Objective: The aim of our study was to assess the use of aEEG in our pediatric intensive care unit (PICU), indications for neuromonitoring and its findings, utility for seizure detection, and associations with outcome. Design: We retrospectively analyzed non-neonates who were treated in our PICU and received amplitude-integrated EEG (aEEG). Patients: 27 patients aged between 29 days and 10 0/12 years (median 7.3 months) were included, who received a total of 35 aEEGS. Measurements: aEEG tracings were assessed for background (BG) pattern and its evolution, seizures, and side differences using a visual classification (Hellström-Westas). Clinical data were collected from patients' histories and analyzed for correlation with aEEG findings. Main results: While rare in early years, there was an increase in use over time. Most aEEGs were conducted because of (suspected) seizures or for management of antiepileptic treatment. aEEG had low sensitivity but high specificity for recognition of pathological BG pattern with reference to conventional EEG. Worsening of BG pattern or failure to improve was associated with death. Seizure detection rates by aEEG were higher than by clinical observation, especially for identification of non-convulsive epileptic state (ES). Side differences in aEEG were rare, but if present, they were associated with unilateral brain injury. Conclusions: aEEG is useful for the detection of seizures and ES in pediatric intensive care patients. Abnormal BG pattern and poor evolution of BG are negatively associated with survival. aEEG is a potential supplement to conventional EEG, facilitating long-term surveillance of cerebral function when continuous full-channel EEG is not available. Further investigation is needed.

Background: Evidence supporting continuous EEG monitoring in pediatric intensive care is increasing, but continuous full-channel EEG is a scarce resource. Amplitude-integrated EEG (aEEG) monitors are broadly available in children's hospitals due to their use in neonatology and can easily be applied to older patients. Objective: The aim of this survey was to evaluate the use of amplitude-integrated EEG in German and Swiss pediatric intensive care units (PICUs). Design: An online survey was sent to German and Swiss PICUs that were identified via databases provided by the German Pediatric Association (DGKJ) and the Swiss Society of Intensive Care (SGI). The questionnaire contained 18 multiple choice questions including the PICU size and specialization, indications for aEEG use, perceived benefits from aEEG, and data storage. Main results: Forty-three (26%) PICUs filled out the questionnaire. Two thirds of all interviewed PICUs use aEEG in non-neonates. Main indications were neurological complications or disease and altered mental state. Features assessed were mostly seizures and side differences, less frequently height of amplitude and background pattern. Interpretation of raw EEG also played an important role. All interviewees would appreciate the establishment of reference values for toddlers and children. Conclusions: aEEG is used in a large proportion of the interviewed PICUs. The widespread use without validation of data generates the need for further evaluation of this technique and the establishment of reference values for non-neonates.

Amplitude-integrated EEG (aEEG) is an easily accessible technique to monitor the electrocortical activity in preterm and term infants in neonatal intensive care units (NICUs). This method was first used to monitor newborns after asphyxia, providing information about future neurological outcomes. The aEEG is also helpful to select newborns who benefit from cooling. The aEEG monitoring of preterm infants is becoming more widespread, as various studies have shown that neurodevelopmental outcome is related to early aEEG tracings. Here, we demonstrate the application of the aEEG monitoring system and present typical patterns that depend upon gestational age and pathophysiological conditions. Furthermore, we mention pitfalls in the interpretation of the aEEG, as this method requires accurate fixation and localization of the electrodes. Additionally, the raw EEG can be used to detect neonatal seizures or to identify aEEG application problems. In conclusion, aEEG is a safe and generally well-tolerated method for the bedside monitoring of neonatal cerebral function; it can even provide information about long-term outcome.

Neurodevelopmental outcome after prematurity is crucial. The aim was to compare two amplitude-integrated EEG (aEEG) classifications (Hellström-Westas (HW), Burdjalov) for outcome prediction. We recruited 65 infants ≤32 weeks gestational age with aEEG recordings within the first 72 h of life and Bayley testing at 24 months corrected age or death. Statistical analyses were performed for each 24 h section to determine whether very immature/depressed or mature/developed patterns predict survival/neurological outcome and to find predictors for mental development index (MDI) and psychomotor development index (PDI) at 24 months corrected age. On day 2, deceased infants showed no cycling in 80% (HW, p = 0.0140) and 100% (Burdjalov, p = 0.0041). The Burdjalov total score significantly differed between groups on day 2 (p = 0.0284) and the adapted Burdjalov total score on day 2 (p = 0.0183) and day 3 (p = 0.0472). Cycling on day 3 (HW; p = 0.0059) and background on day 3 (HW; p = 0.0212) are independent predictors for MDI (p = 0.0016) whereas no independent predictor for PDI was found (multiple regression analyses). Conclusion: Cycling in both classifications is a valuable tool to assess chance of survival. The classification by HW is also associated with long-term mental outcome. What is Known: •Neurodevelopmental outcome after preterm birth remains one of the major concerns in neonatology. •aEEG is used to measure brain activity and brain maturation in preterm infants. What is New: •The two common aEEG classifications and scoring systems described by Hellström-Westas and Burdjalov are valuable tools to predict neurodevelopmental outcome when performed within the first 72 h of life. •Both aEEG classifications are useful to predict chance of survival. The classification by Hellström-Westas can also predict long-term outcome at corrected age of 2 years.

