Abstract 223: Impact of Differences in Once- vs Twice-Daily Medications Adherence on the Risk of Bleed and Stroke in Non-Valvular Atrial Fibrillation: Analysis of Randomized Trials and Claims Data Sources
Bibliographic record
Abstract
Background: In non-valvular atrial fibrillation (NVAF) patients, those receiving once-daily (QD) versus twice-daily (BID) non vitamin-K antagonist oral anticoagulants (NOACs) may have better medication adherence. The impact on stroke and bleed risk is not known. Objective: To estimate the impact of adherence differences between QD vs BID therapies on bleed and stroke risks in NVAF patients. Methods: The relation between adherence (proportion of days covered [PDC]) for QD vs BID NOACs and one year bleed risk was modeled using claims data from Truven Health Analytics MarketScan databases (7/2012-10/2015). Next, the relation between adherence and bleeding was calibrated to match that seen in the placebo and NOAC arms of previous randomized controlled trials (RCTs). Finally, we used adherence rates for QD (PDC=0.849) and BID (PDC=0.738) cardiovascular medications from a meta-analysis (Coleman et al.). These rates were used in the calibrated model to estimate bleeds. An analogous method was applied to evaluate the impact of QD vs BID adherence on stroke risk. Results: The relation between PDC and risks of bleed and stroke was modeled using claims data (N=65,022) and calibrated using RCTs. In the calibrated model, compared with BID dosing, QD dosing was associated with 81 fewer strokes (34% reduction) and 14 more bleeds (6% more) per 10,000 patients/year (Figure). Conclusion: Among NVAF patients, better adherence to QD dosing was associated with a significantly lower stroke risk of QD but similar risk of bleed.
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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 | 0.094 | 0.176 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.043 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".