The impact of health-system pharmacists on hospitalizations in heart failure: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Previous evidence has established the role of pharmacists in heart failure (HF) care. However, the specific role of health-system pharmacists within in- and outpatient settings for HF patients has been left unexplored. This systematic review and meta-analysis aimed to evaluate the impact of health-system pharmacy interventions on all-cause and HF hospitalizations. Methods A systematic literature search was performed using PUBMED and EMBASE, following PRISMA guidelines. Randomized controlled trials (RCTs) published up to May 2024 that evaluated the effects of health-system pharmacy interventions on hospitalizations in HF patients were included. The quality of the included RCTs was assessed using Cochrane’s risk-of-bias tool. Meta-analyses were performed using random-effects models, with odds ratios (OR) as summary measure. Heterogeneity was assessed using the I 2 statistic and Cochrane’s Q test. Results In total, 11 RCTs involving 3576 patients were included in our review. The meta-analysis of 9 RCTs assessing all-cause hospitalizations (3472 patients, 927 events) demonstrated a significant reduction with pharmacist care (OR 0.67, 95% CI: 0.49–0.92, p=0.0119). The second meta-analysis of also 9 RCTs, focusing on HF hospitalizations (3442 patients, 504 events), showed similar results (OR 0.64, 95% CI: 0.48–0.87, p=0.0038). Heterogeneity was moderate for both meta-analyses. Sensitivity analyses confirmed the robustness of the results. Subgroup analyses indicated greater effectiveness in outpatient settings and for extended interventions. Conclusions Health-system pharmacist interventions significantly reduce both all-cause and HF-specific hospitalizations in HF patients. Our findings highlight the importance of integrating pharmacists into multidisciplinary teams to improve HF management for in- and outpatients (PROSPERO: CRD42024593583).
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.046 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".