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

Not yet recruiting

• Conditions: Atrial Fibrillation (AF); Ischemic Stroke
• Interventions: Other: observational study

• Registration Number: NCT06611995
• Lead Sponsor: First Affiliated Hospital of Zhejiang University

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.

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 Timeline

• Status Verified: 2024-09
• Study First Submitted: 2024-09-18
• Study First Submitted QC: 2024-09-22
• Last Update Submitted: 2024-09-22
• Study Start: 2024-09-23
• Study First Posted: 2024-09-25
• Last Update Posted: 2024-09-25
• Primary Completion: 2026-09-30
• Study Completion: 2026-09-30

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 1500

Inclusion Criteria

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.

Exclusion Criteria

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

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
People with atrial persistent fibrillation but without ischemic stroke	observational study	-
People with atrial persistent fibrillation and ischemic stroke	observational study	-

Primary Outcome Measures

Name	Time	Method
Performance of a Deep Learning Framework for Predicting Ischemic Stroke Risk in AF Patients.	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.

Trial Locations

Locations (1)

The First Affiliated Hospital, Zhejiang University School of Medicine; 🇨🇳 Hangzhou, Zhejiang, China