Evidence map›Paper›PMID 27342582›Full record

ArticleNephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association2016

Common chronic conditions do not affect performance of cell cycle arrest biomarkers for risk stratification of acute kidney injury.

Michael Heung, Luis M Ortega, Lakhmir S Chawla, Richard G Wunderink, Wesley H Self, Jay L Koyner, Jing Shi, John A Kellum, Sapphire and Topaz Investigators

Open access · hybridAbstract readMulticenter Study
In one paragraph

Article in Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
4.6field-weighted citation impact, top 5% of its field
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

19 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.

  1. Pooled it
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  10. [Application value of urinary IGFBP7 and TIMP-2 in acute kidney injury with decompensated hepatitis B virus-related liver cirrhosis].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2020
    Article
  11. Review
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  14. [Biomarkers of cardiac surgery-associated acute kidney injury: a narrative review].Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences · 2019
    Review
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  17. Mechanisms Underlying Increased TIMP2 and IGFBP7 Urinary Excretion in Experimental AKI.Journal of the American Society of Nephrology : JASN · 2018
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  19. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 8 institutions in 1 country.

Michael HeungDivision of Nephrology, Department of Medicine, University of Michigan, Ann Arbor, MI, USA.
Luis M OrtegaDivision of Nephrology and Hypertension, Department of Medicine, Allegheny General Hospital, Pittsburgh, PA, USA.
Lakhmir S ChawlaDepartment of Anesthesia and Medicine, George Washington University, Washington, DC, USA.
Richard G WunderinkDivision of Pulmonary and Critical Care Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Wesley H SelfDepartment of Emergency Medicine, Vanderbilt University, Nashville, TN, USA.
Jay L KoynerSection of Nephrology, Department of Medicine, University of Chicago, Chicago, IL, USA.
Jing ShiWalker Bioscience, Carlsbad, CA, USA.
John A KellumCenter for Critical Care Nephrology, Department of Critical Care Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
Sapphire and Topaz Investigators
Allegheny General Hospital · USGeorge Washington University · USNorthwestern University · USUniversity of Chicago · USUniversity of Michigan–Ann Arbor · USUniversity of Pittsburgh Medical Center · USVanderbilt University · USWalker (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIdentification of acute kidney injury (AKI) can be challenging in patients with underlying chronic disease, and biomarkers often perform poorly in this population. In this study we examined the performance characteristics of the novel biomarker panel of urinary tissue inhibitor of metalloproteinases-2 (TIMP2) and insulin-like growth factor-binding protein 7 ([IGFBP7]) in patients with a variety of comorbid conditions.

methodsWe analyzed data from two multicenter studies of critically ill patients in which [TIMP2]•[IGFBP7] was validated for prediction of Kidney Disease: Improving Global Outcomes (KDIGO) Stage 2 or 3 AKI within 12 h. We constructed receiver operating characteristic (ROC) curves for AKI prediction both overall and by comorbid conditions common among patients with AKI, including diabetes mellitus, congestive heart failure (CHF) and chronic kidney disease (CKD).

resultsIn the overall cohort of 1131 patients, 139 (12.3%) developed KDIGO Stage 2 or 3 AKI. [TIMP2]•[IGFBP7] was significantly higher in AKI versus non-AKI patients, both overall and within each comorbidity subgroup. The AUC for [TIMP2]•[IGFBP7] in predicting AKI was 0.81 overall. Higher AUC was noted in patients with versus without CHF (0.89 versus 0.79; P = 0.026) and CKD (0.91 versus 0.80; P = 0.024).

conclusionsWe observed no significant impairment in the performance of cell cycle arrest biomarkers due to the presence of chronic comorbid conditions.

Indexed as

Chronic DiseaseCritical IllnessAcute Kidney InjuryAgedBiomarkersCell Cycle CheckpointsCell Cycle ProteinsFemaleHumansMaleMiddle AgedRisk AssessmentROC CurveBiomarkersCell Cycle Proteinsacute kidney injurybiomarkerschronic kidney disease

Identifiers

PMID27342582
PMCPMC5039344
OpenAlexW2461468019

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

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.