Abstract
ObjectiveA major challenge in the care of preterm infants is the early identification of compromised neurological development. While several measures are routinely used to track anatomical growth, there is a striking lack of reliable and objective tools for tracking maturation of early brain function; a cornerstone of lifelong neurological health. We present a cot‐side method for measuring the functional maturity of the newborn brain based on routinely available neurological monitoring with electroencephalography (EEG).MethodsWe used a dataset of 177 EEG recordings from 65 preterm infants to train a multivariable prediction of functional brain age (FBA) from EEG. The FBA was validated on an independent set of 99 EEG recordings from 42 preterm infants. The difference between FBA and postmenstrual age (PMA) was evaluated as a predictor for neurodevelopmental outcome.ResultsThe FBA correlated strongly with the PMA of an infant, with a median prediction error of less than 1 week. Moreover, individual babies follow well‐defined individual trajectories. The accuracy of the FBA applied to the validation set was statistically equivalent to the training set accuracy. In a subgroup of infants with repeated EEG recordings, a persistently negative predicted age difference was associated with poor neurodevelopmental outcome.InterpretationThe FBA enables the tracking of functional neurodevelopment in preterm infants. This establishes proof of principle for growth charts for brain function, a new tool to assist clinical management and identify infants who will benefit most from early intervention.
Highlights
Preterm birth is a substantial risk to infant health
The quantitative EEG (qEEG) variable that had the highest correlation with postmenstrual age (PMA) was the asymmetry of average burst shape (Fig. 2A), which exhibits a strong linear relationship with bursts becoming more symmetric with increasing PMA (Fig. 2B)
Assessed within a leave-one-out cross-validation, the multivariable functional brain age (FBA) model had a significantly higher correlation with PMA than a single variable model based on the single best variable for models based on bursts, phenomenological, and other newly proposed qEEG variables (Dr = 0.109, 95% CI: 0.059 to 0.162; Dr = 0.095, 95% CI: 0.045 to 0.150; Dr = 0.094, 95% CI: 0.057 to 0.142; n = 177, respectively)
Summary
Preterm birth is a substantial risk to infant health. While mortality rates have dropped considerably over recent years due to improvements in clinical care, these infants remain at significant risk of neurodevelopmental delay and a host of other chronic impairments in later life.[1,2] It is, of critical importance to reduce the exposure of the preterm infant to neurological adversities while in the neonatal intensive care unit (NICU), and to identify those infants who will benefit most from early intervention.[3] Recent advances in neurological care have stressed the need for improving early functional biomarkers of neurodevelopment to expedite cycles within clinical intervention trials.[4]. Monitoring physiological and anatomical growth is crucial for clinicians when optimizing the care of very or extremely preterm infants. Critical time periods for the direction of care are usually the first days after birth, the time of discharge from tertiary care to step-down units,
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