{"doi":"10.34067/kid.0000000988","title":"Variability in eGFR and the Risk of Adverse Kidney Outcomes and All-Cause Mortality","abstract":"Key Points Increased eGFR variability over 3 years independently predicts a higher risk of kidney outcomes and all-cause mortality. This association remained consistent across subgroups and sensitivity analyses. Routine eGFR variability assessment may enable identification of high-risk patients and provide an opportunity for the initiation of interventions. Background eGFR variability may predict adverse outcomes, such as cardiovascular events and mortality, yet its influence on kidney impairment progression in routine clinical practice is not well described. Methods This retrospective cohort study used longitudinal eGFR data from MedicineInsight, a comprehensive primary care database. We included adults (18 years or older) with at least three eGFR measurements over 3 years between January 1, 2011, and December 31, 2018. eGFR variability between visits was assessed using the coefficient of variation and categorized into groups by quintiles. A kidney composite end point, comprising a sustained 40% decline in eGFR from baseline, a sustained eGFR of <15 ml/min per 1.73 m 2 , and all-cause mortality, was tracked over a 3-year follow-up. Cox proportional hazards models quantified the association between eGFR variability and outcomes. Results Among 754,306 patients, with a mean age of 59.1 years and 58.0% female, higher eGFR variability was associated with an increased risk of the kidney composite end point (hazard ratio, 2.17;95% confidence interval, 2.03 to 2.32) for the highest versus lowest fifth after adjusting for mean eGFR and eGFR slope, as well as other cardiovascular disease risk factors and medication use. Similar trends were observed for components of the primary outcome and across all subgroups including age, sex, hypertension, diabetes, and baseline eGFR. Conclusions Increased eGFR variability independently predicts adverse kidney outcomes, underscoring its potential as a clinical biomarker for identifying high-risk patients. Including eGFR variability in routine kidney assessments may improve risk stratification, enabling timely interventions and potentially enhancing patient outcomes in primary care.","journal":"Kidney360","year":2025,"id":579209,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9614,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1489838,"name":"Luke Buizen","orcid":null,"position":1,"is_corresponding":false},{"id":832156,"name":"Katie Harris","orcid":"0000-0002-6104-6948","position":2,"is_corresponding":false},{"id":1016626,"name":"Sunil V. Badve","orcid":"0000-0003-2269-312X","position":3,"is_corresponding":false},{"id":922,"name":"John Chalmers","orcid":"0000-0002-9931-0580","position":4,"is_corresponding":false},{"id":684563,"name":"Martin Gallagher","orcid":"0000-0001-9187-6187","position":5,"is_corresponding":false},{"id":1489442,"name":"Jeffrey T. Ha","orcid":"0000-0002-8558-1697","position":6,"is_corresponding":false},{"id":255771,"name":"Meg Jardine","orcid":"0000-0002-0160-2375","position":7,"is_corresponding":false},{"id":1489443,"name":"Daniel Bekele Ketema","orcid":"0000-0002-7464-7814","position":8,"is_corresponding":false},{"id":1381722,"name":"Sradha Kotwal","orcid":"0000-0002-3294-4087","position":9,"is_corresponding":false},{"id":300854,"name":"Brendon L. Neuen","orcid":"0000-0001-9276-8380","position":10,"is_corresponding":false},{"id":1489444,"name":"Paul E. Ronksley","orcid":"0000-0002-3958-7561","position":11,"is_corresponding":false},{"id":1489445,"name":"Hannah Wallace","orcid":"0000-0002-2728-4299","position":12,"is_corresponding":false},{"id":1038039,"name":"Takashi Yokoo","orcid":"0000-0003-1838-7998","position":13,"is_corresponding":false},{"id":925,"name":"Mark Woodward","orcid":"0000-0001-9800-5296","position":14,"is_corresponding":false},{"id":300857,"name":"Min Jun","orcid":"0000-0003-1460-7535","position":15,"is_corresponding":false},{"id":1489441,"name":"Takaya Sasaki","orcid":"0000-0001-9525-2638","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T02:58:30.282164Z","pmid":"41143921","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}