Evidence map›Paper›PMID 41348427›Full record

ReviewIrish journal of medical science2026

The utility of eGFR difference (eGFRCystatin C - eGFRCreatinine) in the diagnosis, prognosis, and management of chronic kidney disease: a comprehensive review.

Gerry George Mathew

Abstract readReview
PubMed Publisher
In one paragraph

Review in Irish journal of medical science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

1 author.

Gerry George MathewDepartment of Nephrology, SRM Medical College Hospital and Research Centre, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu, 603203, India. gerrygeorge007@gmail.com.ORCID http://orcid.org/0000-0003-1967-4470

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe estimated glomerular filtration rate (eGFR) calculated from serum creatinine has limitations in accurately reflecting kidney function due to its dependence on muscle mass and non-GFR determinants. The eGFR difference (eGFR diff = eGFR Cystatin C - eGFR Creatinine) has emerged as a novel biomarker that may provide additional clinical information beyond that provided by traditional eGFR measurements. METHODOLOGY AND

resultsA comprehensive literature review of peer-reviewed studies investigating eGFR diff in CKD populations, focusing on diagnostic accuracy, prognostic value, and clinical applications was conducted. The eGFR diff reflects the differential effects of non-GFR determinants on creatinine and cystatin C. A positive eGFR diff (eGFRCys eGFRCr) is associated with reduced muscle mass, inflammation, and increased cardiovascular and mortality risks. Conversely, a negative eGFR diff may indicate preserved muscle mass or creatinine elevation due to non-GFR factors. Medical literature reveals that eGFR diff improves the risk prediction for CKD progression, cardiovascular events, and mortality beyond that of traditional risk factors. The eGFR diff is an invaluable clinical tool that enhances CKD risk stratification and provides insights into patient phenotypes beyond kidney function alone.

conclusionThe integration of eGFR diff into clinical practice may improve personalized CKD management and clinical outcomes.

Indexed as

CreatinineCystatin CGlomerular Filtration RateRenal Insufficiency, ChronicBiomarkersHumansPrognosisBiomarkersCreatinineCystatin CBiomarkerChronic kidney diseaseCreatinineCystatin CEGFR differenceMuscle massPrognosis

Identifiers

PMID41348427

What OpenQuestion holds

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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.