New antioxidant drugs for neonatal brain injury

The brain injury concept covers a lot of heterogeneity in terms of aetiology involving multiple factors, genetic, hemodynamic, metabolic, nutritional, endocrinological, toxic, and infectious mechanisms, acting in antenatal or postnatal period. Increased vulnerability of the immature brain to oxidative stress is documented because of the limited capacity of antioxidant enzymes and the high free radicals (FRs) generation in rapidly growing tissue. FRs impair transmembrane enzyme Na(+)/K(+)-ATPase activity resulting in persistent membrane depolarization and excessive release of FR and excitatory aminoacid glutamate. Besides being neurotoxic, glutamate is also toxic to oligodendroglia, via FR effects. Neuronal cells die of oxidative stress. Excess of free iron and deficient iron/binding metabolising capacity are additional features favouring oxidative stress in newborn. Each step in the oxidative injury cascade has become a potential target for neuroprotective intervention. The administration of antioxidants for suspected or proven brain injury is still not accepted for clinical use due to uncertain beneficial effects when treatments are started after resuscitation of an asphyxiated newborn. The challenge for the future is the early identification of high-risk babies to target a safe and not toxic antioxidant therapy in combination with standard therapies to prevent brain injury and long-term neurodevelopmental impairment

Performances of low level hospital health caregivers after a neonatal resuscitation course

Background: High fidelity simulation has been executed to allow the evaluation of technical and non-technical skills of health caregivers. Our objective was to assess technical and non-technical performances of low level hospitals health caregivers who attended a Neonatal Resuscitation course using high fidelity simulation in a standard-setting scenario. Methods: Twenty-three volunteers were asked to manage a simple scenario (infant with secondary apnea) after the course. Technical and non-technical skills were assessed by using previously published scores. Performances were assessed during the scenario and after 2 months by filmed video recordings. Results: Sixteen (69.5%) participants failed to pass the minimum required technical score. Staff experience and participation in previous courses were associated to higher score in technical and non-technical skills, while working in level I or II hospitals did not affect the scores. Previous experience in neonatal resuscitation requiring positive pressure ventilation was associated to better non-technical performance. Technical and non-technical scores were significantly correlated (r = 0.67, p = 0.0005). Delayed and direct evaluation of technical skills provided the same scores. Conclusions: A neonatal resuscitation course, performed by using a high fidelity simulation manikin, had a limited impact on technical and non-technical skills of participants working in low level hospitals. Training programs should be tailored to the participants\u2019 professional background and to the more relevant sessions

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

Feasibility of automated early postnatal sleep staging in extremely and very preterm neonates using dual-channel EEG

Objective: To investigate the feasibility of automated sleep staging based on quantitative analysis of dual-channel electroencephalography (EEG) for extremely and very preterm infants during their first postnatal days. Methods: We enrolled 17 preterm neonates born between 25 and 30 weeks of gestational age. Three-hour behavioral sleep observations and simultaneous dual-channel EEG monitoring were conducted for each infant within their first 72 hours after birth. Four kinds of representative and complementary quantitative EEG (qEEG) metrics (i.e., bursting, synchrony, spectral power, and complexity) were calculated and compared between active sleep, quiet sleep, and wakefulness. All analyses were performed in offline mode. Results: In separate comparison analyses, significant differences between sleep-wake states were found for bursting, spectral power and complexity features. The automated sleep-wake state classifier based on the combination of all qEEG features achieved a macro-averaged area under the curve of receiver operating characteristic of 74.8%. The complexity features contributed the most to sleep-wake state classification. Conclusions: It is feasible to distinguish between sleep-wake states within the first 72 postnatal hours for extremely and very preterm infants using qEEG metrics. Significance: Our findings offer the possibility of starting personalized care dependent on preterm infants’ sleep-wake states directly after birth, potentially yielding long-run benefits for their developmental outcomes

Nurses’ experiences and perspectives on aEEG monitoring in neonatal care: A qualitative study

Purpose: This study aimed to gather nurses’ experiences and perspectives regarding the amplitude-integrated electroencephalogram (aEEG) monitoring system in neonatal intensive care units (NICUs) and to explore potential avenues for future improvements. Design and Methods: This study employed a descriptive qualitative design. Semi-structured interviews were conducted with 20 nurses from the level-III NICU of a Dutch medical center. The collected interview data were analyzed using thematic analysis. Results: Seven main themes emerged: training in aEEG monitoring, proficiency in aEEG electrode placement and pattern interpretation, usual practices of using aEEG, neonatologist-nurse cooperation on aEEG, the performance of the automated seizure detection software, the usefulness of aEEG monitoring in the NICU, and feedback about the current aEEG monitoring system. Conclusions: Nurses confirmed that aEEG is a valuable tool for cerebral function monitoring in the NICU; however, improvements are necessary. For better utilization of aEEG in the NICU, it is recommended to enhance nurses’ aEEG knowledge and skills and apply state-of-art techniques to improve the monitoring system. Practice implications: To enhance the aEEG knowledge of NICU nurses, we suggest introducing structured training programs, conducting routine case-centered discussions, and creating readily available reference resources. To optimize the aEEG monitoring system, it is essential to incorporate innovative electrodes, provide remote accessibility, integrate advanced algorithms, and develop an intuitive graphical user interface

