Transcatheter, sutureless and conventional aortic-valve replacement: a network meta-analysis of 16,432 patients
Bibliographic record
Abstract
BACKGROUND: Minimally invasive surgical techniques pose alternatives to conventional surgery for the treatment of aortic stenosis (AS). We present a Bayesian network analysis comparing Valve Academic Research Consortium-2 clinical outcomes between transcatheter aortic valve implantation (TAVI), sutureless (SL-AVR) and conventional aortic valve replacement (CAVR). METHODS: Electronic searches of databases were conducted and seven two-arm randomized-controlled trials and 25 propensity-score-matched studies comparing clinical outcomes of TAVI, SL-AVR and CAVR for treatment of AS were identified. Bayesian Markov chain Monte Carlo modelling was used to analyze clinical outcomes. RESULTS: The analysis included 16,432 patients who underwent TAVI [7,056], SL-AVR [1,238] or CAVR [8,138]. Compared to CAVR, TAVI and SL-AVR were associated with reduced postoperative major bleeding of 59% (OR 0.41, 95% CI: 0.28-0.59) and 44% (OR 0.56, 95% CI: 0.30-0.99) respectively. TAVI had a 41% reduction in postoperative myocardial infarction (OR 0.59, 95% CI: 0.40-0.86) and SL-AVR had a 40% reduction in postoperative acute kidney injury (AKI) (OR 0.62, 95% CI: 0.42-0.86). Compared to TAVI, CAVR and SL-AVR had a reduction in moderate/severe paravalvular regurgitation of 89% (OR 0.11, 95% CI: 0.07-0.16) and 92% (OR 0.08, 95% CI: 0.03-0.17). CAVR had a 67% decreased postoperative permanent pacemaker (PPM) implantation compared to TAVI (OR 0.33, 95% CI: 0.24-0.45) and a 63% reduction compared to SL-AVR (OR 0.37, 95% CI: 0.22-0.61). There were no differences in 30-day mortality or postoperative stroke between the groups. CONCLUSIONS: In selected patients, minimally invasive surgical interventions including TAVI and SL-AVR for severe AS are viable alternatives to conventional surgery. However, TAVI is associated with increased paravalvular regurgitation, whereas TAVI and SL-AVR are associated with increased conduction disturbances compared to CAVR.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.016 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".