Pharmacotherapy sequencing strategies for patients with heart failure with reduced ejection fraction: a cost–utility analysis
Bibliographic record
Abstract
Background Standard pharmacotherapy for patients with heart failure with reduced ejection fraction (HFrEF) comprises “quadruple therapy” with a renin–angiotensin system inhibitor, a β-blocker, a mineralocorticoid receptor antagonist, and a sodium–glucose cotransporter-2 inhibitor. Randomized controlled trials evaluating different sequencing strategies for initiating and titrating these medications are lacking. We sought to compare costs for various HFrEF quadruple therapy sequencing strategies. Methods We used an individual-based state-transition microsimulation model to compare cost utility for 12 sequencing strategies with either weekly or biweekly medication adjustments for treatment-naive patients with HFrEF. We conducted a probabilistic analysis with a lifetime horizon from the public-payer perspective, along with a deterministic sensitivity analysis and 2 scenario analyses. We estimated costs, quality-adjusted life years (QALYs), incremental net monetary benefit (NMB) at a willingness-to-pay threshold of $50 000/QALY, and incremental cost-effectiveness ratios (ICERs) of each strategy versus the conventional approach. Results Over a lifetime, the 12 strategies resulted in costs ranging from $76 440 to $79 338, QALYs ranging from 9.55 to 9.72, and incremental NMB ranging from $2233 to $6529. Compared with the conventional sequencing strategy of starting and titrating biweekly 1 medication at a time, other strategies had ICERs ranging from $11 175 to $22 492. The simultaneous initiation of all 4 medications with subsequent biweekly adjustment had the highest probability of being the most cost-effective strategy at the specified willingness-to-pay threshold. Interpretation Simultaneous initiation of the 4 standard HFrEF medications with biweekly adjustment provided the highest cost utility compared with other strategies. Systems of care are needed to enable rapid initiation and sustained use of HFrEF medications.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".