Urinary biomarkers may provide prognostic information for subclinical acute kidney injury after cardiac surgery

Accepted Manuscript Urinary biomarkers may provide prognostic information for subclinical acute kidney injury after cardiac surgery Dr. Christian Albert, MD, Dr. Annemarie Albert, MD, Johanna Kube, MD, Prof. Rinaldo Bellomo, MD, Dr. Nicholas Wettersten, MD, Prof. Hermann Kuppe, MD, Prof. Sabine Westphal, MD, Prof. Michael Haase, MD, Dr. Anja Haase-Fielitz, PharmD PII:

S0022-5223(17)33020-9

DOI:

10.1016/j.jtcvs.2017.12.056

Reference:

YMTC 12398

To appear in:

The Journal of Thoracic and Cardiovascular Surgery

Received Date: 14 March 2017 Revised Date:

13 November 2017

Accepted Date: 15 December 2017

Please cite this article as: Albert C, Albert A, Kube J, Bellomo R, Wettersten N, Kuppe H, Westphal S, Haase M, Haase-Fielitz A, Urinary biomarkers may provide prognostic information for subclinical acute kidney injury after cardiac surgery, The Journal of Thoracic and Cardiovascular Surgery (2018), doi: 10.1016/j.jtcvs.2017.12.056. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT

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Urinary biomarkers may provide prognostic information for subclinical acute kidney

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injury after cardiac surgery.

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Dr. Christian Albert, MDa; Dr. Annemarie Albert, MDa; Johanna Kube, MDa,b; Prof. Rinaldo

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Bellomo, MDc; Dr. Nicholas Wettersten, MDd; Prof. Hermann Kuppe, MDe; Prof. Sabine

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Westphal, MDf; Prof. Michael Haase, MDg; Dr. Anja Haase-Fielitz, PharmDh,i

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Clinic of Nephrology & Hypertension, Diabetes & Endocrinology,

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Otto-von-Guericke University Magdeburg, Germany

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Germany

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Magdeburg; MHB, Germany

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Germany

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an der Havel, Germany

Department of Intensive Care, German Heart Center Leipzig, University Clinic, Leipzig,

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Department of Intensive Care, The Austin Hospital, Melbourne, Australia Division of Cardiovascular Medicine, University of California, San Diego, La Jolla, California

Department of Anesthesiology, The German Heart Center, Berlin, Germany

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Institute of Laboratory Medicine, Hospital Dessau, Germany

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Diaverum Deutschland, Potsdam, Germany; Medical Faculty Otto-von-Guericke University

Institute of Social Medicine and Health Economics, Otto-von-Guericke University, Magdeburg,

Research and Science Administration of The Medical School Brandenburg (MHB), Brandenburg

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Correspondence address

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Dr. Anja Haase-Fielitz

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Institute of Social Medicine and Health Economics

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Otto-von-Guericke University Magdeburg

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Leipziger Straße 44, 39120 Magdeburg, Germany

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Tel.: 0391-6724324, Fax: 0391-6724310

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E-mail: [email protected]

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Disclosures

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Relationship with industry

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Dr. Bellomo has acted as paid consultant to Abbott Diagnostics and Biosite Inc.

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Dr. Haase has received honoraria for speaking for Abbott Diagnostics, Alere and Biosite Inc.

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Dr. Albert has received honoraria for speaking for Siemens Healthcare Diagnostics

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Companies are involved in the development of NGAL assays to be applied in clinical practice.

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Sources of funding

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Data base of this study was funded by grants from the German Heart Foundation (Deutsche

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Stiftung für Herzforschung, Frankfurt a. M., Germany), the Else Kröner-Fresenius-Stiftung (Bad

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Homburg, Germany), the Canadian Intensive Care Foundation (Edmonton, Canada), the

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Intensive Care Foundation (Melbourne, Australia), and the Austin Hospital ICU Research Fund

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(Melbourne, Australia).

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Word count: 3500

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Abbreviations

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AKI

acute kidney injury

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CI

confidence interval

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CPB

cardiopulmonary bypass

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CRP

C-reactive protein

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ICU

intensive care unit

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IL-6

Interleukin-6

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MAKE

major adverse kidney events

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NGAL

neutrophil gelatinase-associated lipocalin

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OR

odds ratio

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RIFLE

renal risk, injury, failure, loss of renal function, end stage renal disease classification

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RRT

renal replacement therapy

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Central Message

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Positive urinary kidney-biomarker test result post cardiac surgery carries prognostic information

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regardless of whether renal function will acutely decline (positive RIFLE-criteria) or not.

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Perspective Statement

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Information about subclinical acute kidney injury (AKI) is detectable with urine tests readily

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available in routine clinical practice. Recognition of subclinical AKI carries prognostic

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information and may contribute to refined clinical risk assessment.

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Single biomarker-positive, subclinical AKI deserves further investigation as a novel

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pathophysiological AKI-phenotype.

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Abstract

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Objective

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This study aimed to determine the biomarker-specific outcome patterns and short-and long-term

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prognosis of CS-AKI identified by standard criteria and/or urinary kidney-biomarkers.

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Methods

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Patients

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(NCT00672334). Standard Risk Injury Failure Loss and End-stage (RIFLE) criteria (incl. serum

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creatinine and urine output) and urinary kidney-biomarker test result (NGAL, Midkine,

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Interleukin-6, proteinuria) were used for diagnosis of postoperative CS-AKI. Primary endpoint

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was acute renal replacement therapy (RRT) or in-hospital mortality. Longterm endpoints among

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others included 5-year-mortality. Patients with single-biomarker-positive subclinical-AKI

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(RIFLE-negative) were identified. We controlled for systemic inflammation using C-reactive

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protein.

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Results

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Urinary biomarkers (NGAL, Midkine, IL-6) were identified as independent predictors of the

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primary endpoint. NGAL-, Midkine-, or IL-6-positivity or de-novo/worsening proteinuria

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identified 21.1%, 16.9%, 30.5% and 48.0% more cases, respectively, with likely subclinical-AKI

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(Biomarker-positive/RIFLE-negative) additionally to cases with RIFLE-positivity alone. Patients

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with likely subclinical-AKI (NGAL- or IL-6-positive) had increased risk of primary endpoint

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(adjusted HR 7.18 (95%CI 1.52-33.93, P=0.013) and 6.27 [1.12-35.21], P=0.037), respectively.

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Compared with biomarker-negative/RIFLE-positive patients, NGAL-positive/RIFLE-positive or

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Midkine-positive/RIFLE-positive patients had increased risk of primary endpoint (OR 9.6 [1.4-

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67.3], P=0.033; OR 14.7 [2.0-109.2], P=0.011, respectively). 3-11% of CS-patients appear to be

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(N=200),

originated

from

the

German

‘BIC-Multicenter

Study’

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enrolled

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affected by single-biomarker-positive subclinical-AKI. 5

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During follow up, kidney biomarker-defined short-term outcomes appeared to translate into long-

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term outcomes.

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Conclusions

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Urinary kidney-biomarkers identified RIFLE-negative patients with high-risk subclinical-AKI as

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well as a higher risk-subgroup of patients among RIFLE-AKI-positive patients. These findings

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support the concept that urinary biomarkers define subclinical-AKI and higher risk

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subpopulations with worse long-term prognosis among standard AKI patients.

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Keywords

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acute kidney injury, cardiac surgery, neutrophil gelatinase-associated lipocalin (NGAL),

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Midkine, interleukin-6 (IL-6), subclinical AKI

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Introduction

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Acute kidney injury (AKI) is a common postoperative complication in critically ill patients after

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cardiac surgery.1,2,3 The consensus definition of AKI is currently based on changes in serum

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creatinine and urine-output. However, recent data have challenged this paradigm. A pooled

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analysis of prospective cohort studies highlighted the prognostic relevance of combining routine

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kidney function parameters with kidney injury markers.4 Kidney biomarker test results identified

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patients with AKI being at increased risk of adverse outcomes.4 Also, a condition referred to as

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’subclinical AKI’ (positive biomarker status without creatinine- or urine output-based criteria for

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AKI) was identified.5 On the basis of studies predominantly using neutrophil gelatinase-

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associated lipocalin (NGAL) for outcome prediction,4,6,7 kidney biomarkers have been suggested

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to complement serum creatinine- or urine output-based criteria for AKI diagnosis.8 Despite its

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obvious importance, this new concept has not been sufficiently validated.9

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In this study we chose NGAL, IL-6, Midkine and total protein to be measured in urine as AKI

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biomarkers.10-16

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A concern about all of these biomarkers, however, relates to the fact that they may simply

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represent markers of inflammation rather than renal injury itself because they have not been

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adjusted for the degree of inflammation using non-renal markers of post cardiac surgery

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inflammation such as C-reactive protein (CRP).17,18

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Accordingly, the aim of this study was to evaluate the performance and degree of

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agreement of multiple different urinary kidney biomarkers according to AKI subtypes

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(biomarker-/RIFLE-status) in relation to short- and long-term outcomes of patients undergoing

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cardiac surgery and to control findings for a key marker of systemic inflammation (CRP). Also,

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the frequency and type of subclinical AKI (positivity of any biomarker but RIFLE-negativity)

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should be determined. 7

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Subjects and Methods

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This ancillary study of the ‘BIC-Multicenter Study’ used a cohort of 200 patients who underwent

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elective open-heart surgery with the use of CPB enrolled at one of the study centers, specifically

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the ‘German Heart Center Berlin’, (NCT00672334) from January 2009 through June 2010 (Fig.

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1). Full study details were described previously.19

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Patients

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In brief, we enrolled adult cardiac surgery patients at increased risk of AKI at a tertiary hospital

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(Supplemental Table 9).19 We excluded patients with chronic renal impairment (preoperative

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serum

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immunosuppression and those enrolled in conflicting research study. The institutional Ethics

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Committees granted permission to collect data, biomarker measurement and long-term outcomes

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including contact to patients and their physicians for this study (Charité University Medicine

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Berlin, Germany: ZS EK 11 654/07; University of Magdeburg, Germany: No. 61/14;2014).

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Written informed consent from all patients was obtained.

>300µmol/L),

emergency

cardiac

surgery

procedure,

patients

on

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creatinine

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Biomarker measurements

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Urine and blood samples were obtained preoperatively and at 6 and 24 hours after

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commencement of CPB, immediately centrifuged at 5000rpm and stored at -80°C. For

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measurement of urinary NGAL, the ARCHITECT® platform (Abbott Diagnostics, Abbott Park,

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IL, USA) was used (10ng/mL to 1500 ng/mL, imprecision≤10%). Urinary IL-6 and serum CRP

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were determined using Cobas® e/c411 Immunoassay Analyzer Platform (ROCHE Diagnostics,

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Mannheim,

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Germany)

(IL-6:

1.5–5.000pg/mL,

variability<9%;

CRP

0.3-350mg/L, 8

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variability<5%). Urinary Midkine was measured using human Midkine sandwich ELISA

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development

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spectrophotometer TECAN Infinite 200; Tecan Group Inc., Durham, NC). Urine dipstick for

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assessment of proteinuria (Boehringer Ingelheim Pharma, Germany) was performed at all study

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measurement time points and quantified to the manufacturer’s instructions using the dipstick’s

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scale ‘none’, ‘+’, ‘++’, ‘+++’.

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Biomarker test results were available in 199 out of 200 patients for NGAL, 195 patients for

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Midkine, 190 patients for uIL-6, 199 patients for proteinuria and 194 patients for CRP.

Hamburg,

Germany)

(15-2000pg/mL

analyzed

by

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(PeproTech,

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kit

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AKI and CKD definition

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AKI was defined according to the R-risk, I-injury, or F-failure (RIFLE) criteria using increases of

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postoperative serum creatinine from preoperative baseline and decline in urine-output, both

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assessed during the first 7 postoperative days.20 We used RIFLE criteria to enable comparability

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with previous studies4,6,7 and because literature indicates a trend for higher discriminative value

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in predicting hospital mortality in cardiac surgery patients.21,22 Chronic kidney disease (CKD)

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stages were reported according to the ‘Kidney Disease: Improving Global Outcome’(KDIGO).23

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Patient allocation according to AKI subtypes

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For allocation of patients according to AKI subtypes, RIFLE- and biomarker-status was

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determined for each patient. In our patient cohort, urinary kidney biomarker-positivity(+) or

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biomarker-negativity(-) was defined as a biomarker concentration measured at 6 hours after

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commencement of CPB above or below a biomarker specific cut-off value, as determined by the

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maximum Youden’s Index (optimum cut-off point for combined sensitivity and specificity for

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reported endpoints).24 The following cut-off values for AKI were determined: NGAL ≥50ng/mL,

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Midkine ≥250pg/mL, IL-6 ≥11pg/mL, Proteinuria ≥30mg/dL (dipstick-equivalent), CRP

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≥3mg/L, and CRP at 24 hours ≥67mg/L. AUC-ROC values and ROC-comparisons are provided

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as supplemental material. We applied the terms “NGAL/Midkine/IL-6/proteinuria(+)” or “NGAL/Midkine/IL-

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6/proteinuria(-)” to indicate the presence or absence of biomarker-positive AKI.4 For CRP the

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terms were adapted accordingly. Proteinuria-positivity was defined as early postoperative de-

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novo or worsening proteinuria (according to change in semiquantitative dipstick’s scale)

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compared to preoperative value avoiding carry-over effects of preoperative CKD.

