PH-DyPred: Multimodal dynamic risk prediction in pulmonary hypertension
Research on Dynamic Risk Prediction for Patients With Pulmonary Hypertension Based on Multimodal Data Fusion: A Prospective Observational Study
First Affiliated Hospital of Fujian Medical University · NCT07131241
This project will try to build a prediction model using scans, ECGs, blood tests, and clinical data to see which adults with pulmonary hypertension are at higher risk of hospitalization, worsening symptoms, or death.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 1000 (estimated) |
| Ages | 18 Years and up |
| Sex | All |
| Sponsor | First Affiliated Hospital of Fujian Medical University (other) |
| Locations | 1 site (Fuzhou, Fujian) |
| Trial ID | NCT07131241 on ClinicalTrials.gov |
What this trial studies
This prospective observational cohort will enroll adults with pulmonary hypertension and collect serial multimodal data including echocardiography, cardiac MRI, PET-MR, ECG, blood biomarkers, and clinical information. Biospecimens (whole blood, serum, plasma, urine, stool) will be banked for multi-omics analyses and linked with imaging and outcomes using de-identified codes. Machine learning and data-fusion methods will be used to derive and internally and externally validate a dynamic prognostic model predicting hospitalization, functional decline, and mortality. The aim is to identify key predictive markers and enable individualized early-warning and management strategies.
Who should consider this trial
Good fit: Adults aged 18 or older with pulmonary artery systolic pressure ≥35 mmHg on echocardiography who can provide informed consent and attend baseline and follow-up visits are ideal candidates.
Not a fit: Patients with active cancer treatment, severe hepatic or renal failure, uncontrolled infection or autoimmune disease, recent major surgery, pregnancy, or those unable to complete follow-up may not receive benefit from the study findings.
Why it matters
Potential benefit: If successful, the model could provide earlier warnings and help clinicians tailor monitoring and treatment to reduce hospitalizations and slow disease progression.
How similar studies have performed: Previous work combining imaging, biomarkers, and machine learning has shown promising signals for risk stratification in pulmonary hypertension, but fully integrated multimodal models including PET-MR and multi-omics remain largely novel and unproven.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: * Adults aged 18 years or older * Pulmonary artery systolic pressure (PASP) ≥35 mmHg as estimated by echocardiography * Provided written informed consent Exclusion Criteria: * Severe hepatic or renal insufficiency * Malignancy under active treatment * Severe infection * Active autoimmune disease * Major surgery within the past 3 months * Pregnant or breastfeeding women * Severe psychiatric disorder impairing ability to comply with the study protocol
Where this trial is running
Fuzhou, Fujian
- The First Affiliated Hospital of Fujian Medical University — Fuzhou, Fujian, China (RECRUITING)
Study contacts
- Principal investigator: Dajun Chai, MD — First Affiliated Hospital of Fujian Medical University
- Study coordinator: Dajun Chai, MD
- Email: dajunchai-fy@fjmu.edu.cn
- Phone: 0086059187981637
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.
Conditions: Pulmonary Hypertension, Dynamic Risk Prediction, Multimodal Data Fusion, Echocardiography, Right Heart Function, Cardiac Magnetic Resonance