Effect of dapagliflozin in patients with heart failure on reducing cardiovascular mortality in federal project on the prevention of cardiovascular diseases
Bibliographic record
Abstract
Aim. To assess the effect of dapagliflozin in patients with heart failure with reduced ejection fraction (HFrEF) on reducing cardiovascular mortality as the main goal of a federal project on the prevention of cardiovascular diseases. Material and methods. All adult Russian patients with a documented NYHA class II-IV HFrEF (EF £40%) were considered the target population. The characteristics of the patients corresponded to those of the Russian Hospital Heart Failure Registry (RUS-HFR). The study looked at an increase in the dapagliflozin use in addition to standard therapy by 10% of patients annually in 2021-2023 and calculated the number of deaths that could be prevented. Cardiovascular mortality curve was created by extrapolation of the DAPA-HF study results using the Kaplan-Meier method. Further, the contribution of prevented deaths with dapagliflozin to the achievement of regional and federal targets for reducing cardiovascular mortality was calculated for 1, 2, and 3 years. Results. In case of 10% annual increase in dapagliflozin use in patients with NYHA class II-IV HFrEF, this will allow: — to prevent an additional 1,736 cardiovascular deaths in the first year, achieving the target of federal project on the prevention of cardiovascular diseases in 2021 by 5,9%; — to prevent an additional 3,784 cardiovascular deaths in the second year, achieving the target of federal project on the prevention of cardiovascular diseases in 2022 by 12,9%; — to prevent an additional 5,485 cardiovascular deaths in the third year, achieving the target of federal project on the prevention of cardiovascular diseases in 2023 by 18,7%. Conclusion. The use of dapagliflozin in patients with HFrEF will reduce mortality from cardiovascular diseases.
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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) | 0.001 | 0.001 |
| 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) | 0.000 | 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".