Abstract 189: Identifying Patients at Highest Risk of Developing Atrial Fibrillation and the Role of Remote Prior Stroke: Insights From the REVEAL AF Study
Bibliographic record
Abstract
Introduction: Recent trials using insertable cardiac monitors (ICMs) show high rates of atrial fibrillation (AF) in those at risk of developing AF based on demographics. Further identifying subsets who could benefit most from ICMs is desirable. We evaluated if the recently-developed HAVOC risk score which has predicted AF in cryptogenic stroke patients also predicts AF detection by ICMs in those without a recent stroke. Methods: Participants from the prospective REVEAL AF study, which reported frequent clinically unrecognized AF in patients at risk for AF (CHADS 2 scores ≥3 or =2 with ≥1 additional risk factor), with ICM data and not on anti-arrhythmic drugs were included. Ischemic stroke <1 year ago excluded participation. HAVOC scores were calculated using points for hypertension (2), age ≥75 (2), valvular disease (2), vascular disease (1), obesity (1), congestive heart failure (4), and coronary artery disease (2). Prior stroke, an AF outcome, generally not a cause, is not included. Score classification is: low (0-4), intermediate (5-9), or high (10-14). Corresponding AF detection rates were compared via the log-rank test. Results: Among 391 participants, 95 (24%), 241 (62%), and 55 (14%) had low, intermediate, and high HAVOC scores, respectively. At 18 months, AF incidence was significantly less for those with low (19.5%) vs intermediate (32.1%) or high (34.2%) scores (p=0.045, Figure). At 18 months, AF incidence was similar among those with (n=79) vs. without (n=312) a history of remote stroke (27.1% vs. 29.9%; p>0.05, median time from stroke to ICM insertion=4 years). Conclusions: The HAVOC risk score identified a subset of high risk individuals at greatest risk of developing AF. AF incidence rates were similar among those with and without prior remote stroke. Remote strokes may not be a strong predictor of AF in this population. HAVOC scores could be a useful approach to identify those at high likelihood of manifesting AF, as best documented by long-term 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".