Temporal trends in vascular medication use in 8079 patients with systemic sclerosis: insights to inform future trials and therapeutic strategies from the EUSTAR cohort
Bibliographic record
Abstract
OBJECTIVES: Systemic sclerosis (SSc) is characterized by widespread vascular damage resulting in digital and systemic vasculopathic sequelae. Although there are effective treatments available, vascular disease remains a significant cause of morbidity and mortality in SSc. Our aim was to describe patterns of vascular medication use in SSc, including examination for potential changes over time. METHODS: A cross-sectional study of SSc patients enrolled in the EUSTAR database meeting 2013 ACR/EULAR SSc criteria. Patients were divided into two time periods: 2012-2017 and 2018-2022. We analysed the prescription patterns of endothelin receptor antagonists (ERA), phosphodiesterase type-5 inhibitors (PDE5i), calcium channel blockers (CCB), intravenous iloprost, and antiplatelet therapies. Logistic regression was used to evaluate temporal trends and interaction effects. RESULTS: A total of 8079 patients were included. Significant increases over time were observed in the use of ERA (7% to 12%, P < 0.001), PDE5i (5.4% to 7.2%, P = 0.064), CCB (20% to 32%, P < 0.001) and anti-platelet therapies (15% to 20%, P < 0.001). There was a notable decrease in iloprost use (3.1% to 0.3%, P < 0.001). The prevalence of active digital ulcers (DU) decreased (16% to 13%, P = 0.040), while a history of DU (24% to 30%, P < 0.001) increased. Year-by-year and non-linear increases were noted for ERA and CCB whereas non-linear increase was observed for PDE5i. Year-by-year and non-linear decrease was observed for Iloprost prescription. CONCLUSION: A significant change has occurred over time in vascular medication use in SSc patients, with increased utilization of ERA, PDE5i, CCB and anti-platelet therapies suggesting the adoption of more proactive and/or preventive treatment strategies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".