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Assessment of Neurodevelopmental Outcomes in Children With Congenital Heart Disease Using Magnetic Resonance Imaging
Ming-Cui Fu1, Ye Lin2, Feng Yang1
1Department of Radiology, Children's Hospital of Nanjing Medical University, Nanjing, People's Republic of China.
Insights
Magnetic resonance imaging (MRI) can predict neurodevelopmental abnormalities in children with congenital heart disease (CHD). Larger total brain volume and cortical gray matter volume indicate better neurodevelopmental outcomes.
Area of Science:
- Pediatric Neurology
- Cardiology
- Radiology
Background:
- Congenital heart disease (CHD) affects neurodevelopmental outcomes in children.
- Early identification of neurodevelopmental abnormalities is crucial for timely intervention.
Purpose of the Study:
- To evaluate neurodevelopmental outcomes in children with CHD using MRI.
- To assess brain volume metrics as predictors of neurodevelopmental abnormalities in this population.
Main Methods:
- Retrospective cohort study of 160 children with CHD.
- Classification into normal (DQ ≥ 70) and abnormal (DQ < 70) neurodevelopmental groups.
- MRI to measure brain volumes (total, gray matter, white matter, CSF); ROC analysis for predictive value.
Main Results:
- Children with abnormal neurodevelopment had significantly lower total brain volume and cortical gray matter volume.
- Total brain volume (AUC = 0.968) and cortical gray matter volume (AUC = 0.936) strongly predicted neurodevelopmental abnormalities.
- Abnormal neurodevelopment group scored lower in all developmental domains.
Conclusions:
- Total brain volume and cortical gray matter volume measured by MRI are effective predictors of neurodevelopmental abnormalities in children with CHD.
- These metrics can aid in early neurodevelopmental assessment for children with CHD.
Objective:
This study aims to evaluate the neurodevelopmental outcomes in children with congenital heart disease (CHD) using magnetic resonance imaging (MRI), and to assess the role of brain volume metrics as predictors of neurodevelopmental abnormalities.
Methods:
In this retrospective cohort study, 160 children with CHD treated at Children's Hospital of Nanjing Medical University from January 2020 to December 2023 were analyzed. Patients were classified into normal (DQ ≥ 70, n=106) and abnormal neurodevelopment (DQ < 70, n=54) groups based on Developmental Quotient (DQ) scores. MRI scans were used to measure total brain volume, cortical gray matter, deep gray matter, white matter, and cerebrospinal fluid volumes. Neurodevelopmental assessments focused on adaptive behavior, motor skills, language, and personal-social behavior. ROC analysis was performed to determine the predictive value of brain volume metrics for neurodevelopmental abnormalities.
Results:
Total brain volume in the normal group (341.82 ± 10.43 mL) was significantly higher than in the abnormal group (323.92 ± 10.24 mL) (P < 0.05). Cortical gray matter volume in the normal group (131.47 ± 4.02 mL) was also significantly greater than in the abnormal group (121.63 ± 6.91 mL) (P < 0.05). No significant differences were observed in white matter, deep gray matter, or cerebrospinal fluid volumes. Children in the abnormal group scored significantly lower in all developmental domains (P < 0.05). ROC analysis showed that total brain volume (AUC = 0.968) and cortical gray matter volume (AUC = 0.936) were strong predictors of neurodevelopmental abnormalities (P < 0.001).
Conclusion:
Total brain volume and cortical gray matter volume, as measured by MRI, are effective predictors of neurodevelopmental abnormalities in children with CHD and can serve as valuable tools for early neurodevelopmental assessment.
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