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In summary, four different combinations could be distinguished considering RIFLE status and urinary biomarker status:

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Biomarker(-)/RIFLE(-), Biomarker(+)/RIFLE(-),

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Biomarker(-)/RIFLE(+), Biomarker(+)/RIFLE(+).

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200

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To calculate the proportion of patients with AKI additionally identified by urinary kidney

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biomarkers only and not by RIFLE criteria (subclinical AKI) in relation to the proportion of

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patients diagnosed to have AKI by conventional, RIFLE-based criteria, we used the formula

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(+)/ (−)
(+)/ (−) +
(−)/ (+) +
(+)/ (+) =

        

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Patient follow-up 10

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We obtained patient kidney and vital status at 90 days postoperative as well as at least 5 years

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after discharge through various mechanisms and cross-referenced when possible. We performed

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phone calls and contact by mail to patients’ homes and physicians and reviewed hospital and

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physicians’ records. Serum creatinine, urine protein/albumin concentrations, if available, death

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status and date of death were recorded. As of July 2015, data collection was closed.

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Study endpoints

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For the purpose of this study on AKI subtypes, patient relevant endpoints were predefined before

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conduction of statistical analysis. The primary endpoint was a combination of acute renal

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replacement therapy (RRT) or in-hospital mortality. Secondary endpoint, ‘major adverse kidney

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events’ (MAKE) was defined as the occurrence of one or more of the following criteria: sustained

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AKI (stages I or F without recovery within three days), acute RRT or in-hospital mortality. Long-

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term outcomes consisted of long-term mortality and development of worsening of preoperatively

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present CKD (supplemental material) according to KDIGO stages.

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Statistical analysis

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Clinicians caring for the patients remained blinded to biomarker test results and laboratory

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investigators remained blinded to patient outcomes. ANOVA, Mann-Whitney U-test, Kruskal-

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Wallis test, Pearson’s chi-square test or Fisher exact test were used where appropriate. The odds

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ratio (OR) and 95% confidence interval (CI) were calculated for assessment of risk-disparity

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between biomarker/RIFLE groups. A two-tailed p-value of ≤0.05 was considered statistically

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significant. We performed measurement of agreement among all biomarkers testing for Kappa

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variables and interpreted results according to Landis&Koch (supplemental material).25

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Multivariate regression analyses were undertaken to assess robustness of urinary biomarkers as

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independent predictors of ‘RRT or in-hospital mortality’ based on a previously published

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reference model,26 including a urinary biomarker as well as chronic heart failure, preoperative

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estimated glomerular filtration rate (CKD-EPI), peripheral vascular disease, sex, chronic

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obstructive pulmonary disease, type of surgery and repeated cardiac surgery as independent

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variables.

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Cox proportional-hazard regression models adjusted for baseline group differences were

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performed for confirmation of primary and secondary endpoints (age, type of surgery [Coronary

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artery bypass graft vs. Concomitant surgery], previous cardiac surgery and perioperative fluid

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balance 0-6 hours).

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Influences of cardiovascular risk factors, baseline renal function and biomarker/RIFLE group

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allocation on long-term patients’ survival were assessed using cox proportional-hazard regression

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models. Kaplan-Meier curves are presented. Differences of curves were evaluated using Log-

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Rank test, Breslow-test and Tarone-Ware test. SPSS for Windows, Version 22.0 (IBM Corp.,

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Armonk, New York, USA) and MedCalc Version 17.6 (MedCalc Software, Ostend, Belgium)

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were used for statistical analysis.

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Results

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Pre- and perioperative characteristics of patients

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Patient characteristics according to NGAL/RIFLE-status are presented in Table 1a,b. For

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Midkine-/IL-6-/Proteinuria-/CRP- and RIFLE-status, patient characteristics are shown in

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Supplemental Table 1-4. Patient flow through study is illustrated in Fig.1.

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All urinary biomarkers but not CRP were found to be independent predictors of the primary

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endpoint ‘RRT or in-hospital mortality’ in a multivariate clinical model based on the Cleveland

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risk assessment model provided in Table 2.26

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The majority of patients (66.8 %) was classified as NGAL(-)/RIFLE(-) (no renal injury),

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21.1% as NGAL(+)/RIFLE(-) (subclinical AKI), 4.5% as NGAL(-)/RIFLE(+) (isolated

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functional AKI) and 7.5% as NGAL(+)/RIFLE(+) (higher risk functional AKI) (Table 1).

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Within all four NGAL/RIFLE groups, patients were virtually similar with regard to

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demographic data, comorbidities and preoperative medications except for age. RIFLE-positive

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patients were older than RIFLE-negative patients independent of NGAL-status. More patients

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with NGAL-positivity received non-CABG operations. Significant group differences existed for

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duration of CPB, fluid intake/balance, drain output, urine output and transfusion of packed red

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blood cells and furosemide (all P<0.001).

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AKI subgroups - RIFLE-AKI, subclinical AKI and single-biomarker-positive subclinical AKI

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In our study cohort, 24 (12%) patients developed RIFLE-positive AKI.

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NGAL-positivity in RIFLE-negative patients additionally identified 42 patients with

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suspected ‘subclinical’ AKI (+63.6%) compared to RIFLE-based criteria only. Moreover, using

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Midkine, 33 patients (+57.9%) or IL-6 58 patients (+74.4%) were additionally identified as

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having ‘subclinical’AKI. Finally, using dipstick proteinuria 95 patients (+79.8%) were

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additionally identified. Eight patients out of 200 patients (4%) were only NGAL(+)/RIFLE(-) but not Midkine(+)

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or IL-6(+). Six patients (3%) were Midkine(+)/RIFLE(-) but not NGAL(+) or IL-6(+). Twenty-

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two patients (11%) were only IL-6(+)/RIFLE(-) but not NGAL(+) or Midkine(+).

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Patient outcomes according to urinary kidney biomarker-/RIFLE-status

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In multivariable cox regression analyses adjusted for clinically relevant baseline differences

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allocation to all biomarker/RIFLE groups, was independently associated with the primary and

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secondary endpoints (Supplemental Table 6).

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Independent of RIFLE-status, urinary kidney biomarker-positive patients had worse outcomes

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compared to urinary biomarker-negative patients. Worst outcomes were found in NGAL(+) or

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Midkine(+) or IL-6(+) or proteinuria(+) AND RIFLE(+) patients, whereas best outcomes in

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biomarker(-) and RIFLE(-) patients (Fig.2, Supplemental Table7).

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The outcome patterns for initiation of acute RRT, in-hospital mortality, combined RRT or in-

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hospital mortality, and for MAKE were similar with urinary NGAL-, Midkine- and IL-6 (Fig.2),

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but less pronounced with de-novo or worsening proteinuria (Fig.2d) and not detected by CRP

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(Fig.3).

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Primary endpoint

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Odds ratios (OR) for the primary endpoint (acute RRT or in-hospital mortality), in

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NGAL(+)/RIFLE(-) or in IL-6(+)/RIFLE(-) patients (subclinical AKI) vs. NGAL(-)/RIFLE(-) or

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IL-6(-)/RIFLE(-) patients was 5.86 (95% confidence intervals (CI) 1.34-25.65; P=0.020, adjusted

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Hazard ratio (HR) 7.18 (1.52-33.93, P=0.013, Table3) for NGAL and 6.35 (95% CI 1.24-32.52,

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P=0.020, adjusted HR 6.27 [1.12-35.21], p=0.037), for IL-6 respectively (Fig.2a,b, Table 3).

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OR for the primary endpoint, in NGAL-positive/RIFLE-positive or Midkine-positive/RIFLE-

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positive versus NGAL-negative/RIFLE-positive or Midkine-negative/RIFLE-positive patients

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were 9.6 (95% CI 1.4-67.3 P=0.033) and 14.7 (95% CI 2.0-109.2, P=0.011), respectively. After

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adjustment for baseline differences all biomarkers showed a stepwise increase in adjusted HR for

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development of the primary endpoint. Midkine and Proteinuria detected only non-significantly

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different risk profiles for subclinical AKI versus biomarker(-)/RIFLE(-) patients (Table 3,

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Supplement Table7).

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Biomarker [NGAL/Midkine/IL-6/proteinuria]-positive/RIFLE-positive status indicated

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increased risk for developing the primary endpoint compared with biomarker(-)/RIFLE(-) status

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with adjusted hazard ratios ranging from 66 (for de-novo or worsening proteinuria) to 100 (for

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NGAL), all P<0.001 (Table 3, Supplement Table 7).

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Secondary endpoints

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NGAL(+)/RIFLE(-) vs. NGAL(-)/RIFLE(-) and IL-6(+)/RIFLE(-) vs. IL-6(-)/RIFLE(-) status

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had significantly increased risk of acute RRT, in-hospital mortality or MAKE (OR’s 5.0-13.9, all

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P<0.05) (Fig.2a+2c). Occurrence of MAKE increased from 2.2% in NGAL(-)/RIFLE(-) patients

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to 10.2% in NGAL(+)/RIFLE(-) patients (OR 5.86 [95% CI 1.34-25.65], P=0.020, Fig.1a) and

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from 2.4% in IL-6(-)/RIFLE(-) to 11.0% in IL-6(+)/RIFLE(+) patients (OR 6.35 [95% CI 1.24-

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32.52], P=0.020, Fig.2c). For Midkine(+)/RIFLE(-) vs. Midkine(-)/RIFLE(-) patients, a similar

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pattern for MAKE could be observed (OR 2.66 [95% CI 0.60-11.75]) (Fig.2b).

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Patients with NGAL(+)/RIFLE(-) status had more than ten-times increased in-hospital

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mortality rate over patients without positive biomarker attribute (0.8% in NGAL(-)/RIFLE(-)

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group vs. 9.5% in the NGAL(+)/RILFE(-) group (OR 13.90 [95% CI 1.51-128.04] P=0.01;

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adjusted HR 13.02 [95% CI 1.34-126.96] P=0.027, Table3)

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Again, a similar pattern with similar magnitude was observed for Midkine (OR for in-hospital

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mortality 6.80 [95% CI 1.09-42.49], P=0.05) and IL-6 (OR 8.22 [95% CI 1.0-75.35], P<0.05)

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(Fig.2a+b).

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NGAL(+)/RIFLE(+) vs. NGAL(-)/RIFLE(+) and Midkine(+)/RIFLE(+) vs. Midkine(-)/RIFLE(-)

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status had non-significant increases in the risk of acute RRT, in-hospital mortality or MAKE with

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OR ranging from 5.04-13.9 (Fig.2a+b).

Biomarker [NGAL/Midkine/IL-6/proteinuria]-positive/RIFLE-positive status indicated

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increased risk of acute RRT, in-hospital mortality and MAKE compared with biomarker(-

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)/RIFLE(-) status with OR ranging from 44 (for acute RRT indicated by de-novo or worsening

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proteinuria) to 173 (for MAKE indicated by NGAL), all P<0.001 (Fig.2a-d).

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Long-term outcomes

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Median follow up time was 5.6 years. Data from 147(73.5%) patients were collected at the final

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visit of whom 52(35.4%) had died during follow up. 53(26.5%) patients were lost to follow up

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(Fig.1).

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Long-term follow-up is illustrated in Kaplan-Meier Curves (Fig.4a) showing distinct

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separation between the biomarker/RIFLE groups. Separation for long-term survival comparing

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biomarker(+) with biomarker(-) patients was found independent of RIFLE status (Fig.4a). 16

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Survival curves of AKI subtypes separated early after cardiac surgery and continued during long-

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term follow-up.

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Influences of cardiovascular risk factors, baseline renal function and biomarker/RIFLE group

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allocation were assessed in cox proportional-hazard regression models. We found significant

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effect of all biomarker/RIFLE group allocation on patient’s long-term survival (p<0.001 for all

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biomarkers). Exemplary, adjusted survival probability with regard to follow-up time is illustrated

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in Figure 4b for NGAL/RIFLE groups.

