Atrial Fibrillation (AF)NCT06611995
Prediction of Stroke Risk in Patients with Atrial Fibrillation Based on Chest CT Images
An observational study of Atrial Fibrillation (AF) and Ischemic Stroke, run by First Affiliated Hospital of Zhejiang University at 1 site in China.
Trial summary
This study aims to create and assess a deep learning framework for extracting left atrial appendage features in atrial fibrillation patients and combining them with clinical data to predict ischemic stroke risk. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Atrial Fibrillation (AF), Ischemic Stroke
- What is being tested?
- observational study
- How many people
- 1,500 participants
- Where
- 1 siteChina
- Study dates
- 23 Sep 2024to 30 Sep 2026
- 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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Patients who presented at various research center hospitals.
Who may be able to join
- Diagnosed with atrial fibrillation by ECG, 24-hour Holter monitor, or recordable ECG monitor; atrial fibrillation confirmed by an implanted pacemaker or defibrillator, lasting at least 30 seconds Available chest CT images and complete clinical data.
Who cannot join
- Incomplete clinical data or diagnosis of valvular atrial fibrillation (e.g., rheumatic heart valve disease, post-valve replacement) Poor-quality CT images that prevent complete assessment of left atrial appendage morphology Patients who have undergone left atrial appendage closure Patients who have had radiofrequency ablation or cardioversion with no evidence of recurrence post-procedure
Trial locations and contacts
1 site in 1 country.
China1 site
The First Affiliated Hospital, Zhejiang University School of Medicine
Hangzhou, Zhejiang, 310003
About this study
This study aims to create and assess a deep learning framework for extracting left atrial appendage features in atrial fibrillation patients and combining them with clinical data to predict ischemic stroke risk. Clinical data and chest CT images from patients diagnosed with non-valvular atrial fibrillation will be collected. Patients will be categorized into stroke and non-stroke groups to build a data repository. The dataset will be divided into training and validation sets, with missing data handled and pulmonary vein CTV and virtual non-contrast images annotated. A deep learning model will be used for image segmentation and feature extraction to develop a prediction system.
Read the detailed description
This study aims to develop and evaluate a deep learning framework that can automatically extract imaging features of the left atrial appendage in patients with atrial fibrillation and combine them with clinical features to predict the risk of ischemic stroke in these patients. The study intends to retrospectively collect clinical data (including patients\' general information, medical history, laboratory tests, etc.) and chest CT images, as well as pulmonary vein CTV images (if available), from patients diagnosed with non-valvular atrial fibrillation between January 2018 and June 2024. The patients will be divided into stroke and non-stroke groups based on whether they have experienced an ischemic stroke, and a data analysis repository will be established. The dataset will be split into training and validation sets. Missing data will be handled, and data labeling will be performed on the pulmonary vein CTV sequence images and virtual non-contrast (VNC) sequence images. The left atrial morphology will be delineated, and a deep learning-based image segmentation network model will be developed to extract and select radiomic features for the prediction system.
- Conditions
- Atrial Fibrillation (AF), Ischemic Stroke
- Being tested
- observational study
- Keywords
- Atrial fibrillation, Ischemic Stroke, Deep Learning, Chest CT, Left Atrial Appendage
- Medical subject headings
- Atrial Fibrillation, Ischemic Stroke
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
observational studyOther
Observational study without intervention
| Study group | Role | What they receive |
|---|---|---|
| People with atrial persistent fibrillation but without ischemic stroke | Not described Receives: Other: observational study | |
| People with atrial persistent fibrillation and ischemic stroke | Not described Receives: Other: observational study |
What is being measured
The primary outcome is the main question the study is designed to answer.
Performance of a Deep Learning Framework for Predicting Ischemic Stroke Risk in AF Patients.
This study aims to develop and evaluate a deep learning framework that automatically extracts Left Atrial Appendage (LAA) imaging features from 3D_slicer software and combines them with clinical characteristics to predict ischemic stroke risk in patients with atrial fibrillation (AF). The performance of the developed system will be evaluated using receiver operating characteristic (ROC) curves, area under the curve (AUC), accuracy, sensitivity, and specificity.
Measured: Through study completion, an average of 2 year.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Sep 2024
- Study start23 Sep 2024 (estimated)
- Primary completion30 Sep 2026 (estimated)
- Study completion30 Sep 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Atrial Fibrillation (AF)Studies of the same condition, open and under way.
- More trials run by First Affiliated Hospital of Zhejiang UniversityThe sponsor's other registered studies.
