Evidence map›Paper›PMID 28693441›Full record

ArticleBMC nephrology2017

CKD-EPI creatinine-cystatin C glomerular filtration rate estimation equation seems more suitable for Chinese patients with chronic kidney disease than other equations.

Xiao-Hua Chi, Gui-Ping Li, Quan-Shi Wang, Yong-Shuai Qi, Kai Huang, Qian Zhang, Yao-Ming Xue

Abstract read
In one paragraph

Article in BMC nephrology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
–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

30 citing papers in PubMed.

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  19. The Serum Uric Acid to Serum Creatinine Ratio is an Independent Risk Factor for Diabetic Kidney Disease.Diabetes, metabolic syndrome and obesity : targets and therapy · 2022
    Article
  20. 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

7 authors.

Xiao-Hua ChiDepartment of Nuclear Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, China.
Gui-Ping LiDepartment of Nuclear Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, China.
Quan-Shi WangDepartment of Nuclear Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, China.
Yong-Shuai QiDepartment of Nuclear Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, China.
Kai HuangDepartment of Nuclear Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, China.
Qian ZhangDepartment of Endocrinology and Metabolism, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, China.
Yao-Ming XueDepartment of Endocrinology and Metabolism, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, China. hyxknfyy@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe aim of this study was to identify the optimal equation that accurately estimates the glomerular filtration rate (GFR) and the chronic kidney disease (CKD) stage in the Chinese population.

methodsA total of 1296 Chinese patients aged 18-65 years old were enrolled in this study. The estimated GFRs (eGFRs) calculated separately by three Diet in Renal Disease (MDRD) equations and three Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations were compared with the reference GFR (rGFR) measured by the

resultsBy Bland-Altman analysis, eGFR

conclusionFor a Chinese population, the CKD-EPI

Indexed as

AdultAsian PeopleCreatinineCystatin CFemaleGlomerular Filtration RateHumansMaleMiddle AgedRenal Insufficiency, ChronicRetrospective StudiesCreatinineCystatin CChronic kidney diseaseCystatin CGlomerular filtration rateSerum creatinine

Identifiers

PMID28693441
PMCPMC5504640

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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.