Towards multimodal brain monitoring in asphyxiated newborns with amplitude-integrated EEG and simultaneous somatosensory evoked potentials

Prognostic value of quantitative EEG in early hours of life for neonatal encephalopathy and neurodevelopmental outcomes

BACKGROUND

The ability to determine severity of encephalopathy is crucial for early neuroprotective therapies and for predicting neurodevelopmental outcome. The objective of this study was to assess a novel brain state of newborn (BSN) trend to distinguish newborns with presence of hypoxic ischemic encephalopathy (HIE) within hours after birth and predict neurodevelopmental outcomes at 2 years of age.

METHOD

This is a prospective cohort study of newborns at 36 weeks' gestation or later with and without HIE at birth. The Total Sanart Score (TSS) was calculated based on a modified Sarnat exam within 6 h of life. BSN was calculated from electroencephalogram (EEG) measurements initiated after birth. The primary outcome at 2 year of age was a diagnosis of death or disability using the Bayley Scales of Infant Development III.

RESULTS

BSN differentiated between normal and abnormal neurodevelopmental outcomes throughout the entire recording period from 6 h of life. Additionally, infants with lower BSN values had higher odds of neurodevelopmental impairment and HIE. BSN distinguished between normal (n = 86) and HIE (n = 46) and showed a significant correlation with the concomitant TSS.

CONCLUSION

BSN is a sensitive real-time marker for monitoring dynamic progression of encephalopathy and predicting neurodevelopmental impairment.

IMPACT

This is a prospective cohort study to investigate the ability of brain state of newborn (BSN) trend to predict neurodevelopmental outcome within the first day of life and identify severity of encephalopathy. BSN predicts neurodevelopmental outcomes at 2 years of age and the severity of encephalopathy severity. It also correlates with the Total Sarnat Score from the modified Sarnat exam. BSN could serve as a promising bedside trend aiding in accurate assessment and identification of newborns who may benefit from additional neuroprotection therapies.

An automated bedside measure for monitoring neonatal cortical activity: a supervised deep learning-based electroencephalogram classifier with external cohort validation

BACKGROUND

Electroencephalogram (EEG) monitoring is recommended as routine in newborn neurocritical care to facilitate early therapeutic decisions and outcome predictions. EEG's larger-scale implementation is, however, hindered by the shortage of expertise needed for the interpretation of spontaneous cortical activity, the EEG background. We developed an automated algorithm that transforms EEG recordings to quantified interpretations of EEG background and provides simple intuitive visualisations in patient monitors.

METHODS

In this method-development and proof-of-concept study, we collected visually classified EEGs from infants recovering from birth asphyxia or stroke. We used unsupervised learning methods to explore latent EEG characteristics, which guided the supervised training of a deep learning-based classifier. We assessed the classifier performance using cross-validation and an external validation dataset. We constructed a novel measure of cortical function, brain state of the newborn (BSN), from the novel EEG background classifier and a previously published sleep-state classifier. We estimated clinical utility of the BSN by identification of two key items in newborn brain monitoring, the onset of continuous cortical activity and sleep-wake cycling, compared with the visual interpretation of the raw EEG signal and the amplitude-integrated (aEEG) trend.

FINDINGS

We collected 2561 h of EEG from 39 infants (gestational age 35·0-42·1 weeks; postnatal age 0-7 days). The external validation dataset included 105 h of EEG from 31 full-term infants. The overall accuracy of the EEG background classifier was 92% in the whole cohort (95% CI 91-96; range 85-100 for individual infants). BSN trend values were closely related to the onset of continuous EEG activity or sleep-wake cycling, and BSN levels showed robust difference between aEEG categories. The temporal evolution of the BSN trends showed early diverging trajectories in infants with severely abnormal outcomes.

INTERPRETATION

The BSN trend can be implemented in bedside patient monitors as an EEG interpretation that is intuitive, transparent, and clinically explainable. A quantitative trend measure of brain function might harmonise practices across medical centres, enable wider use of brain monitoring in neurocritical care, and might facilitate clinical intervention trials.

FUNDING

European Training Networks Funding Scheme, the Academy of Finland, Finnish Pediatric Foundation (Lastentautiensäätiö), Aivosäätiö, Sigrid Juselius Foundation, HUS Children's Hospital, HUS Diagnostic Center, National Health and Medical Research Council of Australia.

Sleep State Trend (SST), a bedside measure of neonatal sleep state fluctuations based on single EEG channels

OBJECTIVE

To develop and validate an automated method for bedside monitoring of sleep state fluctuations in neonatal intensive care units.

METHODS

A deep learning-based algorithm was designed and trained using 53 EEG recordings from a long-term (a)EEG monitoring in 30 near-term neonates. The results were validated using an independent dataset from 30 polysomnography recordings. In addition, we constructed Sleep State Trend (SST), a bedside-ready means for visualizing classifier outputs.

