CardiorenalBiomarkersandHeartFailure

NicholasWettersten,MDApril7th,2017

Disclosures

• Stillnone,butlookingforsome

AcuteKidneyInjuryBiomarkers

547in2015

4112asofMarch2017

Case1

• 60yo manpresentedwithanNSTEMIatanOSHfoundtohaveCTOofLADand90%lesionsinRCAandcircumflextransferredforhighriskrevascularization.HisMAPis60uponarrivalandhasanImpella percutaneousLVADplacedandgetsrevascularizationofRCAandcircumflex.

Case2

• 45yo womanwithNICMadmittedfordecompensatedheartfailure.Onexamsheiswarmandwet.Receivesdiureticsfor5daysandonday6creatininegoesfrom0.8to1.2.

WhohasorwilldevelopAKI?Doweneednovelbiomarkers?

• Bothmayormaynot• Weneedbiomarkerstohelpguideus

KidneyDysfunctionisCommoninHeartFailure

was a significant univariate predictor of in-hospital mortality;the mortality odds ratio associated with a 10mL$min$1.73 m2 decrease in GFR was 1.23 (95% CI:1.21e1.25), and GFR remained an independent predictorof mortality in multivariate analysis. The best single pre-dictor of in-hospital mortality was BUN with a c statisticof 0.70. The best 4-variable model remained BUN, sys-tolic blood pressure, pulse, and age with a c statistic of0.76. A model with GFR, systolic blood pressure, pulse,and age had a c statistic of 0.73. In this latter model,the mortality odds ratio associated with a 10mL$min$1.73 m2 decrease in GFR was 1.18 (95% CI:1.17e1.20).

Discussion

Despite significant advances in the treatment of cardio-vascular disease, the prevalence of heart failure continuesto increase and will do so for the foreseeable future.14 Inso-far as clinical heart failure is largely a disease of the elderlyand is often accompanied by hypertension, diabetes, andcoronary artery disease, it is not surprising that renal dys-function frequently coexists with heart failure. Indeed, thepublic health impact and cost of chronic kidney disease isreminiscent of heart failure. Approximately 8 million adultsin the United States currently have stage III or greater kid-ney disease, and the economic cost of treating end-stagekidney disease is approximately $27 billion annually.15,16

In the ADHERE database, at least moderate renal dysfunc-tion (stage III) was seen in 75,382 of 118,465 patients(63.6%), and, of these, 5592 (4.7%) were already receivingchronic dialysis before hospitalization. Only 10.6% of menand 7.5% of women had normal renal function as definedby GFR. Thus, significant renal dysfunction is more therule than the exception in patients with ADHF. Further-more, this renal dysfunction has a profound effect on out-comes. Need for mechanical ventilation, treatment in anintensive care unit, cardiopulmonary resuscitation, durationof hospitalization, and mortality were all related to the de-gree of renal dysfunction at baseline. In contrast, a recentanalysis of national hospital registry data found onlya 27% prevalence of at least moderate renal dysfunctionin patients undergoing coronary artery bypass graft surgery,

6.6

11.5

41.2

30.0

10.67.3

14.6

45.7

7.5

24.9

0.0

10.0

20.0

30.0

40.0

50.0

I II III IV VKidney Function Stage

Pre

va

len

ce

(%

)

MalesFemales

Fig. 1. Prevalence and severity of renal dysfunction in patientsadmitted with ADHF.

Table 2. Treatment by Kidney Function Stage

Kidney Function Stage*

Parameter I (n 5 10,660) II (n 5 32,423) III (n 5 51,553) IV (n 5 15,553) V (n 5 8276) Py

In-Hospital IV Medications, %Any inotrope 7.2 9.0 13.0 20.6 11.2 !.0001

Dobutamine 3.8 4.6 6.7 10.8 5.0 !.0001Dopamine 2.8 4.3 6.6 11.4 7.3 !.0001Milrinone 2.0 2.3 3.0 4.0 1.8 !.0001

Nesiritide 7.0 8.9 12.4 16.9 6.1 !.0001Any diuretic 89.4 90.6 91.4 90.6 58.3 !.0001

O1 diuretic 4.9 5.7 7.5 11.6 6.8 !.0001Total diuretic

dose in initial 24 h, mgfurosemide equivalentsz

n, mean (SD) 8992, 106.4(75.4)

27,479, 109.7(79.2)

43,196, 120.7(92.6)

12,259, 142.6(121.0)

4205, 150.1(143.7)

!.0001

Q1 [median] Q3 60.0 [80.0]120.0

60.0 [80.0]140.0

80.0 [100.0]160.0

80.0 [120.0]180.0

80.0 [100.0]180.0

Non-IV DischargeMedications, %

ACE inhibitor/ARB 71.2 70.6 62.4 39.6 44.1 !.0001ACE inhibitor 63.1 60.5 50.0 28.3 33.1 !.0001ARB 9.3 11.5 13.6 12.4 12.6 !.0001

ß-blocker 52.9 55.9 55.9 55.6 56.6 !.0001Calcium channel blocker 17.4 17.6 19.3 25.2 36.1 !.0001Digoxin 33.4 35.7 33.8 24.2 13.5 !.0001Nitrates 19.7 24.2 31.1 38.4 31.1 !.0001

IV, intravenous; SD, standard deviation; Q, quartile; ACE, angiotensin-converting enzyme; ARB, angiotensin II receptor blocker.*For description of kidney function stages, see Table 1.yAcross stages using chi-square tests for categoric variables and analysis of variance for continuous variables.zConversion factors: 1 mg of bumetanide 5 40 mg furosemide and 1 mg torsemide 5 2 mg furosemide.

426 Journal of Cardiac Failure Vol. 13 No. 6 August 2007

>100,000patientswithacuteheartfailureHeywood JT etal,JCardFail.2007,Aug;13(6):422-30

WorseKidneyFunction=WorseOutcomes

although the prognostic implications of this dysfunctionwere similar to those seen in patients with heart failure.17

Inpatient medical therapy varied significantly for patientswith different degrees of renal dysfunction. Dose of di-uretics, frequency of inotrope use, and frequency of nesiri-tide use increased, whereas frequency of ACE inhibitor orARB use decreased, with increasing severity of renal dys-function. Dobutamine was used in 10.8% and dopaminewas used in 11.4% of patients with stage IV kidney func-tion. Similarly, 16.9% of patients with stage IV kidneyfunction received nesiritide. The role of nesiritide in thesepatients is far from clear. Similar to other neurohormonal

blocking agents,3,18 nesiritide significantly increased therisk of worsening renal function at 30 days in a meta-analysis of selected inpatient trials.19 In contrast, nesiritidesignificantly reduced the risk of worsening renal function ina recent prospective, randomized, double-blind, placebo-controlled evaluation of patients with heart failure undergo-ing coronary artery bypass grafting who were administeredthe currently recommended starting dose.20 In this latterevaluation, patients with baseline serum creatinine greaterthan 1.2 mg/dL who received nesiritide had significantlybetter preservation of renal function during hospitalizationor by study day 14 (whichever came first) than similar pa-tients who received placebo, as indicated by a smaller meanmaximum increase in serum creatinine (0.02 mg/dL vs 0.48mg/dL; P 5 .001) and a smaller mean maximum decreasein GFR (0.4 mL$min$1.73 m2 vs 8.9 mL$min$1.73 m2;P 5 .003).

