Clinical trials / NCT07236970
Recruiting

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

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…

ClinicalTrials.gov IDNCT07236970
PhaseNot Applicable
Enrollment350
SponsorAlejandro Cruz Utrilla
Age18 Years
SexALL
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Conditions studied

Pulmonary Hypertension, Systemic Sclerosis (SSc), Systemic Sclerosis-Associated PAH

About this study

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.

Primary outcomes

Eligibility information

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 locations

Before you participate: Varda Clinical is an independent discovery tool, not the study sponsor or a medical provider. Eligibility can only be determined by the official study team. Discuss potential risks and benefits with a qualified healthcare professional.

Source: ClinicalTrials.gov. Record last refreshed by Varda Clinical: 2026-09-27.