Long-Term Data on Efficacy and Safety of Selexipag for Digital Systemic Sclerosis Vasculopathy
Bibliographic record
Abstract
Objective Raynaud phenomenon (RP) and digital ulcers (DUs) are the main signs of digital vasculopathy in systemic sclerosis (SSc). Selexipag is an oral prostacyclin agonist approved for SSc-related pulmonary arterial hypertension. Following our previous preliminary short-course report, we herein present long-term data on selexipag safety and efficacy in the treatment of SSc digital vasculopathy. Methods Selexipag was administered to patients with SSc with severe digital vasculopathy refractory or with contraindication to all other vasoactive therapies. Each subject was assessed at baseline and after 3, 6, and 12 months. Clinical outcomes related to RP and DUs were evaluated along with modified Rodnan skin score of the fingers. Digital perfusion was assessed by laser speckle contrast analysis (LASCA). Nailfold videocapillaroscopy (NVC) was also performed. Results Eight patients with SSc (63% female, mean age 50.1 years) received selexipag. After 12 months of treatment, RP was reported to significantly decrease in the number of daily episodes and mean duration (P< 0.001 andP= 0.01, respectively). All patients achieved a complete healing of their DUs (P= 0.03) within 6 months. A progressive reduction of fingers skin score was observed (P= 0.03). No structural changes of capillaries were noted on NVC. Conversely, LASCA revealed an important increase in total digital perfusion (P= 0.004) despite seasonal variability. The safety profile was consistent with that reported in the literature. Conclusion We observed a sustained efficacy of selexipag on SSc digital vasculopathy during 1 year of administration. Our promising results encourage the design of a new randomized controlled trial to evaluate the effect of selexipag on SSc digital vasculopathy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".