Abstract 12734: A Sex-Stratified Analysis of Social Determinants of Health and Adverse Cardiovascular Outcomes in Atrial Fibrillation
Bibliographic record
Abstract
Introduction: Atrial fibrillation (AF) is associated with a heightened risk of adverse cardiovascular outcomes. Current prediction models often overlook potential sex differences, as well as the combined influence of gendered social determinants of health (SDOH) with traditional cardiovascular risk factors. The objective of this study is to compare the associations between social determinants and adverse outcomes in models stratified by sex. Methods: Participants were sourced from the EURObservational Research Programme-Atrial Fibrillation (ESC-EHRA EORP-AF) General Long-Term Registry. Alongside CHA 2 DS 2 -VASc variables, SDOH factors, including education, living arrangements, gender inequality index (GII), and EQ-5D-5L questionnaire subscales, were evaluated. The primary outcome was a composite of major adverse cardiovascular events (MACE) and all-cause mortality. We evaluated the predictive performance of various models using C-statistics, including individual CHA2DS2-VASc components, SDOH only, and a combined model. Results: Among 11,096 patients (mean age: 69.2 years; 40.7% females) from 27 European countries, sex-stratified analyses indicated fewer SDOH associations with composite outcomes in females compared to males. For both females and males, higher self-reported health and regular exercise were associated with lower risk of composite outcomes. In the male model only, a higher education level, higher GII, reduced mobility, reduced self-care ability, alcohol use, and smoking were all associated with an increased risk of MACE and all-cause mortality (Figure 1). The combined model demonstrated modest superior predictive performance, while the improvement when combining CHA2DS2-VASc and SDOH components appears more pronounced in males compared to females. Conclusions: The study highlights the distinct impact of SDOH in AF, with males exhibiting a wider range of SDOH factors associated with outcomes.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".