Dapagliflozin and Recurrent Heart Failure Hospitalizations in Heart Failure With Reduced Ejection Fraction: An Analysis of DAPA-HF
Bibliographic record
Abstract
Background: Patients with heart failure (HF) and reduced ejection fraction will experience multiple hospitalizations for heart failure during the course of their disease. We assessed the efficacy of dapagliflozin on reducing the rate of total (ie, first and repeat) hospitalizations for heart failure in the DAPA-HF trial (Dapagliflozin and Prevention of Adverse-Outcomes in Heart Failure). Methods: The total number of HF hospitalizations and cardiovascular deaths was examined by using the proportional-rates approach of Lei-Wei-Yang-Ying and a joint frailty model for each of recurrent HF hospitalizations and time to cardiovascular death. Variables associated with the risk of recurrent hospitalizations were explored in a multivariable Lei-Wei-Yang-Ying model. Results: Of 2371 participants randomly assigned to placebo, 318 experienced 469 hospitalizations for HF; of 2373 assigned to dapagliflozin, 230 patients experienced 340 admissions. In a multivariable model, factors associated with a higher risk of recurrent HF hospitalizations included higher heart rate, higher N-terminal pro-B-type natriuretic peptide, and New York Heart Association class. In the Lei-Wei-Yang-Ying model, the rate ratio for the effect of dapagliflozin on recurrent HF hospitalizations or cardiovascular death was 0.75 (95% CI, 0.65–0.88), P =0.0002. In the joint frailty model, the rate ratio for total HF hospitalizations was 0.71 (95% CI, 0.61–0.82), P <0.0001, whereas, for cardiovascular death, the hazard ratio was 0.81 (95% CI, 0.67–0.98), P =0.0282. Conclusions: Dapagliflozin reduced the risk of total (first and repeat) HF hospitalizations and cardiovascular death. Time-to-first event analysis underestimated the benefit of dapagliflozin in HF and reduced ejection fraction. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT03036124.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".