Predicting low cognitive ability at age 5 years using perinatal data and machine learning.

BACKGROUND: There are no early, accurate, scalable methods for identifying infants at high risk of poor cognitive outcomes in childhood. We aim to develop an explainable predictive model, using machine learning and population-based cohort data, for this purpose. METHODS: Data were from 8858 participants in the Growing Up in Ireland cohort, a nationally representative study of infants and their primary caregivers (PCGs). Maternal, infant, and socioeconomic characteristics were collected at 9-months and cognitive ability measured at age 5 years. Data preprocessing, synthetic minority oversampling, and feature selection were performed prior to training a variety of machine learning models using ten-fold cross validated grid search to tune hyperparameters. Final models were tested on an unseen test set. RESULTS: A random forest (RF) model containing 15 participant-reported features in the first year of infant life, achieved an area under the receiver operating characteristic curve (AUROC) of 0.77 for predicting low cognitive ability at age 5. This model could detect 72% of infants with low cognitive ability, with a specificity of 66%. CONCLUSIONS: Model performance would need to be improved before consideration as a population-level screening tool. However, this is a first step towards early, individual, risk stratification to allow targeted childhood screening. IMPACT: This study is among the first to investigate whether machine learning methods can be used at a population-level to predict which infants are at high risk of low cognitive ability in childhood. A random forest model using 15 features which could be easily collected in the perinatal period achieved an AUROC of 0.77 for predicting low cognitive ability. Improved predictive performance would be required to implement this model at a population level but this may be a first step towards early, individual, risk stratification.

Meeting the need for effective and standardized neonatology training: a pan-European Master's Curriculum.

Neonatology is a pediatric sub-discipline focused on providing care for newborn infants, including healthy newborns, those born prematurely, and those who present with illnesses or malformations requiring medical care. The European Training Requirements (ETR) in Neonatology provide a framework for standardized quality and recognition of equality of training throughout Europe. The latest ETR version was approved by the Union of European Medical Specialists (UEMS) in April 2021. Here, we present the curriculum of the European School of Neonatology Master of Advanced Studies (ESN MAS), which is based on the ETR in Neonatology and aims to provide a model for effective and standardized training and education in neonatal medicine. We review the history and theory that form the foundation of contemporary medical education and training, provide a literature review on best practices for medical training, pediatric training, and neonatology training specifically, including educational frameworks and evidence-based systems of evaluation. The ESN MAS Curriculum is then evaluated in light of these best practices to define its role in meeting the need for a standardized empirically supported neonatology training curriculum for physicians, and in the future for nurses, to improve the quality of neonatal care for all infants. IMPACT STATEMENT: A review of the neonatology training literature was conducted, which concluded that there is a need for standardized neonatology training across international contexts to keep pace with growth in the field and rapidly advancing technology. This article presents the European School of Neonatology Master of Advanced Studies in Neonatology, which is intended to provide a standardized training curriculum for pediatricians and nurses seeking sub-specialization in neonatology. The curriculum is evaluated in light of best practices in medical education, neonatology training, and adult learning theory.

Consensus definition and diagnostic criteria for neonatal encephalopathy-study protocol for a real-time modified delphi study.

BACKGROUND: 'Neonatal encephalopathy' (NE) describes a group of conditions in term infants presenting in the earliest days after birth with disturbed neurological function of cerebral origin. NE is aetiologically heterogenous; one cause is peripartum hypoxic ischaemia. Lack of uniformity in the terminology used to describe NE and its diagnostic criteria creates difficulty in the design and interpretation of research and complicates communication with families. The DEFINE study aims to use a modified Delphi approach to form a consensus definition for NE, and diagnostic criteria. METHODS: Directed by an international steering group, we will conduct a systematic review of the literature to assess the terminology used in trials of NE, and with their guidance perform an online Real-time Delphi survey to develop a consensus diagnosis and criteria for NE. A consensus meeting will be held to agree on the final terminology and criteria, and the outcome disseminated widely. DISCUSSION: A clear and consistent consensus-based definition of NE and criteria for its diagnosis, achieved by use of a modified Delphi technique, will enable more comparability of research results and improved communication among professionals and with families. IMPACT: The terms Neonatal Encephalopathy and Hypoxic Ischaemic Encephalopathy tend to be used interchangeably in the literature to describe a term newborn with signs of encephalopathy at birth. This creates difficulty in communication with families and carers, and between medical professionals and researchers, as well as creating difficulty with performance of research. The DEFINE project will use a Real-time Delphi approach to create a consensus definition for the term 'Neonatal Encephalopathy'. A definition formed by this consensus approach will be accepted and utilised by the neonatal community to improve research, outcomes, and parental experience.

