The ectodomain of KIM-1 is shed from cells into the urine after proximal tubular kidney injury.⁴⁰ Thus, NGAL and KIM-1 represent the structural damage. Cys-C, a 13-kDa protein, is one of the most critical extracellular inhibitors of cysteine proteases. Cys-C is freely filtered by the glomerulus, reabsorbed, and catabolized by the tubules, but is not secreted by the tubules. The kinetics of changes in serum Cys-C and creatinine concentrations were mimicked.⁴¹ In contrast to Cr, Cys-C appears to be independent of sex, age, and muscle mass, and is elevated rapidly after kidney injury.⁴² Thus, we selected Cys-C as a functional marker and conducted our investigation according to the hypothesis that biomarkers can be classified as those representing changes in renal function and those reflecting kidney damage. This delineation permits the simultaneous utilization of biomarkers from each category to delineate the spectrum of AKI. As shown in Figure 1A, patients can be categorized among 4 quadrants according to the changes in the representative functional and damage marker tests. Patients belonging to the upper-left quadrant exhibit no signs of AKI. Patients in the upper-right quadrant manifest kidney damage without evidence of functional change; they may not present symptoms or signs of AKI, but further investigation to detect AKI is warranted. In our study, AKI was diagnosed in nearly 50% of these patients within 7 days of admission (Table 6). Screening for renal insult and the underlying etiologies in this group would be useful in clinical practice. Patients in the lower-right quadrant might be at risk of the most adverse outcomes; additional studies might emphasize early intervention in cases involving the rapid deterioration of renal function to improve outcome. In patients who exhibit functional change without damage (lower-left quadrant), prerenal azotemia or reversible function after volume repletion or cardiotropic agents may be required. This framework provides a novel approach to assessing patients with AKI for diagnosis and staging, differential diagnosis, and prognosis. Our results demonstrate the viability of this framework and that further clinical investigation is warranted.

Study Limitations

Despite the promising results, this study had several limitations. First, only one measurement of the biomarkers was used. Comparative analyses between time-course changes in the biomarkers and time-course development of AKI are lacking. Repeat measurements to reclassify the patients into different categories may improve assessment accuracy. Second, this study considered AKI identified in a 7-day period, excluding the possibility of a second injury after sample collection; thus, studies with long-term follow-up periods are warranted for exploring the relationship between mortality and the biomarkers. Third, this research examined ADHF with heterogeneous etiologies, and further subgroup investigation is necessary to explore the relationships between specific disease types and the biomarkers. Finally, considering the small sample size, ICU setting, and observational design, additional prospective randomized trials are warranted for verifying the cost efficacy of using these markers to modify clinical pathways.

Conclusion

In summary, we present 2 inferences of this investigation. First, a combination of 2 categories of biomarkers can improve the diagnostic accuracy for AHDF. Further research with different biomarker combinations is warranted. Second, patients with increased damage markers but no functional change should be monitored for the development of syndromic AKI during treatment. In patients with functional change but no damage, careful inspection and management of the correctable condition can improve the clinical outcome. Further investigation using biomarkers that represent different etiologies, drug toxicities, injury sites, and severity could guide clinicians in detecting AKI. Accordingly, careful assessment of medication and therapy choice and early intervention in patients exhibiting increased biomarker levels might improve the outcomes of kidney injury.

Acknowledgments

The authors thank Yi-Ching Ko, Ya-Ting Zhuang, and Shu-Yun Wang for their assistance in analysis, sampling, and data collection. This study was partially supported by grants from the Ministry of Science and Technology, and Chang Gung Memorial Hospital Research Program (NSC 103-2314-B-182A-040, 103-2314-B-182A-018-MY3, 104-2314-B-182A-066 -MY3 CMRPG1B0581, CIRPG3B0042, and CMRPG3E0111).

Supplementary Files

Supplementary File 1

Figure S1. Area under curve according to different biomarkers.

Figure S2. Kaplan-Meier survival curves of acute kidney injury (AKI) occurrence along with a log-rank test to compare the 7 predictive groups.

Please find supplementary file(s);

http://dx.doi.org/10.1253/circj.CJ-15-0886

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