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Peer-reviewedCC BY 4.0PLOS One

Study variation in estimating Glomerular Filtration Rate (GFR) from serum creatinine, cystatin C, age, and sex

  • Nora Fino1
  • Ben Haaland2
  • Lesley A. Inker3
  • Andrew Levey4
  • Josef Coresh
  • Ogechi M. Adingwupu
  • Tom Greene
4 affiliations
  1. 1Division of Epidemiology, Department of Internal Medicine, University of Utah Health, Salt Lake City, Utah, United States of America
  2. 2Pentara Corporation, Millcreek, Utah, United States of America
  3. 3Department of Medicine, Division of Nephrology, Tufts Medical Center, Boston, Massachusetts, United States of America
  4. 4Department of Population Health, NYU Langone, New York, New York, United States of America
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Abstract

Introduction: In routine clinical practice, kidney function is assessed by estimating the glomerular filtration rate (GFR) using equations that combine serum creatinine and/or cystatin C with age and sex. These equations are typically created by pooling datasets from multiple research studies and populations into a single dataset. We investigate implications of study and population variation on the relationship of GFR with its predictor variables. Methods: We combined data used to develop and validate the 2021 and 2012 The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations based on creatinine or cystatin C (13,391 individuals in 26 studies). We used random effects modeling to partition the deviations between measured GFR (mGFR) and estimated GFR (eGFR) into four components: predictable variation between studies of CKD vs. non-CKD populations, non-predictable variation between studies, patient-level error in eGFR within studies, and measurement error in mGFR. Results: Overall, the average eGFR was similar across different levels of the predictor variables between the random effect models and the original pooled CKD-EPI equations. However, the random effect model demonstrated substantial variation between studies, both between and within CKD and non-CKD populations. After accounting for the population type (CKD or non-CKD), approximately 10–40% of the deviations between eGFR and mGFR was attributable to study variation, 20–40% to measurement error in mGFR, and 20–70% to patient level errors in eGFR. Both pooled and random‐effects models generalized well across studies, whereas using equations derived from a single study leads to substantial overfitting and larger prediction errors. Conclusions: Single‐study GFR equations may underestimate cross-study prediction error by ignoring study‐level variability, especially in non-CKD populations. Our results largely corroborate the use of pooled datasets and the current form of the CKD-EPI equations. Despite measureable study-level heterogeneity, the pooled CKI-EPI apparach remains a pragmatic and broadly available solution for GFR estimation.

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Cite

BibTeXRIS
BibTeX
@article{fino2026study,
  title = {{Study variation in estimating Glomerular Filtration Rate (GFR) from serum creatinine, cystatin C, age, and sex}},
  author = {Nora Fino and Ben Haaland and Lesley A. Inker and Andrew Levey and Josef Coresh and Ogechi M. Adingwupu and Tom Greene},
  journal = {PLOS One},
  year = {2026},
  month = sep,
  doi = {10.1371/journal.pone.0357209},
  url = {https://doi.org/10.1371/journal.pone.0357209},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Study variation in estimating Glomerular Filtration Rate (GFR) from serum creatinine, cystatin C, age, and sex
AU  - Nora Fino
AU  - Ben Haaland
AU  - Lesley A. Inker
AU  - Andrew Levey
AU  - Josef Coresh
AU  - Ogechi M. Adingwupu
AU  - Tom Greene
PY  - 2026
DA  - 2026/09/28
JO  - PLOS One
DO  - 10.1371/journal.pone.0357209
UR  - https://doi.org/10.1371/journal.pone.0357209
AB  - Introduction: In routine clinical practice, kidney function is assessed by estimating the glomerular filtration rate (GFR) using equations that combine serum creatinine and/or cystatin C with age and sex. These equations are typically created by pooling datasets from multiple research studies and populations into a single dataset. We investigate implications of study and population variation on the relationship of GFR with its predictor variables. Methods: We combined data used to develop and validate the 2021 and 2012 The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations based on creatinine or cystatin C (13,391 individuals in 26 studies). We used random effects modeling to partition the deviations between measured GFR (mGFR) and estimated GFR (eGFR) into four components: predictable variation between studies of CKD vs. non-CKD populations, non-predictable variation between studies, patient-level error in eGFR within studies, and measurement error in mGFR. Results: Overall, the average eGFR was similar across different levels of the predictor variables between the random effect models and the original pooled CKD-EPI equations. However, the random effect model demonstrated substantial variation between studies, both between and within CKD and non-CKD populations. After accounting for the population type (CKD or non-CKD), approximately 10–40% of the deviations between eGFR and mGFR was attributable to study variation, 20–40% to measurement error in mGFR, and 20–70% to patient level errors in eGFR. Both pooled and random‐effects models generalized well across studies, whereas using equations derived from a single study leads to substantial overfitting and larger prediction errors. Conclusions: Single‐study GFR equations may underestimate cross-study prediction error by ignoring study‐level variability, especially in non-CKD populations. Our results largely corroborate the use of pooled datasets and the current form of the CKD-EPI equations. Despite measureable study-level heterogeneity, the pooled CKI-EPI apparach remains a pragmatic and broadly available solution for GFR estimation.
SN  - 1932-6203
ER  -