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Additional data is presented as supplemental material (Table 8 and Fig.2a-d).

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Discussion

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Key findings

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We studied a cohort of 200 patients undergoing cardiac surgery to determine the short-term and

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long-term prognosis of AKI identified by standard criteria and/or urinary kidney biomarkers

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immediately after cardiac surgery. We found that urinary kidney biomarkers identified

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approximately 60% more cases with likely ‘subclinical AKI’ than RIFLE-status alone. The

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diagnosis of subclinical AKI was supported by evidence of increased risk of RRT initiation and

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in-hospital mortality. We found that novel urinary biomarker-positive patients, and less

362

pronounced so, proteinuria-positive patients, also carried higher risk of in-hospital adverse

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outcomes than biomarker-negative patients independent of RIFLE status. Three to eleven percent

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of patients after a cardiac surgery procedure appear to be affected by single-biomarker-positive,

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subclinical AKI. Moreover, patients who were biomarker-negative and RIFLE-positive, implying

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potential loss of renal function without evidence of acute tubular injury, showed intermediate

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outcomes. As expected, biomarker- and RIFLE-positive patients had worst outcomes regarding

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need for RRT initiation, mortality, and combined endpoints. The outcome pattern observed in all

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urinary kidney biomarker-based patient subgroups was similar regardless which urinary

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biomarker was used with significant overlap among urinary biomarkers. In contrast, CRP did not

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separate patient outcomes beyond association with RIFLE-status and urinary kidney biomarker

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grouping did not overlap with CRP-based patient groups. Finally, during long-term follow up

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over 5 years, patient survival was associated with RIFLE-status but also with early kidney

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biomarker-status.

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Relationship with previous studies

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Pooled data from critically ill and cardiac surgical patients and data from two subsequent

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prospective studies enrolling patients treated in an Emergency Department, assessed the short-

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term prognostic relevance of likely “subclinical AKI” (NGAL-positive and RIFLE-negative

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status). Such patients were at increased risk of subsequent RRT and increased in-hospital

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mortality.4,6,7

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The proportion of patients distributed among the biomarker/RIFLE subgroups were similar

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in our study cohort compared with these studies.4,6,7 The proportion of patients additionally

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identified as having ‘subclinical AKI’ (biomarker-positive/RIFLE-negative) was higher in the

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present study (58-74% depending on biomarker) compared with previous studies (~40-45%).

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Across studies, including the present study, in various clinical settings, prognostic pattern defined

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by urinary kidney biomarkers was similar – with best prognosis for kidney biomarker(-)/RIFLE(-

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) patients and worst prognosis for kidney biomarker(+)/RIFLE(+) patients. More recently, Basu

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and coworkers27 demonstrated that a composite of a kidney injury biomarker and Cystatin-C as

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glomerular filtration marker was superior in prediction of AKI than changes of creatinine in

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children undergoing cardiac surgery.

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Only one study reported outcome pattern using more than one kidney biomarker,6 however,

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overlap of patients between biomarker-defined subgroups was not assessed. None of the previous

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prospective studies reporting on urinary kidney biomarker/RIFLE status verified study results

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against non-kidney related prognostic biomarkers such as C-reactive protein. Furthermore, no

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study has yet informed about RIFLE status and long-term outcomes. Our findings suggest partial

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translation of kidney biomarker-defined short-term outcomes to long-term outcomes. A causal

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relationship between early postoperative biomarker-detected tubular injury and long-term kidney

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prognosis may exist, as reported by Cooper and coworkers.28

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Implications of study findings

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To the best of our knowledge, this is the first study to investigate on short- and long-term

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outcomes including RRT, mortality and composite end-points according to urinary biomarker-

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and RIFLE-based kidney status in patients immediately after cardiac surgery. Our study implies

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that biomarkers of kidney injury identify a population at clearly increased risk of renal and

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clinical adverse events. Such renal impairment is not detected by routine clinical measurements

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(creatinine and urine output) and should logically be considered to define a state of ‘subclinical

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AKI’. Patients with subclinical AKI subsequently developing positive RIFLE –criteria based AKI

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may be regarded as pre-clinical AKI diagnosed early by positive kidney biomarker status. Also,

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our study implies that such findings are minimally affected by the biomarker used and are likely

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conceptually robust. Moreover, the verification of data against the prognostic discriminative

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ability of a routine inflammatory marker (CRP) implies a level of kidney specificity for our

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observations referring to the ability of NGAL and other biomarkers to detect decline in kidney

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function before changes in serum creatinine are seen.8

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Subclinical AKI (without RIFLE-positivity developing over the first postoperative week) and

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early biomarker-identified AKI (pre-clinical AKI- with subsequent postoperative RIFLE-

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positivity) were detected within several hours post cardiac surgery implying the ability to deliver

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early prognosis and, in future, early risk-stratified intervention.29 Implementation of the KDIGO

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care bundles30 (ie. avoidance of nephrotoxic agents, discontinuation of ACEi and ARBs for the

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first 48h after surgery, optimization of volume status and hemodynamic parameters31) and goal-

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directed patient individualized protocols32 compared with standard care reduced the frequency

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and severity of AKI after cardiac surgery. In our cohort, the condition of clinical AKI without

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positive biomarker test results may cautiously be interpreted as ‘prerenal’ AKI subgroup,

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supported by more severely impaired cardiac dysfunction but not by signs of hypovolemia. Finally, our study cautiously implies that there is a single biomarker-positive, subclinical

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AKI phenotype affecting up to 10% of cardiac surgical patients, a potentially novel category of

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AKI, which may deserve further attention.

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Study Strengths and Limitations

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Our study reported on outcome pattern using more than one kidney biomarker6 and quantified

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overlap of patients between biomarker-defined subgroups. Also, we verified study results against

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non-kidney related prognostic biomarkers such as CRP, assessed biomarkers against multiple

434

relevant outcomes and provided long-term follow-up.

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The outcome of this analysis critically depends on current creatinine and urine output

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based definition of AKI that is consensus-driven, established in clinical routine and non-

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verifiable as there is no real-time GFR-monitoring available. We cannot exclude type II error and

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potential study bias due to limited patient access of the initial multicenter study. However, non-

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participating study-centers did not collect enough urine samples for measurement of additional

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biomarkers. Overall rate of patients developing RIFLE-AKI after cardiac surgical procedure in

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our cohort 12% (N=24) resembles a rather typical proportion with this postoperative

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complication,21 however the study sample size and event rate limited the ability to perform

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combinatorial biomarker sensitivity analyses. The internal validity of our findings, however, is

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strengthened by similar outcome patterns of three independent urine kidney biomarkers,

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exclusion of preoperative AKI and choice of cardiac surgery patients at increased renal risk

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enabling concept evaluation in a relatively homogenous, well-defined patient cohort with

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assessable timing of AKI onset

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Finally, biomarker measurement at 6 hours after commencement of CPB may not reflect

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the point of best discriminatory power of these markers.13 Nonetheless, findings appeared to be

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robust and consistent. The present study does not preclude the conclusion that no other urinary biomarkers may

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improve AKI risk prediction as previously shown.33 However, so far no combined assessment of

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glomerular (RIFLE) and tubular (dys)function on short and long-term outcomes has been

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reported. Our study was initiated prior to studies reporting on the predictive ability of cell cycle

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arrest for adverse outcome in patients with AKI.34 Yet, such biomarkers will require assessment

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in terms of long-term prognosis. Unfortunately, short-term outcome for patients with AKI was

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affected by high mortality rates and long-term data acquisition was compromised by 53 (26.5%)

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of patients being lost to follow up. The number of surviving patients was too low and urine

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protein excretion was rarely recorded during ambulatory patients’ care, thus limiting our long-

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term analysis by incomplete CKD detection and limited predictive value of urinary kidney

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biomarkers for the development of worsening CKD. These drawbacks could be improved in

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future studies by larger patient numbers and follow-up examination at the study center.

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Conclusions

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This hypothesis-generating substudy or a RCT found that early postoperative measurement of

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urinary kidney biomarkers has important clinical implications for patient risk assessment and

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long-term prognosis. In cardiac surgery patients, early postoperative urinary biomarker-positivity

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added prognostic information regardless of RIFLE-AKI. Thus, our study provides new evidence

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that urinary kidney biomarkers identify AKI subtypes with incrementally worsening prognosis

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and may therefore define a state of clinically relevant subclinical AKI.

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Acknowledgment

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We thank Prof. Siegfried Kropf, Institute for Biometrics and Medical Informatics, Otto-von-

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Guericke University Magdeburg, for his advice and substantive comments on an earlier draft of

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this manuscript.

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Basu RKB, Wong HR, Krawczeski CD, et al. Combining Functional and Tubular Damage Biomarkers Improves Diagnostic Precision for Acute Kidney Injury After Cardiac Surgery. J Am Coll Cardiol. 2014;64(25):2753-2762. doi:10.1016/j.jacc.2014.09.066.

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Cooper DS, Claes D, Goldstein SL, et al. Follow-Up Renal Assessment of Injury Long-Term After Acute Kidney Injury (FRAIL-AKI). Clin J Am Soc Nephrol. 2016;11(1):21-29. doi:10.2215/CJN.04240415.

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Yi Ng S, Sanagou M, Wolfe R, Cochrane A, Smith JA, Reid CM. Prediction of acute kidney injury within 30 days of cardiac surgery. J Thorac Cardiovasc Surg. 2014;147(6):1875–1883.e1. doi:10.1016/j.jtcvs.2013.06.049.

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Meersch M, Schmidt C, Hoffmeier A, et al. Prevention of cardiac surgery-associated AKI by implementing the KDIGO guidelines in high risk patients identified by biomarkers: the PrevAKI randomized controlled trial. Intensive Care Med. January 2017:1-11. doi:10.1007/s00134-016-4670-3.

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Table 1a. Preoperative patient characteristics according to NGAL/RIFLE Subgroup NGAL(-)/RIFLE(-) (n= 133, 66.8 %)

Age, years Sex, female, n (%) Body mass index Insulin dependent diabetes mellitus, n (%) Non-insulin dependent diabetes mellitus, n (%) Arterial hypertension, n (%) Hypercholesterolemia, n (%) Chronic obstructive pulmonary disease, n (%) Smoking habit, current, n (%) Preoperative serum creatinine, µmol/L Peripheral vascular disease, n (%) Left ventricular dysfunction, n (%)* Left ventricular ejection fraction, %

68 (58-73) 40 (30.1) 26.5 (23.4-29.4) 7 (5.3) 25 (18.8) 97 (72.9) 83 (62.4) 21 (15.8) 23 (17.3) 86.6 (76.0-106.1) 36 (27.1) 28 (21.1) 55.0 (40.0-60.0)

70.5 (62.8-76.3) 11 (26.2) 26.1 (24.5-28.4) 2 (4.8) 9 (21.4) 33 (78.6) 30 (71.4) 6 (14.3) 4 (9.5) 97.2 (80.4-114.9) 8 (19) 6 (14.3) 50 (43.8-60)

73 (69-77) 2 (22.2) 28 (25.6-30.6) 0 (0) 0 (0) 9 (100) 7 (77.8) 2 (22.2) 3 (33.3) 97.2 (86.6-123.8) 4 (44.4) 3 (33.3) 45 (30-60)

72 (67-77) 6 (40.0) 26.1 (24.6-31.6) 0 (0) 5 (33.3) 14 (93.3) 10 (66.7) 3 (20.0) 1 (6.7) 97.2 (73.4-132.6) 4 (26.7) 4 (26.7) 55 (50-60)

0.033 0.763 0.181 1.000 0.261 0.109 0.611 0.844 0.227 0.092 0.414 0.454 0.436

68 (51.1) 96 (72.2) 39 (29.3) 77 (57.9) 81 (60.9)

28 (66.7) 32 (76.2) 10 (23.8) 24 (57.1) 27 (64.3)

6 (66.7) 7 (77.8) 4 (44.4) 6 (66.7) 6 (66.7)

12 (80) 11 (73.3) 6 (40) 9 (60) 14 (93.3)

0.070 0.978 0.444 0.958 0.102

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Preoperative medication ACE inhibitor/AT-II antagonist, n (%) Beta blocker, n (%) Calcium channel blocker, n (%) HMG-CoA reductase inhibitor, n (%) Diuretics, n (%)

NGAL(+)/RIFLE(-) NGAL(-)/RIFLE(+) NGAL(+)/RIFLE(+) (n= 42, 21.1 %) (n= 9, 4.5 %) (n= 15, 7.5 %)

