Biomarker-Based Risk Prediction With the ABC-AF Scores in Patients With Atrial Fibrillation Not Receiving Oral Anticoagulation
Bibliographic record
Abstract
BACKGROUND: The novel ABC (Age, Biomarkers, Clinical History) scores outperform traditional risk scores for stroke, major bleeding, and death in patients with atrial fibrillation (AF) receiving oral anticoagulation. To refine their utility, the ABC-AF scores needed to be validated in patients not receiving oral anticoagulation. METHODS: We measured plasma levels of the ABC biomarkers (N-terminal pro-B-type natriuretic peptide, cardiac troponin-T, and growth-differentiation factor 15) to apply the previously developed ABC-AF scores in patients with AF receiving aspirin (n=3195) or aspirin and clopidogrel (n=1110) in 2 large clinical trials. Calibration was assessed by comparing estimated with observed 1-year risks. Cox regression models were used for recalibration. Discrimination was evaluated separately for the aspirin-only and the overall cohort (n=4305). RESULTS: The ABC-AF-stroke score yielded a c-index of 0.70 (95% CI, 0.67-0.73) in both cohorts. The ABC-AF-bleeding score had a c-index of 0.76 (95% CI, 0.71-0.81) in the aspirin-only cohort and 0.73 (95% CI, 0.69-0.77) overall. Both scores were superior to risk scores recommended by current guidelines. The ABC-AF-death score yielded a c-index of 0.78 (95% CI, 0.76-0.80) overall. Calibrated in patients receiving oral anticoagulation, the ABC-AF-stroke score underestimated and the ABC-AF-bleeding score overestimated the risk of events in both cohorts. These scores were recalibrated for prediction of absolute event rates in the absence of oral anticoagulation. CONCLUSIONS: The biomarker-based ABC-AF scores showed better discrimination than traditional risk scores and were recalibrated for precise risk estimation in patients not receiving oral anticoagulation. They can now provide improved decision support on treatment of an individual patient with AF.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".