Are Simple Magnetic Resonance Imaging Biomarkers Predictive of Neurodevelopmental Outcome at Two Years in Very Preterm Infants?

BACKGROUND: Preterm infants are at increased risk of neurodevelopmental impairment due to the vulnerability of the immature brain. Early risk stratification is necessary for predicting outcome in the period of highest neuroplasticity. Several biomarkers in magnetic resonance imaging (MRI) at term equivalent age (TEA) have therefore been suggested. OBJECTIVE: To assess the predictive value of simple brain metrics and the total abnormality score (TAS) - a modified score for brain injury and growth - in relation to neurodevelopmental outcome of very preterm infants in MRI at TEA. METHODS: Single-centre cohort study including preterm infants with gestational age (GA) ≤32 weeks and birth weight ≤1,500 g. Biparietal width (BPW), interhemispheric distance, transcerebellar diameter (TCD) and TAS were assessed. To detect subtle haemorrhages, additional susceptibility-weighted imaging (SWI) was used in addition to conventional MRI to evaluate its clinical relevance. Neurodevelopment was tested by the Mental and Psychomotor Developmental Index (MDI/PDI) of the Bayley Scales of Infant Development II at a corrected age of 24 months. RESULTS: One hundred twenty-nine children with median GA of 28.1 weeks and median birth weight of 980 g were included. BPW significantly correlated with PDI (p= 0.01, R2 = 0.06) and TCD with MDI (p < 0.01, R2 = 0.05) and PDI (p < 0.01, R2 = 0.06) but explained variances were low. TAS was not predictive of neurodevelopmental outcome. By using SWI, additional 4 cases of low grade haemorrhages were identified compared to conventional sequences. In one case this additional information was clinically relevant (MDI/PDI below average). CONCLUSION: Simple brain metrics and TAS did not reliably predict neurodevelopmental outcome in a cohort with low prevalence of high grade brain injury. The additional value of SWI is yet to be determined in larger cohorts. The combination of imaging and functional biomarkers may be advisable for the prediction of neurodevelopmental outcome.

Relationship between brain function (aEEG) and brain structure (MRI) and their predictive value for neurodevelopmental outcome of preterm infants.

To improve the prediction of neurodevelopmental outcome in very preterm infants, this study used the combination of amplitude-integrated electroencephalography (aEEG) within the first 72 h of life and cranial magnetic resonance imaging (MRI) at term equivalent age. A single-center cohort of 38 infants born before 32 weeks of gestation was subjected to both investigations. Structural measurements were performed on MRI. Multiple regression analysis was used to identify independent factors including functional and structural brain measurements associated with outcome at a corrected age of 24 months. aEEG parameters significantly correlated with MRI measurements. Reduced deep gray matter volume was associated with low Burdjalov Score on day 3 (p < 0.0001) and day 1-3 (p = 0.0012). The biparietal width and the transcerebellar diameter were related to Burdjalov Score on day 1 (p = 0.0111; p = 0.0002). The final multiple regression analysis revealed independent predictors of neurodevelopmental outcome: intraventricular hemorrhage (p = 0.0060) and interhemispheric distance (p = 0.0052) for mental developmental index; Burdjalov Score day 1 (p = 0.0201) and interhemispheric distance (p = 0.0142) for psychomotor developmental index. CONCLUSION: Functional aEEG parameters were associated with altered brain maturation on MRI. The combination of aEEG and MRI contributes to the prediction of outcome at 24 months. What is Known: • Prematurity remains a risk factor for impaired neurodevelopment. • aEEG is used to measure brain activity in preterm infants and cranial MRI is performed to identify structural gray and white matter abnormalities with impact on neurodevelopmental outcome. What is New: • aEEG parameters observed within the first 72 h of life were associated with altered deep gray matter volumes, biparietal width, and transcerebellar diameter at term equivalent age. • The combination of aEEG and MRI contributes to the prediction of neurodevelopmental outcome at 2 years of corrected age in very preterm infants.

Comparison of two common aEEG classifications for the prediction of neurodevelopmental outcome in preterm infants.

Neurodevelopmental outcome after prematurity is crucial. The aim was to compare two amplitude-integrated EEG (aEEG) classifications (Hellström-Westas (HW), Burdjalov) for outcome prediction. We recruited 65 infants ≤32 weeks gestational age with aEEG recordings within the first 72 h of life and Bayley testing at 24 months corrected age or death. Statistical analyses were performed for each 24 h section to determine whether very immature/depressed or mature/developed patterns predict survival/neurological outcome and to find predictors for mental development index (MDI) and psychomotor development index (PDI) at 24 months corrected age. On day 2, deceased infants showed no cycling in 80% (HW, p = 0.0140) and 100% (Burdjalov, p = 0.0041). The Burdjalov total score significantly differed between groups on day 2 (p = 0.0284) and the adapted Burdjalov total score on day 2 (p = 0.0183) and day 3 (p = 0.0472). Cycling on day 3 (HW; p = 0.0059) and background on day 3 (HW; p = 0.0212) are independent predictors for MDI (p = 0.0016) whereas no independent predictor for PDI was found (multiple regression analyses). CONCLUSION: Cycling in both classifications is a valuable tool to assess chance of survival. The classification by HW is also associated with long-term mental outcome. What is Known: •Neurodevelopmental outcome after preterm birth remains one of the major concerns in neonatology. •aEEG is used to measure brain activity and brain maturation in preterm infants. What is New: •The two common aEEG classifications and scoring systems described by Hellström-Westas and Burdjalov are valuable tools to predict neurodevelopmental outcome when performed within the first 72 h of life. •Both aEEG classifications are useful to predict chance of survival. The classification by Hellström-Westas can also predict long-term outcome at corrected age of 2 years.