Pre‐Implantation Balloon Aortic Valvuloplasty and Clinical Outcomes Following Transcatheter Aortic Valve Implantation: A Propensity Score Analysis of the UK Registry
Bibliographic record
Abstract
BACKGROUND: Aortic valve predilation with balloon aortic valvuloplasty (BAV) is recommended before transcatheter aortic valve implantation (TAVI), despite limited data around the requirement of this preprocedural step and the potential risks of embolization. This study aimed to investigate the trends in practice and associations of BAV on short-term outcomes in the UK TAVI registry. METHODS AND RESULTS: Eleven clinical endpoints were investigated, including 30-day mortality, myocardial infarction, aortic regurgitation, valve dysfunction, and composite early safety. All endpoints were defined as per the VARC-2 definitions. Odd ratios of each endpoint were estimated using logistic regression, with data analyzed in balloon- and self-expandable valve subgroups. Propensity scores were calculated using patient demographics and procedural variables, which were included in the models of each endpoint to adjust for measured confounding. Between 2007 and 2014, 5887 patients met the study inclusion criteria, 1421 (24.1%) of whom had no BAV before TAVI valve deployment. We observed heterogeneity in the use of BAV nationally, both temporally and by center experience; rates of BAV in pre-TAVI workup varied between 30% and 97% across TAVI centers. All endpoints were similar between treatment groups in SAPIEN (Edwards Lifesciences Inc., Irvine, CA) valve patients. After correction for multiple testing, none of the endpoints in CoreValve (Medtronic, Minneapolis, MN) patients were significantly different between patients with or without predilation. CONCLUSIONS: Performing TAVI without predilation was not associated with adverse short-term outcomes post procedure, especially when using a balloon-expandable prosthesis. Randomized trials including different valve types are required to provide conclusive evidence regarding the utility of predilation before-TAVI.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".