PH-DyPred: A Multimodal Dynamic Risk Prediction Study in Pulmonary Hypertension

Official title

Research on Dynamic Risk Prediction for Patients With Pulmonary Hypertension Based on Multimodal Data Fusion: A Prospective Observational Study

Overview

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.

Detailed description

This is a prospective, observational cohort study designed to investigate dynamic risk prediction in patients diagnosed with pulmonary hypertension (PH). The study will collect multimodal clinical data-comprising imaging (echocardiography, cardiac MRI, PET-MR), electrocardiographic parameters, blood-based biomarkers, and demographic and clinical information-at baseline and follow-up intervals. The core objective is to develop a data fusion-based prognostic model capable of predicting adverse outcomes such as hospitalization, functional deterioration, or mortality. Machine learning methods will be employed to identify key predictive features. The model will be validated internally and externally across different subgroups. The study seeks to inform individualized risk-based decision-making and advance precision screening in PH care.

In addition, biospecimens will be collected to support comprehensive multi-omics profiling. Whole blood, serum, plasma, urine, and stool samples will be obtained and processed using standardized protocols. Blood-derived samples will be used for genomic, proteomic, metabolomic, and microRNA analyses; urine specimens will support metabolomic and renal biomarker assays; and stool samples will be used for gut microbiome sequencing. All biospecimens will be stored in a secure biobank and linked with clinical, imaging, and longitudinal follow-up data using de-identified subject codes to enable integrated multimodal analyses and facilitate future exploratory investigations of disease mechanisms and biomarker discovery.

Health economic evaluation, including cost-effectiveness and budget impact analyses, will be conducted using collected data on healthcare resource utilization, direct medical costs, and clinical outcomes to inform future policy and reimbursement decision-making.

Primary outcome measures

• Time to clinical worsening [Time frame: Up to 36 months]
• All-cause mortality [Time frame: Up to 36 months]

Secondary outcome measures (12)

• Composite risk score performance (AUC) [Time frame: At baseline and follow-up every 6 months]
• Changes in NT-proBNP levels [Time frame: Baseline, 6, 12, 24, 36 months]
• Hospitalization rate for PH-related causes [Time frame: Up to 36 months]
• Change in Tricuspid Annular Plane Systolic Excursion (TAPSE) Measured by Transthoracic Echocardiography [Time frame: Baseline, 6, 12, 24, 36 months]
• Change in Right Ventricular Diameter Measured by Transthoracic Echocardiography [Time frame: Baseline, 6, 12, 24, 36 months]
• Change in Right Ventricular Fractional Area Change (RVFAC) Measured by Transthoracic Echocardiography [Time frame: Baseline, 6, 12, 24, 36 months]
• Change in Right Ventricular Ejection Fraction (RVEF) Measured by Cardiac Magnetic Resonance Imaging [Time frame: Baseline, 6, 12, 24, 36 months]
• Change in Right Ventricular End-Diastolic Volume Measured by Cardiac Magnetic Resonance Imaging [Time frame: Baseline, 6, 12, 24, 36 months]
• Change in Right Ventricular Mass Measured by Cardiac Magnetic Resonance Imaging [Time frame: Baseline, 6, 12, 24, 36 months]
• Change in Right Ventricular FAPI Uptake (SUVmean) Measured by FAPI PET-MR [Time frame: Baseline, 12, 24, and 36 months]
• Change in Right Ventricular FAPI Uptake (SUVmax) Measured by FAPI PET-MR [Time frame: Baseline, 12, 24, and 36 months]
• Change in Right Ventricular FAPI Uptake Ratio Relative to Left Ventricle (SUVratio) Measured by FAPI PET-MR [Time frame: Baseline, 12, 24, and 36 months]

Eligibility 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

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Observational model

Cohort

Study locations

China · 1 center

• The First Affiliated Hospital of Fujian Medical University — Fuzhou

Publications

• Fauvel C, Gomberg-Maitland M, Benza RL. Risk Stratification in Pulmonary Hypertension: We Need to "GoDeeper"! Chest. 2024 Sep;166(3):420-422. doi: 10.1016/j.chest.2024.05.020. No abstract available. PMID 39260943
• Lorenzatti D, Motwani M. Cardiovascular magnetic resonance in pulmonary hypertension: Keeping it simple. Prog Cardiovasc Dis. 2025 May-Jun;90:116-118. doi: 10.1016/j.pcad.2025.04.010. Epub 2025 Apr 26. No abstract available. PMID 40294712
• Kjellstrom B, Lindholm A, Ostenfeld E. Cardiac Magnetic Resonance Imaging in Pulmonary Arterial Hypertension: Ready for Clinical Practice and Guidelines? Curr Heart Fail Rep. 2020 Oct;17(5):181-191. doi: 10.1007/s11897-020-00479-7. PMID 32870447
• Meyer GMB, Spilimbergo FB, Altmayer S, Pacini GS, Zanon M, Watte G, Marchiori E, Hochhegger B. Multiparametric Magnetic Resonance Imaging in the Assessment of Pulmonary Hypertension: Initial Experience of a One-Stop Study. Lung. 2018 Apr;196(2):165-171. doi: 10.1007/s00408-018-0097-7. Epub 2018 Feb 12. PMID 29435739
• van de Veerdonk MC, Kind T, Marcus JT, Mauritz GJ, Heymans MW, Bogaard HJ, Boonstra A, Marques KM, Westerhof N, Vonk-Noordegraaf A. Progressive right ventricular dysfunction in patients with pulmonary arterial hypertension responding to therapy. J Am Coll Cardiol. 2011 Dec 6;58(24):2511-9. doi: 10.1016/j.jacc.2011.06.068. PMID 22133851
• Small M, Perchenet L, Bennett A, Linder J. The diagnostic journey of pulmonary arterial hypertension patients: results from a multinational real-world survey. Ther Adv Respir Dis. 2024 Jan-Dec;18:17534666231218886. doi: 10.1177/17534666231218886. PMID 38357903
• Hameed A, Condliffe R, Swift AJ, Alabed S, Kiely DG, Charalampopoulos A. Assessment of Right Ventricular Function-a State of the Art. Curr Heart Fail Rep. 2023 Jun;20(3):194-207. doi: 10.1007/s11897-023-00600-6. Epub 2023 Jun 5. PMID 37271771
• Rachedi NS, Tang Y, Tai YY, Zhao J, Chauvet C, Grynblat J, Akoumia KKF, Estephan L, Torrino S, Sbai C, Ait-Mouffok A, Latoche JD, Al Aaraj Y, Brau F, Abelanet S, Clavel S, Zhang Y, Guillermier C, Kumar NVG, Tavakoli S, Mercier O, Risbano MG, Yao ZK, Yang G, Ouerfelli O, Lewis JS, Montani D, Humbert M, Steinhauser ML, Anderson CJ, Oldham WM, Perros F, Bertero T, Chan SY. Dietary intake and glutamin PMID 38701775

Identifiers

NCT: NCT07131241 · MRCTA,ECFAH of FMU[2025]716

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