Abstract WP318: Cancer and Atrial Cardiopathy: A Secondary Analysis of the ARCADIA Clinical Trial
Bibliographic record
Abstract
Background: Approximately 50% of ischemic strokes in patients with cancer are classified as cryptogenic after standard evaluation. As cancer and its treatments are associated with increased risk for atrial fibrillation (AF), the pathological substrate for AF—atrial cardiopathy—could be the underlying etiology for some of these cancer-related cryptogenic strokes. However, the relationship between cancer and atrial cardiopathy is uncertain. Methods: We conducted a post-hoc cross-sectional analysis of baseline data collected from patients enrolled into the ARCADIA trial from 2018-2023 at 185 North American sites. Patients had to be age 45 years or older with a clinical diagnosis of cryptogenic ischemic stroke within the past 180 days and without AF of any duration. Enrolled patients consented to screening for atrial cardiopathy, the presence of which was required for randomization. Linear regression models were used to examine associations between a history of cancer and the three log-transformed atrial cardiopathy biomarkers: serum N-terminal pro-B-type natriuretic peptide (NT-proBNP), P-wave terminal force in ECG lead V 1 (PTFV 1 ), and left atrial diameter index (LADI) on echocardiogram. Results: Among 3,745 patients with cryptogenic stroke, 506 (13.5%) had a history of cancer. Patients with, compared to those without, a history of cancer were older (71 versus 65 years) and more often White (88% versus 74%) and non-Hispanic (94% versus 90%). Medical comorbidities were similar between groups. History of cancer was associated with higher median values of NT-proBNP (126 versus 103 pg/mL, p<0.001) and LADI (1.9 versus 1.8 cm/m 2 , p<0.001) but similar median values of PTFV 1 (3000 versus 3025, p=0.08). After adjusting for demographics and tobacco use, no association remained between history of cancer and NT-proBNP (beta-coefficient, -0.09; 95% CI, -0.20 to 0.03), PTFV 1 (beta-coefficient, -0.04; 95% CI, -0.10 to 0.02), or LADI (beta-coefficient, 0.01; 95% CI, -0.01 to 0.04). Conclusions: In a multicenter prospective cohort of patients with cryptogenic stroke, history of cancer was not independently associated with selected biomarkers for atrial cardiopathy. These data suggest that, in the absence of apparent AF, cancer and atrial cardiopathy may not be linked in patients with stroke.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".