Evaluating Glomerular Filtration Rate Slope as a Surrogate End Point for ESKD in Clinical Trials: An Individual Participant Meta-Analysis of Observational Data
Bibliographic record
Abstract
Significance Statement Randomized clinical trials of treatments to slow CKD progression often require large sample sizes and long follow-up to understand their effects on clinical events. This is especially true in patients with earlier stages of kidney disease who are unlikely to experience ESKD for many years. Surrogate study end points that occur earlier during disease progression could help. To evaluate whether eGFR decline over time may be a good surrogate end point, the authors did a meta-analysis of 14 cohorts. They found that slower eGFR decline was significantly associated with lower risk of ESKD in all populations, including those with better kidney function. The results suggest that change in the slope of eGFR decline may be a good surrogate end point for ESKD in clinical trials, particularly in longer trials with patients with rapidly progressive disease. Background Decline in eGFR is a biologically plausible surrogate end point for the progression of CKD in clinical trials. However, it must first be tested to ensure strong associations with clinical outcomes in diverse populations, including patients with higher eGFR. Methods To investigate the association between 1-, 2-, and 3-year changes in eGFR (slope) with clinical outcomes over the long term, we conducted a random effects meta-analysis of 3,758,551 participants with baseline eGFR≥60 ml/min per 1.73 m 2 and 122,664 participants with eGFR<60 ml/min per 1.73 m 2 from 14 cohorts followed for an average of 4.2 years. Results Slower eGFR decline by 0.75 ml/min per 1.73 m 2 per year over 2 years was associated with lower risk of ESKD in participants with baseline eGFR≥60 ml/min per 1.73 m 2 (adjusted hazard ratio, 0.70; 95% CI, 0.68 to 0.72) and eGFR<60 ml/min per 1.73 m 2 (0.71; 95% CI, 0.68 to 0.74). The relationship was stronger with 3-year slope. For a rapidly progressing population with predicted 5-year risk of ESKD of 8.3%, an intervention that reduced eGFR decline by 0.75 ml/min per 1.73 m 2 per year over 2 years would reduce the ESKD risk by 1.6%. For a hypothetical low-risk population with a predicted 5-year ESKD risk of 0.58%, the same intervention would reduce the risk by only 0.13%. Conclusions Slower decline in eGFR was associated with lower risk of subsequent ESKD, even in participants with eGFR≥60 ml/min per 1.73 m 2 , but those with the highest risk would be expected to benefit the most.
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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 | 0.040 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".