Pulmonary EmbolismNCT06541353
Prognostic Model for Long-Term Cardiac Function After Pulmonary Embolism Based on Dynamic Electrocardial Signal and Circulating Biomarkers
An observational study of Pulmonary Embolism, run by China-Japan Friendship Hospital at 1 site in China.
Trial summary
Pulmonary embolism (PE) is a highly morbid and fatal cardiovascular disease. Right ventricular dysfunction (RVD) secondary to PE indicates a poor prognosis and serves as a critical basis for risk stratification. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Pulmonary Embolism
- How many people
- 500 participants
- Where
- 1 siteChina
- Study dates
- 1 Jul 2024to 30 Jun 2026
- How long will I take part?
- 1 Year
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients diagnosed with acute PE and stratified as intermediate-low risk, intermediate-high risk or high-risk according to the 2019 ESC Guidelines
Who may be able to join
- Age ≥18 years;
- Patients with confirmed diagnosis of PE (refer to the 2019 ESC Guidelines for the diagnosis and management of acute pulmonary embolism);
- Onset ≤14 days from diagnosis; (4) Risk stratification of intermediate-low risk, intermediate-high risk, and high-risk according to the 2019 ESC Guidelines for the diagnosis and management of acute pulmonary embolism;
- 5) Patients agree to sign informed consent.
Who cannot join
- A previous diagnosis of VTE with no evidence of recurrence, re-hospitalisation or treatment.
- Unable to attach the cardiac acquisition system due to chest surgery, localised damage, allergy, etc.
- Unable to complete the 1-year follow-up.
- Unable to operate portable mapping due to cognitive impairment, lack of a smartphone, etc.
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
China-Japan Friendship Hospital
Beijing, Beijing Municipality, 100029
Zhenguo Zhai, Ph.D, 18611706133, zhaizhenguo2011@126.com
Dingyi Wang, Ph.D
About this study
Pulmonary embolism (PE) is a highly morbid and fatal cardiovascular disease. Right ventricular dysfunction (RVD) secondary to PE indicates a poor prognosis and serves as a critical basis for risk stratification. Recent studies have shown that over one-third of patients continue to experience RVD one year after PE, with the mechanisms and regression remaining unclear. Although electrocardiography (ECG) is the most commonly used test for cardiac disease, its diagnostic specificity for PE is limited.
In recent years, artificial intelligence (AI) has successfully extracted hundreds of features from data that are difficult for the human eye to recognize. The correlation between daily vital signs monitored by wearable devices and functional signs of chronic cardiovascular disease suggests the potential of AI in detecting disease progression. There is a lack of specific markers for right ventricular function post-PE, and the significance and changes of these markers in disease progression have not yet been explored.
Read more
This study aims to develop a predictive model for the progression of RVD after PE using AI, combining electromyography, wearable devices, and vitality markers. In this prospective cohort study, 500 patients with acute PE at intermediate or higher risk were enrolled. Approximately 200 patients with RVD at discharge were followed for one year, with daily electromyographic data collected using portable electromyographs. Biospecimens were collected at the following time points: admission, discharge, and follow-up at 3, 6, and 12 months and a variety of inflammatory markers were measured using a multifactorial assay on liquid suspension cores. These data were integrated into a continuous disease diagnostic model based on a deep learning restrictive updating strategy.
Ultimately, a continuous disease diagnosis and prognosis algorithm was developed, yielding a model for predicting the progression of RVD after PE using multifactorial assays on liquid suspension cores to measure various inflammatory markers.
Read the detailed description
Long-term functional impairment after acute PE has been increasingly concerned in recent years. Our previous meta-analysis indicated that 34% PE patients had RVD at 1 year after an acute episode. However, the mechanism and prognosis of long-term RVD are unknown but largely influence patients' life expectancy and quality. In recent years, hundreds of ECG features have been successfully identified by the development of artificial intelligence (AI) and electrocardio signal monitored by wearable devices have also been used to identify cardiac disease and may be promising in detecting potential manifestations for long-term cardiac function in PE patients. Inflammation is known to have an important role in RVD after PE, but the prognostic predicting value have not yet been explored, especially time-variant changes. Therefore, this study is to obtain a prognostic model to predict the occurrence and outcome of long-term RVD after PE, based on artificial intelligence and wearable devices, combining dynamic changes of ECG and biomarkers.
