Prediction of Stroke Risk in Patients with Atrial Fibrillation Based on Chest CT Images

NCT06611995 | Not Yet Recruiting

• 1 other identifier: FAHZJU-Ethics-2024-NO.0990
• Study Type: observational
• Target: 1,500
• Locations: 1 country
• Sites: 1

Brief 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. 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.

Conditions

Atrial Fibrillation (AF); Ischemic Stroke

Keywords

Atrial fibrillation; Ischemic Stroke; Deep Learning; Chest CT; Left Atrial Appendage

Outcome Measures

Primary Outcomes (1)

• 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.

  Through study completion, an average of 2 year.

Study Arms (2)

People with atrial persistent fibrillation but without ischemic stroke

Other: observational study

People with atrial persistent fibrillation and ischemic stroke

Other: observational study

Interventions

observational study OTHER

Observational study without intervention

People with atrial persistent fibrillation and ischemic stroke; People with atrial persistent fibrillation but without ischemic stroke

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients who presented at various research center hospitals.

You may qualify if:

• 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.

You may not qualify if:

• 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

Sponsors & Collaborators

• First Affiliated Hospital of Zhejiang University: lead
• The Fourth Affiliated Hospital of Zhejiang University School of Medicine: collaborator
• Taizhou Hospital: collaborator
• Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University: collaborator
• Hangzhou Hospital of Traditional Chinese Medicine: collaborator
• Ningbo Medical Center Lihuili Hospital: collaborator
• Sanmen People Hospital: collaborator
• Zhejiang Rongjun Hospital: collaborator

Study Sites (1)

The First Affiliated Hospital, Zhejiang University School of Medicine

Hangzhou, Zhejiang, 310003, China

Location

MeSH Terms

Conditions

Atrial Fibrillation; Ischemic Stroke

Interventions

Observation

Condition Hierarchy (Ancestors)

Arrhythmias, Cardiac; Heart Diseases; Cardiovascular Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Stroke; Cerebrovascular Disorders; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Vascular Diseases

Intervention Hierarchy (Ancestors)

Methods; Investigative Techniques

Central Study Contacts

Xiaosheng Hu

CONTACT

+86 13588004492; debora886@163.com

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Chief Physician

Study Record Dates

• First Submitted: September 18, 2024
• First Posted: September 25, 2024
• Study Start: September 23, 2024
• Primary Completion (Estimated): September 30, 2026
• Study Completion (Estimated): September 30, 2026
• Last Updated: September 25, 2024

Record last verified: 2024-09

Locations

CN (1)