Pharmacokinetics and safety of brivaracetam in neonates with repeated electroencephalographic seizures: A multicenter, open-label, single-arm study

OBJECTIVE

To evaluate the pharmacokinetics (PK), safety, and tolerability of brivaracetam (BRV) in neonates with repeated electroencephalographic seizures not controlled with previous antiseizure medications (ASMs).

METHODS

Phase 2/3, multicenter, open-label, single-arm study (N01349/NCT03325439) in neonates with repeated electroencephalographic seizures (lasting ≥10 s) confirmed by video-electroencephalography, and inadequate seizure control with at least one ASM. A screening period (up to 36 h) was followed by a 48-h evaluation period during which patients received 0.5 mg/kg BRV twice daily (b.i.d) intravenously (IV). Patients who benefitted from BRV (investigator's opinion) could continue 0.5 mg/kg b.i.d (IV or oral solution) in an extension period. Outcomes included plasma concentrations of BRV following the first dose (primary), and incidence of treatment-emergent adverse events (TEAEs).

RESULTS

Six patients (median [range] postnatal age: 1.5 [1.0, 6.0] days) received ≥1 dose of BRV. All six patients completed the evaluation period; two entered and completed the extension period. Overall (evaluation and extension periods), three patients received one dose of 0.5 mg/kg BRV and three received more than one dose. The median (range) duration of exposure to BRV (IV and oral solution) was 1.5 (1.0, 29.0) days (n = 6). At 0.5-1, 2-4, and 8-12 h following IV BRV administration, the GeoMean (GeoCV) plasma concentrations of BRV were 0.53 mg/L (15.40% [n = 5]), 0.50 mg/L (28.20% [n = 6]), and 0.34 mg/L (13.20% [n = 5]), respectively. Individual and population BRV PK profiles were estimated, and individual PK parameters were calculated using Bayesian feedback. The observed concentrations were consistent with the predicted PK. Three patients experienced four TEAEs, none of which were considered related to BRV.

SIGNIFICANCE

BRV plasma concentrations in neonates were consistent with data in older children receiving BRV oral solution, and with data from adults receiving a nominal IV dose of 25 mg b.i.d. BRV was well tolerated, with no drug-related TEAEs reported.

PLAIN LANGUAGE SUMMARY

Few drugs are available to treat seizures in newborn babies. Brivaracetam is approved to treat focal-onset seizures in children and adults in Europe (patients 2 years of age and older) and the United States (patients 1 month of age or older). In this study, six newborns with repeated seizures were treated with intravenous brivaracetam. The study doctors took samples of blood from the newborns and measured the levels of brivaracetam. The concentrations of brivaracetam in the newborns' blood plasma were consistent with data from studies in older children and in adults. No brivaracetam-related medical problems were reported.

Treatment of seizures in the neonate: Guidelines and consensus-based recommendations-Special report from the ILAE Task Force on Neonatal Seizures

Seizures are common in neonates, but there is substantial management variability. The Neonatal Task Force of the International League Against Epilepsy (ILAE) developed evidence-based recommendations about antiseizure medication (ASM) management in neonates in accordance with ILAE standards. Six priority questions were formulated, a systematic literature review and meta-analysis were performed, and results were reported following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 standards. Bias was evaluated using the Cochrane tool and risk of Bias in non-randomised studies - of interventions (ROBINS-I), and quality of evidence was evaluated using grading of recommendations, assessment, development and evaluation (GRADE). If insufficient evidence was available, then expert opinion was sought using Delphi consensus methodology. The strength of recommendations was defined according to the ILAE Clinical Practice Guidelines development tool. There were six main recommendations. First, phenobarbital should be the first-line ASM (evidence-based recommendation) regardless of etiology (expert agreement), unless channelopathy is likely the cause for seizures (e.g., due to family history), in which case phenytoin or carbamazepine should be used. Second, among neonates with seizures not responding to first-line ASM, phenytoin, levetiracetam, midazolam, or lidocaine may be used as a second-line ASM (expert agreement). In neonates with cardiac disorders, levetiracetam may be the preferred second-line ASM (expert agreement). Third, following cessation of acute provoked seizures without evidence for neonatal-onset epilepsy, ASMs should be discontinued before discharge home, regardless of magnetic resonance imaging or electroencephalographic findings (expert agreement). Fourth, therapeutic hypothermia may reduce seizure burden in neonates with hypoxic-ischemic encephalopathy (evidence-based recommendation). Fifth, treating neonatal seizures (including electrographic-only seizures) to achieve a lower seizure burden may be associated with improved outcome (expert agreement). Sixth, a trial of pyridoxine may be attempted in neonates presenting with clinical features of vitamin B6-dependent epilepsy and seizures unresponsive to second-line ASM (expert agreement). Additional considerations include a standardized pathway for the management of neonatal seizures in each neonatal unit and informing parents/guardians about the diagnosis of seizures and initial treatment options.

Early emollient bathing is associated with subsequent atopic dermatitis in an unselected birth cohort study

BACKGROUND

Skin barrier dysfunction is a key component of the pathogenesis of atopic dermatitis (AD). Recent research on barrier optimization to prevent AD has shown mixed results. The aim of this study was to assess the relationship between emollient bathing at 2 months and the trajectory of AD in the first 2 years of life in a large unselected observational birth cohort study.

METHODS

The Babies After SCOPE: Evaluating the Longitudinal Impact Using Neurological and Nutritional Endpoints Birth Cohort study enrolled 2183 infants. Variables extracted from the database related to early skincare, skin barrier function, parental history of atopy, and AD outcomes. Statistical analysis was performed to adjust for potential confounding variables.

