A Multimodal AI Prediction Model for Complications After Transcatheter Closure of Perimembranous VSD in Children
Condition(s) studied
Study 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.
Eligibility
Primary outcome measure(s)
- Composite Procedure-Related Complications After Transcatheter Closure of Perimembranous VSD — Up to 30 Days After Transcatheter Closure
Occurrence of a composite endpoint of procedure-related complications, including arrhythmia requiring treatment, new-onset or worsened valvular regurgitation, residual shunt requiring reintervention, and device embolization.
Trial sites (1)
| Facility | City | Region | Status |
|---|---|---|---|
| Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine | Shanghai | Shanghai Municipality | Recruiting |
More Xinhua Hospital, Shanghai Jiao Tong University School of Medicine trials in China
Other trials for the same condition
This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.
View NCT07375602 on ClinicalTrials.gov ↗ ← All trials in China