Cardiac Biomarkers and Acute KidneyInjury After Cardiac SurgeryEmily M. Bucholz, MPHa,b, Richard P. Whitlock, MDc, Michael Zappitelli, MD, MScd, Prasad Devarajan, MDe,John Eikelboom, MD, MBBSc,f, Amit X. Garg, MD, PhD, MA, FACPg,h, Heather Thiessen Philbrook, MMath, AStatg,Philip J. Devereaux, MD, PhDi, Catherine D. Krawczeski, MDj, Peter Kavsak, PhDk, Colleen Shorttk, Chirag R. Parikh, MD, PhDl,m,for the TRIBE-AKI Consortium

abstractOBJECTIVES: To examine the relationship of cardiac biomarkers with postoperative acute kidneyinjury (AKI) among pediatric patients undergoing cardiac surgery.

METHODS: Data from TRIBE-AKI, a prospective study of children undergoing cardiac surgery,were used to examine the association of cardiac biomarkers (N-type pro–B-type natriureticpeptide, creatine kinase-MB [CK-MB], heart-type fatty acid binding protein [h-FABP], andtroponins I and T) with the development of postoperative AKI. Cardiac biomarkers werecollected before and 0 to 6 hours after surgery. AKI was defined as a $50% or 0.3 mg/dLincrease in serum creatinine, within 7 days of surgery.

RESULTS: Of the 106 patients included in this study, 55 (52%) developed AKI after cardiac surgery.Patients who developed AKI had higher median levels of pre- and postoperative cardiacbiomarkers compared with patients without AKI (all P , .01). Preoperatively, higher levels ofCK-MB and h-FABP were associated with increased odds of developing AKI (CK-MB: adjusted oddsratio 4.58, 95% confidence interval [CI] 1.56–13.41; h-FABP: adjusted odds ratio 2.76, 95% CI1.27–6.03). When combined with clinical models, both preoperative CK-MB and h-FABP providedgood discrimination (area under the curve 0.77, 95% CI 0.68–0.87, and 0.78, 95% CI 0.68–0.87,respectively) and improved reclassification indices. Cardiac biomarkers collected postoperativelydid not significantly improve the prediction of AKI beyond clinical models.

CONCLUSIONS: Preoperative CK-MB and h-FABP are associated with increased risk of postoperativeAKI and provide good discrimination of patients who develop AKI. These biomarkers may beuseful for risk stratifying patients undergoing cardiac surgery.

WHAT’S KNOWN ON THIS SUBJECT: Acute kidneyinjury (AKI) occurs in up to 50% of children aftercardiopulmonary bypass and is associated withadverse outcomes. Renal biomarkers have beenshown to predict postoperative AKI, but fewstudies have examined cardiac biomarkers forrisk classification.

WHAT THIS STUDY ADDS: Preoperative levels ofcreatine kinase-MB and heart-type fatty acidbinding protein are strongly associated with thedevelopment of postoperative AKI after pediatriccardiac surgery and can be used to improvepreoperative clinical risk prediction.

lDepartment of Internal Medicine, aSchool of Medicine, and bDepartment of Chronic Disease Epidemiology, School ofPublic Health, Yale University, New Haven, Connecticut; cDivision of Cardiac Surgery, Population Health Research Institute,and iDepartments of Clinical Epidemiology and Biostatistics, fMedicine, and kPathology and Molecular Medicine,McMaster University, Hamilton, Ontario, Canada; dDivision of Nephrology, Department of Pediatrics, Montreal Children’sHospital, McGill University Health Centre, Montreal, Quebec, Canada; eDepartment of Nephrology, Cincinnati Children’sHospital Medical Center, Cincinnati, Ohio; gDivision of Nephrology, Department of Medicine, and hDepartment ofEpidemiology and Biostatistics, University of Western Ontario, London, Canada; jDivision of Pediatric Cardiology, LucilePackard Children’s Hospital, Stanford University School of Medicine, Palo Alto, California; and mClinical EpidemiologyResearch Center, Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut

Ms Bucholz conceptualized and designed this study, analyzed the data, and drafted the initialmanuscript; Drs Whitlow and Eikelboom and Ms Shortt supervised data collection, and reviewedand revised the manuscript; Drs Zappitelli, Devarajan, Garg, Devereaux, Krawczeski, and Kavsakdesigned the initial Translational Research Investigating Biomarker Endpoints cohort study,reviewed and revised the manuscript, and obtained funding for the study; Ms Thiessen-Philbrooksupervised data collection, analyzed the data, and reviewed and revised the manuscript; Dr Parikhconceptualized and designed this study, designed the initial Translational Research InvestigatingBiomarker Endpoints cohort study, reviewed and revised the manuscript, and obtained funding forthe study; and all authors approved the final manuscript as submitted.

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Acute kidney injury (AKI) occurs inup to 50% of children aftercardiopulmonary bypass (CPB),1,2

and is independently associated withadverse outcomes, including longerlengths of stay (LOSs), prolongedmechanical ventilation, and highermortality.3,4 Early identification ofpatients at risk for developing AKImay allow implementation ofstrategies to reduce related morbidityand mortality.5,6

Preliminary evidence suggests thatbiomarkers can help to identifypatients at risk for postoperativeAKI.2 In both pediatric and adultpopulations, renal biomarkers, suchas serum cystatin C and neutrophilgelatinase-associated lipocalin(NGAL), have been shown to predictpostoperative AKI and improve riskclassification when combined withother clinical models.7–11 In addition,studies in adults have shown thatpre- and postoperative levels ofcardiac biomarkers, such as B-typenatriuretic peptide (BNP), stronglypredict postoperative AKI risk.12 Only2 studies in children have examinedthe prognostic value of BNP onpostoperative AKI.13,14 Both foundthat although BNP was not anindependent predictor of AKI, it wasassociated with longer intubationtimes and higher postoperativemortality. To our knowledge, nostudies have examined other cardiacbiomarkers, such as troponins,creatine kinase-MB (CK-MB), orheart-type fatty acid binding protein(h-FABP), in children or adults.Preoperative elevations in cardiacbiomarkers may reflect the severity ofthe underlying heart disease,15

whereas postoperative levels ofcardiac biomarkers reflect thecomplexity of the surgery and thedegree of intraoperative cardiacdamage, which can increase patients’risk of postoperative complications,such as AKI.

The purpose of this study wastwofold. First we evaluated theprognostic utility of 5 cardiac

biomarkers (N-type [NT] pro-BNP,CK-MB, h-FABP, high-sensitivitycardiac troponin T [hs-cTnT], andcardiac troponin I [cTnI]), measuredpreoperatively and immediately aftersurgery in children, to predictpostoperative AKI risk and LOS. Wethen determined whether thesebiomarkers provided additionalbenefit for predicting AKI above thatafforded by clinical models.

METHODS

Data from the Translational ResearchInvestigating Biomarker Endpoints inAcute Kidney Injury consortiumstudy (TRIBE-AKI) were used forthese analyses.16,17 In brief, thistranslational study was designed tovalidate novel kidney injurybiomarkers by using a prospectivecohort design with serial samplecollections on 3 consecutive days.Participants included children aged1 month to 18 years undergoingcardiac surgery at 3 academichospitals. To increase the numberof children at risk for AKI, weoversampled children undergoingrisk adjustment of congenital heartsurgery-1 (RACHS-1) category $2surgeries. Children were recruitedpreoperatively and followedpostoperatively until discharge(n = 319). Institutional review boardapproval was obtained at eachparticipating center, and all patientsprovided written informed consent. Tocapture cardiac biomarker kineticsaround the time of cardiac surgery,only patients with a full set of pre- andpostoperative samples were included(n = 106). Subject selection was notbased on any clinical criteria.

Data Collection

Data on patient demographics andmedical history were recorded beforesurgery. Details about the surgicalprocedure (eg, type of cardiacabnormality, procedure, bypass time,elective or urgent, and severity) wereobtained from the medical record byusing standardized definitions of theSociety of Thoracic Surgeons data

collection tool. Severity of conditionand surgical risk were evaluatedusing the RACHS-1 method.18,19

Venous blood samples were collectedpreoperatively, within 6 hours aftersurgery, and on postoperative days 2and 3. Blood was collected in EDTAtubes and centrifuged to separateplasma, divided into bar-coded0.5-mL cryovials, and stored at –80°C.One vial from each time-point wasused for biomarker measurementswith a single freeze-thaw. Biomarkerswere measured with a Rocheautomated analyzer (Roche Elecsys2010; Roche Diagnostics, Basel,Switzerland) for NT pro-BNP (pmol/L)(coefficient of variation [CV] range3.6%–7.7%) and hs-cTnT (ng/L)(CV range 2.5%–10.5%), the BeckmanCoulter Access II instrument(Beckman Coulter, Brea, CA) for theAccuTnI assay cTnI (µg/L) (CV range5.4%–20%) and CK-MB (µg/L)(CV range 2.7%–8.2%), and theEvidence Investigator CytokineCustom Array (Randox Crumlin,United Kingdom) for h-FABP (µg/L)(CV 17%). Preoperative serumcreatinine (SCr) was measured aspart of routine clinical care usingmodified Jaffe or enzymatic assays,and preoperative glomerularfiltration rates (GFRs) were estimatedby using the Schwartz equation.

Outcome Definition

The primary outcome in this studywas development of AKI, which wasdefined as rise in SCr of $50% or0.3 mg/dL from preoperative baselinewithin the first 7 days after surgery.Severe AKI was defined as eithera doubling of creatinine or dialysisrequirement.20–22 Secondaryoutcomes included in-hospitalmortality, hospital and intensive careunit (ICU) LOS, and time toextubation.

Statistical Analyses

Sample characteristics werecompared among patients whodeveloped severe AKI, mild AKI, andno AKI by using analysis of variance

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or Kruskal-Wallis tests for continuousvariables and x2 or Fisher’s exact testfor categorical variables. Medianbiomarker values were plotted andcompared across AKI groups by usingKruskal-Wallis tests for NT pro-BNP,cTnI, hs-cTnT, CK-MB, and h-FABP.Colinearity between biomarkers wasassessed by using scatterplot andcorrelation matrices.

