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Objective: Despite strong evidence for its utility in clinical management and diagnosis of intracranial hemorrhage (ICH), the use of neonatal cranial point‐of‐care ultrasound (POCUS) has not been standardized in neonatal intensive care units (NICUs) in the United States. The primary aim of this study was to evaluate the feasibility of training NICU providers to perform cranial POCUS by tracking the quality of image acquisition following training. Methods: Observational single‐center cohort study of cranial POCUS images obtained by trained neonatal practitioners (attendings, fellows, and advanced practice providers) using a protocol developed by a radiologist and neonatologist. Exams were performed on infants born ≤1250 g and/or ≤30 weeks gestation within the first 3 days after birth. A survey to assess attitudes regarding cranial POCUS was given before each of three training sessions. Demographic and clinical data collection were portrayed with descriptive statistics. Metrics of image quality were assessed by a radiologist and sonographer independently. Analysis of trends in quality of POCUS images over time was performed using a multinomial Cochran‐Armitage test. Results: Eighty‐two cranial POCUS scans were performed over a 2‐year period. Infant median age at exam was 14 hours (IQR 7–22 hours). Metrics of image quality depicted quarterly demonstrated a significant improvement in depth (P =.01), gain (P =.048), and quality of anatomy images captured (P <.001) over time. Providers perceived increased utility and safety of cranial POCUS over time. Conclusion: Cranial POCUS image acquisition improved significantly following care team training, which may enable providers to diagnose ICH at the bedside. [ABSTRACT FROM AUTHOR]

Organotypic brain slice models are an ideal technological platform to investigate therapeutic options for hypoxic‐ischemic (HI) brain injury, a leading cause of morbidity and mortality in neonates. The brain exhibits regional differences in the response to HI injury in vivo. This can be modeled using organotypic brain slices, which maintain three‐dimensional regional structures and reflect the regional differences in injury response. Here, we developed an organotypic whole hemisphere (OWH) slice culture model of HI injury using the gyrencephalic ferret brain at a developmental stage equivalent to a full‐term human infant in order to better probe region‐specific cellular responses to injury. Each slice encompassed the cortex, corpus callosum, subcortical white matter, hippocampus, basal ganglia, and thalamus. Regional responses to treatment with either erythropoietin (Epo) or the ketone body acetoacetate (AcAc) were highly heterogenous. While both treatments suppressed global injury responses and oxidative stress, significant neuroprotection was only seen in a subset of regions, with others displaying no response or potential exacerbation of injury. Similar regional heterogeneity was seen in the morphology and response of microglia to injury and treatment, which mirrored those seen after injury in vivo. Within each region, machine‐learning‐based classification of microglia morphological shifts in response to injury predicted the neuroprotective response to each therapy, with different morphologies associated with different treatment responses. This suggests that the ferret OWH slice culture model provides a platform for examining regional responses to injury in the gyrencephalic brain, as well as for screening combinations of therapeutics to provide global neuroprotection after injury. [ABSTRACT FROM AUTHOR]

The article reports that the interventions aimed at improving adherence to an Intraventricular Hemorrhage Prevention Bundle (IVHPB) led to a reduction in the incidence of severe intraventricular hemorrhage (IVH) in high-risk neonates.

