Rhythm monitoring strategies for atrial fibrillation detection in patients with cryptogenic stroke: A systematic review and meta-analysis
Bibliographic record
Abstract
Objective To summarize data on atrial fibrillation (AF) detection rates and predictors across different rhythm monitoring strategies in patients with cryptogenic stroke (CS) or embolic stroke of undetermined source (ESUS). Methods MEDLINE, Embase, and Web of Science were searched to identify all published studies providing relevant data through July 6, 2020. Random-effects meta-analysis method was used to pool estimates. Results We included 47 studies reporting on a pooled population of 8,215 patients with CS or ESUS. Using implantable cardiac monitor (ICM), the pooled rate of AF was 12.2% (95% CI 9.4–15.0) at 3 months, 16.0% (95% CI 13.2–18.8) at 6 months, 18.7% (95% CI 15.7–21.7) at 12 months, 22.8% (95% CI 19.1–26.5) at 24 months, and 28.5% (95% CI 17.6–39.3) at 36 months. AF rates were significantly higher in patients with ESUS vs CS (22.0% vs 14.2%; p < 0.001) at 6 months, and in studies using Reveal LINQ vs Reveal XT ICM (19.1% vs 13.0%; p = 0.001) at 12 months. Using mobile cardiac outpatient telemetry (MCOT), the pooled rate of AF was 13.7% (95% CI 10.2–17.2) at 1 month. Predictors of AF detection with ICM included older age, CHA 2 DS 2 -VASc score, left atrial enlargement, P wave maximal duration and prolonged PR interval. Conclusion The yield of ICM increases with the duration of monitoring. More than a quarter of patients with CS or ESUS will be diagnosed with AF during follow-up. About one in seven patients had AF detected within a month of MCOT, suggesting that a non-invasive rhythm monitoring strategy should be considered before invasive monitoring.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.024 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".