Evaluating initiation and real-world tolerability of dapagliflozin for the management of HFrEF
Bibliographic record
Abstract
Untreated heart failure with reduced ejection fraction (HFrEF) has a one-year mortality rate of 40%. The DAPA-HF trial found that dapagliflozin reduces mortality and heart failure (HF) hospitalisation by 17% and 30%, respectively. We describe the initiation and real-world tolerability of dapagliflozin for the management of HFrEF at a large university teaching hospital in central London. We reviewed 118 HFrEF patients initiated on dapagliflozin from January to August 2021 in both inpatient and outpatient settings using the Trust's electronic records. A total of 69 (58.4%) patients were on optimised HF pharmacological therapy upon initiation of dapagliflozin. Dapagliflozin was discontinued in 12 (13.0%) patients. Twenty-three (42.6%) patients either discontinued or had a dose reduction in loop diuretics post-initiation of dapagliflozin. In clinical practice, early initiation of dapagliflozin is safe, well-tolerated and resulted in earlier discontinuation or dose reduction in loop diuretics, providing opportunities to further optimise other HF medicines. This retrospective observational study supports the safety of the updated European Society of Cardiology (ESC) guidelines to initiate all four key HF medicines to minimise delays in HF treatment optimisation, which could translate to reduced National Health Service healthcare costs through fewer HF hospitalisations.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".