Early initiation of short-term emollient use for the prevention of atopic dermatitis in high-risk infants-The STOP-AD randomised controlled trial.

BACKGROUND: Protecting the skin barrier in early infancy may prevent atopic dermatitis (AD). We investigated if daily emollient use from birth to 2 months reduced AD incidence in high-risk infants at 12 months. METHODS: This was a single-center, two-armed, investigator-blinded, randomized controlled clinical trial (NCT03871998). Term infants identified as high risk for AD (parental history of AD, asthma or allergic rhinitis) were recruited within 4 days of birth and randomised 1:1 to either twice-daily emollient application for the first 8 weeks of life (intervention group), using an emollient specifically formulated for very dry, AD-prone skin, or to standard routine skin care (control group). The primary outcome was cumulative AD incidence at 12 months. AD <6 months was diagnosed based on clinical presence of AD. The UK Working Party Diagnostic Criteria were applied when diagnosing AD between 6 and 12 months. RESULTS: Three hundred twenty-one were randomised (161 intervention and 160 control), with 61 withdrawals (41 intervention, 20 control). The cumulative incidence of AD at 12 months was 32.8% in the intervention group vs. 46.4% in the control group, p = 0.036 [Relative risk (95%CI): 0.707 (0.516, 0.965)]. One infant in the intervention group was withdrawn from the study following development of a rash that had a potential relationship with the emollient. There was no significant difference in the incidence of skin infections between the intervention and control groups during the intervention period (5.0% vs. 5.7%, p > 0.05). CONCLUSIONS: This study has demonstrated that early initiation of daily specialized emollient use until 2 months reduces the incidence of AD in the first year of life in high-risk infants.

Poor Quality Diets Characterized by Low-Nutrient Density Foods Observed in One-Quarter of 2-Year-Olds in a High Resource Setting.

BACKGROUND: Young children have high nutritional requirements relative to their body size, making healthy diets critical for normal growth and development. OBJECTIVE: We aimed to integrate analysis of dietary patterns among 2-y-old children with indicators of dietary quality, micronutrient status, and body weight status. METHODS: Data from the 2-y follow-up of the Cork BASELINE Birth Cohort included dietary assessment using a 2-d weighed food diary, vitamin D and iron status biomarkers, and anthropometry (n = 468). K-means cluster analysis identified predominant dietary patterns based on energy contributions and associations with nutrient intakes and status and body weight were investigated. RESULTS: Four dietary patterns emerged: "Cows' milk" (unmodified cows' milk: 32% of total energy (TE)); "Traditional" (wholemeal breads, butter, fresh meat, fruit); "Low Nutrient Density (LND) foods" (confectionary, processed meat, convenience foods) and "Formula" (young child formula: 23%TE). The LND pattern was associated with excessive free sugar intake (14%TE) and salt intake (153% of daily limit). No differences in patterns of overweight were observed between the 4 groups; however, the LND group had 3-fold higher odds of being underweight [aOR (95% CI): 3.2 (1.2, 8.5)]. Children consuming >400ml/d of cows' milk or formula exhibited lower dietary variety, fewer family-type meals, and continued use of feeding bottles (75% and 81%, respectively, vs. 35-37% in the other groups). CONCLUSIONS: Unhealthy eating habits are common among young children. Dietary guidance to support families to provide healthy diets needs to maintain currency with eating habits and focus on food choices for meals, snacks, and beverages.

Machine learning for the early prediction of infants with electrographic seizures in neonatal hypoxic-ischemic encephalopathy.

OBJECTIVE: To assess if early clinical and electroencephalography (EEG) features predict later seizure development in infants with hypoxic-ischemic encephalopathy (HIE). METHODS: Clinical and EEG parameters <12 h of birth from infants with HIE across eight European Neonatal Units were used to develop seizure-prediction models. Clinical parameters included intrapartum complications, fetal distress, gestational age, delivery mode, gender, birth weight, Apgar scores, assisted ventilation, cord pH, and blood gases. The earliest EEG hour provided a qualitative analysis (discontinuity, amplitude, asymmetry/asynchrony, sleep-wake cycle [SWC]) and a quantitative analysis (power, discontinuity, spectral distribution, inter-hemispheric connectivity) from full montage and two-channel amplitude-integrated EEG (aEEG). Subgroup analysis, only including infants without anti-seizure medication (ASM) prior to EEG was also performed. Machine-learning (ML) models (random forest and gradient boosting algorithms) were developed to predict infants who would later develop seizures and assessed using Matthews correlation coefficient (MCC) and area under the receiver-operating characteristic curve (AUC). RESULTS: The study included 162 infants with HIE (53 had seizures). Low Apgar, need for ventilation, high lactate, low base excess, absent SWC, low EEG power, and increased EEG discontinuity were associated with seizures. The following predictive models were developed: clinical (MCC 0.368, AUC 0.681), qualitative EEG (MCC 0.467, AUC 0.729), quantitative EEG (MCC 0.473, AUC 0.730), clinical and qualitative EEG (MCC 0.470, AUC 0.721), and clinical and quantitative EEG (MCC 0.513, AUC 0.746). The clinical and qualitative-EEG model significantly outperformed the clinical model alone (MCC 0.470 vs 0.368, p-value .037). The clinical and quantitative-EEG model significantly outperformed the clinical model (MCC 0.513 vs 0.368, p-value .012). The clinical and quantitative-EEG model for infants without ASM (n = 131) had MCC 0.588, AUC 0.832. Performance for quantitative aEEG (n = 159) was MCC 0.381, AUC 0.696 and clinical and quantitative aEEG was MCC 0.384, AUC 0.720. SIGNIFICANCE: Early EEG background analysis combined with readily available clinical data helped predict infants who were at highest risk of seizures, hours before they occur. Automated quantitative-EEG analysis was as good as expert analysis for predicting seizures, supporting the use of automated assessment tools for early evaluation of HIE.

Big data, machine learning, and population health: predicting cognitive outcomes in childhood.

The application of machine learning (ML) to address population health challenges has received much less attention than its application in the clinical setting. One such challenge is addressing disparities in early childhood cognitive development-a complex public health issue rooted in the social determinants of health, exacerbated by inequity, characterised by intergenerational transmission, and which will continue unabated without novel approaches to address it. Early life, the period of optimal neuroplasticity, presents a window of opportunity for early intervention to improve cognitive development. Unfortunately for many, this window will be missed, and intervention may never occur or occur only when overt signs of cognitive delay manifest. In this review, we explore the potential value of ML and big data analysis in the early identification of children at risk for poor cognitive outcome, an area where there is an apparent dearth of research. We compare and contrast traditional statistical methods with ML approaches, provide examples of how ML has been used to date in the field of neurodevelopmental disorders, and present a discussion of the opportunities and risks associated with its use at a population level. The review concludes by highlighting potential directions for future research in this area. IMPACT: To date, the application of machine learning to address population health challenges in paediatrics lags behind other clinical applications. This review provides an overview of the public health challenge we face in addressing disparities in childhood cognitive development and focuses on the cornerstone of early intervention. Recent advances in our ability to collect large volumes of data, and in analytic capabilities, provide a potential opportunity to improve current practices in this field. This review explores the potential role of machine learning and big data analysis in the early identification of children at risk for poor cognitive outcomes.

ASQ-3 and BSID-III's concurrent validity and predictive ability of cognitive outcome at 5 years.

