Pulmonary HypertensionARENASNCT07236970
Multicenter Study on the Development of Pulmonary Arterial Hypertension Screening Models Based on Artificial Intelligence for Patients With Systemic Sclerosis
An observational study of Pulmonary Hypertension and Systemic Sclerosis (SSc), run by Alejandro Cruz Utrilla at 5 sites in Spain.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- Pulmonary Hypertension, Systemic Sclerosis (SSc), Systemic Sclerosis-Associated PAH
- How many people
- 350 participants
- Where
- 5 sitesSpain
- Study dates
- 30 May 2025to Dec 2027
- How long will I take part?
- 1 Day
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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)
Who cannot join
- Missing data in the main variables at diagnosis (clinical assessment, blood tests, electrocardiogram, transthoracic echocardiogram).
- Inability to provide informed consent
Trial locations and contacts
5 sites in 1 country. Contact the study team or a site near you to ask about taking part.
5 sites in 1 country
Spain5 sites, 5 recruiting
- Recruiting
Hospital Universitario Vall d'Hebron
Barcelona, Catalonia, 08035
Manuel López Meseguer Department of Respiratory Medicine, 934893000, manuel.lopez@vallhebron.cat
- Recruiting
Hospital Universitario Clínico San Cecilio
Granada, Andalusia, 18007
Marta Garcia Morales Department of Respiratory Medicine, 958023000, info@ibsgranada.es
- Recruiting
Hospital Universitario Ramón y Cajal
Madrid, Madrid, 28034
Andrés Tenes Mayen Department of Respiratory Medicine, 913368000, jtenes@salud.madrid.org
- Recruiting
Hospital Universitario 12 de Octubre
Madrid, Madrid, 28041
Alejandro Cruz Utrilla, MD, PhD, 635765340, acruzutrilla@gmail.com
- Recruiting
Hospital Universitario Marqués de Valdecilla
Santander, Cantabria, 39008
Amaya Martínez Meñaca Department of Respiratory Medicine, 942202520, amaya.martinez@scsalud.es
About this study
Official title: "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.
Read more
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.
Read the 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.
- Keywords
- Pulmonary Hypertension, Systemic Sclerosis, algorhythm, artificial intelligence, machine learning
- Medical subject headings
- Hypertension, Pulmonary, Scleroderma, Systemic
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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 being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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).
Measured: 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.
Measured: Baseline and 24 months
Secondary outcomes (10)
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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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).
Measured: 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.
Measured: 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.
Measured: 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
Measured: 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.
Measured: From baseline to 24 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Nov 2025
- Study start30 May 2025
- Primary completionDec 2026 (estimated)
- Study completionDec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Pulmonary HypertensionStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 350 participants (estimated)
- Target duration
- 1 Day
Sponsor and investigators
- Lead sponsor
- Alejandro Cruz Utrilla
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Alejandro Cruz Utrilla, Hospital Universitario 12 de Octubre
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07236970
- Sponsor's protocol ID
- 25/046_ARENAS
- Other IDs
- PI24/1880
- Acronym
- ARENAS
- Status last verified
- Nov 2025
- First submitted
- 29 Sep 2025
- Last updated
- 19 Nov 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 19 Nov 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- "Artificial Intelligence in PAH-SSc (ARENAS) "
- ClinicalTrials.gov ID
- NCT07236970
- Acronym / sponsor ID
- ARENAS / 25/046_ARENAS
- Keywords
- Pulmonary Hypertension, Systemic Sclerosis, algorhythm, artificial intelligence, machine learning
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 350 participants (estimated)
- Lead sponsor
- Alejandro Cruz UtrillaUniversity, hospital or other
- Study start
- 30 May 2025 (actual)
- Primary completion
- Dec 2026 (estimated)
- Study completion
- Dec 2027 (estimated)
- First posted
- 19 Nov 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Observational model
- Cohort
- Time perspective
- Other
- Target follow-up
- 1 Day
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| 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 |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (4 listed)
- 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)
Key exclusion criteria (2 listed)
- Missing data in the main variables at diagnosis (clinical assessment, blood tests, electrocardiogram, transthoracic echocardiogram).
- Inability to provide informed consent
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Diagnostic accuracy of AI-based screening models for pulmonary arterial hypertension (PAH) in systemic sclerosis (SSc)
Time frame: At baseline (cross-sectional assessment at study entry)
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.
Event-free survival in patients with systemic sclerosis-associated PAH
Time frame: Up to 24 months of follow-up
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).
Patient-reported quality of life in systemic sclerosis-associated PAH
Time frame: Baseline and 24 months
Change in quality-of-life scores measured with validated questionnaires from baseline to follow-up.
10 secondary outcomes
Correlation of circulating activina A with event-free survival in SSc-PAH
Time frame: From baseline to 24 months.
Correlation of circulating activina B with event-free survival in SSc-PAH
Time frame: From baseline to 24 months
Correlation of circulating inhibina alfa with event-free survival in SSc-PAH
Time frame: From baseline to 24 months
Correlation of circulating follistatin with event-free survival in SSc-PAH
Time frame: From baseline to 24 months
Correlation of circulating FSTL3 with event-free survival in SSc-PAH
Time frame: From baseline to 24 months
Correlation of RVFAC with event-free survival in SSc-PAH
Time frame: From baseline to 24 months
Correlation of TAPSE with event-free survival in SSc-PAH
Time frame: From baseline to 24 months
Correlation of right ventricular global longitudinal strain with event-free survival in SSc-PAH
Time frame: From baseline to 24 months
Correlation of right ventricular free wall strain with event-free survival in SSc-PAH
Time frame: From baseline to 24 months
Correlation of pulmonary artery systolic pressure with event-free survival in SSc-PAH
Time frame: From baseline to 24 months
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 19 Nov 2025.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
5 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Pulmonary Hypertension and Systemic Sclerosis (SSc), run by Alejandro Cruz Utrilla at 5 sites in Spain.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 19 Nov 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 5 sites in Spain. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic accuracy of AI-based screening models for pulmonary arterial hypertension (PAH) in systemic sclerosis (SSc) (time frame: At baseline (cross-sectional assessment at study entry)). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 19 Nov 2025.