Predictors of pacemaker implantation after transcatheter aortic valve implantation according to kind of prosthesis and risk profile: a systematic review and contemporary meta-analysis
Bibliographic record
Abstract
AIMS: Permanent pacemaker implantation (PPI) may be required after transcatheter aortic valve implantation (TAVI). Evidence on PPI prediction has largely been gathered from high-risk patients receiving first-generation valve implants. We undertook a meta-analysis of the existing literature to examine the incidence and predictors of PPI after TAVI according to generation of valve, valve type, and surgical risk. METHODS AND RESULTS: We made a systematic literature search for studies with ≥100 patients reporting the incidence and adjusted predictors of PPI after TAVI. Subgroup analyses examined these features according to generation of valve, specific valve type, and surgical risk. We obtained data from 43 studies, encompassing 29 113 patients. Permanent pacemaker implantation rates ranged from 6.7% to 39.2% in individual studies with a pooled incidence of 19% (95% CI 16-21). Independent predictors for PPI were age [odds ratio (OR) 1.05, 95% confidence interval (CI) 1.01-1.09], left bundle branch block (LBBB) (OR 1.45, 95% CI 1.12-1.77), right bundle branch block (RBBB) (OR 4.15, 95% CI 3.23-4.88), implantation depth (OR 1.18, 95% CI 1.11-1.26), and self-expanding valve prosthesis (OR 2.99, 95% CI 1.39-4.59). Among subgroups analysed according to valve type, valve generation and surgical risk, independent predictors were RBBB, self-expanding valve type, first-degree atrioventricular block, and implantation depth. CONCLUSIONS: The principle independent predictors for PPI following TAVI are age, RBBB, LBBB, self-expanding valve type, and valve implantation depth. These characteristics should be taken into account in pre-procedural assessment to reduce PPI rates. PROSPERO ID CRD42020164043.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.027 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".