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Variable

Numbers denote median (25.-75. percentile) or frequency (%) where appropriate. BMI, body-mass-index; ACE, angiotensin-converting enzyme; AT-II, angiotensin-II; HMG-CoA, 3-hydroxy-3-methyl-glutaryl-CoA; * Left ventricular ejection fraction <35% NGAL, urine neutrophil gelatinase-associated lipocalin; RIFLE, risk injury failure end-stage renal disease classification20 598

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Table 1b: Perioperative patient characteristics according to NGAL/RIFLE Subgroup

Perioperative fluid balance and medication (0-24 h) Fluid intake, ml Urine output, ml Packed red blood cells, ml Drain output, ml Fluid balance, ml Furosemide, mg urine NGAL, ng/mL 0 hrs 6 hrs 24 hrs

3 (7.1) 13 (31.0) 20 (47.6) 6 (14.3) 0 (0) 16 (38.1) 140 (109-190) 34 (28.8-38)

P-value

3 (33.3) 2 (22.2) 3 (33.3) 0 (0) 1 (11.1) 3 (33.3) 157 (87-189) 31 (22-36.5)

1 (6.7) 10 (66.7) 3 (20) 1 (6.7) 0 (0) 7 (46.7) 209 (129-368) 31 (24-41)

0.048 0.037 0.008 0.575 0.170 0.016 <0.001 0.209

7162 (5346-9213) 5227 (4097-5796) 500 (187-1000) 500 (368-841) 1872 (556-3271) 40 (20-52)

6356 (5506-7565) 4020 (2795-4277) 500 (0-1625) 525 (299-1550) 2972 (2296-3889) 60 (20-110)

5856 (3724-7712) 3850 (3125-4490) 1000 (500-2500) 950 (700-2125) 1696 (-99-4756) 80 (20-272)

<0.001 <0.001 <0.001 0.001 <0.001 <0.001

2.65 (0-5.25) 201.7 (86.6-410.7) 7.7 (1.8-34.9)

5.3 (0-7.1) 6.9 (2.3-15.5) 12 (2.9-15.8)

7.4 (2.6-36.7) 292.3 (142.3-570.4) 52.9 (19.8-119.8)

0.064 <0.001 <0.001

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2.1 (0-6.1) 4.4 (0-12.4 5.2 (0-11.8)

NGAL(-)/RIFLE(+) (n= 9, 4.5 %)

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29 (21.8) 64 (48.1) 28 (21.1) 11 (8.3) 1 (0.8) 26 (19.5) 115 (90-140) 35 (30-41) 8512 (7012-9764) 5490 (4502-6525) 250 (0-500) 500 (350-695) 2952 (1847-4199) 20 (0-40)

NGAL(+)/RIFLE(+) (n= 15, 7.5 %)

0.381 6 (66.7) 3 (33.3) 0 (0)

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RIFLE stages Risk Injury Failure

NGAL(+)/RIFLE(-) (n= 42, 21.1 %)

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Procedures CABG surgery, n (%) Valvular surgery, n (%) CABG and valvular surgery, n (%) Thoracic aortic surgery, n (%) Ventricular assist device, n (%) Redo cardiac surgery, n (%) Duration of cardiopulmonary bypass, min Lowest intraoperative MAP, mmHg

NGAL(-)/RIFLE(-) (n= 133, 66.8 %)

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7 (46.7) 4 (26.7) 4 (26.7)

Numbers denote median (25.-75. percentile) or frequency (%) where appropriate. CABG, coronary artery bypass graft; MAP, mean arterial pressure; NGAL, urine neutrophil gelatinase associated lipocalin, RIFLE, risk injury failure end-stage renal disease classification20 Table 2. Multivariate logistic regression analysis of risk factors for the prediction of 'RRT or in-hospital mortality' based on the Cleveland risk assessment model.

Model 0

Model 1

Model 2

Model 3

Model 4

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Exp(B)

Exp(B)

Exp(B)

P

P (95% CI)

Exp(B) P

(95% CI)

P

(95% CI)

RI PT

(95% CI)

Exp(B) P

(95% CI)

1.264 (0.397-4.027)

0.692

1.556 (0.454-5.331)

0.482

1.262 (0.363-4.391)

0.714

1.849 (0.630-5.427)

0.263

1.414 (0.405-4.945)

0.587

Preoperative eGFR

0.970 (0.946-0.995)

0.020

0.967 (0.940-0.994)

0.017

0.963 (0.937-0.991)

0.009

0.969 (0.942-0.996)

0.024

0.964 (0.128-1.899)

0.009

PVD

0.418 (0.114-1.530)

0.187

0.554 (0.144-2.130)

0.390

0.553 (0.144-2.117)

0.387

2.141 (0.772-5.936)

0.772

0.494 (0.128-1.899)

0.305

COPD

1.639 (0.535-5.022)

0.387

1.855 (0.572-6.018)

0.304

2.152 (0.677-6.843)

0.194

1.672 (0.515-5.430)

0.392

1.583 (0.487-5.144)

0.445

Sex

0.394 (0.145-1.069)

0.067

0.290 (0.098-0.857)

0.025

0.484 (0.166-1.410)

0.183

1.816 (0.547-6.035)

0.330

0.471 (0.158-1.404)

0.177

Type of surgery

0.738 (0.246-2.216)

0.588

0.547 (0.169-1.767)

0.313

0.731 (0.236-2.266)

0.587

0.715 (0.218-2.342)

0.580

0.442 (0.125-1.560)

0.204

Previous cardiac surgery

1.325 (0.421-4.169)

0.630

2.359 (0.628-8.859)

0.203

1.321 (0.402-4.341)

0.646

0.498 (0.168-1.475)

0.208

2.001 (0.537-7.459)

0.302

1.001 (1.000-1.001)

0.05 0.992 (0.957-10.28)

0.652

M AN U

Urinary NGAL, ng/mL

SC

CHF

1.003 (1.001-1.005)

Urinary Midkine, pg/mL

<0.001

1.001 (1.000-1.001)

Urinary IL-6, pg/mL

TE D

Plasma CRP, mg/L

0.001

We included each biomarker one after another (model 1 to 4) into multivariate regression analysis to exclude interaction. Multivariate logistic regression analysis included variables based on the Cleveland reference model (26) for prediction of the primary endpoint 'in-hospital mortality or RRT' chronic heart failure, preoperative estimated glomerular filtration rate (CKD-EPI), peripheral vascular disease, sex , chronic obstructive pulmonary disease, Type of surgery, previous cardiac surgery.

EP

All assessed urinary biomarkers could be identified as independent predictors of 'RRT or in-hospital mortality'.

AC C

Goodness of fit of the model improved considerably after adding urinary biomarkers: Model 0 Hosmer-Lemeshow P 0.629 and −2 log likelihood=119.30, Nagelkerke R Square 0.148; Model 1 Hosmer-Lemeshow P 0.127 and −2 log likelihood=104.58, Nagelkerke R Square 0.282;

Model 2 Hosmer-Lemeshow P 0.752 and −2 log likelihood=106.84, Nagelkerke R Square 0.256;

Model 3 Hosmer-Lemeshow P 0.272 and −2 log likelihood=91.90, Nagelkerke R Square 0.286;

Model 4 Hosmer-Lemeshow P 0.308 and −2 log likelihood=112.53, Nagelkerke R Square 0.165

NGAL, neutrophil gelatinase associated-lipocalin. PVD, peripheral vascular disease. CHF, chronic heart failure. COPD, chronic obstructive pulmonary disease. CABG coronary arterial bypass graft.

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Table 3: Cox regression analysis for primary and secondary endpoints P value

Hazard Ratio (95% CI)

Hazard Ratio

P value

(95% CI)

Hazard Ratio

P value

(95% CI)

P value

Hazard Ratio (95% CI)

RI PT

Variable

"In-Hospital Mortality or RRT" as dependent endpoint 1.06 (1.01-1.12)

0.015

1.10 (1.00-1.21)

0.032

1.05 (0.95-1.17)

0.357

1.00 (0.94-1.08)

0.901

Previous cardiac surgery

0.90 (0.32-2.52)

0.840

2.53 (0.29-22.35)

0.403

0.99 (0.50-1.87)

0.979

0.79 (0.18-3.57)

0.761

CABG vs. Concomitant surgery

0.73 (0.28-1.93)

0.525

0.57 (0.13-2.56)

0.460

1.27 (0.16-9.97)

0.819

4.96 (1.22-20.19)

0.025

Perioperative fluid balance

1.00 (0.99-1.00)

0.017

1.00 (0.99-1.00)

0.517

1.00 (0.99-1.00)

0.769

1.00 (0.99-1.00)

0.424

7.18 (1.52-33.93)

0.013

100.45 (19.14527.29)

<0.001

NGAL(+)/RIFLE(-) vs. NGAL(-)/RIFLE(-)

16.79 (1.85-152.40)

NGAL(+)/RIFLE(+) vs. NGAL(-)/RIFLE(-)

M AN U

NGAL(-)/RIFLE(+) vs. NGAL(-)/RIFLE(-)

SC

Age

0.012

"In-Hospital Mortality" as dependent endpoint 1.06 (0.99-1.13)

0.08

Previous cardiac surgery

0.99 (0.52-1.93)

0.994

CABG vs. Concomitant surgery

0.73 (0.22-2.42)

0.607

Perioperative fluid balance

0.99 (0.99-1.00)

0.007

NGAL(+)/RIFLE(-) vs. NGAL(-)/RIFLE(-) NGAL(-)/RIFLE(+) vs. NGAL(-)/RIFLE(-) NGAL(+)/RIFLE(+) vs. NGAL(-)/RIFLE(-)

1.12 (0.98-1.28)

0.105

1.11 (0.90-1.37)

0.321

1.01 (0.90-1.13)

0.866

1.37 (0.43-4.34)

0.589

1.29 (0.62-3.21)

0.487

0.91 (0.33-2.49)

0.853

0.58 (0.90-3.74)

0.566

0.48 (0.16-14.30)

0.672

3.77 (0.63-22.70)

0.148

1.00 (0.99-1.00)

0.726

1.00 (0.99-1.01)

0.665

0.99 (0.99-1.00)

0.181

21.51 (1.62-285.31)

0.020 61.66 (6.70-543.48)

<0.001

TE D

Age

13.02 (1.34-126.96)

0.027

Age Previous cardiac surgery

EP

"Major adverse kidney events (MAKE)" as dependent endpoint 0.004

1.10 (1.01-1.21)

0.032

1.047 (0.95-1.20)

0.350

1.00 (0.94-1.08)

0.881

0.65 (0.26-1.60)

0.345

2.53 (0.29-22.35)

0.403

2.90 (0.22-37.86)

0.416

0.77 (0.17-3.46)

0.737

0.81 (0.32-2.01)

0.642

0.57 (0.13-2.56)

0.460

1.62 (0.23-11.47)

0.631

5.01 (1.23-20.40)

0.024

0.032

1.00 (0.99-1.00)

0.517

1.00 (0.99-1.00)

0.745

1.00 (0.99-1.00)

0.445

7.18 (1.52-33.93)

0.013 43.38 (5.52-340.87)

<0.001 108.99 (20.96566.74)

<0.001

AC C

CABG vs. Concomitant surgery

1.07 (1.02-1.13)

Perioperative fluid balance

1.00 (0.99-1.00)

NGAL(+)/RIFLE(-) vs. NGAL(-)/RIFLE(-)

NGAL(-)/RIFLE(+) vs. NGAL(-)/RIFLE(-)

NGAL(+)/RIFLE(+) vs. NGAL(-)/RIFLE(-)

Model adjusted for age, type of surgery (Coronary artery bypass graft vs. Concomittant surgery), previous cardiac surgery, perioperative fluid balance 0-6 hrs CI, Confidence Interval; NGAL, neutrophil gelatinase associated lipocalin; Group comparisons performed versus NGAL(-)/RIFLE(-) as a reference group All group comparisons were inserted into the model one after another to exclude interaction.

599 30

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600

Figure titles and legends

601

Central Figure (Figure 0)

602

NGAL/RIFLE status. Biomarker (NGAL) positivity associates with worse patient outcome

603

irrespective of RIFLE-status

RI PT

Proportion of patients with defined endpoints according to

604

Fig. 1

Patient flow chart through the study (exemplary for NGAL/RIFLE status)

607

Fig. 2

Proportion of patients with defined endpoints according to

608

(a) NGAL-/ (b) Midkine- / (c) Interleukin-6- / (d) Proteinuria-status and RIFLE-status.

