Clinical trial NCT06611995: Prediction of Stroke Risk in Patients with Atrial Fibrillation Based on Chest CT Images
Official title: Prediction of Stroke Risk in Patients with Atrial Fibrillation Based on Chest CT Images
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Atrial Fibrillation (AF)
Ischemic Stroke
Keywords: Atrial fibrillation
Ischemic Stroke
Deep Learning
Chest CT
Left Atrial Appendage
Lead sponsor: First Affiliated Hospital of Zhejiang University
Sponsor class: OTHER
Collaborators: name: The Fourth Affiliated Hospital of Zhejiang University School of Medicine
name: Taizhou Hospital
name: Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University
name: Hangzhou Hospital of Traditional Chinese Medicine
name: Ningbo Medical Center Lihuili Hospital
name: Sanmen People Hospital
name: Zhejiang Rongjun Hospital
First posted: 2024-09-25
Enrollment: 1500
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_CONTROL
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients who presented at various research center hospitals.

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\&#39; 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.

Eligibility criteria: 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

Primary outcomes:
measure: Performance of a Deep Learning Framework for Predicting Ischemic Stroke Risk in AF Patients.
timeFrame: Through study completion, an average of 2 year.
Secondary outcomes:
Other outcomes:
Locations:
city: Hangzhou
country: China
facility: The First Affiliated Hospital, Zhejiang University School of Medicine
state: Zhejiang
Study officials:
References: