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Идёт набор NCT05566002

Artificial Intelligence-assisted Evaluation of Pulmonary HYpertension

— Наблюдательное Pulmonary Hypertension Pulmonary Arterial 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
В протоколе указаны: Right heart catheterization.
Кому может быть актуально
Состояния в реестре: Pulmonary Hypertension, Pulmonary Arterial 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
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Официальное название

Artificial Intelligence-Assisted Evaluation of Pulmonary Hypertension

Обзор

Pulmonary hypertension represents a challenging and heterogeneous condition that is associated with high mortality and morbidity if left untreated. Artificial intelligence is used to study and develop theories and methods that simulate and extend human intelligence, which is being applied in fields related to cardiovascular diseases. The study intends to combine multimodal clinical data of patients who undergo right heart catheterization at Fuwai Hospital with artificial intelligence techniques to create programs that can screen and diagnose pulmonary hypertension.

Подробное описание

Patients with pulmonary hypertension (PH) represent a challenging and heterogeneous cohort with high morbidity and mortality if left untreated. To make a definitive diagnosis of PH, one needs to conduct an invasive right heart catheterization (RHC) in order to assess the mean pulmonary artery pressure (mPAP). As PH occurs sporadically in various medical conditions, including connective tissue disease, and congenital heart disease, and presenting symptoms are non-specific, there is a need to raise the suspicion of PH early in the community. For this reason, noninvasive tools that are widely available for upfront screening would be ideal to enable timely diagnosis of PH. Transthoracic echocardiography has emerged as the mainstay for screening of PH, yet the sensitivity and specificity of this approach remain limited even in experienced hands. As high-throughput technologies advance and access to PH big data improve, it will be critical to prudently select artificial intelligence approaches for data analysis, visualization, and interpretation. By combining the multimodal clinical data (such as indicators from chest X-ray, electrocardiography, and echocardiography), this study aims to develop artificial intelligence-assisted programs to assist the screening and diagnosis of PH, and to evaluate its diagnostic accuracy for PH as compared with RHC, and to estimate whether this approach outperforms the conventional echocardiographic method.

Вмешательства

  • Диагностический тест Right heart catheterization
    RHC is commonly used essential test to make gold-standard diagnosis of PH with mPAP \>20 mmHg. All multimodal data from patients eligible for inclusion would be randomly assigned to development datasets (70% of the study population) to train the artificial intelligence models for the detection of PH, which would be validated and tested by other datasets (30% of the study population).

Первичные конечные точки

  • Accuracy of diagnosis by artificial intelligence-assisted algorithm [Срок оценки: Baseline]
Вторичные конечные точки (2)
  • Sensitivity of diagnosis by artificial intelligence algorithm [Срок оценки: Baseline]
  • Specificity of diagnosis by artificial intelligence algorithm [Срок оценки: Baseline]

Критерии участия

Критерии включения

  • Age ≥18 years old
  • Patients previously received chest X-ray, electrocardiography, echocardiography, other routine examinations, and RHC at the Fuwai Hospital, CAMS \& PUMC, Beijing, China

Критерии исключения

  • Patients without RHC
  • The quality of routine examinations and RHC cannot meet the requirement for further analysis
  • Severe loss of results of routine examinations (chest X-ray, electrocardiography, echocardiography, etc.)

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

Здоровые добровольцы: Да

Дизайн исследования

Модель наблюдения
Только случаи

Центры проведения

Китай · 1 центр
  • Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College — Пекин

Публикации

  • Huang Z, Diao X, Huo Y, Zhao Z, Geng J, Zhao Q, Liu J, Xi Q, Xia Y, Xu O, Li X, Duan A, Zhang S, Gao L, Wang Y, Li S, Luo Q, Liu Z, Zhao W. Deep Learning-Enhanced Noninvasive Detection of Pulmonary Hypertension and Subtypes via Chest Radiographs, Validated by Catheterization. Chest. 2025 Nov;168(5):1215-1230. doi: 10.1016/j.chest.2025.06.008. Epub 2025 Jun 18. PMID 40541737
  • 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. PMID 40205021

Идентификаторы

NCT: NCT05566002 · AIPHY Project

Первоисточники (государственные реестры)

Открыть это исследование на ClinicalTrials.gov ↗