609

The primary study endpoint, combination of acute RRT or in-hospital mortality, is highlighted.

610

Urinary biomarkers show a stepwise increase in all evaluated endpoints with biomarker positivity

611

irrespective of RIFLE-status. Underlying table shows unadjusted odds ratios and 95% confidence

612

interval (CI) for risk assessment between groups. Significance level for Pearson’s chi-square or

613

Fisher exact test where appropriate: *P<0.05, **P<0.001.

614

Figure Abbreviations

615

NGAL, neutrophil gelatinase-associated lipocalin; IL-6, Interleukine-6; CRP, C-reactive protein;

616

RRT, renal replacement therapy; MAKE, major adverse kidney events including in-hospital

617

mortality, RRT initiation and renal recovery from AKI; RIFLE, risk injury failure end-stage renal

618

disease classification;20 OR, odds ratio.

605

619

AC C

EP

TE D

M AN U

SC

606

620

Fig. 3

Proportion of patients with defined endpoints according to

621

(a) CRP-status at 6 hours / (b) CRP-status at 24 hours after commencement of cardiopulmonary

622

bypass and RIFLE-status.

623

The primary study endpoint, combination of acute RRT or in-hospital mortality, is highlighted. 31

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624

As opposed to urinary kidney biomarkers, for CRP (at 6 hours) no additive prognostic

625

information was derived beyond RIFLE-status. Abbreviations as in Figure 2.

626

RI PT

627

Fig. 4a

Kaplan-Meier survival curve showcasing NGAL/RIFLE groups. Median follow

629

up time was 5.6 years. Survival curves differ significantly early after cardiac surgery until the end

630

of follow up (All p<0.001 for Log-Rank, Breslow-test and Tarone-Ware test). Graphs include the

631

confidence limits as well as the tabled number of patients at risk periodically over time of follow

632

up.

633

Figure 4b

634

Survival curves are adjusted for preoperative creatinine value, peripheral vascular disease, arterial

635

hypertension, past history of myocardial infarction 6 months prior to surgery. NGAL / RIFLE

636

group allocation: p<0.001.

637

Video Legend: Dr. Christian Albert comments on the key study findings. Early postoperative

638

measurement of urinary kidney biomarkers has important clinical implications for patient risk

639

assessment. Positive urinary kidney-biomarker test results post cardiac surgery carries relevant

640

prognostic information and identifies AKI subtypes with incrementally limited outcome.

M AN U

SC

628

AC C

EP

TE D

Multivariable-adjusted survival curves by NGAL/RIFLE group allocation.

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RI PT

Supplemental Material

Table 1a. Preoperative patient characteristics according to Midkine/RIFLE Subgroup

•

Table 1b: Perioperative patient characteristics according to Midkine/RIFLE Subgroup

•

Table 2a. Preoperative patient characteristics according to uIL-6/RIFLE Subgroup

•

Table 2b: Perioperative patient characteristics according to IL-6/RIFLE Subgroup

•

Table 3a. Preoperative patient characteristics according to CRP/RIFLE Subgroup

•

Table 3b: Perioperative patient characteristics according to CRP/RIFLE Subgroup

•

Table 4a. Preoperative patient characteristics according to Proteinuria/RIFLE Subgroup

•

Table 4b: Perioperative patient characteristics according to Proteinuria/RIFLE Subgroup

•

Table 5a: AUC-ROC Values for biomarkers prediction of AKI

•

Table 5b: Comparisons of ROC Curves

•

Table 6a,b,c: Cox Regression Analysis for primary and secondary outcome (in-hospital mortality or RRT, in-hospital mortality, major adverse kidney events (MAKE))

Table 7: Cox regression analysis for in-hospital adverse events (primary and secondary endpoints)

EP

•

TE D

M AN U

SC

•

According to Biomarker/RIFLE groups compared to reference: Biomarker(-)/RIFLE(-) Table 8: Cox regression analysis for long term overall survival (long-term outcome)

•

Table 9: Inclusion and Exclusion Criteria of the original RCT (ClinicalTrials.gov NCT00672334 )

• • •

Table 10: Measurement of agreement among biomarkers Figure 1 a + b Figure 2a-d: Kaplan Meier survival curves for Midkine, IL-6, Proteinuria, CRP - Groups

AC C

•

• Supplemental Results: CRP and controlling outcome pattern for systemic inflammation

33

Table 1a. Preoperative patient characteristics according to Midkine/RIFLE Subgroup

Midkine(-)/RIFLE(-) Midkine(+)/RIFLE(-) Midkine(-)/RIFLE(+) Midkine(+)/RIFLE(+) (n= 138, 70.8 %) (n= 33, 16.9 %) (n= 10, 5.1 %) (n= 14, 7.2 %) 73.5 (69-77.5) 2 (20) 27.9 (26-32.5) 0 (0) 1 (10) 10 (100) 8 (80) 2 (20) 3 (30) 114.5 (88.2-123.8) 4 (40) 3 (30) 47 (30-56)

71.5 (66.5-77) 6 (42.9) 26 (24.5-31.4) 0 (0) 4 (28.6) 13 (92.9) 9 (64.3) 3 (21.4) 1 (7.1) 88.4 (73-118.7) 4 (28.6) 4 (28.6) 57.5 (45-60)

0.003 0.669 0.051 1.000 0.667 0.138 0.792 0.672 0.471 0.114 0.455 0.635 0.519

75 (54.3) 99 (71.7) 39 (28.3) 81 (58.7) 87 (63.0)

19 (57.6) 25 (75.8) 10 (30.3 16 (48.5) 21 (63.6)

6 (60) 6 (60) 5 (50) 8 (80) 8 (80)

12 (85.7) 12 (85.7) 5 (35.7) 7 (50) 12 (85.7)

0.161 0.523 0.468 0.312 0.301

M AN U

SC

73 (64-78) 10 (30.3) 26.1 (24.6-28.1) 1 (3) 5 (15.2) 25 (75.8) 20 (60.6) 6 (18.2) 4 (12.1) 88.4 (79.6-106.1) 10 (30.3) 6 (18.2) 50 (40-60)

AC C

Preoperative medication ACE inhibitor/AT-II antagonist, n (%) Beta blocker, n (%) Calcium channel blocker, n (%) HMG-CoA reductase inhibitor, n (%) Diuretics, n (%)

P-value

67 (58-72) 40 (29) 26.2 (23.4-29.3) 8 (5.8) 28 (20.3) 102 (73.9) 89 (64.5) 19 (13.8) 21 (15.2) 88.4 (76-106.1) 31 (22.5) 27 (19.6) 55 (40-60)

EP

Age, years Sex, female, n (%) Body mass index Insulin dependent diabetes mellitus, n (%) Non-insulin dependent diabetes mellitus, n (%) Arterial hypertension, n (%) Hypercholesterolemia, n (%) Chronic obstructive pulmonary disease, n (%) Smoking habit, current, n (%) Preoperative serum creatinine, µmol/L Peripheral vascular disease, n (%) Left ventricular dysfunction, n (%)* Left ventricular ejection fraction, %

TE D

Variable

RI PT

ACCEPTED MANUSCRIPT

Numbers denote median (25.-75. percentile) or frequency (%) where appropriate. * Left ventricular ejection fraction <35%; BMI, body-mass-index; ACE, angiotensin-converting enzyme; AT-II, angiotensin-II; HMG-CoA, 3-hydroxy-3-methyl-glutaryl-CoA; RIFLE, risk injury failure end-stage renal disease classification (20)

34

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Table 1b: Perioperative patient characteristics according to Midkine/RIFLE Subgroup

29 (21) 64 (46.4) 35 (25.4) 9 (6.5) 1 (0.7) 31 (22.5) 118 (91-140) 34.5 (31-40)

1 (3) 12 (36.4) 12 (36.4) 8 (24.2) 0 (0) 10 (30.3) 146 (113-222) 32 (27-40)

8227 (6800-9709) 5470 (4586-6455) 250 (0-500) 459 (350-675) 2766 (1727-3851) 20 (8.75-40)

urine Midkine, pg/mL 0hrs 6hrs 24hrs

94.4 (48.9-147.5) 101.3 (73.9-161) 77.3 (49.1-102)

EP AC C

RIFLE stages Risk Injury Failure

P-value

3 (30) 3 (30) 3 (30) 0 (0) 1 (10) 5 (50) 149 (91-173) 31.5 (24-35.5)

1 (7.1) 9 (64.3) 3 (21.4) 1 (7.1) 0 (0) 5 (35.7) 227 (142-370) 31 (23-41.2)

0.023 0.260 0.591 0.024 0.157 0.229 <0.001 0.452

8456 (6493-9734) 4890 (4317-6782) 500 (0-1000) 650 (472-962) 2902 (1000-5147) 20 (5-40)

6481 (5677-8191) 4040 (2678-4246) 525 (0-1437) 672 (214-1525) 3592 (2496-5024) 65 (20-242)

5731 (3496-7449) 3836 (3064-4620) 1125 (438-2688) 1010 (688-2243) 1450 (-119-2843) 60 (20-113)

0.001 <0.001 0.005 <0.001 0.131 <0.001

88 (51.8-195.3) 474 (313-1800) 104.5 (55.3-144)

58.6 (20.9-109.8) 114.5 (63.5-151.2) 43.1 (28.8-278.7)

97.7 (43.4-217.3) 1127.5 (431.3-3502.4) 98.9 (63.9-270.9)

0.361 <0.001 0.147

6 (60) 3 (30) 1 (10)

7 (50) 4 (28.6) 3 (21.4)

TE D

Perioperative fluid balance and medication (0-24 h) Fluid intake, ml Urine output, ml Packed red blood cells, ml Drain output, ml Fluid balance, ml Furosemide, mg

Midkine(-)/RIFLE(+) Midkine(+)/RIFLE(+) (n= 10, 5.1 %) (n= 14, 7.2 %)

RI PT

Midkine(+)/RIFLE(-) (n= 33, 16.9 %)

SC

Procedures CABG surgery, n (%) Valvular surgery, n (%) CABG and valvular surgery, n (%) Thoracic aortic surgery, n (%) Ventricular assist device, n (%) Redo cardiac surgery, n (%) Duration of cardiopulmonary bypass, min Lowest intraoperative MAP, mmHg

Midkine(-)/RIFLE(-) (n= 138, 70.8 %)

M AN U

Variable

0.862

Numbers denote median (25.-75. percentile) or frequency (%) where appropriate. CVP, central venous pressure; CABG, coronary artery bypass graft; MAP, mean arterial pressure; RIFLE, risk injury failure end-stage renal disease classification (20)

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Table 2a. Preoperative patient characteristics according to uIL-6/RIFLE Subgroup

P-value

68 (56-72) 38 (33.9) 26 (23.4-29.2) 6 (5.4) 22 (19.6) 85 (75.9) 75 (67) 15 (13.4) 14 (12.5) 88.4 (75.3-106.1) 31 (27.7) 20 (17.9) 55 (40-60)

70 (62.7-76.3) 13 (22.4) 26.8 (24.6-29.3) 3 (5.2) 12 (20.7) 40 (69) 35 (60.3) 11 (19) 12 (20.7) 87 (77.8-106.0) 12 (20.7) 13 (22.4) 50 (40-60)

76 (69-77.5) 1 (20) 27 (24.8-38.7) 0 (0) 1 (20) 5 (100) 4 (80) 1 (20) 1 (20) 123.8 (90.6-128.2) 2 (40) 2 (40) 50 (30-55)

72 (69-75) 4 (26.7) 28 (24.6-31.3) 0 (0) 3 (20) 14 (93.3) 10 (66.7) 4 (26.7) 2 (13.3) 88.4 (85.8-123.8) 6 (40) 5 (33.3) 50 (30-60)

0.016 0.457 0.141 0.738 1.000 0.155 0.781 0.388 0.449 0.139 0.345 0.273 0.346

36 (62.1) 43 (74.1) 17 (29.3) 31 (53.4) 40 (69)

3 (60) 2 (40) 3 (60) 4 (80) 3 (60)

13 (86.7) 14 (93.3) 5 (33.3) 8 (53.3) 13 (86.7)

0.039 0.102 0.517 0.657 0.087

M AN U

SC

RI PT

IL-6(+)/RIFLE(+) (n= 15, 7.9 %)

AC C

Preoperative medication ACE inhibitor/AT-II antagonist, n (%) Beta blocker, n (%) Calcium channel blocker, n (%) HMG-CoA reductase inhibitor, n (%) Diuretics, n (%)

IL-6(-)/RIFLE(+) (n= 5, 2.6 %)

