Computed tomography‐derived membranous septum length as predictor of conduction abnormalities and permanent pacemaker implantation after TAVI: A meta‐analysis of observational studies
Bibliographic record
Abstract
BACKGROUND: Permanent pacemaker implantation (PPI) after transcatheter aortic valve implantation (TAVI) is associated with higher risk of mortality and rehospitalization for heart failure. Efforts to prevent conduction abnormalities (CA) requiring PPI after TAVI should be made. The membranous septum (MS) length and its interaction with implantation depth (ID-ΔMSID) could provide useful information about the risk of CA/PPI following TAVI. OBJECTIVES: To identify MS length and ΔMSID as predictors of CA/PPI following TAVI. METHODS: Study-level meta-analysis of studies published by September 30, 2022. RESULTS: Eighteen studies met our eligibility including 5740 patients. Shorter MS length was associated with a significantly higher risk of CA/PPI (per 1 mm decrease: odds ratio [OR] 1.60, 95% confidence interval [CI] 1.28-1.99, p < 0.001). Similarly, lower ΔMSID was associated with a significantly higher risk of CA/PPI (per 1 mm decrease: OR 1.75, 95% CI 1.32-2.31, p < 0.001). Meta-regression analyses revealed a statistically significant modulation of the effect of shorter MS length and lower ΔMSID on the outcome (CA/PPI) by balloon postdilatation (positive regression coefficients with p < 0.001); with increasing use of balloon postdilatation, the effect of shorter MS length and lower ΔMSID on the outcome increased. MS length and ΔMSID demonstrated excellent discriminative abilities, with diagnostic ORs equaling 9.49 (95% CI 4.73-19.06), and 7.19 (95% CI 3.31-15.60), respectively. CONCLUSION: Considering that short MS length and low ΔMSID are associated with higher risk of CA and PPI, we should include measurement of MS length in the pre-TAVI planning with MDCT and try to establish optimal ID values before the procedure to avoid CA/PPI.
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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) | 0.003 | 0.019 |
| Bibliometrics | 0.001 | 0.001 |
| 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".