Evaluation of <scp>PHARM‐HF</scp>, a pharmacist‐led heart failure medication titration clinic: A pre‐post study
Bibliographic record
Abstract
Abstract Introduction The PHARM‐HF clinic is a novel, pharmacist‐led medication optimization clinic for patients with heart failure with reduced ejection fraction (HFrEF). PHARM‐HF aims to achieve maximum‐tolerated HFrEF guideline‐directed medical therapy (GDMT) as outlined by the latest Canadian Cardiovascular Society heart failure guidelines. Methods This retrospective pre‐post study evaluated consecutive patients attending PHARM‐HF (January 2021–August 2022). The primary outcome was the modified Optimal Medication Therapy (mOMT) score, an aggregate score of HFrEF quadruple therapy. The mOMT score was categorized as suboptimal (score 0–4), acceptable (score 5–7), or optimal (score 8; all four drugs at maximum tolerated dose). Secondary outcomes included change in left ventricular ejection fraction (LVEF) from baseline to 1 year, and Kansas City Cardiomyopathy Questionnaire‐12 (KCCQ; range 0 [worst] to 100 [best]) from baseline to discharge. Results Of 81 included patients, median age was 68 years, 21% were female, 61% had New York Heart Association (NYHA) class 2 functional capacity, and median LVEF was 30%. Median mOMT improved from 6 (interquartile range [IQR] 4–7) at baseline to 8 (IQR 7–8) at clinic discharge (p < 0.001; increase from 7% to 73% categorized as optimal). LVEF improved from a median of 30% to 38% at 1 year (p < 0.001). Among 16 patients who completed the KCCQ‐12 at both time points, the score was 62 at baseline and 77 at discharge (p = 0.42). Conclusions Uptake of GDMT significantly increased from admission to discharge from the PHARM‐HF medication optimization clinic, during which other cardiovascular clinicians did not modify HFrEF medications. Furthermore, there was an improvement in LVEF at 1 year. A pilot randomized controlled trial is currently underway to guide the development of a multicenter trial to provide definitive evidence for the role of pharmacist‐led medication optimization in 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".