In addition, the use of an ACE inhibitor or ARB declinedas GFR decreased. Suppression of the renin-angiotensinsystem improves survival in patients with heart failurewho have systolic dysfunction, reduces hospitalizations inpatients with heart failure who have preserved systolicfunction, and slows renal deterioration in diabetic patientswith proteinuria.21,22 Although this suppression can tran-siently reduce GFR in patients with heart failure whohave underlying renal dysfunction, it is still recommended,even in patients with elevated serum creatinine.7 In a sub-group analysis of the Heart and Estrogen/Progestin Re-placement Study, women with heart failure and renal

Table 3. Procedures and Outcomes by Kidney Function Stage

Kidney Function Stage*

Parameter I (n 5 10,660) II (n 5 32,423) III (n 5 51,553) IV (n 5 15,553) V (n 5 8276) Py

Procedures, %Cardiopulmonary resuscitation 0.7 0.9 1.2 1.9 1.8 !.0001Cardiac catheterization 14.5 13.3 9.1 4.9 6.9 !.0001Defibrillation 0.5 0.6 0.8 0.9 .8 .0001Dialysis

New onset 0.1 0.1 0.5 4.1 9.7 !.0001Total 0.1 0.1 0.5 4.1 67.8 !.0001

Mechanical ventilation 3.5 4.0 4.9 5.3 6.7 !.0001Ultrafiltration 0.1 0.2 0.3 1.9 23.1 !.0001OutcomesDeath, % 1.9 2.3 3.9 7.6 6.5 !.0001Admitted to ICU/CCU, % 17.2 16.6 17.7 21.8 25.7 !.0001ICU/CCU time, dz

Mean (SD) 3.8 (4.9) 3.7 (4.7) 4.1 (5.9) 4.2 (5.2) 3.6 (4.3) !.0001Q1 [median] Q3 1.1 [2.2] 4.4 1.2 [2.3] 4.3 1.3 [2.6] 4.8 1.5 [3.0] 5.0 1.2 [2.3] 4.1

Total length of hospital stay, dMean (SD) 5.3 (5.6) 5.4 (5.0) 6.0 (5.9) 7.0 (6.1) 6.3 (6.3) !.0001Q1 [median] Q3 2.6 [4.0] 6.3 2.7 [4.1] 6.4 2.9 [4.5] 7.2 3.3 [5.2] 8.7 2.7 [4.5] 7.9

Change in weight, kgMean (SD) !3.0 (5.1) !2.9 (4.4) !2.9 (4.7) !3.0 (4.9) !3.2 (4.7) !.0001Q1 [median] Q3 !5.0 [!2.3] !0.4 !4.8 [!2.3] !0.5 !4.7 [!2.3] !0.5 !5.0 [!2.3] !0.3 !5.2 [!2.7] !0.4

Change in weight, kg/dMean (SD) !0.7 (1.3) !0.7 (1.3) !0.6 (1.2) !0.5 (1.1) !0.8 (1.4) !.0001Q1 [median] Q3 !1.2 [!0.5] !0.1 !1.1 [!0.5] !0.1 !1.0 [!0.5] !0.1 !0.9 [!0.4] 0.0 !1.2 [!0.5] !0.1

ICU, intensive care unit; CCU, coronary care unit; SD, standard deviation; Q, quartile.*For description of kidney function stages, see Table 1.yAcross stages using chi-square tests for categoric variables and analysis of variance for continuous variables.zIn patients admitted to the ICU/CCU.

2.3

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0

2

4

6

8

I II III IV VKidney Function Stage

In-h

os

pit

al

Mo

rta

lity

(%

)

Fig. 2. In-hospital mortality by kidney function stage for patientsadmitted with ADHF. Error bars depict the 95% CIs for the pointestimates.

Renal Dysfunction in Patients With ADHF " Heywood et al 427

Heywood JT etal,JCardFail.2007,Aug;13(6):422-30

WorseningRenalFunctionisCommoninAcuteHeartFailure

dicting in-hospital mortality and length of stay of at least10 days are shown in Table 3 and Fig. 2. Twenty-sixpatients died and 247 patients had a prolonged hospital-ization of at least 10 days. Various definitions of wors-ening renal function are shown. Sensitivity of smallincreases was high, with poor specificity. Increasingthresholds of creatinine improved specificity, but identi-fied fewer patients with adverse outcomes. This is

indicated graphically using receiving operating charac-teristic (ROC) curves in Fig. 3. The ROC curves depictthe various definitions of renal deterioration for predict-ing a length of stay of at least 10 days.

An increase in creatinine identified most patients witha prolonged hospitalization. An increase of 0.1 mg/dLwas highly sensitive, but not specific. Adding the re-quirement of a final creatinine of at least 2 mg/dL

Fig. 1. The time course of development of increasing serum creatinine of various extents. When the creatinine increased, it occurredsoon after hospital admission.

Table 3. Sensitivity and Specificity of Various Definitions of Worsening Renal Function for Length of Stay > 10 Days and forMortality

Mortality LOS ! 10 days

Sensitivity Specificity Sensitivity Specificity

Increase in creatinine of0.1 92 29 87 310.2 88 48 74 500.3 81 62 64 650.4 73 74 54 770.5 61 82 46 85

Increase in creatinine of10% 88 46 75 4820% 69 71 54 7430% 54 88 36 8940% 46 96 22 9650% 27 99 12 99

Final creatinine of ! 1.5 mg/dl and increase in creatinine of0.1 81 62 68 650.2 77 67 62 690.3 73 72 57 750.4 73 78 52 810.5 65 84 45 86

Final creatinine of ! 2.0 mg/dl and increase in creatinine of0.1 69 79 42 810.2 65 81 40 830.3 65 83 38 850.4 65 85 36 870.5 62 88 34 90

Final creatinine of ! 2.0 mg/dl and increase in creatinine of10% 65 83 37 8520% 54 90 33 9230% 50 94 25 9640% 42 97 17 9850% 23 98 10 99

Definitions of Renal Dysfunction in CHF O Gottlieb et al 139

GottliebSS etal,JCardFail.2002Jun;8(3):136-41

WorseningRenalFunctionIncreasesMortality

Figure 2 Forest plot of combined all-cause mortality for CKD vs. no CKD, stratified by acute and chronic heart failure. CKD, chronic kidneydisease.

Renal impairment, WRF, and outcome in HF patients 463

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Figure 2 Forest plot of combined all-cause mortality for CKD vs. no CKD, stratified by acute and chronic heart failure. CKD, chronic kidneydisease.

Renal impairment, WRF, and outcome in HF patients 463

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DammanK etal,Eur HeartJ.2014Feb;35(7):455-69

Cardiorenal Syndrome (CRS) General Definition:A pathophysiologic disorder of the heart and kidneyswhereby acute or chronic dysfunction in one organ mayinduce acute or chronic dysfunction in the other organCRS Type I (Acute Cardiorenal Syndrome)Abrupt worsening of cardiac function (e.g. acute cardiogenic shock or decompensated congestive heart failure) leading to acute kidney injury

CRS Type II (Chronic Cardiorenal Syndrome)Chronic abnormalities in cardiac function (e.g. chronic congestive heart failure) causing progressive and permanent chronic kidney disease

CRS Type III (Acute Renocardiac Syndrome)Abrupt worsening of renal function (e.g. acute kidney ischaemia or glomerulonephritis) causing acute cardiac disorder (e.g. heart failure, arrhythmia, ischemia)

CRS Type IV (Chronic Renocardiac Syndrome)Chronic kidney disease (e.g. chronic glomerular disease) contributing to decreased cardiac function, cardiac hypertrophy and/or increased risk of adverse cardiovascular events

CRS Type V (Secondary Cardiorenal Syndrome)Systemic condition (e.g. diabetes mellitus, sepsis) causing both cardiac and renal dysfunction

Ronco Cetal.JAmColl Cardiol 2008;52:1527-39

ComplexPathophysiologyHemodynamicEffects

DecreasedoutputVenousCongestion

NeurohormonalActivationRAASactivation

SNS

ExogenousFactorsDiuretics

ACEinhibitorsContrast

InflammationImmunemediated

HowtoDefineKidneyInjury?

