Short‐Term Change in eGFR and Risk of Cardiovascular Events
Bibliographic record
Abstract
BACKGROUND: Lower estimated glomerular filtration rate (eGFR) on a single occasion is associated with risk of cardiovascular events; whether the degree of change in eGFR during a 1-year period adds prognostic information is unknown. METHODS AND RESULTS: We included adults who had ≥2 outpatient eGFR measurements (≥6 months apart) during a 1-year accrual period in Alberta, Canada. According to recent guidelines, we used a change in eGFR category (≥90, 60 to 89, 45 to 59, 30 to 44, 15 to 29, and <15 mL/min per 1.73 m(2)), and the presence/absence of a ≥25% change from baseline to classify participants into 5 groups: certain drop, uncertain drop, stable (no change), uncertain rise, and certain rise. We calculated adjusted rates of cardiovascular events (per 10 000 person-years) for each group. We estimated the adjusted risks of cardiovascular events associated with each category of change in eGFR, in reference to stable kidney function. Among the 526 388 participants, 76.1% (n=400 560) had stable, 2.6% (n=13 668) had a certain drop, and 3.3% (n=17 499) had a certain rise in eGFR. Compared with participants with stable kidney function, adjusted risks of myocardial infarction, heart failure, and stroke were 27%, 51%, and 20% higher, respectively, for those with a certain drop in kidney function. After adjusting for the last eGFR at the end of the accrual period, the observed association diminished. CONCLUSION: Clinically relevant changes in eGFR are associated with increased risk of cardiovascular events. However, most of the apparent increase in risk can be accounted for by assessing comorbidity and baseline kidney function.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".