Preprocedural CT and ECG Markers for Predicting Post-TAVR Pacemaker Requirement in High-Risk Patients
Bibliographic record
Abstract
Background: Need for permanent pacemaker implantation (PPI) following transcatheter aortic valve replacement (TAVR) remains a common complication. We aimed to assess computed tomography (CT)-based anatomical and electrocardiogram (ECG)-based parameters in a predictive model for PPI following TAVR. Methods: We assessed CT-based parameters, including the predicted course of the conduction axis from atrioventricular node to left bundle branch origin relative to the aortic virtual basal ring. Electrophysiological variables were combined in assessing a model to predict post-TAVR PPI. Results: Among 433 patients (mean age 82.0 [9.0] years, 54.0% female), 90 (21.0%) required PPI. Multiple binary logistic modeling demonstrated a shallower position of the membranous septum inferior margin midpoint increased the odds of PPI by 20% for every 1 mm (adjusted odds ratio [aOR]: 1.20) adjusted for the CT assessment phase. Increasing aortic root rotational angle associated with lower PPI odds (odds ratio [OR]: 0.98; 95% CI [0.95-1.00]), while an angle between the membranous septum midpoint and noncoronary leaflet nadir associated with increased PPI odds (OR: 1.04; 95% CI [1.01-1.08]). Preprocedural right bundle branch block and first-degree atrioventricular block associated with increased odds for PPI (OR: 3.76; 95% CI [1.71-8.21]; and OR: 1.84; 95% CI [1.06-3.18], respectively). The model had an area under the curve of 0.73 (95% CI [0.67-0.79]), sensitivity of 0.74 (95% CI [0.47-0.93]), and specificity of 0.65 (95% CI [0.40-0.87]) for predicting PPI requirement. Conclusions: A predictive model for determining the risk of PPI following TAVR is reported, combining comprehensive conduction-specific anatomical measurements relative to the aortic root and electrical measurements with clinical parameters. This model requires prospective application to understand its performance in the real-world.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".