Artificial Intelligence-assisted Evaluation of Pulmonary HYpertension

Recruiting

• Conditions: Pulmonary Arterial Hypertension; Pulmonary Hypertension
• Interventions: Diagnostic Test: Right heart catheterization

• Registration Number: NCT05566002
• Lead Sponsor: Chinese Pulmonary Vascular Disease Research Group

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.

Study Timeline

• Study Start: 2022-06-01
• Study First Submitted: 2022-09-30
• Study First Submitted QC: 2022-09-30
• Study First Posted: 2022-10-04
• Status Verified: 2025-04
• Last Update Submitted: 2025-04-06
• Last Update Posted: 2025-04-08
• Primary Completion: 2025-12-31
• Study Completion: 2025-12-31

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 2000

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.)

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Patients without pulmonary hypertension	Right heart catheterization	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.
Patients with pulmonary hypertension	Right heart catheterization	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.

Primary Outcome Measures

Name	Time	Method
Accuracy of diagnosis by artificial intelligence-assisted algorithm	Baseline	The investigators will calculate the area under the receiver operating characteristic curve of diagnosis by artificial intelligence-assisted algorithm and compare this index between artificial intelligence-assisted algorithm and RHC.

Secondary Outcome Measures

Name	Time	Method
Sensitivity of diagnosis by artificial intelligence algorithm	Baseline	The investigators will calculate the sensitivity of diagnosis by artificial intelligence-assisted algorithm and compare this index between artificial intelligence-assisted algorithm and RHC.
Specificity of diagnosis by artificial intelligence algorithm	Baseline	The investigators will calculate the sensitivity of diagnosis by artificial intelligence-assisted algorithm and compare this index between artificial intelligence-assisted algorithm and RHC.

Trial Locations

Locations (1)

Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College; 🇨🇳 Beijing, Beijing, China