ArticlePloS one2026
Study variation in estimating Glomerular Filtration Rate (GFR) from serum creatinine, cystatin C, age, and sex.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
introductionIn 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.
methodsWe 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.
resultsOverall, 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.
conclusionsSingle-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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