Abstract WMP65: Prospective Validation of Predictive Features of Paroxysmal Atrial Fibrillation (PROPhecy): An Interim Analysis
Bibliographic record
Abstract
Background: Atrial fibrillation (AF) has a distinct antithrombotic regimen for secondary stroke prevention. While 30-day cardiac monitoring has a greater detection rate for AF than 24-hour Holter, it is not widely accessible. Risk stratification may identify patients who could benefit most from prolonged monitoring. PROPhecy aims to prospectively validate predictive features for detection of AF found in EMBRACE, a trial using 30-day monitoring in individuals with an embolic stroke of undetermined source (ESUS). Methods: Participants were > 55 years and within six months of ESUS, without evidence of AF/flutter on 24-hour Holter. All were given an event-triggered external loop recorder for 30 days. Primary outcome was detection of sustained ( > 30 sec) or non-sustained AF/flutter on 30-day monitoring. Results: 150 of a planned 250 participants have completed long-term monitoring to date. Baseline characteristics are compared to EMBRACE (Table 1). Any AF/flutter was detected in 19.3% (EMBRACE, 16.1%). Burden of atrial premature beats in PROPhecy was low in comparison to EMBRACE and did not predict presence of AF on monitoring (Table 2). Left atrial volume index was a significant predictor of AF in both univariable and multivariable regression adjusted for age and sex (OR1.04 per mL/m 2 , 95% 1.01-1.08, p=0.02). Conclusion: Recruitment is ongoing. AF was detected in ~1/5 participants. The burden of atrial ectopy in our cohort is much lower than in EMBRACE despite similar patient characteristics and AF burden. Further work is required to assess the nature of these differences. Left atrial volume index may be helpful for risk stratification.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".