The Relationship Between Daily Atrial Tachyarrhythmia Burden From Implantable Device Diagnostics and Stroke Risk
Bibliographic record
Abstract
BACKGROUND: It is unknown if brief episodes of device-detected atrial fibrillation (AF) increase thromboembolic event (TE) risk. METHODS AND RESULTS: TRENDS was a prospective, observational study enrolling patients with > or = 1 stroke risk factor (heart failure, hypertension, age > or = 65 years, diabetes, or prior TE) receiving pacemakers or defibrillators that monitor atrial tachycardia (AT)/AF burden (defined as the longest total AT/AF duration on any given day during the prior 30-day period). This time-varying exposure was updated daily during follow-up and related to TE risk. Annualized TE rates were determined according to AT/AF burden subsets: zero, low (<5.5 hours [median duration of subsets with nonzero burden]), and high (> or = 5.5 hours). A multivariate Cox model provided hazard ratios including terms for stroke risk factors and time-varying AT/AF burden and antithrombotic therapy. Patients (n=2486) had at least 30 days of device data for analysis. During a mean follow-up of 1.4 years, annualized TE risk (including transient ischemic attacks) was 1.1% for zero, 1.1% for low, and 2.4% for high burden subsets of 30-day windows. Compared with zero burden, adjusted hazard ratios (95% CIs) in the low and high burden subsets were 0.98 (0.34 to 2.82, P=0.97) and 2.20 (0.96 to 5.05, P=0.06), respectively. CONCLUSIONS: The TE rate was low compared with patients with traditional AF with similar risk profiles. The data suggest that TE risk is a quantitative function of AT/AF burden. AT/AF burden > or = 5.5 hours on any of 30 prior days appeared to double TE risk. Additional studies are needed to more precisely investigate the relationship between stroke risk and AT/AF burden.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".