Fazekas score predicts cognitive decline & frailty in older adults: insights from the SAGE-AF cohort study
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation (AF) is a common condition in older adults, often associated with increased risks of cognitive decline and frailty. White matter hyperintensities (WMH), visible on neuroimaging and quantified by the Fazekas score, have been linked to both cognitive and physical impairments. However, the relationship between WMH, cognitive decline, and frailty in older adults with AF remains relatively underexplored. METHODS: This study analyzed data from 86 participants in the SAGE-AF cohort, a two-year prospective multicenter cohort study of older adults with AF, who also had neuroimaging performed for clinical indications. WMH severity was assessed by independent reviewers using Fazekas scores from brain imaging. Cognitive function was measured using the Montreal Cognitive Assessment (MoCA), and frailty was assessed at baseline as well as 1- and 2-year follow-up visits by trained examiners as part of the SAGE-AF study protocol. Participants were characterized based on the severity of their white matter hyperintensities and compared to baseline and two-year cognitive and physical functional status. Longitudinal regression models were used to adjust for demographic, clinical, and geriatric covariates. RESULTS: Participants with higher Fazekas scores (grades 2-3) demonstrated significantly lower baseline and follow-up MoCA scores and were more likely to meet frailty criteria over a two-year follow-up period. After adjusting for multiple factors known to influence cognitive decline, greater white matter hyperintensity (Fazekas grades 2-3) remained associated with a 2.6-fold increased risk of cognitive impairment at (p = 0.04) and a 2.7-fold increased risk of frailty at (p = 0.02). CONCLUSION: Higher Fazekas scores are related to cognitive decline and frailty in older adults with AF, emphasizing WMH as a critical biomarker for aging-related impairments. Neuroimaging tools like Fazekas scoring could enhance risk stratification and inform targeted interventions for this vulnerable population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 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".