Risk Stratification in Left Bundle Branch Block After Transcatheter Aortic Valve Implantation
Bibliographic record
Abstract
BACKGROUND: Managing left bundle branch block (LBBB) after transcatheter aortic valve implantation (TAVI) remains challenging. OBJECTIVES: The aim of this study was to develop a novel, simplified electrocardiogram (ECG) algorithm for predicting infranodal conduction delay in LBBB (both new onset as well as preexisting) patients after TAVI and to compare its performance vs current European Society of Cardiology (ESC) ECG criteria. METHODS: A multicenter analysis of prospectively enrolled patients undergoing electrophysiology testing for preexisting or new-onset LBBB after TAVI was conducted. The novel algorithm was developed by analyzing various combinations of the PR interval, QRS duration pre-TAVI and post-TAVI, and changes in these parameters to identify patients with infranodal conduction delay (defined as a His-ventricular interval ≥70 milliseconds). RESULTS: A total of 769 patients with LBBB (12% preexisting) after TAVI underwent risk stratification using electrophysiology testing at 7 institutions (mean age 81 ± 7 years; 57% female; 21% His-ventricular ≥70 milliseconds). A novel algorithm using solely a PR interval of 190 milliseconds and a QRS interval of 160 milliseconds revealed a sensitivity of 88% and an negative predictive value of 92% for the rule-out of infranodal conduction delay (PR interval post-TAVI <190 milliseconds AND QRS duration post-TAVI <160 milliseconds) and a specificity and positive predictive value of 85% and 41%, respectively, for the rule-in of infranodal conduction delay (PR interval post-TAVI ≥190 milliseconds AND QRS duration post-TAVI ≥160 milliseconds). By comparison, the ESC ECG criteria showed a sensitivity of 72%, a negative predictive value of 88%, specificity of 53%, and a positive predictive value of 28%. CONCLUSIONS: The novel, simplified ECG algorithm showed a superior performance for the rule-out and rule-in of infranodal conduction delay compared with current ESC ECG criteria.
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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 | 0.000 | 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 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".