RESULTS

One thousand five hundred five children had data on AD status available at 6, 12, and 24 months. Prevalence of AD was 18.6% at 6 months, 15.2% at 12 months, and 16.5% at 24 months. Adjusted for potential confounding variables, the odds of AD at any point were higher among infants who had emollient baths at 2 months (OR (95% CI): 2.41 (1.56 to 3.72), p < .001). Following multivariable analysis, the odds of AD were higher among infants who had both emollient baths and frequent emollient application at 2 months, compared with infants who had neither (OR (95% CI) at 6 months 1.74 (1.18-2.58), p = .038), (OR (95% CI) at 12 months 2.59 (1.69-3.94), p < .001), (OR (95% CI) at 24 months 1.87 (1.21-2.90), p = .009).

CONCLUSION

Early emollient bathing was associated with greater development of AD by 2 years of age in this population-based birth cohort study.

Postnatal maternal mental health and postnatal attachment

AIMS

Maternal mental illness has a significant influence on negative maternal and childhood outcomes. Few studies have focused on both maternal depression and anxiety, or explored the interplay of maternal mental illness and the mother-infant bond. We aimed to examine the relationship between early postnatal attachment and mental illness at 4 and 18 months postpartum.

METHODS

This was a secondary analysis of 168 mothers recruited from the BabySmart Study. All women delivered healthy term infants. Depression and anxiety symptoms were measured via the Edinburgh Postnatal Depression Scale (EPDS) and Beck's Depression and Anxiety Inventory at 4 and 18 months respectively. Maternal Postnatal Attachment Scale (MPAS) was completed at 4 months. Negative binomial regression analysis investigated associated risk factors at both time points.

RESULTS

The prevalence of postpartum depression fell from 12.5% at 4 months to 10.7% at 18 months. Anxiety rates increased from 13.1% to 17.9% at similar time points. At 18 months, both symptoms were new in almost two-thirds of women, 61.1% and 73.3% respectively. There was a strong correlation between the anxiety scale of the EPDS and the total EPDS p-score (R=0.887, p<0.001). Early postpartum anxiety was an independent risk factor for later anxiety and depression. High attachment scores were an independent protective factor for depression at 4 months (RR=0.943, 95%CI: 0.924-0.962, p<0.001) and 18 months (RR=0.971, 95%CI: 0.949-0.997, p=0.026), and protected against early postpartum anxiety (RR=0.952, 95%CI: 0.933-0.97, p<0.001).

CONCLUSION

The prevalence of postnatal depression at 4 months was similar to national and international rates, although clinical anxiety increased over time with almost 1 in 5 women scoring in the clinical anxiety range at 18 months. Strong maternal attachment was associated with decreased reported symptoms of both depression and anxiety. The effect of persistent maternal anxiety on maternal and infant health needs to be determined.

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.

Parental atopy and risk of atopic dermatitis in the first two years of life in the BASELINE birth cohort study

BACKGROUND

Atopic dermatitis (AD) has a strong genetic basis. The objective of this study was to assess the association between parental atopy and AD development by 2 years.

METHODS

A secondary data analysis of the BASELINE Birth Cohort study was performed (n = 2183). Parental atopy was self-reported at 2 months. Infants were examined for AD by trained health care professionals at 6, 12, and 24 months. Variables extracted from the database related to skin barrier function, early skincare, parental atopy, and AD. Statistical analysis adjusted for potential confounding variables.

RESULTS

Complete data on AD status were available for 1505 children at 6, 12, and 24 months. Prevalence of AD was 18.6% at 6 months, 15.2% at 12 months, and 16.5% at 24 months. Adjusted odds ratios (95% CIs) following multivariable analysis were 1.57 (1.09-2.25) at 6 months and 1.66 (1.12-2.46) at 12 months for maternal AD; 1.90 (1.28-2.83) at 6 months and 1.85 (1.20-2.85) at 24 months for paternal AD; 1.76 (1.21-2.56) at 6 months and 1.75 (1.16-2.63) at 12 months for maternal asthma; and 1.70 (1.19-2.45) at 6 months, 1.86 (1.26-2.76) at 12 months, and 1.99 (1.34-2.97) at 24 months for paternal asthma. Parental rhinitis was only associated with AD with maternal rhinitis at 24 months (aOR (95% CI): 1.79 (1.15-2.80)).

CONCLUSION

Parental AD and asthma were associated with increased risk of objectively diagnosed AD in offspring in this contemporary cohort.

Study protocol: assessing SleeP IN infants with early-onset atopic Dermatitis by Longitudinal Evaluation (The SPINDLE study)

BACKGROUND

Atopic dermatitis (AD) is the most common chronic inflammatory skin condition in childhood. Most (50-60%) children with AD report sleep disturbance, which is secondary to itch, dry skin, inflammation, and abnormal circadian rhythm. Sleep is essential for brain development, learning, and growth. Sleep disruption in early life is associated with cognitive and psychological dysfunction in later life. The aim of this study is to describe in detail the sleep architecture of infants with early-onset atopic dermatitis (AD), compared to controls, by using EEG polysomnography, sleep actigraphy, and parental reporting.

METHODS

This observational study will recruit six- to eight-month old infants with moderate to severe AD and age-matched control infants who do not have AD. At six-eight months diurnal sleep electroencephalography and polysomnography will be performed in our research center. Nocturnal sleep actigraphy will be performed at home for five consecutive nights at six-eight months and 12 months. Between six and 12 months, monthly questionnaires will capture data on quantitative sleep and parental sleep. Skin barrier and immune profiles will be captured at six-eight and 12 months. AD will be assessed using standardized severity assessment tools and treated according to protocol. A neurodevelopmental assessment will be performed at 18 months to assess cognition and behaviour. An estimated sample size of 50 participants in each group is required to power the primary outcome of disturbed macrostructure of sleep and secondary outcomes of disturbed microstructure of sleep, and disturbed parental sleep, assuming an attrition rate of 60%. Potential confounding factors which will be controlled for in the data analysis will include parental educational level, parental depression, feeding practice, and number of siblings.