EP

Age, years Sex, female, n (%) Body mass index Insulin dependent diabetes mellitus, n (%) Non-insulin dependent diabetes mellitus, n (%) Arterial hypertension, n (%) Hypercholesterolemia, n (%) Chronic obstructive pulmonary disease, n (%) Smoking habit, current, n (%) Preoperative serum creatinine, µmol/L Peripheral vascular disease, n (%) Left ventricular dysfunction, n (%)* Left ventricular ejection fraction, %

IL-6(-)/RIFLE(-) IL6(+)/RIFLE(-) (n= 112, 58.9 %) (n= 58, 30.5 %)

TE D

Variable

57 (50.9) 80 (71.4) 32 (28.6) 67 (59.8) 64 (57.1)

Numbers denote median (25.-75. percentile) or frequency (%) where appropriate. * Left ventricular ejection fraction <35%; BMI, body-mass-index; ACE, angiotensin-converting enzyme; AT-II, angiotensin-II; HMG-CoA, 3-hydroxy-3-methyl-glutaryl-CoA; * Left ventricular ejection fraction <35% IL-6, urine Interleukin-6, RIFLE, risk injury failure end-stage renal disease classification (20)

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Table 2b: Perioperative patient characteristics according to IL-6/RIFLE Subgroup

23 (20.5) 48 (42.9) 29 (25.9) 11 (9.8) 1 (0.9) 23 (20.5) 115 (88-138) 36 (31-41)

9 (15.5) 25 (43.1) 18 (31) 6 (10.3) 0 (0) 17 (29.3) 137 (105-180) 33 (28.8-38.2)

EP

RIFLE stages Risk Injury Failure

3.36 (2.42-5.38) 4.65 (3.48-7.01) 6.58 (3.66-12.12)

AC C

urine Interleukin-6, pg/mL 0hrs 6hrs 24hrs

IL-6(+)/RIFLE(+) (n= 15, 7.9 %)

P-value

2 (40) 3 (60) 0 (0) 0 (0) 0 (0) 3 (60) 117 (81-267) 34 (32.5-40.5)

2 (13.3) 6 (40) 5 (33.3) 1 (6.7) 1 (6.7) 4 (26.7) 168 (129-246) 31 (24-39)

0.466 0.917 0.488 1.000 0.292 0.144 <0.001 0.454

8109 (6198-9671) 5110 (4213-6463) 500 (0-750) 562 (400-806) 2759 (1564-4069) 20 (0-43)

6112 (4215-8922) 4020 (1678-4188) 500 (0-1000) 219 (200-515) 4021 (200-6108) 70 (15-240)

5856 (4912-7362) 3575 (2880-4340) 1000 (250-2500) 1330 (700-2125) 2696 (384-3426) 60 (20-150)

0.002 <0.001 0.025 <0.001 0.673 0.002

4.54 (2.99-8.72) 26.27 (14.83-68.52) 12.10 (5.65-23.74)

3.07 (2.18-4.66) 4.63 (4.05-8.70) 8.89 (3.99-11.31)

5.22 (2.96-10.45) 25.04 (19.02-77.41) 13.29 (5.18-23.23)

0.047 <0.001 0.007

3 (60) 1 (20) 1 (20)

10 (66.7) 3 (20) 2 (13.3)

TE D

Perioperative fluid balance and medication (0-24 h) Fluid intake, ml 8263 (6819-9715) Urine output, ml 5468 (4496-6485) Packed red blood cells, ml 250 (0-500) Drain output, ml 460 (331-675) Fluid balance, ml 2727 (1597-4106) Furosemide, mg 20 (10-40)

IL-6(-)/RIFLE(+) (n= 5, 2.6 %)

RI PT

IL6(+)/RIFLE(-) (n= 58, 30.5 %)

SC

Procedures CABG surgery, n (%) Valvular surgery, n (%) CABG and valvular surgery, n (%) Thoracic aortic surgery, n (%) Ventricular assist device, n (%) Redo cardiac surgery, n (%) Duration of cardiopulmonary bypass, min Lowest intraoperative MAP, mmHg

IL-6(-)/RIFLE(-) (n= 112, 58.9 %)

M AN U

Variable

1.000

Numbers denote median (25.-75. percentile) or frequency (%) where appropriate. CVP, central venous pressure; IL-6, Interleukin-6; CABG, coronary artery bypass graft; MAP, mean arterial pressure; RIFLE, risk injury failure end-stage renal disease classification (20)

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Table 3a. Preoperative patient characteristics according to CRP/RIFLE Subgroup CRP(+)/RIFLE(-) (n= 87, 44.8 %)

Age, years Sex, female, n (%) Body mass index Insulin dependent diabetes mellitus, n (%) Non-insulin dependent diabetes mellitus, n (%) Arterial hypertension, n (%) Hypercholesterolemia, n (%) Chronic obstructive pulmonary disease, n (%) Smoking habit, current, n (%) Preoperative serum creatinine, µmol/L Peripheral vascular disease, n (%) Left ventricular dysfunction, n (%)* Left ventricular ejection fraction, %

67 (59.3-72.8) 26 (31) 26.3 (23.6-28.7) 4 (4.8)

70 (58-75) 25 (28.7) 26.2 (23.4-30.3) 5 (5.7)

13 (15.5) 62 (73.8) 53 (63.1) 13 (15.5) 10 (11.9) 82.7 (73.4-97.2) 23 (27.4) 12 (14.3) 57.5 (50.0-60.0)

CRP(+)/RIFLE(+) (n= 13, 6.7 %)

P-value

75.5 (68.8-77.3) 3 (30) 25.5 (24.5-30.4) 0 (0)

72 (68-75.5) 4 (30.8) 29.2 (26.1-35.1) 0 (0)

0.022 0.991 0.017 1.000

20 (23) 65 (74.7) 58 (66.7) 13 (14.9) 16 (18.4) 97.2 (76.6-114.9) 19 (21.8) 22 (25.3) 50 (39.2-60)

0 (0) 10 (100) 8 (80) 1 (10) 1 (10) 97.2 (82.7-136.4) 1 (10) 3 (30) 52 (30-61.3)

5 (38.5) 12 (92.3) 9 (69.2) 4 (30.8) 2 (15.4) 105.2 (86.2-123.8) 7 (53.8) 4 (30.8) 50 (35-60)

0.072 0.153 0.775 0.511 0.702 0.002 0.074 0.162 0.054

46 (52.9) 63 (72.4) 27 (31) 52 (59.8) 58 (66.7)

7 (70) 6 (60) 5 (50) 7 (70) 8 (80)

11 (84.6) 11 (84.6) 4 (30.8) 8 (61.5) 11 (84.6)

0.145 0.631 0.444 0.880 0.119

M AN U

TE D

EP

48 (57.1) 61 (72.6) 22 (26.2) 48 (57.1) 47 (56)

AC C

Preoperative medication ACE inhibitor/AT-II antagonist, n (%) Beta blocker, n (%) Calcium channel blocker, n (%) HMG-CoA reductase inhibitor, n (%) Diuretics, n (%)

CRP(-)/RIFLE(+) (n= 10, 5.2 %)

RI PT

CRP(-)/RIFLE(-) (n= 84, 43.3 %)

SC

Variable

Numbers denote median (25.-75. percentile) or frequency (%) where appropriate. * Left ventricular ejection fraction <35%; BMI, body-mass-index; ACE, angiotensin-converting enzyme; AT-II, angiotensin-II; HMG-CoA, 3-hydroxy-3-methyl-glutaryl-CoA; CRP, serum C-reactive protein; RIFLE, risk injury failure end-stage renal disease classification (20)

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Table 3b: Perioperative patient characteristics according to CRP/RIFLE Subgroup

EP

RIFLE stages Risk Injury Failure

0.9 (0.4-1.4) 1.6 (1.1-2.3) 56.1 (38-82.4)

AC C

serum CRP mg/L 0 hours 6 hours 24 hours

P-value

1 (10) 6 (60) 3 (30) 0 (0) 0 (0) 4 (40) 209 (150-370) 29.5 (22.75-40.25)

3 (23.1) 5 (38.5) 3 (23.1) 1 (7.7) 1 (7.7) 5 (38.5) 147 (94-207) 32 (29-40)

0.879 0.294 0.343 0.877 0.224 0.475 0.003 0.304

8556 (6782-9706) 5490 (4705-6160) 500 (0-750) 450 (350-700) 2832 (1566-4542) 30 (10-50)

5556 (3459-7283) 3922 (2806-4563) 750 (0-2312) 738 (451-2038) 1314 (-906-4292) 0 (0-40)

6356 (5587-7765) 3850 (2820-4278) 750 (375-2250) 1070 (610-1750) 2696 (1451-4257) 60 (30-175)

0.002 <0.001 0.022 0.009 0.342 <0.001

5.1 (3.3-12.1) 6.3 (4.4-11.1) 77.5 (60.8-106.8)

1.0 (0.7-1.7) 1.3 (0.8-2.4) 43.6 (28.5-70.8)

8.7 (2.9-11.8) 5.9 (3.8-11.0) 81.1 (67.2-124.6)

<0.001 <0.001 <0.001

4 (40) 3 (30) 3 (30)

9 (69.2) 3 (23.1) 1 (7.7)

17 (19.5) 33 (37.9) 29 (33.3) 7 (8.0) 1 (1.1) 22 (25.3) 119 (88-148) 35 (31-40)

TE D

Perioperative fluid balance and medication (0-24 h) Fluid intake, ml 8131 (6473-9694) Urine output, ml 5135 (4271-6770) Packed red blood cells, ml 250 (0-500) Drain output, ml 510 (371-700) Fluid balance, ml 2658 (1578-3814) Furosemide, mg 20 (0-40)

CRP(+)/RIFLE(+) (n= 13, 6.7 %)

RI PT

15 (17.9) 42 (50) 18 (21.4) 9 (10.7) 0 (0) 20 (23.8) 120 (93-147) 34 (28-40)

CRP(-)/RIFLE(+) (n= 10, 5.2 %)

SC

Procedures CABG surgery, n (%) Valvular surgery, n (%) CABG and valvular surgery, n (%) Thoracic aortic surgery, n (%) Ventricular assist device, n (%) Redo cardiac surgery, n (%) Duration of cardiopulmonary bypass, min Lowest intraoperative MAP, mmHg

CRP(-)/RIFLE(-) CRP(+)/RIFLE(-) (n= 84, 43.3 %) (n= 87, 44.8 %)

M AN U

Variable

0.275

Numbers denote median (25.-75. percentile) or frequency (%) where appropriate. CABG, coronary artery bypass graft; CRP, C-reactive protein, RIFLE, risk injury failure end-stage renal disease classification (20)

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Table 4a. Preoperative patient characteristics according to Proteinuria/RIFLE Subgroup

AC C

Preoperative medication ACE inhibitor/AT-II antagonist, n (%) Beta blocker, n (%) Calcium channel blocker, n (%) HMG-CoA reductase inhibitor, n (%) Diuretics, n (%)

Prot(+)/RIFLE(+) (n= 14, 7.0 %)

69 (58.3-74.5) 27 (33.8) 25.9 (23.4-29.4) 2 (2.5) 15 (18.8) 59 (73.8) 48 (60.0) 15 (18.8) 12 (15.0) 88.4 (77.8-106.1) 17 (21.3) 21 (26.3) 50.0 (37.0-60.0)

67.0 (59.5-74.8) 23 (24.2) 27.0 (24.2-29.2) 6 (6.3) 19 (20.0) 70 (73.7) 64 (67.4) 12 (12.6) 15 (15.8) 86.2 (77.1-106.1) 27 (28.4) 13 (13.7) 55.0 (45.0-60.0)

73.0 (70.5-77.3) 2 (20.0) 27.5 (25.6-32.8) 0 (0) 3 (30.0) 9 (90.0) 6 (60.0) 2 (20.0) 1 (10) 96.8 (87.3-126.0) 4 (40.0) 4 (40.0) 50.0 (23.8-60.0)

71.5 (66.5-77) 6 (42.9) 26.9 (24.5-31.2) 0(0) 2 (14.3) 14 (100) 11 (78.6) 3 (21.4) 3 (21.4) 97.2 (72.9-123.8) 4 (28.6) 3 (21.4) 55.0 (41.3-60.0)

0.097 0.305 0.281 0.625 0.801 0.086 0.510 0.556 0.898 0.248 0.457 0.065 0.346

42 (52.5) 61 (76.3) 22 (27.5) 42 (52.5) 57 (71.3)

54 (56.8) 66 (69.5) 28 (29.5) 58 (61.1) 51 (53.7)

8 (80.0) 8 (80.0) 5 (50.0) 5 (50.0) 7 (70.0)

