Cost-Effectiveness of Earlier Transition to Angiotensin Receptor Neprilysin Inhibitor in Patients With Heart Failure and Reduced Ejection Fraction
Bibliographic record
Abstract
Background Angiotensin receptor neprilysin inhibitor (ARNi) therapy improves clinical outcomes in patients with heart failure and reduced left ventricular ejection fraction. However, ARNi therapy uptake remains modest, potentially in part due to perceived cost considerations of early transition from angiotensin converting enzyme inhibitor or angiotensin receptor blocker therapy. Methods We constructed a decision-analytic Markov model to assess cost-effectiveness of 3 different ARNi initiation strategies according to timing of initiation: (1) de novo , or immediate initiation at baseline, (2) Early or after 3 months, or (3) Late, or after 9 months. Initiation strategies were compared with (4) current care, with utilization of ARNi derived from a large observational database. Total costs, quality-adjusted life-years (QALYs), and the incremental cost-effectiveness ratio (ICER) were estimated over a 5-year time horizon in the base case analysis. Results Current care was associated with the lowest total cost (CAD$26,664) and accrued benefit (3.28 QALYs). The de novo strategy yielded an ICER of $34,727 per QALY gained, whereas Early and Late initiation strategies yielded a less favourable ICER per QALY gained of $35,871 and $40,234, respectively. The model was most sensitive to the cost of ARNi therapy. Conclusion A strategy of de novo ARNi initiation is economically attractive and becomes less favourable as the delay of initiation increases. Our results suggest that ARNi therapy should be initiated as soon as possible for patients with heart failure and reduced left ventricular ejection fraction.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".