Clinical trial NCT05566002: Artificial Intelligence-assisted Evaluation of Pulmonary HYpertension
Official title: Artificial Intelligence-Assisted Evaluation of Pulmonary Hypertension
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Pulmonary Hypertension
Pulmonary Arterial Hypertension
Keywords: pulmonary hypertension
pulmonary vascular disease
right heart catheterization
echocardiography
electrocardiography
chest X-ray
artificial intelligence
machine learning
deep learning
screening
diagnosis
Lead sponsor: Chinese Pulmonary Vascular Disease Research Group
Sponsor class: OTHER
First posted: 2022-10-04
Enrollment: 2000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: CASE_ONLY
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Adult patients previously received chest X-rays, electrocardiography, echocardiography, other routine examinations, and RHC at the Fuwai Hospital, CAMS \& PUMC, Beijing, China.

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.

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.)

Primary outcomes:
measure: Accuracy of diagnosis by artificial intelligence-assisted algorithm
timeFrame: Baseline
Secondary outcomes:
measure: Sensitivity of diagnosis by artificial intelligence algorithm
timeFrame: Baseline
measure: Specificity of diagnosis by artificial intelligence algorithm
timeFrame: Baseline
Other outcomes:
Locations:
city: Beijing
country: China
facility: Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
state: Beijing
Study officials:
affiliation: Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
name: Zhihong Liu, MD, PhD
role: PRINCIPAL_INVESTIGATOR
References: