Artificial Intelligence-assisted Evaluation of Pulmonary HYpertension

NCT ID: NCT05566002

Last Updated: 2025-04-08

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 2000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2022-06-01
• Study Completion Date: 2025-12-31

Brief Summary

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.

Detailed Description

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.

Conditions

Pulmonary Hypertension; Pulmonary Arterial Hypertension

Study Design

• Observational Model Type: CASE_ONLY
• Study Time Perspective: RETROSPECTIVE

Study Groups

Patients with pulmonary hypertension

A series of routine examinations, including chest X-ray, electrocardiography, echocardiography, etc, would be performed on consecutive patients at Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. An RHC with an mPAP of \>20 mmHg would confirm the diagnosis of PH. All these data will be collected as a source for machine learning or other artificial intelligence-assisted programs.

Right heart catheterization

Intervention Type: DIAGNOSTIC_TEST

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).

Patients without pulmonary hypertension

A series of routine examinations, including chest X-ray, electrocardiography, echocardiography, etc, would be performed on consecutive patients at Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. An RHC with an mPAP of ≤20 mmHg would confirm the absence of PH. All these data will be collected as a source for machine learning or other artificial intelligence-assisted programs.

Right heart catheterization

Intervention Type: DIAGNOSTIC_TEST

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).

Interventions

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).

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

• 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

Exclusion Criteria

• 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.)

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Chinese Pulmonary Vascular Disease Research Group

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Zhihong Liu, MD, PhD

Role: PRINCIPAL_INVESTIGATOR

Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College

Locations

Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College

Beijing, Beijing Municipality, China

Site Status: RECRUITING

Countries

China

Central Contacts

Zhihong Liu, MD, PhD

Role: CONTACT

zhihongliufuwai@163.com

13269276067

Facility Contacts

Zhihong Liu, MD, PhD

Role: primary

zhihongliufuwai@163.com

13269276067

References

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 Jun 18:S0012-3692(25)00702-0. doi: 10.1016/j.chest.2025.06.008. Online ahead of print.

Reference Type: DERIVED

PMID: 40541737 (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: DERIVED

PMID: 40205021 (View on PubMed)

Other Identifiers

AIPHY Project

Identifier Type: -

Identifier Source: org_study_id