Pulmonary Arterial Hypertension Incidence in Patients With Systemic Sclerosis Treated With Bosentan for Digital Ulcers: Evidence From the SPRING-SIR Registry
Bibliographic record
Abstract
OBJECTIVE: Bosentan (BOS) is approved for treating pulmonary arterial hypertension (PAH) and preventing digital ulcers (DU) in systemic sclerosis (SSc). Our study aimed to evaluate whether BOS prescribed for DU could reduce the incidence of PAH in a large SSc cohort from the Systemic Sclerosis Progression Investigation (SPRING) registry. METHODS: Patients with SSc from the SPRING registry, meeting 2013 American College of Rheumatology/European Alliance of Associations for Rheumatology classification criteria with data on PAH onset, DU status, BOS exposure, and at least 1 year of follow-up between 2015 and 2020, and having no known PAH at baseline, were included. PAH was diagnosed with right heart catheterization during the follow-up, and its incidence rate (IR) was calculated. Kaplan-Meier curves were determined, and multivariate regression identified PAH risk factors. RESULTS: Among 727 eligible patients with SSc, followed for a median of 2.0 years, 54 (7.4%) developed PAH (IR 3.71 per 100 patient-years [PYs]). Patients with DU who were never exposed to BOS had a higher incidence of PAH (IR 4.90 per 100 PYs) compared to those exposed to BOS, whose rates matched those without DU and who were never exposed to BOS. Risk factors independently associated with PAH development included DU (hazard ratio [HR] 1.86), age (HR 1.05), modified Rodnan skin score > 4 (HR 2.07), interstitial lung disease (HR 2.29), and acetylsalicylic acid treatment (HR 1.78). CONCLUSION: In our cohort, the presence of DU was confirmed as a leading risk factor for PAH development, and BOS use for DU prevention may reduce this risk. Only patients with DU who were not using BOS had an increased PAH incidence.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".