KDIGOStage BasedonsCroreGFR BasedonUrine

Output

1

1)sCr=1.5-1.9xbaseline(withinprior7days)OR2)≥0.3mg/dLinsCr(prior48h)^

IdenticaltoAKIN2 sCr=2.0-2.9xbaseline

3

1)sCr=3xbaselineOR2)sCr≥4mg/dLOR3)LOFrequiringrenalreplacementtherapyOR4)If<18yearsold,eGFRto<35mL/min/1.73m2

sCr=serumcreatinine;eGFR=estimatedglomerularfiltrationrate;*changeover7days;**withshorttermriseof≥0.5mg/dL;^shorttermrise(≤48h);conversiontoSIunitsis1mg/dL=88.4µmol/L

Complicatedinheartfailurebydiuretics

NewParadigmforKidneyInjury

No Damage/InjuryOR

Loss of Function

Damage/InjuryBUT

No Functional Change

No Damage/InjuryBut

Loss of Function

Damage/InjuryAND

Loss of Function

TimeforNovelBiomarkerssystemic circulation and undergo glomerular fi ltration (that is, markers of glomerular function), enzymes that are released by tubular cells into the urine after tubular cell injury (that is, markers of tubular damage) or infl am-matory mediators released by renal cells or infi ltrating infl ammatory cells (that is, markers of degree of damage and indicators of site of injury) (Figure  1 and Table  2). Th e aetiology of AKI is far from uniform, however, and defi ning new biomarkers for AKI is thus extremely challenging. Consequently, investigators have tended to study their chosen biomarker(s) in well defi ned clinical settings where the timing of renal injury is known, that is, after cardiopulmonary bypass surgery, coronary angio-graphy or following renal transplantation (Table 3). Further-more, a signifi cant number of studies were done in paediatric populations where comorbidities such as CKD, diabetes mellitus and chronic infl ammatory diseases are less likely to present as confounding variables. As a result, not all studies are easily generalized to heterogenous populations, including critically ill patients in the ICU. Th e eff ects of baseline renal function, comorbidities, age, and duration of renal injury have led to confl icting results in various studies (Table 3).

Specifi c clinical scenariosDiff erentiation between ‘pre-renal’ and ‘intrinsic’ acute kidney injuryTh e diff erentiation between a transient serum creatinine rise caused by perturbations in renal perfusion and direct damage to the kidney leading to sustained AKI can be diffi cult, especially in acute patients. Th e diagnosis of pre-renal AKI is usually retrospective after a transient rise in serum creatinine with recovery of function within 24 to 72  hours. In a study involving 510 critically ill patients, De Geus and colleagues [5] confi rmed that serial measurement of urinary neutrophil gelatinase-associated lipocalin (NGAL) could distinguish between these two

conditions. Adding the results to a clinical prediction model, however, only led to marginal improvement of the area under the receiver operating characteristics curve (AUC) from 0.79 to 0.82. Nickolas and colleagues [6] evaluated fi ve diff erent urinary biomarkers (NGAL, kidney injury molecule (KIM)-1, liver-type fatty acid-binding protein (L-FABP), IL-18, and cystatin C) in 1,635 patients who presented to the emergency department and were subsequently hospitalized for more than 24  hours. Th e entire cohort was divided into three subgroups: patients with sustained AKI (that is, AKI that persisted for more than 72 hours); patients with transient AKI (that is, AKI that resolved within 72  hours); and patients without AKI. All markers were raised in patients with sustained AKI but only urinary NGAL (uNGAL) and urinary cystatin C were able to distinguish patients with sustained AKI from those with transient or pre-renal AKI. Hall and colleagues [7] measured uNGAL,

Table 1. Desirable criteria for any potentially clinically useful candidate acute kidney injury biomarker(s)To provide information above that of traditional clinical evaluation and investigation

To be non-invasive, utilising easily accessible samples

To provide results rapidly and both sensitive and specifi c to AKI

To have specifi c cutoff values to distinguish between normal and abnormal renal function

To distinguish intrinsic AKI from pre-renal azotaemia

To provide insight into aetiology of AKI

To diff erentiate between AKI and chronic kidney disease

To be specifi c for renal injury in the presence of concomitant dysfunction of other organs

To be indicative of the severity of AKI

To ideally allow some estimate as to the timing of the onset of renal injury

To guide initiation of therapies and to monitor the response to interventions

To aid prognostication in terms of potential renal recovery, need for RRT and mortality

AKI, acute kidney injury; RRT, renal replacement therapy.

Figure 1. Origin of acute kidney injury biomarkers within a single nephron. GST, glutathione S-transferase; GT, glutamyl transpeptidase; KIM, kidney injury molecule; L-FABP, liver-type fatty acid-binding protein; NAG, N-acetyl-β-D-glucosaminidase; NGAL, neutrophil gelatinase-associated lipocalin; RBP, retinol binding protein.

Ostermann et al. Critical Care 2012, 16:233 http://ccforum.com/content/16/5/233

Page 2 of 13

Ostermann,CritCare.2012Sep21;16(5):233

NeutrophilGelatinase-AssociatedLipocalin(NGAL)

• Smallmoleculeoflipocalinfoundinneutrophilsandrenaltubularcells

• Physiologycomplex,butreleasedduringacutephaseoftoxicorischemickidneyinjury,mainlyinloopofHenle anddistaltubule

• Measurableinplasmaandurine• EvaluatedinheartfailureintheAcuteKidneyInjuryN-galEvaluationofSymptomaticheartfaIlure Study (AKINESIS)

AKINESIS

• 927patientswithAHF• Primaryoutcomeofacutekidneyinjurydefinedasincreaseincreatinine≥0.5mg/dLor≥50%OR initiationofdialysis

• Secondaryoutcomein-hospitaladverseevents• EvaluatedinitialandpeakNGALvalues

AUC 95%CIPeakNGAL 0.656 0.589-0.723FirstNGAL 0.647 0.579-0.715FirstCrea?nine 0.652 0.576-0.729

2D Graph 1

1 - Specificity

0.0 0.2 0.4 0.6 0.8 1.0

Sen

sitiv

ity

0.0

0.2

0.4

0.6

0.8

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Peak NGALInitial NGALInitial Creatinine

Peak NGAL First NGAL First Creatinine

2D Graph 1

1 - Specificity

0.0 0.2 0.4 0.6 0.8 1.0

Sen

sitiv

ity

0.0

0.2

0.4

0.6

0.8

1.0

Peak NGALInitial NGALInitial Creatinine

NGALnoBetterthanCreatinineforPrimaryOutcome

MaiselAetal,JAmColl Cardiol.2016Sep27;68(13):1420-31

NorAnyBetterforSecondaryOutcome

AUC 95%CIPeakNGAL 0.653 0.601-0.704FirstNGAL 0.691 0.643-0.740FirstCrea=nine 0.686 0.634-0.738

2D Graph 1

1 - Specificity

0.0 0.2 0.4 0.6 0.8 1.0

Sen

sitiv

ity

0.0

0.2

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Peak NGALInitial NGALInitial Creatinine

Peak NGAL First NGAL First Creatinine

2D Graph 1

1 - Specificity

0.0 0.2 0.4 0.6 0.8 1.0

Sen

sitiv

ity

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Peak NGALInitial NGALInitial Creatinine

MaiselAetal,JAmColl Cardiol.2016Sep27;68(13):1420-31

NGALMayIdentifyaLowRiskPopulation

Predic/vePerformance

eGFR<60NGAL<150mg/mL

Sensi/vity 84.5% (CI76.0%-90.5%)

Specificity 41.7% (CI36.8%-46.7%)

Posi/vePredic/veValue 27.7% (CI23.0%-33.0%)

Nega/vePredic/veValue 91.0% (CI85.8%-94.5%)

MaiselAetal,JAmColl Cardiol.2016Sep27;68(13):1420-31

UrineNGALNotLookingMuchBetter0.

000.

250.

500.

751.

00Se

nsitiv

ity

0.00 0.25 0.50 0.75 1.001-Specificity

xb1 ROC area: 0.5935 xb2 ROC area: 0.567xb3 ROC area: 0.6228 ReferenceuNGAL admitROCCreat admitROC

uNGAL peakROC

KidneyInjuryMolecule1(KIM-1)

• Trans-membraneglycoproteininproximaltubule

• Usuallynotdetectable,butupregulatedinacutetubularnecrosisandischemia

• Proposedtobeinvolvedinrenalrepairfollowinginjury

• Detectableinbothurineandplasma

UrineKIM-1PredictsWorseningRenalFunctioninChronicHeartFailure

3.07 to 4.30; p < 0.001). Table 4 presents the multivariableanalysis, showing that the occurrence of WRF remainedindependently associated with impaired outcome, whereasbaseline urine albumin to creatinine ratio did not. Also inthis subanalysis, NAG showed prognostic information,independent from the occurrence of WRF, whereas eGFRshowed a trend toward an effect with outcome; NGAL and

KIM-1 were not independently associated with outcome inthis analysis.

Discussion

This is the first study to assess urinary tubular proteins aspredictors of deterioration of renal function in chronic HF.

