Real-life use of tolvaptan in ADPKD: a retrospective analysis of a large Canadian cohort
Bibliographic record
Abstract
Abstract Tolvaptan is the first disease-modifying drug proven to slow eGFR decline in high-risk patients with ADPKD. However, barriers from the patient perspective to its use in real-life settings have not been systemically examined in a large cohort. This was a single-center, retrospective study of 523 existing or new patients with ADPKD followed at the Center for Innovative Management of PKD in Toronto, Ontario, between January 1, 2016 to December 31, 2018. All patients underwent clinical assessment including total kidney volume measurements and Mayo Clinic Imaging Class (MCIC). Those who were deemed to be at high risk were offered tolvaptan with their preference (yes or no) and reasons for their choices recorded. Overall, 315/523 (60%) patients had MCIC 1C-1E; however, only 96 (30%) of them were treated with tolvaptan at their last follow-up. Among these high-risk patients, those not treated versus treated with tolvaptan were more likely to have a higher eGFR (82 ± 26 vs. 61 ± 27 ml/min/1.73 m2), CKD stages 1–2 (79% vs. 41%), and MCIC 1C (63% vs. 31%). The most common reasons provided for not taking tolvaptan were lifestyle preference related to the aquaretic effect (51%), older age ≥ 60 (12%), and pregnancy/family planning (6%). 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 early stages of CKD with well-preserved eGFR and as such, were prime targets for tolvaptan therapy to slow disease progression. Given that the most common reason for tolvaptan refusal was the concern for intolerability of the aquaretic side-effect, strategies to mitigate this may help to reduce this 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 | 0.005 |
| 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) | 0.002 | 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".