Abstract 4121589: Sex differences in Social Determinants of Health and Adverse Outcomes in Atrial Fibrillation: A UK Biobank Study
Bibliographic record
Abstract
Background: Beyond the risk of thromboembolic stroke, people with atrial fibrillation (AF) are substantially burdened with a higher incidence of cardiovascular mortality, even among patients treated with anticoagulants. The reasons behind these outcomes are only partially understood. Social determinants of health (SDOH), strong independent predictors of major adverse cardiovascular events (MACE) in several cardiac diseases, may play a role; however, their impact on AF prognosis and the differences in SDOH by sex have been insufficiently explored. Objective: We investigated the sex differences in the association between SDOH and MACE in patients with AF. Methods: Data from the UK Biobank included participants enrolled from 2006 to 2010. An incident AF patient cohort, free of stroke and MI was created. Seventeen SDOH derived from three domains:: socio-economic status, psychosocial factors, and neighborhood/living environment were identified Cox proportional hazards models were used to evaluate the associations of individual SDOH components with the risk of MACE stratified by sex. Covariates including all variables of the CHA 2 DS 2 -VASc Score (Congestive heart failure, Hypertension, Age, Diabetes, prior stroke or transient ischemic attack, Vascular disease and Sex), current use of antiplatelet therapy or anticoagulation, smoking and body mass index. The primary outcome was a composite of MACE (including stroke, transient ischemic attack and arterial thromboembolic event, MI and cardiovascular mortality) and all-cause mortality. Results: A total of 23,113 participants with AF (mean age, 62.44 ± 5.88 years; female sex 39.7%) were included. The composite outcome occurred in 5,151 (22%) of participants over 10-year follow up. In the multivariate adjusted model, several SDOH were independently associated with an increased risk of adverse outcomes. Males showed a broader range of SDOH that were significantly associated with outcomes, including unfavourable economic factors and low social support. Despite females had fewer significant SDOH, education-related factors and local crime rates were significant predictors of adverse outcomes (Figure 1). Conclusions: Adverse SDOHs are associated with a higher risk of MACE and all-cause mortality in AF, with sex-specific variations. These findings underscore the need of incorporating routinely SDOH assessment into clinical practice to more accurately stratify risk and tailor preventive strategies based on sex-specific data.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".