BACKGROUND: Early detection of cognitive disability is challenging. We assessed the domain-specific, concurrent validity of the ages and stages questionnaire (ASQ-3) and the Bayley Scales of Infant and Toddler Development (BSID-III), and their ability to predict cognitive delay at school age. METHODS: Within a longitudinal birth cohort study, a nested cohort of children was assessed using ASQ-3 and BSID-III at 24 months, and at 5 years using the Kaufmann brief IQ test (KBIT). RESULTS: 278 children were assessed using BSID-III and ASQ-3 at 24-months; mean(SD) BW = 3445(506) grams, M:F ratio=52:48. ASQ-3 had reasonable predictive ability (AUROC, p value, sensitivity:specificity) of same domain delay for motor (0.630, p = 0.008, 50%:76.1%) and language (0.623, p = 0.010, 25%:99.5%) at 2 years, but poor ability to detect cognitive delay compared to BSID-III (0.587, p = 0.124, 20.7%/96.8%;). 204/278 children were assessed at 5 years. BSID-III language and cognition domains showed better correlation with verbal and nonverbal IQ (R = 0.435, p < 0.001 and 0.388, p < 0.001 respectively). Both assessments showed high specificity and low sensitivity for predicting delay at 5 years. CONCLUSIONS: The ASQ-3 cognitive domain showed poor concurrent validity with BSID-III cognitive score. Both ASQ-3 and BSID-III at 2 years poorly predict cognitive delay at 5 years. IMPACT: The ASQ-3 does not adequately detect cognitive delay or predict cognitive delay at 5 years, particularly for children with mild to moderate delay. The ASQ-3 shows reasonable concurrent validity with the motor and language subscales of the BSID-III. Neither early screening nor formal developmental testing demonstrated significant predictive validity to screen for cognitive delay at school age. This article highlights the need to analyse our existing model of using the ASQ-3 to screen for cognitive delay in children aged 2 years.

Validation of a touchscreen assessment tool to screen for cognitive delay at 24 months.

AIM: To validate a touchscreen assessment as a screening tool for mild cognitive delay in typically developing children aged 24 months. METHOD: Secondary analysis of data was completed from an observational birth cohort study (The Cork Nutrition & Microbiome Maternal-Infant Cohort Study [COMBINE]), with children born between 2015 and 2017. Outcome data were collected at 24 months of age, at the INFANT Research Centre, Ireland. Outcomes were the Bayley Scales of Infant and Toddler Development, Third Edition cognitive composite score and a language-free, touchscreen-based cognitive measure (Babyscreen). RESULTS: A total of 101 children (47 females, 54 males) aged 24 months (mean = 24.25, SD = 0.22) were included. Cognitive composite scores correlated with the total number of Babyscreen tasks completed, with moderate concurrent validity (r = 0.358, p < 0.001). Children with cognitive composite scores lower than 90 (1 SD below the mean, defined as mild cognitive delay) had lower mean Babyscreen scores than those with cognitive scores equal to or greater than 90 (8.50 [SD = 4.89] vs 12.61 [SD = 3.68], p = 0.001). The area under the receiver operating characteristic curve for the prediction of a cognitive composite score less than 90 was 0.75 (95% confidence interval = 0.59-0.91; p = 0.006). Babyscreen scores less than 7 were equivalent to less than the 10th centile and identified children with mild cognitive delay with 50% sensitivity and 93% specificity. INTERPRETATION: Our 15-minute, language-free touchscreen tool could reasonably identify mild cognitive delay among typically developing children.

Parent-led massage and sleep EEG for term-born infants: A randomized controlled parallel-group study.

AIM: To examine the impact of parent-led massage on the sleep electroencephalogram (EEG) features of typically developing term-born infants at 4 months. METHOD: Infants recruited at birth were randomized to intervention (routine parent-led massage) and control groups. Infants had a daytime sleep EEG at 4 months and were assessed using the Griffiths Scales of Child Development, Third Edition at 4 and 18 months. Comparative analysis between groups and subgroup analysis between regularly massaged and never-massaged infants were performed. Groups were compared for sleep stage, sleep spindles, quantitative EEG (primary analysis), and Griffiths using the Mann-Whitney U test. RESULTS: In total, 179 out of 182 infants (intervention: 83 out of 84; control: 96 out of 98) had a normal sleep EEG. Median (interquartile range) sleep duration was 49.8 minutes (39.1-71.4) (n = 156). A complete first sleep cycle was seen in 67 out of 83 (81%) and 72 out of 96 (75%) in the intervention and control groups respectively. Groups did not differ in sleep stage durations, latencies to sleep and to rapid eye movement sleep. Sleep spindle spectral power was greater in the intervention group in main and subgroup analyses. The intervention group showed greater EEG magnitudes, and lower interhemispherical coherence on subgroup analyses. Griffiths assessments at 4 months (n = 179) and 18 months (n = 173) showed no group differences in the main and subgroup analyses. INTERPRETATION: Routine massage is associated with distinct functional brain changes at 4 months. WHAT THIS PAPER ADDS: Routine massage of infants is associated with differences in sleep electroencephalogram biomarkers at 4 months. Massaged infants had higher sleep spindle spectral power, greater sleep EEG magnitudes, and lower interhemispherical coherence. No differences between groups were observed in total nap duration or first cycle macrostructure.