10 (71.4) 10 (71.4) 5 (37.7) 10 (71.4) 13 (92.9)

0.260 0.774 0.472 0.449 0.007

SC

RI PT

Prot(-)/RIFLE(+) (n= 10, 5.0 %)

EP

Age, years Sex, female, n (%) Body mass index Insulin dependent diabetes mellitus, n (%) Non-insulin dependent diabetes mellitus, n (%) Arterial hypertension, n (%) Hypercholesterolemia, n (%) Chronic obstructive pulmonary disease, n (%) Smoking habit, current, n (%) Preoperative serum creatinine, µmol/L Peripheral vascular disease, n (%) Left ventricular dysfunction, n (%)* Left ventricular ejection fraction, %

Prot(+)/RIFLE(-) (n= 95, 47.5 %)

M AN U

Prot(-)/RIFLE(-) (n= 80, 40.2 %)

TE D

Variable

P-value

Numbers denote median (25.-75. percentile) or frequency (%) where appropriate. BMI, body-mass-index; ACE, angiotensin-converting enzyme; AT-II, angiotensin-II; HMG-CoA, 3-hydroxy-3-methyl-glutaryl-CoA; * Left ventricular ejection fraction <35% RIFLE, risk injury failure end-stage renal disease classification (20)

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Table 4b: Perioperative patient characteristics according to Proteinuria/RIFLE Subgroup

Procedures CABG surgery, n (%) Valvular surgery, n (%) CABG and valvular surgery, n (%) Thoracic aortic surgery, n (%) Ventricular assist device, n (%) Redo cardiac surgery, n (%) Duration of cardiopulmonary bypass, min Lowest intraoperative MAP, mmHg

Prot(+)/RIFLE(+) (n= 14, 7.0 %)

P-value

15 (15.8) 39 (41.1) 29 (30.5) 12 (12.6) 0 (0) 23 (24.2) 127 (94-184) 34 (28-39)

3 (30.0) 3 (30.0) 2 (20.0) 1 (10.0) 1 (10.0) 2 (20) 144 (93-256) 31 (28.25-34)

1 (7.1) 9 (64.3) 4 (28.6) 0 (0) 0 (0) 8 (57.1) 198 (149-300) 29.5 (23.75-41)

0.443 0.265 0.774 0.324 0.054 0.077 <0.001 0.058

8709 (7343-9706) 5400 (4639-6433) 250 (0-500.0) 500 (300-700) 2982 (2119-4476) 20.0 (10.0-40.0)

7540 (6062-9747) 5377 (4433-6488) 500 (0-750) 485 (375-700) 2387 (1162-3709) 20.0 (0-47.5)

5984 (4289-7325) 4110 (2805-4246) 1000 (375-2500) 1135 (542-1625) 2124 (999-3509) 70.0 (32.5-218-0)

6584 (5282-8782) 3836 (2838-4773) 525 (0-2000) 813 (509-2038) 2711 (-118-4842) 55.0 (20.0-150.0)

<0.001 <0.001 0.013 0.006 0.091 <0.001

M AN U

TE D

EP

RIFLE stages Risk Injury Failure

Prot(-)/RIFLE(+) (n= 10, 5.0 %)

16 (20.0) 39 (48.8) 19 (23.8) 5 (6.3) 1 (1.3) 19 (23.8) 116 (88-137) 37 (31-41)

AC C

Perioperative fluid balance and medication (0-24 h) Fluid intake, ml Urine output, ml Packed red blood cells, ml Drain output, ml Fluid balance, ml Furosemide, mg

Prot(+)/RIFLE(-) (n= 95, 47.5 %)

RI PT

Prot(-)/RIFLE(-) (n= 80, 40.2 %)

SC

Variable

0.461 7 (70.0) 2 (20.0) 1 (10.0)

6 (42.9) 5 (35.7) 3 (21.4)

Numbers denote median (25.-75. percentile) or frequency (%) where appropriate. CABG, coronary artery bypass graft; RIFLE, risk injury failure end-stage renal disease classification (20)

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Table 5a: Area under the ROC curve for acute kidney injury as endpoint

NGAL Midkine Interleukin-6 CRP

AUC-ROC (CI)

P value

0.724 (0.612-0.836) 0.719 (0.598-0.840) 0.732 (0.625-0.839) 0.501 (0.369-0.632)

<0.001 0.001 0.001 0.992

RI PT

Biomarker

M AN U

SC

NGAL, Neutrophil gelatinase-associated lipocalin; CRP, C-reactive protein; AUC-ROC, Area under the Reciever-operating characteristic Curve; CI, confidence interval

Table 5b: Comparison of the area under the ROC curve for all biomarker combinations Difference area A—area B

EP

0 0.01 0.01 0.22 0.23 0.22

Std. Error

+/- 0.087 +/- 0.0906 +/- 0.0907 +/- 0.089 +/- 0.0925 +/- 0.089

P

1.0 0.912 0.912 0.013 0.013 0.013

AC C

NGAL - Midkine NGAL - uIL-6 uIL-6 - Midkine NGAL - CRP uIL-6 - CRP Midkine - CRP

TE D

paired biomarkers

NGAL, Neutrophil gelatinase-associated lipocalin; CRP, C-reactive protein; AUC-ROC, Area under the Reciever-operating characteristic Curve Comparison of the area under the ROC curves for AKI as dependent endpoint did not show differences comparing NGAL, Midkine and IL-6 with each other. However, the AUC for CRP differed significantly compared to all urinary biomarkers

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Table 6a: Cox regression analysis for 'in-hospital mortality or RRT' as dependent endpoint Variables in the equation

1.04 (0.98-1.10) 1.56 (0.52-4.87) 1.00 (0.36-2.82) 1.00 (0.99-1.00) 3.54 (2.36-5.31)

0.179 0.412 1.000 1.000 <0.001

SC

Model 2: Midkine / RIFLE Age Previous cardiac surgery CABG vs. Concomitant Perioperative fluid balance Midkine / RIFLE Group allocation

P value

RI PT

Model 1: NGAL / RIFLE Age Previous cardiac surgery CABG vs. Concomitant Perioperative fluid balance NGAL / RIFLE Group allocation

Hazard Ratio (95% CI)

0.183 0.740 0.811 0.177 <0.001

1.06 (1.00-1.12) 1.68 (0.46-6.10) 0.58 (0.19-1.78) 1.00 (0.99-1.00) 3.72 (2.34-5.92)

0.064 0.433 0.341 0.310 <0.001

Model 4: Proteinuria / RIFLE Age Previous cardiac surgery CABG vs. Concomitant Perioperative fluid balance Proteinuria / RIFLE Group allocation

1.05 (0.99-1.10) 2.07 (0.65-6.59) 0.56 (0.20-1.56) 0.99 (0.99-1.00) 4.38 (2.66-7.22)

0.089 0.219 0.269 0.014 <0.001

Model 5: CRP / RIFLE Age Previous cardiac surgery CABG vs. Concomitant Perioperative fluid balance CRP / RIFLE Group allocation

1.05 (1.00-1.11) 1.53 (0.49-4.81) 0.67 (0.23-1.92) 1.00 (0.99-1.00) 2.92 (1.90-4.49)

0.052 0.467 0.451 0.031 <0.001

TE D

AC C

EP

Model 3: IL-6 / RIFLE Age Previous cardiac surgery CABG vs. Concomitant Perioperative fluid balance IL-6 / RIFLE Group allocation

M AN U

1.04 (0.98-1.09) 1.19 (0.41-3.49) 1.13 (0.41-3.14) 1.00 (0.99-1.00) 3.43 (2.31-5.09)

CABG, Coronary artery bypass graft; Concomitant surgery, CABG plus Valvular Surgery CRP, C-reactive Protein; IL-6, Interleukin-6; NGAL, neutrophil gelatinase associated lipocalin; RIFLE, Risk Injury Failure Classification of Acute kidney injury (20)

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Table 6b: Cox regression analysis for 'in-hospital mortality' as dependent endpoint Variables in the equation

1.04 (0.97-1.12) 1.27 (0.64-2.53) 0.79 (0.23-2.73) 0.99 (0.99-1.00) 3.07 (1.91-4.93)

0.271 0.501 0.708 0.035 <0.001

SC

Model 2: Midkine / RIFLE Age Previous cardiac surgery CABG vs. Concomitant Perioperative fluid balance (0-6hrs) Midkine / RIFLE Group allocation

P value

RI PT

Model 1: NGAL / RIFLE Age Previous cardiac surgery CABG vs. Concomitant Perioperative fluid balance (0-6hrs) NGAL / RIFLE Group allocation

Hazard Ratio (95% CI)

0.324 0.615 0.931 0.044 <0.001

1.06 (0.98-1.15) 1.83 (0.64-5.22) 0.50 (0.12-2.12) 1.00 (0.99-1.00) 2.81 (1.63-4.84)

0.138 0.261 0.347 0.154 <0.001

Model 4: Proteinuria / RIFLE Age Previous cardiac surgery CABG vs. Concomitant Perioperative fluid balance (0-6hrs) Proteinuria / RIFLE Group allocation

1.05 (0.98-1.13) 1.43 (0.70-2.94) 0.46 (0.13-1.63) 0.99 (0.99-1.00) 3.93 (2.15-7.20)

0.190 0.329 0.231 0.008 <0.001

Model 5: CRP / RIFLE Age Previous cardiac surgery CABG vs. Concomitant Perioperative fluid balance (0-6hrs) CRP / RIFLE Group allocation

1.07 (0.99-1.14) 1.39 (0.64-3.06) 0.58 (0.16-2.11) 0.99 (0.99-1.00) 2.44 (1.45-4.10)

0.083 0.408 0.408 0.012 0.001

AC C

EP

TE D

Model 3: IL-6 / RIFLE Age Previous cardiac surgery CABG vs. Concomitant Perioperative fluid balance (0-6hrs) IL-6 / RIFLE Group allocation

M AN U

1.04 (0.97-1.11) 1.19 (0.61-2.34) 0.95 (0.28-3.24) 0.99 (0.99-1.00) 3.10 (1.94-4.96)

CABG, Coronary artery bypass graft; Concomitant surgery, CABG plus Valvular Surgery CRP, C-reactive Protein; IL-6, Interleukin-6; NGAL, neutrophil gelatinase associated lipocalin; RIFLE, Risk Injury Failure Classification of Acute kidney injury ( 20)

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Table 6c: Cox regression analysis for 'major adverse kidney events (MAKE)' as dependent endpoint Hazard Ratio (95% CI)

Variables in the equation

SC

1.04 (0.99-1.10) 1.39 (0.49-3.96) 1.16 (0.43-3.10) 1.00 (0.99-1.00) 3.71 (2.52-5.46)

0.102 0.541 0.768 0.150 <0.001

0.083 0.936 0.584 0.202 <0.001

1.06 (1.00-1.12) 1.31 (0.41-4.19) 0.63 (0.22-1.84) 1.00 (0.99-1.00) 4.16 (2.61-6.64)

0.035 0.650 0.398 0.284 <0.001

Model 4: Proteinuria / RIFLE Age Previous cardiac surgery CABG vs. Concomitant Perioperative fluid balance Proteinuria / RIFLE Group allocation

1.05 (1.00-1.11) 1.81 (0.61-5.38) 0.64 (0.24-1.69) 0.99 (0.99-1.00) 4.79 (2.94-7.80)

0.047 0.286 0.369 0.014 <0.001

1.06 (1.00-1.11) 1.09 (0.40-2.97) 0.78 (0.28-2.16) 1.00 (0.99-1.00) 3.33 (2.17-5.11)

0.036 0.861 0.633 0.049 <0.001

TE D

EP

Model 3: IL-6 / RIFLE Age Previous cardiac surgery CABG vs. Concomitant Perioperative fluid balance IL-6 / RIFLE Group allocation

M AN U

1.05 (0.99-1.01) 0.96 (0.36-2.55) 1.31 (0.49-3.44) 1.00 (0.99-1.00) 3.75 (2.54-5.54)

AC C

Model 2: Midkine / RIFLE Age Previous cardiac surgery CABG vs. Concomitant Perioperative fluid balance Midkine / RIFLE Group allocation

RI PT

Model 1: NGAL / RIFLE Age Previous cardiac surgery CABG vs. Concomitant Perioperative fluid balance NGAL / RIFLE Group allocation

P value

Model 5: CRP / RIFLE Age Previous cardiac surgery CABG vs. Concomitant Perioperative fluid balance CRP / RIFLE Group allocation

