Variability in eGFR and Adverse Kidney Outcomes in Routine Clinical Practice
Bibliographic record
Abstract
Background: Variability in estimated glomerular filtration rate (eGFR) may indicate underlying kidney instability, but its prognostic value for kidney disease progression in routine primary care remains insufficiently studied. Methods: We conducted a retrospective cohort study using MedicineInsight, an Australian primary care database. Adults (≧18 years) with ≧3 eGFR measurements over 3 years (2011-2018) were included. eGFR variability was measured using the coefficient of variation (CV) and categorized using quintiles. The primary outcome was a composite of sustained ≧40% eGFR decline, sustained eGFR <15 mL/min/1.73 m2, or all-cause mortality, assessed over a 3-year follow-up period. Cox proportional hazards models were adjusted for mean eGFR, eGFR slope, demographics, comorbidities, and medications. Results: Among 754,306 patients (mean age 59.1 years, 58.0% female), 14,239 (1.9%) experienced the composite kidney outcome. The cumulative incidence of the composite outcome increased significantly in accordance with higher eGFR variability (Figure). Compared with the lowest fifth, patients in the highest fifth had a significantly increased risk (adjusted hazard ratio: 2.17 [95% CI: 2.03-2.32]; P <0.001). These associations were consistent across subgroups stratified by age, sex, hypertension, diabetes, and baseline eGFR, and remained robust in sensitivity analyses. When treated as a continuous variable, eGFR variability showed a positive, log-linear association with the adverse outcomes, particularly with all-cause mortality and ≧40% eGFR decline. Conclusion: Greater eGFR variability was independently associated with increased risk of adverse kidney outcomes and mortality in a large, unselected primary care cohort. Incorporating eGFR variability into routine assessments may improve risk stratification and guide earlier interventions to slow chronic kidney disease progression. Funding: Commercial Support - This study was supported by the Renal Division of The George Institute for Global Health, which is supported by the University of New South Wales Scientia Program and a sponsorship provided by Boehringer Ingelheim and Eli Lilly Alliance., Private Foundation Support, Government Support – Non-U.S.Figure
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".