Clustering of Social Determinants of Health and their Association with Adverse Cardiovascular Outcomes in Atrial Fibrillation
Bibliographic record
Abstract
Abstract Background Despite improvements in diagnostic and therapeutic options, individuals with atrial fibrillation (AF) remain at high risk for major adverse cardiovascular events (MACE). Social determinants of health (SDOH) are strongly associated with cardiovascular outcomes, yet the complex patterns through which these factors cluster to create distinct vulnerability profiles remain poorly understood. Methods We conducted a latent class analysis utilizing data from the UK Biobank cohort, examining 15 SDOH indicators across economic, psychological, and neighborhood domains among 3,842 participants with AF (35.1% female). Multi-group latent class analysis (LCA) evaluated differences in vulnerability pattern distribution, and explored whether SDOH clusters of vulnerability varies by sex. Cox proportional hazards models assessed the association between identified SDOH clusters and composite cardiovascular outcomes comprising major adverse cardiovascular events (MACE) and all-cause mortality. Results We identified five distinct SDOH vulnerability clusters based on multi-group LCA: 1) low vulnerability across all domains; 2) primarily economic vulnerability; 3) primarily neighborhood-related vulnerability; 4) economic and neighborhood vulnerability with favorable psychological conditions; and 5) high vulnerability across all domains. Male participants demonstrated a higher representation in more advantaged profiles than their female counterpart (Cluster 1: 37.0% vs. 26.6%; Cluster 4: 25.3% vs. 14.1%). Female participants exhibited the greater representation in the overall highest vulnerability cluster(Cluster 5: 35.1% vs. 25.2%) as compared with male participants. Compared to Cluster 1, individuals with AF from classes with adverse economic conditions (Cluster 2, 4 and 5) had a higher risk of MACE events with individuals in Cluster 5 having twice the risk. No sex interactions were observed with SDOH clusters in their association with MACE. Conclusions We identified five distinct SDOH vulnerability patterns among individuals with AF, revealing economic determinants as pivotal drivers of cardiovascular risk. These findings provide an evidence-based framework for implementing precision medicine approaches that incorporate comprehensive SDOH assessment into AF clinical management.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".