50 patients with acute intermediate and higher risk PE will be prospectively recruited. ECG signal will be collected by a wearable single-lead long-range ECG acquisition system during hospitalization. And those with RVD at discharge (approximately 20 patients) are followed up for 1 year after discharge. Daily ECG data will be collected using a portable ECG monitor device. Blood and urine samples will be obtained at the following time points: admission, discharge, and follow-up at 3, 6, and 12 months, to measure time-variant inflammatory markers using a multiplex immunoassay for inflammatory cytokines quantitation. According to baseline ECG, biomarkers and clinical features, a model based on deep learning algorithm predicting RVD at discharge in study population will be obtained.
- Conditions
- Pulmonary Embolism
- Keywords
- acute pulmonary embolism, ECG, biomarker, Long-Term Cardiac Function
- Medical subject headings
- Pulmonary Embolism
What is being measured
The primary outcome is the main question the study is designed to answer.
the prevalence of right ventricle dysfunction
the prevalence of RVD defined by functional parameters of the right ventricle through echocardiography according to Guidelines for the Echocardiographic Assessment of the Right Heart in Adults: A Report from the American Society of Echocardiography.
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Aug 2024
- Study start1 Jul 2024
- Primary completion30 Jun 2026 (estimated)
- Study completion30 Jun 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Pulmonary EmbolismStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 500 participants (estimated)
- Target duration
- 1 Year
Sponsor and investigators
- Lead sponsor
- China-Japan Friendship Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Zhenguo Zhai,MD,PhD, China-Japan Friendship Hospital
- Investigators
- Zhenguo Zhai, Ph.D, Principal investigator, China-Japan Friendship Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06541353
- Sponsor's protocol ID
- CURES-CARE
- Status last verified
- Aug 2024
- First submitted
- 26 Jun 2024
- Last updated
- 7 Aug 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Xu F, Lyu Y, Li Z, Fan G, Ma J, Zhang X, Xu X, Qiao L, Liang Y, Qi Y, Shang J, Han J, Liu W, Gao C, Yan K, Li F, Wang X, Li Y, Cheng Z, Geng S, Li A, Yang P, Hong S, Zhai Z, Wang D. Artificial Intelligence-Enhanced Electrocardiogram for Longitudinal Evaluation of Right Ventricular Dysfunction After Acute Pulmonary Embolism: Rational and Design of CURES-CARE Study. Pulm Circ. 2026 May 20;16(2):e70312. doi: 10.1002/pul2.70312. eCollection 2026 Jun.
PubMed 42254875
Study at a glance
From the registry record, last updated by the sponsor on 7 Aug 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prognostic Model for Long-Term Cardiac Function After Pulmonary Embolism Based on Dynamic Electrocardial Signal and Circulating Biomarkers
- ClinicalTrials.gov ID
- NCT06541353
- Acronym / sponsor ID
- CURES-CARE
- Conditions
- Pulmonary Embolism
- Keywords
- acute pulmonary embolism, ECG, biomarker, Long-Term Cardiac Function
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- China-Japan Friendship HospitalUniversity, hospital or other
- Study start
- 1 Jul 2024 (actual)
- Primary completion
- 30 Jun 2026 (estimated)
- Study completion
- 30 Jun 2026 (estimated)
- First posted
- 7 Aug 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Pulmonary embolism (PE) is a highly morbid and fatal cardiovascular disease. Right ventricular dysfunction (RVD) secondary to PE indicates a poor prognosis and serves as a critical basis for risk stratification. Recent studies have shown that over one-third of patients continue to experience RVD one year after PE, with the mechanisms and regression remaining unclear. Although electrocardiography (ECG) is the most commonly used test for cardiac disease, its diagnostic specificity for PE is limited.
In recent years, artificial intelligence (AI) has successfully extracted hundreds of features from data that are difficult for the human eye to recognize. The correlation between daily vital signs monitored by wearable devices and functional signs of chronic cardiovascular disease suggests the potential of AI in detecting disease progression. There is a lack of specific markers for right ventricular function post-PE, and the significance and changes of these markers in disease progression have not yet been explored.
