Rhythmic EEG patterns in extremely preterm infants: Classification and association with brain injury and outcome

Current Evidence: Seizures in Extremely Low Gestational Age Newborns (ELGANs)

Extremely low gestational age newborns (ELGANs) are born at or below 28 weeks of gestational age. Despite improved obstetric care, the incidence of preterm birth continues to rise in advanced countries. Preterm birth remains a major cause of infant mortality, and for infants who survive, neonatal seizures are a significant predictor of later neurologic morbidity. However, little is known about risk factors for neonatal seizures in ELGANs. Understanding the association between neonatal seizures and the development of other neurologic disorders is important given the increasing prevalence of ELGANs. Identifying risk factors that contribute to the development of neonatal seizures in ELGANs may offer insights into novel mechanisms of epileptogenesis in the developing brain and improvements in the prevention or treatment of seizures in preterm infants, including ELGANs. In this literature review, we outline the limitations of epidemiologic studies of neonatal seizures in ELGANs and discuss risk factors for neonatal seizures.

Variable Association of Physiologic Changes With Electrographic Seizure-Like Events in Infants Born Preterm

OBJECTIVES

To determine the incidence of seizure-like events in a cohort of infants born preterm as well as the prevalence of associated vital sign changes (heart rate [HR], respiratory rate, and pulse oximetry [SpO2]).

STUDY DESIGN

We performed prospective conventional video electroencephalogram monitoring on infants born at 23-30 weeks of gestational age during the first 4 postnatal days. For detected seizure-like events, simultaneously captured vital sign data were analyzed during the pre-event baseline and during the event. Significant vital sign changes were defined as HR or respiratory rate >±2 SD from the infant's own baseline physiologic mean, derived from a 10-minute interval before the seizure-like event. Significant change in SpO2 was defined as oxygen desaturation during the event with a mean SpO2 <88%.

RESULTS

Our sample included 48 infants with median gestational age of 28 weeks (IQR 26-29) and birth weight of 1125 g (IQR 963-1265). Twelve (25%) infants had seizure-like discharges with a total of 201 events; 83% (10/12) of infants had vital sign changes during these events, and 50% (6/12) had significant vital sign changes during the majority of the seizure-like events. Concurrent HR changes occurred the most frequently.

CONCLUSIONS

Individual infant variability was observed in the prevalence of concurrent vital sign changes with electroencephalographic seizure-like events. Physiologic changes associated with preterm electrographic seizure-like events should be investigated further as a potential biomarker to assess the clinical significance of such events in the preterm population.

From Alpha to Zeta: A Systematic Review of Zeta Waves

PURPOSE

Electroencephalogram is used for prognostication and diagnosis in critically ill patients and is vital in developing clinical management algorithms. Unique waveforms on EEG may distinguish neurological disorders and define a potential for seizures. To better characterize zeta waves, we sought to define their electroclinical spectrum.

METHODS

We performed a systematic review using MEDLINE, Embase, Cochrane Central Register of Controlled Trials and Cochrane Database of Systematic Review [through Ovid], Scopus, Science Citation Index Expanded and Emerging Sources Citation Index [through the Web of Science], and Epistemonikos. Grey literature resources were searched.

RESULTS

Five hundred thirty-seven articles were identified. After excluding duplicates and reviewing titles, abstracts, and bodies and bibliographies of articles, four studies reported 64 cases describing data from patients with zeta waves, with a prevalence of 3 to 4%. Various and often incomplete clinical, neuroimaging, and EEG data were available. 57 patients (89.1%) had a focal cerebral lesion concordant with the location of zeta waves on EEG. 26 patients (40.6%) had clinical seizures, all but one being focal onset. Thirteen patients (20%) had epileptiform activity on EEG. Typically, zeta waves were located in the frontal head regions, often with generalized, frontal, predominant, rhythmic delta activity and associated with focal EEG suppression.

CONCLUSIONS

Zeta waves frequently represent an underlying focal structural lesion. Their presence suggests a heightened risk for seizures. The small number of retrospective cases series in the literature reporting zeta waves might be an underrepresentation. We suggest a need for prospective studies of cEEG in critically ill patients to determine their clinical significance.

