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ObjectiveHypoxic-ischaemic encephalopathy (HIE) remains a leading cause of neonatal mortality and neurodisability. We aimed to determine the incidence of HIE and management patterns against national guidelines.DesignRetrospective cohort study using the National Neonatal Research Database.SettingNeonatal units in England and Wales.PatientsInfants 34–42 weeks gestational age (GA) with a recorded diagnosis of HIE.Main outcomesIncidence of HIE, mortality and treatment with therapeutic hypothermia (TH) were the main outcomes. Temporal changes were compared across two epochs (2011–2013 and 2014–2016).ResultsAmong 407 462 infants admitted for neonatal care, 12 195 were diagnosed with HIE. 8166 infants ≥36 weeks GA had moderate/severe HIE, 62.1% (n=5069) underwent TH and mortality was 9.3% (n=762). Of infants with mild HIE (n=3394), 30.3% (n=1027) underwent TH and 6 died. In late preterm infants (34–35 weeks GA) with HIE (n=635, 5.2%), 33.1% (n=210) received TH and 13.1% (n=83) died. Between epochs (2011–2013 vs 2014–2016), mortality decreased for infants ≥36 weeks GA with moderate/severe HIE (17.5% vs 12.3%; OR 0.69, 95% CI 0.59 to 0.81, p<0.001). Treatment with TH increased significantly between epochs in infants with mild HIE (24.9% vs 35.8%, p<0.001) and those born late preterm (34.3% vs 46.6%, p=0.002).ConclusionsMortality of infants ≥36 weeks GA with moderate/severe HIE has reduced over time, although many infants diagnosed with moderate/severe HIE do not undergo TH. Increasingly, mild HIE and late preterm infants with HIE are undergoing TH, where the evidence base is lacking, highlighting the need for prospective studies to evaluate safety and efficacy in these populations.

Neurodevelopment is a highly intricate process, and early detection of abnormalities is critical for optimizing outcomes through timely intervention. Accurate and cost-effective diagnostic methods for neurological disorders, particularly in infants, remain a significant challenge due to the heterogeneity of data and the variability in neurodevelopmental conditions. This study recruited twelve parent–infant pairs, with infants aged 3 to 12 months. Approximately 25 min of 2D video footage was captured, documenting natural play interactions between the infants and toys. We developed a novel, open-source method to classify and analyse infant movement patterns using deep learning techniques, specifically employing a transformer-based fusion model that integrates multiple video features within a unified deep neural network. This approach significantly outperforms traditional methods reliant on individual video features, achieving an accuracy of over 90%. Furthermore, a sensitivity analysis revealed that the pose estimation contributed far less to the model’s output than the pre-trained transformer and convolutional neural network (CNN) components, providing key insights into the relative importance of different feature sets. By providing a more robust, accurate and low-cost analysis of movement patterns, our work aims to enhance the early detection and potential prediction of neurodevelopmental delays, whilst providing insight into the functioning of the transformer-based fusion models of diverse video features.

Neurodevelopmental delay following extremely preterm birth or birth asphyxia is common but diagnosis is often delayed as early milder signs are not recognised by parents or clinicians. Early interventions have been shown to improve outcomes. Automation of diagnosis and monitoring of neurological disorders using non-invasive, cost effective methods within a patient’s home could improve accessibility to testing. Furthermore, said testing could be conducted over a longer period, enabling greater confidence in diagnoses, due to increased data availability. This work proposes a new method to assess the movements in children. Twelve parent and infant participants were recruited (children aged between 3 and 12 months). Approximately 25 min 2D video recordings of the infants organically playing with toys were captured. A combination of deep learning and 2D pose estimation algorithms were used to classify the movements in relation to the children’s dexterity and position when interacting with a toy. The results demonstrate the possibility of capturing and classifying children’s complexity of movements when interacting with toys as well as their posture. Such classifications and the movement features could assist practitioners to accurately diagnose impaired or delayed movement development in a timely fashion as well as facilitating treatment monitoring.

Decision-making in perinatal management of extremely preterm infants is challenging. Mortality prediction tools may support decision-making. We used population-based routinely entered electronic patient record data from 25,902 infants born between 23+0-27+6 weeks' gestation and admitted to 185 English and Welsh neonatal units from 2010-2020 to develop and internally validate an online tool to predict mortality before neonatal discharge. Comparing nine machine learning approaches, we developed an explainable tool based on stepwise backward logistic regression (https://premoutcome.shinyapps.io/Death/). The tool demonstrated good discrimination (area under the receiver operating characteristics curve (95% confidence interval) of 0.746 (0.729-0.762)) and calibration with superior net benefit across probability thresholds of 10%-70%. Our tool also demonstrated superior calibration and utility performance than previously published models. Acceptable performance was demonstrated in a multinational, external validation cohort of preterm infants. This tool may be useful to support high-risk perinatal decision-making following further evaluation.

