Prosthesis-patient mismatch following transcatheter aortic valve replacement for degenerated transcatheter aortic valves: the TRANSIT-PPM international project
Bibliographic record
Abstract
Background A severe prosthesis-patient mismatch (PPM) is associated with adverse outcomes following transcatheter aortic valve replacement (TAVR) for de novo aortic stenosis or a failed surgical bioprosthesis. The impact of severe PPM in patients undergoing TAV-in-TAVR is unknown. Aim We sought to investigate the incidence and 1-year outcomes of different grades of PPM in patients undergoing TAV-in-TAVR. Materials and methods The TRANSIT-PPM is an international registry, including cases of degenerated TAVR treated with a second TAVR. PPM severity, as well as in-hospital, 30-day, and 1-year outcomes were defined according to the Valve Academic Research Consortium-3 (VARC-3) criteria. Results Among 28 centers, 155 patients were included. Severe PPM was found in 6.5% of patients, whereas moderate PPM was found in 14.2% of patients. The rate of severe PPM was higher in patients who underwent TAV-in-TAVR with a second supra-annular self-expanding (S-SE) TAVR (10%, p = 0.04). Specifically, the rate of severe PPM was significantly higher among cases of a SE TAVR implanted into a balloon-expandable (BE) device (19%, p = 0.003). At 1-year follow-up, the rate of all-cause mortality, and the rate of patients in the New York Heart Association (NYHA) class III/IV were significantly higher in the cohort of patients with severe PPM ( p = 0.016 and p = 0.0001, respectively). Almost all the patients with a severe PPM after the first TAVR had a failed < 23 mm BE transcatheter heart valve (THV): the treatment with an S-SE resolved the severe PPM in the majority of the cases. Conclusion After TAV-in-TAVR, in a fifth of the cases, a moderate or severe PPM occurred. A severe PPM is associated with an increased 1-year all-cause mortality. Clinical trial registration [ https://clinicaltrials.gov ], identifier [NCT04500964].
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".