Artificial Intelligence-assisted Evaluation of Pulmonary HYpertension

Study Purpose

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.

Recruitment Criteria

Accepts Healthy Volunteers Healthy volunteers are participants who do not have a disease or condition, or related conditions or symptoms	Yes
Study Type An interventional clinical study is where participants are assigned to receive one or more interventions (or no intervention) so that researchers can evaluate the effects of the interventions on biomedical or health-related outcomes. An observational clinical study is where participants identified as belonging to study groups are assessed for biomedical or health outcomes. Searching Both is inclusive of interventional and observational studies.	Observational
Eligible Ages	18 Years and Over
Gender	All

More Inclusion & Exclusion 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.)

Trial Details

Trial ID: This trial id was obtained from ClinicalTrials.gov, a service of the U.S. National Institutes of Health, providing information on publicly and privately supported clinical studies of human participants with locations in all 50 States and in 196 countries.	NCT05566002
Phase Phase 1: Studies that emphasize safety and how the drug is metabolized and excreted in humans. Phase 2: Studies that gather preliminary data on effectiveness (whether the drug works in people who have a certain disease or condition) and additional safety data. Phase 3: Studies that gather more information about safety and effectiveness by studying different populations and different dosages and by using the drug in combination with other drugs. Phase 4: Studies occurring after FDA has approved a drug for marketing, efficacy, or optimal use.
Lead Sponsor The sponsor is the organization or person who oversees the clinical study and is responsible for analyzing the study data.	Chinese Pulmonary Vascular Disease Research Group
Principal Investigator The person who is responsible for the scientific and technical direction of the entire clinical study.	Zhihong Liu, MD, PhD
Principal Investigator Affiliation	Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
Agency Class Category of organization(s) involved as sponsor (and collaborator) supporting the trial.	Other
Overall Status	Recruiting
Countries	China
Conditions The disease, disorder, syndrome, illness, or injury that is being studied.	Pulmonary Hypertension, Pulmonary Arterial Hypertension

Additional Details

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.

Arms & Interventions

Arms

: 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.

: 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.

Interventions

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).

Contact a Trial Team

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