Adherence and Quality of Life in Non-Valvular Atrial Fibrillation With Direct Oral Anticoagulants Versus Vitamin K Antagonists: A Systematic Review
Bibliographic record
Abstract
Abstract Background Direct anticoagulants (DOACs) and vitamin K antagonists (VKAs) differ in pharmacokinetic characteristics, intensity of required laboratory monitoring, and costs. These differences could affect patients' adherence to treatment and quality of life (QoL). Objective To assess whether patients with non-valvular atrial fibrillation (AF) using DOACs have better treatment adherence and QoL when compared to patients using VKAs. Methods We conducted a systematic review in Medline, Embase, LILACS, SciELO, CINAHL, and Cochrane Central, until June 9, 2021. We included studies that estimated and compared treatment adherence and QoL between DOACs and VKAs in adults with non-valvular AF. The methodological quality of the studies was assessed using the Joanna Briggs Institute (JBI) tools. The protocol was registered in the PROSPERO (CRD 42020165238). Results Sixteen studies, including 122,458 patients with non-valvular AF, evaluated adherence, and eleven studies, including 5,687 patients, assessed QoL. A variety of methods was used to measure adherence. Eleven studies showed no difference in adherence between DOACs and VKAs, while three studies favored VKAs over DOACs and two studies favored DOACs over VKAs. QoL was measured by specific (n = 3) or generic questionnaires (n = 8); results favored DOACs over VKAs in four studies, while in the other seven studies the results showed no difference between the groups. Meta-analyses were not performed due to high methodological heterogeneity among studies. Conclusions Available evidence regarding treatment adherence and QoL with DOACs and VKAs is characterized by methodological heterogeneity and conflicting findings.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".