RESULTS

The accuracy of quiet sleep detection in the training data was 90%, and the accuracy was comparable (85-86 %) in all bipolar derivations available from the 4-electrode recordings. The algorithm generalized well to a polysomnography dataset, showing 81% overall accuracy despite different signal derivations. SST allowed an intuitive, clear visualization of the classifier output.

CONCLUSIONS

Fluctuations in sleep states can be detected at high fidelity from a single EEG channel, and the results can be visualized as a transparent and intuitive trend in the bedside monitors.

SIGNIFICANCE

The Sleep State Trend (SST) may provide caregivers and clinical studies a real-time view of sleep state fluctuations and its cyclicity.

Early brain activity: Translations between bedside and laboratory

Neural activity is both a driver of brain development and a readout of developmental processes. Changes in neuronal activity are therefore both the cause and consequence of neurodevelopmental compromises. Here, we review the assessment of neuronal activities in both preclinical models and clinical situations. We focus on issues that require urgent translational research, the challenges and bottlenecks preventing translation of biomedical research into new clinical diagnostics or treatments, and possibilities to overcome these barriers. The key questions are (i) what can be measured in clinical settings versus animal experiments, (ii) how do measurements relate to particular stages of development, and (iii) how can we balance practical and ethical realities with methodological compromises in measurements and treatments.

Prognostic value of somatosensory-evoked potentials in the newborn with hypoxic-ischemic encephalopathy after the introduction of therapeutic hypothermia

To establish the ability of somatosensory-evoked potentials (SEPs) to detect neurological damage in neonatal patients with hypoxic-ischemic encephalopathy (HIE) treated with therapeutic hypothermia (TH). Retrospective study including 84 neonates ≥ 36 weeks of gestational age with HIE and TH with SEPs performed in the first 14 days of life. SEPs from the median nerve were performed after completion of TH. Either unilateral or bilateral absence of N20, or unilateral or bilateral latency ≥ 36 ms, was considered pathological. All newborns underwent a cerebral resonance imaging (MRI) at between days 7 and 14 of life and a neurodevelopmental evaluation using the Brunet-Lezine test at two years of age; a global Brunet-Lezine test score < 70 was considered unfavorable. The risk of moderate-to-severe alteration on basal ganglia-thalamic (BGT) and/or white matter areas on MRI for pathological SEPs was as follows: odds ratio 95% IC: 23.1 (6.9-76.9), sensitivity 78.6%, specificity 86.3%, positive predictive value 75.9%, and negative predictive value 88%. The BGT and internal capsule were the areas with the greatest risk of lesion with an altered SEPs: odds ratio 95% IC 93.1 (11.1-777.8). The risk of neurodevelopmental impairment for pathological SEPs was odds ratio 95% IC: 38.5 (4.4-335.3), sensitivity 91.7%, specificity 77.8% positive predictive value 52.4%, and negative predictive value 97.2%.

CONCLUSION

The present study demonstrates the good predictive capacity of SEPs performed in the first two weeks of life in newborns with HIE and TH to detect an increased risk of neuroimaging lesions and neurodevelopmental impairment at two years of age.

WHAT IS KNOWN

• Bilateral absence of the N20 cortical component of somatosensory evoked potentials has been associated with poor neurological outcome in neonates with hypoxic-ischemic encephalopathy.

WHAT IS NEW

• This work confirms the predictive capacity of SEPs by adding two important aspects: the value of latency when interpreting SEPs results and the absence of effect of the hypothermia method used on the results of SEPs.

Prognostic neurobiomarkers in neonatal encephalopathy

Therapeutic hypothermia is now standard-care for infants with moderate-severe neonatal encephalopathy (NE), and improves brain damage on neuroimaging, and neurodevelopmental outcomes. Critically, for effective neuroprotection, hypothermia should be started within 6 h from birth. There is compelling evidence to suggest that a proportion of infants with mild NE have material risk of developing brain damage and poor outcomes. This cohort is increasingly being offered therapeutic hypothermia, despite lack of trial evidence for its benefit. In current practice, infants need to be diagnosed within 6 h of birth for therapeutic treatment, compared to retrospective NE grading in the pre-hypothermia era. This presents challenges as NE is a dynamic brain disorder that can worsen or resolve over time. Neurological symptoms of NE can be difficult to discern in the first few hours after birth, and confounded by analgesics and anesthetic treatment. Using current enrolment criteria, a significant number of infants with NE that would benefit from hypothermia are not treated, and vice versa, infants are receiving mild hypothermia when its benefit will be limited. Better biomarkers are needed to further improve management and treatment of these neonates. In the present review, we examine the latest research, and highlight a central limitation of most current biomarkers: that their predictive value is consistently greatest after most neuroprotective therapies are no longer effective.