DISCUSSION

This study will provide a rich analysis of sleep in infants with AD in the first year of life using detailed electroencephalography, novel actigraphy techniques, and longitudinal parent-reported data. It may provide guidance on the optimal treatment of AD to prevent or reduce sleep disruption.

TRIAL REGISTRATION

clinicaltrials.gov NCT05031754 , retrospectively registered on September 2nd, 2021.

Long-term neuropsychological and behavioral outcome of mild and moderate hypoxic ischemic encephalopathy

BACKGROUND

Outcomes for infants who survive mild-moderate hypoxic ischemic encephalopathy (HIE) into adolescence is relatively uncharted.

AIMS

We examined neuropsychological and behavioral outcomes in adolescents with mild and moderate HIE, using both parent and self - informants, and including healthy peers and nearest age siblings as controls.

PARTICIPANTS

23 adolescents with a history of mild-moderate HIE (M age = 14.45 years, SD = 1.03; 14 boys and 9 girls) were recruited from an original cohort of 53. A group of their nearest - age siblings (n = 13), and healthy peers (n = 14) were recruited as controls.

OUTCOME MEASURES

A number of neuropsychological sub-tests, taken from the WISC-V.UK, Children's Memory Scale, NEPSY, WIAT-III.UK, Rey Complex Figure Copy Test and British Picture Vocabulary Scale were administered. Behavioral adjustment was assessed using the Strengths and Difficulties Questionnaire and the competence subscales of the Child Behavior Checklist.

RESULTS

No differences in neuropsychological and behavioral outcomes were observed between mild and moderate HIE cohorts. Together they had significantly lower scores on tests of attention/executive functioning, verbal reasoning and sensory-motor ability compared to healthy peers, with moderate to large effect sizes. Remedial provision at school was greater in the HIE group. Parents reported elevated levels of peer problems in the HIE group compared to both siblings and healthy peers. Reduced competencies were also observed.

CONCLUSIONS

We found evidence that both mild and moderate survivors of HIE experience neuropsychological, school and peer relationship problems in adolescence.

Denoising EEG Signals for Real-World BCI Applications Using GANs

As a measure of the brain's electrical activity, electroencephalography (EEG) is the primary signal of interest for brain-computer-interfaces (BCI). BCIs offer a communication pathway between a brain and an external device, translating thought into action with suitable processing. EEG data is the most common signal source for such technologies. However, artefacts induced in BCIs in the real-world context can severely degrade their performance relative to their in-laboratory performance. In most cases, the recorded signals are so heavily corrupted by noise that they are unusable and restrict BCI's broader applicability. To realise the use of portable BCIs capable of high-quality performance in a real-world setting, we use Generative Adversarial Networks (GANs) that can adopt both supervised and unsupervised learning approaches. Although our approach is supervised, the same model can be used for unsupervised tasks such as data augmentation/imputation in the low resource setting. Exploiting recent advancements in Generative Adversarial Networks (GAN), we construct a pipeline capable of denoising artefacts from EEG time series data. In the case of denoising data, it maps noisy EEG signals to clean EEG signals, given the nature of the respective artefact. We demonstrate the capability of our network on a toy dataset and a benchmark EEG dataset developed explicitly for deep learning denoising techniques. Our datasets consist of an artificially added mains noise (50/60 Hz) artefact dataset and an open-source EEG benchmark dataset with two artificially added artefacts. Artificially inducing myogenic and ocular artefacts for the benchmark dataset allows us to present qualitative and quantitative evidence of the GANs denoising capabilities and rank it among the current gold standard deep learning EEG denoising techniques. We show the power spectral density (PSD), signal-to-noise ratio (SNR), and other classical time series similarity measures for quantitative metrics and compare our model to those previously used in the literature. To our knowledge, this framework is the first example of a GAN capable of EEG artefact removal and generalisable to more than one artefact type. Our model has provided a competitive performance in advancing the state-of-the-art deep learning EEG denoising techniques. Furthermore, given the integration of AI into wearable technology, our method would allow for portable EEG devices with less noisy and more stable brain signals.

Multi-organ dysfunction scoring in neonatal encephalopathy (MODE Score) and neurodevelopmental outcomes

AIM

Neonatal encephalopathy (NE) is associated with an increased risk of multi-organ injury. The lack of standardised definitions for multi-organ dysfunction in NE hinders accurate quantification of these complications.

METHODS

A simple multi-organ dysfunction in neonatal encephalopathy scoring (MODE) system was created to include the cardiovascular, respiratory, gastrointestinal, haematological and neurological systems with a maximum score of 15. The MODE score was then compared with the grade of NE, Bayley Scales of Infant Development (Bayley-III) at 2 years of age and mortality. The Bayley score was used as it gave an objective score making it easier to compare the MODE score. Bayley score of <90 and/or abnormal MRI as an adverse outcome.

RESULTS

Infants with perinatal asphyxia (PA:n = 85) were prospectively enrolled (PA only n = 9; NE I = 23; NE II = 42; NE III = 11). Infants with higher MODE scores were significantly more likely to have moderate/severe NE (NE II/III: median scores (IQR) 7(5-10) versus mild NE 2 (1-3); p-value < 0.001) The MODE score was highly predictive of mortality (AUC 0.96, p-value = 0.002). Infants who had an abnormal neurological examination at discharge or abnormal Bayley-III scores had significantly higher MODE scores (p-value = 0.001).

CONCLUSION

Quantifying multi-organ injury is important to plan optimal early management and long-term follow-up. Additional use of clinical biomarkers may be useful as surrogate endpoints in future clinical trials and link to multi-organ longer-term developmental follow-up.

Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach

Ischemic heart disease is the highest cause of mortality globally each year. This puts a massive strain not only on the lives of those affected, but also on the public healthcare systems. To understand the dynamics of the healthy and unhealthy heart, doctors commonly use an electrocardiogram (ECG) and blood pressure (BP) readings. These methods are often quite invasive, particularly when continuous arterial blood pressure (ABP) readings are taken, and not to mention very costly. Using machine learning methods, we develop a framework capable of inferring ABP from a single optical photoplethysmogram (PPG) sensor alone. We train our framework across distributed models and data sources to mimic a large-scale distributed collaborative learning experiment that could be implemented across low-cost wearables. Our time-series-to-time-series generative adversarial network (T2TGAN) is capable of high-quality continuous ABP generation from a PPG signal with a mean error of 2.95 mmHg and a standard deviation of 19.33 mmHg when estimating mean arterial pressure on a previously unseen, noisy, independent dataset. To our knowledge, this framework is the first example of a GAN capable of continuous ABP generation from an input PPG signal that also uses a federated learning methodology.

Management of seizures in neonates with neonatal encephalopathy treated with hypothermia

Neonatal encephalopathy (NE) is the most common etiology of acute neonatal seizures - about half of neonates treated with therapeutic hypothermia for NE have EEG-confirmed seizures. These seizures are best identified with continuous EEG monitoring, as clinical diagnosis leads to under-diagnosis of subclinical seizures and over-treatment of events that are not seizures. High seizure burden, especially status epilepticus, is thought to augment brain injury. Treatment, therefore, is aimed at minimizing seizure burden. Phenobarbital remains the mainstay of treatment, as it is more effective than levetiracetam and easier to administer than fosphenytoin. Emerging evidence suggests that, for many neonates, it is safe to discontinue the phenobarbital after acute seizures resolve and prior to hospital discharge.

Spindle-AI: Sleep spindle number and duration estimation in infant EEG

OBJECTIVE

Sleep spindle features show developmental changes during infancy and have the potential to provide an early biomarker for abnormal brain maturation. Manual identification of sleep spindles in the electroencephalogram (EEG) is time-consuming and typically requires highly-trained experts. Automated detection of sleep spindles would greatly facilitate this analysis. Research on the automatic detection of sleep spindles in infant EEG has been limited to-date.

METHODS

We present a random forest-based sleep spindle detection method (Spindle-AI) to estimate the number and duration of sleep spindles in EEG collected from 141 ex-term born infants, recorded at 4 months of age. The signal on channel F4-C4 was split into a training set (81 ex-term) and a validation set (30 ex-term). An additional 30 ex-term infant EEGs (channel F4-C4 and channel F3-C3) were used as an independent test set. Fourteen features were selected for input into a random forest algorithm to estimate the number and duration of spindles and the results were compared against sleep spindles annotated by an experienced clinical physiologist.

RESULTS

The prediction of the number of sleep spindles in the independent test set demonstrated 93.3% to 93.9% sensitivity, 90.7% to 91.5% specificity, and 89.2% to 90.1% precision. The duration estimation of sleep spindle events in the independent test set showed a percent error of 5.7% to 7.4%.

CONCLUSION AND SIGNIFICANCE

Spindle-AI has been implemented as a web server that has the potential to assist clinicians in the fast and accurate monitoring of sleep spindles in infant EEGs.

Altered inflammasome activation in neonatal encephalopathy persists in childhood

Neonatal encephalopathy (NE) is characterized by altered neurological function in term infants and inflammation plays an important pathophysiological role. Inflammatory cytokines interleukin (IL)‐1β, IL‐1ra and IL‐18 are activated by the nucleotide‐binding and oligomerization domain (NOD)‐, leucine‐rich repeat domain (LRR)‐ and NOD‐like receptor protein 3 (NLRP3) inflammasome; furthermore, we aimed to examine the role of the inflammasome multiprotein complex involved in proinflammatory responses from the newborn period to childhood in NE. Cytokine concentrations were measured by multiplex enzyme‐linked immunosorbent assay (ELISA) in neonates and children with NE in the absence or presence of lipopolysaccharide (LPS) endotoxin. We then investigated expression of the NLRP3 inflammasome genes, NLRP3, IL‐1β and ASC by polymerase chain reaction (PCR). Serum samples from 40 NE patients at days 1 and 3 of the first week of life and in 37 patients at age 4–7 years were analysed. An increase in serum IL‐1ra and IL‐18 in neonates with NE on days 1 and 3 was observed compared to neonatal controls. IL‐1ra in NE was decreased to normal levels at school age, whereas serum IL‐18 in NE was even higher at school age compared to school age controls and NE in the first week of life. Percentage of LPS response was higher in newborns compared to school‐age NE. NLRP3 and IL‐1β gene expression were up‐regulated in the presence of LPS in NE neonates and NLRP3 gene expression remained up‐regulated at school age in NE patients compared to controls. Increased inflammasome activation in the first day of life in NE persists in childhood, and may increase the window for therapeutic intervention.

