International Journal of Cardiology 197 (2015) 48–55

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Spot urine albumin to creatinine ratio outperforms novel acute kidney injury biomarkers in patients with acute myocardial infarction☆ Dimitrios Tziakas a,⁎, Georgios Chalikias a, Dimitra Kareli a, Christina Tsigalou b, Ali Risgits a, Petros Kikas a, Dimitrios Makrygiannis c, Sofia Chatzikyriakou d, Georgia Kampouromiti b, David Symeonidis c, Vassilis Voudris d, Stavros Konstantinides a a

Cardiology Department, Medical School, Democritus University of Thrace, Alexandroupolis, Greece Microbiology Department, Medical School, Democritus University of Thrace, Alexandroupolis, Greece c Cardiology Department, General Hospital of Kavala, Kavala, Greece d Second Department of Interventional Cardiology, Onassis Cardiac Surgery Center, Athens, Greece b

a r t i c l e

i n f o

Article history: Received 30 December 2014 Received in revised form 31 May 2015 Accepted 16 June 2015 Available online 18 June 2015 Keywords: Acute kidney injury Myocardial infarction Spot urine albumin to creatinine ratio Diagnosis Biomarkers

a b s t r a c t Background: Acute kidney injury (AKI) is a frequent complication in patients hospitalized for acute myocardial infarction (AMI), and is associated with in-hospital and long-term morbidity and mortality. We prospectively assessed the diagnostic performance of spot urine albumin to creatinine ratio (uACR) in an adequately sized multicenter cohort of patients admitted to hospital with AMI. We further compared uACR to novel renal injury associated biomarkers regarding their diagnostic ability. Methods: We enrolled 805 consecutive patients presenting with acute ST-elevation and non-ST elevation AMI. Patients were assessed for presence of AKI at 48 h post-admission and at hospital discharge using the Acute Kidney Injury Network (AKIN), the Acute Dialysis Quality Initiative [Risk, Injury and Failure (RIFLE)] criteria and the Kidney Disease: Improving Global Outcomes (KDIGO) criteria. Blood and urine sampling for neutrophil gelatinase-associated lipocalin (NGAL), interleukin-18 (IL-18), cystatin-C, and uACR assessment was performed during admission. Results: The predictive accuracy of uACR was good (Area Under the Curve (AUC), 0.725; 95% CI 0.676–0.774) and was better compared to urine NGAL (P = 0.007), urine (P b 0.001) and plasma Cystatin-C (P = 0.001). ROC analysis identified concentrations of ≥66.7 μg/mg as having the best diagnostic accuracy. The use of uACR exhibited good discriminating ability independent to possible cofounders and additive regarding the use of novel biomarkers. Conclusions: The use of uACR can easily be applied in the clinical setting, allows for robust risk assessment and offers the potential to improve the management of AMI patients at risk for acute kidney injury. © 2015 Elsevier Ireland Ltd. All rights reserved.

1. Introduction Acute kidney injury (AKI) is, when present during hospitalization for acute myocardial infarction (AMI), associated with poor prognosis both in the short-term and in the long-term [1]. This observation is of high clinical importance since incidence of AKI has been estimated to be between 10 and 15% in this hospitalized population [2–6]. The acute ischemic myocardial insult as observed during AMI can cause AKI through a variety of mechanisms such as kidney hypoperfusion due to low cardiac output; renal side-effects of various

☆ All authors take responsibility for all aspects of the reliability and freedom from bias of the data presented and their discussed interpretation. ⁎ Corresponding author at: Cardiology Department, Medical School, Democritus University of Thrace, Dragana, 68100 Alexandroupolis, Greece. E-mail address: [email protected] (D. Tziakas).

http://dx.doi.org/10.1016/j.ijcard.2015.06.019 0167-5273/© 2015 Elsevier Ireland Ltd. All rights reserved.

