One-Year Change in Kidney Function Is Associated with an Increased Mortality Risk
Bibliographic record
Abstract
BACKGROUND: Serum creatinine is routinely measured to estimate glomerular filtration rate (GFR). Long-term cohort studies report that death is a likelier outcome than progression to kidney failure. However, it is unclear how short-term changes in estimated GFR (eGFR) over a 1-year period relate to subsequent mortality risk. METHODS: Using a provincial laboratory registry from Alberta, Canada, we identified 598,397 adults who had ≥2 outpatient eGFR measurements at least 6 months apart during a 1-year accrual period. Change in kidney function was categorized by both changes in eGFR category and percent change ≥25% into 5 groups: certain drop, uncertain drop, stable (no change in CKD category), uncertain rise, and certain rise. Cox proportional hazards models, adjusting for baseline covariates, kidney function, and proteinuria were used to estimate the risk of all-cause mortality associated with each group change in kidney function in reference to stable kidney function. RESULTS: Among the study participants, 447,570 (74.8%) had stable kidney function, 19,591 (3.3%) had a certain drop, and 22,171 (3.7%) had a certain rise in kidney function. Participants with change in kidney function (both drop and rise) were older, more likely to be female, and had a higher prevalence of comorbidities in comparison to those with stable kidney function. There were 51,473 (8.6%) deaths during a median follow-up of 3.5 years. Compared to participants with stable kidney function, those with a certain drop had an almost twofold increased mortality risk (hazard ratio 1.89, 95% CI 1.83-1.95) adjusted for baseline eGFR, proteinuria, and covariates. Participants with a certain rise (3.7%) in kidney function also experienced an increased mortality risk (hazard ratio 1.51, 95% CI 1.46-1.56) compared to those with stable kidney function. Risk of death was similarly increased with adjustment for eGFR at the last visit. CONCLUSION: Change in kidney function of ≥25% in any direction over a 1-year period is associated with a substantially increased risk of mortality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".