Deep Learning for EEG Seizure Detection in Preterm Infants

EEG is the gold standard for seizure detection in the newborn infant, but EEG interpretation in the preterm group is particularly challenging; trained experts are scarce and the task of interpreting EEG in real-time is arduous. Preterm infants are reported to have a higher incidence of seizures compared to term infants. Preterm EEG morphology differs from that of term infants, which implies that seizure detection algorithms trained on term EEG may not be appropriate. The task of developing preterm specific algorithms becomes extra-challenging given the limited amount of annotated preterm EEG data available. This paper explores novel deep learning (DL) architectures for the task of neonatal seizure detection in preterm infants. The study tests and compares several approaches to address the problem: training on data from full-term infants; training on data from preterm infants; training on age-specific preterm data and transfer learning. The system performance is assessed on a large database of continuous EEG recordings of 575[Formula: see text]h in duration. It is shown that the accuracy of a validated term-trained EEG seizure detection algorithm, based on a support vector machine classifier, when tested on preterm infants falls well short of the performance achieved for full-term infants. An AUC of 88.3% was obtained when tested on preterm EEG as compared to 96.6% obtained when tested on term EEG. When re-trained on preterm EEG, the performance marginally increases to 89.7%. An alternative DL approach shows a more stable trend when tested on the preterm cohort, starting with an AUC of 93.3% for the term-trained algorithm and reaching 95.0% by transfer learning from the term model using available preterm data. The proposed DL approach avoids time-consuming explicit feature engineering and leverages the existence of the term seizure detection model, resulting in accurate predictions with a minimum amount of annotated preterm data.

Parental involvement in a multidisciplinary PhD programme in neonatal brain injury

Parental and patient and public involvement (PPI) involvement is a core element of the Neonatal Brain Consortium Ireland Ireland (NBCI) since its inception. PPI  representatives were critical to the development of the Consortium and the animations for parent information as well as the NEPTUNE Neonatal Encephalopathy PhD programme in which they are core members involved in PhD supervision, publications, study days and educational outreach. Key outputs have also included national clinical guidelines and parent information.

Neonatal EEG Interpretation and Decision Support Framework for Mobile Platforms

This paper proposes and implements an intuitive and pervasive solution for neonatal EEG monitoring assisted by sonification and deep learning AI that provides information about neonatal brain health to all neonatal healthcare professionals, particularly those without EEG interpretation expertise. The system aims to increase the demographic of clinicians capable of diagnosing abnormalities in neonatal EEG. The proposed system uses a low-cost and low-power EEG acquisition system. An Android app provides single-channel EEG visualization, traffic-light indication of the presence of neonatal seizures provided by a trained, deep convolutional neural network and an algorithm for EEG sonification, designed to facilitate the perception of changes in EEG morphology specific to neonatal seizures. The multifaceted EEG interpretation framework is presented and the implemented mobile platform architecture is analyzed with respect to its power consumption and accuracy.

System Level Framework for Assessing the Accuracy of Neonatal EEG Acquisition

Significant research has been conducted in recent years to design low-cost alternatives to the current EEG monitoring systems used in healthcare facilities. Testing such systems on a vulnerable population such as newborns is complicated due to ethical and regulatory considerations that slow down the technical development. This paper presents and validates a method for quantifying the accuracy of neonatal EEG acquisition systems and electrode technologies via clinical data simulations that do not require neonatal participants. The proposed method uses an extensive neonatal EEG database to simulate analogue signals, which are subsequently passed through electrical models of the skin-electrode interface, which are developed using wet and dry EEG electrode designs. The signal losses in the system are quantified at each stage of the acquisition process for electrode and acquisition board losses. SNR, correlation and noise values were calculated. The results verify that low-cost EEG acquisition systems are capable of obtaining clinical grade EEG. Although dry electrodes result in a significant increase in the skin-electrode impedance, accurate EEG recordings are still achievable.

Heart Rate Variability during Periods of Low Blood Pressure as a Predictor of Short-Term Outcome in Preterms

Efficient management of low blood pressure (BP) in preterm neonates remains challenging with a considerable variability in clinical practice. The ability to assess preterm wellbeing during episodes of low BP will help to decide when and whether hypotension treatment should be initiated. This work aims to investigate the relationship between heart rate variability (HRV), BP and the short-term neurological outcome in preterm infants less than 32 weeks gestational age (GA). The predictive power of common HRV features with respect to the outcome is assessed and shown to improve when HRV is observed during episodes of low mean arterial pressure (MAP) - with a single best feature leading to an AUC of 0.87. Combining multiple features with a boosted decision tree classifier achieves an AUC of 0.97. The work presents a promising step towards the use of multimodal data in building an objective decision support tool for clinical prediction of short-term outcome in preterms who suffer episodes of low BP.

Prediction of short-term health outcomes in preterm neonates from heart-rate variability and blood pressure using boosted decision trees

BACKGROUND AND OBJECTIVE

Efficient management of low blood pressure (BP) in preterm neonates remains challenging with considerable variability in clinical practice. There is currently no clear consensus on what constitutes a limit for low BP that is a risk to the preterm brain. It is argued that a personalised approach rather than a population based threshold is more appropriate. This work aims to assist healthcare professionals in assessing preterm wellbeing during episodes of low BP in order to decide when and whether hypotension treatment should be initiated. In particular, the study investigates the relationship between heart rate variability (HRV) and BP in preterm infants and its relevance to a short-term health outcome.

METHODS

The study is performed on a large clinically collected dataset of 831 h from 23 preterm infants of less than 32 weeks gestational age. The statistical predictive power of common HRV features is first assessed with respect to the outcome. A decision support system, based on boosted decision trees (XGboost), was developed to continuously estimate the probability of neonatal morbidity based on the feature vector of HRV characteristics and the mean arterial blood pressure.

RESULTS

It is shown that the predictive power of the extracted features improves when observed during episodes of hypotension. A single best HRV feature achieves an AUC of 0.87. Combining multiple HRV features extracted during hypotensive episodes with the classifier achieves an AUC of 0.97, using a leave-one-patient-out performance assessment. Finally it is shown that good performance can even be achieved using continuous HRV recordings, rather than only focusing on hypotensive events - this had the benefit of not requiring invasive BP monitoring.

CONCLUSIONS

The work presents a promising step towards the use of multimodal data in providing objective decision support for the prediction of short-term outcome in preterm infants with hypotensive episodes.