pharmaceutical agent used during hospitalization for myocardial infarction; nephrotoxicity of contrast agent used in urgent cardiac catheterization procedures; and detrimental effects of acute cardiac injury-related immunological and hormonal mechanisms [7,8]. During the last decade, many papers on the use of new urinary and plasma/serum biomarkers for the diagnosis and prognostication of AKI were published including novel molecules such as interleukin18 (IL-18), neutrophil gelatinase-associated lipocalin (NGAL) and cystatin-C [9,10]. However, uncertainty still exists as to whether these biomarkers possess adequate prognostic accuracy for early detection of AKI [9,10]. At the other end of the spectrum of available options, albuminuria is a robust and validated urine marker for diagnosing preclinical kidney dysfunction in various clinical settings [11]. With the present study we aimed to investigate the diagnostic value of novel plasma and urine biomarkers regarding occurrence of AKI in patients presenting with acute ST-elevation and non-ST elevation

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myocardial infarction (STEMI and NSTEMI), and compare them to an established ‘older’ less cumbersome biomarker reflecting albuminuria such as the spot urine albumin-to-creatinine ratio (uACR). 2. Methods 2.1. Study design and patients The present study was designed as a prospective observational cohort study. Consecutive patients with acute STEMI or NSTEMI were recruited if they fulfilled the following inclusion criteria: 1) age ≥ 18 years; 2) ability to provide written, informed consent; and 3) acute, spontaneous (type 1) AMI. The main exclusion criteria were the presence of pre-existing renal disease and AMI-related symptom onset ≥72 h from hospital admission. Patients with active malignancy, or infection, hepatic, thyroid, pulmonary or auto-immune disease at the time of inclusion or under treatment with anti-inflammatory drugs were excluded. Finally patients referred for urgent coronary artery bypass grafting and patients suffering a fatal event during the index hospitalization were also excluded from the study. A total of 805 consecutive patients admitted to the Coronary Care Unit from 3 different Cardiology Departments (Alexandroupolis; Kavala; Athens) in Greece, were recruited from July 2010 to May 2014. STEMI patients underwent either primary percutaneous coronary intervention (PCI), or fibrinolysis followed by rescue or elective PCI as indicated [12,13]. NSTEMI patients underwent urgent (b120 min), early invasive (b 24 h), invasive (b72 h) or primarily conservative (followed by elective PCI) strategy according to current indications [13,14]. All patients received standard treatment post-MI medical therapy consisting of angiotensin-converting enzyme (ACE) inhibitors or angiotensin receptor blockers (ARB), aldosterone antagonists, betablockers, anti-platelets, and statins upon indication for each one of the pharmaceutical compounds [12,14]. Patients were assessed for presence of AKI at 48 h post-admission using the Acute Kidney Injury Network (AKIN) [15] and the Acute Dialysis Quality Initiative [Risk, Injury and Failure (RIFLE)] criteria [16] and also at hospital discharge using the RIFLE criteria and the Kidney Disease: Improving Global Outcomes (KDIGO) criteria, slightly modified (Appendix Table 1) [17]. Patients were followed post-discharge for up to 24 months after admission using a standardized protocol that included telephone contacts. Follow-up contacts included the recording of information about cardiovascular adverse events and additionally focused on kidney function. The study protocol was approved by the institutional Ethics Committee, and all subjects gave written informed consent. 2.2. Definitions and study end-points Definitions of various clinical terms used throughout the present manuscript are explained in Appendix Table 2. AMI either STEMI or NSTEMI was diagnosed according to published guidelines [12–14].

