Real-World Adherence and Persistence to Direct Oral Anticoagulants in Patients With Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND: Stroke reduction with direct oral anticoagulants (DOACs) in atrial fibrillation (AF) is dependent on adherence and persistence in the real-world setting. Individual study estimates of DOAC adherence/persistence rates have been discordant. Our aims were to characterize real-world observational evidence for DOAC adherence/persistence and evaluate associated clinical outcomes in patients with AF. METHODS AND RESULTS: PubMed, EMBASE, and CINAHL were searched from inception to June 2018. Observational studies that reported real-world DOAC adherence/persistence in patients with AF were included. Study quality was assessed using the Newcastle-Ottawa Scale. Meta-analyses for pooled estimates were performed using DerSimonian and Laird random-effects models. Outcomes included DOAC mean proportion of days covered or medication possession ratio, proportion of good adherence (proportion of days covered/medication possession ratio ≥80%), persistence, DOAC versus vitamin K antagonists persistence, and clinical outcomes associated with nonadherence/nonpersistence. Forty-eight observational studies with 594 784 unique patients with AF (59% male; mean age 71 years) were included. The overall pooled mean proportion of days covered/medication possession ratio was 77% (95% CI, 75%-80%), proportion of patients with good adherence was 66% (95% CI, 63%-70%), and proportion persistent was 69% (95% CI, 65%-72%). The pooled proportion of patients with good adherence was 71% (95% CI, 64%-78%) for apixaban, 60% (95% CI, 52%-68%) for dabigatran, and 70% (95% CI, 64%-75%) for rivaroxaban. Similar patterns were found for pooled persistence by agent. The pooled persistence was higher with DOACs than vitamin K antagonists (odds ratio, 1.44 [95% CI, 1.12-.86]). DOAC nonadherence was associated with an increased risk of stroke (hazard ratio, 1.39 [95% CI, 1.06-1.81]). CONCLUSIONS: Suboptimal adherence and persistence to DOACs was common in patients with AF, with 1 in 3 patients adhering to their DOAC <80% of the time, which was associated with poor clinical outcomes in nonadherent patients. Although it is convenient that DOACs do not require laboratory monitoring, greater effort in monitoring for and interventions to prevent nonadherence may be necessary to optimize stroke prevention. Increased clinician awareness of DOAC nonadherence may help identify at-risk patients.
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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 | 0.028 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.012 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".