Prenatal maternal cortisol, stress and anxiety, and childhood obesity at 5 years: a nested case-control study.

BACKGROUND: Paediatric obesity is a global public health issue. Prenatal maternal mental health is potentially implicated in the development of childhood obesity. This study examined associations between prenatal maternal cortisol, self-reported stress, anxiety and depression in the second trimester, and childhood overweight and obesity at 5 years of age. METHODS: A nested case-control study was conducted using data from the Irish prospective longitudinal birth cohort SCOPE BASELINE. Cases were children with overweight or obesity, operationalised as having a BMI z-score above +2 standard deviations. Controls were children with a BMI z-score between -0.5 and 0.5 standard deviations at 5 years of age. Two to one matching by sex was conducted. Thirty-eight cases and 83 sex-matched controls were included. Maternal serum cortisol concentration and self-reported stress, anxiety and depression were measured at 15 ± 1 and 20 ± 1 weeks gestation. Conditional logistic regression analyses were conducted to examine associations between prenatal maternal cortisol and self-reported stress, anxiety and depression, and childhood overweight and obesity. RESULTS: Despite some evidence for associations between anxiety and depression, and child BMI z-scores in univariate analyses, adjusted models indicated no associations between prenatal maternal stress (OR: 1.02, 95% CI: 0.94-1.12), anxiety (OR: 1.03, 95% CI: 0.97-1.09), depression (OR: 1.04, 95% CI: 0.91-1.19), or cortisol concentration (OR: 0.99, 95% CI: 0.99-1.00) and child BMI z-score. CONCLUSION: Our findings do not provide support for associations between foetal exposure during the second trimester of pregnancy and maternal cortisol, stress and anxiety, and childhood overweight or obesity at 5 years of age.

Neonatal Seizure Management: Is the Timing of Treatment Critical?

OBJECTIVE: To assess the impact of the time to treatment of the first electrographic seizure on subsequent seizure burden and describe overall seizure management in a large neonatal cohort. STUDY DESIGN: Newborns (36-44 weeks of gestation) requiring electroencephalographic (EEG) monitoring recruited to 2 multicenter European studies were included. Infants who received antiseizure medication exclusively after electrographic seizure onset were grouped based on the time to treatment of the first seizure: antiseizure medication within 1 hour, between 1 and 2 hours, and after 2 hours. Outcomes measured were seizure burden, maximum seizure burden, status epilepticus, number of seizures, and antiseizure medication dose over the first 24 hours after seizure onset. RESULTS: Out of 472 newborns recruited, 154 (32.6%) had confirmed electrographic seizures. Sixty-nine infants received antiseizure medication exclusively after the onset of electrographic seizure, including 21 infants within 1 hour of seizure onset, 15 between 1 and 2 hours after seizure onset, and 33 at >2 hours after seizure onset. Significantly lower seizure burden and fewer seizures were noted in the infants treated with antiseizure medication within 1 hour of seizure onset (P = .029 and .035, respectively). Overall, 258 of 472 infants (54.7%) received antiseizure medication during the study period, of whom 40 without electrographic seizures received treatment exclusively during EEG monitoring and 11 with electrographic seizures received no treatment. CONCLUSIONS: Treatment of neonatal seizures may be time-critical, but more research is needed to confirm this. Improvements in neonatal seizure diagnosis and treatment are also needed.

The predictive value of the ages and stages questionnaire in late infancy for low average cognitive ability at age 5.

AIM: This retrospective, longitudinal study examined the predictive value of the ages and stages questionnaire (ASQ) in late infancy for identifying children who progressed to have low cognitive ability at 5 years of age. METHODS: The ASQ was performed on 755 participants from the Irish BASELINE birth cohort at 24 or 27 months of age. Intelligence quotient was measured at age 5 with the Kaufmann Brief Intelligence Test, Second Edition, and low cognitive ability was defined as a score more than 1 standard deviation below the mean. The ASQ's predictive value was examined, together with other factors associated with low cognitive ability at 5 years. RESULTS: When the ASQ was performed at 24 or 27 months, the overall sensitivity for identifying low cognitive ability at 5 years was 20.8% and the specificity was 91.1%. Using a total score cut-off point increased the sensitivity to 46.6% and 71.4% at 24 and 27 months, but specificity fell to 74.1% and 67.2%, respectively. After adjusting for ASQ performance, maternal education and family income were strongly associated with cognitive outcomes at 5 years. CONCLUSION: The ASQ did not detect the majority of children with low cognitive ability at age 5. Alternative methods need investigation.