CABG, Coronary artery bypass graft; Concomitant surgery, CABG plus Valvular Surgery CRP, C-reactive Protein; IL-6, Interleukin-6; NGAL, neutrophil gelatinase associated lipocalin; RIFLE, Risk Injury Failure Classification of Acute kidney injury (20) Table 7: Cox regression analysis for in-hospital adverse events (primary and secondary endpoints) According to Biomarker/RIFLE groups compared to reference: Biomarker(-)/RIFLE(-) 45

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Groups compared

Hazard Ratio (95% CI)

M AN U

SC

RI PT

"In-Hospital Mortality or RRT" as dependent endpoint Midkine(+)/RIFLE(-) vs. Midkine(-)/RIFLE(-) 2.33 (0.53-10.35) Midkine(-)/RIFLE(+) vs. Midkine(-)/RIFLE(-) 6.27 (0.91-43.03) Midkine(+)/RIFLE(+) vs Midkine(-)/RIFLE(-) 67.59 (17.32-263.78) IL-6(+)/RIFLE(-) vs. IL-6(-)/RIFLE(-) 6.27 (1.12-35.21( IL-6(-)/RIFLE(+) vs. IL-6(-)/RIFLE(-) 22.19 (1.2-417.38) IL-6(+)/RIFLE(+) vs. IL-6(-)/RIFLE(-) 91.08 (10.71-774.30) Prot(+)/RIFLE(-) vs. Prot(-)/RIFLE(-) 2.15 (0.41-11.27) Prot(-)/RIFLE(+) vs. Prot(-)/RIFLE(-) 29.50 (3.11-280.01) Prot(+)/RIFLE(+) vs. Prot(-)/RIFLE(-) 66.63 (11.82-375.48) CRP(+)/RIFLE(-) vs. CRP(-)/RIFLE(-) 0.92 (0.21-4.01) CRP(-)/RIFLE(+) vs. CRP(-)/RIFLE(-) 13.39 (3.16-56.78) CRP(+)/RIFLE(+) vs. CRP(-)/RIFLE(-) 11.41 (2.86-45.42)

TE D

In-Hospital Mortality as dependent endpoint Midkine(+)/RIFLE(-) vs. Midkine(-)/RIFLE(-) 4.27 (0.66-27.56) Midkine(-)/RIFLE(+) vs. Midkine(-)/RIFLE(-) 6.65 (0.46-95.73) Midkine(+)/RIFLE(+) vs Midkine(-)/RIFLE(-) 42.48 (7.74-233.06) IL-6(+)/RIFLE(-) vs. IL-6(-)/RIFLE(-) 5.41 (0.58-50.26) IL-6(-)/RIFLE(+) vs. IL-6(-)/RIFLE(-) 0.99 (0.99-1.00) IL-6(+)/RIFLE(+) vs. IL-6(-)/RIFLE(-) 159.87 (3.39-7532.42) Prot(+)/RIFLE(-) vs. Prot(-)/RIFLE(-) 4.03 (0.43-37.79) Prot(-)/RIFLE(+) vs. Prot(-)/RIFLE(-) 67.57 (2.47-1850.67) Prot(+)/RIFLE(+) vs. Prot(-)/RIFLE(-) 62.92 (4.95-800.1) CRP(+)/RIFLE(-) vs. CRP(-)/RIFLE(-) 0.51 (0.08-3.21) CRP(-)/RIFLE(+) vs. CRP(-)/RIFLE(-) 6.49 (1.22-34.49) CRP(+)/RIFLE(+) vs. CRP(-)/RIFLE(-) 9.69 (1.88-49.90)

AC C

EP

Major adverse kidney events (MAKE) as dependent endpoint Midkine(+)/RIFLE(-) vs. Midkine(-)/RIFLE(-) 2.33 (0.53-10.35) Midkine(-)/RIFLE(+) vs. Midkine(-)/RIFLE(-) 16.03 (3.07-83.82) Midkine(+)/RIFLE(+) vs Midkine(-)/RIFLE(-) 67.59 (17.32-263.78) IL-6(+)/RIFLE(-) vs. IL-6(-)/RIFLE(-) 6.27 (1.12-35.21) IL-6(-)/RIFLE(+) vs. IL-6(-)/RIFLE(-) 45.33 (2.98-689.49) IL-6(+)/RIFLE(+) vs. IL-6(-)/RIFLE(-) 104.85 (12.32-892.54) Prot(+)/RIFLE(-) vs. Prot(-)/RIFLE(-) 2.15 (0.41-11.27) Prot(-)/RIFLE(+) vs. Prot(-)/RIFLE(-) 34.98 (3.77-324.49) Prot(+)/RIFLE(+) vs. Prot(-)/RIFLE(-) 78.41 (13.85-444.0) CRP(+)/RIFLE(-) vs. CRP(-)/RIFLE(-) 0.92 (0.21-4.01) CRP(-)/RIFLE(+) vs. CRP(-)/RIFLE(-) 18.48 (4.40-77.64) CRP(+)/RIFLE(+) vs. CRP(-)/RIFLE(-) 13.64 (3.57-52.01) Model adjusted for age, type of surgery (Coronary artery bypass graft vs. Concomittant surgery), previous cardiac surgery and perioperative fluid balance 0-6 NGAL, neutrophil gelatinase associated lipocalin; IL-6, Interleukin-6, Prot, Proteinuria RIFLE, Risk Injury Failure Classification of Acute kidney injury (20)

P value

0.265 0.062 <0.001 0.037 0.038 <0.001 0.366 0.003 <0.001 0.913 <0.001 0.001

0.128 0.164 <0.001 0.137 0.994 0.01 0.222 0.013 0.001 0.476 0.028 0.007

0.265 0.001 <0.001 0.037 0.006 <0.001 0.366 0.002 <0.001 0.913 <0.001 <0.001

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Table 8: Cox regression analysis for long term overall survival (long-term outcome) P value

RI PT

Hazard Ratio (95% CI)

Variables in the equation

2.251 (1.676-3.022) 1.002 (0.991-1.014) 0.770 (0.407-1.457) 1.544 (0.670-3.557) 1.476 (0.637-3.421)

<0.001 0.690 0.421 0.308 0.363

Model 2: Midkine / RIFLE Midkine / RIFLE Group allocation Preoperative serum creatinine Peripheral vascular disease Arterial hypertension Myocardial infarction 6 months prior to surgery

2.154 (1.655-2.804) 1.002 (0.990-1.013) 0.799 (0.397-1.479) 1.558 (0.677-3.585) 1.388 (0.534-3.610)

<0.001 0.790 0.428 0.297 0.501

Model 3: IL-6 / RIFLE IL-6 / RIFLE Group allocation Preoperative serum creatinine Peripheral vascular disease Arterial hypertension Myocardial infarction 6 months prior to surgery

2.027 (1.527-2.700) 1.011 (1.000-1.022) 0.873 (0.455-1.675) 1.509 (0.653-3.486) 1.260 (0.544-2.918)

<0.001 0.058 0.682 0.335 0.589

1.721 (1.266-2,339) 1.005 (0.994-1.016) 0.789 (0.415-1.497) 1.874 (0.826-4.253) 1.470 (0.639-3.384)

0.001 0.377 0.468 0.133 0.365

EP

TE D

M AN U

SC

Model 1: NGAL / RIFLE NGAL / RIFLE Group allocation Preoperative serum creatinine Peripheral vascular disease Arterial hypertension Myocardial infarction 6 months prior to surgery

AC C

Model 4: Proteinuria / RIFLE Proteinuria / RIFLE Group allocation Preoperative serum creatinine Peripheral vascular disease Arterial hypertension Myocardial infarction 6 months prior to surgery

CRP, C-reactive Protein; IL-6, Interleukin-6; NGAL, neutrophil gelatinase associated lipocalin; RIFLE, Risk Injury Failure Classification of Acute kidney injury (20)

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Table 9: Inclusion and Exclusion Criteria of the original RCT (ClinicalTrials.gov NCT00672334) (19)

M AN U

SC

RI PT

Inclusion criteria. Cardiac surgical patients in whom the use of cardiopulmonary bypass was planned and having one or more of the following risk factors for postoperative acute kidney injury: - Age above 70 years Pre-existing renal impairment (preoperative plasma creatinine concentration - >120 mmol/l) - New York Heart Association class III/IV or impaired left ventricular function (left ventricular ejection fraction <35%) Valvular surgery or concomitant valvular and coronary artery bypass graft - surgery - Redo cardiac surgery - Insulin-dependent Type 2 diabetes mellitus Exclusion criteria

AC C

EP

TE D

End stage renal disease (serum creatinine concentration >300 - mmol/l) Emergency cardiac - surgery - Planned off-pump cardiac surgery - Known blood-borne infectious disease Chronic inflammatory disease on - immunosuppression Chronic moderate to high dose corticosteroid therapy (>10 mg/d prednisone - or equivalent) - Enrolled in conflicting research study - Age <18 years

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Supplemental Table 10: Measurement of agreement among biomarkers The degree of agreement among all biomarkers allocating patients to AKI subtype groups is presented. We found ‘moderate’ to ‘substantial’

RI PT

agreement between NGAL and Midkine and ‘fair’ agreement with proteinuria. However, there was only ‘poor’ to ‘slight’ agreement between CRP

0.579 0.452 0.428 0.322 0.250 0.238 0.180 0.144 0.018 0.097

+/- 0.060 +/- 0.060 +/- 0.062 +/- 0.052 +/- 0.061 +/- 0.053 +/- 0.065 +/- 0.058 +/- 0.061 +/- 0.056

Significance level

Strength of agreement*

TE D

M AN U

Std. Error

EP

NGAL - Midkine NGAL - IL-6 IL-6 - Midkine NGAL - Proteinuria IL-6 - Proteinuria Midkine - Proteinuria IL-6 - CRP NGAL - CRP Proteinuria - CRP Midkine - CRP

Kappa

<0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.001 0.003 0.738 0.032

moderate-substantial moderate moderate fair fair fair slight slight poor-slight poor-slight

AC C

Paired biomarkers

SC

with kidney biomarkers and proteinuria.

*Measurement of agreement and interpretation according to recommendation by Landis JR, Koch GG25 Graduation on recommended basis of "poor, slight, fair, moderate, substantial, almost perfect". NGAL, neutrophil gelatinase-associated lipocalin; IL-6, Interleukine-6; CRP, C-reactive protein

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Legends to Appendix Fig. 1

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Overall, 42 of 105 patients (40.0%) developed worsening of CKD, but no patient developed dependence on chronic hemodialysis. Patients with NGAL-positive or Midkine-positive subclinical AKI, showed a non-significant increase in the chance of developing worsening CKD compared to NGAL(-)/RIFLE(-) patients (OR 1.66 (95% CI 0.57-4.87), P=0.381, Supplemental Fig.1a) or Midkine(-

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Long-term worsening chronic kidney disease according to biomarker status

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)/RIFLE(-) patients (OR 1.49 (95% CI 0.47-4.65), P=0.537, Supplemental Fig.1b), respectively.

(A) Proportion of NGAL-positive/RIFLE-negative patients developing worsening chronic kidney disease during follow-up period of 5.6 years compared to NGAL-negative/RIFLE-negative patients

(B) Proportion of Midkine-positive/RIFLE-negative patients developing worsening chronic kidney disease during follow-up period of 5.6

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years compared to Midkine-negative/RIFLE-negative patients

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classification 16

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CKD, chronic kidney disease; NGAL, neutrophil gelatinase-associated lipocalin, RIFLE, risk injury failure end-stage renal disease

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Appendix Figure 2.a-d

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Kaplan-Meier Survival curves illustrate significant differences for all assessed biomarker/RIFLE groups. For all biomarkers assessed, survival distribution between the groups differed significantly early after cardiac surgery as well as for short-term and long-term followup as indicated by Log-Rank test, Breslow-test and Tarone-Ware test who consider (weighted) left, middle and right parts of the Kaplan-

Supplemental Results

CRP and controlling outcome pattern for systemic inflammation

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Meier curve (all p<0.001). Graphs include the confidence limits as well as the number of patients at risk periodically over time of follow up.

presented in supplemental Tables 3a+b.

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Baseline and perioperative characteristics, biomarker courses and group status for CRP and consecutive patient adverse outcomes are

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Using CRP levels at 6 hours after commencement of CPB, no additive prognostic information for the primary endpoint, in-hospital

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mortality, RRT or MAKE was found beyond RIFLE-status in adjusted and unadjusted models (Fig.3a, Supplemental Table 7). Prognostic patterns and biomarker agreement over AKI subtypes using CRP-concentrations measured at 24 hours remained essentially unchanged (Fig.3b).

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