Neuromonitoring in neonatal critical care part I: neonatal encephalopathy and neonates with possible seizures

The blooming of neonatal neurocritical care over the last decade reflects substantial advances in neuromonitoring and neuroprotection. The most commonly used brain monitoring tools in the neonatal intensive care unit (NICU) are amplitude integrated EEG (aEEG), full multichannel continuous EEG (cEEG), and near-infrared spectroscopy (NIRS). While some published guidelines address individual tools, there is no consensus on consistent, efficient, and beneficial use of these modalities in common NICU scenarios. This work reviews current evidence to assist decision making for best utilization of neuromonitoring modalities in neonates with encephalopathy or with possible seizures. Neuromonitoring approaches in extremely premature and critically ill neonates are discussed separately in the companion paper. IMPACT: Neuromonitoring techniques hold promise for improving neonatal care. For neonatal encephalopathy, aEEG can assist in screening for eligibility for therapeutic hypothermia, though should not be used to exclude otherwise eligible neonates. Continuous cEEG, aEEG and NIRS through rewarming can assist in prognostication. For neonates with possible seizures, cEEG is the gold standard for detection and diagnosis. If not available, aEEG as a screening tool is superior to clinical assessment alone. The use of seizure detection algorithms can help with timely seizures detection at the bedside.

Newborns at high risk for brain injury: the role of the amplitude-integrated electroencephalography

OBJECTIVE

Amplitude-integrated electroencephalography (aEEG) is a simplified bedside neurophysiology tool that has been implemented in the neonatal intensive care unit and studied in an extensive range of clinical applications in the past decade. This critical review aimed to evaluate a variety of clinical applications of aEEG monitoring in diagnosis, clinical management, and prognosis assessment in critically ill neonates.

SOURCES

The databases of Pubmed, SciELO, Lilacs, and Cochrane, books, and other online resources were consulted, as well as sources of professional experiences.

SUMMARY OF FINDINGS

The clinical use of aEEG to access real-time brain function, background activity, and utility in seizures detection has been described. A critical review was realized considering the authors' professional experience. Newborns with hypoxic-ischemic encephalopathy and seizures screening represent the most common studied population. However, several studies have shown interesting applications on preterm infants, newborns with congenital heart disease, and other clinical situations of high risk of injury to the developing brain.

CONCLUSION

The aEEG has shown to be a useful non-invasive bedside monitor that aids in evaluating brain function, background activity, and cyclicity. aEEG findings have also demonstrated good prognostic value in a group of critically ill neonates. The aEEG seizure diagnosis capability has limitations, which have been already well established. The use of neonatal brain monitoring such as aEEG was shown to give valuable information in several high-risk clinical situations.

Seizure burden in preterm infants and smaller brain volume at term-equivalent age

BACKGROUND

Seizures are underrecognized in preterm infants, and little is known about their impact on brain growth. We aimed to define the association between early seizures and subsequent brain growth.

METHODS

Infants <30 weeks gestation underwent 72 h of prospective amplitude-integrated electroencephalography (aEEG) monitoring, term-equivalent age (TEA) magnetic resonance imaging (MRI), and 2-year neurodevelopmental testing. Seizures were defined as trains of sharp waves >10 s, evolving in frequency/amplitude/morphology, and identified using automated algorithms with manual review. Using T2-weighted images, cortical surface area (CSA) and gyrification index (GI) were calculated and volumes were segmented into five tissue classes: cerebrospinal fluid, gray matter, white matter (WM), deep nuclear gray matter, and cerebellum. Correlations between total seizure burden and tissue-specific volumes were evaluated, controlling for clinical variables of interest.

RESULTS

Ninety-nine infants underwent aEEG/MRI assessments (mean GA = 26.3 weeks, birthweight = 899 g). Seizure incidence was 55% with a median of two events; median length = 66 s and mean burden = 285 s. Greater seizure burden was associated with smaller CSA and volumes across all tissue types, most prominently in WM (R2 = -0.603, p < 0.01), even after controlling for confounders. There was no association with GI.

