Predicting death or long-term neurodevelopmental outcome in term newborns after hypoxic ischemic encephalopathy

Hypoxic-ischemic encephalopathy after perinatal asphyxia is a severe neonatal disease with a high mortality and morbidity rate despite recent improvements in medical care in the Neonatal Intensive Care Unit. In the diagnostic and prognostic workup of these patients, a wide range of biochemical, neurophysiological and radiological tests is performed. Although many of these predictive parameters have been studied, an internationally accepted, validated prediction model to predict the long-term neurodevelopmental outcome in this high-risk population is currently lacking. This thesis aimed to investigate and contribute to the current evidence on long-term outcome prediction of newborns with hypoxic ischemic encephalopathy treated with controlled therapeutic hypothermia. The systematic review performed confirmed that to date there is no clinically applicable multivariate prediction model available for long-term outcome in these infants. The additional studies showed that the MRI Weeke score is a reliable predictor of outcome and should be implemented in clinical practice. It was demonstrated that multiple organ dysfunction should not be taken into account when predicting or discussing the outcome of these infants. Neither the presence of seizures, nor the severity of seizures (described by the number of anti-epileptic drugs needed) are associated with the combined outcome up until the age of five years after correction for important confounders. Finally, a novel prediction model for the combined outcome death or NDI at two years of age was build and internally validated

Automated EEG background analysis to identify neonates with hypoxic-ischemic encephalopathy treated with hypothermia at risk for adverse outcome: A pilot study

Background: To improve the objective assessment of continuous video-EEG (cEEG) monitoring of neonatal brain function, the aim was to relate automated derived amplitude and duration parameters of the suppressed periods in the EEG background (dynamic Interburst Interval= dIBIs) after neonatal hypoxic-ischemic encephalopathy (HIE) to favourable or adverse neurodevelopmental outcome. Methods: Nineteen neonates (gestational age 36-41 weeks) with HIE underwent therapeutic hypothermia and had cEEG-monitoring. EEGs were retrospectively analyzed with a previously developed algorithm to detect the dynamic Interburst Intervals. Median duration and amplitude of the dIBIs were calculated at 1h-intervals. Sensitivity and specificity of automated EEG background grading for favorable and adverse outcomes were assessed at 6h-intervals. Results: Dynamic IBI values reached the best prognostic value between 18 and 24h (AUC of 0.93). EEGs with dIBI amplitude ≥15 μV and duration 10s were specific for adverse outcome (89-100%) at 18-24h (n = 10). Extremely low voltage and invariant EEG patterns were indicative of adverse outcome at all time points. Conclusions: Automated analysis of the suppressed periods in EEG of neonates with HIE undergoing TH provides objective and early prognostic information. This objective tool can be used in a multimodal strategy for outcome assessment. Implementation of this method can facilitate clinical practice, improve risk stratification and aid therapeutic decision-making. A multicenter trial with a quantifiable outcome measure is warranted to confirm the predictive value of this method in a more heterogeneous dataset

Neonatal EEG graded for severity of background abnormalities in hypoxic-ischaemic encephalopathy

This report describes a set of neonatal electroencephalogram (EEG) recordings graded according to the severity of abnormalities in the background pattern. The dataset consists of 169 hours of multichannel EEG from 53 neonates recorded in a neonatal intensive care unit. All neonates received a diagnosis of hypoxic-ischaemic encephalopathy (HIE), the most common cause of brain injury in full term infants. For each neonate, multiple 1-hour epochs of good quality EEG were selected and then graded for background abnormalities. The grading system assesses EEG attributes such as amplitude and frequency, continuity, sleep--wake cycling, symmetry and synchrony, and abnormal waveforms. Background severity was then categorised into 4 grades: normal or mildly abnormal EEG, moderately abnormal EEG, severely abnormal EEG, and inactive EEG. The data can be used as a reference set of multi-channel EEG for neonates with HIE, for EEG training purposes, or for developing and evaluating automated grading algorithms

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

Peer reviewe

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

Abstract: 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

Building an Open Source Classifier for the Neonatal EEG Background: A Systematic Feature-Based Approach From Expert Scoring to Clinical Visualization

Neonatal brain monitoring in the neonatal intensive care units (NICU) requires a continuous review of the spontaneous cortical activity, i.e., the electroencephalograph (EEG) background activity. This needs development of bedside methods for an automated assessment of the EEG background activity. In this paper, we present development of the key components of a neonatal EEG background classifier, starting from the visual background scoring to classifier design, and finally to possible bedside visualization of the classifier results. A dataset with 13,200 5-minute EEG epochs (8–16 channels) from 27 infants with birth asphyxia was used for classifier training after scoring by two independent experts. We tested three classifier designs based on 98 computational features, and their performance was assessed with respect to scoring system, pre- and post-processing of labels and outputs, choice of channels, and visualization in monitor displays. The optimal solution achieved an overall classification accuracy of 97% with a range across subjects of 81–100%. We identified a set of 23 features that make the classifier highly robust to the choice of channels and missing data due to artefact rejection. Our results showed that an automated bedside classifier of EEG background is achievable, and we publish the full classifier algorithm to allow further clinical replication and validation studies.Peer reviewe

