Abstract 15996: Pre-Operative Predictors of Permanent Pacemaker Implantation Post Aortic Valve Replacement: A Retrospective Cohort Evaluation
Bibliographic record
Abstract
Introduction: Permanent pacemaker (PPM) implantation following Aortic Valve Replacement (AVR) is common. While AVR modality choice (i.e. surgical vs. transcatheter) affects PPM risk, identifying pre-operative factors associated with increased PPM risk can benefit enhanced risk assessment and care decisions. Methods: This study is a retrospective evaluation of pre-operative factors associated with PPM risk within 1-month post-AVR at a tertiary care centre (2014-2020). Pre-AVR conduction abnormalities were classified as left bundle (LBBB), right bundle branch blocks (RBBB), and other abnormalities (left anterior/posterior fascicular block, intraventricular conduction delay) in combination with RBBB, LBBB, or alone. Results: Of the 776 (mean age= 74, 66% male) patients, 89 (11.5%) subsequently received PPM. As per pre-AVR electrocardiogram, 76 (9.8) were not in sinus rhythm and 214 (27.6%) had conduction abnormalities, including: 81 (10.4%) LBBB and LBBB + other, 91 (11.7%) RBBB and RBBB + other, and 42 (5.4%) other abnormalities. Pre-AVR rhythm (18.4 vs. 10.7%, p=0.05) and conduction abnormalities (19.2 vs. 8.5%, p<0.0001) were significantly associated with the need for post-AVR PPM. No statistically significant differences were observed in other demographic-clinical characteristics. Logistic regression, adjusted for age and sex, indicated significantly higher post-AVR PPM risk for patients with RBBB and RBBB + other (OR= 3.84, 95%CI: 2.21-6.66, p<0.0001) and other miscellaneous (OR= 2.58, 95%CI: 1.13-5.93, p<0.03) abnormalities, but not for LBBB and LBBB + other (OR= 1.14, 95%CI: 0.52-2.50, p<0.75) compared to participants with no pre-AVR conduction abnormalities. Females experienced significantly lower need for PPM (OR= 0.60, 95%CI: 0.35-1.00, p= 0.05). Conclusions: Pre-AVR RBBB and other conduction abnormalities alone or together, but not LBBB are associated with significantly higher need for post-AVR PPM.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".