Isoprostanes as Biomarker for Patent Ductus Arteriosus in Preterm Infants

Context: It has been reported that isoprostanes (IPs) have a role in the pathophysiology of ductus arteriosus during the fetal and neonatal period. Our aim in this study was to assess if urinary IPs (uIPs) levels correlate with the risk of developing a hemodynamically significant patent ductus arteriosus (hsPDA) in preterm infants. Materials and methods: Infants with 23 + 0 - 33 + 6 weeks of gestational age and respiratory distress syndrome (RDS) were consecutively enrolled. Urine samples were collected on the 2nd and 10th day of life (DOL) for uIPs measurement. Echocardiography for hsPDA diagnosis was performed between 24 and 48 h of life. Regression analysis was performed to assess the correlation between uIPs and hsPDA. Receiver operating characteristic (ROC) curve analysis was used to evaluate the accuracy of the uIPs in predicting the occurrence of hsPDA. Results: Sixty patients were studied: 33 (55%) developed a hsPDA, 27 (45%) had ibuprofen hsPDA closure, and six (10%) required surgical closure. uIPs levels decreased from the 2nd to the 10th DOL. Adjusted regression analysis demonstrated that uIPs on the 2nd DOL were associated (p = 0.02) with the risk of developing a hsPDA. A cut-off level of 1627 ng/mg of creatinine of uIPs predicted the development of a hsPDA with a sensitivity of 82% and a specificity of 73%. Conclusion: Early measurement of uIPs on the 2nd DOL is a reliable biomarker of hsPDA development and, alone or combined with other markers, might represent a non-invasive tool useful for planning the management of PDA in preterm infants

Automated cot-side tracking of functional brain age in preterm infants

Objective A major challenge in the care of preterm infants is the early identification of compromised neurological development. While several measures are routinely used to track anatomical growth, there is a striking lack of reliable and objective tools for tracking maturation of early brain function; a cornerstone of lifelong neurological health. We present a cot-side method for measuring the functional maturity of the newborn brain based on routinely available neurological monitoring with electroencephalography (EEG). Methods We used a dataset of 177 EEG recordings from 65 preterm infants to train a multivariable prediction of functional brain age (FBA) from EEG. The FBA was validated on an independent set of 99 EEG recordings from 42 preterm infants. The difference between FBA and postmenstrual age (PMA) was evaluated as a predictor for neurodevelopmental outcome. Results The FBA correlated strongly with the PMA of an infant, with a median prediction error of less than 1 week. Moreover, individual babies follow well-defined individual trajectories. The accuracy of the FBA applied to the validation set was statistically equivalent to the training set accuracy. In a subgroup of infants with repeated EEG recordings, a persistently negative predicted age difference was associated with poor neurodevelopmental outcome. Interpretation The FBA enables the tracking of functional neurodevelopment in preterm infants. This establishes proof of principle for growth charts for brain function, a new tool to assist clinical management and identify infants who will benefit most from early intervention.Peer reviewe

Morphine exposure and neurodevelopmental outcome in infants born extremely preterm

AIM: To investigate the association between morphine exposure in the neonatal period and neurodevelopment at 2 and 5 years of age while controlling for potential confounders. METHOD: We performed a retrospective, single-centre cohort study on 106 infants (60 males, 46 females; mean gestational age 26 weeks [SD 1]) born extremely preterm (gestational age < 28 weeks). Morphine administration was expressed as cumulative dose (mg/kg) until term-equivalent age. Neurodevelopmental outcome was assessed at 2 years with the Bayley Scales of Infant and Toddler Development, Third Edition, Dutch version and at 5 years with the Wechsler Preschool and Primary Scale of Intelligence, Third Edition, Dutch version. Multiple linear regression analysis was used to assess the association between morphine exposure and outcome. RESULTS: Sixty-four out of 106 (60.4%) infants included in the study received morphine. Morphine exposure was not associated with poorer motor, cognitive, and language subscores of the Bayley Scales of Infant and Toddler Development, Third Edition, Dutch version at 2 years. Morphine exposure was associated with lower Full-Scale IQ scores (p = 0.008, B = -9.3, 95% confidence interval [CI] = -15.6 to -3.1) and Performance IQ scores (p = 0.005, B = -17.5, 95% CI = -27.9 to -7) at 5 years of age. INTERPRETATION: Morphine exposure in infants born preterm is associated with poorer Full-Scale IQ and Performance IQ at 5 years. Individualized morphine administration is advised in infants born extremely preterm

NutriBrain: protocol for a randomised, double-blind, controlled trial to evaluate the effects of a nutritional product on brain integrity in preterm infants

