Evidence map›Paper›PMID 31179104›Full record

ArticleJournal of thoracic disease2019

Simple pre-procedure risk stratification tool for contrast-induced nephropathy.

Zhonghan Ni, Yan Liang, Nianjin Xie, Jin Liu, Guoli Sun, Shiqun Chen, Jianfeng Ye, Yibo He, Wei Guo, Ning Tan and 4 more

Erratum issuedOpen access · diamondAbstract read
In one paragraph

Article in Journal of thoracic disease, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
1.8field-weighted citation impact, top 16% 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, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.

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  6. Observational
  7. Observational
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors at 3 institutions in 1 country.

Zhonghan NiDepartment of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China.
Yan LiangDepartment of Cardiology, Maoming People's Hospital, Maoming 525000, China.
Nianjin XieDepartment of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China.
Jin LiuDepartment of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China.
Guoli SunDepartment of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China.
Shiqun ChenDepartment of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China.
Jianfeng YeDepartment of Cardiology, Dongguan People's Hospital, Dongguan 523059, China.
Yibo HeDepartment of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China.
Wei GuoDepartment of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China.
Ning TanDepartment of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China.
Jiyan ChenDepartment of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China.
Yong LiuDepartment of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China.
Zhujun ChenDepartment of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China.
Shouhong WangDepartment of Critical Care Medicine, Guangdong Geriatric Institute, Guangdong Provincial People's Hospital and Guangdong Academy of Medical Sciences, Guangzhou 510080, China.
Guangdong Provincial People's Hospital · CNGuangdong Academy of Medical Sciences · CNDongguan People’s Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundA few simple and pre-procedural risk models have been developed for predicting contrast-induced nephropathy (CIN), which allow for early administration of preventative strategies before coronary angiography (CAG). The study aims to develop and validate simple pre-procedure tools for predicting risk of CIN following CAG.

methodsWe retrospectively analyzed the data from 3,469 consecutive patients undergoing CAG, who were randomly assigned to a development dataset (n=2,313) and a validation dataset (n=1,156). CIN was defined as an increase in serum creatinine (SCr) ≥0.5 mg/dL from baseline within 72 hours after CAG. Multivariate logistic regression was applied to identify independent predictors of CIN to develop risk models. The possible predictors included age >75 years, hypotension, acute myocardial infarction (AMI), SCr ≥1.5 mg/dL, and congestive heart failure (CHF).

resultsThe incidences of CIN were 3.20% and 3.55% in the training and validation dataset respectively. Compared to classical Mehran' and ACEF CIN risk score, the new score across the validation dataset exhibited similar discrimination and predictive ability on CIN (c-statistic: 0.829, 0.832, 0.812 respectively) and in-hospital mortality (c-statistic: 0.909, 0.937, 0.866 respectively) (all P>0.05).

conclusionsThe easy-to-use pre-procedural prediction model only containing 5 factors had similar predictive ability on CIN and mortality.

Indexed as

Contrast-induced nephropathy (CIN)coronary angiography (CAG)percutaneous coronary interventionrisk score

Identifiers

PMID31179104
PMCPMC6531701
OpenAlexW2943225080

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.