Background: Neonatal intraventricular hemorrhage prevention bundles for preterm infants commonly defer daily weighing for the first 72 h, with reweighing occurring on day 4. Clinicians rely on maintaining stable sodium values as a proxy of fluid status to inform fluid management decisions over the first 96 h after birth. Yet, there exists a paucity of research evaluating whether serum sodium or osmolality are appropriate proxies for weight loss and whether increasing variability in sodium or osmolality during this early transitional period is associated with adverse in-hospital outcomes., Objectives: To evaluate whether serum sodium or osmolality change in the first 96 h after birth was associated with percent weight change from birth weight, and to assess potential associations between serum sodium and osmolality variability with in-hospital outcomes., Methods: This retrospective, cross-sectional study included neonates born at ≤30 gestational weeks or ≤1250 g. We evaluated associations between serum sodium coefficient of variation (CoV), osmolality CoV, and maximal weight loss percentage in the first 96 h after birth with in-hospital neonatal outcomes., Results: Among 205 infants, serum sodium and osmolality were poorly correlated with percent weight change in individual 24-h increments ( R 2 = 0.01-0.14). For every 1% increase in sodium CoV, there was an associated 2-fold increased odds of surgical necrotizing enterocolitis and 2-fold increased odds of in-hospital mortality (odds ratio, 2.07; 95% CI: 1.02, 4.54; odds ratio, 1.95; 95% CI: 1.10, 3.64, respectively). Sodium CoV was more strongly associated with outcomes than absolute sodium maximal change., Conclusions: In the first 96 h, serum sodium and osmolality are poor proxies for assessing percent weight change. Increasing variability of serum sodium is associated with later development of surgical necrotizing enterocolitis and all-cause in-hospital mortality. Prospective research is needed to evaluate whether reducing sodium variability in the first 96 h after birth, as assessed by CoV, improves newborn health outcomes., (© 2023 Published by Elsevier Inc. on behalf of American Society for Nutrition.)

Background: Infants born extremely preterm (<28 weeks' gestation) are at high risk of neurodevelopmental impairment (NDI) with 50% of survivors showing moderate or severe NDI when at 2 years of age. We sought to develop novel models by which to predict neurodevelopmental outcomes, hypothesizing that combining baseline characteristics at birth with medical care and environmental exposures would produce the most accurate model., Methods: Using a prospective database of 692 infants from the Preterm Epo Neuroprotection (PENUT) Trial, which was carried out between December 2013 and September 2016, we developed three predictive algorithms of increasing complexity using a Bayesian Additive Regression Trees (BART) machine learning approach to predict both NDI and continuous Bayley Scales of Infant and Toddler Development 3rd ed subscales at 2 year follow-up using: 1) the 5 variables used in the National Institute of Child Health and Human Development (NICHD) Extremely Preterm Birth Outcomes Tool, 2) 21 variables associated with outcomes in extremely preterm (EP) infants, and 3) a hypothesis-free approach using 133 potential variables available for infants in the PENUT database., Findings: The NICHD 5-variable model predicted 3-4% of the variance in the Bayley subscale scores, and predicted NDI with an area under the receiver operator curve (AUROC, 95% CI) of 0.62 (0.56-0.69). Accuracy increased to 12-20% of variance explained and an AUROC of 0.77 (0.72-0.83) when using the 21 pre-selected clinical variables. Hypothesis-free variable selection using BART resulted in models that explained 20-31% of Bayley subscale scores and AUROC of 0.87 (0.83-0.91) for severe NDI, with good calibration across the range of outcome predictions. However, even with the most accurate models, the average prediction error for the Bayley subscale predictions was around 14-15 points, leading to wide prediction intervals. Higher total transfusion volume was the most important predictor of severe NDI and lower Bayley scores across all subscales., Interpretation: While the machine learning BART approach meaningfully improved predictive accuracy above a widely used prediction tool (NICHD) as well as a model utilizing NDI-associated clinical characteristics, the average error remained approximately 1 standard deviation on either side of the true value. Although dichotomous NDI prediction using BART was more accurate than has been previously reported, and certain clinical variables such as transfusion exposure were meaningfully predictive of outcomes, our results emphasize the fact that the field is still not able to accurately predict the results of complex long-term assessments such as Bayley subscales in infants born EP even when using rich datasets and advanced analytic methods. This highlights the ongoing need for long-term follow-up of all EP infants., Funding: Supported by the National Institute of Neurological Disorders and StrokeU01NS077953 and U01NS077955., Competing Interests: S.E.J. declares receiving royalties from editing Avery's Diseases of the Newborn. S.E.J., B.A.C., D.E.M., and P.J.H. received salary support as part of funding for the PENUT trial. All other authors declare that they have no relevant conflicts of interest., (© 2022 The Author(s).)