286Predictors of atrial fibrillation in patients with embolic stroke of undetermined source: an analysis of the RE-SPECT ESUS trial
Bibliographic record
Abstract
Abstract Background A proportion of patients with embolic stroke of undetermined source (ESUS) may have silent atrial fibrillation (AF) or develop AF after the initial evaluation. Better understanding of risk for identification is critical to implement optimal monitoring strategies with the goal of preventing recurrent stroke. The RE-SPECT ESUS trial provides an opportunity to assess predictors for developing AF and associated recurrent stroke. Methods RE-SPECT ESUS was a randomized, controlled trial (564 sites, 42 countries) assessing dabigatran versus aspirin for the prevention of recurrent stroke in patients with ESUS. Of 5390 patients enrolled and followed for a median of 19 months, 403 (7.5%) were found to develop AF reported as an adverse event or using cardiac monitoring per standard clinical care. Univariable and multivariable regression analyses for predictors of AF were conducted. Results In a multivariable analysis, clinical predictors for developing AF were: older age, history of heart failure, lower heart rate, hypertension, higher body mass index, and being from Western Europe (Table). Using several published predictive models, including HAVOC, C2HEST, AS5F, ARIC, and CHA2DS2-VASc, high scores were associated with increased rates of AF. In patients who developed AF, recurrent stroke occurred in 7.0% per year, versus 4.2% per year in patients who did not develop AF (hazard ratio 1.75; 95% CI 1.30–2.35, p=0.0002). Conclusion Besides age as the most important variable, several other factors, including lower heart rate, higher body mass index, and hypertension, are independent predictors of AF after ESUS. Understanding who is at higher risk of developing AF may help identify patients requiring more intense, long-term cardiac monitoring. Acknowledgement/Funding Funded by Boehringer Ingelheim
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".