Predictors of permanent pacemaker implantation for transcatheter self‐expandable aortic valve implant in the cusp overlap era
Bibliographic record
Abstract
BACKGROUND: Predictors of permanent pacemaker implantation (PPMI) after self-expanding transcatheter aortic valve implant (TAVI) were described. Is unknown if PPMI predictors remain in the era of high implants using the cusp overlap (COP). METHODS: Single-center, prospective, consecutive case series of patients undergoing self-expanding TAVI with the COP approach. The status of PPMI and other clinical events were ascertained at 30 days. RESULTS: A total of 261 patients were included (84% with Evolut, n = 219). Implant depth >4 mm was infrequent (13.8%). TAVI depth (OR 1.259; p = 0.005), first or second-degree auriculo-ventricular block (OR 3.406; p = 0.033), right-bundle (OR 15.477; p < 0.0001), and incomplete left-bundle branch block (OR 7.964; p = 0.036) were found to be independent predictors of PPMI. The risk of PPMI with deep implant and no electrical disturbances was 3%, and 0% with high implant and no prior electrical disturbances. Those who received PPMI had no statistically significant increased risk of death, myocardial infarction, stroke, bleeding events, or vascular complications at 30 days, but longer hospital stay (mean difference 1.43 days more, p = 0.003). CONCLUSIONS: Implant depth and prior conduction abnormalities remain the main predictors of PPMI using self-expanding TAVI in the COP era. Patients with high implants and no prior conduction abnormalities may be candidates for early discharge after uneventful self-expanding TAVI, while the rest may need inpatient monitoring regardless of achieving a high implant. The need for PPMI was associated with longer hospital stays.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.006 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".