Atrial fibrillation and atherosclerosis cause different vascular brain lesions on magnetic resonance imaging
Bibliographic record
Abstract
BACKGROUND AND AIMS: Atrial fibrillation (AF) and atherosclerosis pre-dispose to the occurrence of vascular brain lesions compared with the general population, yet direct comparisons of brain lesion patterns between these two cardiovascular patient groups are lacking. This study sought to compare the prevalence and distribution of vascular brain lesions on cerebral magnetic resonance imaging (MRI) between patients with AF and those with atherosclerosis. METHODS: Baseline clinical data and standardized brain MRI scans from the Swiss Atrial Fibrillation cohort study (Swiss-AF; representing patients with AF) and the COMPASS MRI sub-study (COMPASS MIND; representing patients with atherosclerosis without AF) were used to compare the prevalence of lacunar and non-lacunar infarcts, periventricular and deep white matter hyperintensities (WMH), and cerebral micro-bleeds (CMB) between groups. RESULTS: Overall, 3508 patients were included (AF: n = 1748; atherosclerosis: n = 1760). Mean age was 73 (±8) years in the AF cohort and 71 (±6) years in the atherosclerosis cohort, 28% and 23% were female, 90% of the AF patients took oral anti-coagulation, 93% of the atherosclerosis patients took anti-platelet therapy. AF patients were more likely to have non-lacunar infarcts (22% vs 10%; P < .001), and atherosclerosis patients were more likely to have lacunar infarcts (21% vs 26%; P = .001). A higher grade of periventricular WMH was seen in AF patients (49% vs 37%; P < .001). The presence of CMB were more common in atherosclerosis patients (22% vs 29%; P < .001). In multi-variable analyses, AF patients had a higher odds ratio (OR) of non-lacunar infarcts (OR 2.28, 95% confidence interval [CI] 1.86-2.81; P < .001), lower odds of lacunar infarcts (OR 0.66, 95% CI 0.56-0.79; P < .001), and higher odds of severe periventricular WMH (OR 1.42, 95% CI 1.22-1.67; P < .001) compared with atherosclerosis patients. CONCLUSIONS: Patients with AF had a higher rate of non-lacunar infarcts, multi-infarct patterns and more severe periventricular white matter disease compared with patients with atherosclerosis. These findings support disease-specific mechanisms in the development of vascular brain lesions in patients with cardiovascular disease.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".