A practical approach toward interpretation of amplitude integrated electroencephalography in preterm infants

Effect of Combining Intrauterine Cerebral Blood Flow Changes with Electrical Activity on Prognostic Evaluation of Brain Injury

OBJECTIVES

We sought to investigate the value of combining intrauterine cerebral blood flow changes with brain electrical activity examination in evaluating the prognosis of brain injury.

METHODS

A total of 90 preterm infants were enrolled and divided into 2 groups: the brain damaged preterm infants group (n = 55) and the nonbrain damaged preterm infants group (n = 35). The diagnostic efficacy of combining intrauterine cerebral blood flow changes with electroencephalogram (EEG) activity examination in predicting the prognosis of preterm infants with brain injury was evaluated using T-test. Pearson linear correlation was applied to analyze the relationship between fetal intrauterine cerebral blood flow changes combined with electrical activity examination and the prognosis of brain injury.

RESULTS

Significant differences were seen in pulse index, the ratio of peak systolic velocity to end diastolic velocity ratio, and other indexes between the 2 groups (P < 0.05). The combined approach of intrauterine cerebral blood flow changes with EEG activity examination demonstrated significantly higher values for area under the curve, sensitivity and negative predictive value compared to using intrauterine cerebral blood flow changes or EEG activity examination alone (P < 0.05). A positive correlation was found between fetal intrauterine cerebral blood flow and electrical activity examination (P < 0.05).

CONCLUSIONS

Combining the assessment of intrauterine cerebral blood flow changes with cerebral electrical activity examination proved beneficial in diagnosing the prognosis of brain injury and provided an important reference for early clinical intervention.

Clinical outcome prediction with an automated EEG trend, Brain State of the Newborn, after perinatal asphyxia

OBJECTIVE

To evaluate the utility of a fully automated deep learning -based quantitative measure of EEG background, Brain State of the Newborn (BSN), for early prediction of clinical outcome at four years of age.

METHODS

The EEG monitoring data from eighty consecutive newborns was analyzed using the automatically computed BSN trend. BSN levels during the first days of life (a of total 5427 hours) were compared to four clinical outcome categories: favorable, cerebral palsy (CP), CP with epilepsy, and death. The time dependent changes in BSN-based prediction for different outcomes were assessed by positive/negative predictive value (PPV/NPV) and by estimating the area under the receiver operating characteristic curve (AUC).

RESULTS

The BSN values were closely aligned with four visually determined EEG categories (p < 0·001), as well as with respect to clinical milestones of EEG recovery in perinatal Hypoxic Ischemic Encephalopathy (HIE; p < 0·003). Favorable outcome was related to a rapid recovery of the BSN trend, while worse outcomes related to a slow BSN recovery. Outcome predictions with BSN were accurate from 6 to 48 hours of age: For the favorable outcome, the AUC ranged from 95 to 99% (peak at 12 hours), and for the poor outcome the AUC ranged from 96 to 99% (peak at 12 hours). The optimal BSN levels for each PPV/NPV estimate changed substantially during the first 48 hours, ranging from 20 to 80.

CONCLUSIONS

We show that the BSN provides an automated, objective, and continuous measure of brain activity in newborns.

SIGNIFICANCE

The BSN trend discloses the dynamic nature that exists in both cerebral recovery and outcome prediction, supports individualized patient care, rapid stratification and early prognosis.

[Effects of antenatal corticosteroid therapy in pregnant women on the brain development of preterm infants as assessed by amplitude-integrated electroencephalography]

OBJECTIVES

To investigate the effects of antenatal corticosteroid (ACS) therapy in pregnant women on the brain development of preterm infants using amplitude-integrated electroencephalography (aEEG).

METHODS

A retrospective analysis was conducted on 211 preterm infants with a gestational age of 28 to 34+6 weeks. The infants were divided into an ACS group (131 cases) and a control group (80 cases) based on whether antenatal dexamethasone was given for promoting fetal lung maturity. The first aEEG monitoring (referred to as aEEG1) was performed within 24 hours after birth, and the second aEEG monitoring (referred to as aEEG2) was performed between 5 to 7 days after birth. The aEEG results were compared between the two groups.

RESULTS

In preterm infants with a gestational age of 28 to 31+6 weeks, the ACS group showed a more mature periodic pattern and higher lower amplitude boundary in aEEG1 compared to the control group (P<0.05). In preterm infants with a gestational age of 32 to 33+6 weeks and 34 to 34+6 weeks, the ACS group showed a higher proportion of continuous patterns, more mature periodic patterns and higher Burdjalov scores in aEEG1 (P<0.05). And the ACS group exhibited a higher proportion of continuous patterns, more mature periodic patterns, higher lower amplitude boundaries, narrower bandwidths, and higher Burdjalov scores in aEEG2 (P<0.05).

