STAGED-PKD: Patient Enrichment and Modeling-Driven Efficient ADPKD Trial Design
Bibliographic record
Abstract
Background: Total kidney volume (TKV) and eGFR slope are key endpoints in autosomal dominant polycystic kidney disease (ADPKD) trials, indicative of cyst growth and kidney function decline. To date, unequivocal demonstration of drug effect on these endpoints required two trials. STAGED-PKD assesses the effect of glucosylceramide synthase inhibition with venglustat on both endpoints in one efficient, short-duration trial. Methods: Retrospective analysis of TKV and eGFR slope data from CRISP (3-yr) and HALT-A combined identified rapidly progressing patients for enrichment. A statistical relationship between TKV growth vs eGFR slope was derived by modeling. Metaanalysis was conducted of randomized clinical trials assessing treatment impact on both TKV and eGFR. These analyses enabled study powering for both endpoints. Comparison of design efficiency was performed vs prior trials. Results: Retrospective analysis of CRISP and HALT-A confirmed a significant correlation between TKV growth and eGFR slope (correlation 0.346, p<0.0001; Figure). Different statistical approaches showed that in rapidly progressing ADPKD patients, 50% reduction in TKV growth is associated with a ˜30% reduction in eGFR slope. Thus, STAGED-PKD is powered to detect 50% reduction in TKV growth and 30% reduction in eGFR slope. STAGED-PKD is highly efficient vs HALT-A and -B, TEMPO 3:4, and REPRISE. Conclusions: Modeling allowed the design and powering of a two-stage study to assess venglustat impact on TKV growth and eGFR slope. STAGED-PKD improves study efficiency via modeling and patient enrichment to reduce patient number and trial duration. Funding: Commercial Support - Sanofi GenzymeModeling of the Relationship Between TKV Growth Rate and eGFR Decline
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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 | 0.064 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".