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Early Conventional MRI for Prediction of Neurodevelopmental Impairment in Extremely-Low-Birth-Weight Infants
Laurel A Slaughter1, Eliana Bonfante-Mejia, Susan R Hintz
1Division of Neurology, Department of Pediatrics, Nationwide Children's Hospital and The Ohio State University College of Medicine, Columbus, Ohio, USA.
Insights
Brain MRI in extremely-low-birth-weight infants shows high specificity for predicting neurodevelopmental outcomes. However, sensitivity is limited, indicating a need for additional clinical factors and biomarkers for accurate prognostication.
Area of Science:
- Neonatal neurology
- Neuroimaging
- Developmental pediatrics
Background:
- Extremely-low-birth-weight (ELBW) infants face significant risks for neurodevelopmental impairments.
- Conventional brain MRI at term-equivalent age is a key tool for outcome prediction in ELBW infants.
- Optimal prediction models, particularly for cognitive outcomes, require further investigation.
Purpose of the Study:
- To assess the predictive accuracy of a novel data-driven MRI scoring system for neurodevelopmental impairments in ELBW infants.
- To compare the performance of this system against a commonly used MRI scoring method.
Main Methods:
- Brain MRI was performed at term-equivalent age in 122 ELBW infants.
- Conventional MRI findings were analyzed using a standardized algorithm and multivariable regression.
- Neurodevelopmental impairment was defined by specific criteria at 18-24 months corrected age.
Main Results:
- Moderate-to-severe gyral maturational delay significantly predicted overall neurodevelopmental impairment and cognitive delay.
- Diffuse cystic abnormality was a strong predictor of cerebral palsy.
- While predictors showed high specificity (94-99%), sensitivity for outcomes was suboptimal (30-67%).
- A commonly cited scoring system did not show significant associations with neurodevelopmental impairment.
Conclusions:
- Conventional brain MRI at term-equivalent age demonstrates high specificity for predicting neurodevelopmental outcomes in ELBW infants.
- Suboptimal sensitivity highlights the need for integrating additional clinical factors and biomarkers for improved prognostication.
- Further research is essential to enhance the accuracy of neurodevelopmental outcome prediction in this vulnerable population.
Background:
Extremely-low-birth-weight (ELBW; ≤1,000 g) infants are at high risk for neurodevelopmental impairments. Conventional brain MRI at term-equivalent age is increasingly used for prediction of outcomes. However, optimal prediction models remain to be determined, especially for cognitive outcomes.
Objective:
The aim was to evaluate the accuracy of a data-driven MRI scoring system to predict neurodevelopmental impairments.
Methods:
122 ELBW infants had a brain MRI performed at term-equivalent age. Conventional MRI findings were scored with a standardized algorithm and tested using a multivariable regression model to predict neurodevelopmental impairment, defined as one or more of the following at 18-24 months' corrected age: cerebral palsy, bilateral blindness, bilateral deafness requiring amplification, and/or cognitive/language delay. Results were compared with a commonly cited scoring system.
Results:
In multivariable analyses, only moderate-to-severe gyral maturational delay was a significant predictor of overall neurodevelopmental impairment (OR: 12.6, 95% CI: 2.6, 62.0; p < 0.001). Moderate-to-severe gyral maturational delay also predicted cognitive delay, cognitive delay/death, and neurodevelopmental impairment/death. Diffuse cystic abnormality was a significant predictor of cerebral palsy (OR: 33.6, 95% CI: 4.9, 229.7; p < 0.001). These predictors exhibited high specificity (range: 94-99%) but low sensitivity (30-67%) for the above outcomes. White or gray matter scores, determined using a commonly cited scoring system, did not show significant association with neurodevelopmental impairment.
Conclusions:
In our cohort, conventional MRI at term-equivalent age exhibited high specificity in predicting neurodevelopmental outcomes. However, sensitivity was suboptimal, suggesting additional clinical factors and biomarkers are needed to enable accurate prognostication.
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