Direct Oral Anticoagulants for Stroke Prevention in Patients With Device-Detected Atrial Fibrillation: A Study-Level Meta-Analysis of the NOAH-AFNET 6 and ARTESiA Trials
Bibliographic record
Abstract
BACKGROUND: Device-detected atrial fibrillation (also known as subclinical atrial fibrillation or atrial high-rate episodes) is a common finding in patients with an implanted cardiac rhythm device and is associated with an increased risk of ischemic stroke. Whether oral anticoagulation is effective and safe in this patient population is unclear. METHODS: We performed a systematic review of MEDLINE and Embase for randomized trials comparing oral anticoagulation with antiplatelet or no antithrombotic therapy in adults with device-detected atrial fibrillation recorded by a pacemaker, implantable cardioverter defibrillator, cardiac resynchronization therapy device, or implanted cardiac monitor. We used random-effects models for meta-analysis and rated the quality of evidence using the Grading of Recommendations Assessment, Development and Evaluation framework (GRADE). The review was preregistered (PROSPERO CRD42023463212). RESULTS: From 785 citations, we identified 2 randomized trials with relevant clinical outcome data: NOAH-AFNET 6 (Non–Vitamin K Antagonist Oral Anticoagulants in Patients With Atrial High Rate Episodes; 2536 participants) evaluated edoxaban, and ARTESiA (Apixaban for the Reduction of Thrombo-Embolism in Patients With Device-Detected Sub-Clinical Atrial Fibrillation; 4012 participants) evaluated apixaban. Meta-analysis demonstrated that oral anticoagulation with these agents reduced ischemic stroke (relative risk [RR], 0.68 [95% CI, 0.50–0.92]; high-quality evidence). The results from the 2 trials were consistent (I 2 statistic for heterogeneity=0%). Oral anticoagulation also reduced a composite of cardiovascular death, all-cause stroke, peripheral arterial embolism, myocardial infarction, or pulmonary embolism (RR, 0.85 [95% CI, 0.73–0.99]; I 2 =0%; moderate-quality evidence). There was no reduction in cardiovascular death (RR, 0.95 [95% CI, 0.76–1.17]; I 2 =0%; moderate-quality evidence) or all-cause mortality (RR, 1.08 [95% CI, 0.96–1.21]; I 2 =0%; moderate-quality evidence). Oral anticoagulation increased major bleeding (RR, 1.62 [95% CI, 1.05–2.50]; I²=61%; high-quality evidence). CONCLUSIONS: The results of the NOAH-AFNET 6 and ARTESiA trials are consistent with each other. Meta-analysis of these 2 large randomized trials provides high-quality evidence that oral anticoagulation with edoxaban or apixaban reduces the risk of stroke in patients with device-detected atrial fibrillation and increases the risk of major bleeding.
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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) | 0.001 | 0.001 |
| 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".