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NCT Number: NCT07131241

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

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.

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Key information

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

The First Affiliated Hospital of Fujian Medical University

Fuzhou, Fujian, 350011, China

Location status: Recruiting

Location contact

Biyun Chen, MSc

CONTACT

[email protected]

008613876168899

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Primary outcomes

  1. Time to clinical worsening

    Time frame: Up to 36 months

    Defined as any of the following: hospitalization for PH, escalation of therapy, 6MWD decrease >15%, WHO-FC worsening, or death. Measured from baseline.

  2. All-cause mortality

    Time frame: Up to 36 months

    Death from any cause during follow-up, as confirmed by medical records or death registry.

Secondary outcomes

  1. Composite risk score performance (AUC)

    Time frame: At baseline and follow-up every 6 months

    Area under the ROC curve for the multimodal model predicting adverse outcomes.

  2. Changes in NT-proBNP levels

    Time frame: Baseline, 6, 12, 24, 36 months

    Evaluate biomarker dynamics and predictive value.

  3. Hospitalization rate for PH-related causes

    Time frame: Up to 36 months

    Frequency of hospital admissions due to PH complications.

  4. Change in Tricuspid Annular Plane Systolic Excursion (TAPSE) Measured by Transthoracic Echocardiography

    Time frame: Baseline, 6, 12, 24, 36 months

    TAPSE (mm) will be measured via transthoracic echocardiography to evaluate longitudinal right ventricular systolic function over time.

  5. Change in Right Ventricular Diameter Measured by Transthoracic Echocardiography

    Time frame: Baseline, 6, 12, 24, 36 months

    Right ventricular internal diameter (mm) will be assessed by echocardiography as an indicator of RV structural remodeling.

  6. Change in Right Ventricular Fractional Area Change (RVFAC) Measured by Transthoracic Echocardiography

    Time frame: Baseline, 6, 12, 24, 36 months

    RVFAC (%) will be calculated as the percentage change in RV area between end-diastole and end-systole to assess systolic function.

  7. Change in Right Ventricular Ejection Fraction (RVEF) Measured by Cardiac Magnetic Resonance Imaging

    Time frame: Baseline, 6, 12, 24, 36 months

    RVEF (%) will be quantified using cardiac magnetic resonance imaging to evaluate global systolic function.

  8. Change in Right Ventricular End-Diastolic Volume Measured by Cardiac Magnetic Resonance Imaging

    Time frame: Baseline, 6, 12, 24, 36 months

    Right ventricular end-diastolic volume (mL) will be measured by CMR to assess structural remodeling over time.

  9. Change in Right Ventricular Mass Measured by Cardiac Magnetic Resonance Imaging

    Time frame: Baseline, 6, 12, 24, 36 months

    Right ventricular mass (grams) will be measured by CMR as an index of ventricular hypertrophy and remodeling.

  10. Change in Right Ventricular FAPI Uptake (SUVmean) Measured by FAPI PET-MR

    Time frame: Baseline, 12, 24, and 36 months

    tandardized uptake value mean (SUVmean) of FAPI in the right ventricular free wall will be quantified using PET-MR imaging to assess fibroblast activation and myocardial fibrotic activity over time in patients with pulmonary hypertension.

  11. Change in Right Ventricular FAPI Uptake (SUVmax) Measured by FAPI PET-MR

    Time frame: Baseline, 12, 24, and 36 months

    Maximum standardized uptake value (SUVmax) of FAPI in the right ventricle will be measured via PET-MR as an indicator of peak regional fibroblast activation.

  12. 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

    The ratio of right ventricular to left ventricular myocardial FAPI uptake (SUVmean RV/SUVmean LV) will be calculated as a normalized index of right ventricular fibrotic remodeling.

Other outcomes

  1. Longitudinal changes in health-related quality of life (HRQoL) among patients with suspected or confirmed pulmonary hypertension

    Time frame: Baseline, 6, 12, 24, and 36 months

    Health-related quality of life (HRQoL) will be assessed using validated instruments such as the EQ-5D-3L and/or the emPHasis-10 questionnaire, both commonly used in pulmonary hypertension research. The emPHasis-10 is a disease-specific, patient-reported outcome measure developed for individuals with pulmonary hypertension, covering domains such as breathlessness, fatigue, social limitation, and psychological burden. Changes in HRQoL scores will be evaluated over time and correlated with clinical events (e.g., hospitalization, WHO functional class deterioration), imaging parameters (e.g., RV function), biomarkers (e.g., NT-proBNP), and model-predicted risk strata. Analyses will include repeated measures ANOVA or mixed-effects modeling to examine within-subject longitudinal chan

  2. Area Under the ROC Curve (AUC) of the Multimodal Risk Prediction Model

    Time frame: Baseline, 12, 24, 36 months

    The area under the receiver operating characteristic curve (AUC) will be calculated to evaluate the discrimination performance of the multimodal dynamic risk prediction model for adverse outcomes.

  3. Harrell's Concordance Index (C-index) of the Multimodal Risk Prediction Model

    Time frame: Baseline, 12, 24, 36 months

    The concordance index (C-index) will be computed to assess the predictive accuracy of the multimodal risk prediction model.

  4. Calibration Slope of the Multimodal Risk Prediction Model

    Time frame: Baseline, 12, 24, 36 months

    Calibration slope will be estimated to measure agreement between predicted and observed risks in the dynamic risk prediction model.

  5. Net Benefit Derived from Decision Curve Analysis of the Multimodal Risk Prediction Model

    Time frame: Baseline, 12, 24, 36 months

    Net benefit will be calculated using decision curve analysis to evaluate the clinical usefulness of the multimodal risk prediction model in guiding clinical decision-making.

  6. Correlation between NT-proBNP Concentration (pg/mL) and Right Ventricular Ejection Fraction (RVEF)

    Time frame: Baseline, 6, 12, 24, and 36 months

    NT-proBNP concentration will be measured in plasma using a standardized immunoassay (pg/mL). Pearson correlation coefficients (r) will be calculated between NT-proBNP levels and right ventricular ejection fraction (RVEF, %) measured by cardiac MRI.

    Unit of Measure: Correlation coefficient (r)

  7. Correlation between NT-proBNP Concentration (pg/mL) and Right Ventricular End-Diastolic Volume (RVEDV)

    Time frame: Baseline, 6, 12, 24, and 36 months

    NT-proBNP concentration measured by immunoassay (pg/mL) will be correlated with right ventricular end-diastolic volume (mL) assessed by cardiac MRI. Pearson correlation coefficients (r) will be calculated at each follow-up time point.

    Unit of Measure: Correlation coefficient (r)

  8. Correlation between NT-proBNP Concentration (pg/mL) and TAPSE

    Time frame: Baseline, 6, 12, 24, and 36 months

    NT-proBNP levels (pg/mL) obtained by immunoassay will be correlated with tricuspid annular plane systolic excursion (TAPSE, mm) measured on transthoracic echocardiography using Pearson correlation.

    Unit of Measure: Correlation coefficient (r)

  9. Correlation between NT-proBNP Concentration (pg/mL) and Right Ventricular Longitudinal Strain

    Time frame: Baseline, 6, 12, 24, and 36 months

    NT-proBNP values (pg/mL) will be correlated with right ventricular longitudinal strain (%) measured by speckle-tracking echocardiography. Pearson correlation coefficients (r) will be reported.

    Unit of Measure: Correlation coefficient (r)

  10. Correlation between NT-proBNP Concentration (pg/mL) and Right Ventricular Fractional Area Change (RVFAC)

    Time frame: Baseline, 6, 12, 24, and 36 months

    Plasma NT-proBNP level (pg/mL) will be correlated with right ventricular fractional area change (RVFAC, %) measured using echocardiography, and correlation coefficients (r) will be calculated over time.

    Unit of Measure: Correlation coefficient (r)

  11. Association between Baseline NT-proBNP Concentration and Time to Clinical Worsening

    Time frame: Up to 36 months

    Baseline plasma NT-proBNP levels (pg/mL), measured using a standardized immunoassay, will be analyzed using Cox proportional hazards models to assess their association with time to clinical worsening, defined as hospitalization for pulmonary hypertension, WHO functional class deterioration, or death.

    Unit of Measure: Hazard ratio (HR)

  12. Association between Baseline NT-proBNP Concentration and Model-Predicted Risk Categories

    Time frame: Baseline, 6, 12, 24, and 36 months

    Baseline NT-proBNP concentration (pg/mL), measured using a standardized immunoassay, will be analyzed using multinomial or logistic regression models to evaluate its association with model-predicted risk categories (e.g., low-, intermediate-, and high-risk strata) generated by the multimodal dynamic risk prediction model.

    Unit of Measure: Odds ratio (OR)

Study contacts

Contact information is provided by the study sponsor or research team.

Biyun Chen, MSc

CONTACT

[email protected]

008613876168899

Dajun Chai, MD

CONTACT

[email protected]

0086059187981637

Sponsors and collaborators

Lead sponsor

First Affiliated Hospital of Fujian Medical University

Other

Registry information

Official study title

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

Acronym: PH-DyPred

Important dates

Study start
2025
Primary completion
2028
Study completion
2029
First posted
Aug 20, 2025
Registry last updated
Aug 28, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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