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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".