Risk stratification of patients with LBBB after TAVI: an international multicentric comparative study of a novel, simplified ECG algorithm and current ESC-ECG-criteria
Bibliographic record
Abstract
Abstract Background Managing left-bundle-branch-block (LBBB) following transcatheter aortic valve implant (TAVI) is challenging. The European Society of Cardiology (ESC) guidelines recommend electrophysiological (EP) testing in LBBB patients meeting certain electrocardiographic (ECG) criteria. Objective to develop a novel, simplified ECG algorithm for the prediction of infranodal conduction delay in LBBB patients after TAVI and compare it to current ESC-ECG criteria. Methods We performed a international multicenter analysis of prospectively enrolled patients undergoing EP testing for LBBB after TAVI. A novel algorithm was developed by testing various combinations of the PR interval, QRS duration pre- and post-TAVI, as well as changes in these parameters for the identification of patients with infranodal conduction delay, defined as His-to ventricular (HV) interval ≥ 70 ms. The performance in predicting a HV interval ≥ 70 ms of the novel algorithm was then compared to that of the established ESC-ECG criteria. Results 769 patients with LBBB after TAVI underwent risk stratification using EP testing at seven institutions (mean age 82 ± 7 years, 55% women, 21% HV ≥ 70 ms). A novel, simplified ECG algorithm showed a sensitivity of 88% and an NPV of 92% for the rule-out of infranodal conduction delay (PR interval post-TAVI <190 ms OR QRS duration post-TAVI <160 ms) and a specificity and PPV of 85% and 41%, respectively, for the rule-in of infranodal conduction delay (PR interval post-TAVI ≥190 ms AND QRS duration post-TAVI ≥160 ms). In comparison, the ESC-ECG criteria had a sensitivity of 72%, NPV 88%, specificity of 53%, and PPV of 28%. Conclusion A novel, simplified ECG algorithm showed improved performance for the rule-out and rule-in of infranodal conduction delay compared to current ESC-ECG criteria.Graphical Abstract Proposed Workflow for Management
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".