Abstract 64: Biomarkers of Atrial Cardiopathy and Outcomes After Cryptogenic Stroke: The ARCADIA Trial
Bibliographic record
Abstract
Background: Atrial cardiopathy is associated with incident ischemic stroke in the absence of clinical atrial fibrillation in prospective cohorts. Whether biomarkers of atrial cardiopathy predict recurrent stroke is less clear. Methods: In the ARCADIA trial, a multicenter randomized trial of apixaban versus aspirin in patients with cryptogenic stroke and evidence of atrial cardiopathy, we explored whether the biomarkers P-wave terminal force in ECG lead V 1 (PTFV1), N-terminal pro-Brain natriuretic peptide (NT-proBNP), or left atrial diameter index (LADI) predicted primary and secondary trial outcomes: (1) recurrent stroke of any type; (2) recurrent ischemic stroke or systemic embolism; and (3) recurrent stroke of any type or death. We analyzed the full cohort using Cox proportional hazard models , sequentially adjusting for treatment effect, demographic factors, and clinical risk factors. NT-proBNP concentrations were log-transformed. Results: With 1,015 of the target 1,100 participants enrolled (mean age 68 years, 54.3% female, 21.1% Black, and 8.1% Hispanic) and mean follow-up of 1.8 years, the trial was stopped for futility. Recurrent stroke occurred in 80 patients (annualized rate, 4.4%) and death in 54 patients. NT-proBNP significantly predicted recurrent stroke; recurrent ischemic stroke or systemic embolism; and recurrent stroke or death after adjusting for other risk factors, while PTFV1 and LADI did not (Table). NT-proBNP predicted recurrent stroke and death most strongly and with little change after adjusting for other risk factors. Treatment effect was not significant in any of the models. Conclusions: In this population of patients with cryptogenic stroke and evidence of atrial cardiopathy, NT-proBNP is a predictor of recurrent stroke and, with greater magnitude, of a composite of stroke or death. The role of NT-proBNP measurement in management after stroke deserves further study.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".