Impact of macitentan on the health-related quality of life (HRQoL) in pulmonary arterial hypertension (PAH): Results from a long-term randomised controlled trial
Bibliographic record
Abstract
The impact of macitentan, a novel endothelin receptor antagonist, on HRQoL was assessed in patients enrolled in the SERAPHIN study (randomised 1:1:1 to placebo, macitentan 3 or 10mg q.d.). Literate PAH patients (≥14 years), in WHO functional class II–IV, answered the Short Form 36-item (SF-36v2) at baseline, 6 and 12 months, and end-of-treatment (EOT). Placebo-corrected treatment effects for change from baseline to Month 6 and 12 are reported for the physical (PCS) and mental component summary (MCS) scores of the SF-36, with missing data imputed. Kaplan–Meier analyses of the time to a ≥5-point decrease (considered clinically relevant) from baseline in the PCS and MCS scores up to EOT were performed. Treatment groups were compared using log rank tests. Both doses of macitentan showed significant treatment effects vs placebo on the PCS and MCS at 6 and 12 months. Placebo-corrected treatment effect (mean [95% CI]) on PCS and MCS* Month 6 Month 12 Macitentan 3mg Macitentan 10mg Macitentan 3mg Macitentan 10mg PCS 2.7 (1.1-4.3) 3.0 (1.5-4.5) 2.4 (0.8-4.1) 2.4 (0.9-4.0) MCS 3.5 (1.3-5.8) 3.4 (1.2-5.6) 3.5 (1.2-5.9) 2.6 (0.2-4.9) *Normalised to the US 1998 general population Both doses reduced the risk of HRQoL deterioration, as measured by the time to ≥5-point decrease in the PCS (3mg: HR 0.70, 95%CI 0.54–0.92, P=0.008; 10mg: HR 0.65, 95%CI 0.50–0.85, P=0.001) and the MCS scores (3mg: HR 0.81, 95%CI 0.63–1.03, P=0.085; 10mg: HR 0.79, 95%CI 0.61–1.01, P=0.053) across the total study duration. In conclusion, macitentan improved HRQoL scores vs placebo and reduced the risk of decline in HRQoL over the duration of SERAPHIN.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".