CONCLUSIONS

Seizures in preterm infants are common and associated with smaller TEA brain volumes. This relationship was strongest for WM and independent of clinical factors.

IMPACT

Seizures in preterm infants are common. Little is known about the association between early seizures and later brain growth. Greater seizure burden is linked with smaller volumes of all brain tissue types, most prominently the WM. This relationship is true even controlling for other factors. Additional study is needed to identify the optimal EEG monitoring and seizure treatment strategy for improved brain growth and neurodevelopmental outcomes.

Neuromonitoring bei zerebralen Anfällen im Neugeborenenalter – Chancen und Herausforderungen

Angesichts der vielen Kinder mit neonatalen Risikofaktoren für erworbene ZNS-Läsionen und zerebrale Anfälle ist das EEG zunehmend relevant für eine optimierte Diagnostik und Therapieüberwachung 1 2. Folgender Artikel gibt einen Überblick über Besonderheiten des neonatalen EEG und über aktuelle Empfehlungen zum Stellenwert des Langzeit-EEG-Monitorings bei neonatalen Anfällen und epileptischen Enzephalopathien im Früh- und Neugeborenalter.

Precision Medicine in Neonates: A Tailored Approach to Neonatal Brain Injury

Despite advances in neonatal care to prevent neonatal brain injury and neurodevelopmental impairment, predicting long-term outcome in neonates at risk for brain injury remains difficult. Early prognosis is currently based on cranial ultrasound (CUS), MRI, EEG, NIRS, and/or general movements assessed at specific ages, and predicting outcome in an individual (precision medicine) is not yet possible. New algorithms based on large databases and machine learning applied to clinical, neuromonitoring, and neuroimaging data and genetic analysis and assays measuring multiple biomarkers (omics) can fulfill the needs of modern neonatology. A synergy of all these techniques and the use of automatic quantitative analysis might give clinicians the possibility to provide patient-targeted decision-making for individualized diagnosis, therapy, and outcome prediction. This review will first focus on common neonatal neurological diseases, associated risk factors, and most common treatments. After that, we will discuss how precision medicine and machine learning (ML) approaches could change the future of prediction and prognosis in this field.

Neuromonitoring in Neonatal-Onset Epileptic Encephalopathies

Considering the wide spectrum of etiologies of neonatal-onset epileptic encephalopathies (EE) and their unfavorable consequences for neurodevelopmental prognoses, neuromonitoring at-risk neonates is increasingly important. EEG is highly sensitive for early identification of electrographic seizures and abnormal background activity. Amplitude-integrated EEG (aEEG) is recommended as a useful bedside monitoring method but as a complementary tool because of methodical limitations. It is of special significance in monitoring neonates with acute symptomatic as well as structural, metabolic and genetic neonatal-onset EE, being at high risk of electrographic-only and prolonged seizures. EEG/aEEG monitoring is established as an adjunctive tool to confirm perinatal hypoxic-ischemic encephalopathy (HIE). In neonates with HIE undergoing therapeutic hypothermia, burst suppression pattern is associated with good outcomes in about 40% of the patients. The prognostic specificity of EEG/aEEG is lower compared to cMRI. As infants with HIE may develop seizures after cessation of hypothermia, recording for at least 24 h after the last seizure is recommended. Progress in the identification of genetic etiology of neonatal EE constantly increases. However, presently, no specific EEG changes indicative of a genetic variant have been characterized, except for individual variants associated with typical EEG patterns (e.g., KCNQ2, KCNT1). Long-term monitoring studies are necessary to define and classify electro-clinical patterns of neonatal-onset EE.

Mathematical Analysis of EEG Concordance in Preterm Twin Infants

PURPOSE

Preterm twins are at higher risk of neurodisability than preterm singletons, with monochorionic-diamniotic (MCDA) twins at higher risk than dichorionic-diamniotic (DCDA) twins. The impact of genetic influences on EEG concordance in preterm twins <32 weeks of gestational age is not established. This study aims to investigate EEG concordance in preterm MCDA and dichorionic-diamniotic twins during maturation.

