Multiplex protein screening of biomarkers associated with major bleeding in patients with atrial fibrillation treated with oral anticoagulation
Bibliographic record
Abstract
BACKGROUND: Oral anticoagulants (OAC) in patients with atrial fibrillation (AF) prevent thromboembolic events, but are associated with significant risk of bleeding. OBJECTIVES: To explore associations between a wide range of biomarkers and bleeding risk in patients with AF on OAC. METHOD: Biomarkers were analyzed in a random sample of 4200 patients, 204 cases with major bleedings, from ARISTOTLE. The replication cohort included 344 cases with major bleeding and 1024 random controls from RE-LY. Plasma samples obtained at randomization were analyzed by the Olink Proximity Extension Assay cardiovascular and inflammation panels and conventional immunoassays. The associations between biomarker levels and major bleeding over 1 to 3 years of follow-up were evaluated by random survival forest/Boruta analyses and Cox regression analyses to assess linear associations and hazard ratios for identified biomarkers. RESULTS: Out of 268 proteins, nine biomarkers were independently associated with bleeding in both cohorts. In the replication cohort the linear hazard ratios (95% confidence intervals) per interquartile range were for these biomarkers: TNF-R1 1.748 (1.456, 2.098), GDF-15 1.653 (1.377, 1.985), EphB4 1.575 (1.320, 1.880), suPAR 1.548 (1.294, 1.851), OPN 1.476 (1.240, 1.757), OPG 1.397 (1.156, 1.688), TNF-R2 1.360 (1.144,1.616), cTnT-hs 1.232 (1.067, 1.423), and TRAIL-R2 1.202 (1.069, 1.351). CONCLUSIONS: In patients with AF on OAC, GDF-15, cTnT-hs, and seven novel biomarkers were independently associated with major bleedings and reflect pathophysiologic processes of inflammation, apoptosis, oxidative stress, vascular calcification, coagulation, and fibrinolysis. Investigations of the utility of these markers to refine risk stratification and guide the management of patients at high risk of bleeding are warranted.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".