Clinical Impact of Standardized TAVR Technique and Care Pathway
Bibliographic record
Abstract
BACKGROUND: Procedural success and clinical outcomes after transcatheter aortic valve replacement (TAVR) have improved, but residual aortic regurgitation (AR) and new permanent pacemaker implantation (PPI) rates remain variable because of a lack of uniform periprocedural management and implantation. OBJECTIVES: The Optimize PRO study evaluates valve performance and procedural outcomes using an "optimized" TAVR care pathway and the cusp overlap technique (COT) in patients receiving the Evolut PRO/PRO+ (Medtronic) self-expanding valves. METHODS: Optimize PRO, a nonrandomized, prospective, postmarket study conducted in the United States, Canada, Europe, Middle East, and Australia, is enrolling patients with severe symptomatic aortic stenosis and no pre-existing pacemaker. Sites follow a standardized TAVR care pathway, including early discharge and a conduction disturbance management algorithm, and transfemoral deployment using the COT. RESULTS: A total of 400 attempted implants from the United States and Canada comprised the main cohort of this second interim analysis. The mean age was 78.7 ± 6.6 years, and the mean Society of Thoracic Surgeons predictive risk of mortality was 3.0 ± 2.4. The median length of stay was 1 day. There were no instances of moderate or severe AR at discharge. At 30 days, all-cause mortality or stroke was 3.8%, all-cause mortality was 0.8%, disabling stroke was 0.7%, hospital readmission was 10.1%, and cardiovascular rehospitalization was 6.1%. The new PPI rate was 9.8%, 5.8% with 4-step COT compliance. In the multivariable model, right bundle branch block and the depth of the implant increased the risk of PPI, whereas using the 4-step COT lowered 30-day PPI. CONCLUSIONS: The use of the TAVR care pathway and COT resulted in favorable clinical outcomes with no moderate or severe AR and low PPI rates at 30 days while facilitating early discharge and reproducible outcomes across various sites and operators. (Optimize PRO; NCT04091048).
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".