Brain lesions and cognitive decline in patients with atrial fibrillation
Bibliographic record
Abstract
Abstract Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): Swiss National Science Foundation, Swiss Heart Foundation Background In addition to clinical stroke, atrial fibrillation (AF) is associated with a high burden of various vascular brain lesions, the majority of which are silent. However, the impact of these lesions on cognitive performance remains unclear. Purpose Our aim was to assess the association between vascular brain lesions and cognitive decline in clinically asymptomatic AF patients. Methods In a prospective multicentre cohort trial, we included 1536 clinically stable AF patients aged ≥65 years and a limited number of patients aged 45–64 years (90% on oral anticoagulation therapy). Patients underwent brain magnetic resonance imaging (bMRI) for the detection of any brain lesions at baseline (ischemic brain lesions and microbleeds) and yearly cognitive assessment using different standardized tests. Cognitive decline was defined as a measurement >1 standard deviation of the age-education standardized baseline population, compared with individual baseline levels. Multivariable adjusted Cox regression analyses were performed to assess the relationship of baseline brain lesions presence with cognitive decline during follow-up. Results At the time of inclusion, 1030 (67%) of 1536 patients (mean age 72±8 years, 73% male) had one or more vascular brain lesions on baseline MRI. Based on the Montreal Cognitive Assessment score (MoCA), cognitive decline developed in 159 (10%) patients during a mean follow-up of 4.8 years. The incidence rate (per 100 person-years) for cognitive decline (MoCA score) was 3.64 and 1.82 in patients with and without brain lesions, respectively. After multivariable adjustment, the hazard ratio (95% CI) for cognitive decline (MoCA score) was 1.29 (0.85-1.96). The association of brain lesions with cognitive decline was 1.57 (1.02-2.40) for the Digit Symbol Substitution Test (DSST), 1.28 (1.01 to 1.63) for the semantic fluency test (SFT), and 0.91 (0.69-1.21) for the Trail Making Test Part A (TMT-A). Conclusion In our contemporary AF cohort, two thirds of patients had brain lesions on baseline MRI, and these lesions were predictive of worse cognitive outcomes in the mid-term on some of the tests used. The full effects on cognitive outcome will be obtained during even longer follow-up.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".