METHODS

Infants <32 weeks of gestational age had multichannel EEG recordings for up to 72 postnatal hours, with repeat recordings at 32 and 35 weeks of postmenstrual age. Twin pairs had synchronous recordings. Mathematical EEG features were generated to represent EEG power, discontinuity, and symmetry. Intraclass correlations, while controlling for gestational age, estimated similarities within twins.

RESULTS

EEGs from 10 twin pairs, 4 MCDA and 6 dichorionic-diamniotic pairs, and 10 age-matched singleton pairs were analyzed from a total of 36 preterm infants. For MCDA twins, 17 of 22 mathematical EEG features had significant (>0.6; P < 0.05) intraclass correlations at one or more time points, compared with 2 of 22 features for DCDA twins and 0 of 22 for singleton pairs. For MCDA twins, all 10 features of discontinuity and all four features of symmetry were significant at one or more time-points. Three features of the MCDA twins (spectral power at 3-8 Hz, EEG skewness at 3-15 Hz, and kurtosis at 3-15 Hz) had significant intraclass correlations over all three time points.

CONCLUSIONS

Preterm twin EEG similarities are subtle but clearly evident through mathematical analysis. MCDA twins showed stronger EEG concordance across different postmenstrual ages, thus confirming a strong genetic influence on preterm EEG activity at this early development stage.

How Early Can a Seizure Happen? Pathophysiological Considerations of Extremely Premature Infant Brain Development

Seizures in neonates represent a neurologic emergency requiring prompt recognition, determination of etiology, and treatment. Yet, the definition and identification of neonatal seizures remain challenging and controversial, in part due to the unique physiology of brain development at this life stage. These issues are compounded when considering seizures in premature infants, in whom the complexities of brain development may engender different clinical and electrographic seizure features at different points in neuronal maturation. In extremely premature infants (< 28 weeks gestational age), seizure pathophysiology has not been explored in detail. This review discusses the physiological and structural development of the brain in this developmental window, focusing on factors that may lead to seizures and their consequences at this early time point. We hypothesize that the clinical and electrographic phenomenology of seizures in extremely preterm infants reflects the specific pathophysiology of brain development in that age window.

Outcome in preterm infants with seizures

Most neonatal seizures in preterm newborns are of acute symptomatic origin with a prevalence higher than in full-term infants. To date, recommendations for management of seizures in preterm newborns are scarce and do not differ from those in full-term newborns. Mortality in preterm newborns with seizures has significantly declined over the last decades, from figures of 84%-94% in the 1970s and 1980s to 22%-45% in the last years. However, mortality is significantly higher in those with a birth weight<1000g and a gestational age<28 weeks. Seizures are a strong predictor of unfavorable outcomes, including not only cerebral palsy, epilepsy, and intellectual disability, but also vision, hearing impairment, and microcephaly. The majority of patients with developmental delay are severely affected and this is usually associated with cerebral palsy. Furthermore, the incidence of epilepsy after neonatal seizures seems to be lower in preterm than in full-term infants but the risk is approximately 40 times greater than in the general population. Clinical studies cannot disentangle the specific and independent contributions of seizure-induced functional changes and the role of etiology and brain damage severity in determining the long-term outcomes in these newborns.

Diagnosis of seizures and encephalopathy using conventional EEG and amplitude integrated EEG

Seizures are more common in the neonatal period than at any other time of life, partly due to the relative hyperexcitability of the neonatal brain. Brain monitoring of sick neonates in the NICU using either conventional electroencephalography or amplitude integrated EEG is essential to accurately detect seizures. Treatment of seizures is important, as evidence increasingly indicates that seizures damage the brain in addition to that caused by the underlying etiology. Prompt treatment has been shown to reduce seizure burden with the potential to ameliorate seizure-mediated damage. Neonatal encephalopathy most commonly caused by a hypoxia-ischemia results in an alteration of mental status and problems such as seizures, hypotonia, apnea, and feeding difficulties. Confirmation of encephalopathy with EEG monitoring can act as an important adjunct to other investigations and the clinical examination, particularly when considering treatment strategies such as therapeutic hypothermia. Brain monitoring also provides useful early prognostic indicators to clinicians. Recent use of machine learning in algorithms to continuously monitor the neonatal EEG, detect seizures, and grade encephalopathy offers the exciting prospect of real-time decision support in the NICU in the very near future.

