Evidence map›Paper›PMID 38479785›Full record

ArticleNephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association2024

Pre-donation assessment of cystatin C to improve prediction of pre- and post-donation GFR in potential living kidney donors.

Jessica van der Weijden, Daan Kremer, Lisa B Westenberg, Jan-Stephan F Sanders, Robert A Pol, Ilja M Nolte, Martin H De Borst, Stefan P Berger, Stephan J L Bakker, Marco van Londen

Open access · hybridAbstract read
In one paragraph

Article in Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
2.7field-weighted citation impact, top 11% 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

3 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Article
  3. Observational
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 1 institution in 1 country.

Jessica van der WeijdenDivision of Nephrology, Department of Internal Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.ORCID 0000-0003-0247-7526
Daan KremerDivision of Nephrology, Department of Internal Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.ORCID 0000-0003-0011-115X
Lisa B WestenbergDivision of Nephrology, Department of Internal Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.ORCID 0000-0002-6438-3587
Jan-Stephan F SandersDivision of Nephrology, Department of Internal Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Robert A PolDepartment of Surgery, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Ilja M NolteDepartment of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Martin H De BorstDivision of Nephrology, Department of Internal Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.ORCID 0000-0002-4127-8733
Stefan P BergerDivision of Nephrology, Department of Internal Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Stephan J L BakkerDivision of Nephrology, Department of Internal Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Marco van LondenDivision of Nephrology, Department of Internal Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.ORCID 0000-0001-7145-3240
University Medical Center Groningen · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAccurate estimation of glomerular filtration rate (GFR) is crucial in living kidney donation. While most estimated GFR (eGFR) equations are based on plasma creatinine, its levels are strongly influenced by muscle mass. Application of cystatin C (cysC)-based estimates before donation may improve both estimation of current GFR and prediction of post-donation GFR.

methodsWe assessed the performance of Chronic Kidney Disease Epidemiology Collaboration equations based on creatinine (eGFRcreat-2009, eGFRcreat-2021), cysC (eGFRCysC-2012) or both (eGFRcombined-2012, eGFRcombined-2021) for estimating pre- and post-donation (mGFR) GFR in 486 living kidney donors. We subsequently focused on a subgroup of individuals with high/low muscle mass (25% highest/lowest 24-hour urinary creatinine excretion, sex stratified and height indexed).

resultsPre-donation eGFRcombined-2012 and eGFRcombined-2021 showed the strongest associations with pre- and post-donation mGFR. Pre-donation eGFRcombined-2021 was most accurate for estimating both pre-donation (bias 0.01 ± 11.9 ml/min/1.73 m2) and post-donation mGFR (bias 1.3 ± 8.5 ml/min/1.73 m2). In donors with high/low muscle mass, cysC-based equations (with or without creatinine) performed better compared with equations based on only creatinine.

conclusionsCombined eGFR equations yielded a better estimate of pre- and post-donation mGFR compared with estimates based on creatinine or cysC only. The added value of cysC seems particularly pronounced in donors with high or low muscle mass.

Indexed as

CreatinineCystatin CGlomerular Filtration RateKidney TransplantationLiving DonorsAdultBiomarkersFemaleFollow-Up StudiesHumansKidney Function TestsMaleMiddle AgedNephrectomyPrognosisBiomarkersCreatinineCystatin Ccystatin Cglomerular filtration rateliving kidney donationmuscle masspost-donation GFR

Identifiers

PMID38479785
PMCPMC11648953
OpenAlexW4392791143

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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.