ObjectiveTherapeutic hypothermia (TH) for neonatal hypoxic-ischaemic encephalopathy (HIE), delivered mainly in tertiary cooling centres (CCs), reduces mortality and neurodisability. It is unknown if birth in a non-cooling centre (non-CC), without active TH, impacts short-term outcomes.DesignRetrospective cohort study using National Neonatal Research Database and propensity score-matching.SettingUK neonatal units.PatientsInfants ≥36 weeks gestational age with moderate or severe HIE admitted 2011–2016.InterventionsBirth in non-CC compared with CC.Main outcome measuresPrimary outcome was survival to discharge without recorded seizures. Secondary outcomes were recorded seizures, mortality and temperature on arrival at CCs following transfer.Results5059 infants were included with 2364 (46.7%) born in non-CCs. Birth in a CC was associated with improved survival without seizures (35.1% vs 31.8%; OR 1.15, 95% CI 1.02 to 1.31; p=0.02), fewer seizures (60.7% vs 64.6%; OR 0.84, 95% CI 0.75 to 0.95, p=0.007) and similar mortality (15.8% vs 14.4%; OR 1.11, 95% CI 0.93 to 1.31, p=0.20) compared with birth in a non-CC. Matched infants from level 2 centres only had similar results, and birth in CCs was associated with greater seizure-free survival compared with non-CCs. Following transfer from a non-CC to a CC (n=2027), 1362 (67.1%) infants arrived with a recorded optimal therapeutic temperature but only 259 (12.7%) of these arrived within 6 hours of birth.ConclusionsAlmost half of UK infants with HIE were born in a non-CC, which was associated with suboptimal hypothermic treatment and reduced seizure-free survival. Provision of active TH in non-CC hospitals prior to upward transfer warrants consideration.

Neonatal stroke is a devastating condition that causes brain injury in babies and often leads to lifelong neurological impairment. Recent prospective population studies of neonatal stroke are lacking. Neonatal strokes are different from those in older children and adults. A better understanding of its aetiology, current management, and outcomes could reduce the burden of this rare condition. The study aims to explore the incidence and 2 year outcomes of neonatal stroke across an entire population in the UK and Republic of Ireland. This is an active national surveillance study using a purpose-built integrated case notification-data collection online platform. Over a 13 month period, with a potential 6 month extension, clinicians will notify neonatal stroke cases presenting in the first 90 days of life electronically via the online platform monthly. Clinicians will complete a primary questionnaire via the platform detailing clinical information, including neuroimaging, for analysis and classification. An outcome questionnaire will be sent at 2 years of age via the platform. Appropriate ethics and regulatory approvals have been received. The neonatal stroke study represents the first multinational population surveillance study delivered via a purpose-built integrated case notification-data collection online platform and data safe haven, overcoming the challenges of setting up the study.

ObjectiveEarly postnatal transfer (PNT) of extremely preterm infants is associated with adverse outcomes compared with in utero transfer (IUT). We aimed to explore recent national trends of IUT and early PNT.DesignObservational cohort study using the National Neonatal Research Database.SettingNeonatal units in England, Scotland and Wales.PatientsExtremely preterm infants 23+0–27+6 weeks’ gestation admitted for neonatal care from 2011 to 2016.Main outcomeThe incidence of IUT or PNT within 72 hours of life. Secondary outcomes included mortality, hospital transfer level between centres and temporal changes across two equal epochs, 2011–2013 (epoch 1 (Ep1)) and 2014–2016 (epoch 2 (Ep2)).Results14 719 infants were included (Ep1=7363 and Ep2=7256); 4005 (27%) underwent IUT; and 3042 (20.7%) had PNT. IUTs decreased significantly between epochs from 28.3% (Ep1=2089) to 26.0% (Ep2=1916) (OR 0.90, 95% CI 0.84 to 0.97, p<0.01). Conversely, PNTs increased from 19.8% (Ep1=1416) to 21.5% (Ep2=1581) (OR 1.11, 95% CI 1.02 to 1.20, p=0.01). PNTs between intensive care centres increased from 8.1% (Ep1=119) to 10.2% (Ep2=161, p=0.05). Mortality decreased from 21.6% (Ep1=1592) to 19.3% (Ep2=1421) (OR 0.90, 95% CI 0.83 to 0.97, p=0.01). Survival to 90 days of age was significantly lower in infants undergoing PNT compared with IUT (HR 1.31, 95% CI 1.18 to 1.46), with the greatest differences observed in infants <25 weeks’ gestational age.ConclusionIn the UK, IUT of extremely preterm infants has significantly decreased over the study period with a parallel increase in early PNT. Strategies to reverse these trends, improve IUT pathways and optimise antenatal steroid use could significantly improve survival and reduce brain injury for these high-risk infants.

