Evidence map›Paper›PMID 38567244›Full record

ArticleKidney medicine2024

Cystatin C- and Creatinine-based Estimated GFR Differences: Prevalence and Predictors in the UK Biobank.

Debbie C Chen, Kaiwei Lu, Rebecca Scherzer, Jennifer S Lees, Elaine Rutherford, Patrick B Mark, O Alison Potok, Dena E Rifkin, Joachim H Ix, Michael G Shlipak and 1 more

Abstract read
In one paragraph

Article in Kidney medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
–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

13 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Testosterone deficiency and chronic kidney disease.Journal of clinical & translational endocrinology · 2024
    Review
  13. 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

11 authors.

Debbie C ChenDivision of Nephrology, Department of Medicine, University of California, San Francisco, San Francisco, CA.
Kaiwei LuKidney Health Research Collaborative, San Francisco VA Health Care System & University of California, San Francisco, San Francisco, CA.
Rebecca ScherzerKidney Health Research Collaborative, San Francisco VA Health Care System & University of California, San Francisco, San Francisco, CA.
Jennifer S LeesSchool of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK.
Elaine RutherfordSchool of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK.
Patrick B MarkSchool of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK.
O Alison PotokDivision of Nephrology and Hypertension, Department of Medicine, University of California, San Diego, San Diego, CA.
Dena E RifkinDivision of Nephrology and Hypertension, Department of Medicine, University of California, San Diego, San Diego, CA.
Joachim H IxDivision of Nephrology and Hypertension, Department of Medicine, University of California, San Diego, San Diego, CA.
Michael G ShlipakKidney Health Research Collaborative, San Francisco VA Health Care System & University of California, San Francisco, San Francisco, CA.
Michelle M EstrellaDivision of Nephrology, Department of Medicine, University of California, San Francisco, San Francisco, CA.

Funding

The Clinical Applications of the Difference in Estimated Glomerular Filtration Rate by Cystatin C vs. Creatinine (eGFRDiff)K23DK128604 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI POTOK, OLIVIA ALISON · 2021 to 2025
$951k
NIDDK NIH HHS K23 DK128604
6 · The paper itself

Abstract

Rationale & Objective: Large differences between estimated glomerular filtration rate (eGFR) based on cystatin C (eGFRcys) and creatinine (eGFRcr) occur commonly. A comprehensive evaluation of factors that contribute to these differences is needed to guide the interpretation of discrepant eGFR values. Study Design: Cohort study. Setting & Participants: 468,969 participants in the UK Biobank. Exposures: Candidate sociodemographic, lifestyle factors, comorbidities, medication usage, and physical and laboratory predictors. Outcomes: eGFRdiff, defined as eGFRcys minus eGFRcr, categorized into 3 levels: lower eGFRcys (eGFRdiff, less than -15 mL/min/1.73 m Analytical Approach: Multinomial logistic regression models were constructed to identify predictors of lower eGFRcys or lower eGFRcr. We developed 2 prediction models comprising 375,175 participants: (1) a clinical model using clinically available variables and (2) an enriched model additionally including lifestyle variables. The models were internally validated in an additional 93,794 participants. Results: Mean ± standard deviation of eGFRcys was 88 ± 16 mL/min/1.73 m Limitations: Limited generalizability. Conclusions: This study highlights the multitude of demographic, lifestyle, and health characteristics that are associated with large eGFRdiff. The clinical model may identify individuals who are likely to have discrepant eGFR values and thus should be prioritized for cystatin C testing.

Indexed as

Chronic kidney diseasecreatininecystatin Cestimated glomerular filtration rateprediction

Identifiers

PMID38567244
PMCPMC10986041

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.