Recommendations for the design of therapeutic trials for neonatal seizures

Although seizures have a higher incidence in neonates than any other age group and are associated with significant mortality and neurodevelopmental disability, treatment is largely guided by physician preference and tradition, due to a lack of data from well-designed clinical trials. There is increasing interest in conducting trials of novel drugs to treat neonatal seizures, but the unique characteristics of this disorder and patient population require special consideration with regard to trial design. The Critical Path Institute formed a global working group of experts and key stakeholders from academia, the pharmaceutical industry, regulatory agencies, neonatal nurse associations, and patient advocacy groups to develop consensus recommendations for design of clinical trials to treat neonatal seizures. The broad expertise and perspectives of this group were invaluable in developing recommendations addressing: (1) use of neonate-specific adaptive trial designs, (2) inclusion/exclusion criteria, (3) stratification and randomization, (4) statistical analysis, (5) safety monitoring, and (6) definitions of important outcomes. The guidelines are based on available literature and expert consensus, pharmacokinetic analyses, ethical considerations, and parental concerns. These recommendations will ultimately facilitate development of a Master Protocol and design of efficient and successful drug trials to improve the treatment and outcome for this highly vulnerable population.

Coagulation Profiles Are Associated With Early Clinical Outcomes in Neonatal Encephalopathy

Introduction: Neonatal encephalopathy (NE) is associated with coagulation abnormalities. We aimed to investigate the serial alterations in coagulation profiles in term infants with NE and correlate with their clinical outcomes. This was a prospective cohort study in a tertiary referral, university-affiliated maternity hospital. Neonates exposed to perinatal asphyxia were recruited (n = 82) and 39 received therapeutic hypothermia. Infants had serial coagulation tests including platelets, prothrombin time (PT), activated partial thromboplastin time (aPTT) and fibrinogen in the first week of life. The main outcome measures included MRI brain and EEG seizures. Our results show that mortality was predicted on day 1 by decreased Fibrinogen (AUC = 0.95, p = 0.009) and by PT on day 2 with a cutoff of 22 s. An abnormal MRI was predicted by Fibrinogen on day 3 with a cut-off value of 2 g/L. For prediction of grade II/III NE, PT on day 2 of life was strongest with a cut-off value of 14 s. Only elevated APTT levels on day 1 of life were predictive of seizures (AUC = 0.65, p = 0.04). Conclusion: Coagulation parameters are strong predictors of outcomes such as abnormal NE grade, seizures, and mortality.

Modelling interactions between blood pressure and brain activity in preterm neonates

Hypotension or low blood pressure (BP) is a common problem in preterm neonates and has been associated with adverse short and long-term outcomes. Deciding when and whether to treat hypotension relies on an understanding of the relations between blood pressure and brain function. This study aims to investigate the interaction between BP and multichannel EEG in preterm infants less than 32 weeks gestational age. The mutual information is chosen to model interaction. This measure is independent of absolute values of BP and electroencephalography (EEG) power and quantifies the level of coupling between the short-term dynamics in both signals. It is shown that while adverse health conditions as measured by higher clinical risk indices for babies (CRIB II) are accompanied by consistently lower blood pressure (r=0.43), no significant correlation was observed between CRIB scores and EEG spectral power. More importantly, the chosen measure of interaction between dynamics of EEG and BP was found to be more closely related to CRIB scores (r=0.49, p-value=0.012), with higher CRIB score associated with lower levels of interaction.

Investigating the Impact of CNN Depth on Neonatal Seizure Detection Performance

This study presents a novel, deep, fully convolutional architecture which is optimized for the task of EEG-based neonatal seizure detection. Architectures of different depths were designed and tested; varying network depth impacts convolutional receptive fields and the corresponding learned feature complexity. Two deep convolutional networks are compared with a shallow SVMbased neonatal seizure detector, which relies on the extraction of hand-crafted features. On a large clinical dataset, of over 800 hours of multichannel unedited EEG, containing 1389 seizure events, the deep 11-layer architecture significantly outperforms the shallower architectures, improving the AUC90 from 82.6% to 86.8%. Combining the end-to-end deep architecture with the feature-based shallow SVM further improves the AUC90 to 87.6%. The fusion of classifiers of different depths gives greatly improved performance and reduced variability, making the combined classifier more clinically reliable.

Lost in translation? The potential psychobiotic Lactobacillus rhamnosus (JB-1) fails to modulate stress or cognitive performance in healthy male subjects

BACKGROUND

Preclinical studies have identified certain probiotics as psychobiotics - live microorganisms with a potential mental health benefit. Lactobacillus rhamnosus (JB-1) has been shown to reduce stress-related behaviour, corticosterone release and alter central expression of GABA receptors in an anxious mouse strain. However, it is unclear if this single putative psychobiotic strain has psychotropic activity in humans. Consequently, we aimed to examine if these promising preclinical findings could be translated to healthy human volunteers.

OBJECTIVES

To determine the impact of L. rhamnosus on stress-related behaviours, physiology, inflammatory response, cognitive performance and brain activity patterns in healthy male participants.

METHODS

An 8week, randomized, placebo-controlled, cross-over design was employed. Twenty-nine healthy male volunteers participated. Participants completed self-report stress measures, cognitive assessments and resting electroencephalography (EEG). Plasma IL10, IL1β, IL6, IL8 and TNFα levels and whole blood Toll-like 4 (TLR-4) agonist-induced cytokine release were determined by multiplex ELISA. Salivary cortisol was determined by ELISA and subjective stress measures were assessed before, during and after a socially evaluated cold pressor test (SECPT).