- 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
- Case-control
- Time perspective
- Cross-sectional
- Enrolment
- 1,500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- First Affiliated Hospital of Zhejiang University
- Sponsor type
- University, hospital or other
- Collaborators
- The Fourth Affiliated Hospital of Zhejiang University School of Medicine, Taizhou Hospital, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Hangzhou Hospital of Traditional Chinese Medicine, Ningbo Medical Center Lihuili Hospital, Sanmen People Hospital, Zhejiang Rongjun Hospital
- Responsible party
- Principal investigator, Hu Xiaosheng, First Affiliated Hospital of Zhejiang University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06611995
- Sponsor's protocol ID
- FAHZJU-Ethics-2024-NO.0990
- Status last verified
- Sep 2024
- First submitted
- 18 Sep 2024
- Last updated
- 25 Sep 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 25 Sep 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prediction of Stroke Risk in Patients with Atrial Fibrillation Based on Chest CT Images
- ClinicalTrials.gov ID
- NCT06611995
- Acronym / sponsor ID
- FAHZJU-Ethics-2024-NO.0990
- Conditions
- Atrial Fibrillation (AF), Ischemic Stroke
- Keywords
- Atrial fibrillation, Ischemic Stroke, Deep Learning, Chest CT, Left Atrial Appendage
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,500 participants (estimated)
- Lead sponsor
- First Affiliated Hospital of Zhejiang UniversityUniversity, hospital or other
- Collaborators
- The Fourth Affiliated Hospital of Zhejiang University School of Medicine, Taizhou Hospital, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Hangzhou Hospital of Traditional Chinese Medicine, Ningbo Medical Center Lihuili Hospital, Sanmen People Hospital, Zhejiang Rongjun Hospital
- Study start
- 23 Sep 2024 (estimated)
- Primary completion
- 30 Sep 2026 (estimated)
- Study completion
- 30 Sep 2026 (estimated)
- First posted
- 25 Sep 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.
This study aims to create and assess a deep learning framework for extracting left atrial appendage features in atrial fibrillation patients and combining them with clinical data to predict ischemic stroke risk. Clinical data and chest CT images from patients diagnosed with non-valvular atrial fibrillation will be collected. Patients will be categorized into stroke and non-stroke groups to build a data repository. The dataset will be divided into training and validation sets, with missing data handled and pulmonary vein CTV and virtual non-contrast images annotated. A deep learning model will be used for image segmentation and feature extraction to develop a prediction system.
Detailed description
This study aims to develop and evaluate a deep learning framework that can automatically extract imaging features of the left atrial appendage in patients with atrial fibrillation and combine them with clinical features to predict the risk of ischemic stroke in these patients. The study intends to retrospectively collect clinical data (including patients\' general information, medical history, laboratory tests, etc.) and chest CT images, as well as pulmonary vein CTV images (if available), from patients diagnosed with non-valvular atrial fibrillation between January 2018 and June 2024. The patients will be divided into stroke and non-stroke groups based on whether they have experienced an ischemic stroke, and a data analysis repository will be established. The dataset will be split into training and validation sets. Missing data will be handled, and data labeling will be performed on the pulmonary vein CTV sequence images and virtual non-contrast (VNC) sequence images. The left atrial morphology will be delineated, and a deep learning-based image segmentation network model will be developed to extract and select radiomic features for the prediction system.
Study design
- Observational model
- Case-control
- Time perspective
- Cross-sectional
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| People with atrial persistent fibrillation but without ischemic stroke | – | Other: observational study |
| People with atrial persistent fibrillation and ischemic stroke | – | Other: observational study |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients who presented at various research center hospitals.
Key inclusion criteria (1 listed)
- Diagnosed with atrial fibrillation by ECG, 24-hour Holter monitor, or recordable ECG monitor; atrial fibrillation confirmed by an implanted pacemaker or defibrillator, lasting at least 30 seconds Available chest CT images and complete clinical data.
Key exclusion criteria (1 listed)
- Incomplete clinical data or diagnosis of valvular atrial fibrillation (e.g., rheumatic heart valve disease, post-valve replacement) Poor-quality CT images that prevent complete assessment of left atrial appendage morphology Patients who have undergone left atrial appendage closure Patients who have had radiofrequency ablation or cardioversion with no evidence of recurrence post-procedure
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| observational studyObservational study without intervention | Other | People with atrial persistent fibrillation and ischemic stroke, People with atrial persistent fibrillation but without ischemic stroke |
Primary outcome
Performance of a Deep Learning Framework for Predicting Ischemic Stroke Risk in AF Patients.
Time frame: Through study completion, an average of 2 year.
This study aims to develop and evaluate a deep learning framework that automatically extracts Left Atrial Appendage (LAA) imaging features from 3D_slicer software and combines them with clinical characteristics to predict ischemic stroke risk in patients with atrial fibrillation (AF). The performance of the developed system will be evaluated using receiver operating characteristic (ROC) curves, area under the curve (AUC), accuracy, sensitivity, and specificity.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 25 Sep 2024.
- Individual participant dataSharing planned: Undecided
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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.
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 Atrial Fibrillation (AF) and Ischemic Stroke, run by First Affiliated Hospital of Zhejiang University 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 1 inclusion and 1 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 25 Sep 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 Xiaosheng Hu (+86 13588004492, debora886@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Performance of a Deep Learning Framework for Predicting Ischemic Stroke Risk in AF Patients. (time frame: Through study completion, an average of 2 year.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 25 Sep 2024.