Real-World Switching to Riociguat: Management and Practicalities in Patients with PAH and CTEPH
Bibliographic record
Abstract
PURPOSE: A proportion of patients with pulmonary arterial hypertension (PAH) and chronic thromboembolic pulmonary hypertension (CTEPH) do not achieve treatment goals or experience side effects on their current therapy. In such cases, switching patients to a new drug while discontinuing the first may be a viable and appropriate treatment option. CAPTURE was designed to investigate how physicians manage the switching of patients to riociguat in real-world clinical practice. Observations from the study were used to assess whether recommendations in the riociguat prescribing information are reflected in clinical practice. METHODS: CAPTURE was an international, multicenter, uncontrolled, retrospective chart review that collected data from patients with PAH or inoperable or persistent/recurrent CTEPH who switched to riociguat from another pulmonary hypertension (PH)-targeted medical therapy. The primary objective of the study was to understand the procedure undertaken in real-world clinical practice for patients switching to riociguat. RESULTS: Of 127 patients screened, 125 were enrolled in CAPTURE. The majority of patients switched from a phosphodiesterase type 5 inhibitor (PDE5i) to riociguat and the most common reason for switching was lack of efficacy. Physicians were already using the recommended treatment-free period when switching patients to riociguat from sildenafil, but a slightly longer period than recommended for tadalafil. In line with the contraindication, the majority of patients did not receive riociguat and PDE5i therapy concomitantly. Physicians also followed the recommended dose-adjustment procedure for riociguat. CONCLUSION: Switching to riociguat from another PH-targeted therapy may be feasible in real-world clinical practice in the context of the current recommendations.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".