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.

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.

Temporal evolution of electrographic seizures in newborn infants with hypoxic-ischaemic encephalopathy requiring therapeutic hypothermia: a secondary analysis of the ANSeR studies.

BACKGROUND: Despite extensive research on neonatal hypoxic-ischaemic encephalopathy, detailed information about electrographic seizures during active cooling and rewarming of therapeutic hypothermia is sparse. We aimed to describe temporal evolution of seizures and determine whether there is a correlation of seizure evolution with 2-year outcome. METHODS: This secondary analysis included newborn infants recruited from eight European tertiary neonatal intensive care units for two multicentre studies (a randomised controlled trial [NCT02431780] and an observational study [NCT02160171]). Infants were born at 36+0 weeks of gestation with moderate or severe hypoxic-ischaemic encephalopathy and underwent therapeutic hypothermia with prolonged conventional video-electroencephalography (EEG) monitoring for 10 h or longer from the start of rewarming. Seizure burden characteristics were calculated based on electrographic seizures annotations: hourly seizure burden (minutes of seizures within an hour) and total seizure burden (minutes of seizures within the entire recording). We categorised infants into those with electrographic seizures during active cooling only, those with electrographic seizures during cooling and rewarming, and those without seizures. Neurodevelopmental outcomes were determined using the Bayley's Scales of Infant and Toddler Development, Third Edition (BSID-III), the Griffiths Mental Development Scales (GMDS), or neurological assessment. An abnormal outcome was defined as death or neurodisability at 2 years. Neurodisability was defined as a composite score of 85 or less on any subscales for BSID-III, a total score of 87 or less for GMDS, or a diagnosis of cerebral palsy (dyskinetic cerebral palsy, spastic quadriplegia, or mixed motor impairment) or epilepsy. FINDINGS: Of 263 infants recruited between Jan 1, 2011, and Feb 7, 2017, we included 129 infants: 65 had electrographic seizures (43 during active cooling only and 22 during and after active cooling) and 64 had no seizures. Compared with infants with seizures during active cooling only, those with seizures during and after active cooling had a longer seizure period (median 12 h [IQR 3-28] vs 68 h [35-86], p<0·0001), more seizures (median 12 [IQR 5-36] vs 94 [24-134], p<0·0001), and higher total seizure burden (median 69 min [IQR 22-104] vs 167 min [54-275], p=0·0033). Hourly seizure burden peaked at about 20-24 h in both groups, and infants with seizures during and after active cooling had a secondary peak at 85 h of age. When combined, worse EEG background (major abnormalities and inactive background) at 12 h and 24 h were associated with the seizure group: compared with infants with a better EEG background (normal, mild, or moderate abnormalities), infants with a worse EEG background were more likely to have seizures after cooling at 12 h (13 [54%] of 24 vs four [14%] of 28; odds ratio 7·09 [95% CI 1·88-26·77], p=0·0039) and 24 h (14 [56%] of 25 vs seven [18%] of 38; 5·64 [1·81-17·60], p=0·0029). There was a significant relationship between EEG grade at 12 h (four categories) and seizure group (p=0·020). High total seizure burden was associated with increased odds of an abnormal outcome at 2 years of age (odds ratio 1·007 [95% CI 1·000-1·014], p=0·046), with a medium negative correlation between total seizure burden and BSID-III cognitive score (rS=-0·477, p=0·014, n=26). INTERPRETATION: Overall, half of infants with hypoxic-ischaemic encephalopathy had electrographic seizures and a third of those infants had seizures beyond active cooling, with worse outcomes. These results raise the importance of prolonged EEG monitoring of newborn infants with hypoxic-ischaemic encephalopathy not only during active cooling but throughout the rewarming phase and even longer when seizures are detected. FUNDING: Wellcome Trust, Science Foundation Ireland, and the Irish Health Research Board.

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.

Prediction of 2-Year Cognitive Outcomes in Very Preterm Infants Using Machine Learning Methods.

