[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100603032":3},{"organization":4,"outcomesModule":7,"designInfo":95,"detailedDescription":99,"studyPopulation":100,"armGroups":101,"interventions":96,"overallOfficials":105,"centralContacts":109,"locations":118,"responsibleParty":135,"collaborators":96,"id":138,"slug":96,"hasResults":139,"nctId":140,"briefTitle":141,"officialTitle":142,"acronym":143,"eligibilityCriteria":144,"healthyVolunteers":139,"sex":145,"minAge":146,"maxAge":96,"enrollmentInfo":147,"targetDuration":150,"studyType":151,"phases":96,"briefSummary":152,"conditions":153,"keywords":155,"overallStatus":121,"whyStopped":96,"lastUpdateSubmitDate":161,"lastUpdatePostDateStruct":162,"startDateStruct":165,"completionDateStruct":167,"leadSponsor":169,"locationsCount":170},{"fullName":5,"class":6},"First Affiliated Hospital of Fujian Medical University","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":16,"otherOutcomes":56},[9,13],{"measure":10,"description":11,"timeFrame":12},"Time to clinical worsening","Defined as any of the following: hospitalization for PH, escalation of therapy, 6MWD decrease \\>15%, WHO-FC worsening, or death. Measured from baseline.","Up to 36 months",{"measure":14,"description":15,"timeFrame":12},"All-cause mortality","Death from any cause during follow-up, as confirmed by medical records or death registry.",[17,21,25,28,31,34,37,40,43,46,50,53],{"measure":18,"description":19,"timeFrame":20},"Composite risk score performance (AUC)","Area under the ROC curve for the multimodal model predicting adverse outcomes.","At baseline and follow-up every 6 months",{"measure":22,"description":23,"timeFrame":24},"Changes in NT-proBNP levels","Evaluate biomarker dynamics and predictive value.","Baseline, 6, 12, 24, 36 months",{"measure":26,"description":27,"timeFrame":12},"Hospitalization rate for PH-related causes","Frequency of hospital admissions due to PH complications.",{"measure":29,"description":30,"timeFrame":24},"Change in Tricuspid Annular Plane Systolic Excursion (TAPSE) Measured by Transthoracic Echocardiography","TAPSE (mm) will be measured via transthoracic echocardiography to evaluate longitudinal right ventricular systolic function over time.",{"measure":32,"description":33,"timeFrame":24},"Change in Right Ventricular Diameter Measured by Transthoracic Echocardiography","Right ventricular internal diameter (mm) will be assessed by echocardiography as an indicator of RV structural remodeling.",{"measure":35,"description":36,"timeFrame":24},"Change in Right Ventricular Fractional Area Change (RVFAC) Measured by Transthoracic Echocardiography","RVFAC (%) will be calculated as the percentage change in RV area between end-diastole and end-systole to assess systolic function.",{"measure":38,"description":39,"timeFrame":24},"Change in Right Ventricular Ejection Fraction (RVEF) Measured by Cardiac Magnetic Resonance Imaging","RVEF (%) will be quantified using cardiac magnetic resonance imaging to evaluate global systolic function.",{"measure":41,"description":42,"timeFrame":24},"Change in Right Ventricular End-Diastolic Volume Measured by Cardiac Magnetic Resonance Imaging","Right ventricular end-diastolic volume (mL) will be measured by CMR to assess structural remodeling over time.",{"measure":44,"description":45,"timeFrame":24},"Change in Right Ventricular Mass Measured by Cardiac Magnetic Resonance Imaging","Right ventricular mass (grams) will be measured by CMR as an index of ventricular hypertrophy and remodeling.",{"measure":47,"description":48,"timeFrame":49},"Change in Right Ventricular FAPI Uptake (SUVmean) Measured by FAPI PET-MR","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.","Baseline, 12, 24, and 36 months",{"measure":51,"description":52,"timeFrame":49},"Change in Right Ventricular FAPI Uptake (SUVmax) Measured by FAPI PET-MR","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.",{"measure":54,"description":55,"timeFrame":49},"Change in Right Ventricular FAPI Uptake Ratio Relative to Left Ventricle (SUVratio) Measured by FAPI PET-MR","The ratio of right ventricular to left ventricular myocardial FAPI uptake (SUVmean RV\u002FSUVmean LV) will be calculated as a normalized index of right ventricular fibrotic remodeling.",[57,61,65,68,71,74,77,80,83,86,89,92],{"measure":58,"description":59,"timeFrame":60},"Longitudinal changes in health-related quality of life (HRQoL) among patients with suspected or confirmed pulmonary hypertension","Health-related quality of life (HRQoL) will be assessed using validated instruments such as the EQ-5D-3L and\u002For 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","Baseline, 6, 12, 24, and 36 months",{"measure":62,"description":63,"timeFrame":64},"Area Under the ROC Curve (AUC) of the Multimodal Risk Prediction Model","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.","Baseline, 12, 24, 36 months",{"measure":66,"description":67,"timeFrame":64},"Harrell's Concordance Index (C-index) of the Multimodal Risk Prediction Model","The concordance index (C-index) will be computed to assess the predictive accuracy of the multimodal risk prediction model.",{"measure":69,"description":70,"timeFrame":64},"Calibration Slope of the Multimodal Risk Prediction Model","Calibration slope will be estimated to measure agreement between predicted and observed risks in the dynamic risk prediction model.",{"measure":72,"description":73,"timeFrame":64},"Net Benefit Derived from Decision Curve Analysis of the Multimodal Risk Prediction Model","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.",{"measure":75,"description":76,"timeFrame":60},"Correlation between NT-proBNP Concentration (pg\u002FmL) and Right Ventricular Ejection Fraction (RVEF)","NT-proBNP concentration will be measured in plasma using a standardized immunoassay (pg\u002FmL). Pearson correlation coefficients (r) will be calculated between NT-proBNP levels and right ventricular ejection fraction (RVEF, %) measured by cardiac MRI.