Impact of pharmacy-supported interventions on proportion of patients receiving non-indicated acid suppressive therapy upon discharge: A systematic review and meta-analysis

Objective: Conduct a systematic review and meta-analysis to estimate the impact of pharmacy-supported interventions on the proportion of patients discharged from the hospital on inappropriate acid suppressive therapy (AST). Methods: To identify studies, the following databases were systematically searched on October 14th, 2018 and repeated on September 12th, 2019: Ovid MEDLINE(R) and In-Process & Other Non-Indexed Citations and Daily, Embase.com, CINAHL, Web of Science, Cochrane CENTRAL (EBSCO), and ClinicalTrials.gov. Eligible studies consisted of adults, intervention and historical/usual care groups, description of active pharmacy-supported intervention, and proportion of patients discharged on inappropriate AST. Qualitative assessments and quantitative analyses were performed. Modified funnel plot analysis assessed heterogeneity. Preferred reporting items of systematic reviews and meta-analyses (PRISMA) methodology was used to evaluate studies in this review. Results: Seventeen publications resulting in 16 studies were included in the review. Using random effects model, meta-analysis showed a significant reduction in the odds of being discharged on inappropriate AST from the hospital in the pharmacist-supported intervention arm versus comparator (Odds Ratio 0.33 [95%CI 0.20 to 0.53]), with significant heterogeneity (I2 = 86%). Eleven studies favored pharmacy-supported interventions, four were inconclusive and one favored usual care. Using modified funnel plot analysis, our final evaluation was distilled to 11 studies and revealed a similar outcome (OR 0.36 [95%CI 0.27 to 0.48]), but with less heterogeneity (I2 = 36%). Conclusion: This systematic review and meta-analysis showed that pharmacy-supported interventions were associated with a significantly reduced probability of patients discharged on inappropriate AST. However, heterogeneity was high and may affect interpretation of results. Using funnel plot optimization method, three positive and two negative studies were objectively removed from analyses, resulting in a similar effect size, but with less heterogeneity. To improve study quality, future researchers should consider utilizing a pre-post, multi-arm, prospective design with sampling randomization, training of data extractors (preferably two extractors), re-evaluating a small dataset to check for agreement and providing a comprehensive methodology in subsequent publications.