Evidence map›Paper›PMID 31049747›Full record

ArticleClinical and experimental nephrology2019

A simple risk score model for predicting contrast-induced nephropathy after coronary angiography in patients with diabetes.

Jun-Feng Zeng, Shi-Qun Chen, Jian-Feng Ye, Yi Chen, Li Lei, Xiao-Qi Liu, Yong Liu, Yi Wang, Ji-Jin Lin, Ji-Yan Chen

Abstract readValidation Study
PubMed Publisher
In one paragraph

Article in Clinical and experimental nephrology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.9field-weighted citation impact, top 15% 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

9 citing papers in PubMed, 22 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

10 authors at 6 institutions in 1 country.

Jun-Feng ZengGuangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China.
Shi-Qun ChenGuangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China.
Jian-Feng YeDongguan People's Hospital, Dongguan, Guangdong, China.
Yi ChenGuangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China.
Li LeiThe Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, Guangdong, China.
Xiao-Qi LiuGuangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Department of Pharmacy, Guangdong Cardiovascular Institute, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China. vinki82@163.com.
Yong LiuGuangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China. liuyong2099@126.com.
Yi WangSchool of Pharmacy, Guangdong Pharmaceutical University, Guangzhou, 510000, Guangdong, China.
Ji-Jin LinGuangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China.
Ji-Yan ChenGuangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China.
Guangdong General Hospital · CNGuangdong Pharmaceutical University · CNDongguan People’s Hospital · CNGuangdong Academy of Medical Sciences · CNSouthern Medical University · CNZhuhai People's Hospital · CN

Funding

Science and Technology Planning Project of Dongguan City(DAMI) 2015108101022Science and Technology Planning Project of Guangdong Province 2014B070706010The National Science Foundation for Young Scientist of China 81500520The Progress in Science and Technology Project of Guangdong Province 2015A030302037The Science and Technology Program of Guangzhou 201510010190
6 · The paper itself

Abstract

backgroundContrast-induced nephropathy (CIN) is a common complication in patients undergoing coronary angiography (CAG) or percutaneous coronary intervention (PCI) and associated with poor outcome. Some previous studies have already set up models to predict CIN, but there is no model for patients with diabetes mellitus (DM) especially. Therefore, we aim to develop and validate a simple risk score for predicting the risk of CIN in patients with DM undergoing CAG/PCI.

methodsA total of 1157 consecutive patients with DM undergoing CAG/PCI were randomly assigned to a development cohort (n = 771) and a validation cohort (n = 386). The primary endpoint was CIN, which was defined as an absolute increase in serum creatinine (SCr) by 0.5 mg/dL from the baseline within 48-72 h after contrast exposure. The independent predictors for CIN were identified by multivariate logistic regression, and the discrimination and calibration of the risk score were assessed by ROC curve and Hosmer-Lemeshow test, respectively.

resultsThe overall incidence of CIN was 45 (3.9%). The new simple risk score (Chen score), which included four independent variables (age > 75 years, acute myocardial infarction, SCr > 1.5 mg/dL, the use of intra-aortic balloon pump), exhibited a similar discrimination and predictive ability on CIN (AUC 0.813, 0.843, 0.796, P > 0.05, respectively), mortality (AUC 0.735, 0.771, 0.826, respectively) and MACEs when being compared with the classical Mehran or ACEF risk score.

conclusionOur data suggest that the new simple risk score might be a good tool for predicting CIN in patients with DM undergoing CAG/PCI.

Indexed as

Decision Support TechniquesAgedAge FactorsBiomarkersContrast MediaCoronary AngiographyCoronary Artery DiseaseCreatinineDiabetes MellitusFemaleHumansIncidenceIntra-Aortic Balloon PumpingKidney DiseasesMaleMiddle AgedBiomarkersContrast MediaCreatinineContrast-induced nephropathyCoronary angiographyDiabetes mellitusPercutaneous coronary interventionRisk score model

Identifiers

PMID31049747
OpenAlexW2943306858

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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.