Allied health professional-physician collaboration to enhance guideline-directed medical therapy of heart failure: expanding therapeutic opportunities
Bibliographic record
Abstract
Abstract Background Guideline-directed medical therapy (GDMT) is the cornerstone of treatment for heart failure patients with reduced ejection fraction (HFrEF). Optimization of GDMT at a targeted allied professional medication titration clinic represents a key opportunity to provide greater access to care and further refine patient care models, leading to improvements in clinical outcomes. Purpose To evaluate the effectiveness of an allied health practitioner optimization clinic model in HFrEF as indicated by changes in rates of triple and quadruple GDMT therapy (see associated table). Methods Qualified patients were either: proactively screened from clinic lists, having demonstrated a diagnosis of heart failure, reduced ejection fraction, and not receiving triple or quadruple therapy, or were physician-referred after initial optimization during 2021-2022. Participants subsequently underwent medication review and optimization by an allied health professional, involving introduction, up-titration, down-titration, or withdrawal of medications across relevant medication classes as tolerated. Results 52 (78.8% male) patients with a mean age of 70.3 (11.4) years and mean ejection fraction of 30.1 (6.7) percent underwent medication optimization. Along with HFrEF, all patients exhibited cardio-metabolic multi-morbidity (2-3 cardio-metabolic diagnoses), including hypertension (59.6%), dyslipidemia (71.2%), coronary artery disease (61.5%), atrial fibrillation (42.3%), and type 2 diabetes (21.2%). Pre-optimization prescription rates ranged from: 53.8% for ACEi/ARB, 30.8% for ARNI, 80.8% for beta-blockers, 59.6% for MRA, 40.4% for diuretics, and 13.5% for SGLT2i. Medication review and optimization spanned an average of 2.8 (1-7) meetings, and lead to an average of 1.9 (range 0-4) medication changes. Among 49 patients considered for ARNI optimization, there were 43 (87.7%) successful introductions and progressive dose adjustments, including 12 (27.9%) dose adjustments among those already prescribed. Additional changes involved optimizations in: beta-blockers (5 introductions, 5 dose-changes), MRA (8 introductions, 4 dose-changes, 2 withdrawals), diuretics (1 introduction, 7 dose-changes, 2 withdrawals), and SGLT2i (17 introductions). Collaboration between allied health professionals and physicians in optimization demonstrated significant improvements in rates of triple (42.3% vs 63.5%, P= 0.003) and quadruple (5.8% vs 26.9%, P= 0.001) therapy post-optimization. Discussion A Collaborative approach to medication optimization increased the use of GDMT in ambulatory patients with HFrEF. Collaboration presents both clinicians and health systems with greater opportunities to improve healthcare access, therapeutic inertia, and disease management.Pre-post Changes in GDMT Rates
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".