Multicenter Study on the Development of Pulmonary Arterial Hypertension Screening Models Based on Artificial Intelligence for Patients With Systemic Sclerosis (ARENAS)

"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.

Study Overview

• Status: Recruiting
• Conditions: Pulmonary Hypertension; Systemic Sclerosis-Associated PAH; Systemic Sclerosis (SSc)

Detailed Description

Pulmonary arterial hypertension (PAH) is a rare and serious disease, affecting fewer than 50 people per million inhabitants. Its diagnosis requires right heart catheterization, an invasive procedure. PAH is a diverse condition and is often linked to autoimmune diseases such as systemic sclerosis (SSc), which affects about 277 people per million inhabitants in Spain, meaning that over 12,000 people may have the disease in the country. PAH develops in around 10% of SSc patients and is the main cause of death in this group. Although there is no cure, pulmonary vasodilator drugs have helped patients live longer, sometimes at the cost of reduced quality of life.

In more advanced stages of PAH, continuous intravenous or subcutaneous therapies are often needed. Traditional treatments mainly focus on widening the blood vessels in the lungs to reduce heart problems. More recently, new drugs have been developed that act directly on the mechanisms causing the disease, with the goal of improving blood flow in the lungs.

Artificial intelligence (AI) and a better understanding of disease mechanisms are changing healthcare. However, it is not yet known how useful AI might be in screening, diagnosing, and predicting outcomes in patients with SSc-associated PAH (SSc-PAH). In past decades, screening programs using clinical data, lab tests, and echocardiography have been developed to detect PAH before symptoms appear. These programs have helped identify patients earlier and reduce mortality. However, their low specificity can lead to many unnecessary right heart catheterizations. This problem may have increased since the 2022 update of pulmonary hypertension diagnostic criteria, which now use less strict hemodynamic thresholds, potentially making early diagnosis more difficult.

This is an ambispective observational study, combining retrospective data from existing patient records with prospective follow-up of newly enrolled patients.

The aim is to improve early detection of PAH in SSc patients by using AI-based algorithms that integrate simple and reproducible clinical data, such as electrocardiograms and echocardiograms. It is expected that these AI models will perform better than traditional screening programs, allowing earlier detection of PAH in many patients. Earlier and more accurate screening could also reduce the number of unnecessary invasive procedures, benefiting both clinical outcomes and patients' experience of their health.

The study will also examine protein expression in SSc-PAH patients, detailed measures of right heart function using echocardiography at rest and during exercise, and patient-reported health status. This will help determine how useful these factors are for predicting outcomes and for guiding treatment, supporting more personalized care and improving both clinical results and patient-reported health.

Through the collaboration of reference centers for pulmonary hypertension and systemic autoimmune diseases, together with patient associations, this study aims to ensure that many affected patients can access earlier and better care, ultimately improving survival and quality of life.

• Study Type: Observational
• Enrollment (Estimated): 350

Contacts and Locations

Study Locations

• Spain
  • Andalusia
    • Granada, Andalusia, Spain, 18007
      • Recruiting
      • Hospital Universitario Clínico San Cecilio
      • Contact: Marta Garcia Morales Department of Respiratory Medicine; Phone Number: 958023000; Email: info@ibsgranada.es
  • Cantabria
    • Santander, Cantabria, Spain, 39008
      • Recruiting
      • Hospital Universitario Marques de Valdecilla
      • Contact: Amaya Martínez Meñaca Department of Respiratory Medicine; Phone Number: 942202520; Email: amaya.martinez@scsalud.es
  • Catalonia
    • Barcelona, Catalonia, Spain, 08035
      • Recruiting
      • Hospital Universitario Vall d'Hebron
      • Contact: Manuel López Meseguer Department of Respiratory Medicine; Phone Number: 934893000; Email: manuel.lopez@vallhebron.cat
  • Madrid
    • Madrid, Madrid, Spain, 28034
      • Recruiting
      • Hospital Universitario Ramon y Cajal
      • Contact: Andrés Tenes Mayen Department of Respiratory Medicine; Phone Number: 913368000; Email: jtenes@salud.madrid.org
    • Madrid, Madrid, Spain, 28041
      • Recruiting
      • Hospital Universitario 12 de Octubre
      • Contact: Alejandro Cruz Utrilla, MD, PhD; Phone Number: 635765340; Email: acruzutrilla@gmail.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

The study population consists of patients with systemic sclerosis (SSc), with and without pulmonary arterial hypertension (PAH), recruited from the Spanish national PAH registry (REHAP), the systemic sclerosis registry at the Rheumatology Department, and the CSUR national referral unit for pulmonary hypertension at Hospital Universitario 12 de Octubre, as well as participating centers.

Cohort 1 (development cohort): 300 SSc patients without PAH and 50 SSc patients with confirmed PAH, retrospectively selected from 2016 to 2024.

Cohort 2 (external validation cohort): 200 SSc patients without PAH and 50 with PAH-SSc, prospectively recruited from participating centers and REHAP during the first 2 years of the study.

