AI-based screening models for pulmonary arterial hypertension in people with systemic sclerosis

"Artificial Intelligence in PAH-SSc (ARENAS) "

Observational Hospital Universitario 12 de Octubre · NCT07236970

This project will test whether artificial intelligence models using clinical, lab, ECG, and echocardiogram data can better screen adults with systemic sclerosis for pulmonary arterial hypertension.

Quick facts

Study typeObservational
Enrollment350 (estimated)
Ages18 Years to 80 Years
SexAll
SponsorHospital Universitario 12 de Octubre Academic / other
Locations5 sites (Granada, Andalusia and 4 other locations)
Trial IDNCT07236970 on ClinicalTrials.gov

What this trial studies

This multicenter observational study will develop and compare several artificial intelligence algorithms that use clinical, laboratory, pulmonary function, electrocardiographic, and echocardiographic data to identify pulmonary arterial hypertension (PAH) in patients with systemic sclerosis (SSc). Algorithms will be trained on retrospectively and/or prospectively collected data and compared with existing classical screening programs, then externally validated across hospitals in Spain. Cases are defined by right heart catheterization using the updated hemodynamic criteria and controls are SSc patients without PAH (with limited inclusion of other PH types). The study also explores circulating proteins related to metabolic pathways as secondary biomarkers to complement the models.

Who should consider this trial

Good fit: Adults (≥18) with a clinical diagnosis of systemic sclerosis who can provide informed consent are ideal, including both patients with right-heart-catheterization–confirmed PAH and SSc patients without PAH for controls.

Not a fit: People without sufficient clinical/laboratory/ECG/echo data, minors, or patients whose pulmonary hypertension is from non-SSc causes are unlikely to benefit from these screening models.

Why it matters

Potential benefit: If successful, the approach could enable earlier and more accurate detection of PAH in people with SSc, allowing earlier treatment and potentially better outcomes.

How similar studies have performed: Classical screening programs for SSc-PAH have improved early detection and outcomes, but applying artificial intelligence to this screening task is relatively new and has limited prior external validation.

Eligibility criteria

Show full inclusion / exclusion criteria
Inclusion Criteria:

* Age ≥ 18 years
* Clinical diagnosis of systemic sclerosis (SSc) according to ACR/EULAR criteria
* For controls (SSc without PAH): absence of pulmonary arterial hypertension; patients with isolated or combined post-capillary pulmonary hypertension (pulmonary capillary pressure \> 15 mmHg) or Group 3 pulmonary hypertension may be included, limited to 20% of this group
* For cases (SSc-associated PAH): confirmed PAH by right heart catheterization (mean pulmonary arterial pressure \> 20 mmHg, pulmonary capillary pressure \< 15 mmHg, pulmonary vascular resistance \> 2 Wood Units)

Exclusion Criteria:

* Missing data in the main variables at diagnosis (clinical assessment, blood tests, electrocardiogram, transthoracic echocardiogram).
* Inability to provide informed consent

Where this trial is running

Granada, Andalusia and 4 other locations

How to participate

  1. Review the eligibility criteria above with your treating physician.
  2. Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
  3. Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.
Conditions Pulmonary HypertensionSystemic SclerosisSystemic Sclerosis-Associated PAHalgorhythmartificial intelligencemachine learning
Last reviewed 2026-06-13 by the Find a Trial editorial team. Information on this page is for educational purposes and is not medical advice. Always consult qualified healthcare professionals about clinical trial participation.