Cost-Effectiveness of Sacubitril/Valsartan Compared with Enalapril in Patients with Heart Failure with Reduced Ejection Fraction: A Systematic Review
Bibliographic record
Abstract
Background: To assess the cost-effectiveness of sacubitril/valsartan compared with enalapril in patients with heart failure with reduced ejection (HFrEF). Methods: A systematic literature search was conducted searching in major electronic databases from inception to January 1, 2021. All relevant full economic evaluation studies of sacubitril/valsartan versus enalapril for the treatment of patients with HFrEF were identified using ad hoc search strategies. Mortality, hospital admissions, quality-adjusted life years (QALYs), life-years (LYQs), annual drug costs, total lifetime costs, and incremental cost-effectiveness ratio (ICER) were considered as the outcomes. The quality of the included studies was assessed using the CHEERS checklist. This study was conducted and reported in accordance with the "Preferred Reporting Items for Systematic Reviews and Meta-Analyses" (PRISMA) guidelines. Results: The initial search yielded a pool of 1026 articles, of which 703 unique articles were screened, 65 full-text articles were assessed for eligibility and 15 studies finally included in the qualitative synthesis. Studies show that sacubitril/valsartan reduces mortality and hospitalization rate. The mean of death risk ratio and hospitalization were computed at 0.843 and 0.844, respectively. Sacubitril/valsartan produced higher annual and total lifetime costs. The lowest and highest lifetime costs for sacubitril/valsartan were found in Thailand ($4,756) and Germany ($118,815), respectively. The lowest ICER was reported in Thailand ($4857/QALY) and the highest in the USA ($143,891/QALY). Conclusion: Sacubitril/valsartan is associated with better outcomes and may be cost-effective compared to enalapril for the management of HFrEF. However, in developing countries such as Thailand, sacubitril-valsartan costs must be reduced to yield an ICER below the threshold.
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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) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".