Evidence map›Paper›PMID 38441895›Full record

SynthesisJAMA network open2024

Bias and Accuracy of Glomerular Filtration Rate Estimating Equations in the US: A Systematic Review and Meta-Analysis.

Alice F Yan, Michelle Y Williams, Zumin Shi, Richard Oyekan, Carol Yoon, Raffick Bowen, Glenn M Chertow

Open access · goldAbstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in JAMA network open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
6.1field-weighted citation impact, top 3% 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

13 citing papers in PubMed, 18 citations in OpenAlex.

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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 at 3 institutions in 2 countries.

Alice F YanDepartment of Research, Patient Care Services, Stanford Healthcare, Palo Alto, California.
Michelle Y WilliamsDepartment of Research, Patient Care Services, Stanford Healthcare, Palo Alto, California.
Zumin ShiHuman Nutritition Department, College of Health Sciences, QU Health, Qatar University, Doha 2713, Qatar.
Richard OyekanDepartment of Research, Patient Care Services, Stanford Healthcare, Palo Alto, California.
Carol YoonDepartment of Research, Patient Care Services, Stanford Healthcare, Palo Alto, California.
Raffick BowenDepartment of Pathology, Stanford Healthcare, Palo Alto, California.
Glenn M ChertowDivision of Nephrology, Department of Medicine, Stanford University School of Medicine, Palo Alto, California.
Stanford Health Care · USQatar University · QAStanford University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: There is increasing concern that continued use of a glomerular filtration rate (GFR) estimating equation adjusted for a single racial group could exacerbate chronic kidney disease-related disparities and inequalities. Objective: To assess the performance of GFR estimating equations across varied patient populations. Data Sources: PubMed, Embase, Web of Science, ClinicalTrials.gov, and Scopus databases were systematically searched from January 2012 to February 2023. Study Selection: Inclusion criteria were studies that compared measured GFR with estimated GFR in adults using established reference standards and methods. A total of 6663 studies were initially identified for screening and review. Data Extraction and Synthesis: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 2 authors independently extracted data on studies that examined the bias and accuracy of GFR estimating equations. For each outcome, a random-effects model was used to calculate pooled estimates. Data analysis was conducted from March to December 2023. Main Outcomes and Measures: The primary outcomes were bias and accuracy of estimated GFRs in Black vs non-Black patients, as well as in individuals with chronic conditions. Bias was defined as the median difference between the measured GFR and the estimated GFR. Accuracy was assessed with P30 (the proportion of persons in a data set whose estimated GFR values were within 30% of measured GFR values) and measures of heterogeneity. Results: A total of 12 studies with a combined 44 721 patients were included. Significant heterogeneity was found in the bias of various GFR estimation equations. Race-corrected equations and creatinine-based equations tended to overestimate GFR in Black populations and showed mixed results in non-Black populations. For creatinine-based equations, the mean bias in subgroup analysis was 2.1 mL/min/1.73 m2 (95% CI, -0.2 mL/min/1.73 m2 to 4.4 mL/min/1.73 m2) in Black persons and 1.3 mL/min/1.73 m2 (95% CI, 0.0 mL/min/1.73 m2 to 2.5 mL/min/1.73 m2) in non-Black persons. Equations using only cystatin C had small biases. Regarding accuracy, heterogeneity was high in both groups. The overall P30 was 84.5% in Black persons and 87.8% in non-Black persons. Creatinine-based equations were more accurate in non-Black persons than in Black persons. For creatinine-cystatin C equations, the P30 was higher in non-Black persons. There was no significant P30 difference in cystatin C-only equations between the 2 groups. In patients with chronic conditions, P30 values were generally less than 85%, and the biases varied widely. Conclusions and Relevance: This systematic review and meta-analysis of GFR estimating equations suggests that there is bias in race-based GFR estimating equations, which exacerbates kidney disease disparities. Development of a GFR equation independent of race is a crucial starting point, but not the sole solution. Addressing the disproportionate burden of kidney failure on Black individuals in the US requires an enduring, multifaceted approach that should include improving diagnostics, tackling social determinants of health, confronting systemic racism, and using effective disease prevention and management strategies.

Indexed as

Cystatin CRenal Insufficiency, ChronicAdultBiasCreatinineGlomerular Filtration RateHumansCreatinineCystatin C

Identifiers

PMID38441895
PMCPMC10915689
OpenAlexW4392455196

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.