RESULTS

There was no overall effect of probiotic treatment on measures of mood, anxiety, stress or sleep quality and no significant effect of probiotic over placebo on subjective stress measures, or the HPA response to the SECPT. Visuospatial memory performance, attention switching, rapid visual information processing, emotion recognition and associated EEG measures did not show improvement over placebo. No significant anti-inflammatory effects were seen as assessed by basal and stimulated cytokine levels.

CONCLUSIONS

L. rhamnosus was not superior to placebo in modifying stress-related measures, HPA response, inflammation or cognitive performance in healthy male participants. These findings highlight the challenges associated with moving promising preclinical studies, conducted in an anxious mouse strain, to healthy human participants. Future interventional studies investigating the effect of this psychobiotic in populations with stress-related disorders are required.

Classification of hypoxic-ischemic encephalopathy using long term heart rate variability based features

Hypoxic-ischemic HI injury at the time of birth could lead to severe neurological dysfunction at an older age. Therapeutic hypothermia can be used to treat HI if severity of injury is determined within 6 hours of birth. EEG is generally used to assess the brain injury but it is neither widely recorded after birth nor is the expertise to interpret it commonly available. This study presents a novel system to classify HI injury using heart rate variability. The system makes decisions based on long-term statistical features extracted from the short-term HRV features. The preliminary results show the promising performance and robustness of the proposed method given a poor quality dataset. This tool can serve as decision support system in remote maternity units to help clinical staff to initiate hypothermia.

Pilot evaluation of the population pharmacokinetics of bumetanide in term newborn infants with seizures

Recent experimental data suggest bumetanide as a possible therapeutic option in newborn infants with seizures after birth asphyxia. Because pharmacokinetic (PK) data are lacking in this population, who very often benefit from therapeutic cooling, which can modify the PK behavior of a drug, a PK study was conducted in term infants with seizures caused by hypoxic-ischemic encephalopathy. Fourteen infants were included, 13 of them being cooled. Forty-nine blood samples were available for the determination of the plasma concentration of bumetanide. Concentration-time data were analyzed by the use of a population approach performed with Monolix Software. Bumetanide was found to follow a 2-compartment model. The mean values were 0.063 L/h for clearance, 0.28 and 0.44 L for the central and peripheral distribution volumes, respectively, and 0.59 L/h for the distribution clearance. Birth body weight explained the interindividual variability of bumetanide clearance via an allometric model. No relationship was found between bumetanide exposure and its efficacy (reduction in seizure burden) or its toxicity (hearing loss). This study describes the first PK model of bumetanide in hypothermia-treated infants with seizures.

Assessment of quality of ECG for accurate estimation of Heart Rate Variability in newborns

Heart Rate Variability has been recently used to determine the severity of Hypoxic Ischemic Encephalopathy in neonates. However, it was shown that ECG and subsequently Instantaneous Heart Rate can be heavily corrupted by artefacts which have to be manually removed. This work analyses a set of features to assess their sensitivity to normal and corrupted ECG in newborns. Specifically, the IHR signal is obtained by detecting R-Peaks using the Pan-Tompkins algorithm. Four features are extracted from both ECG and IHR signal using various temporal resolutions to discriminate normal and corrupted signal. The performance of these features in discrimination is then assessed using statistical tests.

Systematic review of neonatal seizure management strategies provides guidance on anti-epileptic treatment

There is a lack of scientific evidence to support the best management of neonatal seizures. Current strategies for neonatal seizure management were investigated by analysis of all surveys published during the time period 2000-2012. Methods for seizure diagnosis and availability of electroencephalogram (EEG), including monitoring, varied. Phenobarbital was the drug of first choice, and the use of off-label drugs and treatment times varied.

CONCLUSION

We conclude that there is an urgent need for more evidence-based studies to guide neonatal seizure management.

Clinical implementation of a neonatal seizure detection algorithm

Technologies for automated detection of neonatal seizures are gradually moving towards cot-side implementation. The aim of this paper is to present different ways to visualize the output of a neonatal seizure detection system and analyse their influence on performance in a clinical environment. Three different ways to visualize the detector output are considered: a binary output, a probabilistic trace, and a spatio-temporal colormap of seizure observability. As an alternative to visual aids, audified neonatal EEG is also considered. Additionally, a survey on the usefulness and accuracy of the presented methods has been performed among clinical personnel. The main advantages and disadvantages of the presented methods are discussed. The connection between information visualization and different methods to compute conventional metrics is established. The results of the visualization methods along with the system validation results indicate that the developed neonatal seizure detector with its current level of performance would unambiguously be of benefit to clinicians as a decision support system. The results of the survey suggest that a suitable way to visualize the output of neonatal seizure detection systems in a clinical environment is a combination of a binary output and a probabilistic trace. The main healthcare benefits of the tool are outlined. The decision support system with the chosen visualization interface is currently undergoing pre-market European multi-centre clinical investigation to support its regulatory approval and clinical adoption.

Epilepsy after neonatal seizures: literature review

INTRODUCTION

Acute neonatal seizures are the most frequent neurological complication in the neonatal intensive care units and the seizing newborns have an increased risk of long-term morbidity. However, the relationship between neonatal seizures and the development of epilepsy later in life is still unclear.

METHODS

We performed a literature review using the search terms "neonatal seizures AND outcome", "neonatal seizures AND epilepsy", "neonatal seizures AND post-neonatal epilepsy", including secondary sources of data such as reference lists of articles reviewed. From the studies in which data were available, the incidence of epilepsy was calculated by dividing the number of all subjects who developed epilepsy in the different studies considered with the number of all newborns enrolled to the studies less the number of patients lost at follow-up.

RESULTS

We found 44 studies published between 1954 and 2013, of which 4 were population-based studies and the remaining were hospital-based case series. The overall population evaluated was 4538 newborns and 17.9% developed post-neonatal epilepsy, with an onset within the first year of life in 68.5% of the patients. In 80.7%, epilepsy was associated with other neurological impairments.