This study aims to develop a predictive model for the progression of RVD after PE using AI, combining electromyography, wearable devices, and vitality markers. In this prospective cohort study, 500 patients with acute PE at intermediate or higher risk were enrolled. Approximately 200 patients with RVD at discharge were followed for one year, with daily electromyographic data collected using portable electromyographs. Biospecimens were collected at the following time points: admission, discharge, and follow-up at 3, 6, and 12 months and a variety of inflammatory markers were measured using a multifactorial assay on liquid suspension cores. These data were integrated into a continuous disease diagnostic model based on a deep learning restrictive updating strategy.
Ultimately, a continuous disease diagnosis and prognosis algorithm was developed, yielding a model for predicting the progression of RVD after PE using multifactorial assays on liquid suspension cores to measure various inflammatory markers.
Detailed description
Long-term functional impairment after acute PE has been increasingly concerned in recent years. Our previous meta-analysis indicated that 34% PE patients had RVD at 1 year after an acute episode. However, the mechanism and prognosis of long-term RVD are unknown but largely influence patients' life expectancy and quality. In recent years, hundreds of ECG features have been successfully identified by the development of artificial intelligence (AI) and electrocardio signal monitored by wearable devices have also been used to identify cardiac disease and may be promising in detecting potential manifestations for long-term cardiac function in PE patients. Inflammation is known to have an important role in RVD after PE, but the prognostic predicting value have not yet been explored, especially time-variant changes. Therefore, this study is to obtain a prognostic model to predict the occurrence and outcome of long-term RVD after PE, based on artificial intelligence and wearable devices, combining dynamic changes of ECG and biomarkers.
50 patients with acute intermediate and higher risk PE will be prospectively recruited. ECG signal will be collected by a wearable single-lead long-range ECG acquisition system during hospitalization. And those with RVD at discharge (approximately 20 patients) are followed up for 1 year after discharge. Daily ECG data will be collected using a portable ECG monitor device. Blood and urine samples will be obtained at the following time points: admission, discharge, and follow-up at 3, 6, and 12 months, to measure time-variant inflammatory markers using a multiplex immunoassay for inflammatory cytokines quantitation. According to baseline ECG, biomarkers and clinical features, a model based on deep learning algorithm predicting RVD at discharge in study population will be obtained.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 1 Year
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients diagnosed with acute PE and stratified as intermediate-low risk, intermediate-high risk or high-risk according to the 2019 ESC Guidelines
Key inclusion criteria (4 listed)
- Age ≥18 years;
- Patients with confirmed diagnosis of PE (refer to the 2019 ESC Guidelines for the diagnosis and management of acute pulmonary embolism);
- Onset ≤14 days from diagnosis; (4) Risk stratification of intermediate-low risk, intermediate-high risk, and high-risk according to the 2019 ESC Guidelines for the diagnosis and management of acute pulmonary embolism;
- 5) Patients agree to sign informed consent.
Key exclusion criteria (4 listed)
- A previous diagnosis of VTE with no evidence of recurrence, re-hospitalisation or treatment.
- Unable to attach the cardiac acquisition system due to chest surgery, localised damage, allergy, etc.
- Unable to complete the 1-year follow-up.
- Unable to operate portable mapping due to cognitive impairment, lack of a smartphone, etc.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
the prevalence of right ventricle dysfunction
Time frame: 1 year
the prevalence of RVD defined by functional parameters of the right ventricle through echocardiography according to Guidelines for the Echocardiographic Assessment of the Right Heart in Adults: A Report from the American Society of Echocardiography.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 7 Aug 2024.
- Derived from this study: PubMed 42254875Xu F, Lyu Y, Li Z, Fan G, Ma J, Zhang X, Xu X, Qiao L, Liang Y, Qi Y, Shang J, Han J, Liu W, Gao C, Yan K, Li F, Wang X, Li Y, Cheng Z, Geng S, Li A, Yang P, Hong S, Zhai Z, Wang D. Artificial Intelligence-Enhanced Electrocardiogram for Longitudinal Evaluation of Right Ventricular Dysfunction After Acute Pulmonary Embolism: Rational and Design of CURES-CARE Study. Pulm Circ. 2026 May 20;16(2):e70312. doi: 10.1002/pul2.70312. eCollection 2026 Jun.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Zhenguo Zhai, Ph.D principal investigator
China-Japan Friendship Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Pulmonary Embolism, run by China-Japan Friendship Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 7 Aug 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Zhenguo Zhai, Ph.D (18611706133, zhaizhenguo2011@126.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
the prevalence of right ventricle dysfunction (time frame: 1 year). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06541353, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 7 Aug 2024.