The role of pharmacists in the management of patients with atrial fibrillation: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: Pharmacist-led interventions have demonstrated benefits across various medical conditions; however, their impact on atrial fibrillation (AF) remains unexplored. This study aims to synthesize the available evidence regarding the pharmacist’s role in AF management. Methods: A systematic review with searches in PubMed, Scopus, and Web of Science was performed (PROSPERO: CRD42025647848). Randomized and non-randomized trials, as well as cohort studies reporting clinical, process, and humanistic outcomes, were included. Findings were pooled through pairwise meta-analyses. Dichotomous outcomes were reported as risk ratios (RRs) and continuous variables as standardized mean differences (SMDs) with 95% confidence intervals (CIs). The quality of the randomized and non-randomized studies was assessed using RoB 2.0 and ROBINS-I tools, respectively. Evidence was graded using the GRADE approach. Results: Seventeen studies (n = 11,428 participants) published between 2008 and 2024, predominantly as non-randomized trials/cohorts (77%), were included. Pharmacist-led interventions varied widely in scope, including anticoagulation management services, medication therapy management, and prescribing. Meta-analyses showed that pharmacists improved time in therapeutic range (SMD 0.35; 95% CI, 0.13–0.56) and reduced major bleeding events (RR 0.76; 95% CI, 0.61–0.95) and strokes (RR 0.65; 95% CI, 0.44–0.94) compared with usual care. Pharmacist care also increased appropriate prescription rates (RR 1.36, 95% CI, 1.18–1.56). No significant differences were found for other outcomes. Evidence was of low-to-moderate certainty. Interpretation: Pharmacist-led interventions have been shown to improve certain clinical and process outcomes in AF. Conclusions: High-quality randomized studies with well-defined interventions are still needed to better refine the pharmacist’s role in AF care and to identify the most effective intervention in practice (see Graphical Abstract). Can Pharm J (Ott) 2025;158:xx-xx.
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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 | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.047 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".