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Patients were included in the study only if they presented at the hospital within 72 h from symptom presentation and had spontaneous (type 1) AMI according to the 3rd Universal Definition of Myocardial Infarction [18]. Glomerular filtration rate (GFR) was estimated using the Cockcroft–Gault method [19]. Renal function was categorized according to the stages proposed by the National Kidney Foundation [20]. Pre-existing renal disease was defined as at least one of the following: history of or previous admission for renal artery stenosis; acute renal failure; acute or chronic glomerulonephritis; renal obstruction; overt hematuria, nephrotic syndrome; nephrectomy; reduced renal function at baseline (defined by serum creatinine levels ≥2.5 mg/dl or calculated GFR b30 ml/min); permanent renal replacement therapy; history of kidney transplantation. The primary end-point of the study was the incidence of AKI during hospitalization. Incidence of AKI was assessed twice during hospitalization: a) 48 h post-admission using the AKIN and RIFLE criteria, and b) at discharge using the RIFLE criteria and KDIGO criteria slightly modified (changes in creatinine, or GFR were presumed to have occurred within hospitalization [15–17]. For both assessments serum creatinine on admission were considered as baseline. Secondary end-point of the study, assessed with telephone contacts during follow-up, was the combination of: i) death from any or cardiovascular causes (cardiovascular death was defined as death due to AMI, stroke, pulmonary emboli, aortic events, arrhythmias, heart failure, or cardiac surgery/interventions, all other deaths were considered non-cardiovascular), ii) new non-fatal recurrent AMI, iii) hospitalization for unstable or stable angina, iv) any coronary revascularization [PCI or coronary artery bypass grafting (CABG)] different from the index event, v) development of heart failure symptoms requiring hospitalization and vi) any deterioration of kidney function including any of the following: hospitalization for acute renal failure; regular follow-up at outpatient renal department; transient or permanent renal replacement therapy; history of or planned kidney transplantation. Peripheral blood samples for measurement of blood chemistry (renal function) and full blood count were obtained from all patients on admission, 48 h after the index event, and also daily until discharge. Blood and urine sampling for NGAL, IL-18, cystatin-C, and uACR assessment was performed only during admission. In addition, in each patient a transthoracic echocardiography study was performed during hospitalization using standard techniques, in which left ventricular (LV) ejection fraction (EF) was assessed (Simpson's method) [21]. 2.3. Biochemical analysis Blood samples were drawn from a peripheral vein of the patients in vacutainer tubes containing EDTA as an anticoagulant. Samples were immediately centrifuged at 4000 rpm for 10 min at ambient temperature, and the extracted plasma was stored in aliquots and frozen at − 70 °C until use. Urine samples were collected in aliquots from spot urine during admission and were immediately frozen uncentrifuged at −70 °C until use [22].

Table 1 Incidence of acute kidney injury using the AKIN, RIFLE and KQIGO criteria. AKIN criteria

RIFLE criteria

Either AKIN or RIFLE criteria

At 48 h since admission Incidence Staging Stage 1 Stage 2 Stage 3 Stage 4

58 (7.2%)

54 (6.7%)

52 (90%) 4 (7%) 2 (3%) n/a

46 (85%) 6 (11%) 2 (4%) 0

Mod. KDIGO criteria a

Mod. RIFLE criteria a

Either mod. KDIGO or mod. RIFLE criteria a

102 (12.7%)

94 (11.7%)

118 (14.7%)

82 (80%) 12 (12%) 8 (8%) n/a

78 (83%) 12 (13%) 4 (4%) 0

During hospitalization 76 (9.4%)

AKIN, Acute Kidney Injury Network; KDIGO, Kidney Disease: Improving Global Outcomes and the Acute Dialysis Quality Initiative; mod, modified; RIFLE, Risk, Injury and Failure. a According to modified RIFLE and KDIGO criteria changes in creatinine, or GFR were presumed to have occurred within hospitalization

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2.4. Interleukin-18 (IL-18) Plasma and urine IL-18 levels were determined using enzymelinked immunosorbent assay (ELISA) with a commercially available kit (Biorbyt, United Kingdom). The limit of analytical detection was 15.6 pg/mL. The coefficients of variation for intra- and inter-assay precision were b5% and b 10%, respectively. 2.5. Neutrophil gelatinase-associated lipocalin (NGAL) Similarly, plasma and urine NGAL levels were determined with an ELISA assay using a commercially available kit (Biorbyt, United Kingdom). The lower detection limit was 156 pg/mL. The coefficients of variation for intra- and inter-assay precisions were b 5% and b7%, respectively.

Table 2 Discriminating ability (area under the curve) of the under investigation variables regarding the incidence of AKI during hospitalization. AUC

95% CI

P value

Sens

Spec

+PV

−PV

Urine IL-18 NGAL Cystatin-C uACR

0.538 0.616 0.573 0.725

0.476–0.600 0.562–0.669 0.514–0.632 0.676–0.774

0.188 b0.001 0.011 b0.001

75 64 68

45 55 76

19 20 32

91 90 93

Plasma IL-18 NGAL Cystatin-C

0.529 0.521 0.571

0.466–0.592 0.462–0.581 0.525–0.628

0.311 0.464 0.013

61

56

19

89

AKI, acute kidney injury; AUC, area under the curve; CI, confidence interval; IL-18, interleukin-18; NGAL, neutrophil gelatinase-associated lipocalin; n/a, non-applicable; Sens, sensitivity; Spec, specificity; uACR, urine albumin-to-creatinine ratio; +PV, positive predictive value; −PV, negative predictive value.

2.6. Cystatin C A commercially available kit with a competitive ELISA assay (Biorbyt, United Kingdom) was also used for determination of plasma and urine Cystatin C levels. The minimum detectable value was 0.312 ng/mL. The coefficients of variation for intra- and inter-assay precisions were b8% and b8%, respectively. 2.7. Urine albumin-to-creatinine ratio (uACR) Urine albumin and creatinine levels were determined in automated biochemistry analyzer (Clinical Chemistry System ADVIA 2400, Siemens) using commercially available kits (Medicon, Hellas). A microalbumin detection kit was used for albumin quantification, with detection range 0.17–45 mg/dL and coefficient of variation for intraand inter-assay precisions b 0.94% and b 2.5% respectively. Similarly, the detection range for creatinine values was 0.07–500 mg/dL and coefficient of variation for intra- and inter-assay precisions was b1.03% and b2.5% respectively. For albumin and creatinine levels exceeding the detection range additional dilution of the sample was performed.

We included all variables that were different between patients with and without AKI. After fitting the model, we then ranked all patients by their estimated propensity score and grouped patients within quintiles. We calculated the OR and 95% CI for AKI occurrence, comparing within each quintile those who developed AKI and those who did not. A combined difference was estimated by averaging the OR across the quintiles using weights proportional to the inverse of the variance of the estimates [23,24]. According to power analysis and a hypothesized 15% incidence of AKI, 800 patients will be needed in order for the under-investigation biomarkers to have at least 70% statistical power to detect AKI with an odds ratio ≥1.3 and a possibility of error at 5%. A P value b 0.05 was considered to indicate statistical significance; all tests were two-sided. The IBM SPSS Statistics 20.0 statistical software

2.8. Statistical analysis Data are presented as percentages for categorical data, as means ± standard deviation (SD) for continuous variables that were normally distributed and as medians with interquartile range (IQR) for non-normally distributed data. Normality was tested using the Kolmogorov–Smirnov test. Comparisons between categorical variables were performed by chi-square test or Fisher's exact test when required. Differences in continuous variables between two groups were assessed using the Student's t-test or the Mann–Whitney's U-test as appropriate. Τhe diagnostic accuracy of the under investigation biomarkers was determined by calculating the area under the curve (AUC), sensitivity, specificity, and positive and negative predictive values using receiver operating characteristic (ROC) curve analysis. The association of study biomarkers with AKI incidence or the combined survival end-point was evaluated in univariable logistic regression analysis models. Independence was assessed in multivariable logistic regression analysis models using variables that could act as possible cofounders for AKI incidence or prognosis. For all logistic regression analysis models, odds ratios (OR) with 95% confidence intervals were calculated. Variables which were not normally distributed were mathematically transformed as required to approach normal distribution and to obtain equal variances in order to be included in all models. Variables retained in all final models were chosen with a backward stepwise selection method. Finally, we created a propensity score for the likelihood of AKI during hospitalization. Multiple logistic regression with AKI occurrence as the dependent variable was used in the development of the propensity score; it also incorporated patient, treatment and hospital characteristics.

Fig. 1. Comparison of predictive accuracy for AKI of under investigation urine markers using ROC analysis in the study cohort. Blue line, uACR; green line, IL-18; purple line, Cystatin-C; gray line, NGAL. AKI, acute kidney injury; IL-18, interleukin-18; NGAL, neutrophil gelatinase-associated lipocalin; ROC, receiver operating curve; uACR, urine albumin to creatinine ratio.

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package (SPSS Inc., Chicago, Illinois, USA) was used for all calculations with an exception of AUC comparison for which MedCalc 19.2 Statistical Software (MedCalc Software, Mariakerke, Belgium) was used. 3. Results 3.1. Study population Baseline demographic, clinical and angiographic characteristics as well as main hospitalization data are listed in Appendix Table 3. Study

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patients had a mean age of 62 years, with one third of the population being over 70 years of age. Approximately 70% of study participants were hospitalized for STEMI whereas 30% for NSTEMI, admitted at a median of 6 h since symptom presentation. The majority of the patients had an anterior AMI, with no evidence of pulmonary congestion and a blood pressure of N 90 mm Hg. The majority of the patients were managed invasively during hospitalization and one fourth of the population experienced at least one in-hospital adverse event. Seventeen percent of the patient had moderately or severely reduced LV EF on echocardiography.

Fig. 2. A. Incidence of acute kidney injury in the study cohort according to increasing quartiles of uACR. Numbers within bars reflect incidence of AKI (%). AKI, acute kidney injury; uACR, urine albumin to creatinine ratio. Q1, b5 μg/mg; Q2, 5.1 μg/mg to 26 μg/mg; Q3, 26.1 μg/mg to 93 μg/mg; Q4, N93 μg/mg. B. Correlation between uACR levels and predicted incidence acute kidney injury. AKI, acute kidney injury; uACR, urine albumin to creatinine ratio.

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The incidence of AKI in our study population ranged from 6.7% to 14.7% (Table 1) depending on timing (at 48 h vs. during hospitalization) and on definition used (AKIN vs. RIFLE vs. KDIGO). The majority of the patients had stage 1 kidney injury whereas none of the patients required dialysis during hospitalization. For further analysis, patients were considered to have AKI using the modified KDIGO or RIFLE criteria during hospitalization on practicality reasons. Plasma and urine concentrations of the under-investigation laboratory variables (IL-18, NGAL, Cystatin-C and uACR) at 48 h within study participants are presented in Appendix Table 4. Urine levels indexed to body mass index (BMI), to body surface area (BSA) and urine creatinine levels are also reported.

3.2. Diagnostic performance From the under investigation variables only uACR, urine NGAL, urine and plasma Cystatin-C were capable of detecting AKI. The discriminating ability (regarding the incidence of AKI during hospitalization) of the urine concentration of the parameters under investigation ranged from good to moderate (Table 2). uACR had a better diagnostic accuracy for AKI as assessed with ROC analysis (AUC 0.725, 95% CI 0.676–0.774) compared to urine NGAL (P = 0.007), urine (P b 0.001) and plasma Cystatin-C (P = 0.001) (Fig. 1). ROC analysis identified a value of 66.7 μg/mg as having the best diagnostic accuracy in predicting the occurrence of AKI. The sensitivity of uACR was 68% and the specificity was 76%, with a negative predictive value of 93% and a positive predictive value of 32%. Indexing these markers to BMI, BSA, and urine creatinine or using their urine to plasma ratio did not improve their diagnostic accuracy (Appendix Table 5). The observed incidence of AKI across quartiles of uACR is presented in Fig. 2A showing an increase on AKI incidence with quartiles of uACR (Cochran-Armitage test for trend with increasing quartile values; z score, 59.9; P b 0.001). The association between increasing levels of uACR and the predicted incidence of AKI suggests, as shown in Fig. 2B, a linear effect. Finally, uACR levels exhibited good discriminating ability across the pre-defined subgroups of the study population (Appendix Table 6).

3.3. Multivariate modeling Levels of uACR were associated with AKI incidence in a univariate model (OR per 1 SD, 1.32 95% CI 1.12–1.56, P = 0.001). The observed association was more robust compared to those observed with urine NGAL levels (OR per 1 SD, 1.15 95% CI 0.99–1.34, P = 0.073), urine Cystatin-C levels (OR per 1 SD, 1.19 95% CI 1.01–.141, P = 0.042) and plasma Cystatin-C levels (OR per 1 SD, 1.13 95% CI 0.96–1.33, P = 0.141). Levels of uACR continued to be associated with AKI incidence in a multivariate model that included all possible confounding variables (Appendix Table 7) (OR per 1 SD 1.21 95% CI 1.03–1.48, P = 0.046). Of importance, urine NGAL (P = 0.837), urine Cystatin-C (P = 0.229) and plasma Cystatin-C (P = 0.249) concentrations were not independent predictors of AKI in multivariable models. The addition of uACR levels in predictive models including each one of the 3 comparator biomarkers resulted in increased discriminating ability (Table 3). Furthermore, the addition of uACR levels to a predictive model including all three biomarkers (urine NGAL, urine and plasma Cystatin-C) showed a better predictive performance (Table 3). Table 4 shows the association between uACR levels and incidence of AKI stratified by quintiles of propensity score. In all quintiles, there was a significant or marginally significant predictive ability of uACR for the presence of AKI. The combined propensity score-adjusted odds for AKI prediction were 2.65 with 95% CI 1.10–7.9 and similar to those obtained with logistic regression alone (OR for log uACR levels 2.36; 95% CI 1.85–3.01).

Table 3 Performance measures of biomarker multivariate models predicting AKI.

uNGAL (model A) Model A + uACR uCyst C (model B) Model B + uACR pCyst C (model C) Model C + uACR uNGAL + uCystC + p CystC (model D) Model D + uACR

−2LL

χ2

df

C-statistic

667.9 656.1 667.1 656.6 668.9 656.5 664.7 653.4

3 14.8 3.8 14.4 1.9 14.4 6.3 17.5

1 2 1 2 1 2 3 4

0.616 0.695 0.573 0.654 0.571 0.718 0.597 0.677

P value 0.024 0.028 b0.001 0.026

P value corresponds to comparison of c-statistic of the two models compared using z score (Hanley JA, McNeil BJ. The meaning and use of the area under a Receiver Operating Characteristic (ROC) curve. Radiology, 1982, 143, 29–36.) −2LL, −2 Log Likelihood; pCyst C, plasma cystatin C; uACR, urine albumin to creatinine ratio; uCyst C, urine cystatin C; uNGAL, urine neutrophil gelatinase-associated lipocalin.

3.4. Prognosis Patients with AKI were characterized by longer hospitalization (6 ± 3 days vs. 5 ± 2 days; P b 0.001) compared to patients who did not develop AKI. Under the same notion, patients with AKI had an increased rate of in-hospital adverse events (41% vs. 21%; P b 0.001). AKI incidence during hospitalization did not result in renal replacement therapy in any of the patients. During the 2 year follow-up, 66 (8%) deaths were observed among the whole study population. Fifty two deaths (79%) were attributed to cardiovascular causes whereas the remaining 21% were attributed to other causes mainly cancer (data not shown). Patients with AKI had increased total mortality compared to patients not developing AKI during follow-up (22% vs. 6%; P b 0.001) (Fig. 3). This was also true for cardiovascular mortality (18% vs. 5%; P b 0.001). Re-hospitalization and revascularization rates were similar among the two groups (10% vs. 15%; P = 0.250 and 8% vs. 14%; P = 0.138 respectively) (Fig. 3); however, patients developing AKI at hospitalization were associated with increased incidence of chronic renal disease during follow-up (12% vs. 2%; P b 0.001) (Fig. 3). 4. Discussion In the present study we assessed the prognostic ability of 3 novel biomarkers of kidney injury (NGAL, Cystatin-C and IL-18) using plasma and urine concentrations regarding the incidence of AKI in patients hospitalized for AMI (STEMI or NSTEMI) irrespective of management, compared to a known cardiovascular and more easily applicable biomarker reflecting albuminuria such as spot urine ACR. We furthermore assessed indexed measures of these biomarkers relative to demographic variables (such as BMI and BSA) or laboratory variables (spot urine creatinine concentrations and plasma to urine ratios).