Importance: Early intervention can improve cognitive outcomes for very preterm infants but is resource intensive. Identifying those who need early intervention most is important. Objective: To evaluate a model for use in very preterm infants to predict cognitive delay at 2 years of age using routinely available clinical and sociodemographic data. Design, Setting, and Participants: This prognostic study was based on the Swedish Neonatal Quality Register. Nationwide coverage of neonatal data was reached in 2011, and registration of follow-up data opened on January 1, 2015, with inclusion ending on September 31, 2022. A variety of machine learning models were trained and tested to predict cognitive delay. Surviving infants from neonatal units in Sweden with a gestational age younger than 32 weeks and complete data for the Bayley Scales of Infant and Toddler Development, Third Edition cognitive index or cognitive scale scores at 2 years of corrected age were assessed. Infants with major congenital anomalies were excluded. Exposures: A total of 90 variables (containing sociodemographic and clinical information on conditions, investigations, and treatments initiated during pregnancy, delivery, and neonatal unit admission) were examined for predictability. Main Outcomes and Measures: The main outcome was cognitive function at 2 years, categorized as screening positive for cognitive delay (cognitive index score <90) or exhibiting typical cognitive development (score ≥90). Results: A total of 1062 children (median [IQR] birth weight, 880 [720-1100] g; 566 [53.3%] male) were included in the modeling process, of whom 231 (21.8%) had cognitive delay. A logistic regression model containing 26 predictive features achieved an area under the receiver operating curve of 0.77 (95% CI, 0.71-0.83). The 5 most important features for cognitive delay were non-Scandinavian family language, prolonged duration of hospitalization, low birth weight, discharge to other destination than home, and the infant not receiving breastmilk on discharge. At discharge from the neonatal unit, the full model could correctly identify 605 of 650 infants who would have cognitive delay at 24 months (sensitivity, 0.93) and 1081 of 2350 who would not (specificity, 0.46). Conclusions and Relevance: The findings of this study suggest that predictive modeling in neonatal care could enable early and targeted intervention for very preterm infants most at risk for developing cognitive impairment.

Neurodevelopmental outcome of low-risk moderate to late preterm infants at 18 months.

Background: Of the 15 million preterm births that occur worldwide each year, approximately 80% occur between 32 and 36 + 6 weeks gestational age (GA) and are defined as moderate to late preterm (MLP) infants. This percentage substantiates a need for a better understanding of the neurodevelopmental outcome of this group. Aim: To describe neurodevelopmental outcome at 18 months in a cohort of healthy low-risk MLP infants admitted to the neonatal unit at birth and to compare the neurodevelopmental outcome to that of a healthy term-born infant group. Study design and method: This single-centre observational study compared the neurodevelopmental outcome of healthy MLP infants to a group of healthy term control (TC) infants recruited during the same period using the Griffith's III assessment at 18 months. Results: Seventy-five MLP infants and 92 TC infants were included. MLP infants scored significantly lower in the subscales: Eye-hand coordination (C), Personal, Social and Emotional Development (D), Gross Motor Development (E) and General Developmental (GD) (p < 0.001 for each) and Foundations of Learning (A), (p = 0.004) in comparison to the TC infant group with Cohen's d effect sizes ranging from 0.460 to 0.665. There was no statistically significant difference in mean scores achieved in subscale B: Language and Communication between groups (p = 0.107). Conclusion: MLP infants are at risk of suboptimal neurodevelopmental outcomes. Greater surveillance of the neurodevelopmental trajectory of this group of at-risk preterm infants is required.

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.

Electroencephalography Quality and Application Times in a Pediatric Emergency Department Setting: A Feasibility Study.

BACKGROUND: Status epilepticus is the most common neurological emergency presenting to pediatric emergency departments. Nonconvulsive status epilepticus can be extremely challenging to diagnose, however, requiring electroencephalographic (EEG) confirmation for definitive diagnosis. We aimed to determine the feasibility of achieving a good-quality pediatric EEG recording within 20 minutes of presentation to the emergency department. METHODS: Single-center prospective feasibility study in Cork University Hospital, Ireland, between July 2021 and June 2022. Two-channel continuous EEG was recorded from children (1) aged <16 years and (2) with Glasgow Coma Scale <11 or a reduction in baseline Glasgow Coma Scale in the case of a child with a neurodisability. RESULTS: Twenty patients were included. The median age at presentation was 65.8 months (interquartile range, 23.2 to 119.0); 50% had a background diagnosis of epilepsy. The most common reason for EEG monitoring was status epilepticus (85%) followed by suspected nonconvulsive status (10%) and reduced consciousness of unknown etiology (5%). The mean length of recording was 93.1 minutes (S.D. 47.4). The mean time to application was 41.3 minutes (S.D. 11.7). The mean percent of artifact in all recordings was 19.3% (S.D. 15.9). Thirteen (65%) EEGs had <25% artifact. Artifact was higher in cases in which active airway management was ongoing. CONCLUSIONS: EEG monitoring can be achieved in a pediatric emergency department setting within one hour of presentation. Overall, artifact percentage was low outside of periods of airway manipulation. Future studies are required to determine its use in early seizure detection and its support role in clinical decision-making in these patients.

Developmental and behavioural outcomes at 2 years in babies born during the COVID-19 pandemic: communication concerns in a pandemic birth cohort.

