Abstract P2094: Atrial Fibrillation and Risk of Incident Cognitive Impairment: The Reasons for Geographic and Racial Differences in Stroke (REGARDS) Study
Bibliographic record
Abstract
Introduction: Atrial fibrillation (AF) and cognitive impairment are expected to double in prevalence over the next 20 years. While most literature on AF and risk of cognitive disorders is focused on dementia, there is little evidence on cognitive impairment or non-stroke populations. We addressed this question in the REGARDS cohort. Hypothesis: Baseline AF is associated with increased risk of incident cognitive impairment (ICI) in REGARDS participants with and without prevalent stroke. We tested for differences in this association by prevalent stroke, race (Black and White), and oral anticoagulant use, and for the attenuating effect of selected biomarkers on associations. Methods: REGARDS enrolled 30,239 persons > 45 years old in 2003-07. AF was defined by self-report or ECG. ICI was defined during follow-up by robust cognitive norms based on the Montreal Cognitive Assessment and Six-Item Screener. HRs of time to ICI with AF were calculated by Cox proportional hazards models (Table footnote), with censoring at death or last follow-up. Considered individually, interaction terms for prevalent stroke, race, and oral anticoagulant use with AF were assessed. Results: Among 23,638 participants with mean follow-up 12.8 years (mean age 64.3 years, 56.3% women, 37.6% Black), the association of AF with ICI was modest (HR 1.21; see Table). The adjusted HR was greater in those with prevalent stroke than without stroke (1.69 vs. 1.05). Among those with prevalent stroke, there was little impact of added adjustment for anticoagulant use; with added adjustment for each biomarker individually, there was small-modest attenuation by each biomarker. HRs did not differ by race or oral anticoagulant use (both p interactions >0.4). Conclusion: Independent of other factors including anticoagulant use, AF was associated with ICI in those with prevalent stroke, but not in those without prevalent stroke. Biomarkers had modest attenuating effects on the association among those with prevalent stroke. Results underscore the importance of considering cognitive impairment after stroke in those with AF and identifying underlying causes, which could involve inflammation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".