Evidence map›Paper›PMID 31650729›Full record

ArticleAnnals of laboratory medicine2020

Urinary Biomarkers may Complement the Cleveland Score for Prediction of Adverse Kidney Events After Cardiac Surgery: A Pilot Study.

Christian Albert, Michael Haase, Annemarie Albert, Siegfried Kropf, Rinaldo Bellomo, Sabine Westphal, Mark Westerman, Rüdiger Christian Braun-Dullaeus, Anja Haase-Fielitz

Abstract read
In one paragraph

Article in Annals of laboratory medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Christian AlbertFaculty of Medicine, Otto-von-Guericke University Magdeburg, Magdeburg, Germany.ORCID https://orcid.org/0000-0002-6956-9962
Michael HaaseFaculty of Medicine, Otto-von-Guericke University Magdeburg, Magdeburg, Germany.ORCID https://orcid.org/0000-0001-8212-7416
Annemarie AlbertDiaverum Renal Services, MVZ Potsdam, Potsdam, Germany.ORCID https://orcid.org/0000-0002-5611-1506
Siegfried KropfInstitute for Biometrics and Medical Informatics, Otto-von-Guericke University Magdeburg, Magdeburg, Germany.ORCID https://orcid.org/0000-0002-5197-3481
Rinaldo BellomoSchool of Medicine, University of Melbourne, Melbourne, Australia.ORCID https://orcid.org/0000-0002-1650-8939
Sabine WestphalInstitute of Laboratory Medicine, Hospital Dessau, Dessau, Germany.ORCID https://orcid.org/0000-0002-5846-3345
Mark WestermanIntrinsic LifeSciences, La Jolla, CA, USA.
Rüdiger Christian Braun-DullaeusDepartment of Internal Medicine, University Clinic for Cardiology and Angiology, Otto-von-Guericke University Magdeburg, Magdeburg, Germany.ORCID https://orcid.org/0000-0003-3888-6532
Anja Haase-FielitzDepartment of Cardiology, Immanuel Diakonie Bernau, Heart Center Brandenburg, Brandenburg Medical School Theodor Fontane (MHB), Germany.ORCID https://orcid.org/0000-0001-6881-2249

Funding

Else Kröner-Fresenius-StiftungGerman Heart Foundation (Deutsche Stiftung für Herzforschung)
6 · The paper itself

Abstract

backgroundThe ability of urinary biomarkers to complement established clinical risk prediction models for postoperative adverse kidney events is unclear. We assessed the effect of urinary biomarkers linked to suspected pathogenesis of cardiac surgery-induced acute kidney injury (AKI) on the performance of the Cleveland Score, a risk assessment model for postoperative adverse kidney events.

methodsThis pilot study included 100 patients who underwent open-heart surgery. We determined improvements to the Cleveland Score when adding urinary biomarkers measured using clinical laboratory platforms (neutrophil gelatinase-associated lipocalin [NGAL], interleukin-6) and those in the preclinical stage (hepcidin-25, midkine, alpha-1 microglobulin), all sampled immediately post-surgery. The primary endpoint was major adverse kidney events (MAKE), and the secondary endpoint was AKI. We performed ROC curve analysis, assessed baseline model performance (odds ratios [OR], 95% CI), and carried out statistical reclassification analyses to assess model improvement.

resultsNGAL (OR [95% CI] per 20 concentration-units wherever applicable): (1.07 [1.01-1.14]), Interleukin-6 (1.51 [1.01-2.26]), midkine (1.01 [1.00-1.02]), 1-hepcidin-25 (1.08 [1.00-1.17]), and NGAL/hepcidin-ratio (2.91 [1.30-6.49]) were independent predictors of MAKE and AKI (1.38 [1.03-1.85], 1.08 [1.01-1.15], 1.01 [1.00-1.02], 1.09 [1.01-1.18], and 3.45 [1.54-7.72]). Category-free net reclassification improvement identified interleukin-6 as a model-improving biomarker for MAKE and NGAL for AKI. However, only NGAL/hepcidin-25 improved model performance for event- and event-free patients for MAKE and AKI.

conclusionsNGAL and interleukin-6 measured immediately post cardiac surgery may complement the Cleveland Score. The combination of biomarkers with hepcidin-25 may further improve diagnostic discrimination.

Indexed as

Acute Kidney InjuryAgedArea Under CurveBiomarkersCardiac Surgical ProceduresFemaleHumansInterleukin-6Lipocalin-2MaleMiddle AgedOdds RatioPilot ProjectsRisk FactorsROC CurveSeverity of Illness IndexBiomarkersInterleukin-6LCN2 protein, humanLipocalin-2Acute kidney injuryCardiac surgeryCleveland ScoreHepcidinInterleukin-6Major adverse kidney eventsMidkineNeutrophil gelatinase-associated lipocalinReclassification analysis

Identifiers

PMID31650729
PMCPMC6822001

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.