Cohort 3 (prognostic cohort): 100 patients with PAH-SSc prospectively recruited from REHAP and participating centers between January 2025 and December 2026.

Description

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

Study Plan

How is the study designed?

Number of groups / cohorts

3

Cohorts and Interventions

Group / Cohort
Cohort 1; Development cohort for an AI model based on widely available clinical data. 300 controls with Systemic Sclerosis (SSc) without Pulmonary Hypertension (PAH) and 50 cases of SSc with PAH
Cohort 2; External validation of the screening model: 200 controls with SSc without PAH and 50 cases with PAH associated with SSc
Cohort 3; Prognostic models including protein analysis, cardiac imaging, PREMS and PROMS: 100 patients with PAH-SSc

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Diagnostic accuracy of AI-based screening models for pulmonary arterial hypertension (PAH) in systemic sclerosis (SSc)	Sensitivity, specificity, and area under the ROC curve (AUC) of machine learning and Bayesian network-based algorithms compared with classical screening algorithms, using right heart catheterization as the diagnostic gold standard.	At baseline (cross-sectional assessment at study entry)
Event-free survival in patients with systemic sclerosis-associated PAH	Time to first clinical event defined as all-cause mortality, hospitalization due to PAH, or clinical worsening (progression of WHO functional class, decline in 6-minute walk distance, or worsening hemodynamics).	Up to 24 months of follow-up
Patient-reported quality of life in systemic sclerosis-associated PAH	Change in quality-of-life scores measured with validated questionnaires from baseline to follow-up.	Baseline and 24 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Correlation of circulating activina A with event-free survival in SSc-PAH	Correlation between plasma concentration of activina A (pg/mL, measured by ELISA) and event-free survival (time to death, hospitalization for PAH, or clinical worsening) in patients with systemic sclerosis-associated PAH.	From baseline to 24 months.
Correlation of circulating activina B with event-free survival in SSc-PAH	Correlation between plasma concentration of activina B (pg/mL, measured by ELISA) and event-free survival in patients with SSc-PAH.	From baseline to 24 months
Correlation of circulating inhibina alfa with event-free survival in SSc-PAH	Correlation between plasma concentration of inhibina alfa (pg/mL, measured by ELISA) and event-free survival in patients with SSc-PAH.	From baseline to 24 months
Correlation of circulating follistatin with event-free survival in SSc-PAH	Correlation between plasma concentration of follistatin (pg/mL, measured by ELISA) and event-free survival in patients with SSc-PAH.	From baseline to 24 months
Correlation of circulating FSTL3 with event-free survival in SSc-PAH	Correlation between plasma concentration of FSTL3 (pg/mL, measured by ELISA) and event-free survival in patients with SSc-PAH.	From baseline to 24 months
Correlation of RVFAC with event-free survival in SSc-PAH	Correlation between RVFAC (%) measured by 2D transthoracic echocardiography and event-free survival (time to death, hospitalization for PAH, or clinical worsening).	From baseline to 24 months
Correlation of TAPSE with event-free survival in SSc-PAH	Correlation between TAPSE (cm, measured by 2D echocardiography) and event-free survival in patients with SSc-PAH.	From baseline to 24 months
Correlation of right ventricular global longitudinal strain with event-free survival in SSc-PAH	Correlation between RV global longitudinal strain (%) measured by 2D echocardiography and event-free survival in patients with SSc-PAH.	From baseline to 24 months
Correlation of right ventricular free wall strain with event-free survival in SSc-PAH	Correlation between RV free wall strain (%) measured by 2D echocardiography and event-free survival in patients with SSc-PAH	From baseline to 24 months
Correlation of pulmonary artery systolic pressure with event-free survival in SSc-PAH	Correlation between PSAP (mmHg, measured by 2D echocardiography at rest and during exercise) and event-free survival in patients with SSc-PAH.	From baseline to 24 months

Collaborators and Investigators

• Sponsor: Alejandro Cruz Utrilla

Study record dates

Study Major Dates

• Study Start (Actual): May 30, 2025
• Primary Completion (Estimated): December 1, 2026
• Study Completion (Estimated): December 1, 2027

Study Registration Dates

• First Submitted: September 29, 2025
• First Submitted That Met QC Criteria: November 17, 2025
• First Posted (Actual): November 19, 2025

Study Record Updates

• Last Update Posted (Actual): November 19, 2025
• Last Update Submitted That Met QC Criteria: November 17, 2025
• Last Verified: November 1, 2025

More Information

Terms related to this study

• Keywords: machine learning; Pulmonary Hypertension; artificial intelligence; Systemic Sclerosis; algorhythm
• Additional Relevant MeSH Terms: Vascular Diseases; Cardiovascular Diseases; Connective Tissue Diseases; Respiratory Tract Diseases; Lung Diseases; Skin Diseases; Hypertension; Skin and Connective Tissue Diseases; Scleroderma, Systemic; Hypertension, Pulmonary
• Other Study ID Numbers: 25/046_ARENAS; PI24/1880 (Other Grant/Funding Number: Instituto de Salud Carlos III (ISCIII))

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No