Validation of the Emory Risk Score in the Transcatheter Aortic Valve Implantation Population: A Canadian Perspective
Bibliographic record
Abstract
Background Permanent pacemaker (PPM) implantation may be indicated post-transcatheter aortic valve implantation (TAVI). The Emory Risk Score (ERS) is a validated predictive risk score of the need for a PPM post-TAVI using a balloon-expandable valve. Our objectives were to determine the validity of the ERS in our local TAVI population with both balloon-expandable and self-expanding valves and to identify additional electrocardiographic (ECG) parameters predictive of the need for a PPM post-TAVI. Methods Retrospective chart and electronic database reviews were performed to collect demographic and procedural information. Two expert readers reviewed all ECGs. Independent factors associated with PPM implantation were examined with multivariable logistic regression via a stepwise selection process with calculation of the area under the receiver operating characteristic curve to assess model discrimination. Results The overall PPM implantation rate was 11.7%; rates were 9% for the Sapien 3 valves, 10% for the Evolut Pro valves, and 17% for the Evolut R valves. The ERS was found to not be predictive of need for PPM post-TAVI for the entire cohort. Right bundle branch block was the only ERS parameter independently associated with new PPM implant (8.5% vs 25%, odds ratio = 3.59, P = 0.01). No additional ECG parameters met the criteria for statistical significance. Conclusions The poor predictive value of the ERS in determining the need for a PPM post-TAVI in our patient population suggests that further refinement of a formula (or risk-calculator) is warranted. Identification of a precise risk-calculator is likely to facilitate patient mobilization and reduce inpatient healthcare resource utilization.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".