Figure 1 Relationship Among Quartiles of Tubular Markers, KDOQI Stages, Albuminuria, and WRF

(A) Relationship between quartiles of kidney injury molecule (KIM)-1 and occurrence of worsening renal failure (WRF); log-rank p < 0.001. (B) Relationship between quartiles ofN-acetyl-beta-D-glucosaminidase (NAG) and occurrence of WRF; log-rank p < 0.001. (C) Relationship between quartiles of neutrophil gelatinase-associated lipocalin (NGAL) andoccurrence of WRF; log-rank p < 0.001. (D) Relationship between Kidney Disease Outcomes Quality Initiative (KDOQI) stage of chronic kidney disease and occurrence of WRF;log-rank p < 0.001. Estimated glomerular filtration rate (eGFR) in ml/min/1.73 m2. (E) Relationship between albuminuria and occurrence of WRF; log-rank p < 0.001.Normoalbuminuria ¼ urinary albumin to creatinine ratio (UACR) <30 mg/g; microalbuminuria ¼ UACR 30 to 299 mg/g; macroalbuminuria ¼ UACR "300 mg/g.

JACC: Heart Failure Vol. 1, No. 5, 2013 Damman et al.October 2013:417–24 Tubular Damage and Worsening Renal Function

421

DammanK etal,JACCHeartFail.2013Oct;1(5):417-24

Hazardratioof1.23forworseningrenalfunction

UrineKIM-1MayTrackwithRenalCongestion

function (20). KIM-1 is thought to be expressed in the urinewhen (hypoxic) tubular dysfunction develops, whereas theresponse time of urinary NAG is somewhat slower and lessspecific (21,22). The absence of any alteration in NGALlevels in urine or serum remains obscure. NGAL levels maynot only be dependent of tubular dysfunction, but also ofother comorbid organ dysfunction and inflammation (23).Furthermore, urinary NGAL levels are more dependent on

production of NGAL in the distal tubule after injury,whereas both KIM-1 and NAG are markers that representproximal tubular injury (23). Although proximal tubularinjury may also result in higher urinary NGAL levels, this isa reflection of serum NGAL that has been filtered throughthe glomerulus and not reabsorbed in the proximal tubule.Given the absence of changes in serum NGAL, this may bea reason for a lack of an effect on NGAL. Together, this

Figure 4 Effect of Diuretic Reinitiation on Urinary KIM-1 and NAG and Urinary and Plasma NGAL

(A) Urinary KIM-1 and NAG. Median and interquartile ranges are presented. *p ! 0.05 versus day 4, 0 h. (B) Urinary and plasma NGAL.Median and interquartile ranges are presented. i.v. " intravenous; other abbreviations as in Figure 3.

2239JACC Vol. 57, No. 22, 2011 Damman et al.May 31, 2011:2233–41 Volume Status, Diuretics, and Tubular Function

their baseline values after diuretic reinitiation, which maysuggest that a decrease in CVP accompanied the reiniti-ation of diuretics and the fall in both BNP and urinaryKIM-1/NAG. We also found that patients with compro-mised kidney function, lower hemoglobin levels, andlower blood pressures at baseline were especially atincreased risk for deteriorating tubular function. Thisgroup of patients may be more susceptible to volume/diuretic changes that can alter renal perfusion and oxygen

delivery and may have limited reserve capacity to preservetubular function.

These tubular markers are sensitive and are known torespond extremely quickly after induction of tubular dys-function or the occurrence of acute kidney injury (10,18,19).We recently showed that the concentrations of these mark-ers were increased in congestive heart failure, which maysuggest that this patient group also suffers from chronichypoxic tubular damage in addition to reduced glomerular

Figure 3 Effect of Diuretic Withdrawal on Urinary KIM-1 and NAG and Serum and Urinary NGAL

(A) Urinary kidney injury molecule (KIM)-1 and N-acetyl-beta-D-glucosaminidase (NAG). Median and interquartile ranges are presented. *p ! 0.01; †p " 0.075 versusday 1, baseline. (B) Serum and urinary neutrophil gelatinase associated lipocalin (NGAL). Median and interquartile ranges are presented.

2238 Damman et al. JACC Vol. 57, No. 22, 2011Volume Status, Diuretics, and Tubular Function May 31, 2011:2233–41

DammanK etal,JAmColl Cardiol.2011May31;57(22):2233-41

PlasmaKIM-1NotasPredictiveofOutcomes

• 874AHFpatientsfromASCENDtrial– HigherbaselineplasmaKIM-1levelsassociatedwithWRFanddecreaseddiuresis

– KIM-1measuredat30days(notbaselineor48-72hoursafter)associatedwith180mortality

• 1588AHFpatientsfromPROTECTtrial– BaselineKIM-1onlyassociatedwith60dayHFrehospitalization,butnotmortality

Grodin K etal,JACCHeartFail.2015Oct;3(10):777-85Emmens etal,Eur JHeartFail.2016Jun;18(6):641-9

Proenkephalin (PENK)

• Anendogenousopioid(enkephalins,endorphins,anddynorphins)

• Associatedwithcardiodepressiveeffects(negativeinotropy,lowerBP,lowerHR)

• Morestablethanotherforms• Reflectsglomerularfiltrationearlierthancreatinine

• AssociatedwithrenaldysfunctionandpooroutcomesinACS,cardiacsurgery,andsepsis

1714PatientswithAHF,PENKStrongAssociationwithWRF

quartiles. Patients with higher PENK levels wereolder, had a lower body mass index, were more oftenfemale, and had comorbidities such as histories ofhypertension, ischemic heart disease, HF, and renalimpairment; their initial systolic blood pressures(SBP) and heart rates were also lower. With increasingPENK quartiles, renal function deteriorated, andnatriuretic peptide levels increased. Higher PENK also

was associated with more frequent prescription ofloop diuretics and aldosterone antagonists.

CORRELATION ANALYSIS AND EFFECTS OF

CHANGES IN PROENKEPHALIN A. Spearman ana-lysis (rs, p value) showed that PENK was correlatedwith age (0.366; p < 0.0005), eGFR (!0.752;p < 0.0005), plasma creatinine (0.668; p < 0.0005),plasma urea (0.641; p < 0.0005), heart rate (!0.165;p < 0.0005), SBP (!0.100; p < 0.0005), troponin T(0.373; p < 0.0005), and z-score of log natriureticpeptide (0.419; p< 0.0005). There were nonsignificantcorrelations with plasma sodium. A univariate generallinear model indicated the following independentpredictors of PENK level, in descending order ac-cording to variance accounted for in the model(Table 3): eGFR, plasma urea, natriuretic peptidelevels, age, sex, past history of renal impairment, SBP,and heart rate. These variables accounted for 60.6%of the variance of PENK levels, and of these, 2 mea-sures of renal function (eGFR and plasma urea)accounted for 46.8% of the model.

Of the 1,714 patients with data on plasma creati-nine within 5 days of hospitalization, 264 had devel-oped a rise in plasma creatinine of $26.5 mmol/l or50% higher than the admission value. Using clinicalvariables, use of nephrotoxic drugs on admission

TABLE 4 Significant Predictors

Predictor p Value

Male 0.026

Past history of renal failure <0.0005

Systolic BP 0.007

Plasma urea 0.014

Creatinine <0.0005

PENK 0.001

Ordinal regression for WRF stages

Systolic BP 0.016

Plasma urea 0.028

Creatinine 0.03

Sodium 0.037

Past history of renal failure 0.012

PENK <0.0005

WRF ¼ worsening renal function; other abbreviations as in Tables 1 and 2.

FIGURE 1 Predictors of WRF

0.25 0.5Odds Ratio

PENKNatriuretic Peptide

DiureticACE/ARB

Plasma Sodium

Plasma UreaHeart RateSystolic BP

PH DiabetesPH Renal Failure

PH HypertensionPH IHD

PH Heart FailureMale

Agep Value

NSNSNSNSNS

NS

NSNSNS

NSNSNS

.004

.009

<0.0005

.041Plasma Creatinine

1 2

Forest plots of a multivariable analysis shows odds ratio for clinical variables, natriuretic peptides, and amino acids 119 to 159 of proenkephalinA for prediction of worsening renal function (WRF) during initial hospitalization. ACE ¼ angiotensin-converting enzyme; ARB ¼ angiotensinreceptor blocker; BP ¼ blood pressure; IHD ¼ ischemic heart disease; PENK ¼ proenkephalin A assay; PH ¼ past history; NS ¼ not significant.

