Pharmacist interventions in hypertension care: Network meta-analysis to identify what works best
Bibliographic record
Abstract
Abstract Background Hypertension management remains a major public health challenge. Recent hypertension guidelines recommend the involvement of pharmacists and other nonphysician healthcare providers for team-based care management. Nevertheless, implementation of pharmacist interventions is lacking and knowledge of which interventions work best is needed. We are therefore performing a network meta-analysis to compare the effectiveness of different pharmacist interventions on blood pressure (BP) among outpatients with hypertension. Methods We have completed systematic searches of randomized controlled trials assessing the effect of pharmacist interventions, alone or in collaboration, on BP among outpatients with hypertension compared to usual care. The outcome was the change in systolic and diastolic BP. We compared the effectiveness of different types of pharmacist interventions based on the Cochrane Effective Practice and Organisation of Care (EPOC) classification. The systematic review with simple pairwise meta-analysis is registered in PROSPERO (CRD42021279751) and published in an open-access peer-reviewed journal. Network meta-analysis with random effects is ongoing to identify which interventions worked best to decrease BP. Results Out of 2,330 study records identified by searches of electronic databases, we included 95 studies, with 31,168 participants, published between 1973 and 2023. Pharmacist interventions included patient education in 88%, feedback to healthcare providers in 49%, and patient reminders in 24% of the studies. Meta-analysis showed a reduction of − 5.3 mmHg (95% CI: −6.3 to − 4.4; I2 = 86%) in systolic BP and −2.3 mmHg (95% CI: −2.9 to − 1.8; I2 = 75%) in diastolic BP. The network meta-analysis is ongoing and results will be presented at the congress. Conclusions Pharmacist interventions were on average effective to decrease BP. This network meta-analysis aims to identify which type of pharmacist interventions work best to improve hypertension management. Key messages • Recent hypertension guidelines recommend pharmacist involvement in hypertension care management. • This network meta-analysis aims to identify which type of pharmacist interventions in hypertension management work best.
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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 | 0.049 | 0.105 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.030 | 0.082 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".