Wednesday, June 15, 2016

MRI for prediction of neurodevelopmental impairment in extremely-low-birth-weight infants.

Slaughter LA, Bonfante-Mejia E, Hintz SR, Dvorchik I, Parikh NA. Early Conventional MRI for Prediction of Neurodevelopmental Impairment in Extremely-Low-Birth-Weight Infants. Neonatology. 2016 Apr 7;110(1):47-54.

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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