CONCLUSION

Estimates on epilepsy after neonatal seizures vary widely depending on selection criteria and length of the follow-up. However, it represents a common outcome of these newborns, especially in those with severe brain injury and additional neurodevelopmental disabilities.

Neonatal EEG audification for seizure detection

Technologies for automated detection of neonatal seizures are gradually moving towards cot-side implementation. The aim of this paper is to present an alternative way to visualize the output of a neonatal seizure detection algorithm. For this purpose audified neonatal EEG is considered. The EEG is audified with the aid of the neonatal seizure detection algorithm which selects the representative channels for stereo audio image and controls the signal gain. A survey on the usefulness and accuracy of the presented audification method has been performed. The results of the audification method compare favourably to that of using amplitude integrated EEG for detection of neonatal seizures.

A novel dictionary for neonatal EEG seizure detection using atomic decomposition

The development of automated methods of electroencephalogram (EEG) seizure detection is an important problem in neonatology. This paper proposes improvements to a previously described method of seizure detection based on atomic decomposition by developing a new time-frequency (TF) dictionary that is highly coherent with the newborn EEG seizure. We compare the performance of the proposed dictionary on neonatal EEG signals with that achieved using Gabor, Fourier and wavelet dictionaries. Through the analysis of real newborn EEG data, we show first, that dictionary selection can influence the seizure detection accuracy and second, that the proposed dictionary outperforms other dictionaries by at least 10% in seizure detection accuracy and 5% improvement in the area under the Receiver Operator Characteristic curve.

Women in hospital medicine: facts, figures and personal experiences

Although females represent a high proportion of medical graduates, women are under represented at consultant level in many hospital specialties. Qualitative and quantitative analyses were undertaken which established female representation at all levels of the medical workforce in Ireland in 2011 and documented the personal experiences of a sample of female specialists. The proportions of female trainees at initial and higher specialist training levels are 765 (53%) and 656 (55%) respectively but falls to 1,685 (32%) at hospital specialist level (p < 0.0001). Significantly fewer women are found at specialist as compared to training levels in anaesthesia (p = 0.04), emergency medicine (p = 0.02), medicine (p < 0.0001), obstetrics/gynaecology (p = 0.0005), paediatrics (p = 0.006), pathology p = 0.03) and surgery (p < 0.0001). The lowest proportion of female doctors at specialist level exists in the combined surgical specialties 88 (10%); the highest is in psychiatry 380 (53%). Qualitative findings indicate that females who complete specialist training are wary of pursuing either flexible training or part time work options and experience discrimination at a number of levels. They appear to be resilient to this and tolerate it. Balancing motherhood and work commitments is the biggest challenge faced by female doctors with children and causes some to change career pathways.

Inclusion of temporal priors for automated neonatal EEG classification

The aim of this paper is to use recent advances in the clinical understanding of the temporal evolution of seizure burden in neonates with hypoxic ischemic encephalopathy to improve the performance of automated detection algorithms. Probabilistic weights are designed from temporal locations of neonatal seizure events relative to time of birth. These weights are obtained by fitting a skew-normal distribution to the temporal seizure density and introduced into the probabilistic framework of the previously developed neonatal seizure detector. The results are validated on the largest available clinical dataset, comprising 816.7 h. By exploiting these priors, the receiver operating characteristic area is increased by 23% (relative) reaching 96.74%. The number of false detections per hour is decreased from 0.45 to 0.25, while maintaining the correct detection of seizure burden at 70%.

Dynamic time warping based neonatal seizure detection system

Neonatal seizures patterns evolve with changing frequency, morphology and propagation. This study is an initial attempt to incorporate the characteristics of temporal evolution of neonatal seizures into our developed neonatal seizure detector. The previously designed SVM-based neonatal seizure detector is modified by substituting the Gaussian kernel with the Gaussian dynamic time warping kernel, to enable the SVM to classify variable length sequences of feature vectors of neonatal seizures. The preliminary results obtained compare favorably with the conventional SVM. The fusion of the two approaches is expected to improve the current state of the art neonatal seizure detection system.

Temporal evolution of seizure burden for automated neonatal EEG classification

The aim of this paper is to use recent advances in the clinical understanding of the temporal evolution of seizure burden in neonates with hypoxic ischemic encephalopathy to improve the performance of automated detection algorithms. Probabilistic weights are designed from temporal locations of neonatal seizure events relative to time of birth. These weights are obtained by fitting a skew-normal distribution to the temporal seizure density and introduced into the probabilistic framework of the previously developed neonatal seizure detector. The results are validated on the largest available clinical dataset, comprising 816.7 hours. By exploiting these priors, the ROC area is increased by 23% (relative) reaching 96.75%. The number of false detections per hour is decreased from 0.72 to 0.36, while maintaining the correct detection of seizure burden at 75%.

EEG signal description with spectral-envelope-based speech recognition features for detection of neonatal seizures

In this paper, features which are usually employed in automatic speech recognition (ASR) are used for the detection of seizures in newborn EEG. In particular, spectral envelope-based features, composed of spectral powers and their spectral derivatives are compared to the established feature set which has been previously developed for EEG analysis. The results indicate that the ASR features which model the spectral derivatives, either full-band or localized in frequency, yielded a performance improvement, in comparison to spectral-power-based features. Indeed it is shown here that they perform reasonably well in comparison with the conventional EEG feature set. The contribution of the ASR features was analyzed here using the support vector machines (SVM) recursive feature elimination technique. It is shown that the spectral derivative features consistently appear among the top-rank features. The study shows that the ASR features should be given a high priority when dealing with the description of the EEG signal.