Artificial Intelligence-assisted Evaluation of Pulmonary HYpertension
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Right heart catheterization.
- Who it may be relevant to
- Registry conditions: Pulmonary Hypertension, Pulmonary Arterial Hypertension. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Artificial Intelligence-Assisted Evaluation of Pulmonary Hypertension
Overview
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.
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).
Primary outcome measures
- Accuracy of diagnosis by artificial intelligence-assisted algorithm [Time frame: Baseline]
Secondary outcome measures (2)
- Sensitivity of diagnosis by artificial intelligence algorithm [Time frame: Baseline]
- Specificity of diagnosis by artificial intelligence algorithm [Time frame: Baseline]
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.)
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Observational model
- Case-only
Study locations
China · 1 center
- Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College — Beijing
Publications
- Huang Z, Diao X, Huo Y, Zhao Z, Geng J, Zhao Q, Liu J, Xi Q, Xia Y, Xu O, Li X, Duan A, Zhang S, Gao L, Wang Y, Li S, Luo Q, Liu Z, Zhao W. Deep Learning-Enhanced Noninvasive Detection of Pulmonary Hypertension and Subtypes via Chest Radiographs, Validated by Catheterization. Chest. 2025 Nov;168(5):1215-1230. doi: 10.1016/j.chest.2025.06.008. Epub 2025 Jun 18. PMID 40541737
- Zhao W, Huang Z, Diao X, Yang Z, Zhao Z, Xia Y, Zhao Q, Sun Z, Xi Q, Huo Y, Xu O, Geng J, Li X, Duan A, Zhang S, Gao L, Wang Y, Li S, Luo Q, Liu Z. Development and validation of multimodal deep learning algorithms for detecting pulmonary hypertension. NPJ Digit Med. 2025 Apr 10;8(1):198. doi: 10.1038/s41746-025-01593-3. PMID 40205021
Identifiers
NCT: NCT05566002 · AIPHY Project