We evaluated unadjusted associationsbetween cardiac biomarkers and thedevelopment of AKI by using logisticregression. Because 95% of AKI casesoccurred in the first 2 postoperativedays, we focused on cardiacbiomarkers collected preoperativelyand immediately postoperatively(within 6 hours of surgery).Biomarker levels were introducedinto the models as logtransformations to normalize thedistributions of these values. Analyseswere repeated adjusting fordemographic and preoperativecharacteristics, including patient age,preoperative estimated GFR (eGFR)percentile, hospital site, RACHS-1category $3, and CPB time .120minutes. Covariates for the modelswere selected by using a combinationof previously reported AKI predictorsin this cohort16,23 and significancetesting (P , .1).

We calculated the area under thereceiver operating characteristic(ROC) curve (AUC) for eachbiomarker alone to determine itsability to discriminate betweenpatients who did and did not developAKI. Contingency tables were used todetermine the optimal cutpoint foreach biomarker. We used 2approaches to assess the added valueof biomarkers in the clinicalprediction models. First, we evaluatedincremental changes in the AUC whenindividual biomarkers were added tothe clinical model. This approach,however, is often an insensitivemeasure of the ability of a newmarker to add value to a preexistingmodel because the c-statisticminimally moves after a few powerful

risk factors are already in the model.Accordingly, we also calculated thecontinuous net reclassificationimprovement (NRI) and integrateddiscrimination improvement (IDI)indices. The NRI indicates how muchmore frequently appropriatereclassification of AKI risk occursthan inappropriate reclassificationwith use of the model containing thebiomarker, whereas the IDI measureshow far individuals move on averagealong the continuum of predictiverisk. These measures have advantagesover the AUC because they directlyquantify the appropriateness andamount of reclassification whenbiomarker values are added to theclinical prediction models.24–26 Boththe NRI and IDI have been usedextensively in cardiovascularoutcomes and nephrologyresearch.27–32

We then evaluated the associationbetween these biomarkers with ICU

and hospital LOS by using logisticregression. ICU and hospital stayswere dichotomized by using themedian value of each (3 days for ICUstay and 7 days for hospital stay) toquantify the relation betweenelevations in cardiac biomarkers andrisk of prolonged hospitalization byusing risk measures. Models wereadjusted for patient and clinicalcharacteristics as well as AKI statusto determine whether AKI explainedthe longer LOS in patients with highcardiac biomarker values.

RESULTS

Of the 106 pediatric patients enrolledin this study, 23 (21.7%) developedsevere AKI and 32 (30.2%) developedmild AKI after cardiac surgery(Table 1). Patients who developedAKI tended to be younger and to havelower preoperative weights. Patientswho developed severe AKI had longerhospital and ICU LOS than patients

TABLE 1 Patient Characteristics AKI Status

No AKI,n = 51

Mild AKI,n = 32

Severe AKI,n = 23

P

Demographic characteristics and medical historyAge, mo, mean (SD) 53.1 (64) 33.2 (45) 13.3 (23) .01Male gender, n (%) 28 (55) 20 (63) 13 (57) .79Weight, kg, mean (SD) 18.8 (22) 12.3 (12) 7.0 (5) .01Previous cardiothoracic surgery, n (%) 20 (42) 16 (52) 13 (57) .45

Preoperative characteristicsPreoperative eGFR (mL/min per 1.73 m2),

mean (SD)82 (22) 91 (35) 101 (33) .04

Preoperative eGFR (percentile), mean (SD) 45 (33) 60 (35) 76 (30) .001Perioperative characteristicsUrgency of surgery, n (%) .24Elective 41 (80) 30 (94) 19 (83)Urgent 10 (20) 2 (6) 4 (17)

Type of surgery, n (%) .92Septal defect repair 14 (28) 9 (28) 6 (26)Inflow/outflow tract or valve procedure 15 (29) 10 (31) 5 (22)Combined procedure 22 (43) 13 (41) 12 (52)

RACHS-1 score, n (%) .542 24 (48) 15 (47) 12 (52)3 23 (46) 17 (53) 9 (39)4 3 (6) 0 (0) 2 (9)

CPB time, min, mean (SD) 118 (64) 101 (40) 134 (80) .14CPB time .120 min, n (%) 19 (37.3) 9 (28.1) 13 (56.5) .10Cross-clamp time, min, mean (SD) 57 (51) 46 (41) 61 (40) .47

OutcomesIn-hospital mortality, n (%) 0 (0) 0 (0) 2 (9) .046ICU LOS, d, median (IQR) 3.0 (2.0–4.0) 3.0 (2.0–5.5) 5.5 (3.0–9.0) .003Hospital LOS, d, median (IQR) 6.0 (5.0–10.0) 5.5 (4.0–12.5) 12.0 (9.0–15.0) .002

IQR, interquartile range.

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with mild or no AKI. Only 2 deathsoccurred in-hospital, both amongpatients with severe AKI.

The trajectories of the 5 cardiacbiomarkers during the first 3 days ofhospitalization stratified by AKIstatus are displayed in Fig 1. Markersof cardiac injury (CK-MB, h-FABP,cTnI, and hs-cTnT) peaked within6 hours after surgery, whereasmarkers of cardiac function (NTpro-BNP) peaked on day 2. Acrossall 4 time-points, median levels ofcardiac biomarkers were higheramong patients who developed severeAKI compared with mild or no AKI.

Preoperative Cardiac Biomarkers

Fig 2 shows the distributions of the5 preoperative biomarkers stratified

by AKI status. There were significantdifferences across AKI groups inpreoperative CK-MB, hs-cTnT, andh-FABP levels (P = .001, P = .048,and P , .001, respectively), butpreoperative levels of NT pro-BNPand cTnI were comparable acrossgroups (P = .2 and P = .3,respectively).

Higher preoperative levels of CK-MBand h-FABP were associated withhigher crude odds of developing AKI(Table 2). After adjustment forpatient characteristics, theassociation of preoperative CK-MBand h-FABP with development ofpostoperative AKI remainedsignificant. A 1-unit increase in logCK-MB was associated with a fivefoldincrease in the odds of developing

AKI after surgery. Similarly, a 1-unitincrease in log h-FABP was associatedwith a threefold increase in the oddsof developing AKI. No otherpreoperative biomarkers wereassociated with the development ofAKI after adjustment for patientcharacteristics (all P . .1).

ROC analyses revealed preoperativeCK-MB and h-FABP to be candidatebiomarkers with high discriminatoryvalue for predicting AKI (Fig 3 Aand B). When used alone, preoperativeCK-MB and h-FABP had relatively highAUCs (CK-MB: AUC 0.70, 95%confidence interval [CI] 0.60–0.81;h-FABP: AUC 0.70, 95% CI 0.60–0.81)(Table 3). Adding preoperative CK-MBor h-FABP to the clinical model didnot significantly improve the AUC of

FIGURE 1Median biomarker values by AKI group collected preoperatively and 0 to 6 hours, 2 days, and 3 days after surgery.

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the clinical model (P = .60 and P = .51,respectively); however, bothbiomarkers were associated withsignificant NRI and IDI values(Table 4). Given that changes in theAUC are insensitive when comparingnested models, it is not surprising thatpreoperative CK-MB and h-FABPadded significant discriminative valueto the clinical model when assessed byNRI and IDI, but increased the AUConly slightly in ROC analyses. Theoptimal cutoff for detectingpostoperative AKI was 2.9 µg/L(sensitivity 64.2%, specificity 64.6%)for preoperative CK-MB and 2.6 pg/mL(sensitivity 68%, specificity 68.8%) forpreoperative h-FABP (Table 5).

Secondary analyses showed thathigher preoperative hs-cTnT, CK-MB,and h-FABP were associated withlonger ICU and hospital staysindependent of AKI status (Table 6).Preoperative NT pro-BNP and cTnIwere associated with longer ICU butnot hospital stays. Of the 5preoperative biomarkers tested,CK-MB and h-FABP exhibited thestrongest association with LOS.

Postoperative Day 1 CardiacBiomarkers

Biomarker distributions onpostoperative day 1 are presented inFig 4. Median levels of biomarkersdiffered significantly across AKI

groups for all biomarkers exceptCK-MB (P, .05 for NT pro-BNP, cTnI,hs-cTnT, and h-FABP). In unadjustedanalyses, only postoperative hs-cTnTwas borderline associated with thedevelopment of AKI, but adjustmentfor patient and operativecharacteristics attenuated thisrelationships (Table 2). All 5biomarkers were nonsignificant inmultivariable analyses.

Postoperative hs-cTnT yielded thehighest AUC for predicting AKI inboth unadjusted and adjusted ROCanalyses (Fig 3C). When used alone,postoperative hs-cTnT had amoderately high AUC (0.62, 95%CI 0.51–0.73), but addition of hs-cTnT

FIGURE 2Distribution of serum values of preoperative cardiac biomarkers by AKI status.

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to the clinical model did not significantlyimprove the AUC of the clinical model(P = .74) (Table 3). Similarly, neither theNRI nor IDI proved significant forhs-cTnT or any other postoperativebiomarkers (all P . .05) (Table 4).

In secondary analyses, higherpostoperative levels of NT pro-BNPpredicted longer ICU and hospitalLOS, which persisted even afteradjustment for AKI status (Table 6).Postoperative hs-cTnT and h-FABP

also were associated with increasedICU but not hospital stay.

DISCUSSION

In this multicenter, prospective studyof pediatric patients undergoingcardiac surgery for congenitalconditions, we found thatpreoperative CK-MB and h-FABPwere strong and independentpredictors of postoperative AKI. A1-unit increase in preoperative logCK-MB was associated with a fivefoldincrease in the odds of AKI, anda 1-unit elevation in preoperativelog h-FABP increased the odds ofdeveloping AKI by 3 times. We foundthat a single measurement of each ofthese biomarkers provided gooddiscrimination between patients whodeveloped AKI and those who didnot when used alone and incombination with clinical models.Previous studies examining theassociation of preoperative renalbiomarkers with postoperative AKIhave yielded AUC values ranging0.44 to 0.72 for NGAL, cystatin C,and kidney injury molecule-1.33–36

TABLE 2 Logistic Regression Models for Prediction Development of AKI by Using Pre- andPostoperative Biomarker Values

