Incidence, Predictor, and Clinical Outcomes of Multiple Resheathing With Self‐Expanding Valves During Transcatheter Aortic Valve Replacement
Bibliographic record
Abstract
Background No study has evaluated the impact of the additional manipulation demanded by multiple resheathing (MR) in patients undergoing transcatheter aortic valve replacement with repositionable self‐expanding valves. Methods and Results This study included a real‐world, multicenter registry involving 16 centers from Canada, Germany, Latin America, and Spain. All consecutive patients who underwent transcatheter aortic valve replacement with the Evolut R, Evolut PRO, and Portico valves were included. Patients were divided according to the number of resheathing: no resheathing, single resheathing (SR), and MR. The primary end point was device success. Secondary outcomes included procedural complications, early safety events, and 1‐year mortality. In 1026 patients, the proportion who required SR and MR was 23.9% and 9.3%, respectively. MR was predicted by the use of Portico and moderate/severe aortic regurgitation at baseline (both with P <0.01). Patients undergoing MR had less device success (no resheathing=89.9%, SR=89.8%, and MR=80%; P =0.01), driven by more need for a second prosthesis and device embolization. At 30 days, there were no differences in safety events. At 1 year, more deaths occurred with MR (no resheathing=10.5%, SR=8.0%, and MR=18.8%; P =0.014). After adjusting for baseline differences and center experience by annual volume, MR associated with less device success (odds ratio, 0.42; P =0.003) and increased 1‐year mortality (hazard ratio, 2.06; P =0.01). When including only the Evolut R/PRO cases (N=837), MR continued to have less device success ( P <0.001) and a trend toward increased mortality ( P =0.05). Conclusions Repositioning a self‐expanding valve is used in a third of patients, being multiple in ≈10%. MR, but not SR, was associated with more device failure and higher 1‐year mortality, regardless of the type of valve implanted.
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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 | 0.005 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".