Safety and effectiveness of direct oral anticoagulants in AF patients with nonmechanical valves
Bibliographic record
Abstract
BACKGROUND: Direct oral anticoagulants (DOACs) have been studied in nonvalvular atrial fibrillation (AF) (NVAF). Data are lacking in patients with nonmechanical valvular (NMV) AF. OBJECTIVE: We hypothesized that DOACs were safe and effective in patients with NMV AF. METHODS: A retrospective cohort study was designed within administrative health care databases. The cohort entry date was the first DOAC dispensation or 3 months after the NMV procedures. Follow-up was until the end of data availability or exposure to DOACs. The primary outcome was ischemic stroke or embolism. The secondary outcome was major bleeding. Event rates were compared with those in patients with NVAF in the same databases and included in a meta-analysis. RESULTS: A total of 692 patients were included. Of those, 100 patients (14.4%) received dabigatran, 229 (33.1%) rivaroxaban, and 363 (52.5%) apixaban. Owing to low event incidence, data were pooled. There were 7 ischemic strokes/embolisms in 699 person-years, an incidence rate of 1.00 per 100 person-years (95% confidence interval [CI] 0.48-2.10), and 12 major bleedings in 689 person-years, an incidence rate of 1.74 per 100 person-years (95% CI 0.99-3.07). NVAF cohorts demonstrated 554 ischemic strokes/embolisms in 6707.58 person-years, an incidence rate of 0.83 per 100 person-years (95% CI 0.76-0.90; P = .613), and 1907 major bleedings in 64,178.27 person-years, an incidence rate of 2.97 per 100 person-years (95% CI 2.84-3.11; P = .065). Event rates were overlapping with other studies. CONCLUSION: This is the largest cohort of patients with NMV AF and DOACs to be described. Based on low event rates, our data support the prescribing of DOACs in this population.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".