Evidence map›Paper›PMID 36632770›Full record

ArticleRenal failure2023

Validation of the EKFC equation for glomerular filtration rate estimation and comparison with the Asian-modified CKD-EPI equation in Chinese chronic kidney disease patients in an external study.

Li Zhao, Huan-Li Li, Hui-Jing Liu, Jin Ma, Wei Liu, Jian-Min Huang, Ling-Ge Wei, Peng Xie

Abstract read
In one paragraph

Article in Renal failure, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. 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

8 authors.

Li ZhaoDepartment of Laboratory Medicine, The Third Hospital of Hebei Medical University, Shijiazhuang, PR China.
Huan-Li LiDepartment of ophthalmology Medicine, Hebei General Hospital, Shijiazhuang, PR China.
Hui-Jing LiuDepartment of Laboratory Medicine, The Third Hospital of Hebei Medical University, Shijiazhuang, PR China.
Jin MaDepartment of Laboratory Medicine, The Third Hospital of Hebei Medical University, Shijiazhuang, PR China.
Wei LiuDepartment of Laboratory Medicine, The Third Hospital of Hebei Medical University, Shijiazhuang, PR China.
Jian-Min HuangDepartment of Nuclear Medicine, The Third Hospital of Hebei Medical University, Shijiazhuang, PR China.
Ling-Ge WeiDepartment of Nuclear Medicine, The Third Hospital of Hebei Medical University, Shijiazhuang, PR China.
Peng XieDepartment of Nuclear Medicine, The Third Hospital of Hebei Medical University, Shijiazhuang, PR China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThe aim of this study is to determine whether new European Kidney Function Consortium (EKFC) equation is more applicable than Asian-modified CKD-EPI equation in clinical practice, having a higher accuracy in estimating GFR in our external CKD population.

methodsWe calculated estimated GFR

resultsTotally, 160 CKD patients were recruited in our external cohort. GFR

conclusionsThe overall performance of EKFC equation is acceptable. There is no clinically meaningful difference in the performance of the Asian-modified CKD-EPI and EKFC equations within the limits imposed by the small sample size.

Indexed as

Glomerular Filtration RateRenal Insufficiency, ChronicChinaCreatinineEast Asian PeopleHumansCreatinineAsian-modified CKD-EPI equationChronic kidney disease (CKD)EKFC equationglomerular filtration rate (GFR)

Identifiers

PMID36632770
PMCPMC9848359

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.