Table 4 Propensity score adjusted predictive accuracy of uACR levels regarding AKI occurrence. Propensity quintile score (range)

n

AKI incidence (%)

ORa

95% CI

P value

1 (0 to 0.033) 2 (N0.033 to 0.053) 3 (N0.053 to 0.096) 4 (0.096 to 0.218) 5 (N0.218 to 0.971) Overall

160 162 162 161 160 805

2.6 2.5 8.6 17.4 42.5 14.7

4.19 3.03 1.83 2.67 1.55 2.65

1.06–16.49 0.85–12.77 0.97–3.43 1.53–4.64 1.10–2.17 1.10–7.90

0.041 0.083 0.06 0.001 0.011 n/a

AKI, acute kidney injury; CI, confidence intervals, OR, odds ratio; uACR, urine albumin to creatinine ratio. a Odds ratios reflect a 1 unit change in the log uACR scale.

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Fig. 3. Follow-up survival data between patient with and without acute kidney injury. Mortality refers to total (any or cardiovascular deaths); re-hospitalization refers to hospitalization for new non-fatal recurrent myocardial infarction, for unstable or stable angina, and for heart failure; revascularization refers to any coronary revascularization (percutaneous coronary intervention or coronary artery bypass grafting); chronic renal disease refers to any of the following : hospitalization for acute renal failure; regular follow-up at outpatient renal department; transient or permanent renal replacement therapy; history of or planned kidney transplantation.

AKI occurred in 14.7% of the study population using a slightly modified definition (presence of KDIGO or RIFLE criteria during hospitalization). The uACR concentrations were characterized by good discriminating ability (AUC 0.725, 95% CI 0.676–0.774). Urine NGAL, urine Cystatin-C and plasma Cystatin-C were also capable of detecting AKI however with lesser potency (AUC 0.616 95% CI 0.562–0.669 for urine NGAL, AUC 0.573 95% CI 0.514–0.632 for urine Cystatin-C and AUC 0.571 95% CI 0.525–0.628 for plasma Cystatin-C). Furthermore, uACR levels had independent and additive diagnostic value regarding AKI incidence as shown in the multivariable and propensity score models. Sensitivity analysis also showed good diagnostic performance across a variety of clinically significant sub-groups. Using indexed measures of the novel renal injury related biomarkers yielded similar results (data not shown). Finally, examination of the performance of uACR using different definitions of AKI yielded a similarly robust predictive ability (data also not shown). Using a cut-off value of 66.7 μg/mg, our model exhibited an overall sensitivity of 68% and a specificity of 76%, corresponding to a negative predictive value of 93% and a positive predictive value of 32%. Moreover, the uACR cut-off was associated with a positive likelihood ratio (+LR) of 2.8 (95% CI 2.3–3.3) and a negative ratio (−LR) of 0.4 (95% CI 0.3– 0.6). Applying Bayes' theorem, if we consider 15% as the pre-test probability for developing AKI, the post-test probability for developing AKI, when uACR levels are ≥ 66.7 μg/mg, is doubled to 33% (95% CI, 30–37). Similarly, the post-test probability for developing AKI, when the uACR concentrations are b66.7 μg/mg, is only 7% (95% CI 6–9). Consequently, we apply uACR measurements, one out of three patients with levels N66.7 μg/ml will be expected to develop AKI during hospitalization whereas approximately every patient with levels b66.7 μg/mg will not develop AKI. In terms of clinical implications, our study shows that patients with a low probability of developing AKI

