Using artificial intelligence to screen for lung high blood pressure in systemic sclerosis
Studies AI models to screen for pulmonary arterial hypertension in adults with systemic sclerosis.
Plain-language title generated from the registry record
Registry title: Multicenter Study on the Development of Pulmonary Arterial Hypertension Screening Models Based on Artificial Intelligence for Patients With Systemic Sclerosis
Key facts
- Where
- Barcelona, Granada, Madrid, and Santander, SpainSee the 5 sites
Could this be for you?
Answer for yourself. This is not a decision: only the study team can say whether you can take part.
- Are you between 18 and 80 years old?
Source: NCT07236970, who can take part
Where in Spain
- Hospital Universitario Vall d'HebronBarcelona, SpainTaking people
- Hospital Universitario Clínico San CecilioGranada, SpainTaking people
- Hospital Universitario 12 de OctubreMadrid, SpainTaking people
- Hospital Universitario Ramón y CajalMadrid, SpainTaking people
- Hospital Universitario Marqués de ValdecillaSantander, SpainTaking people
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- Alejandro Cruz Utrilla
- Trial code
- NCT07236970
- Record updated
- 19 November 2025
- Original description
Pulmonary Arterial Hypertension (PAH) is a rare and severe condition that can be associated with Systemic Sclerosis (SSc), significantly worsening the prognosis of the latter disease. Screening programs based on clinical, laboratory, pulmonary function test, electrocardiographic, and echocardiographic data have been shown to enable earlier diagnosis and improve the prognosis of PAH associated with SSc. However, the hemodynamic criteria for the diagnosis of PAH have recently changed, and the usefulness of these screening programs in this new context is unknown.Read full original descriptionShow less
"Artificial Intelligence in PAH-SSc (ARENAS) "
Pulmonary Arterial Hypertension (PAH) is a rare and severe condition that can be associated with Systemic Sclerosis (SSc), significantly worsening the prognosis of the latter disease. Screening programs based on clinical, laboratory, pulmonary function test, electrocardiographic, and echocardiographic data have been shown to enable earlier diagnosis and improve the prognosis of PAH associated with SSc. However, the hemodynamic criteria for the diagnosis of PAH have recently changed, and the usefulness of these screening programs in this new context is unknown.
The primary objective of this study is to develop a PAH screening program in patients with SSc through the use of different artificial intelligence algorithms, comparing these algorithms with classical screening programs. These algorithms will be externally validated in different hospitals in Spain.
As secondary objectives, the study will assess the usefulness of various proteins involved in the metabolic pathways related to the development of PAH, as well as certain parameters of right ventricular function and measures of quality-of-life impact, in the prognostic evaluation of PAH associated with SSc.
To this end, simple and reproducible clinical data will be used, such as electrocardiogram, echocardiogram, and different quality-of-life scales obtained from major PAH and SSc registries. Machine learning techniques and Bayesian networks will be applied to generate artificial intelligence models for screening and prognostic assessment.
Inclusion: 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: Missing data in the main variables at diagnosis (clinical assessment, blood tests, electrocardiogram, transthoracic echocardiogram); Inability to provide informed consent.