Optimal Usage of Sacubitril/Valsartan for the Treatment of Heart Failure: The Importance of Optimizing Heart Failure Care in Canada
Bibliographic record
Abstract
BACKGROUND: Heart failure (HF) with reduced ejection fraction represents approximately 50% of the 600,000 Canadians currently living with HF and over 90,000 new cases diagnosed each year. The angiotensin receptor neprilysin inhibitor, sacubitril/valsartan, demonstrated superior efficacy in reducing cardiovascular death and HF hospitalization over standard of care therapy. METHODS: The potential magnitude of benefit in Canada with respect to preventing or postponing deaths and reducing hospitalizations resulting from its optimal implementation in patients with HF with an ejection fraction <40% was estimated based on published sources. RESULTS: Of the potentially eligible 225,562 patients, this would amount to the prevention of 4699 cardiovascular deaths and first HF hospitalizations, 3698 thirty-day HF readmissions, and 2820 deaths due to all-cause mortality. The number of patients receiving sacubitril/valsartan nationally in 2018 was 27,267. This represents approximately 12% of the calculated eligible population for this therapy in Canada. CONCLUSIONS: The findings from this analysis suggest that a substantial number of deaths, hospitalizations, and HF readmissions could potentially be avoided by optimal usage of sacubitril/valsartan therapy in Canada. This emphasizes the importance of rapidly and appropriately implementing evidence-based medications into routine clinical practice, to achieve the best possible outcomes for our patients with HF and to reduce the high burden and cost of HF in Canada.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".