Unadjusted Adjusteda

OR (95% CI) P OR (95% CI) P

Preoperative biomarkersNT Pro-BNP, n = 105 1.08 (0.84–1.38) .55 0.94 (0.68–1.30) .71cTnI, n = 100b 1.01 (0.68–1.49) .97 092 (0.56–1.49) .73hs-cTnT, n = 106c 1.22 (0.92–1.62) .17 1.09 (0.75–1.57) .65CK-MB, n = 100 4.19 (1.85–9.48) ,.001 5.09 (1.64–15.82) .005h-FABP, n = 98 2.72 (1.48–5.01) .001 3.02 (1.35–6.75) .01

First postoperative biomarkersNT Pro-BNP, n = 103 1.20 (0.94–1.55) .15 1.15 (0.81–1.64) .43cTnI, n = 101 1.29 (0.95–1.77) .11 1.14 (0.73–1.80) .56hs-cTnT, n = 106 1.38 (0.99–1.94) .06 1.26 (0.75–2.11) .38CK-MB, n = 100 1.41 (0.93–2.14) .11 1.04 (0.58–1.88) .90h-FABP, n = 105 1.29 (0.86–1.92) .21 1.18 (0.69–2.05) .54

OR, odds ratio.All values of biomarkers have been log transformed.a Preoperative biomarker models adjusted for age (years), site, preoperative eGFR percentile, and RACHS-1 category $3.Postoperative biomarker models adjusted for age (years), site, CPB time .120 minutes, preoperative eGFR, and RACHS-1category $3.b Of the 100 patients with available cTnI values, only 45 had detectable values (.0.01 mg/L). Sensitivity analyses using onlypatients with detectable cTnI values showed similar results to those in the table (unadjusted OR 0.89, 95% CI 0.53–1.50;adjusted OR 0.94, 95% CI 0.49–1.80).c Of the 106 patients with available hs-cTnT values, only 68 had detectable values (.3 ng/L). Sensitivity analyses using onlypatients the detectable hs-cTnT values showed similar results to those in the table (unadjusted OR 1.33, 95% CI 0.86–2.04;adjusted OR 1.26, 95% CI 0.76–2.10).

FIGURE 3Receiver operating curves for biomarkers alone and combined with clinical model. A, Preoperative CK-MB. B, Preoperative h-FABP. C, Postoperative hs-cTnT.Solid blue lines are for models containing biomarkers alone, red dotted lines are for clinical models, and punctuated green lines are for models withbiomarker and clinical variables.

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Additionally, most clinical risk modelsfor the prediction of postoperativeAKI have produced AUCs in the rangeof 0.65 to 0.83, depending on thedefinition of AKI and the number ofclinical parameters included.11,37–40

When placed in the context ofprevious studies, our findings suggestthat CK-MB and h-FABP may beuseful biomarkers for assessingpreoperative risk of AKI in childrenundergoing cardiac surgery. However,larger follow-up studies are neededto verify the results of this study andconfirm the utility of thesebiomarkers as risk stratification toolsfor AKI.

This is the first prospective studyanalyzing the use of multiple cardiac

biomarkers to identify patients athigh risk of postoperative AKI inchildren. Although several studieshave evaluated the utility ofbiomarkers for AKI risk stratificationafter cardiac surgery in children, mostof these studies have focused on renalbiomarkers.41–45 To our knowledge,only 1 study in adults and 2 studies inchildren have examined cardiacbiomarkers for the prediction of AKIafter cardiac surgery.12–14 However,in all 3 studies, the authors examinedonly BNP and observed only modestassociations between biomarkerlevels and postoperative AKI. In thepediatric studies, Cantinotti et al13

found that pre- and postoperativeBNP were associated with AKI in

unadjusted analyses but lost theirpredictive value once urinary NGALand other conventional risk factorswere added to the model. Similarly,Hornik et al14 found thatpreoperative BNP was not associatedwith increased risk of postoperativeAKI. Our results for NT pro-BNP areconsistent with those reportedpreviously.

Our study expands the currentliterature by examining additionalcardiac biomarkers in the predictionof AKI, including markers of cardiacfunction (NT pro-BNP) and cardiacinjury (cTnI, hs-cTnT, CK-MB, andh-FABP). In our study, preoperativeCK-MB and h-FABP outperformedNT pro-BNP in predicting postoperativeAKI as assessed by larger odds ratiosand higher AUCs. We also found thathigher preoperative levels of CK-MBand h-FABP were associated withextended ICU and hospital LOS inaddition to AKI, which is consistentwith previous studies.46–49 However,addition of AKI to these models didnot attenuate the risk ratios,suggesting that the relationshipbetween these biomarkers andextended LOS is mediated by factorsother than AKI. These findingssuggest that CK-MB and h-FABP maybe superior to BNP in evaluating riskof postoperative AKI in children.