J A C C V O L . 6 9 , N O . 1 , 2 0 1 7 Ng et al.J A N U A R Y 3 / 1 0 , 2 0 1 7 : 5 6 – 6 9 Proenkephalin in Acute Heart Failure

61

Ngetal,JAmColl Cardiol.2017Jan3;69(1):56-69

HigherPENK,HigherMortality

increasing PENK quartiles (p < 0.0005). Comparisonof PENK quartiles revealed significant differencesamong all of them (p < 0.001), except comparingquartile 1 versus quartile 2 (p ¼ 0.009). Figure 3Bshows a graded increase in event rates for deathand/or HF hospitalization with increasing PENKquartiles (p < 0.0005). Apart from quartile 1 versus 2(p ¼ 0.015), all other quartile comparisons were sta-tistically different (p < 0.0005).

Figure 4A illustrates the univariable hazard ratiosfor factors affecting the outcome of all-cause mor-tality at 1 year, by using Cox proportional hazardsurvival analysis. Model 1 (Figure 4B) included rele-vant clinical variables and z-transformed natriureticpeptide levels, with independent predictors beingage, past history of hypertension, SBP, plasma urea,sodium, eGFR, and natriuretic peptide levels. Addi-tion of PENK to this base model (Figure 4C) showedthat it had independent predictive value for death, itsadded value being statistically significant (p <

0.0005) using the increment in log likelihood ratiochi-square for nested regression models. For theendpoint of death at 3 and 6 months, the multivari-able adjusted HR for PENK remained significant forboth time points (3 months: HR: 1.49; 95% CI: 1.20 to1.85; p < 0.0005; 6 months: HR: 1.40; 95% CI: 1.17 to1.68; p < 0.0005).

The C statistic for 1-year mortality was 0.741 (in thebase model using the foregoing demographic andclinical chemistry variables), and it rose to 0.754(p ¼ 0.021) and 0.751 (p ¼ 0.051) with addition ofnatriuretic peptide and PENK, respectively, and to0.759 (p¼ 0.007) with the addition of both biomarkers.

Figure 5A reports the HRs for the outcome of deathor HF at 1 year in the Leicester and Basel cohorts.Model 1 (Figure 5B) is a multivariable model thatincluded the independent predictors: age, past his-tory of HF, hypertension, ischemic heart disease,diabetes, SBP, plasma urea, and natriuretic peptidelevels. Addition of PENK (model 2) showed that ithad independent predictive value for death or HF(p ¼ 0.003) (Figure 5C), and the increment in loglikelihood ratio chi-square was statistically significant(p ¼ 0.001). For the endpoint of death or HF at 3 and6 months, the multivariable adjusted HR for PENKremained significant for both time points (3 monthsHR: 1.27; 95% CI: 1.06 to 1.53; p ¼ 0.011; 6 months HR:1.32; 95% CI: 1.13 to 1.54; p < 0.0005).

Using the base model, the C statistic for 1-yeardeath or HF was 0.692, and it rose to 0.702(p ¼ 0.079) and 0.700 (p ¼ 0.09) with addition ofnatriuretic peptide and PENK, respectively, and to0.706 (p ¼ 0.039) with the addition of bothbiomarkers.

FIGURE 3 Outcomes According to PENK Levels

PENK QuartileTime (Days)

Even

t Rat

e %

A B

0

0

10

20

30

40

50

60

100 200 300 400

1 2 3 4

60

50

40

0 100 200Time (Days)

Even

t Rat

e %

300 400

30

20

10

0

Cumulative incidence of all-cause mortality (A) and the composite endpoint of death and/or heart failure hospitalization (B) rose with higher proenkephalinA (PENK) quartiles.

J A C C V O L . 6 9 , N O . 1 , 2 0 1 7 Ng et al.J A N U A R Y 3 / 1 0 , 2 0 1 7 : 5 6 – 6 9 Proenkephalin in Acute Heart Failure

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[TIMP-2][IGFBP7]• Insulin-likegrowthfactor-bindingprotein7(IGFBP7)

• Tissueinhibitorofmetalloproteinases-2(TIMP-2)• Discoveredfrom340candidateproteins• Bothinvolvedincellcyclearrestduringearlyphaseofinjury

• Measuredinurine• Veryhighnegativepredictivevalue,goodprognosticvalueoverall(AUC0.8)

• Value<0.3highlyunlikelytodevelopAKI

Intensive Care MedDOI 10.1007/s00134-016-4670-3

SEVEN-DAY PROFILE PUBLICATION

Prevention of cardiac surgery-associated AKI by implementing the KDIGO guidelines in high risk patients identified by biomarkers: the PrevAKI randomized controlled trialMelanie Meersch1, Christoph Schmidt1, Andreas Hoffmeier2, Hugo Van Aken1, Carola Wempe1, Joachim Gerss3 and Alexander Zarbock1*

© 2017 Springer-Verlag Berlin Heidelberg and ESICM

Abstract Purpose: Care bundles are recommended in patients at high risk for acute kidney injury (AKI), although they have not been proven to improve outcomes. We sought to establish the efficacy of an implementation of the Kidney Dis-ease Improving Global Outcomes (KDIGO) guidelines to prevent cardiac surgery-associated AKI in high risk patients defined by renal biomarkers.

Methods: In this single-center trial, we examined the effect of a “KDIGO bundle” consisting of optimization of volume status and hemodynamics, avoidance of nephrotoxic drugs, and preventing hyperglycemia in high risk patients defined as urinary [TIMP-2]·[IGFBP7] > 0.3 undergoing cardiac surgery. The primary endpoint was the rate of AKI defined by KDIGO criteria within the first 72 h after surgery. Secondary endpoints included AKI severity, need for dialy-sis, length of stay, and major adverse kidney events (MAKE) at days 30, 60, and 90.

Results: AKI was significantly reduced with the intervention compared to controls [55.1 vs. 71.7%; ARR 16.6% (95 CI 5.5–27.9%); p = 0.004]. The implementation of the bundle resulted in significantly improved hemodynamic param-eters at different time points (p < 0.05), less hyperglycemia (p < 0.001) and use of ACEi/ARBs (p < 0.001) compared to controls. Rates of moderate to severe AKI were also significantly reduced by the intervention compared to controls. There were no significant effects on other secondary outcomes.

Conclusion: An implementation of the KDIGO guidelines compared with standard care reduced the frequency and severity of AKI after cardiac surgery in high risk patients. Adequately powered multicenter trials are warranted to examine mortality and long-term renal outcomes.

Keywords: Acute kidney injury, KDIGO guidelines, Biomarkers, [TIMP-2]·[IGFBP7], Cardiac surgery, Major adverse kidney events

Introduction Acute kidney injury (AKI) is a well-recognized com-plication following cardiac surgery and significantly affects morbidity and mortality [1]. Up to 30% of patients develop AKI after cardiac surgery, whereas severe AKI requiring dialysis is relatively rare [1]. Approximately 1% of all patients undergoing cardiac surgery develop a severe dialysis-dependent AKI, and this severity of AKI

*Correspondence: zarbock@uni-muenster.de 1 Department of Anesthesiology, Intensive Care and Pain Medicine University, Hospital Münster, Albert-Schweitzer-Campus 1, Building A1, 48149 Münster, GermanyFull author information is available at the end of the article

Take home message: An implementation of the KDIGO guidelines compared with standard care reduced the frequency and severity of cardiac surgery-associated AKI (CSA-AKI) in high risk patients identified by biomarkers. Future studies will be needed to address whether this approach has an impact on long-term outcomes.

Intensive Care MedDOI 10.1007/s00134-016-4670-3

SEVEN-DAY PROFILE PUBLICATION

Prevention of cardiac surgery-associated AKI by implementing the KDIGO guidelines in high risk patients identified by biomarkers: the PrevAKI randomized controlled trialMelanie Meersch1, Christoph Schmidt1, Andreas Hoffmeier2, Hugo Van Aken1, Carola Wempe1, Joachim Gerss3 and Alexander Zarbock1*

© 2017 Springer-Verlag Berlin Heidelberg and ESICM

Abstract Purpose: Care bundles are recommended in patients at high risk for acute kidney injury (AKI), although they have not been proven to improve outcomes. We sought to establish the efficacy of an implementation of the Kidney Dis-ease Improving Global Outcomes (KDIGO) guidelines to prevent cardiac surgery-associated AKI in high risk patients defined by renal biomarkers.