might safely undergo more complex therapeutic procedures and/or be discharged earlier. Conversely, patients at high risk should possibly necessitate withholding treatment with possible nephrotoxic sideeffects (such as rennin–angiotensin–aldosterone axis pharmaceutical agents), staged or event-tailored procedures (thus reducing the required volume of contrast agents), and also be scheduled for more detailed scrutiny during follow-up. Using uACR by the bed testing at admission may help to better identify patients most likely to benefit from renal-protective interventions (hydration, N-acetylcystein, sodium bicarbonate) if an invasive strategy has been selected. However, it must be underscored that, uACR testing appears more clinically useful in the identification of patients unlikely to develop AKI. In addition, due to the relatively low positive predictive value, withdrawal of potentially useful drugs (i.e. ACE-inhibitors, ARBs or mineralocorticoid/ aldosterone receptor antagonists) should not be based solely upon the application of uACR testing but also on clinical judgment and current published guidelines. Further studies are needed to validate the practical application of this AKI marker. Although our study was not powered to assess the association of biomarkers of renal injury with survival, it was shown that the presence of AKI is associated with longer hospitalizations, increased morbidity during hospital admission, increased mortality at follow-up and also with a chronic kidney disease sequel. Our findings were in agreement with larger studies showing that AKI is a potent predictor not only of in-hospital mortality, but also of long-term morbidity and mortality [25,26]. This underscores the clinical value of a bedside test such as uACR levels. Defining uACR concentrations at spot urine provides the clinician a practical tool to safely rule-out or predict acute kidney injury during hospitalization for AMI. The commercial availability of tests for the detection of urine albumin and its low cost give it an added advantage for routine clinical use [27].

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Our study is among the first [28] to evaluate the diagnostic value of uACR in predicting AKI in patients hospitalized with AMI since the majority of published literature had the limitation of evaluating only blood renal injury associated biomarkers and in the narrow field of primary or urgent coronary intervention [29]. In support of our findings, it was shown in animal and human models that urine albumin increases as early as 4 h following injury only in the intrinsic renal causes of AKI (ischemia–reperfusion, nephrotoxin, and rhabdomyolysis) and not in either pre-renal (secondary to endotoxin) or post-renal (obstructive uropathy) conditions; of interest, increases in urine albumin occurred before changes in serum creatinine, which increased only after significant azotemia had developed [27,30]. These observations suggest that albuminuria is specific to intrinsic causes of AKI, is not altered in pre-renal or post-renal causes and also precedes changes in circulating creatinine levels [27]. 4.1. Limitations Our results are subject to limitations inherent to the observational nature of a prospectively collected database. However, special attention was given in order to avoid relevant caveats and biases by adjusting our results to concomitant severity of illness, co-morbidities and to clinical relevant in-hospital events. In addition, the inclusion of a relatively small population with borderline power may limit the ability of statistical analyses. Our findings should be confirmed and the application of the risk score should be prospectively validated in larger multi-center trials. Moreover, we did not assess the ability of the biomarkers under investigation to predict in-hospital hemodialysis. In order to assess the prognostic ability of uACR for renal replacement therapy during hospitalization given the rarity of this adverse event, this would have required a vast (N10,000 patients) study population. Further, an inherent potential limitation of the study is that the use of ACE-inhibitors or ARBs in this patient population may affect creatinine and albumin urine concentrations. Since the application of uACR testing was performed upon admission, we have tested the effect of previous use of these agents upon its predictive ability (Appendix Table 6). Previous use of neither ACE-inhibitors nor ARBs affected significantly the predictive ability of uACR testing. However, we cannot exclude the possibility that post-admission prescription of these agents although not affecting uACR testing (admission assessment) could have an effect on AKI incidence. Finally, we did not assess specifically the impact of contrastinduced nephropathy (CIN) on AKI incidence; however the use of a more liberal definition of AKI (KDIGO or RIFLE criteria met during hospitalization) allowed us to include any possible confounding from the development of CIN. 4.2. Conclusions In summary, we assessed in an adequately sized cohort of 805 patients, the diagnostic performance of a simple and easily measurable urine biomarker, the uACR, in comparison to novel and promising relevant biomarkers (NGAL, Cystatin-C and IL-18) regarding incidence of AKI in patients being hospitalized for AMI. The old uACR biomarker showed good discriminative ability outperforming novel biomarkers with especially high negative predictive value, and thus its application may safely rule out the development of acute kidney injury. The uACR is easy to measure and to apply in clinical practice, and, may allow for a more robust, cost-effective risk assessment compared to novel biomarkers. It offers the potential to improve the management of MI patients in the Coronary Care Unit at risk of developing acute kidney injury. Conflict of interest The authors report no relationships that could be construed as a conflict of interest.

Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.ijcard.2015.06.019.

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