Artificial Intelligence-assisted Evaluation of Pulmonary HYpertension

Pulmonary Hypertension Clinical Trial

— AIPHY  

Official title:

Artificial Intelligence-Assisted Evaluation of Pulmonary Hypertension

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05566002
• Other study ID #: AIPHY Project
• Status: Recruiting
• Start date: June 1, 2022
• Est. completion date: December 31, 2024

Study information

• Verified date: November 2023
• Source: Chinese Pulmonary Vascular Disease Research Group
• Contact: Zhihong Liu, MD, PhD
• Phone: 13269276067
• Email: zhihongliufuwai[@]163.com
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 2000
• Est. completion date: December 31, 2024
• Est. primary completion date: December 31, 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

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.)

Related Conditions & MeSH terms

• Hypertension
• Hypertension, Pulmonary
• Pulmonary Arterial Hypertension
• Pulmonary Hypertension

Intervention

Diagnostic Test:
• 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).

Locations

Country	Name	City	State
China	Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College	Beijing	Beijing

Sponsors (1)

Lead Sponsor	Collaborator
Chinese Pulmonary Vascular Disease Research Group

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Accuracy of diagnosis by artificial intelligence-assisted algorithm	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.	Baseline
Secondary	Sensitivity of diagnosis by artificial intelligence algorithm	The investigators will calculate the sensitivity of diagnosis by artificial intelligence-assisted algorithm and compare this index between artificial intelligence-assisted algorithm and RHC.	Baseline
Secondary	Specificity of diagnosis by artificial intelligence algorithm	The investigators will calculate the sensitivity of diagnosis by artificial intelligence-assisted algorithm and compare this index between artificial intelligence-assisted algorithm and RHC.	Baseline