Navigating between Scylla and Charybdis: challenges and strategies for implementing guideline‐directed medical therapy in heart failure with reduced ejection fraction
Bibliographic record
Abstract
Guideline-directed medical therapy (GDMT) has the potential to reduce the risks of mortality and hospitalisation in patients with heart failure (HF) with reduced ejection fraction (HFrEF). However, real-world data indicate that many patients with HFrEF do not receive optimised GDMT, which involves several different medications, many of which require up-titration to target doses. There are many challenges to implementing GDMT, the most important being patient-related factors (comorbidities, advanced age, frailty, cognitive impairment, poor adherence, low socioeconomic status), treatment-related factors (intolerance, side-effects) and healthcare-related factors that influence availability and accessibility of HF care. Accordingly, international disparities in resources for HF management and limited public reimbursement of GDMT, coupled with clinical inertia for treatment intensification combine to hinder efforts to provide GDMT. In this review paper, authors aim to provide solutions based on available evidence, practical experience, and expert consensus on how to utilise evolving strategies, novel medications, and patient profiling to allow the more comprehensive uptake of GDMT. Authors discuss professional education, motivation, and training, as well as patient empowerment for self-care as important tools to overcome clinical inertia and boost GDMT implementation. We provide evidence on how multidisciplinary care and institutional accreditation can be successfully used to increase prescription rates and adherence to GDMT. We consider the role of modern technologies in advancing professional and patient education and facilitating patient-provider communication. Finally, authors emphasise the role of novel drugs (especially sodium-glucose co-transporter 2 inhibitors), and a tailored approach to drug management as evolving strategies for the more successful implementation of GDMT.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".