INTRODUCTION: The CORAL (Impact of Corona Virus Pandemic on Allergic and Autoimmune Dysregulation in Infants Born During Lockdown) study reported a reduction in social communication milestones in 12-month-old infants born into the COVID-19 pandemic. AIMS: To look at 24-month developmental and behavioural outcomes in the CORAL cohort. DESIGN: The CORAL study is a longitudinal prospective observational study of Irish infants born in the first 3 months of the pandemic. At 24 months of age, the Ages and Stages Developmental Questionnaire (ASQ24) and the Child Behaviour Checklist (CBCL) were completed and compared with prepandemic BASELINE (Babies After SCOPE: Evaluating the Longitudinal Impact Using Neurological and Nutritional Impact) cohort. RESULTS: 917 babies (312 CORAL infants and 605 BASELINE infants) were included. At 24 months of age, infants in the CORAL and BASELINE cohorts had similar developmental ASQ24 scores in fine motor, problem solving and personal and social domains but ASQ24 communication scores were significantly lower in the CORAL group compared with the BASELINE cohort (mean (SD) 49.5 (15.1) vs 53.7 (11.6), p<0.01). Infants from the CORAL cohort were more likely to score below standardised cut-offs for developmental concern in the communication domain (11.9% CORAL compared with 5.4% BASELINE, p<0.01). Unadjusted ASQ24 gross motor scores were lower for the pandemic cohort. Fewer CORAL infants fell under 2 SD cut-off in personal-social subdomain. For CBCL, there was no evidence of difference in scores between the cohorts on multivariable analysis. CONCLUSION: 24-month-old pandemic-born infants had largely similar developmental and behavioural scores compared with their prepandemic counterparts. Concerns have been raised in the communication developmental domain.

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.

Reference centiles for infant sleep parameters from 4 to 16 weeks of age: findings from an Irish cohort.

OBJECTIVES: To establish unconditional reference centiles for sleep parameters in infants 4-16 weeks of age. DESIGN AND SETTING: Secondary data analysis of sleep parameters recorded at 4-16 weeks of age in a longitudinal randomised controlled trial (RCT) (BabySMART). PATIENTS: Healthy term infants assigned to the non-intervention arm of the RCT. MAIN OUTCOME MEASURES: Infants' sleep duration was recorded by parents/guardians daily, from week 2-16 of age using a sleep diary. Reference centiles for total, daytime, night-time and longest sleep episode duration were estimated using multilevel modelling. RESULTS: One hundred and six infants, mean (SD) gestational age of 39.9 (1.2) weeks and mean (SD) birth weight of 3.6 (0.5) kg had sleep recorded contributing 1264 measurements for each sleep parameter. Between 4 and 16 weeks of age total sleep duration in a 24-hour period, night-time sleep duration in a 12-hour period and infant's longest sleep episode duration increased, while daytime sleep duration in a 12-hour period decreased. CONCLUSIONS: Reference centiles up to 4 months of age in infants highlight the gradual decrease in daytime sleep and large increases in night-time sleep, which occur in tandem with increasing lengths of sleep episodes. These reference centiles provide useful sleep values for infant sleep trajectory occurring in early life and may be helpful for parents and clinicians. TRIAL REGISTRATION NUMBER: NCT03381027.

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.

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.

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.

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.

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.

Social communication skill attainment in babies born during the COVID-19 pandemic: a birth cohort study.

INTRODUCTION: The SARS-CoV-2 (COVID-19) pandemic was managed with sustained mass lockdowns to prevent spread of COVID-19 infection. Babies born during the early stages of the pandemic missed the opportunity of meeting a normal social circle of people outside the family home. METHODS: We compared 10 parentally reported developmental milestones at 12-month assessment in a cohort of 309 babies born at the onset of the pandemic (CORAL cohort) and 1629 babies from a historical birth cohort (BASELINE cohort recruited between 2008 and 2011). RESULTS: Compared with a historical cohort, babies born into lockdown appeared to have some deficits in social communication. Fewer infants in the pandemic cohort had one definite and meaningful word (76.6% vs 89.3%), could point (83.8% vs 92.8%) or wave bye-bye (87.7% vs 94.4%) at 12-month assessment. Adjusted log-binomial regression analyses demonstrated significant differences in social communication in the CORAL cohort compared with the BASELINE cohort: one definite and meaningful word (relative risk (RR): 0.86 (95% CI: 0.80 to 0.92)), pointing (RR: 0.91 (95% CI: 0.86 to 0.96)) and waving bye-bye (RR: 0.94 (95% CI: 0.90 to 0.99)). DISCUSSION: Parentally reported developmental outcomes in a birth cohort of babies born into lockdown during the COVID-19 pandemic may indicate some potential deficits in early life social communication. It must be noted that milestones are parentally reported and comparison is with a historical cohort with associated limitations. Further studies with standardised testing is required to validate these findings. CONCLUSION: Pandemic-associated social isolation may have impacted on the social communication skills in babies born during the pandemic compared with a historical cohort. Babies are resilient and inquisitive by nature, and it is hoped that with societal re-emergence and increase in social circles, their social communication skills will improve.

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.