A Novel Approach to Medical Management of Heart Failure With Reduced Ejection Fraction
Bibliographic record
Abstract
The advent of newly available medical therapies for heart failure with reduced ejection fraction (HFrEF) has resulted in many potential therapeutic combinations, increasing treatment complexity. Publication of expert consensus guidelines and initiatives aimed to improve implementation of treatment has emphasized sequential stepwise initiation and titration of medical therapy, which is labour intensive. Data taken from heart failure registries show suboptimal use of medications, prolonged titration times, and consequently little change in dose intensity, all of which indicate therapeutic inertia. Recently published evidence indicates that 4 medication classes-renin-angiotensin-neprilysin inhibitors, β-blockers, mineralocorticoid antagonists, and sodium-glucose cotransporter inhibitors-which we refer to as Foundational Therapy, confer rapid and robust reduction in both morbidity and mortality in most patients with HFrEF and that they work in additive fashion. Additional morbidity and mortality may be observed following addition of several personalized therapies in specific subgroups of patients. In this review, we discuss mechanisms of action of these therapies and propose a framework for their implementation, based on several principles. These include the critical importance of rapid initiation of all 4 Foundational Therapies followed by their titration to target doses, emphasis on multiple simultaneous drug changes with each patient encounter, attention to patient-specific factors in choice of medication class, leveraging inpatient care, use of the entire health care team, and alternative (ie, virtual visits) modes of care. We have incorporated these principles into a Cluster Scheme designed to facilitate timely and optimal medical treatment for patients with 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| 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".