Evolution of early cerebral NIRS in hypoxic ischaemic encephalopathy.

AIM: To describe early cerebral oxygenation (cSO2 ) and fractional tissue oxygen extraction (FTOE) values and their evolution over the first days of life in infants with all grades of hypoxic-ischaemic encephalopathy (HIE) and to determine whether cSO2 and FTOE measured early (6 and 12 h) can predict short-term outcome. METHODS: Prospective, observational study of cerebral near-infrared spectroscopy (NIRS) in infants >36 weeks' gestation with HIE. Ten one-hour epochs of cSO2 and FTOE were extracted for each infant over the first 84 h. Infants with moderate and severe HIE received therapeutic hypothermia (TH). Abnormal outcome was defined as abnormal magnetic resonance imaging (MRI) and/or death. RESULTS: Fifty-eight infants were included (28 mild, 24 moderate, 6 severe). Median gestational age was 39.9 weeks (IQR 38.1-40.7) and birthweight was 3.35 kgs (IQR 2.97-3.71). cSO2 increased and FTOE decreased over the first 24 h in all grades of HIE. Compared to the moderate group, infants with mild HIE had significantly higher cSO2 at 6 h (p = 0.003), 9 h (p = 0.009) and 12 h (p = 0.032) and lower FTOE at 6 h (p = 0.016) and 9 h (0.029). cSO2 and FTOE at 6 and 12 h did not predict abnormal outcome. CONCLUSION: Infants with mild HIE have higher cSO2 and lower FTOE than those with moderate or severe HIE in the first 12 h of life. cSO2 increased in all grades of HIE over the first 24 h regardless of TH status.

Up-Regulation of Nfat5 mRNA and Fzd4 mRNA as a Marker of Poor Outcome in Neonatal Hypoxic-Ischemic Encephalopathy.

OBJECTIVE: To evaluate umbilical cord messenger RNA (mRNA) expression as biomarkers for the grade of hypoxic-ischemic encephalopathy (HIE) and long-term neurodevelopment outcome. STUDY DESIGN: Infants were recruited from the BiHiVE1 study, Ireland (2009-2011), and the BiHiVE2 study, Ireland, and Sweden (2013-2015). Infants with HIE were assigned modified Sarnat scores at 24 hours and followed at 18-36 months. mRNA expression from cord blood was measured using quantitative real-time polymerase chain reaction. RESULTS: We studied 124 infants (controls, n = 37; perinatal asphyxia, n = 43; and HIE, n = 44). Fzd4 mRNA increased in severe HIE (median relative quantification, 2.98; IQR, 2.23-3.68) vs mild HIE (0.88; IQR, 0.46-1.37; P = .004), and in severe HIE vs moderate HIE (1.06; IQR, 0.81-1.20; P = .003). Fzd4 mRNA also increased in infants eligible for therapeutic hypothermia (1.20; IQR, 0.92-2.37) vs those who were ineligible for therapeutic hypothermia group (0.81; IQR, 0.46-1.53; P = .017). Neurodevelopmental outcome was analyzed for 56 infants. Nfat5 mRNA increased in infants with severely abnormal (1.26; IQR, 1.17-1.39) vs normal outcomes (0.97; IQR, 0.83-1.24; P = .036), and also in infants with severely abnormal vs mildly abnormal outcomes (0.96; IQR, 0.80-1.06; P = .013). Fzd4 mRNA increased in infants with severely abnormal (2.51; IQR, 1.60-3.56) vs normal outcomes (0.74; IQR, 0.48-1.49; P = .004) and in infants with severely abnormal vs mildly abnormal outcomes (0.97; IQR, 0.75-1.34; P = .026). CONCLUSIONS: Increased Fzd4 mRNA expression was observed in cord blood of infants with severe HIE; Nfat5 mRNA and Fzd4 mRNA expression were increased in infants with severely abnormal long-term outcomes. These mRNA may augment current measures as early objective markers of HIE severity at delivery.

Improvement in the Prediction of Neonatal Hypoxic-Ischemic Encephalopathy with the Integration of Umbilical Cord Metabolites and Current Clinical Makers.

