Management of Postprocedural Conduction Disturbances Using a Prespecified Algorithm in the Optimize PRO Study
Bibliographic record
Abstract
BackgroundLack of standardization in posttranscatheter aortic valve replacement (TAVR) conduction disturbance (CD) identification and treatment may affect permanent pacemaker implantation (PPI) rates and clinical outcomes. The safety and efficacy of a standardized TAVR CD algorithm has not been analyzed. This study analyzes the Optimize PRO post-TAVR CD management algorithm with Evolut PRO/PRO+ valves.MethodsOptimize PRO is a prospective, postmarket study implementing 2 strategies to reduce pacemaker rates: TAVR with cusp overlap technique and a post-TAVR CD algorithm. The 2-hour postprocedural electrocardiogram (ECG) stratified patients to early discharge in the absence of new ECG changes or to CD algorithms for (1) ECG changes with preexisting right or left bundle branch block (LBBB), interventricular conduction delay or first-degree atrioventricular block, (2) new LBBB, or (3) high-degree atrioventricular block (HAVB).ResultsThe interim analysis of the CD cohort consisted of 125/400 TAVR recipients. In the CD cohort, the 30-day new PPI rate was higher (28.1% vs 1.5%; P <.001), and 60 (48%) patients were discharged with a 30-day continuous ECG monitor. At 30 days, 90% of patients discharged with a monitor did not require PPI. Clinical outcomes, including mortality, stroke, bleeding, and reintervention, were similar in patients with and without CDs. No patient experienced sudden cardiac death.ConclusionsEffective management of CDs using a standard algorithm following Evolut TAVR provides similar 30-day safety outcomes to patients without CDs who undergo routine next day discharge. The CD algorithm may provide an effective strategy to recognize arrhythmias early, improve PPI utilization, and facilitate safe monitoring of patients after discharge.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.094 |
| Bibliometrics | 0.000 | 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".