Identifying the optimal time period for detection of atrial fibrillation after ischaemic stroke and TIA: An updated systematic review and meta-analysis of randomized control trials
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation (AF) is a major risk factor for ischaemic stroke (IS) and transient ischaemic attack (TIA). The timely detection of first-diagnosed or "new" AF (nAF) would prompt a switch from antiplatelets to anticoagulation to reduce the risk of stroke recurrence; however, the optimal timing and duration of rhythm monitoring to detect nAF remains unclear. AIMS: We searched MEDLINE, PubMed, Cochrane database, and Google Scholar to undertake a systematic review and meta-analysis of randomized controlled trials (RCT) between 2012 and 2023 investigating nAF detection after IS and TIA. Outcome measures were overall detection of nAF (control; (usual care) compared to intervention; (continuous cardiac monitoring >72 h)) and the time period in which nAF detection is highest (0-14 days, 15-90 days, 91-180 days, or 181-365 days). A random-effects model with generic inverse variance weights was used to pool the most adjusted effect measure from each trial. SUMMARY OF REVIEW: A total of eight RCTs investigated rhythm monitoring after IS, totaling 5820 patients. The meta-analysis of the studies suggested that continuous cardiac monitoring was associated with a pooled odds ratio of 3.81 (95% CI 2.14 to 6.77), compared to usual care (control), for nAF detection. In the time period analysis, the odds ratio for nAF detection at 0-14 days, 15-90 days, 91-180 days, 181-365 days were 1.79 (1.24-2.58); 2.01 (0.63-6.37); 0.98 (0.16-5.90); and 2.92 (1.30-6.56), respectively. CONCLUSION: There is an almost fourfold increase in nAF detection with continuous cardiac monitoring, compared to usual care. The results also demonstrate two statistically significant time periods in nAF detection; at 0-14 days and 6-12 months following monitoring commencement. These data support the utilization of different monitoring methods to cover both time periods and a minimum of 1 year of monitoring to maximize nAF detection in patients after IS and TIA.
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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 | 0.025 | 0.071 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.029 | 0.050 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".