Abstract 8996: Efficacy and Safety of Direct Oral Anticoagulants in Those Identifying as Black or African-American: A Systematic Review and Meta-analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Introduction: Direct oral anticoagulants (DOACs) are indicated for prevention of stroke in non-valvular atrial fibrillation (AF) and for both treatment and prevention of recurrence of venous thromboembolism (VTE). Very little is known about efficacy and safety of these agents in patients identifying as Black or African-American (AA), as landmark trials demonstrating their non-inferiority to vitamin K antagonists (VKAs) are notable for discrepancies in enrollment by patient’s race. As the primary efficacy and safety outcomes for both AF and VTE are similar, we hypothesized that the cumulative efficacy and safety of DOACs in AA patients would be similar to that of the general population. Methods: A systematic review was conducted of clinical trials that investigated the comparison between DOACs and VKAs in patients for both AF and VTE. The Mantel-Haenszel Method was used to estimate pooled risk ratios (RRs) and corresponding confidence intervals (CIs) for the efficacy and safety of DOACs in AAs compared to the general population. Primary efficacy outcome was defined as stroke or systolic embolism in AF or symptomatic, recurrent VTE. Primary safety outcome was defined as major bleeding in both AF and VTE. Results: Nine studies were used for meta-analysis, with 26 eliminated due to lack of randomization or subgroup data for AAs. A total of 80,688 of patients were analyzed with 1.67% being AA. Regarding efficacy, the RR for AAs using DOACs compared to VKAs was 0.98 (95% CI 0.62,1.55; I 2 =0%; p=0.94), while the RR for the entire population was 0.90 (CI 0.78,1.04; I 2 =41%; p=0.1). Regarding safety, the RR for AAs using DOACs compared to VKAs was 0.83 (CI 0.56,1.25; I 2 =0%; p=0.92), while the RR for the entire population was 0.78 (CI 0.65,0.93; I 2 =76%; p<0.01). Conclusions: The use of DOACs versus VKAs in AF and VTE for AAs results in similar efficacy to the general population. DOACs are non-inferior to VKAs with respect to safety outcomes in AF and VTE for AAs. It is uncertain why superiority of safety outcomes seen in the general population are not seen in the AA population, and will require further studies.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.038 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".