Several mechanisms may explain therelationship between preoperativeCK-MB and h-FABP withpostoperative AKI. In children withcongenital heart disease, elevations inCK-MB and h-FABP are most likelyreleased from damaged myocardium,suggesting that patients with higherpreoperative levels may have moresevere underlying cardiac disease.50

These patients may have reducedcardiac reserve at baseline and maybe at higher risk of hemodynamicinstability in the perioperativesetting, which may place them atgreater risk of postsurgicalcomplications, including AKI.Additionally, patients with moresevere baseline disease may require

TABLE 3 ROC Analysis: AUC for Prediction of AKI

Unadjusted AUC (95% CI) Adjusted AUC (95% CI)

Biomarker Only Biomarker + Clinical Model

Preoperative biomarkersClinical modela — 0.75 (0.66–0.85)NT pro-BNP 0.53 (0.42–0.65) 0.77 (0.67–0.86)cTnI 0.47 (0.37–0.58) 0.76 (0.66–0.85)hs-cTnT 0.57 (0.46–0.48) 0.76 (0.67–0.85)CK-MB 0.70 (0.60–0.81) 0.79 (0.70–0.88)h-FABP 0.70 (0.60–0.81) 0.80 (0.71–0.89)

Postoperative biomarkersClinical modelb — 0.76 (0.67–0.85)NT-pro BNP 0.57 (0.46–0.68) 0.77 (0.68–0.86)cTnI 0.61 (0.50–0.72) 0.77 (0.67–0.86)hs-cTnT 0.62 (0.51–0.73) 0.78 (0.69–0.87)CK-MB 0.61 (0.50–0.72) 0.74 (0.65–0.84)h-FABP 0.56 (0.45–0.67) 0.76 (0.67–0.85)

a Preoperative clinical models adjusted for age (years), site, preoperative eGFR percentile, and RACHS-1 category $3.b Postoperative clinical models adjusted for age (years), site, CPB time . 120min, preoperative eGFR percentile, andRACHS-1 category $3.

TABLE 4 Comparison of Models With Cardiac Biomarkers to Clinical Model by UsingReclassification Indices

NRI IDI

Estimate (SE) P Estimate (SE) P

Preoperative biomarkersNT pro-BNP 20.13 (0.20) .53 ,0.01 (0.01) .73cTnI 0.09 (0.20) .67 ,0.01 (0.01) .98hs-cTnT 0.11 (0.20) .56 0.01 (0.01) .40CK-MB 0.46 (0.20) .02 0.05 (0.02) .03h-FABP 0.40 (0.20) .04 0.08 (0.03) .01

Postoperative biomarkersNT pro-BNP 20.25 (0.20) .21 0.01 (0.01) .27cTnI 0.31 (0.20) .13 0.03 (0.02) .08hs-cTnT 0.37 (0.20) .06 0.03 (0.02) .08CK-MB 0.30 (0.20) .13 0.01 (0.01) .47h-FABP 0.12 (0.20) .56 0.01 (0.01) .50

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longer cross-clamping and CPB times,which can independently increase therisk of postoperative AKI.16

Although the proposed mechanismsare plausible, our data showed mixedresults for several of theseexplanations. For example, neithercross-clamping nor CPB duration wasassociated with postoperative AKI in

our sample. Additionally, it is unclearwhy only 2 markers of cardiac injury(CK-MB and h-FABP) predicted AKIbut not preoperative cardiactroponins. One potential explanationis the large number of children withundetectable levels of preoperativecTnI (n = 55) and hs-cTnT (n = 38).Even with low levels of cardiac

damage, the range of cardiac troponinvalues may not have been wideenough to detect subtle differencesbetween children who developedpostoperative AKI and those who didnot. In fact, previous studies also haveshown that cardiac troponin levelsare often undetectable in childrenwith congenital heart abnormalitiesor other cardiac diseases such asKawasaki,51–53 but CK-MB levels arefrequently elevated and vary byseverity and type of cardiacdefect.50,54 Thus, it is possible thatthe kinetics of the cardiac troponins,CK-MB, and h-FABP differ in children.

The high performance of CK-MB andh-FABP in our study may be partlyexplained by the fact that we limitedour sample to only higher-riskpatients undergoing complex cardiacsurgeries. Larger studies are neededto replicate our findings in patientswith varying levels of surgical riskand to determine the cost-effectiveness of monitoring CK-MBand h-FABP preoperatively. Recently,the concept of a renal angina indexhas been proposed to directbiomarker assessment in noncardiacICU patients who meet a certain levelof risk and demonstrate signs ofevolving AKI.55,56 This concept hasarisen due to concerns thatwidespread diagnostic testing mayyield high numbers of false-positiveresults.57 In the cardiac surgerysetting, a similar parallel would beclinical risk scores. In TRIBE-AKI, wefound that using a simple baselineclinical risk model, including age,gender, RACHS-1 category,preoperative eGFR percentile, CPBtime, and study site, predictedpostoperative AKI with AUC 0.77 andwas improved by adding biomarkersto this model.36 Similar risk modelsmay help to increase the specificityand positive predictive value ofbiomarker screening tests.58

Before the use of these biomarkerscan be incorporated into routineclinical practice, however, AKIprevention strategies must be

TABLE 5 Biomarker Cutoff Values for Predicting AKI

Biomarker Value Sensitivity Specificity PPV NPV

Preoperative CK-MB (mg/L)90% Sensitivity 1.6 92.5 27.1 58.3 76.5Optimal 2.9 64.2 64.6 66.7 62.090% Specificity 4.5 30.2 93.8 84.2 54.9

Preoperative FABP (mg/L)90% Sensitivity 1.3 90.0 29.2 57.0 73.7Optimal 2.6 68.0 68.8 69.4 67.490% Specificity 4.8 26.0 91.7 76.5 54.3

Postoperative hs-cTnT (ng/L)90% Sensitivity 344 90.9 9.8 52.1 50.0Optimal 2668 58.2 58.8 60.4 56.690% Specificity 7737 18.2 90.2 66.7 50.6

NPV, negative predictive value; PPV, positive predictive value.

