Trials / Recruiting
RecruitingNCT07375602
A Multimodal AI Prediction Model for Complications After Transcatheter Closure of Perimembranous VSD in Children
Multimodal Clinical Data Integration and Artificial Intelligence Modeling for Predicting Complications Following Pediatric Transcatheter Closure of Perimembranous Ventricular Septal Defect
- Status
- Recruiting
- Phase
- —
- Study type
- Observational
- Enrollment
- 5,249 (estimated)
- Sponsor
- Xinhua Hospital, Shanghai Jiao Tong University School of Medicine · Academic / Other
- Sex
- All
- Age
- 18 Years
- Healthy volunteers
- Not accepted
Summary
The goal of this observational study is to develop and validate a multimodal artificial intelligence prediction model for treatment-related complications in children with perimembranous ventricular septal defect (pmVSD) undergoing transcatheter device closure. The main question it aims to answer is: Can an AI model that integrates demographics, laboratory results, electronic health record text, echocardiography reports, chest radiographs, and electrocardiogram accurately predict the risk of complications at the individual patient level? Data will be retrospectively collected from routine clinical care records of pediatric patients who underwent transcatheter closure for pmVSD. Deep learning methods will be used to extract features from text and images to train and validate the prediction model.
Conditions
- Congenital Heart Disease (CHD)
- Ventricular Septal Defects (VSD)
- Cardiac Catheterization
- Postoperative Complications
Timeline
- Start date
- 2026-02-01
- Primary completion
- 2026-06-15
- Completion
- 2026-06-15
- First posted
- 2026-01-29
- Last updated
- 2026-04-09
Locations
1 site across 1 country: China
Source: ClinicalTrials.gov record NCT07375602. Inclusion in this directory is not an endorsement.