Artificial Intelligence-assisted Evaluation of Pulmonary HYpertension

Study Overview
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Brief Summary
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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
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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.

Official Title
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Artificial Intelligence-Assisted Evaluation of Pulmonary Hypertension

Conditions
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Pulmonary Hypertension, Pulmonary Arterial Hypertension

Intervention / Treatment
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* Diagnostic Test: Right heart catheterization


Participation Criteria
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Eligibility Criteria
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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.)

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Patients with pulmonary hypertension<br>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. | Diagnostic Test: Right heart catheterization<br>* 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).<br>|
| Patients without pulmonary hypertension<br>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. | Diagnostic Test: Right heart catheterization<br>* 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).<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 | 
| 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 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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machine learning, pulmonary hypertension, pulmonary vascular disease, right heart catheterization, echocardiography, electrocardiography, chest X-ray, artificial intelligence, deep learning, screening, diagnosis