Effectiveness of Pharmacist-led Anticoagulation Management on Clinical Outcomes: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVES: We performed this systematic review and meta-analysis to confirm whether patients benefit more from pharmacist-led anticoagulation management than other models. METHODS: We searched PubMed, Embase, Cochrane Library and reference lists of yielded results conducted up to April 25, 2017. RCTs and observational cohort studies and case-control studies which compared the percentage of time within the target therapeutic range (TTR), the percentage of time within the expanded therapeutic range (TER), haemorrhage events, thrombosis events, mortality, patient satisfaction and/or medicine cost saving of pharmacist-led anticoagulation management with other models, and species were limited to humans. Two investigators evaluated methodology and extracted data from included studies independently. Data analysis were performed by STATA 12.0 software and quality of evidence assessment was performed by GRADEprofiler software. RESULTS: 8 RCTs and 9 observational cohort studies with 9919 patients were included eventually with high quality and no publication bias. In RCTs pooled results, TTR (p=0.548 moderate-quality), TER (p=0.285, moderate-quality), total haemorrhage events (p=0.140, low-quality), minor haemorrhage events (p=0.162, low-quality), major haemorrhage events (p=0.237, low-quality), thrombosis events (p=0.615, low-quality) and mortality (p=0.876, low-quality) was not significant between two groups. In observational studies pooled results, TTR (p=0.000, low-quality) was significant higher in pharmacist-led management group and the risk of total haemorrhage events (p=0.000, moderate-quality), minor haemorrhage events (p=0.000, moderate-quality) and thrombosis events (p=0.000, moderate-quality) were significant lower in pharmacist-led management group. Patient satisfaction and medicine cost saving were descriptively reviewed. CONCLUSIONS: According to the grading of evidence, we concluded that the risk of total haemorrhage events, minor haemorrhage events and thrombosis events significantly decreased in pharmacist-led anticoagulation management group compared with other management models and no significant difference in TTR, TER, major haemorrhage events and mortality between two groups. Longer follow-up period RCT studies with large sample size should be done in the future to confirm effectiveness of pharmacist-led anticoagulation management model. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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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 | 0.015 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.013 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".