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.
연구 개요
상태
연구 유형
등록 (추정된)
연락처 및 위치
연구 장소
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Shanghai Municipality
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Shanghai, Shanghai Municipality, 중국, 200092
- Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine
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참여기준
자격 기준
공부할 수 있는 나이
- 어린이
건강한 자원 봉사자를 받아들입니다
샘플링 방법
연구 인구
설명
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.
공부 계획
연구는 어떻게 설계됩니까?
디자인 세부사항
코호트 및 개입
그룹/코호트 |
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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.
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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.
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연구는 무엇을 측정합니까?
주요 결과 측정
결과 측정 |
측정값 설명 |
기간 |
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Occurrence of Perimembranous Ventricular Septal Defect Related Structural Complications
기간: From initial diagnosis through 3 years after diagnosis
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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.
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From initial diagnosis through 3 years after diagnosis
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2차 결과 측정
결과 측정 |
측정값 설명 |
기간 |
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Area Under the Precision-Recall Curve (AUCPR) for Complication Prediction
기간: From initial diagnosis through 3 years after diagnosis
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The area under the precision-recall curve (AUCPR) of the model for predicting PMVSD-related complication endpoint.
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From initial diagnosis through 3 years after diagnosis
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Sensitivity of the Model at a Pre-Specified Risk Threshold
기간: From initial diagnosis through 3 years after diagnosis
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Sensitivity for predicting PMVSD-related complication endpoint at a pre-specified probability (risk) threshold.
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From initial diagnosis through 3 years after diagnosis
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Positive Predictive Value (PPV) of the Model at a Pre-Specified Risk Threshold
기간: From initial diagnosis through 3 years after diagnosis
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Positive predictive value (PPV) for predicting PMVSD-related complication endpoint at the same pre-specified probability (risk) threshold.
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From initial diagnosis through 3 years after diagnosis
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Negative Predictive Value (NPV) of the Model at a Pre-Specified Risk Threshold
기간: From initial diagnosis through 3 years after diagnosis
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Negative predictive value (NPV) for predicting PMVSD-related complication endpoint at the same pre-specified probability (risk) threshold.
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From initial diagnosis through 3 years after diagnosis
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공동 작업자 및 조사자
연구 기록 날짜
연구 주요 날짜
연구 시작 (실제)
기본 완료 (실제)
연구 완료 (추정된)
연구 등록 날짜
최초 제출
QC 기준을 충족하는 최초 제출
처음 게시됨 (실제)
연구 기록 업데이트
마지막 업데이트 게시됨 (실제)
QC 기준을 충족하는 마지막 업데이트 제출
마지막으로 확인됨
추가 정보
이 연구와 관련된 용어
추가 관련 MeSH 약관
기타 연구 ID 번호
- XHEC-C-2026-099
개별 참가자 데이터(IPD) 계획
개별 참가자 데이터(IPD)를 공유할 계획입니까?
약물 및 장치 정보, 연구 문서
미국 FDA 규제 의약품 연구
미국 FDA 규제 기기 제품 연구
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