Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
3993 participants
OBSERVATIONAL
2014-01-01
2024-08-31
Brief Summary
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Detailed Description
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Unlike left ventricular (LV) dysfunction, RV dysfunction primarily results from pressure and volume overloads and has unique pathophysiological characteristics. While LV dysfunction mechanisms are well-studied, less is known about RV, and much of its clinical management is adapted from LV-focused research. The RV, with its thinner and more adaptable walls, remodels efficiently but often tolerate changes for extended periods before failure. Furthermore, RV dysfunction frequently leads to LV dysfunction due to strong interventricular interactions. These differences, along with the RV's irregular shape and orientation, complicate imaging and assessment, requiring advanced imaging techniques, often involving multiple modalities.
Precise evaluation of RV size and function is essential for diagnosing, managing, and predicting outcomes in pediatric cardiac conditions. Echocardiography, as a non-invasive and accessible tool, is the first-line imaging technique for monitoring RV function). However, traditional RV functional measures face limitations in pediatric populations due to significant variability in RV morphology. Among systolic parameters, fractional area change (FAC) has demonstrated stronger correlations with disease severity in advanced heart failure patients and a closer relationship with RV ejection fraction as measured by cardiovascular magnetic resonance (CMR), suggesting its reliability in assessing pediatric RV function. An FAC \<35% is considered abnormal by the guideline. Accurate FAC assessment can guide timely interventions, improve prognosis, and enhance long-term outcomes by enabling better monitoring of RV function over time.
Congenital Heart Disease (CHD) includes a wide range of structural abnormalities and conditions that affect the heart's development before birth, with examples such as Tetralogy of Fallot (TOF) and Pulmonary hypertension(PH). The RV plays a crucial role in TOF diagnosis because its outflow tract obstruction and hypertrophy are primary features of the condition. Pulmonary hypertension increases the afterload (pressure) on the right ventricle, leading to RV structural and functional changes. These changes are typically not seen in the LV unless there is severe biventricular involvement or secondary effects.
Echocardiography is a non-invasive, widely accessible, and essential first-line tool for routine follow-up of various pediatric cardiac conditions affecting the right ventricle (RV). However, assessing RV function in pediatric heart disease remains challenging due to significant variability in RV morphology and physiology. The apical four-chamber (A4C) view is a cornerstone for right ventricular (RV) function assessment due to its ability to evaluate RV size, shape, and systolic function comprehensively. The parasternal short-axis (PSAX) view is often used as a supplementary echocardiographic view alongside the A4C view for assessing right ventricular (RV) function. Advances in AI-driven echocardiography, particularly deep learning, hold promise for enhancing cardiac function assessment. Recent innovations, like EchoNet-Dynamic, have shown the utility of video-based deep learning algorithms for adult LV segmentation and ejection fraction estimation. Building on this progress, the investigators developed EchoNet-Peds , an AI model for pediatric echocardiography that automates LV segmentation and ejection fraction calculations. Beside left ventricle segmentation methods, other deep learning applications include automated quantification of left ventricular structure and function, as well as novel methods for estimating intraventricular hemodynamic parameters on a beat-to-beat basis . However, most deep learning studies focus on LV assessment, with comparatively fewer models dedicated to the RV, mainly addressing segmentation tasks. While some recent advancements have emerged, such as a model estimating RV ejection fraction from echocardiographic images in pulmonary arterial hypertension patients , significant gaps remain in applying AI to pediatric RV functional assessment.
This study aims to develop a deep learning-based framework for right ventricular (RV) segmentation, prediction of RV fractional area change (FAC), and identification of pediatric RV dysfunction. The AI model was designed to distinguish between normal pediatric hearts, pulmonary hypertension (PH), and Tetralogy of Fallot (TOF). To improve diagnostic accuracy, the investigators extended the analysis beyond the A4C view by integrating data from both A4C and PSAX views. Additionally, the framework was applied to predict left ventricular ejection fraction (LV EF), further showcasing its versatility and clinical utility.
Conditions
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Keywords
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Study Design
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CASE_CONTROL
RETROSPECTIVE
Study Groups
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RV FAC Prediction Cohort
This cohort is designed to predict pediatric RV FAC using a deep learning model based on echocardiograms
No interventions assigned to this group
RV Disease Classification Cohort
The cohort is designed to employ a deep learning model to differentiate between normal pediatric hearts and pulmonary hypertension (PH), as well as between Tetralogy of Fallot (TOF) and PH, using echocardiograms.
No interventions assigned to this group
RV Function Assessment and Disease Classification Using A4C and PSAX View Cohorts
The cohort is designed to utilize A4C and PSAX echocardiographic views for pediatric RV function assessment and disease classification using deep learning models.
No interventions assigned to this group
LV Ejection Fraction (EF) Prediction Cohort
The cohort is designed to validate our new deep learning model for LV EF assessment.
No interventions assigned to this group
Eligibility Criteria
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Inclusion Criteria
2. Patients with the following diagnoses (defined as abnormal RVs): premature infants with lung disease, congenital heart disease with systemic right ventricles, surgically repaired congenital heart disease resulting in pressure and/or volume load on the RV (tetralogy of Fallot, Double outlet right ventricle, etc.), and idiopathic pulmonary hypertension.
Exclusion Criteria
18 Years
ALL
Yes
Sponsors
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HBI Solutions Inc.
INDUSTRY
Responsible Party
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Doff McElhinney
Professor of Cardiothoracic Surgery (Pediatric Cardiac Surgery) and of Pediatrics (Cardiology), Stanford University School of Medicine
Locations
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Stanford University
Palo Alto, California, United States
Countries
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Other Identifiers
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ECHO_1
Identifier Type: -
Identifier Source: org_study_id