Methods: In this single-center trial, we examined the effect of a “KDIGO bundle” consisting of optimization of volume status and hemodynamics, avoidance of nephrotoxic drugs, and preventing hyperglycemia in high risk patients defined as urinary [TIMP-2]·[IGFBP7] > 0.3 undergoing cardiac surgery. The primary endpoint was the rate of AKI defined by KDIGO criteria within the first 72 h after surgery. Secondary endpoints included AKI severity, need for dialy-sis, length of stay, and major adverse kidney events (MAKE) at days 30, 60, and 90.

Results: AKI was significantly reduced with the intervention compared to controls [55.1 vs. 71.7%; ARR 16.6% (95 CI 5.5–27.9%); p = 0.004]. The implementation of the bundle resulted in significantly improved hemodynamic param-eters at different time points (p < 0.05), less hyperglycemia (p < 0.001) and use of ACEi/ARBs (p < 0.001) compared to controls. Rates of moderate to severe AKI were also significantly reduced by the intervention compared to controls. There were no significant effects on other secondary outcomes.

Conclusion: An implementation of the KDIGO guidelines compared with standard care reduced the frequency and severity of AKI after cardiac surgery in high risk patients. Adequately powered multicenter trials are warranted to examine mortality and long-term renal outcomes.

Keywords: Acute kidney injury, KDIGO guidelines, Biomarkers, [TIMP-2]·[IGFBP7], Cardiac surgery, Major adverse kidney events

Introduction Acute kidney injury (AKI) is a well-recognized com-plication following cardiac surgery and significantly affects morbidity and mortality [1]. Up to 30% of patients develop AKI after cardiac surgery, whereas severe AKI requiring dialysis is relatively rare [1]. Approximately 1% of all patients undergoing cardiac surgery develop a severe dialysis-dependent AKI, and this severity of AKI

*Correspondence: zarbock@uni-muenster.de 1 Department of Anesthesiology, Intensive Care and Pain Medicine University, Hospital Münster, Albert-Schweitzer-Campus 1, Building A1, 48149 Münster, GermanyFull author information is available at the end of the article

Take home message: An implementation of the KDIGO guidelines compared with standard care reduced the frequency and severity of cardiac surgery-associated AKI (CSA-AKI) in high risk patients identified by biomarkers. Future studies will be needed to address whether this approach has an impact on long-term outcomes.

• IdentifiedhighriskpatientsforAKIaftercardiacsurgeryby[TIMP-2]*[IGFBP7}>0.3measured4hoursaftercardiopulmonarybypass

• InstitutedaKDIGOcarebundle(hemodynamicoptimization,avoidinghyperglycemia,avoidingnephrotoxins)

BiomarkersUsedtoGuideTherapyReducedAKI

90). We offer several possible reasons for these observed results. First, a follow-up duration of 90 days may be too short (e.g., in patients with relatively healthy kidneys and ample renal reserve before surgery). However, the associa-tion was robust at 90  days in prior observational studies [28]. A between-group difference during longer follow-up might be more likely if a benefit was not evident after such a short period. Second, mild to moderate AKI (as defined by the KDIGO classification system [5]) may not cause substantial chronic kidney disease (CKD). Multiple observational studies demonstrate a robust association between mild to moderate AKI and long-term declines in kidney function. However, patients who develop AKI (ver-sus those who do not) in almost all observational studies are sicker and have more comorbidities, which also con-tribute to the development and progression of CKD. Third, the rate of complications was low and the study was not powered to show a difference. These factors may confound some of the observed associations between AKI and PRD [29]. Fourth, we used the KDIGO criteria to diagnose AKI and most AKIs were diagnosed on the basis of a decreased urine output. The association of oliguria with patient-cen-tered outcomes has been shown in a general ICU popula-tion (where it is mainly shown for the higher AKI stages) [30], but not in a cardiac surgery population which could be explained by the relatively higher incidence of prerenal causes [31]. Further studies have to be performed to inves-tigate whether an association of the oliguric component and long-term outcomes in cardiac surgery patients exists.

One strength of the PrevAKI trial is the generalizability of the data, because the number of exclusion criteria was limited. However, our active intervention was only used in selected patients with positive biomarker results.

The study is not without limitations. Hemodynamic opti-mization, glycemic control, and deferring ACEi/ARBs for the first 48 h after cardiac surgery may be already part of the postoperative management in other centers. However, it is known from other fields in medicine (e.g., sepsis) that the compliance to adhere to guidelines is low [32, 33]. Accord-ing to the EuroSCORE, the 30-day mortality rate of patients in our institution is comparable with the treatment in other centers. Therefore, our data suggest that the adherence to guidelines can reduce the occurrence of CSA-AKI. Although a large difference in the AKI rate was detected, this was not a multicenter trial and, as with many single-center studies, the observed effect size is likely inflated. Another reason why larger trials are needed is that small trials cannot avoid small baseline differences. Therefore, an adequately powered mul-ticenter trial is needed to confirm our results and establish a bundle of supportive measures to reduce the occurrence of CSA-AKI. In addition, this study was not blinded, which could contribute to measurement bias. Finally, we found a non-significant trend towards higher adverse kidney events (MAKE, requirement of RRT, and persistent renal dysfunc-tion) in the intervention group early after surgery. Although the power analysis was performed for the primary endpoint AKI within 72 h after cardiac surgery, it might be possible that implementing this bundle of measures in cardiac surgery patients at high risk for AKI causes a deteriorated patient-centered outcome. Therefore, caution needs to be exercised by interpreting these results and future trials addressing this issue are required.Electronic supplementary materialThe online version of this article (doi:10.1007/s00134-016-4670-3) contains supplementary material, which is available to authorized users.

Author details1 Department of Anesthesiology, Intensive Care and Pain Medicine University, Hospital Münster, Albert-Schweitzer-Campus 1, Building A1, 48149 Münster, Germany. 2 Department of Cardiac Surgery, University of Münster, Münster, Germany. 3 Institute of Biostatistics and Clinical Research, University of Mün-ster, Münster, Germany.

Compliance with ethical standards

Conflicts of interestAZ has received unrestricted grant and lecture fees from Astute Medical as well as lecture fees from Fresenius and Braun. MM has received lecture fees from Astute Medical. The remaining authors declare that they have no conflicts of interest.

Funding/supportThe trial is registered at http://apps.who.int/trialsearch/ (Identifier: DRKS00006139). The study was supported by the German Research Founda-tion (428/6-1 to AZ), the European Society of Intensive Care Medicine, the Innovative Medizinische Forschung (to MM), and an unrestricted research grant from Astute Medical.

Received: 15 October 2016 Accepted: 28 December 2016

Fig. 2 Occurrence of cardiac surgery-associated AKI. Rate of CSA-AKI in control and intervention groups

NottheWholeStoryforAKI

Follow-UpThe median (IQR) duration of the initial hospitalization was9 (6–15) days. Five patients died on or before the day ofdischarge from the initial hospitalization. The remaining 594patients were discharged alive from hospital, 1 of whom waslost to follow-up. Data regarding deaths and hospitalizationswere complete for all the other patients. The mean follow-upof these patients was 797!619 days (median [25th, 75thpercentiles], 671 [261, 1275] days) from discharge. Within 1year after discharge, 78 of these patients died (13.1%), 15(2.5%) received a transplant, and 219 (36.9%) were rehospi-talized for AHF.