TABLE 6 Logistic Regression Models for ICU LOS.3 Days and hospital LOS.7 Days (Median LOS)

Unadjusted Adjusted Adjusted for AKI

OR (95% CI) P OR (95% CI) P OR (95% CI) P

Preoperative valuesa

ICU LOSNT Pro-BNP 1.47 (1.12–1.93) .004 1.46 (1.07–1.98) .02 1.50 (1.09–2.06) .01cTnI 2.25 (1.28–3.97) .005 2.16 (1.24–3.76) .007 2.24 (1.27–3.95) .005hs-cTnT 1.70 (1.23–2.34) .001 1.72 (1.17–2.51) .005 1.73 (1.18–2.54) .005CK-MB 2.60 (1.24–5.42) .01 2.97 (1.12–7.89) .03 2.50 (0.92–6.85) .07h-FABP 2.76 (1.47–5.15) .002 3.20 (1.52–6.77) .002 2.96 (1.37–6.40) .006

Hospital LOSNT Pro-BNP 1.28 (0.99–1.66) .06 1.29 (0.94–1.77) .11 1.32 (0.96–1.83) .09cTnI 1.41 (0.92–2.16) .11 1.45 (0.92–2.29) .11 1.48 (0.93–2.33) .10hs-cTnT 1.44 (1.07–1.93) .02 1.46 (1.01–2.10) .04 1.45 (1.01–2.10) .05CK-MB 3.11 (1.45–6.70) .004 3.47 (1.27–9.52) .02 3.27 (1.16–9.28) .03h-FABP 2.38 (1.32–4.30) .004 2.42 (1.17–5.04) .02 2.26 (1.06–4.82) .03

Postoperative valuesb

ICU LOSNT Pro-BNP 1.81 (1.33–2.46) ,.001 1.84 (1.26–2.69) .002 1.84 (1.25–2.71) .002cTnI 1.42 (1.03–1.96) .03 1.33 (0.85–2.07) .21 1.31 (0.84–2.04) .24hs-cTnT 1.67 (1.17–2.39) .005 1.69 (1.00–2.85) .05 1.64 (0.98–2.76) .06CK-MB 1.36 (0.89–2.07) .15 1.11 (0.63–1.94) .73 1.11 (0.62–1.96) .73h-FABP 2.00 (1.28–3.12) .002 2.10 (1.18–3.75) .01 2.11 (1.17–3.82) .01

Hospital LOSNT Pro-BNP 1.39 (1.07–1.81) .01 1.53 (1.06–2.19) .02 1.52 (1.06–2.18) .02cTnI 1.13 (0.83–1.54) .43 0.96–0.61–1.51) .87 0.95 (0.60–1.50) .81hs-cTnT 1.44 (1.07–1.93) .02 1.25 (0.76–2.06) .37 1.23 (0.75–2.03) .41CK-MB 1.14 (0.75–1.71) .54 0.92 (0.51–1.66) .68 0.92 (0.51–1.67) .79h-FABP 1.47 (0.98–2.22) .07 1.49 (0.86–2.58) .16 1.47 (0.85–2.55) .17

OR, odds ratio.All biomarkers have been log transformed.a Adjusted models are adjusted for age (years), site, preoperative eGFR percentile, and RACHS-1 category $3.b Adjusted models are adjusted for age (years), site, CPB time .120 minutes, preoperative eGFR percentile, and RACHS-1category $3.

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developed and shown to beefficacious in patients with elevatedbiomarkers. If replicated, our studyhas important implications fordecisions regarding timing of surgeryand for identifying patients at risk forAKI before surgery that may benefitfrom certain interventions. Althoughthere are no established preventivemeasures or early treatments for AKIin children undergoing cardiacsurgery, risk stratification may help toavoid AKI by minimizing CPB time,avoiding nephrotoxic medications,and optimizing hemodynamicsthrough fluid management andinotropic support. In addition, it maybe worth considering postponing the

surgery in children at high riskundergoing elective procedures.2

This study has a few limitations. First,despite being a multicenterinternational study, we examined onlypatients with RACHS-1 category 2 to4, which may limit thegeneralizability of our results.Although our results are compelling,they need to be validated by largerprospective studies that includechildren with varying levels ofsurgical risk at baseline. Second, weincluded only patients for whom bothpre- and postoperative samples hadbeen collected. This approach mayhave been subject to sampling bias ifbiomarker collection was associated

with disease severity; however, thedistribution of RACHS-1 scores in oursample was similar to that in theoverall pediatric cohort. Third, thereis no true gold standard for AKI.Therefore, we based the definition ofAKI on elevations in SCr, which maybe a potentially flawed outcomevariable for evaluating theperformance of novel biomarkers. Itis possible that this study may haveyielded different results if a differentdefinition had been used. Fourth,TRIBE does not contain informationon a few potentially importantvariables, including preoperativemechanical ventilation, inotropicsupport, and diuretic use, which may

FIGURE 4Distribution of serum values of postoperative day 1 cardiac biomarkers by AKI status.

