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A Multimodal AI Prediction Model for Complications After Transcatheter Closure of Perimembranous VSD in Children

NCT07375602 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 5249

Last updated 2026-08-05

No results posted yet for this 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.

Conditions

  • Congenital Heart Disease (CHD)
  • Ventricular Septal Defects (VSD)
  • Cardiac Catheterization
  • Postoperative Complications

Sponsors & Collaborators

  • Xinhua Hospital, Shanghai Jiao Tong University School of Medicine

    lead OTHER

Eligibility

Max Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-02-01
Primary Completion
2027-06-01
Completion
2027-12-30

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07375602 on ClinicalTrials.gov