Development and Validation of the DOAC Score: A Novel Bleeding Risk Prediction Tool for Patients With Atrial Fibrillation on Direct-Acting Oral Anticoagulants
Bibliographic record
Abstract
BACKGROUND: Current clinical decision tools for assessing bleeding risk in individuals with atrial fibrillation (AF) have limited performance and were developed for individuals treated with warfarin. This study develops and validates a clinical risk score to personalize estimates of bleeding risk for individuals with atrial fibrillation taking direct-acting oral anticoagulants (DOACs). METHODS: Among individuals taking dabigatran 150 mg twice per day from 44 countries and 951 centers in this secondary analysis of the RE-LY trial (Randomized Evaluation of Long-Term Anticoagulation Therapy), a risk score was developed to determine the comparative risk for bleeding on the basis of covariates derived in a Cox proportional hazards model. The risk prediction model was internally validated with bootstrapping. The model was then further developed in the GARFIELD-AF registry (Global Anticoagulant Registry in the Field-Atrial Fibrillation), with individuals taking dabigatran, edoxaban, rivaroxaban, and apixaban. To determine generalizability in external cohorts and among individuals on different DOACs, the risk prediction model was validated in the COMBINE-AF (A Collaboration Between Multiple Institutions to Better Investigate Non-Vitamin K Antagonist Oral Anticoagulant Use in Atrial Fibrillation) pooled clinical trial cohort and the Quebec Régie de l’Assurance Maladie du Québec and Med-Echo Administrative Databases (RAMQ) administrative database. The primary outcome was major bleeding. The risk score, termed the DOAC Score, was compared with the HAS-BLED score. RESULTS: Of the 5684 patients in RE-LY, 386 (6.8%) experienced a major bleeding event, within a median follow-up of 1.74 years. The prediction model had an optimism-corrected C statistic of 0.73 after internal validation with bootstrapping and was well-calibrated based on visual inspection of calibration plots (goodness-of-fit P =0.57). The DOAC Score assigned points for age, creatinine clearance/glomerular filtration rate, underweight status, stroke/transient ischemic attack/embolism history, diabetes, hypertension, antiplatelet use, nonsteroidal anti-inflammatory use, liver disease, and bleeding history, with each additional point scored associated with a 48.7% (95% CI, 38.9%–59.3%; P <0.001) increase in major bleeding in RE-LY. The score had superior performance to the HAS-BLED score in RE-LY (C statistic, 0.73 versus 0.60; P for difference <0.001) and among 12 296 individuals in GARFIELD-AF (C statistic, 0.71 versus 0.66; P for difference = 0.025). The DOAC Score had stronger predictive performance than the HAS-BLED score in both validation cohorts, including 25 586 individuals in COMBINE-AF (C statistic, 0.67 versus 0.63; P for difference <0.001) and 11 945 individuals in RAMQ (C statistic, 0.65 versus 0.58; P for difference <0.001). CONCLUSIONS: In individuals with atrial fibrillation potentially eligible for DOAC therapy, the DOAC Score can help stratify patients on the basis of expected bleeding risk.
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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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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".