Evaluation of PHARM-HF, a pharmacist-led heart failure medication titration clinic
Bibliographic record
Abstract
Abstract Background Optimal medical therapy (OMT) is the bedrock of heart failure with reduced ejection fraction (HFrEF) management. The pharmacist-led PHARM-HF clinic provides rapid, pharmacist-led, telehealth-based initiation, titration, and optimization of medications for HFrEF. We evaluated OMT changes in patients from PHARM-HF referral to discharge. Methods This retrospective pre-post study evaluated patients attending PHARM-HF from January 2021 to February 2023. The primary outcome was a modified version of the OMT score, which integrates utilization and dosing of ACEi/ARBs/ARNI, beta-blockers, MRAs and SGLT2is. Secondary outcomes included OMT score categorized as suboptimal (score 0-4), acceptable (score 5-7) or optimal (score 8; all four drugs at max-tolerated dose); use of individual agents at 1 year follow-up; and change in left ventricular ejection fraction (LVEF) from baseline to 1-year follow-up. Change in modified OMT score from baseline to discharge was compared using the Wilcoxon signed rank test. Results We included 81 patients. Median age was 68 (interquartile range [IQR] 57-74), 21% were female, 44% in NYHA class 2, and median LVEF was 31% (IQR 25%-37%, range 0-8). Patients had a median 5 (IQR 4-7) encounters with the PHARM-HF clinic pharmacist. The median OMT score at baseline was 6 (IQR 4-7, range 0-8), which an improvement to a median score of 8 (IQR 8-8, range 2-8) at discharge (p<0.0001). At baseline, 7% of patients were categorized as optimal, which improved to 78% at discharge, with 16% of patients on target doses of all four medications. Medication use at 1 year follow-up was 83.4% for sacubitril-valsartan, 14.3% for ACEi/ARB, 97.6% for beta-blockers, 85.7% for MRA, 78.6% for SGLT2i. For 31 patients with baseline and 1-year LVEF, median LVEF improved from 30% (IQR 25%-37%) to 38% (IQR 34%-46%). Conclusions A pharmacist-led clinic increased prescription OMT uptake. Future multicentre studies should explore the role of pharmacists as collaborators and prescribers to optimize HFrEF care.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".