#5783 REAL-LIFE USE OF TOLVAPTAN IN ADPKD: A RETROSPECTIVE ANALYSIS OF A LARGE CANADIAN COHORT
Bibliographic record
Abstract
Abstract Background and Aims Tolvaptan is the first disease-modifier therapy proven to slow eGFR decline in high-risk patients with ADPKD. However, barriers to its use in real-life settings have not been examined. Method Single-center, retrospective study of 523 current or new patients with ADPKD followed at the Center for Innovative Management of PKD in Toronto, Ontario between January 1, 2016 to December 30, 2018. All patients had clinical assessment and total kidney volume measurements; those deemed to be at high risk based on their Mayo Clinic Imaging Class (MCIC) 1C, 1D, or 1E, were offered tolvaptan with their preference (yes or no) and reasons for their choices recorded. Results Overall, 315/523 (60.2%) patients had MCIC 1C-1E; however, only 96 (30%) of them were treated with tolvaptan at the last follow-up. Among these high-risk patients, those treated with tolvaptan were more likely to have a higher eGFR (61 ±27 vs. 82 ±26 ml/min/1.73 m2), CKD stages 1–2 (41% vs. 79%), and MCIC 1D-1E (69% vs. 37%). The most common reasons provided for not taking tolvaptan were lifestyle preference related to the aquaretic effect (51%), older age ≥60 (12%), and pregnancy or family planning (6%). Conclusion In this real-world experience, at least 60% of patients with ADPKD considered to be at high risk for progression to ESKD by imaging were not treated with tolvaptan; most of them had earlier stages of CKD with well-preserved eGFR. The most common reason for their refusal to consider tolvaptan is a concern for intolerability of the aquaretic side-effect; strategies to mitigate this side-effect may help to reduce the barrier to tolvaptan therapy.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".