Riociguat for the treatment of pulmonary arterial hypertension (PAH): A responder analysis from the phase III PATENT-1 study
Bibliographic record
Abstract
Background In PATENT-1, riociguat significantly improved 6-min walking distance (6MWD) and a range of secondary endpoints, including hemodynamics, NT-proBNP, and WHO functional class (FC), in patients (pts) with PAH. For several of these endpoints, threshold criteria have been defined that correlate with favorable clinical outcome. Aims To investigate the proportion of pts who fulfilled these criteria in PATENT-1. Methods PATENT-1 was a double-blind randomized trial in which pts with PAH received 12 wks’ oral treatment with placebo, an individual titration of riociguat (up to 2.5 mg tid), or a capped titration of riociguat (up to 1.5 mg tid). Increase in 6MWD ≥40 m, 6MWD ≥380 m, cardiac index (CI) ≥2.5 L/min/m 2 , PVR <500 dyn·s·cm -5 , mixed venous oxygen saturation (SvO 2 ) ≥65%, FC I/II, and NT-proBNP <1800 pg/mL were chosen as criteria of a positive response based on studies showing their prognostic relevance at baseline (BL) and after targeted therapy. Results Similar proportions of pts met the selected criteria in the riociguat and placebo groups at baseline. The proportion of pts who met these criteria at Wk 12 was increased in the riociguat group, while it remained unchanged or decreased in the placebo group. Criteria, % Riociguat individual titration Placebo N BL Wk 12 N BL Wk 12 6MWD increase ≥40 m 254 n/a 43 126 n/a 23 6MWD ≥380 m 254 45 63 126 58 55 CI ≥2.5 L/min/m 2 233 45 76 108 48 44 PVR <500 dyn⋅s⋅cm -5 232 30 49 107 27 30 SvO 2 ≥65% 210 56 73 100 61 47 FC I/II 254 44 60 125 51 54 NT-proBNP <1800 pg/mL 228 82 89 106 79 73 Conclusions Riociguat increased the proportion of pts who fulfilled criteria defining a positive response to therapy.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".