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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".