Background: The gut microbiota and the brain are connected through different mechanisms. Bacterial colonisation of the gut plays a substantial role in normal brain development, providing opportunities for nutritional neuroprotective interventions that target the gut microbiome. Preterm infants are at risk for brain injury, especially white matter injury, mediated by inflammation and infection. Probiotics, prebiotics and L-glutamine are nutritional components that have individually already demonstrated beneficial effects in preterm infants, mostly by reducing infections or modulating the inflammatory response. The NutriBrain study aims to evaluate the benefits of a combination of probiotics, prebiotics and L-glutamine on white matter microstructure integrity (i.e., development of white matter tracts) at term equivalent age in very and extremely preterm born infants. Methods: This study is a double-blind, randomised, controlled, parallel-group, single-center study. Eighty-eight infants born between 24 + 0 and < 30 + 0 weeks gestational age and less than 72 h old will be randomised after parental informed consent to receive either active study product or placebo. Active study product consists of a combination of Bifidobacterium breve M-16V, short-chain galacto-oligosaccharides, long-chain fructo-oligosaccharides and L-glutamine and will be given enterally in addition to regular infant feeding from 48 to 72 h after birth until 36 weeks postmenstrual age. The primary study outcome of white matter microstructure integrity will be measured as fractional anisotropy, assessed using magnetic resonance diffusion tensor imaging at term equivalent age and analysed using Tract-Based Spatial Statistics. Secondary outcomes are white matter injury, brain tissue volumes and cortical morphology, serious neonatal infections, serum inflammatory markers and neurodevelopmental outcome. Discussion: This study will be the first to evaluate the effect of a combination of probiotics, prebiotics and L-glutamine on brain development in preterm infants. It may give new insights in the development and function of the gut microbiota and immune system in relation to brain development and provide a new, safe treatment possibility to improve brain development in the care for preterm infants. Trial registration: ISRCTN, ISRCTN96620855. Date assigned: 10/10/2017

Early qualitative and quantitative amplitude-integrated electroencephalogram and raw electroencephalogram for predicting long-term neurodevelopmental outcomes in extremely preterm infants in the Netherlands: a 10-year cohort study

Background: Extremely preterm infants (<28 weeks of gestation) are at great risk of long-term neurodevelopmental impairments. Early amplitude-integrated electroencephalogram (aEEG) accompanied by raw EEG traces (aEEG−EEG) has potential for predicting subsequent outcomes in preterm infants. We aimed to determine whether and which qualitative and quantitative aEEG–EEG features obtained within the first postnatal days predict neurodevelopmental outcomes in extremely preterm infants. Methods: This study retrospectively analysed a cohort of extremely preterm infants (born before 28 weeks and 0 days of gestation) who underwent continuous two-channel aEEG–EEG monitoring during their first 3 postnatal days at Wilhelmina Children's Hospital, Utrecht, the Netherlands, between June 1, 2008, and Sept 30, 2018. Only infants who did not have genetic or metabolic diseases or major congenital malformations were eligible for inclusion. Features were extracted from preprocessed aEEG–EEG signals, comprising qualitative parameters grouped in three types (background pattern, sleep–wake cycling, and seizure activity) and quantitative metrics grouped in four categories (spectral content, amplitude, connectivity, and discontinuity). Machine learning-based regression and classification models were used to evaluate the predictive value of the extracted aEEG–EEG features for 13 outcomes, including cognitive, motor, and behavioural problem outcomes, at 2–3 years and 5–7 years. Potential confounders (gestational age at birth, maternal education, illness severity, morphine cumulative dose, the presence of severe brain injury, and the administration of antiseizure, sedative, or anaesthetic medications) were controlled for in all prediction analyses. Findings: 369 infants were included and an extensive set of 339 aEEG–EEG features was extracted, comprising nine qualitative parameters and 330 quantitative metrics. The machine learning-based regression models showed significant but relatively weak predictive performance (ranging from r=0·13 to r=0·23) for nine of 13 outcomes. However, the machine learning-based classifiers exhibited acceptable performance in identifying infants with intellectual impairments from those with optimal outcomes at age 5–7 years, achieving balanced accuracies of 0·77 (95% CI 0·62–0·90; p=0·0020) for full-scale intelligence quotient score and 0·81 (0·65–0·96; p=0·0010) for verbal intelligence quotient score. Both classifiers maintained identical performance when solely using quantitative features, achieving balanced accuracies of 0·77 (95% CI 0·63–0·91; p=0·0030) for full-scale intelligence quotient score and 0·81 (0·65–0·96; p=0·0010) for verbal intelligence quotient score. Interpretation: These findings highlight the potential benefits of using early postnatal aEEG–EEG features to automatically recognise extremely preterm infants with poor outcomes, facilitating the development of an interpretable prognostic tool that aids in decision making and therapy planning. Funding: European Commission Horizon 2020