Atrial fibrillation in patients with sick sinus syndrome: the association with PQ-interval and percentage of ventricular pacing
Bibliographic record
Abstract
AIMS: In the recently published DANPACE trial, incidence of atrial fibrillation (AF) was significantly higher with single-lead atrial (AAIR) pacing than with dual-chamber (DDDR) pacing. The present analysis aimed to evaluate the importance of baseline PQ-interval and percentage of ventricular pacing (VP) on AF. METHODS AND RESULTS: We analysed data on AF during follow-up in 1415 patients included in the DANPACE trial. In a subgroup of 650 patients with DDDR pacemaker, we studied whether %VP, baseline PQ-interval, and programmed atrio-ventricular interval (AVI) was associated with AF burden measured as time in mode-switch (MS) detected by the pacemaker. In the entire DANPACE study population, the incidence of AF was significantly higher in patients with baseline PQ-interval >180 ms (P< 0.001). Among 650 patients with DDDR pacemaker, telemetry data were available for 1.337 ± 786 days, %VP was 66 ± 33%, AF was detected at planned follow-up in 160 patients (24.6%), MS occurred in 422 patients (64.9%), and AF burden was marginally higher with baseline PQ-interval >180 ms (P= 0.028). No significant association was detected between %VP and %MS (Spearman's ρ 0.056, P= 0.154). %MS was not different between minimal-paced programmed AVI ≤ 100 and >100 ms (median value), respectively (P= 0.60). CONCLUSIONS: The present study indicates that a longer baseline PQ-interval is associated with an increased risk of AF in patients with sick sinus syndrome. Atrial fibrillation burden is not associated with the percentage of VP or the length of the programmed AVI.
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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 | 0.001 |
| 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) | 0.001 | 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".