Seizures in Premature Infants Born at Less than 28 Weeks' Gestation

BACKGROUND

The incidence of seizures in the neonatal period is thought to be high due to a lower seizure threshold of the immature brain. Data on seizures in extremely premature infants are scarce.

OBJECTIVES

The aim of this study was to determine whether seizures are an independent risk factor for in-hospital death and to determine the incidence of seizures in extremely premature infants.

METHODS

This was a retrospective cohort study. Included were infants born under 28 weeks' gestation and monitored with amplitude-integrated electroencephalography (aEEG) over the first 3 days of life. The number and duration of seizures was retrieved from aEEG recordings together with clinical data. The association of seizures and other parameters with mortality was assessed using univariable analyses methods. Relevant parameters were used for a multivariable Cox regression analysis.

RESULTS

Overall, 229 infants were included in the study. Forty-six infants had at least one seizure episode yielding an incidence of 20%. In univariable analyses, gestational age (p < 0.001), birthweight Z-score (p < 0.001), seizures (p = 0.025), suppressed background aEEG (p < 0.001), and severe intraventricular hemorrhage (IVH; p < 0.001) were associated with death before discharge. In multivariable analysis, gestational age (HR = 0.61, p < 0.001), background aEEG activity (HR = 0.30, p < 0.001), birth weight Z-score (HR = 0.51, p = 0.04), and severe IVH (HR = 2.60, p < 0.001) were found to be significant predictors of mortality while the presence of seizures in the first 3 days of life trended to significantly predict an increased risk of mortality (HR = 1.53, p = 0.09).

CONCLUSIONS

Although seizure incidence was relatively high in this cohort of extremely preterm infants and infants with seizures were more likely to die, seizures alone are not a predictor for early death. However, they may be an important indicator of pathologies that are not immediately diagnosed yet could eventually lead to death among this vulnerable population.

Background EEG features and prediction of cognitive outcomes in very preterm infants: A systematic review

OBJECTIVES

Very preterm infants are at risk of cognitive impairment, but current capacity to predict at-risk infants is sub-optimal. Electroencephalography (EEG) has been used to assess brain function in development. This review investigates the relationship between EEG and cognitive outcomes in very preterm infants.

METHODS

Two reviewers independently conducted a literature search in April 2018 using PubMed, CINAHL, PsycINFO, Cochrane Library, Embase and Web of Science. Studies included very preterm infants (born ≤34 weeks gestational age, GA) who were assessed with EEG at ≤43 weeks postmenstrual age (PMA) and had cognitive outcomes assessed ≥3 months of age. Data on the subjects, EEG, cognitive assessment, and main findings were extracted. Meta-analysis was undertaken to calculate pooled sensitivity and specificity.

RESULTS

31 studies (n = 4712 very preterm infants) met the inclusion criteria. The age of EEG, length of EEG recording, EEG features analysed, age at follow-up, and follow-up assessments were diverse. The included studies were then divided into categories based on their analysed EEG feature(s) for meta-analysis. Only one category had an adequate number of studies for meta-analysis: four papers (n = 255 very preterm infants) reporting dysmature/disorganised EEG patterns were meta-analysed and the pooled sensitivity and specificity for predicting cognitive outcomes were 0.63 (95% CI: 0.53-0.72) and 0.83 (95% CI: 0.74-0.89) respectively.

CONCLUSIONS

There is preliminary evidence that background EEG features can predict cognitive outcomes in very preterm infants. Reported findings were however too heterogeneous to determine which EEG features are best at predicting cognitive outcome.