Abstract WMP101: Atrial Cardiopathy Biomarkers and Brain Infarction in Multiple Territories in ARCADIA
Bibliographic record
Abstract
Introduction: The ARCADIA trial (Apixaban to Prevent Recurrence After Cryptogenic Stroke in Patients With Atrial Cardiopathy) found no benefit of anticoagulation for secondary stroke prevention in patients with cryptogenic stroke and evidence of atrial cardiopathy. It remains unclear if the biomarkers used in the trial reliably identified atrial cardiopathy. We examined the association between biomarkers of atrial cardiopathy and acute brain infarction in multiple arterial territories, an imaging signature of cardioembolic stroke. Hypothesis: Biomarkers of atrial cardiomyopathy are associated with acute brain infarction in multiple arterial territories. Methods: The ARCADIA trial screened patients with cryptogenic stroke for atrial cardiopathy at 185 centers in the U.S. and Canada. Investigators were asked to record the presence and topography of acute brain infarction on baseline imaging. Multi-territorial infarction was defined as acute infarction in at least two of the left middle cerebral artery (MCA), right MCA, and posterior circulation. P-wave terminal force in ECG lead V1 (PTFV1) and left atrial dimension index (LADI) were modeled as continuous variables, whereas N-terminal pro-B-type natriuretic peptide (NT-proBNP) was log-transformed. Relative risk regression was used to examine the association between atrial cardiopathy biomarkers and multi-territorial infarction. Results: Of 3,745 patients enrolled in ARCADIA, 3,301 had available data on atrial cardiomyopathy biomarkers and the topography of acute infarctions seen on baseline imaging. Of these 3,301 patients, 452 (13.7%) had multi-territory brain infarction. We found no association with multi-territory brain infarction for ln(NT-proBNP) (OR per SD, 1.06; 95% CI, 0.97-1.16), PTFV1 (OR per SD, 1.06; 95% CI, 0.98-1.15), or LADI (OR per SD, 0.95; 95% CI, 0.86-1.05). Patients who met criteria for atrial cardiopathy and were randomized into the treatment phase of the study had a similar likelihood of multi-territorial infarction (13.4%) compared with those who were not eligible for randomization (13.8%) (P = 0.77). Conclusions: The atrial cardiopathy biomarkers used in the ARCADIA trial were not associated with patterns of brain infarction suggestive of a cardioembolic source.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".