A pharmacist-led heart failure stewardship initiative for guideline-directed medical therapy in hospitalized patients with reduced ejection fraction
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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%; <i>p</i> < 0.001), mineralocorticoid receptor antagonists (9.5% to 66.7%; <i>p</i> < 0.001) and combination GDMT (9.5% to 52.4%; <i>p</i> = 0.004) from admission to discharge. A statistically significant increase in the prescribing of β-blockers (47.6% to 76.2%; <i>p</i> = 0.004) and angiotensin-converting enzyme inhibitors (21.4% to 40.5%; <i>p</i> = 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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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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 it