Determinants of OutcomesEstimated survival rates in the 4 groups are shown in theFigure (left). The unadjusted risk of death within 1 year of

discharge in patients with WRF alone was not higher thanin patients with neither WRF nor congestion. However,patients with both WRF and congestion were at signifi-cantly higher risk than patients with neither factor. Vari-ables associated with an increased risk of death within 1year after discharge at multivariable analysis were chronicobstructive pulmonary disease, chronic kidney disease,worse NYHA class, higher heart rate, lower blood pres-sure, lower body weight, and lower serum sodium (Table2). After adjustment for these variables, the mortality risksfor patients with either WRF alone or residual congestionat discharge alone were not significantly greater than thatof patients with neither factor. The increased risk appearedto be driven primarily by the presence of congestion(Figure), and the interaction of congestion with WRF wasnot statistically significant (P"0.3074). Patients with both

Table 1. Continued

ParametersAll

(n"594)WRF and

Cong (n"45)No WRF/Cong

(n"31)WRF/No

Cong (n"253)No WRF/No

Cong (n"265)P

Value

Medications

ACE inhibitors and/or ARBs, n (%)

Admission 449 (76) 30 (67) 25 (81) 186 (74) 208 (78) 0.2599

Discharge 458 (77) 28 (62) 24 (77) 187 (74) 219 (83) 0.0085

Aldosterone antagonists, n (%)

Admission 309 (52) 24 (53) 16 (52) 132 (52) 137 (52) 0.9986

Discharge 402 (68) 32 (71) 22 (71) 175 (69) 173 (66) 0.7514

!-Blockers, n (%)

Admission 357 (60) 24 (53) 19 (61) 147 (58) 167 (63) 0.7262

Discharge 477 (80) 31 (69) 27 (87) 209 (83) 210 (79) 0.1305

Furosemide, n (%)

Admission 585 (99) 44 (100) 31 (100) 251 (100) 259 (98) 0.1813

Discharge 564 (95) 45 (100) 31 (100) 241 (95) 247 (93) 0.1239

Furosemide dose, mg/d, mean!SD 108.9!147.49 230.5!202.18 142.7!161.43 124.1!151.84 71.2!113.83 #0.0001

Intravenous therapy duringhospitalization

Nitrates, n (%) 179 (30) 12 (27) 13 (42) 89 (35) 65 (25) 0.0238

Inotropes or dopamine, n (%) 148 (25) 18 (40) 7 (23) 80 (32) 43 (16) #0.0001

WRF indicates worsening renal function; Cong, congestion; CVD, cardiovascular disease; CAD, coronary artery disease; COPD, chronic obstructive pulmonarydisease; NYHA, New York Heart Association; LVEF, left ventricular ejection fraction; eGFR, estimated glomerular filtration rate; IQR, interquartile range; BUN, blood ureanitrogen; BNP, brain natriuretic peptide; ACE, angiotensin-converting enzyme; ARB, adrenergic receptor binder.

Figure. Outcome for 1-year death or urgent heart transplantation (Tx) (left) and for the combined end point of 1-year death,urgent heart transplantation, or heart failure (HF) readmission (right) for the patients subdivided on the basis of the developmentof worsening renal function (WRF) and on the presence of signs of congestion (Cong) at discharge. The number of patients at riskis shown at the bottom.

Metra et al Congestion and Worsening Renal Function in AHF 57

at UNIV OF CALIFORNIA SAN DIEGO on June 4, 2016http://circheartfailure.ahajournals.org/Downloaded from

MetraK etal,Circ HeartFail.2012Jan;5(1):54-62

European Journal of Heart Failure (2017)doi:10.1002/ejhf.746

Urinary levels of novel kidney biomarkers andrisk of true worsening renal function andmortality in patients with acute heart failureMateusz Sokolski1,2*, Robert Zymlinski2, Jan Biegus1,2, Paweł Siwołowski2,Sylwia Nawrocka-Millward2, John Todd3, Malli Rama Yerramilli3, Joel Estis3,Ewa Anita Jankowska2,4, Waldemar Banasiak2, and Piotr Ponikowski1,2

1Wroclaw Medical University, Department of Heart Diseases, Wroclaw, Poland; 2Centre for Heart Disease, Clinical Military Hospital, Wroclaw, Poland; 3Singulex, Inc., Alameda,CA, USA; and 4Wroclaw Medical University, Laboratory for Applied Research on Cardiovascular System, Department of Heart Diseases, Wroclaw, Poland

Received 3 April 2016; revised 2 December 2016; accepted 7 December 2016

Aims Recent studies indicate the need to redefine worsening renal function (WRF) in acute heart failure (AHF), linkinga rise in creatinine with clinical status to identify patients who develop ‘true WRF’. We evaluated the usefulness ofserial assessment of urinary levels of neutrophil gelatinase-associated lipocalin (uNGAL), kidney injury molecule-1(uKIM-1), and cystatin C (uCysC) for prediction of ‘true WRF’.

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Methodsand results

In 132 patients with AHF, uNGAL, uKIM-1, and uCysC were measured using a highly sensitive immunoassay basedon a single-molecule counting technology (Singulex, Alameda, CA, USA) at baseline, day 2, and day 3. Patients whodeveloped WRF (a ≥0.3 mg/dL increase in serum creatinine or a >25% decrease in the estimated glomerular filtrationrate from the baseline value) were differentiated into those ‘true WRF’ (presence of deterioration/no improvementin clinical status during hospitalization) vs. ‘pseudo-WRF’ (uneventful clinical course). ‘True WRF’ occurred in 13(10%), ‘pseudo-WRF’ in 15 (11%), whereas the remaining 104 (79%) patients did not develop WRF. Patients with‘true WRF’ were more often females, had higher levels of NT-proBNP, creatinine, and urea on admission, higherurine albumin to creatinine ratio at day 2, higher uNGAL at baseline, day 2, and day 3, and higher KIM-1 at day 2 (vs.pseudo-WRF vs. without WRF, all P < 0.05). Patients with pseudo-WRF did not differ from those without WRF. Inthe multivariable model, elevated uNGAL at all time points and uKIM-1 at day 2 remained independent predictorsof ‘true WRF’.

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Conclusion Elevated levels of uNGAL and uKIM-1 may predict development of ‘true WRF’ in AHF.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Keywords Acute heart failure • Worsening renal function • Urinary biomarkers • Neutrophil

gelatinase-associated lipocalin • Kidney injury molecule-1 • Cystatin C

IntroductionAlthough deterioration in renal function is one of the most impor-tant co-morbidities1 and often complicates the natural courseof hospital stay in patients with acute heart failure (AHF), theterminology and optimal assessment of renal function in this clin-ical condition remain controversial.2 In everyday practice, serumcreatinine is used to monitor renal function, with a rise in its level

*Corresponding author. Wroclaw Medical University, Department of Heart Diseases, Rudolfa Weigla 5, 50–981 Wroclaw, Poland. Tel: +48 261660250, Fax: +48 261660250, Email:matsok@gmail.com

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.. traditionally considered as indicating worsening renal function(WRF) and linked with inevitably poor outcomes.3 However, theresults of recent studies have challenged this approach, showingthat not every increase in serum creatinine levels in AHF indicateskidney injury/dysfunction with unfavourable consequences.4,5

Effective decongestion may be associated with a reduction inintravascular volume, leading to a rise in serum creatinine lev-els but at the same being related to better outcomes.6 These

© 2017 The AuthorsEuropean Journal of Heart Failure © 2017 European Society of Cardiology

• 132patientswithAHF• SerialurineNGAL,KIM-1,andcystatin C• WRFdefinedas≥0.3mg/dLincreasecreatinineor≥25%decreaseGFR

• ‘TrueWRF’– deteriorationornoimprovement• ‘PseudoWRF’– uneventfulclinicalcourse

AUC0.74-0.83foruNGAL anduKIM-1fortrueWRF

4 M. Sokolski et al.

Table 2 Correlation between new and traditionalkidney biomarkers

uKIM-1 uNGAL uCysC. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Creatinine r=−0.077 r= 0.079 r=−0.113eGFR r= 0.035 r=−0.178 r= 0.083Urea r=−0.029 r= 0.139 r=−0.064UACR r= 0.693* r= 0.502* r= 0.643*

uKIM-1 r= 0.569* r= 0.672*

uNGAL r= 0.569* r= 0.528*

uCysC r= 0.672 r= 0.528*

eGFR, estimated glomerular filtration rate; UACR, urine albumin to creatinineratio; uCysC, urinary cystatin C; uKIM-1, urinary kidney injury molecule-1;uNGAL, urinary neutrophil gelatinase-associated lipocalin.*P< 0.001.

baseline uNGAL

uNGAL at day 2

uNGAL at day 3

uKIM-1 at day 2

0,0 0,2 0,4 0,6 0,8 1,0

1-Specificity

0,0

0,2

0,4

0,6

0,8

1,0

ytivitisneS

Figure 1 Receiver operating characteristic curves for novelbiomarkers to predict true worsening renal function. uNGAL,urinary neutrophil gelatinase-associated lipocalin; uKIM-1, urinarykidney injury molecule-1.

and uKIM-1, as predictors of true WRF, showed the followingarea under the curve with 95% CIs: for baseline uNGAL, 0.76(0.63–0.90); for uNGAL at day 2, 0.83 (0.73–0.93); for uNGAL atday 3, 0.77 (0.60–0.94); and for uKIM-1 at day 2, 0.74 (0.59–0.90)(Figure 1). The optimal cut-off values were as follows: for baselineuNGAL, 29.2 μg/gCr (sensitivity 77%, specificity 69%); for uNGALat day 2, 24.4 μg/gCr (sensitivity 91%, specificity 69%); for uNGALat day 3, 32.5 μg/gCr (sensitivity 73%, specificity 78%); and foruKIM-1 at day 2, 1510 ng/gCr (sensitivity 80%, specificity 66%).

