Trends in the occurrence of new conduction abnormalities after transcatheter aortic valve implantation
Bibliographic record
Abstract
OBJECTIVES: The aim of the study was to investigate trends over time in the occurrence of left bundle branch block (LBBB) and permanent pacemaker implantation (PPI) after transcatheter aortic valve implantation (TAVI) with the Medtronic CoreValve System (MCS) and Edwards SAPIEN Valve (ESV). BACKGROUND: TAVI-induced conduction abnormalities (TAVI-CAs) such as LBBB and the need for PPI are frequent postoperative complication. New techniques, procedural refinements, and increased awareness are focused on the reduction of these abnormalities. METHODS: Electrocardiograms of 549 patients without preprocedural LBBB and/or pacemaker were assessed to determine the frequency and nature of TAVI-CAs. To study the effect of experience, patients were subdivided per center into tertiles based on the number of procedures. Univariate and multivariate logistic regression was used to study predictors of TAVI-induced LBBB (TAVI-LBBB) and PPI. RESULTS: TAVI-LBBB occurred in 185 patients (33.7%) and significantly decreased over time, from 42.6% to 27.3% (P=0.006). This effect was only significant after implantation of the MCS (59.6% vs. 46.5% vs. 31.1%, P=0.001, ESV: 22.6% vs. 13.1% vs. 24.8%, P=0.11). Between tertiles there was no difference in the frequency of PPI after TAVI (n=73, 13.1% vs. 14.8% vs. 12%, P=0.74). Multivariate analysis revealed that, independent from valve type, depth of implantation was the only significant predictor of TAVI-LBBB (OR [95% C.I.]: 1.16 [1.10-1.24], P<0.001). In case of PPI pre-existing RBBB (OR [95% C.I.]: 7.22 [3.28-15.88], P<0.001) was the only significant predictor. CONCLUSIONS: Over time the frequency of LBBB after TAVI decreased significantly, especially in patients undergoing TAVI with the MCS. Experience and the subsequent reduction in depth of implantation seem responsible for this reduction. Contrary to TAVI-LBBB, the incidence of PPI remained unchanged over time and was not affected by experience. Although experience has led to a decrease in new CAs after TAVI, elucidation of pathophysiologic mechanisms underlying these CAs and subsequent changes in patient stratification, valve design and the procedure are needed to further reduce this complication.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".