"Artificial Intelligence in PAH-SSc (ARENAS) "
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 350
- 试验地点
- 5
- 主要终点
- Diagnostic accuracy of AI-based screening models for pulmonary arterial hypertension (PAH) in systemic sclerosis (SSc)
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Other
入排标准
- 年龄范围
- 18 Years 至 80 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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)
排除标准
- •Missing data in the main variables at diagnosis (clinical assessment, blood tests, electrocardiogram, transthoracic echocardiogram).
- •Inability to provide informed consent
结局指标
主要结局
Diagnostic accuracy of AI-based screening models for pulmonary arterial hypertension (PAH) in systemic sclerosis (SSc)
时间窗: 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
时间窗: 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
时间窗: Baseline and 24 months
Change in quality-of-life scores measured with validated questionnaires from baseline to follow-up.
次要结局
- Correlation of right ventricular global longitudinal strain with event-free survival in SSc-PAH(From baseline to 24 months)
- Correlation of circulating activina A with event-free survival in SSc-PAH(From baseline to 24 months.)
- Correlation of circulating inhibina alfa with event-free survival in SSc-PAH(From baseline to 24 months)
- Correlation of circulating FSTL3 with event-free survival in SSc-PAH(From baseline to 24 months)
- Correlation of circulating activina B with event-free survival in SSc-PAH(From baseline to 24 months)
- Correlation of circulating follistatin with event-free survival in SSc-PAH(From baseline to 24 months)
- Correlation of TAPSE with event-free survival in SSc-PAH(From baseline to 24 months)
- Correlation of pulmonary artery systolic pressure with event-free survival in SSc-PAH(From baseline to 24 months)
- Correlation of RVFAC with event-free survival in SSc-PAH(From baseline to 24 months)
- Correlation of right ventricular free wall strain with event-free survival in SSc-PAH(From baseline to 24 months)
研究者
Alejandro Cruz Utrilla
Cardiologist Consultant in the Pulmonary Hypertension Unit, Principal Investigator
Hospital Universitario 12 de Octubre