Cognitive impairment, age, quality of life, and treatment strategy for atrial fibrillation in older adults: The <scp>SAGE‐AF</scp> study
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation (AF) treatment includes anticoagulation for high stroke risk individuals and either rate or rhythm control strategies. We aimed to investigate the impact of age, geriatric factors, and medical comorbidities on choice of rhythm versus rate control strategy in older adults. METHODS: VASc score ≥2 and eligible for anticoagulation were recruited for the Systematic Assessment of Geriatric Elements-AF (SAGE-AF) prospective cohort study. An interview that included measures of HRQoL, cognitive function, vision, hearing, and frailty was performed. The association between these elements and AF treatment strategy was examined by multivariable logistic regression models. RESULTS: One thousand two hundred forty-four participants (mean age 76 years; 49% female; 85% non-Hispanic white) were enrolled. Rate and rhythm control were used in 534 and 710 participants, respectively. Compared to participants <75 years, those ≥75 were more likely to be treated with a rate control strategy (age 75-84 adjusted odds ratio [aOR] 1.37 [95% CI 0.99, 1.88]; age 85+ aOR = 2.05, 95% CI 1.30, 3.21). Those treated with a rate control strategy were more likely to have cognitive impairment (aOR = 1.50, 95% CI 1.13, 1.99), and peripheral vascular disease (PVD) (aOR = 1.82, 95% CI 1.22, 2.72) but less likely to have visual impairment (aOR 0.73 [0.55, 0.98]), congestive heart failure (CHF; aOR 0.68 [0.49, 0.94]) or receive anticoagulation (aOR 0.53, 95% CI 0.36, 0.78). CONCLUSION: Older age, cognitive impairment, and PVD were associated with use of rate control strategy. Visual impairment, CHF, and anticoagulation use were associated with a rhythm control strategy. There was no difference in HRQoL between the rate and rhythm control groups. This study suggests that certain geriatric elements may be associated with AF treatment strategies. Further study is needed to evaluate how these decisions affect 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".