ObjectiveNewborn resuscitation relies on accurate heart rate (HR) assessment, which, during auscultation, is prone to error. We investigated if a 6 s visual timer (VT) could improve HR assessment accuracy during newborn simulation.DesignProspective observational study of newborn healthcare professionals.SettingThree-phase developmental approach: phase I: HR auscultation during newborn simulation using a standard clock timer (CT) or the VT; phase II: repeat phase I after using a bespoke training app (NeoRate); phase III: following the Newborn Life Support course, participants assessed random HRs using the CT or VT.Main outcome measuresHR accuracy (within ±10 beats/min, correct HR category, i.e. <60, 60–100 and >100 beats/min), assessment time and error-free rates were compared.ResultsOverall, 1974 HR assessments were performed with participants more accurate using the VT for ±10 beats/min (70% CT vs 86% VT, p<0.001) and correct HR category (78% CT vs 84% VT, p<0.01). The VT improved accuracy across all three phases. Additionally, following app training in phase II, the HR accuracy of both the CT and VT improved. The VT resulted in faster HR assessment times of 11 s (IQR 9–13) compared with the CT at 15 s (IQR 9–23, p<0.001). Error-free scenarios increased from 24% using the CT to 57% using the VT (p<0.001), with a shorter assessment time (CT 116 s (IQR 65–156) vs VT 53 s (IQR 50–64), p<0.001).ConclusionUsing a VT to assess simulated newborn HR combined with a training app significantly improves accuracy and reduces assessment time compared with standard methods. Evaluation in the clinical setting is required to determine potential benefits.

Prediction models could identify infants at the greatest risk of bronchopulmonary dysplasia (BPD) and allow targeted preventative strategies. We performed a systematic review and meta-analysis with external validation of identified models. Studies using predictors available before day 14 of life to predict BPD in very preterm infants were included. Two reviewers assessed 7628 studies for eligibility. Meta-analysis of externally validated models was followed by validation using 62,864 very preterm infants in England and Wales. A total of 64 studies using 53 prediction models were included totalling 274,407 infants (range 32–156,587/study). In all, 35 (55%) studies predated 2010; 39 (61%) were single-centre studies. A total of 97% of studies had a high risk of bias, especially in the analysis domain. Following meta-analysis of 22 BPD and 11 BPD/death composite externally validated models, Laughon’s day one model was the most promising in predicting BPD and death (C-statistic 0.76 (95% CI 0.70–0.81) and good calibration). Six models were externally validated in our cohort with C-statistics between 0.70 and 0.90 but with poor calibration. Few BPD prediction models were developed with contemporary populations, underwent external validation, or had calibration and impact analyses. Contemporary, validated, and dynamic prediction models are needed for targeted preventative strategies. Impact This review aims to provide a comprehensive assessment of all BPD prediction models developed to address the uncertainty of which model is sufficiently valid and generalisable for use in clinical practice and research. Published BPD prediction models are mostly outdated, single centre and lack external validation. Laughon’s 2011 model is the most promising but more robust models, using contemporary data with external validation are needed to support better treatments.

Aim  The pelvic radiograph in children with cerebral palsy (CP) can inform the degree of hip displacement by calculation of the migration percentage. However, concerns have arisen about the reliability of this measurement. The present study examined the reliability of radiographic assessment of displacement and the importance of positioning and reporting experience. Method  Two pelvic radiographs, taken at least an hour apart, were performed in 20 children (total 40 hips) in the standard position by a trained paediatric radiographer. Children (13 males, seven females) were aged 30 months to 10 years with severe bilateral spastic CP in Gross Motor Function Classification System levels IV (n=10) and V (n=10). The migration percentage of each hip was measured on two occasions 3 months apart by two experienced radiologists independently. Comparisons of migration percentage were made in three ways by (1) the same observer at the same time, (2) the same observer 3 months apart, and (3) different observers 3 months apart. Results  Migration percentage (mean [SD]) was (1) 3.2% (3.5), (2) 3.3% (3.2), and (3) 3.7% (3.8) respectively. Interpretation  Reliable measures of migration percentage can be obtained with correct positioning and if reported by suitably experienced radiologists, making this a valid surveillance method. Clinical decisions can be made taking into account an expected error in hip displacement measurements.