Adherence to oral anticoagulants among patients with atrial fibrillation: a systematic review and meta-analysis of observational studies
Bibliographic record
Abstract
INTRODUCTION: Medications cannot exert their effect if not taken as prescribed by patients. Our objective was to summarise the observational evidence on adherence to oral anticoagulants (OACs) among patients with atrial fibrillation (AF). METHODS: In March 2019, we systematically searched PubMed/Medline, Embase, CINAHL and PsycINFO (from inception) for observational studies measuring adherence, its determinants and impacts in patients with AF. Mean adherence measures and corresponding proportions of adherent patients were pooled using random effects models. Factors shown to be independently associated with adherence were extracted as well as the clinical and economic outcomes of adherence. RESULTS: We included 30 studies. Pooled mean adherence scores of over half a million patients with AF 6 months and 1 year after therapy initiation were 77 (95% CI: 74-79) and 74 (68-79) out of 100, respectively. Drug-specific pooled mean adherence score at 6 months and 1 year were as follows: rivaroxaban: 78 (73-84) and 77 (69-86); apixaban: 77 (75-79) and 82 (74-89); dabigatran: 74 (69-79) and 75 (68-82), respectively. There was inadequate information on warfarin for inclusion in meta-analysis.Factors associated with increased adherence included: older age, higher stroke risk, once-daily regimen, history of hypertension, diabetes or stroke, concomitant cardiovascular medications, living in rural areas and being an experienced OAC user. Non-adherent patients were more likely to experience stroke and death, and incurred higher medical costs compared with patients with poor adherence. CONCLUSIONS: Our findings show that up to 30% of patients with AF are non-adherent, suggesting an important therapeutic challenge in this patient 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 | 0.022 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.007 | 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) | 0.002 | 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".