Abstract 12706: Stroke Risk and Oral Anticoagulation Use With Extended Cardiac Monitoring for Atrial Fibrillation versus Usual Care: A Systematic Review With Meta-Analysis
Bibliographic record
Abstract
INTRODUCTION: Prolonged cardiac monitoring is frequently used to detect atrial fibrillation (AF) in high-risk populations, with the goal of preventing thromboembolic events. We sought to determine the impact of prolonged cardiac monitoring on the incidence of stroke and systemic embolism (SSE) or transient ischemic attack (TIA) METHODS: We performed a systematic review and meta-analysis of randomized trials evaluating prolonged monitoring versus usual care (PROSPERO #CRD42021277611). Studies were identified through CENTRAL, MEDLINE, and Embase. We included studies with ≥100 participants and ≥30 days follow-up. The primary outcome was a composite of SSE/TIA, as reported in the original trials. Secondary outcomes included AF detection, oral anticoagulation (OAC) initiation, and major bleeding. Sensitivity analysis examining the impact of monitoring device, and indication for monitoring were performed. Meta-analyses were performed with R using a random-effects model. RESULTS: From 1411 records, we included 9 RCTs (n = 10,205). Mean age was 70 years, 40% were female, and mean CHADS2 score was 4.0. Studies used implantable cardiac monitors (n = 4), external cardiac monitors (n = 3), or handheld ECG devices (n = 2). Study populations included post-stroke (n = 5), high risk for AF or stroke (n = 2), and post-cardiac surgery (n = 1). Mean follow-up was 16 months (range 3-65). Extended monitoring did not significantly reduce the primary outcome (Figure, random effects risk ratio [RR] 0.87, 95% confidence interval [CI] 0.72-1.06, I2 = 0%) or its individual components. Extended monitoring increased AF detection (RR 4.56, 95% CI 3.01-6.92, I2 = 65%) and OAC usage (RR 2.25, 95% CI 2.01-2.53, I2 = 0%), but did not impact major bleeding (RR 1.23, 95% CI 0.84-1.82, I2 = 0%). CONCLUSION: Prolonged monitoring was associated with increased AF detection and OAC use, without significantly reducing the occurrence of thromboembolic events.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.049 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".