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have confounded the relationshipbetween cardiac biomarkers andpostoperative AKI. Nevertheless, weadjusted for RACHS-1 category as anoverall assessment of preoperativeillness severity. Fifth, neonates wereexcluded from this study becauseprevious studies have shown thatusing SCr to diagnose AKI in neonatescan be problematic.59 Neonates havehigher baseline levels of SCr at birth,which gradually decline during thefirst few weeks of life due to ongoingrenal functional development.60,61

These changes, combined with thelower muscle mass in neonates, makeit difficult to interpret changes in SCrwith AKI. Thus, our findings are not

generalizable to children ,1 year ofage, and future multicenter studiesshould be performed in that agegroup. Finally, the number of patientsexperiencing dialysis or death in ourstudy was low, limiting our ability toevaluate the association ofbiomarkers with these outcomes.

CONCLUSIONS

In summary, preoperative levels ofCK-MB and h-FABP were highlyassociated with the development ofAKI and prolonged LOS andmechanical ventilation after pediatriccardiac surgery. Both biomarkersimproved clinical risk prediction of

postoperative AKI; however,additional studies are needed toconfirm these findings in patientswith varying levels of surgical riskand to evaluate the cost-effectivenessof obtaining these biomarkers forrisk-stratification purposes. Theability to predict postoperativesequelae using preoperative clinicaldata has important implications foridentifying patients who maybenefit from earlier interventionsfor AKI. If replicated by futurestudies, these findings suggest thatpreoperative measurement ofCK-MB and h-FABP may be useful forrisk stratifying patients beforesurgery.

www.pediatrics.org/cgi/doi/10.1542/peds.2014-2949

DOI: 10.1542/peds.2014-2949

Accepted for publication Jan 7, 2015

Address correspondence to Chirag R. Parikh, MD, PhD, Department of Internal Medicine, Yale University School of Medicine, 60 Temple Street, Suite 6C, New Haven,

CT 06510. E-mail: chirag.parikh@yale.edu

PEDIATRICS (ISSN Numbers: Print, 0031-4005; Online, 1098-4275).

Copyright © 2015 by the American Academy of Pediatrics

FINANCIAL DISCLOSURE: Drs Devarajan and Devereaux are co-inventors on patents submitted for the use of neutrophil gelatinase-associated lipocalin as

a biomarker of kidney disease. Dr Devereaux has received research grants from Abbott Diagnostics and Roche Diagnostics. Dr Kavsak has received honorarium and

research grants from Abbott Laboratories, Beckman Coulter, Ortho Clinical Diagnostics, Randox Laboratories, and Roche Diagnostics; he has consulted for Abbott

Laboratories and Roche Diagnostics. The other authors have indicated they have no financial relationships relevant to this article to disclose.

FUNDING: Supported by the National Institutes of Health (NIH) (R01HL085757 to Dr Parikh) to fund the TRIBE-AKI Consortium to study novel biomarkers of acute

kidney injury in cardiac surgery and the O’Brien Center Award from the National Institute of Diabetes and Digestive and Kidney Diseases (P30DK079310). Dr Parikh is

also supported by NIH (K24DK090203). Drs Garg and Parikh are also members of the NIH-sponsored Assess, Serial Evaluation, and Subsequent Sequelae in Acute

Kidney Injury Consortium (U01DK082185). Biomarker measurements were supported by Roche Diagnostics, Beckman Coulter, and Randox Laboratories. The

granting agencies and Roche Diagnostics, Beckman Coulter, and Randox Laboratories did not participate in the design and conduct of the study; collection,

management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. Funded by the National Institutes of Health (NIH).

POTENTIAL CONFLICT OF INTEREST: Drs Devarajan is a co-inventor on patents submitted for the use of neutrophil gelatinase-associated lipocalin as a biomarker of

kidney disease. Dr Devereaux has received research grants from Abbott Diagnostics and Roche Diagnostics. Dr Kavsak has received honorarium and research

grants from Abbott Laboratories, Beckman Coulter, Ortho Clinical Diagnostics, Randox Laboratories, and Roche Diagnostics; he has consulted for Abbott

Laboratories and Roche Diagnostics. The other authors have indicated they have no potential conflicts of interest to disclose.

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DOI: 10.1542/peds.2014-2949 originally published online March 9, 2015; 2015;135;e945Pediatrics 

TRIBE-AKI ConsortiumCatherine D. Krawczeski, Peter Kavsak, Colleen Shortt, Chirag R. Parikh and for the

Eikelboom, Amit X. Garg, Heather Thiessen Philbrook, Philip J. Devereaux, Emily M. Bucholz, Richard P. Whitlock, Michael Zappitelli, Prasad Devarajan, John

Cardiac Biomarkers and Acute Kidney Injury After Cardiac Surgery

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