Variability in eGFR and the Risk of Adverse Kidney Outcomes and All-Cause Mortality
Bibliographic record
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".