Predicting how pulmonary hypertension changes over time in patients
Observes adults with pulmonary hypertension, combining scans, tests and clinical data to predict risk.
Plain-language title generated from the registry record
Registry title: PH-DyPred: A Multimodal Dynamic Risk Prediction Study in Pulmonary Hypertension
Key facts
- Where
- Fuzhou, ChinaSee the site
Where in China
- The First Affiliated Hospital of Fujian Medical UniversityFuzhou, ChinaTaking 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
- First Affiliated Hospital of Fujian Medical University
- Trial code
- NCT07131241
- Record updated
- 6 October 2026
- Original description
Pulmonary hypertension (PH) is a progressive cardiopulmonary disease characterized by elevated pulmonary artery pressure and vascular remodeling, which leads to right heart failure and increased mortality. Despite advances in diagnostics, risk stratification remains limited due to the disease's heterogeneity. This study aims to develop and validate a dynamic risk prediction model for PH by integrating multimodal data-including echocardiography, Cardiac MRI, PET-MR, ECG, biomarkers, and clinical features-using advanced machine learning algorithms. The study will establish a prospective cohort of PH patients to explore predictive markers, stratify prognosis, and provide a scientific basis for early warning and individualized management.Read full original descriptionShow less
Research on Dynamic Risk Prediction for Patients With Pulmonary Hypertension Based on Multimodal Data Fusion: A Prospective Observational Study
Pulmonary hypertension (PH) is a progressive cardiopulmonary disease characterized by elevated pulmonary artery pressure and vascular remodeling, which leads to right heart failure and increased mortality. Despite advances in diagnostics, risk stratification remains limited due to the disease's heterogeneity. This study aims to develop and validate a dynamic risk prediction model for PH by integrating multimodal data-including echocardiography, Cardiac MRI, PET-MR, ECG, biomarkers, and clinical features-using advanced machine learning algorithms. The study will establish a prospective cohort of PH patients to explore predictive markers, stratify prognosis, and provide a scientific basis for early warning and individualized management.
Inclusion: Adults aged 18 years or older; Pulmonary artery systolic pressure (PASP) ≥35 mmHg as estimated by echocardiography; Provided written informed consent.
Exclusion: Major surgery within the past 3 months; Severe hepatic or renal insufficiency; Malignancy under active treatment; Severe infection; Active autoimmune disease; Severe psychiatric disorder impairing ability to comply with the study protocol; Pregnant or breastfeeding women.