{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T12:17:31Z","timestamp":1793276251250,"version":"4.5.0"},"reference-count":15,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T00:00:00Z","timestamp":1790553600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000062","name":"National Institute of Diabetes and Digestive and Kidney Diseases","doi-asserted-by":"publisher","award":["R01DK116790"],"award-info":[{"award-number":["R01DK116790"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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\u201340% of the deviations between eGFR and mGFR was attributable to study variation, 20\u201340% to measurement error in mGFR, and 20\u201370% to patient level errors in eGFR. Both pooled and random\u2010effects models generalized well across studies, whereas using equations derived from a single study leads to substantial overfitting and larger prediction errors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Single\u2010study GFR equations may underestimate cross-study prediction error by ignoring study\u2010level 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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0357209","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T17:26:58Z","timestamp":1790616418000},"page":"e0357209","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["Study variation in estimating Glomerular Filtration Rate (GFR) from serum creatinine, cystatin C, age, and sex"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6209-1740","authenticated-orcid":true,"given":"Nora","family":"Fino","sequence":"first","affiliation":[{"name":"University of Utah Health","place":["Salt Lake City, Utah, United States of America"],"department":["Division of Epidemiology, Department of Internal Medicine"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ben","family":"Haaland","sequence":"additional","affiliation":[{"name":"Pentara Corporation","place":["Millcreek, Utah, United States of America"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2820-9482","authenticated-orcid":true,"given":"Lesley A.","family":"Inker","sequence":"additional","affiliation":[{"name":"Tufts Medical Center","place":["Boston, Massachusetts, United States of America"],"department":["Department of Medicine, Division of Nephrology"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Levey","sequence":"additional","affiliation":[{"name":"Tufts Medical Center","place":["Boston, Massachusetts, United States of America"],"department":["Department of Medicine, Division of Nephrology"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Josef","family":"Coresh","sequence":"additional","affiliation":[{"name":"NYU Langone","place":["New York, New York, United States of America"],"department":["Department of Population Health"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7475-4171","authenticated-orcid":true,"given":"Ogechi M.","family":"Adingwupu","sequence":"additional","affiliation":[{"name":"Tufts Medical Center","place":["Boston, Massachusetts, United States of America"],"department":["Department of Medicine, Division of Nephrology"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tom","family":"Greene","sequence":"additional","affiliation":[{"name":"University of Utah Health","place":["Salt Lake City, Utah, United States of America"],"department":["Division of Epidemiology, Department of Internal 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