\n\nUnit of Measure: Correlation coefficient (r)",{"measure":78,"description":79,"timeFrame":60},"Correlation between NT-proBNP Concentration (pg\u002FmL) and Right Ventricular End-Diastolic Volume (RVEDV)","NT-proBNP concentration measured by immunoassay (pg\u002FmL) 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.\n\nUnit of Measure: Correlation coefficient (r)",{"measure":81,"description":82,"timeFrame":60},"Correlation between NT-proBNP Concentration (pg\u002FmL) and TAPSE","NT-proBNP levels (pg\u002FmL) obtained by immunoassay will be correlated with tricuspid annular plane systolic excursion (TAPSE, mm) measured on transthoracic echocardiography using Pearson correlation.\n\nUnit of Measure: Correlation coefficient (r)",{"measure":84,"description":85,"timeFrame":60},"Correlation between NT-proBNP Concentration (pg\u002FmL) and Right Ventricular Longitudinal Strain","NT-proBNP values (pg\u002FmL) will be correlated with right ventricular longitudinal strain (%) measured by speckle-tracking echocardiography. Pearson correlation coefficients (r) will be reported.\n\nUnit of Measure: Correlation coefficient (r)",{"measure":87,"description":88,"timeFrame":60},"Correlation between NT-proBNP Concentration (pg\u002FmL) and Right Ventricular Fractional Area Change (RVFAC)","Plasma NT-proBNP level (pg\u002FmL) will be correlated with right ventricular fractional area change (RVFAC, %) measured using echocardiography, and correlation coefficients (r) will be calculated over time.\n\nUnit of Measure: Correlation coefficient (r)",{"measure":90,"description":91,"timeFrame":12},"Association between Baseline NT-proBNP Concentration and Time to Clinical Worsening","Baseline plasma NT-proBNP levels (pg\u002FmL), 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.\n\nUnit of Measure: Hazard ratio (HR)",{"measure":93,"description":94,"timeFrame":60},"Association between Baseline NT-proBNP Concentration and Model-Predicted Risk Categories","Baseline NT-proBNP concentration (pg\u002FmL), 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.\n\nUnit of Measure: Odds ratio (OR)",{"allocation":96,"interventionModel":96,"interventionModelDescription":96,"primaryPurpose":96,"observationalModel":97,"timePerspective":98,"maskingInfo":96},null,"COHORT","PROSPECTIVE","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.\n\nIn 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.\n\nHealth 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.","The study population includes adult patients (≥18 years) undergoing transthoracic echocardiography. Participants with a pulmonary artery systolic pressure (PASP) ≥35 mmHg on echocardiography will be included as suspected pulmonary hypertension cases. This cohort represents a real-world population at risk for or with early-stage pulmonary hypertension, suitable for developing dynamic risk prediction models based on multimodal data integration.",[102],{"label":103,"type":96,"description":104,"interventionNames":96},"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.",[106],{"name":107,"affiliation":5,"role":108},"Dajun Chai, MD","PRINCIPAL_INVESTIGATOR",[110,114],{"name":107,"role":111,"phone":112,"phoneExt":96,"email":113},"CONTACT","0086059187981637","dajunchai-fy@fjmu.edu.cn",{"name":115,"role":111,"phone":116,"phoneExt":96,"email":117},"Biyun Chen, MSc","008613876168899","heraty@sina.com",[119],{"facility":120,"status":121,"city":122,"state":123,"zip":124,"country":125,"countryCode":126,"cosmosGeoPoint":127,"geoPoint":132,"contacts":133},"The First Affiliated Hospital of Fujian Medical University","RECRUITING","Fuzhou","Fujian","350011","China","CN",{"type":128,"coordinates":129},"Point",[130,131],119.30611,26.06139,{"lat":131,"lon":130},[134],{"name":115,"role":111,"phone":116,"phoneExt":96,"email":117},{"type":108,"investigatorFullName":136,"investigatorTitle":137,"investigatorAffiliation":5,"oldNameTitle":96,"oldOrganization":96},"Dajun Chai","Professor and Chief Physician, Department of Cardiology","100603032",false,"NCT07131241","PH-DyPred: A Multimodal Dynamic Risk Prediction Study in Pulmonary Hypertension","Research on Dynamic Risk Prediction for Patients With Pulmonary Hypertension Based on Multimodal Data Fusion: A Prospective Observational Study","PH-DyPred","Inclusion Criteria:\n\n* Adults aged 18 years or older\n* Pulmonary artery systolic pressure (PASP) ≥35 mmHg as estimated by echocardiography\n* Provided written informed consent\n\nExclusion Criteria:\n\n* Severe hepatic or renal insufficiency\n* Malignancy under active treatment\n* Severe infection\n* Active autoimmune disease\n* Major surgery within the past 3 months\n* Pregnant or breastfeeding women\n* Severe psychiatric disorder impairing ability to comply with the study protocol","ALL","18 Years",{"count":148,"type":149},1000,"ESTIMATED","2 Years","OBSERVATIONAL","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.",[154],"Pulmonary Hypertension",[154,156,157,158,159,160],"Dynamic Risk Prediction","Multimodal Data Fusion","Echocardiography","Right Heart Function","Cardiac Magnetic Resonance","2025-08-27",{"date":163,"type":164},"2025-08-28","ACTUAL",{"date":166,"type":164},"2025-06-27",{"date":168,"type":149},"2029-06-30",{"name":5,"class":6},1]