Neuromonitoring during newborn transition

Background: Newborn infant neurological function can be measured by monitoring electrical activity (electroencephalography) or cerebral oxygenation via NIRS (near infrared spectroscopy). In practice the clinical applications of electroencephalography (EEG) are limited to monitoring infants following moderate to severe hypoxic ischemic injury (HIE), and for the detection of seizures in at risk infants. NIRS monitoring has been the focus of a number of research trials but has no clinical applications in the immediate newborn period to date, and is not routinely performed in neonatal units. Aim: To assess the feasibility of infant neuromonitoring in the immediate period in two important clinical scenarios. Firstly, to assess the feasibility of monitoring brain activity during the first minutes of life in healthy term infants. Secondly, to assess the feasibility and utility of monitoring newborn preterm infants’ brain activity and cerebral oxygenation in the context of an interventional randomized controlled trial. Methods: 1. Healthy term newborn infants had EEG monitoring performed for the first ten minutes of life. EEG was assessed both qualitatively and quantitatively. All infants had respiratory function monitoring performed simultaneously. 2. Forty-five infants (< 32 weeks gestation) were randomly assigned to different methods of newborn infant cord clamping. All infants had EEG and NIRS monitoring for the first 72 hours of life. Quantitative features of EEG and median NIRS values were compared between groups at 6 and 12 hours of life as a primary outcome measure. Results: 1. Forty-nine infants had EEG recordings. Median (IQR) age at time of initial EEG recording was 3.0 (2·5 to 3·8) minutes. End tidal CO2 and tidal volumes increased over the first 3 minutes of life and then stabilized. Good quality EEG, with continuous mixed frequency activity with a range of 25-50μV, was observed in all infants. The majority of EEG spectral power was within the delta band. 2. There were 45 infants included. One infant died in the delivery room. Median time (IQR) from birth until EEG application was 3.05 (1.85 to 5.38) hrs. For primary outcome measures, data was available for 42/44 (95%) at 6 hrs and 44/44 (100%) at 12 hours. There was no significant difference between groups for measures for EEG values or cerebral NIRS. Conclusion: Infant neuromonitoring in the immediate newborn period is feasible in the first minutes of life in healthy term infants and within the first hours of life in preterm infants. Normative quantitative data for electrical activity in healthy newborn term infants during the first minutes of life is described for the first time. Neuromonitoring during the first day of life as an outcome measure for preterm interventional trials is possible and the outcomes from this research is promising for further trials

Laskennalliset muuttujat vastasyntyneen aivomonitoroinnin arvioinnissa

The aim of this thesis was to find out if computational electroencephalography (EEG) features can be used in the automated monitoring of newborns after asphyxia. EEG is already widely used in the neonatal intensive care units but there is a need for quantitative measures that can be obtained without the presence of a clinical expert. One of the biggest challenges in the treatment of newborns with asphyxia is also to correctly estimate the severity of the resulting neurological problems. Eight different feature classes were computed for 42 full-term babies from periods of quiet and active sleep. These feature classes measured correlations of amplitude and phase, interhemispheric synchrony, multifractality and spectral properties. We then studied the ability of these features to distinguish between different severity groups and also tested a classification algorithm to predict the outcome of the babies. Quiet sleep was noted to be more sensitive when separating groups with different grades of severity and most of the used feature classes showed significant results in statistical testing between the groups. The babies with the normal outcome were classified more accurately with the EEG based classification algorithm, than with only the clinical estimation.Työn tarkoituksena oli selvittää, onko aivosähkökäyrästä (EEG) laskettuja parametreja mahdollista käyttää happivajeesta kärsineiden vastasyntyneiden automaattisessa monitoroinnissa. EEG on jo nyt yleisesti käytössä vastasyntyneiden teho-osastoilla, mutta tarve kvantitatiivisille mittareille, joiden tulkintaan ei tarvita lääketieteen asiantuntijaa, on suuri. Lisäksi yksi suurimmista haasteista on pystyä arvioimaan tarkasti, kuinka vakaviin neurologisiin ongelmiin happivaje johtaa. Työssä laskettiin kahdeksan erilaista muuttujajoukkoa 42 täysiaikaiselle vauvalle sekä hiljaisen että aktiivisen unen aikana. Nämä muuttujat mittasivat amplitudin ja vaiheen korrelaatioita, aivopuoliskojen välistä synkroniaa, multifraktaalisuutta sekä taajuusjakaumaa. Tämän jälkeen tutkittiin muuttujien kykyä erotella eri vakavuusasteisia ryhmiä ja testattiin luokittelualgoritmia vauvojen tulevan terveydentilan ennustamiseen. Hiljaisen unen huomattiin olevan herkempi havaitsemaan eroja eri vakavuusasteisten ryhmien välillä ja tilastollisen testauksen perusteella suurin osa valituista muuttujajoukoista erotteli merkittävästi eri vakavuusryhmiä. Ne vauvat, jotka toipuivat hapenpuutteesta täysin, pystyttiin löytämään EEG-pohjaisella luokittimella tarkemmin kuin pelkän kliinisen arvion avulla