Evidence map›Paper›PMID 30180211›Full record

Observational studyPloS one2018

Preoperative risk assessment improves biomarker detection for predicting acute kidney injury after cardiac surgery.

Cheng-Chia Lee, Chih-Hsiang Chang, Shao-Wei Chen, Pei-Chun Fan, Su-Wei Chang, Yi-Ting Chen, Yu-Yun Nan, Pyng-Jing Lin, Feng-Chun Tsai

Open access · goldAbstract readObservational Study
In one paragraph

Observational study in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.2field-weighted citation impact, top 13% 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

8 citing papers in PubMed, 17 citations in OpenAlex.

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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 1 institution in 1 country.

Cheng-Chia LeeKidney Research Center, Department of Nephrology, Chang Gung Memorial Hospital, Linkou branch, College of Medicine, Chang Gung University, Taoyuan, Taiwan.ORCID 0000-0002-6973-5908
Chih-Hsiang ChangKidney Research Center, Department of Nephrology, Chang Gung Memorial Hospital, Linkou branch, College of Medicine, Chang Gung University, Taoyuan, Taiwan.ORCID 0000-0002-7342-335X
Shao-Wei ChenGraduate Institute of Clinical Medical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan.
Pei-Chun FanKidney Research Center, Department of Nephrology, Chang Gung Memorial Hospital, Linkou branch, College of Medicine, Chang Gung University, Taoyuan, Taiwan.
Su-Wei ChangClinical Informatics and Medical Statistics Research Center, College of Medicine, Chang Gung University, Taoyuan, Taiwan.
Yi-Ting ChenKidney Research Center, Department of Nephrology, Chang Gung Memorial Hospital, Linkou branch, College of Medicine, Chang Gung University, Taoyuan, Taiwan.
Yu-Yun NanDepartment of Cardiothoracic and Vascular Surgery, Chang Gung Memorial Hospital, Linkou Branch, College of Medicine, Chang Gung University, Taoyuan, Taiwan.
Pyng-Jing LinDepartment of Cardiothoracic and Vascular Surgery, Chang Gung Memorial Hospital, Linkou Branch, College of Medicine, Chang Gung University, Taoyuan, Taiwan.
Feng-Chun TsaiDepartment of Cardiothoracic and Vascular Surgery, Chang Gung Memorial Hospital, Linkou Branch, College of Medicine, Chang Gung University, Taoyuan, Taiwan.
Chang Gung University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlthough urinary neutrophil gelatinase-associated lipocalin (NGAL) has emerged as a promising biomarker for the early detection of kidney injury, previous studies of adult patients who underwent cardiac surgery have reported only moderate discrimination. The age, creatinine, and ejection fraction (ACEF) score is a preoperative validated risk model with satisfactory accuracy for predicting AKI following cardiac surgery. It remains unknown whether combining preoperative risk assessment through ACEF scores followed by urinary NGAL test in a population of high-risk individuals is an optimal approach with improved predictive performance. MATERIAL AND

methodsA total of 177 consecutive patients who underwent cardiac surgery were enrolled. Clinical characteristics, prognostic model scores, and outcomes were assessed. Urinary NGAL were examined within 6 hours after cardiac surgery. Patients were stratified according to preoperative ACEF scores, and comparisons were made using the area under the receiver operator characteristic curve (AUROC) for the prediction of AKI.

resultsA total of 45.8% (81/177) of the patients had AKI. As expected, patients with ACEF scores ≥ 1.1 were older and more likely to have class III or IV heart failure. They were also more likely to have diabetes mellitus, myocardial infarction, and peripheral arterial disease. Urinary NGAL alone moderately predicted AKI, with an AUROC of 0.732. Risk stratification by ACEF scores ≥ 1.1 substantially improved the AUROC of urinary NGAL to 0.873 (95% confidence interval, 0.784-0.961; P < .001).

conclusionsRisk stratification by preoperative ACEF scores ≥ 1.1, followed by postoperative urinary NGAL, provides more satisfactory risk discrimination than does urinary NGAL alone for the early detection of AKI after cardiac surgery. Future studies should investigate whether this strategy could improve the outcomes and cost-effectiveness of care in patients undergoing cardiac surgery.

Indexed as

Acute Kidney InjuryModels, CardiovascularPostoperative ComplicationsAdultAgedBiomarkersCardiac Surgical ProceduresFemaleHumansLipocalin-2MaleMiddle AgedPreoperative CareRisk AssessmentStroke VolumeTime FactorsBiomarkersLCN2 protein, humanLipocalin-2

Identifiers

PMID30180211
PMCPMC6122821
OpenAlexW2889624616

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LicenceCC BY
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Registered trials

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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.