Cardiac mechanics and the risk of atrial fibrillation in a community-based cohort of older adults
Bibliographic record
Abstract
AIMS: Assessment of cardiac structure and function improves risk prediction of new-onset atrial fibrillation (AF) in different populations. We aimed to comprehensively compare standard and newer measures of cardiac structure and function in improving prediction of AF in a cohort of older adults without history of AF and stroke. METHODS AND RESULTS: We included 5050 participants without prevalent AF and stroke (mean age 75 ± 5 years, 59% women, and 22% Black) from the Atherosclerosis Risk in Communities (ARIC) study who underwent complete two-dimensional echocardiography, including speckle-tracking analysis of the left ventricle (LV) and left atrium (LA). We assessed the association of cardiac measures with incident AF (including atrial flutter) and quantified the extent to which these measures improved model discrimination and risk classification of AF compared with the CHARGE-AF score. Over a median follow-up time of 7 years, 676 participants developed AF (incidence rate 2.13 per 100 person-years). LV mass index and wall thickness, E/e', and measures of LA structure and function, but not LV systolic function, were associated with incident AF, after accounting for confounders. Above all, LA reservoir strain, contraction strain, and LA minimal volume index (C-statistics [95% confidence interval]: 0.73 [0.70, 0.75], 0.72 [0.70, 0.75], and 0.72 [0.69, 0.75], respectively) significantly improved the risk discrimination of the CHARGE-AF score (baseline C-statistic: 0.68 [0.65, 0.70]) and achieved the highest category-based net reclassification improvement (29%, 24%, and 20%, respectively). CONCLUSION: In a large cohort of older adults without prevalent AF and stroke, measures of LA function improved the prediction of AF more than other conventional cardiac measures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".