Biomarker, Imaging, and Clinical Factors Associated With Overt and Covert Stroke in Patients With Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation is a major risk factor for stroke and silent brain infarcts. We studied whether a multimodal approach offers additional insights to the CHA 2 DS 2 -VASc score in predicting stroke or new brain infarcts on magnetic resonance imaging (MRI) over a 2-year follow-up. METHODS: Swiss-AF is a prospective, multicenter cohort study of patients with known atrial fibrillation. We included patients with available brain MRI both at enrollment and 2 years later. The dates of the baseline and follow-up visits ranged from March 2014 to November 2020. The primary outcome was assessed 2 years after baseline and was defined as a composite of clinically identified stroke or any new brain infarct on the 2-year MRI. We compared a multivariable logistic regression model including prespecified clinical, biomarker, and baseline MRI variables to the CHA 2 DS 2 -VASc score. RESULTS: We included 1232 patients, 89.8% of them taking oral anticoagulants. The primary outcome occurred in 78 patients (6.3%). The following baseline variables were included in the final multivariate model and were significantly associated with the primary outcome: white matter lesion volume in milliliters (adjusted odds ratio [aOR], 1.91 [95% CI, 1.45–2.56]), NT-proBNP (N-terminal pro-B-type natriuretic peptide; aOR, 1.75 [95% CI, 1.20–2.63]), GDF-15 (growth differentiation factor-15; aOR, 1.68 [95% CI, 1.11–2.53]), serum creatinine (aOR, 1.50 [95% CI, 1.02–2.22]), IL (interleukin)-6 (aOR, 1.37 [95% CI, 1.00–1.86]), and hFABP (heart-type fatty acid–binding protein; aOR, 0.48 [95% CI, 0.31–0.73]). Overall performance and discrimination of the new model was superior to that of the CHA 2 DS 2 -VASc score (C statistic, 0.82 [95% CI, 0.77–0.87] versus 0.64 [95% CI, 0.58–0.70]). CONCLUSIONS: In patients with atrial fibrillation, a model incorporating white matter lesion volume on baseline MRI and selected blood markers yielded new insights on residual stroke risk despite a high proportion of patients on oral anticoagulants. This may be relevant to develop further preventive measures.
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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 | 0.004 |
| 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 | 0.001 |
| 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".