OBJECTIVE: To validate our previously identified candidate metabolites, and to assess the ability of these metabolites to predict hypoxic-ischemic encephalopathy (HIE) both individually and combined with clinical data. STUDY DESIGN: Term neonates with signs of perinatal asphyxia, with and without HIE, and matched controls were recruited prospectively at birth from 2 large maternity units. Umbilical cord blood was collected for later batch metabolomic analysis by mass spectroscopy along with clinical details. The optimum selection of clinical and metabolites features with the ability to predict the development of HIE was determined using logistic regression modelling and machine learning techniques. Outcome of HIE was determined by clinical Sarnat grading and confirmed by electroencephalogram grade at 24 hours. RESULTS: Fifteen of 27 candidate metabolites showed significant alteration in infants with perinatal asphyxia or HIE when compared with matched controls. Metabolomic data predicted the development of HIE with an area under the curve of 0.67 (95% CI, 0.62-0.71). Lactic acid and alanine were the primary metabolite predictors for the development of HIE, and when combined with clinical data, gave an area under the curve of 0.96 (95% CI, 0.92-0.95). CONCLUSIONS: By combining clinical and metabolic data, accurate identification of infants who will develop HIE is possible shortly after birth, allowing early initiation of therapeutic hypothermia.

Multichannel EEG abnormalities during the first 6 hours in infants with mild hypoxic-ischaemic encephalopathy.

BACKGROUND: Infants with mild HIE are at risk of significant disability at follow-up. In the pre-therapeutic hypothermia (TH) era, electroencephalography (EEG) within 6 hours of birth was most predictive of outcome. This study aims to identify and describe features of early EEG and heart rate variability (HRV) (<6 hours of age) in infants with mild HIE compared to healthy term infants. METHODS: Infants >36 weeks with mild HIE, not undergoing TH, with EEG before 6 hours of age were identified from 4 prospective cohort studies conducted in the Cork University Maternity Services, Ireland (2003-2019). Control infants were taken from a contemporaneous study examining brain activity in healthy term infants. EEGs were qualitatively analysed by two neonatal neurophysiologists and quantitatively assessed using multiple features of amplitude, spectral shape and inter-hemispheric connectivity. Quantitative features of HRV were assessed in both the groups. RESULTS: Fifty-eight infants with mild HIE and sixteen healthy term infants were included. Seventy-two percent of infants with mild HIE had at least one abnormal EEG feature on qualitative analysis and quantitative EEG analysis revealed significant differences in spectral features between the two groups. HRV analysis did not differentiate between the groups. CONCLUSIONS: Qualitative and quantitative analysis of the EEG before 6 hours of age identified abnormal EEG features in mild HIE, which could aid in the objective identification of cases for future TH trials in mild HIE. IMPACT: Infants with mild HIE currently do not meet selection criteria for TH yet may be at risk of significant disability at follow-up. In the pre-TH era, EEG within 6 hours of birth was most predictive of outcome; however, TH has delayed this predictive value. 72% of infants with mild HIE had at least one abnormal EEG feature in the first 6 hours on qualitative assessment. Quantitative EEG analysis revealed significant differences in spectral features between infants with mild HIE and healthy term infants. Quantitative EEG features may aid in the objective identification of cases for future TH trials in mild HIE.

Activin A and Acvr2b mRNA from Umbilical Cord Blood Are Not Reliable Markers of Mild or Moderate Neonatal Hypoxic-Ischemic Encephalopathy.

BACKGROUND: Activin A protein and its receptor ACVR2B have been considered viable biomarkers for the diagnosis of hypoxic-ischemic encephalopathy (HIE). This study aimed to assess umbilical cord blood (UCB) levels of Activin A and Acvr2b messenger RNA (mRNA) as early biomarkers of mild and moderate HIE and long-term neurodevelopmental outcome. METHODS: One-hundred and twenty-six infants were included in the analyses from the BiHiVE2 cohort, a multi-center study, recruited in Ireland and Sweden (2013 to 2015). UCB serum Activin A and whole blood Acvr2b mRNA were measured using enzyme-linked immunosorbent assay and quantitative polymerase chain reaction, respectively. RESULTS: Activin A analysis included 101 infants (controls, n = 50, perinatal asphyxia, n = 28, HIE, n = 23). No differences were detected across groups (p = 0.69). No differences were detected across HIE grades (p = 0.12). Acvr2b mRNA analysis included 67 infants (controls, n = 22, perinatal asphyxia, n = 23, and HIE, n = 22), and no differences were observed across groups (p = 0.75). No differences were detected across HIE grades (p = 0.58). No differences were detected in neurodevelopmental outcome in infants followed up to 18 to 36 months in serum Activin A or in whole blood Acvr2b mRNA (p = 0.55 and p = 0.90, respectively). CONCLUSION: UCB Activin A and Acvr2b mRNA are not valid biomarkers of infants with mild or moderate HIE; they are unable to distinguish infants with HIE or infants with poor neurodevelopmental outcomes.

