5770A novel biomarker-based risk score to predict death in patients with atrial fibrillation: Insights from the ARISTOTLE and RE-LY trials
Bibliographic record
Abstract
Background: Biomarker-based ABC risk scores for stroke and bleeding in atrial fibrillation (AF) have been published. However, there are no established risk scores at present for death, which is the most common outcome event in anticoagulated patients with AF. Purpose: To develop and validate a new biomarker-based risk score to improve the prognostication of death in anticoagulated patients with AF. Methods: A new risk score was developed and internally validated in 14,611 patients with AF from the ARISTOTLE trial with biomarkers levels determined at baseline using high-sensitivity assays. The median follow-up was 1.9 years. Biomarkers and clinical variables significantly predicting all-cause mortality were assessed by Cox-regression and each variable obtained a weight proportional to the model coefficients. External validation was performed in 8,548 patients with AF from the RE-LY trial with a median follow-up of 2.1 years. Results: 1047 patients died during follow-up in the derivation cohort. The most important predictors of death were NT-proBNP, cardiac troponin T (cTnT), growth differentiation factor-15 (GDF-15), older age, and heart failure. These variables were therefore included in the ABC (Age, Biomarkers, Clinical history) death risk score. The ABC-death score was well-calibrated (Figure) and yielded a higher c-index than the CHA2DS2-VASc score in both the derivation cohort (0.74 vs. 0.59, p<0.001) and the external validation cohort (0.74 vs. 0.58, p<0.001). The ABC-death risk score performed consistently in several clinically important subgroups.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".