Length of hospital stayThe median (with lower and upper quartiles) length of hospitalstay, after excluding the patients who died during hospitalization,was 7 (6–11) days. Patients with true WRF were hospitalized for 7(6–13) days, those with pseudo-WRF for 4 (4–12) days, and thosewithout WRF for 7 (6–11) days (P= 0.144). Patients with longerlength of stay (above the median) had higher levels of creatinineand urea on admission: 1.3 (1.0–1.6) vs. 1.1 (0.9–1.4) mg/dL,P= 0.039 for creatinine; and 59 (42–89) vs. 48 (37–68) mg/dL, ..

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.. P= 0.035 for urea. There were no significant differences for thenovel biomarkers.

MortalityDuring the 1-year follow-up there were 36 (27%) deaths, made upof 5 (38%) deaths in patients with true WRF, 2 (13%) of those withpseudo-WRF, and 29 (28%) of those without WRF (P= 0.32).

In the univariable Cox model, elevated uNGAL on admission, atdays 2 and 3 were associated with an increased risk of death (HR1.29, 95% CI 1.00–1.67, P= 0.050; HR 1.29, 95% CI 1.04–1.60,P= 0.018; and HR 1.32, 95% CI 1.03–1.70, P= 0.026, respectively;units used for Cox model: 1 Ln μg/gCr). Similarly, elevated uCysCmeasured at day 2 was related to higher mortality (HR 1.35, 95%CI 1.08–1.68, P= 0.007; units used for Cox model: 1 Ln μg/gCr).Adjustment for the other prognosticators which appeared signifi-cant in univariable analysis (systolic blood pressure, catecholamineuse, baseline NT-proBNP, and urea levels) was performed. Ele-vated levels of uNGAL on admission (HR 1.39, 95% CI 1.03–1.87,P= 0.031), at day 2 (HR 1.35, 95% CI 1.04–1.74, P= 0.022), and atday 3 (HR 1.45, 95% CI 1.09–1.92, P= 0.011), and uCysC at day2 (HR 1.34, 95% CI 1.08–1.68, P= 0.009) were the predictors ofmortality (units used for Cox model: 1 Ln μg/gCr). The levels ofuKIM-1 at all analysed time points did not predict mortality.

DiscussionThis study applies the new definition of WRF, placing the inter-pretation of the changes in creatinine/eGFR in the context of theclinical status of patients with AHF. With such an approach, thekey finding of our study is that ∼10% of all AHF patients developedtrue WRF defined as a rise in creatinine/drop in eGFR accompaniedby an adverse clinical course. Female gender, elevated NT-proBNP,less frequent use of ACE inhibitors/ARBs, but mainly changes inrenal biomarkers characterized this group. Importantly, patientswith pseudo-WRF, defined as having only an increase in creatininewith an uneventful clinical course, did not differ from those with-out WRF.

Neither the prevalence nor the predictors of true WRF foundin our study can be directly compared with other reports asthey used a ‘traditional’ definition of WRF based solely on serialassessment of creatinine levels. However, looking at the totalnumber of patients who developed an increase in creatinine levelsfulfilling the widely used definition of WRF used in the literature(i.e. an increase of ≥0.3 mg/dL during hospital stay), we identified21% of such patients in our AHF cohort, which is comparablewith the prevalence of WRF previously reported.3 Importantly,splitting these patients into two categories, i.e. those with trueWRF vs. those with pseudo-WFR, we found an approximatelyequal number of patients in each category. Only the former groupdiffered significantly from patients who did not experience any risein creatinine/drop in eGFR. We believe that this is a relevant findingsupporting the concept proposed by Damman and Testani tocombine changes in renal function measures with clinical responsein order to distinguish and characterize patients developing kidney

© 2017 The AuthorsEuropean Journal of Heart Failure © 2017 European Society of Cardiology

Moretotherelationship

retrospective analysis of GISSI-HF, tubular damage assessed byurinary markers such as N-acetyl-b-D-glucosaminidase (NAG), neu-trophil gelatinase-associated lipocalin (NGAL), and kidney injurymolecule 1 (KIM-1) was frequently present among patients withchronic HFand strongly associated with mortality.44 In acute HF, mul-tiple studies have assessed the prevalence of tubular injury. Most ofthe research focusing on tubular damage markers in acute HF hasbeen focused on the identification of patients at risk of WRF. Innon-HF patient populations, tubular damage markers are sensitiveand specific markers of severe AKI.46 Unfortunately, studies inacute HF that have been conducted thus far have failed to demon-strate clinical usefulness of NGAL to identify patients at risk of clinicalsignificant WRF, and notably in patients that do develop WRF urineNGAL levels do not meaningfully increase.47,48 In chronic HF,urinary KIM-1 levels were the best predictors of WRF.49 Withrespect to therapy, loop diuretics that seem to reduce urinaryNAG and KIM-1 levels in stable HF patients and reducing congestion

has been shown to improve albuminuria in acute HF.50,51 Until wehave more information on the clinical applicability of these novel(tubular) markers, their routine use in patients with HF does notseem justified yet.

Patient identification and prognosticationClearly, identification of patients at high risk of mortality and/or HFhospitalizations should include some measure of ‘renal function’: aGFR, and possibly, albuminuria or a marker of tubular damage.Recent reports have indicated that blood urea nitrogen (BUN)could be an even better prognosticator that resembles (someform) of GFR. However, BUN has been associated with factorsbeyond glomerular filtration, such as neurohormonal activationand haemodynamic status, which could be the reason for the factthat it retains powerful prognostic information even after controllingfor GFR.52

Figure 3 Pathophysiologic pathways of cardiorenal interaction. AKI, acute kidney injury; CO, cardiac output; CVP, central venous pressure; DCM,dilated cardiomyopathy; GFR, glomerular filtration rate; HFPEF, heart failure with preserved ejection fraction; HFREF, heart failure with reducedejection fraction; IL-18, interleukin 18; KIM-1, kidney injury molecule 1; L-FABP, liver type fatty acid binding protein; LVAD, left ventricular assistdevice; NAG, N-acetyl-b-D-glucosaminidase; NGAL, neutrophil gelatinase-associated lipocalin; NTproBNP, N-terminal pro brain natriureticpeptide; RAAS, renin angiotensin aldosterone system; SNS, sympathetic nervous system; WRF, worsening renal function. The diagram illustratespredisposing factors that can cause both cardiac and renal disease. From both ends of the spectrum, disease of one organ can lead to progressivedysfunction leading to heart and renal failure. Both interact with each other through haemodynamic and (neurohormonal) (mal)adaptive processes,and modulating factors further affect these associations. Further progression of disease is caused by (re)hospitalizations. Eventually, patients enter avicious circle of mutual organ dysfunction, resulting either in end stage renal disease, end stage heart failure, or a combination of both. Illustrations(adapted from) Servier Medical Art (http://www.servier.com/Powerpoint-image-bank), under the Creative Commons Attribution 3.0 UnportedLicense (http://creativecommons.org/licenses/by/3.0/).

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FutureDirections

• Definingsignificantkidneyinjury(‘true’AKI)complicatedinHF

• Identifyingnon-significantWRFmaybewhypriorbiomarkersstudieswerenegative

• Apanelofbiomarkersreflectingpathophysiologylikelyneeded

• Forourcases,needtoidentifyinjuryearlyandifsignificantinjuryoccurringtoinstitutemeasurestoreduceprogressionofAKI

ThankYou