A pharmacist-led heart failure stewardship initiative for guideline-directed medical therapy in hospitalized patients with reduced ejection fraction
Bibliographic record
Abstract
Background:Heart failure with reduced ejection fraction (HFrEF) is a progressive disease with high rates of hospitalization and mortality. The Canadian Cardiovascular Society recommends treating patients with HFrEF with medications from 4 standard medication classes—this is known as guideline-directed medical therapy (GDMT). However, despite clear evidence and recommendations, GDMT agents are known to be underutilized in the HFrEF population.Objective:To determine if the implementation of a prescriber-alert stewardship tool for hospitalized patients with HFrEF will increase the frequency of GDMT prescribing with all classes during hospitalization.Methods:Utilization of GDMT in patients with HFrEF between admission and discharge pre- and post-implementation of a prescriber alert stewardship tool was compared. Patients admitted to a cardiology stepdown unit between January and April 2022 had a stewardship-alert tool placed on their chart for physician review, while those admitted during the same time frame 1 year prior did not.Results:Following the use of a prescriber alert, there was a statistically significant increase in prescribing for β-blockers (38.1% to 95.2%; p < 0.001), mineralocorticoid receptor antagonists (9.5% to 66.7%; p < 0.001) and combination GDMT (9.5% to 52.4%; p = 0.004) from admission to discharge. A statistically significant increase in the prescribing of β-blockers (47.6% to 76.2%; p = 0.004) and angiotensin-converting enzyme inhibitors (21.4% to 40.5%; p = 0.008) was still observed without the use of the prescriber alert.Conclusion:A pharmacist-led heart failure stewardship tool initiative increased uptake of GDMT in patients with HFrEF.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".