CONCLUSIONS

ACS-treated preterm infants have more mature aEEG patterns compared to those not treated with ACS, suggesting a beneficial effect of ACS on the brain development of preterm infants.

Introduction of Amplitude-Integrated Electroencephalography (aEEG) Monitoring in a Level 2 NICU: Improving the Quality of Care for Neurologically At-Risk Newborns

Amplitude-integrated electroencephalography (aEEG) is a bedside tool for continuous monitoring of brain activity with the possibility of real-time interpretation. Amplitude-integrated electroencephalography is routinely used in Canadian tertiary NICUs; however, its use in Level 2 NICUs has been limited. A bedside aEEG program was introduced in a Level 2 NICU in order to help facilitate the timely transfer of neurologically compromised infants and keep mother-infant dyads together where reassurance of appropriate neurological status could be attained. A monitoring guideline and educational program were developed. The introduction of aEEG monitoring enhanced the care provided to neurologically at-risk newborns. This experience can be used as a framework for other Level 2 NICUs who may wish to embark upon a similar initiative.

Sleep assessment in preterm infants: Use of actigraphy and aEEG

OBJECTIVE

Objective methods to monitor the sleep of preterm infants at the neonatal intensive care unit (NICU) are required to prevent potentially adverse neurodevelopmental outcomes. This study aimed to determine the concordance of actigraphy and amplitude-integrated electroencephalogram (aEEG) against gold standard direct observation (DO) in assessing sleep/wake states of typically developing preterm infants.

METHODS

This prospective observational study was conducted in a single center level III NICU. Sleep variables were measured using Philips Respironics Mini-Mitter® Actiwatch-2 for 24 h and compared with 8-h matched data of aEEG and DO. Sensitivity-specificity analysis, Cohen's kappa, prevalence-adjusted and bias-adjusted kappa (PABAK), and Bland Altman plots were generated.

RESULTS

Seventeen preterm infants were recruited. A total of 11252 epochs were studied. Sensitivity (86.4%), agreement rate (67.9%), and predictive value for wake (47.9%) for the actigraphy were highest at the automatic activity threshold whereas specificity (54.5%) and predictive value for sleep (75.5%) were highest at low threshold. The sensitivity of aEEG was 79.3% and the specificity was 54.3%. At all thresholds, the agreement was largely equivalent with low kappas (0.14-0.17) and PABAK coefficients (0.22-0.35) for actigraphy and DO. Moderate agreement was observed between aEEG and DO according to the PABAK coefficient (0.44). Mean differences in sleep parameters were not different between DO and aEEG as well as DO/aEEG and actigraphy at medium threshold (p > 0.05).

CONCLUSIONS

Actigraphy at medium threshold can be used in depicting sleep in typically developing preterm infants at NICU. aEEG may be an alternative adjunctive method to actigraphy for the evaluation of sleep/wake states in the NICU setting.

CLINICAL TRIAL REGISTRATION NUMBER

NCT04145362.

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.

Neuromonitoring in neonatal critical care part II: extremely premature infants and critically ill neonates

Neonatal intensive care has expanded from cardiorespiratory care to a holistic approach emphasizing brain health. To best understand and monitor brain function and physiology in the neonatal intensive care unit (NICU), the most commonly used tools are amplitude-integrated EEG, full multichannel continuous EEG, and near-infrared spectroscopy. Each of these modalities has unique characteristics and functions. While some of these tools have been the subject of expert consensus statements or guidelines, there is no overarching agreement on the optimal approach to neuromonitoring in the NICU. This work reviews current evidence to assist decision making for the best utilization of these neuromonitoring tools to promote neuroprotective care in extremely premature infants and in critically ill neonates. Neuromonitoring approaches in neonatal encephalopathy and neonates with possible seizures are discussed separately in the companion paper. IMPACT: For extremely premature infants, NIRS monitoring has a potential role in individualized brain-oriented care, and selective use of aEEG and cEEG can assist in seizure detection and prognostication. For critically ill neonates, NIRS can monitor cerebral perfusion, oxygen delivery, and extraction associated with disease processes as well as respiratory and hypodynamic management. Selective use of aEEG and cEEG is important in those with a high risk of seizures and brain injury. Continuous multimodal monitoring as well as monitoring of sleep, sleep-wake cycling, and autonomic nervous system have a promising role in neonatal neurocritical care.