Heart Failure Spending Function: An Investment Framework for Sequencing and Intensification of Guideline-Directed Medical Therapies
Bibliographic record
Abstract
Heart failure with reduced ejection fraction is managed with increasing numbers of guideline-directed medical therapies (GDMT). Benefits tend to be additive. Burdens can also be additive. We propose a heart failure spending function as a conceptual framework for tailored intensification of GDMT that maximizes therapeutic opportunity while limiting adverse events and patient burden. Each patient is conceptualized to have reserve in physiological and psychosocial domains, which can be spent for a future return on investment. Key domains are blood pressure, heart rate, serum creatinine, potassium, and out-of-pocket costs. For each patient, GDMT should be initiated and intensified in a sequence that prioritizes medications with the greatest expected cardiac benefit while drawing on areas where the patient has ample reserves. When reserve is underspent, patients fail to gain the full benefit of GDMT. Conversely, when a reserve is fully spent, addition of new drugs or higher doses that draw upon a domain will lead to patient harm. The benefit of multiple agents drawing upon varied physiological domains should be balanced against cost and complexity. Thresholds for overspending are explored, as are mechanisms for implementing these concepts into routine care, but further health care delivery research is needed to validate and refine clinical use of the spending function. The heart failure spending function also suggests how newer therapies may be considered in terms of relative value, prioritizing agents that draw on different spending domains from existing GDMT.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".