Early Warning of Structural Complications in Perimembranous VSD

Early Warning of Structural Complications Related to Perimembranous Ventricular Septal Defect Using Unstructured Echocardiographic Text Mining and Machine Learning

The goal of this observational study is to develop and test a AI-based prediction model for children with perimembranous ventricular septal defect, also called PMVSD. PMVSD is a type of hole between the lower chambers of the heart. Some children with PMVSD may later develop heart structure problems, such as aortic valve prolapse or leakage, subaortic fibrous ridge, left ventricle-to-right atrium shunt, or right ventricular outflow tract obstruction.

This study will use past medical records and echocardiography reports from children who received care at six hospitals in China between 2004 and 2022. The main questions it aims to answer are:

Can information written in echocardiography reports help predict which children with PMVSD are more likely to develop heart structure problems? Can natural language processing and machine learning improve early risk prediction when used together with routine clinical information? Researchers will review existing, de-identified medical data. They will use natural language processing to turn written descriptions in echocardiography reports into data that a computer model can analyze. These descriptions may include details about the edge of the heart defect, the direction of blood flow, and the relationship between the defect and nearby heart valves.

Participants will not receive any study treatment or extra tests. The study will only use information already collected during routine medical care. The prediction models will be trained and tested using data from different hospitals to see how well they work across medical centers.

Study Overview

Status

Active, not recruiting

Study Type

Observational

Enrollment (Estimated)

11683

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

    • Shanghai Municipality
      • Shanghai, Shanghai Municipality, China, 200092
        • Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

Pediatric patients with perimembranous ventricular septal defect evaluated at six tertiary referral centers between 2004 and 2022.

Description

Inclusion Criteria:

  • Children aged 14 years or younger at the time of presentation/diagnosis;
  • Confirmed diagnosis of perimembranous ventricular septal defect (PMVSD), or PMVSD accompanied by minor cardiac anomalies that do not alter the hemodynamic status of the VSD, including small patent foramen ovale, atrial septal defect, patent ductus arteriosus, or persistent left superior vena cava;
  • Availability of at least two consecutive follow-up records, with consistency between the electronic echocardiography reports and the Hospital Information System data.

Exclusion Criteria:

  • Age older than 14 years at diagnosis;
  • Presence of complex congenital heart disease;
  • Patients lost to follow-up due to inability to establish contact, resulting in an indeterminate clinical outcome.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Training cohort
Participants from the multicenter retrospective dataset used for model development. Unstructured echocardiographic text and clinical data from this cohort will be used for feature extraction, model training, and internal optimization to establish an early warning model for structural complications related to perimembranous ventricular septal defect.
Validaction cohort
An independent subset of participants from the multicenter retrospective dataset used for model validation. Data from this cohort will be used to evaluate the discrimination, calibration, and generalizability of the developed early warning model for structural complications related to perimembranous ventricular septal defect.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Occurrence of Perimembranous Ventricular Septal Defect Related Structural Complications
Time Frame: From initial diagnosis through 3 years after diagnosis
Occurrence of at least one structural complication related to perimembranous ventricular septal defect, as identified by echocardiography, including: (1) aortic valve prolapse and/or aortic regurgitation, defined as valvular leaflet displacement into the defect and/or regurgitation detected by color Doppler; (2) subaortic fibrous ridge, defined as abnormal fibrous strand or ridge-like echogenicity in the left ventricular outflow tract; (3) left ventricle-to-right atrium shunt; or (4) right ventricular outflow tract obstruction, defined as abnormal muscular bundle hypertrophy with increased Doppler flow velocity and a peak pressure gradient greater than 20 mmHg.
From initial diagnosis through 3 years after diagnosis

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Area Under the Precision-Recall Curve (AUCPR) for Complication Prediction
Time Frame: From initial diagnosis through 3 years after diagnosis
The area under the precision-recall curve (AUCPR) of the model for predicting PMVSD-related complication endpoint.
From initial diagnosis through 3 years after diagnosis
Sensitivity of the Model at a Pre-Specified Risk Threshold
Time Frame: From initial diagnosis through 3 years after diagnosis
Sensitivity for predicting PMVSD-related complication endpoint at a pre-specified probability (risk) threshold.
From initial diagnosis through 3 years after diagnosis
Positive Predictive Value (PPV) of the Model at a Pre-Specified Risk Threshold
Time Frame: From initial diagnosis through 3 years after diagnosis
Positive predictive value (PPV) for predicting PMVSD-related complication endpoint at the same pre-specified probability (risk) threshold.
From initial diagnosis through 3 years after diagnosis
Negative Predictive Value (NPV) of the Model at a Pre-Specified Risk Threshold
Time Frame: From initial diagnosis through 3 years after diagnosis
Negative predictive value (NPV) for predicting PMVSD-related complication endpoint at the same pre-specified probability (risk) threshold.
From initial diagnosis through 3 years after diagnosis

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 1, 2004

Primary Completion (Actual)

January 1, 2022

Study Completion (Estimated)

July 1, 2027

Study Registration Dates

First Submitted

June 1, 2026

First Submitted That Met QC Criteria

June 22, 2026

First Posted (Actual)

June 23, 2026

Study Record Updates

Last Update Posted (Actual)

June 23, 2026

Last Update Submitted That Met QC Criteria

June 22, 2026

Last Verified

June 1, 2026

More Information

Terms related to this study

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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