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Идёт набор NCT07131241

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

Наблюдательное Pulmonary Hypertension

Ориентир для пациента и семьи

Простыми словами

Автоматическая сводка по структурированным данным реестра. Она помогает сориентироваться, но не заменяет официальный протокол или оценку врача.

Что изучают
Это наблюдательное исследование: исследуемое лечение участникам по протоколу не назначают.
Кому может быть актуально
Состояния в реестре: Pulmonary Hypertension. Базовые параметры: от 18 лет · Все.
Что важно проверить
Возраст, диагноз и пол — только базовые ориентиры. Предыдущее лечение, анализы и другие обязательные условия указаны ниже в критериях участия.
Где проводится
Китай
Следующий шаг
Сохраните исследование, покажите его лечащему врачу и уточните актуальный статус у исследовательского центра. Расходы, документы и поездка →
Официальное название

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

Обзор

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.

Подробное описание

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.

Первичные конечные точки

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

Критерии участия

Критерии включения

  • Adults aged 18 years or older
  • Pulmonary artery systolic pressure (PASP) ≥35 mmHg as estimated by echocardiography
  • Provided written informed consent

Критерии исключения

  • 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

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Модель наблюдения
Когортное

Центры проведения

Китай · 1 центр
  • The First Affiliated Hospital of Fujian Medical University — Фучжоу

Публикации

  • 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

Идентификаторы

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

Первоисточники (государственные реестры)

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