Clinical trial NCT06526468: Chinese PE Multimodality Imaging Artificial Intelligence Study
Official title: CHinese pulmOnary Embolism Multimodality Imaging-artifiCial intelligencE Study
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Pulmonary Embolism
Chronic Thromboembolic Pulmonary Hypertension
Chronic Thromboembolic Pulmonary Disease
Keywords: imaging
early diagnosis
differential diagnosis
risk stratification
prognosis assessment
Lead sponsor: China-Japan Friendship Hospital
Sponsor class: OTHER
First posted: 2024-07-29
Enrollment: 1500
Sex: ALL
Minimum age (years): 14
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients suspected of PE in China

Brief summary: The CHinese pulmOnary Embolism Multimodality Imaging-artifiCial intelligencE Study (CHOICE) is a prospective observational multi-center study that will collect imaging text data and raw data of patients with pulmonary embolism (PE) in China. By combining artificial intelligence technology, it aims to identify imaging markers to assist in early diagnosis, differential diagnosis, risk stratification, and prognosis assessment of PE.

Detailed description: Pulmonary embolism (PE) represents a significant public health issue. Timely diagnosis and treatment during the acute phase, as well as appropriate long-term follow-up strategies, are crucial for the management of PE. PE is classified into three stages based on disease course: acute pulmonary embolism (APE), chronic thromboembolic pulmonary disease (CTEPD), and chronic thromboembolic pulmonary hypertension (CTEPH). APE can cause acute right ventricular failure and death if not diagnosed and treated early. CTEPD has the potential to significantly impair patients' quality of life. CTEPH is a rare and potentially life-threatening long-term sequelae of PE, characterized by persistent obstruction of pulmonary arteries by organized clots, leading to redistribution of blood flow and secondary remodeling of the pulmonary microvasculature. Early identification of PE and implementation of targeted treatment plans will significantly improve survival rates and prognosis.

Multimodal imaging tests play a crucial role in the management of PE (including computed tomography pulmonary angiography (CTPA), magnetic resonance imaging (MRI), echocardiography, and lung ventilation/perfusion (V/Q) scan). The guidelines have identified the right ventricle to left ventricle (RV:LV) ratio \>1.0 on CTPA or right heart dysfunction signs from echocardiography as important indicators for risk stratification of APE. Patients stratified as high risk require closer monitoring in an inpatient setting. Whereas, those stratified as low risk are suitable for early discharge.

Therefore, exploring novel imaging markers and integrating these markers into radiology reports may have potential clinical significance. If no quantifiable evidence of right ventricular dysfunction is provided to clinicians to make treatment decisions, patients with high-risk APE may be considered "low-risk" and discharged home. In addition, patients with low-risk APE may require longer hospital stays and may not need to be hospitalized, which undoubtedly increases healthcare costs. For patients with CTEPD or CTEPH, treatment options are diverse, including multimodal therapies such as pulmonary endarterectomy, balloon pulmonary angioplasty and targeted medical therapy. Therefore, multimodal imaging evaluation is meaningful for clinical treatment decision-making and efficacy monitoring. Combined with artificial intelligence (AI) technology, it can provide a variety of metrics to assist in evaluating clots morphology, pulmonary ventilation-perfusion function, cardiac function, hemodynamics, and more. AI can not only assist in finding more clinically significant imaging biomarkers but also customize standardized radiology reports, which are expected to address the current challenges.

This study is a multi-center real-world study aimed at exploring novel imaging markers in combination with AI technology and integrating them into a software for clinical application to provide quantitative parameters, using imaging reports and raw data from Chinese patients with PE. It is hypothesized that AI technology can improve early diagnosis, differential diagnosis, risk stratification, and management of PE by increasing the ability to accurately evaluate PE in a real-world clinical setting. The researchers also hypothesized that the integration of AI technologies would be cost-effective and acceptable to radiologists and clinicians.

Eligibility criteria: Inclusion Criteria:

* 14 Years and older
* Patients suspected of PE

Exclusion Criteria:

* Pregnant women
* Refuse to follow up
* Incomplete or discontinued imaging scans
* Insufficient quality of image data to allow for analysis

Primary outcomes:
measure: Diagnostic rate of PE
timeFrame: 2 years
measure: APE risk stratification rates (low, intermediate low, intermediate high and high risk)
timeFrame: 2 years
measure: Disease severity of chronic thromboembolic pulmonary disease (CTEPD)/chronic thromboembolic pulmonary hypertension (CTEPH)
timeFrame: 2 years
measure: 30 day mortality
timeFrame: 2 years
Secondary outcomes:
measure: Rate of discordant PE cases
timeFrame: 2 years
measure: AI failure rate for PE detection
timeFrame: 2 years
measure: 12 month mortality
timeFrame: 2 years
measure: Length of hospital stay for PE
timeFrame: 2 years
measure: Time from symptom onset to final diagnosis
timeFrame: 3 months
measure: Hospitalization cost for PE using Markov model
timeFrame: 2 years
Other outcomes:
Locations:
city: Beijing
country: China
facility: China-Japan Frendship hospital
Study officials:
affiliation: China-Japan Friendship Hospital
name: Zhenguo Zhai, PhD
role: STUDY_DIRECTOR
References: