Pattern Analysis of Left Ventricular Remodeling Using Cardiac Computed Tomography in Children with Congenital Heart

Hyun Woo Goo1, Sang Hyub Park2

  • 1Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea. ghw68@hanmail.net.

Insights

Cardiac CT can identify left ventricular remodeling patterns in children with congenital heart disease. These patterns may help predict clinical outcomes, aiding in personalized treatment strategies for pediatric cardiac conditions.

Area of Science:

  • Cardiology
  • Pediatric Imaging
  • Medical Diagnostics

Background:

  • Congenital heart disease (CHD) significantly impacts cardiac structure and function in children.
  • Left ventricular (LV) remodeling is a critical adaptation process in pediatric CHD.
  • Non-invasive imaging is crucial for monitoring LV remodeling in this population.

Purpose of the Study:

  • To characterize LV remodeling patterns using cardiac computed tomography (CT) in children with CHD.
  • To correlate identified remodeling patterns with the clinical course of pediatric patients with CHD.

Main Methods:

  • Quantified LV volume and myocardial mass in 17 children with CHD using serial cardiac CT.
  • Categorized LV remodeling into four patterns based on changes in indexed LV myocardial mass (LVMi) and LV mass-volume ratio (LVMVR).
  • Correlated remodeling patterns with clinical outcomes during follow-up.

Main Results:

  • LV remodeling patterns were identified in children with CHD.
  • Pattern 1 (increased LVMi, increased LVMVR) was associated with unfavorable events in 29% of patients.
  • Pattern 3 (increased LVMi, decreased LVMVR) showed a higher rate of unfavorable events (67%) compared to Pattern 2 (decreased LVMi, decreased LVMVR).

Conclusions:

  • Cardiac CT effectively characterizes LV remodeling patterns in pediatric CHD.
  • These patterns demonstrate potential as predictors of clinical course in children with CHD.
  • Utilizing CT-derived remodeling patterns may enhance prognostic assessments and guide management strategies.
Abstract

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