Transcatheter Aortic Valve Implantation (TAVI) for Native Aortic Valve Regurgitation ― A Systematic Review ―
Bibliographic record
Abstract
BACKGROUND: Transcatheter aortic valve implantation (TAVI) has become the standard of care for management of high-risk patients with aortic stenosis. Limited data is available regarding the performance of TAVI in patients with native aortic valve regurgitation (NAVR). METHODS AND RESULTS: We performed a systematic review from 2002 to 2016. The primary outcome was device success as per VARC-2 criteria. Secondary endpoints included procedural complications, and 30-day and 1-year mortality rates. A total of 175 patients were included from 31 studies. Device success was reported in 86.3% of patients - with device failure driven by moderate aortic regurgitation (AR ≥3+) and/or need for a second device. Procedural complications were rare, with no procedural deaths, myocardial infarctions or annular ruptures reported. Procedural safety was acceptable with a low 30-day incidence of stroke (1.5%). The 30-day and 1-year overall mortality rates were 9.6% and 20.0% (cardiovascular death, 3.8% and 10.1%, respectively). Patients receiving 2nd-generation valves demonstrated similar safety profiles with greater device success compared with 1st-generation valves (96.2% vs. 78.4%). This was driven by the higher incidence of second-valve implantation (23.4% vs. 1.7%) and significant paravalvular leak (8.3% vs. 0.0%). CONCLUSIONS: TAVI demonstrates acceptable safety and efficacy in high-risk patients with severe NAVR. Second-generation valves may afford a similar safety profile with improved device success. Dedicated studies are needed to definitively establish the efficacy of TAVI in this population.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".