Severe Atrioventricular Decoupling, Uncoupling, and Ventriculoatrial Coupling During Enhanced Atrial Pacing: Incidence, Mechanisms, and Implications for Minimizing Right Ventricular Pacing in ICD Patients
Bibliographic record
Abstract
UNLABELLED: AV Decoupling During Enhanced AAIR Pacing. BACKGROUND: Enhanced AAI/R pacing minimizes right ventricular pacing but may permit or induce AV decoupling (AV-DC) due to unrestricted AV intervals (AVIs). The purpose of this study was to characterize and quantify AVI behavior in a randomized trial of enhanced AAI/R pacing in ICD patients. METHODS: One hundred twenty-one patients in the Marquis ICD MVPtrade mark Study, a randomized 1-month crossover comparison of cumulative% ventricular pacing (Cum%VP) in enhanced AAIR (MVP) vs DDD/R, were analyzed. AV-DC was defined as >or=40% AVIs >300 ms; VA coupling (VA-C) was defined as%V-atrial pace (AP) intervals <300 ms. Dynamic AVI behavior and increases in Cum%VP due to AV block (AV uncoupling, AV-UC) were characterized using Holters with real-time ICD telemetry. RESULTS: AV-DC occurred in 17 (14%) of patients. Baseline PR, amiodarone, nighttime, lower rate >60 beats/min, rate response, and Cum%AP were associated with longer AVIs. Logistic regression identified baseline PR (odds ratio [OR]= 1.024, 95% confidence interval [CI] 1.007-1.042; P = 0.005), and Cum%AP (OR = 1.089, 95% CI 1.027-1.154; P = 0.004) as predictors of AV-DC. AV-DC was associated with approximately 10-fold increases in both Cum%VP (13.6 +/- 28.3% vs 1.2 +/- 3.9%; P = 0.023) due to transient AV-UC) and VA-C (6.0 +/- 17.5% vs 0.5 +/- 1.2%, P = 0.028). AV coupling (<40% AVIs >300 ms) was preserved in 104 (86%) patients. CONCLUSIONS: AV-DC, VA-C, and AV-UC may be worsened or induced by enhanced AAI/R pacing. Conservative programming of lower rate and rate response should reduce the risk of AV-DC by reducing Cum%AP.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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".