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

NCT ID: NCT07131241

Last Updated: 2025-08-28

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Total Enrollment

1000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2025-06-27

Study Completion Date

2029-06-30

Brief Summary

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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

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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.

Conditions

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Pulmonary Hypertension

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

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Suspected PH by Echocardiography

This study includes a prospective observational cohort of patients with suspected pulmonary hypertension (PH), identified by transthoracic echocardiography (TTE) showing a pulmonary artery systolic pressure (PASP) ≥35 mmHg. No experimental intervention will be applied. Participants will undergo comprehensive data collection, including echocardiography, cardiac magnetic resonance imaging (CMR), electrocardiography (ECG), laboratory testing, and biospecimen sampling (blood, urine, and stool). Follow-up will occur every 6 months for up to 3 years to record clinical outcomes and support the development of a dynamic, multimodal risk prediction model based on artificial intelligence.

No interventions assigned to this group

Eligibility Criteria

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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
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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First Affiliated Hospital of Fujian Medical University

OTHER

Sponsor Role lead

Responsible Party

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Dajun Chai

Professor and Chief Physician, Department of Cardiology

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Dajun Chai, MD

Role: PRINCIPAL_INVESTIGATOR

First Affiliated Hospital of Fujian Medical University

Locations

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The First Affiliated Hospital of Fujian Medical University

Fuzhou, Fujian, China

Site Status RECRUITING

Countries

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China

Central Contacts

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Dajun Chai, MD

Role: CONTACT

0086059187981637

Biyun Chen, MSc

Role: CONTACT

008613876168899

Facility Contacts

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Biyun Chen, MSc

Role: primary

008613876168899

References

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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.

Reference Type BACKGROUND
PMID: 39260943 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 40294712 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 32870447 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 29435739 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 22133851 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 38357903 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 37271771 (View on PubMed)

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 glutamine-serine metabolism control pathologic vascular stiffness. Cell Metab. 2024 Jun 4;36(6):1335-1350.e8. doi: 10.1016/j.cmet.2024.04.010. Epub 2024 May 2.

Reference Type BACKGROUND
PMID: 38701775 (View on PubMed)

Yorke J, Corris P, Gaine S, Gibbs JS, Kiely DG, Harries C, Pollock V, Armstrong I. emPHasis-10: development of a health-related quality of life measure in pulmonary hypertension. Eur Respir J. 2014 Apr;43(4):1106-13. doi: 10.1183/09031936.00127113. Epub 2013 Nov 14.

Reference Type BACKGROUND
PMID: 24232702 (View on PubMed)

Zhao W, Huang Z, Diao X, Yang Z, Zhao Z, Xia Y, Zhao Q, Sun Z, Xi Q, Huo Y, Xu O, Geng J, Li X, Duan A, Zhang S, Gao L, Wang Y, Li S, Luo Q, Liu Z. Development and validation of multimodal deep learning algorithms for detecting pulmonary hypertension. NPJ Digit Med. 2025 Apr 10;8(1):198. doi: 10.1038/s41746-025-01593-3.

Reference Type BACKGROUND
PMID: 40205021 (View on PubMed)

Rich S, Haworth SG, Hassoun PM, Yacoub MH. Pulmonary hypertension: the unaddressed global health burden. Lancet Respir Med. 2018 Aug;6(8):577-579. doi: 10.1016/S2213-2600(18)30268-6. Epub 2018 Jun 29. No abstract available.

Reference Type BACKGROUND
PMID: 30072105 (View on PubMed)

Humbert M, Kovacs G, Hoeper MM, Badagliacca R, Berger RMF, Brida M, Carlsen J, Coats AJS, Escribano-Subias P, Ferrari P, Ferreira DS, Ghofrani HA, Giannakoulas G, Kiely DG, Mayer E, Meszaros G, Nagavci B, Olsson KM, Pepke-Zaba J, Quint JK, Radegran G, Simonneau G, Sitbon O, Tonia T, Toshner M, Vachiery JL, Vonk Noordegraaf A, Delcroix M, Rosenkranz S; ESC/ERS Scientific Document Group. 2022 ESC/ERS Guidelines for the diagnosis and treatment of pulmonary hypertension. Eur Heart J. 2022 Oct 11;43(38):3618-3731. doi: 10.1093/eurheartj/ehac237. No abstract available.

Reference Type BACKGROUND
PMID: 36017548 (View on PubMed)

Study Documents

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Document Type: Individual Participant Data Set

For more information, please contact: [email protected]

View Document

Related Links

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https://www.fjmu.edu.cn

Description: Official website of the First Affiliated Hospital of Fujian Medical University, the lead institution responsible for this study. The site includes institutional information, clinical departments, research governance, and contact information.

Other Identifiers

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MRCTA,ECFAH of FMU[2025]716

Identifier Type: -

Identifier Source: org_study_id

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