The prognostic value of amplitude-integrated EEG in full-term neonates with seizures

Electrographic monitoring for seizure detection in the neonatal unit: current status and future direction

Neonatal neurocritical intensive care is dedicated to safeguarding the newborn brain by prioritising clinical practices that promote early identification, diagnosis and treatment of brain injuries. The most common newborn neurological emergency is neonatal seizures, which may also be the initial clinical indication of neurological disease. A high seizure burden in the newborn period independently contributes to increased mortality and morbidity. The majority of seizures in newborns are subclinical (without clinical presentation), and hence identification may be difficult. Neuromonitoring techniques most frequently used to monitor brain wave activity include conventional electroencephalography (cEEG) or amplitude-integrated EEG (aEEG). cEEG with video is the gold standard for diagnosing and treating seizures. Many neonatal units do not have access to cEEG, and frequently those that do, have little access to real-time interpretation of monitoring. IMPACT: EEG monitoring is of no benefit to an infant without expert interpretation. Whilst EEG is a reliable cot-side tool and of diagnostic and prognostic use, both conventional EEG and amplitude-integrated EEG have strengths and limitations, including sensitivity to seizure activity and ease of interpretation. Automated seizure detection requires a sensitive and specific algorithm that can interpret EEG in real-time and identify seizures, including their intensity and duration.

An Introduction to Neonatal EEG

Newborn care has witnessed significant improvements in survival, but ongoing concerns persist about neurodevelopmental outcome. Protecting the newborn brain is the focus of neurocritical care in the intensive care unit. Brain-focused care places emphasis on clinical practices supporting neurodevelopment in conjunction with early detection, diagnosis, and treatment of brain injury. Technology now facilitates continuous cot-side monitoring of brain function. Neuromonitoring techniques in neonatal intensive care units include the use of electroencephalography (EEG) or amplitude-integrated EEG (aEEG) and near-infrared spectroscopy. This article aims to provide an introduction to EEG, which is appropriate for neonatal healthcare professionals.

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.

Prognostic value of amplitude-integrated EEG in neonates with high risk of neurological sequelae

OBJECTIVE

To determine the efficacy and the prognostic value of amplitude-integrated electroencephalography (aEEG) in term and near-term neonates with high risk of neurological sequelae.

METHODS

Infants of ≥35 weeks of gestation diagnosed with neonatal encephalopathy or with high risk of brain injury were included. All eligible infants underwent aEEG within 6 h after clinical assessment. The infants were followed up 12 months to evaluate neurological development.

RESULTS

A total of 250 infants were eligible, of which 85 had normal aEEG, 81 had mildly abnormal aEEG, and 84 had severely abnormal aEEG. Of these infants, 168 were diagnosed with different neonatal encephalopathies, 27 with congenital or metabolic diseases, and 55 with high risk of brain injury. In all, 22 infants died, 19 were lost to follow-up, and 209 completed the follow-up at 12 months, of which 62 were diagnosed with a neurological disability. Statistical analysis showed that severely abnormal aEEG predicted adverse neurological outcome with a sensitivity of 70.2%, a specificity of 87.1%, a positive predictive value of 75.6%, and a negative predictive value of 83.7%.

INTERPRETATION

aEEG can predict adverse outcomes in high-risk neonates and is a useful method for monitoring neonates with high risk of adverse neurological outcomes.

Clinical Manifestations and Amplitude-integrated Encephalogram in Neonates with Early-onset Epileptic Encephalopathy

Background:

The patients with early-onset epileptic encephalopathy (EOEE) suffer from neurodevelopmental delay. The aim of this study was to analyze the clinical manifestations and amplitude-integrated encephalogram (aEEG) characteristics of infants with EOEE with onset within the neonatal period, to make early diagnosis to improve the prognosis.

Methods:

One-hundred and twenty-eight patients with neonatal seizure were enrolled and followed up till 1 year old. Sixty-six neonates evolved into EOEE were as the EOEE group, the other 62 were as the non-EOEE (nEOEE) group. Then we compared the clinical and aEEG characteristics between the two groups to analyze the manifestations in neonates with EOEE.

Results:

Compared to the nEOEE group, the incidence of daily seizure attacks, more than two types of convulsions, more than two antiepileptic drugs (AEDs) application, severely abnormal aEEG background, absence of cyclicity, and more than two seizures detection were significantly higher in the EOEE group (P < 0.05) (97% vs. 54.8%; 30.3% vs. 14.5%; 97.0% vs. 25.4%; 39.4% vs. 3.2%; 57.6% vs. 9.7%; and 56% vs. 3.2%, respectively). Severely abnormal background pattern (odds ratio [OR] = 0.081, 95% confidence interval [CI]: 0.009–0.729, P = 0.025) and more than two seizures detection by aEEG (OR = 0.158, 95% CI: 0.043–0.576, P = 0.005) were the independent risk factors for the evolvement into EOEE. The upper and lower margins of active sleep (AS) and quiet sleep (QS) were significantly higher in EOEE group than those of the control group (P < 0.05) (34.3 ± 13.6 vs. 21.3 ± 6.4; 9.9 ± 3.7 vs. 6.7 ± 2.2; 41.2 ± 15.1 vs. 30.4 ± 11.4; and 11.9 ± 4.4 vs. 9.4 ± 4.0; unit: μV, respectively). AS upper margin was demonstrated a higher diagnostic specificity and sensitivity for EOEE than another three parameters according to the receiver operating characteristic curves; the area under the curve was 0.827.

Conclusions:

The clinical characteristics of the neonatal seizure which will evolve into EOEE were more than two AEDs application, high seizure frequency (daily attack), and more than two types of the seizure. Significant high voltage, severely abnormal background, absence of cyclicity, and more than two seizures detected on aEEG were the meaningful indicators to the prediction of EOEE.

Sleep-wake cycle on amplitude-integrated EEG and neuroimage outcomes in newborns

BACKGROUND

The aim of this study was to evaluate the results of sleep-wake cycle monitoring using amplitude-integrated EEG (aEEG) and neuroimaging in newborn infants with a possible perinatal hypoxic insult, investigate the correlation between the findings, and determine the relevance of the findings to reasonably predict neurological outcome.

METHODS

aEEG was recorded among newborn infants suspected of perinatal asphyxia between November, 2014 and June, 2015 in one neonatal intensive care unit facility. Brain imaging with serial ultrasonography and MRI when available were performed, and the infants were divided into two groups according to findings and potential neurological outcome: Group I (favorable findings) and Group II (severe findings such as high grade intraventricular hemorrhage, cerebral infarction or white matter injury). Established sleep-wake cycle times after birth was compared between the two groups.

RESULTS

Among 107 newborn infants, 85 subjects were classified as Group I and the remaining 22 subjects as Group II. The total number of aEEG sessions was 207 and recording time was 2,796 h with a mean of 14.43 ± 13.40 h per study. Estimated times of cyclicity were earlier in Group I (113.34 h, 95 % CI 82.31-144.37) as compared to Group II (504.39 h, 95 % CI 319.91-688.88; p < 0.001).

CONCLUSIONS

Delayed cyclicity on aEEG has a strong correlation with unfavorable brain neuroimages in newborns with possible perinatal asphyxia. If sleep-wake cycles do not appear during initial period after birth, follow-up aEEG studies are recommended.

TRIAL REGISTRATION

Retrospectively registered Registration number: BD 2015-148 Name of registry: amplitude integrated EEG in neonate Date of registration: September 9, 2015.

Guiding Antiepileptic Therapy in a Pediatric Patient with Severe Meningoencephalitis and Decompressive Craniectomy with the Use of Amplitude-Integrated Electroencephalography

Introduction Amplitude-integrated electroencephalography (aEEG) is one of the most widely used neuromonitoring tools in neonatology today. However, little is known about its clinical indications and potential benefits in pediatric intensive care patients. Based on limited experience, its impact on therapeutic decision-making in this patient population is unclear. Case Description We report the case of a 16-year-old boy who, after a pansinusitis, developed a severe meningoencephalitis and intracranial empyema with increased intracranial pressure that required drainage and decompressive craniectomy. He subsequently developed status epilepticus despite a combination of various anticonvulsants. Only after the initialization of an aEEG, we were able to adequately diagnose and continuously monitor his seizure activity and titrate the effect of the antiepileptic drugs. During his hospital stay, we were able to clearly monitor and guide our therapy by accurately identifying the termination of status epilepticus and the recurrence of seizures. Discussion With the help of aEEG, it was easy to identify the nonconvulsive status epilepticus (NCSE) and the ongoing seizure activity in this teenage patient. NCSE is a clinical problem with an effect on the outcome of the patient and is often underdiagnosed. AEEG enabled a rapid detection and management of seizure activity and thereby reduced the overall seizure burden, which was associated with better neurologic outcome.

Neonatal EEG and neurodevelopmental outcome in preterm infants born before 32 weeks

OBJECTIVE

To assess the value of neonatal EEG for predicting non-optimal neurodevelopmental outcomes in very preterm infants, using a multimodal strategy of evaluation comprising brain imaging and clinical assessment.

DESIGN AND SETTING

Between 2003 and 2009, we performed an observational, population-based study. Out of 2040 eligible preterm infants born before 32 weeks, 1954 were enrolled in the French regional Loire Infant Follow-Up Team (LIFT) cohort. 1744 (89%) of these completed the follow-up. Neonatal EEGs were recorded prospectively as two EEGs during the first 2 weeks of life and then one every 2 weeks up to 33 weeks.

MAIN OUTCOME MEASURES

The neurodevelopmental outcome was assessed by physical examination, the Brunet-Lézine Test and/or the Age and Stages Questionnaire at 2 years of corrected age.

RESULTS

Of the 1744 infants assessed at 2 years, 422 had a non-optimal outcome. A total of 4804 EEGs were performed, and 1345 infants had at least one EEG. EEG abnormalities were predictive of non-optimal outcomes after controlling for confounding factors such as severe intracranial lesions detected by brain imaging. Transient moderate and severe abnormalities were independent predictors of non-optimal outcomes with an OR and 95% CI of 1.49 (1.08 to 2.04) and 2.38 (1.49 to 3.81), respectively. In the validation group, the predictive risk stratification tree identified severe abnormalities as a factor contributing to the prognosis of two subgroups: infants with severe cranial lesions and infants with a normal examination at discharge and without severe cranial lesions.

Early amplitude-integrated EEG monitoring 6 h after birth predicts long-term neurodevelopment of asphyxiated late preterm infants

The present study aimed to assess the prognostic value of early amplitude-integrated electroencephalogram (aEEG) in late preterm infants who were born at a gestational age between 34 0/7 and 36 6/7 weeks for the prediction of neurobehavioral development. Late preterm infants (n = 170) with normal, mild, and severe asphyxia underwent continuous recording of aEEG for 4-6 h starting 6-8 h after delivery. The recordings were analyzed for background pattern, sleep-wake cycle (SWC), and seizures. Survivors were assessed at 18 months by neurological examination and Bayley Scales of Infant Development II. The incidence of adverse neurological outcome in the asphyxia group was significantly higher than in the normal group. For late preterm infants in the asphyxia group, abnormal aEEG pattern had a predictive potential of neurological outcomes with sensitivity of 78.57% (specificity, 87.80%; positive predictive value [PPV], 68.75%; negative predictive value [NPV], 92.31%; power, 85.45%). Non-SWC and intermediate SWC significantly were increased (25.45 and 52.73%, respectively) in the asphyxia group vs. the normal group. SWC pattern had neurological prognosis value in the asphyxia group with sensitivity of 64.29% (specificity, 87.80%; PPV, 64.29%; NPV, 87.80%; power, 81.82%).

CONCLUSION

Early aEEG patterns are important determinants of long-term prognosis of neurodevelopmental outcome in asphyxiated late preterm infants.