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NCT Number: NCT06526468

Chinese PE Multimodality Imaging Artificial Intelligence Study

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.

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Key information

Age range

14 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Artificial intelligence

Device

AI technology will provide novel imaging markers and generate a radiology report with relevant key slice imaging and evaluation results

Primary outcomes

  1. Diagnostic rate of PE

    Time frame: 2 years

    Comparison before and after AI technique.

  2. APE risk stratification rates (low, intermediate low, intermediate high and high risk)

    Time frame: 2 years

    Comparison before and after AI technique.

  3. Disease severity of chronic thromboembolic pulmonary disease (CTEPD)/chronic thromboembolic pulmonary hypertension (CTEPH)

    Time frame: 2 years

    Comparison before and after AI technique. Assessment of disease severity is comprehensive, referring to the comprehensive risk assessment in pulmonary arterial hypertension (three-strata model) [DOI: 10.1183/13993003.00879-2022], including clinical observations and modifiable variables. The higher the score, the more severe the condition.

  4. 30 day mortality

    Time frame: 2 years

    Patient mortality (death) at 30-days post-PE diagnosis. Comparison before and after AI technique.

Secondary outcomes

  1. Rate of discordant PE cases

    Time frame: 2 years

    False positive and false negative rate

  2. AI failure rate for PE detection

    Time frame: 2 years

    Proportion of scans unable to be interpreted by AI despite suitable CTPA acquisition

  3. 12 month mortality

    Time frame: 2 years

    Patient mortality (death) at 12-months post-PE diagnosis. Comparison before and after AI technique.

  4. Length of hospital stay for PE

    Time frame: 2 years

    Comparison before and after AI technique. Measured as number of days from admission to time of discharge from hospital.

  5. Time from symptom onset to final diagnosis

    Time frame: 3 months

    Comparison before and after AI technique.

  6. Hospitalization cost for PE using Markov model

    Time frame: 2 years

    Comparison before and after AI technique.

Study contacts

Contact information is provided by the study sponsor or research team.

Min Liu, PhD

CONTACT

[email protected]

+86-10-84205056

Sponsors and collaborators

Lead sponsor

China-Japan Friendship Hospital

Other

Registry information

Official study title

CHinese pulmOnary Embolism Multimodality Imaging-artifiCial intelligencE Study

Important dates

Study start
2010
Primary completion
2027
Study completion
2028
First posted
Jul 29, 2024
Registry last updated
Aug 28, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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