BACKGROUND AND OBJECTIVES The worldwide SARS-CoV-2 virus pandemic challenges adolescents’ mental health. The aim of this study was to compare the number of pediatric intensive care unit (PICU) admissions after suicide attempts during the first German lockdown and one year later during a second, prolonged lockdown with pre-pandemic years. METHODS A retrospective multicenter study was conducted among 27 German PICUs. Cases <18 years admitted to the PICU due to accidents or injuries between March 16th and May 31st of 2017-2021 were identified based on ICD-10 codes (German modification) and patient data entered into a database. This study is a subset analysis on suicide attempts in adolescents aged 12–17.9 years. The Federal Statistics Office was queried for data on fatal suicides, which were available only for 2020 in adolescents aged 10–17.9 years. RESULTS Total admissions and suicide attempts declined during the first lockdown in 2020 (standardized morbidity ratio (SMR) 0.74 (95% CI 0.58–0.92) and 0.69 (0.43–1.04), respectively) and increased in 2021 (2.14 (SMR 1.86–2.45) and 2.84 (2.29–3.49), respectively). Fatal suicide rates remained stable between 2017–2019 and 2020 (1.57 v. 1.48/100,000 adolescent years) with monthly numbers showing no clear trend during the course of 2020. CONCLUSIONS This study shows a strong increase in serious suicide attempts among adolescents during the course of the pandemic in Germany. More research is needed to understand the relation between pandemic prevention measures and suicidal ideation to help implement mental health support for adolescents.

IntroductionInterpretation of amplitude-integrated EEG (aEEG) is hindered by lacking knowledge on physiological background patterns in children. The aim of this study was to find out whether aEEG differs between wakefulness and sleep in children.MethodsForty continuous full-channel EEGs (cEEG) recorded during the afternoon and overnight in patients <18 years of age without pathologies or only solitary interictal epileptiform discharges were converted into aEEGs. Upper and lower amplitudes of the C3–C4, P3–P4, C3–P3, C4–P4, and Fp1–Fp2 channels were measured during wakefulness and sleep by two investigators and bandwidths (BW) calculated. Sleep states were assessed according to the American Academy of Sleep Medicine. Median and interquartile ranges (IQR) were calculated to compare the values of amplitudes and bandwidth between wakefulness and sleep.ResultsMedian age was 9.9 years (IQR 6.1–14.7). All patients displayed continuous background patterns. Amplitudes and BW differed between wakefulness and sleep with median amplitude values of the C3–C4 channel 35 μV (IQR: 27–49) for the upper and 13 μV (10–19) for the lower amplitude. The BW was 29 μV (21–34). During sleep, episodes with high amplitudes [upper: 99 μV (71–125), lower: 35 μV (25–44), BW 63 μV (44–81)] corresponded to sleep states N2–N3. High amplitude-sections were interrupted by low amplitude-sections, which became the longer toward the morning [upper amplitude: 39 μV (30–51), lower: 16 μV (11–20), BW 23 μV (19–31)]. Low amplitude-sections were associated with sleep states REM, N1, and N2. With increasing age, amplitudes and bandwidths declined.ConclusionaEEGs in non-critically ill children displayed a wide range of amplitudes and bandwidths. Amplitudes were low during wakefulness and light sleep and high during deep sleep. Interpretation of pediatric aEEG background patterns must take into account the state of wakefulness in in clinical practice and research.

To improve the prediction of neurodevelopmental outcome in very preterm infants, this study used the combination of amplitude-integrated electroencephalography (aEEG) within the first 72 h of life and cranial magnetic resonance imaging (MRI) at term equivalent age. A single-center cohort of 38 infants born before 32 weeks of gestation was subjected to both investigations. Structural measurements were performed on MRI. Multiple regression analysis was used to identify independent factors including functional and structural brain measurements associated with outcome at a corrected age of 24 months. aEEG parameters significantly correlated with MRI measurements. Reduced deep gray matter volume was associated with low Burdjalov Score on day 3 (p < 0.0001) and day 1–3 (p = 0.0012). The biparietal width and the transcerebellar diameter were related to Burdjalov Score on day 1 (p = 0.0111; p = 0.0002). The final multiple regression analysis revealed independent predictors of neurodevelopmental outcome: intraventricular hemorrhage (p = 0.0060) and interhemispheric distance (p = 0.0052) for mental developmental index; Burdjalov Score day 1 (p = 0.0201) and interhemispheric distance (p = 0.0142) for psychomotor developmental index.Conclusion: Functional aEEG parameters were associated with altered brain maturation on MRI. The combination of aEEG and MRI contributes to the prediction of outcome at 24 months. What is Known: • Prematurity remains a risk factor for impaired neurodevelopment.• aEEG is used to measure brain activity in preterm infants and cranial MRI is performed to identify structural gray and white matter abnormalities with impact on neurodevelopmental outcome. What is New: • aEEG parameters observed within the first 72 h of life were associated with altered deep gray matter volumes, biparietal width, and transcerebellar diameter at term equivalent age.• The combination of aEEG and MRI contributes to the prediction of neurodevelopmental outcome at 2 years of corrected age in very preterm infants.Electronic supplementary materialThe online version of this article (10.1007/s00431-018-3166-2) contains supplementary material, which is available to authorized users.

Aim Amplitude‐integrated electroencephalography (aEEG) is used in children beyond neonatal age, but systematic investigations have been lacking. This mini‐review summarised aEEG studies on children aged one month to 18 years, evaluated the usefulness of aEEG and identified knowledge gaps or limitations. Methods We searched the PubMed database for articles published in English up to September 2020, and 23 papers were identified. Results aEEG was frequently used to compensate for the absence of continuous full‐channel EEG monitoring, particularly for detecting seizures. Interpreting background patterns was based on neonatal classifications, as reference values for older infants and children are lacking. It is possible that aEEG could predict outcomes after paediatric cardiac arrests and other conditions. Gaps in our knowledge exist with regard to normal values in healthy children and the effects of sedation on aEEG background patterns in children. Conclusion The main application of aEEG was detecting and treating paediatric seizures. Further research should determine reference values and investigate the potential to predict outcome after critical events or in acute neurological disease. It is likely that aEEG will play a role in paediatric critical care in the future.