Eating behaviour, physical activity, TV exposure and sleeping habits in five year olds: a latent class analysis.

BACKGROUND: Diet, physical activity, sedentary behaviours, and sleep time are considered major contributory factors of the increased prevalence of childhood overweight and obesity. The aims of this study were to (1) identify behavioural clusters of 5 year old children based on lifestyle behaviours, (2) explore potential determinants of class membership, and (3) to determine if class membership was associated with body measure outcomes at 5 years of age. METHODS: Data on eating behaviour, engagement in active play, TV watching, and sleep duration in 1229 5 year old children from the Cork BASELINE birth cohort study was obtained through in-person interviews with parent. Latent class analysis was used to identify behavioural clusters. Potential determinants of cluster membership were investigated using multinomial logistic regression. Associations between the identified classes and cardio metabolic body measures were examined using multivariate logistic and linear regression, with cluster membership used as the independent variable. RESULTS: 51% of children belonged to a normative class, while 28% of children were in a class characterised by high scores on food avoidance scales in combination with low enjoyment of food, and 20% experienced high scores on the food approach scales. Children in both these classes had lower conditional probabilities of engaging in active play for at least 1 hour per day and sleeping for a minimum of 10 h, and higher probability of watching TV for 2 hours or more, compared to the normative class. Low socioeconomic index (SEI) and no breastfeeding at 2 months were found to be associated with membership of the class associated with high scores on the food avoidance scale, while lower maternal education was associated with the class defined by high food approach scores. Children in the class with high scores on the food approach scales had higher fat mass index (FMI), lean mass index (LMI), and waist-to-height ratio (WtHR) compared to the normative class, and were at greater risk of overweight and obesity. CONCLUSION: Findings suggest that eating behaviour appeared to influence overweight and obesity risk to a greater degree than activity levels at 5 years old. Further research of how potentially obesogenic behaviours in early life track over time and influence adiposity and other cardio metabolic outcomes is crucial to inform the timing of interventions.

A detailed exploration of early infant milk feeding in a prospective birth cohort study in Ireland: combination feeding of breast milk and infant formula and early breast-feeding cessation.

Breast-feeding initiation and continuation rates in the UK and Ireland are low relative to many European countries. As a core outcome of the prospective Cork Nutrition and Development Maternal-Infant Cohort (COMBINE) study (Cork, Ireland), we aimed to describe infant milk feeding practices in detail and examine the prevalence and impact of combination feeding of breast milk and infant formula on breast-feeding duration. COMBINE recruited 456 nulliparous mothers (2015-2017) for maternal-infant follow-up via interview at hospital discharge (median 3 (interquartile range (IQR) 2, 4) d (n 453)), 1 (n 418), 2 (n 392), 4 (n 366), 6 (n 362) and 9 (n 345) months of age. Median maternal age was 32 (IQR 29, 34) years, 97 % of mothers were of white ethnicity, 79 % were Irish-born and 75 % were college-educated. Overall, 75 % breastfed to any extent at discharge and 44 % breastfed solely. At 1, 2, 4, 6 and 9 months, respectively, 40, 36, 33, 24 and 19 % breastfed solely. Combination feeding of breast milk and infant formula was common at discharge (31 %) and 1 month (20 %). Reasons for combination feeding at 1 month included perceived/actual hunger (30 %), healthcare professional advice (31 %) and breast-feeding difficulties (13 %). Of mothers who breastfed to any extent at discharge, 45 % stopped within 4 months. Mothers who combination fed were more likely to cease breast-feeding than those who breastfed solely (relative risk 2·3 by 1 month and 12·0 by 2 months). These granular data provide valuable insight to early milk feeding practices and indicate that supporting early breast-feeding without formula use may be key to the successful continuation of breast-feeding.