Sacubitril/valsartan and quality of life assessed using the EuroQol Five-dimension Three-level questionnaire level sum score (EQ-5D-3L-LSS) in patients with HFrEF and HFmrEF/HFpEF
Bibliographic record
Abstract
AIMS: To investigate the EQ-5D-3L Level Sum Score (LSS) in patients with heart failure (HF) and reduced (HFrEF) and mildly reduced or preserved ejection fraction (HFmrEF/HFpEF) and the effect of sacubitril/valsartan on this score using patient-level data from the PARADIGM-HF and PARAGON-HF trials. METHODS AND RESULTS: The LSS was calculated by summing the three levels (1-3) for each of the five domains (minimum sum score = 5; maximum sum score = 15). Patient characteristics and outcomes were compared across LSS tertiles (T1-T3) at baseline. Cox models were used to evaluate the primary endpoint [first HF hospitalization or cardiovascular death (CVD)] according to tertiles of LSS. Changes in LSS severity at 8 months were analysed using ordinal logistic regression models to estimate the effect of sacubitril/valsartan vs. enalapril or valsartan. Of 13 195 patients, 12 974 had a baseline LSS. Compared to lower LSS, patients with higher (worse) scores were older, more often women and White, and had more comorbidities and more severe HF. At 8 months, patients assigned to sacubitril/valsartan experienced more improvement and less worsening of LSS vs. the comparator: OR:1.16 (95%CI: 1.08-1.24). Sacubitril/valsartan also reduced the risk of the primary outcome across LSS tertiles: T1: HR: 0.87 (95%CI: 0.75-1.00); T2: 0.80 (95%CI: 0.71-0.90); T3: 0.87 (95%CI: 0.77-0.97); Pinteraction = 0.59. Higher LSS was independently associated with a greater risk of the primary endpoint, and the achieved LSS at 8 months may be more strongly associated with subsequent outcomes. CONCLUSION: Sacubitril/valsartan significantly reduced the risk of HF events and improved health status across the LSS spectrum in HFrEF and HFmrEF/HFpEF. CLINICAL TRIAL REGISTRATION: https://www.clinicaltrials.gov. Unique identifiers: NCT01920711 (PARAGON-HF), NCT01035255 (PARADIGM-HF).
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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 | 0.001 |
| 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 | 0.001 |
| 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".