Sudden unexpected clinical deterioration or cardiorespiratory instability is common in neonates and is often referred as a “crashing” neonate. The established resuscitation guidelines provide an excellent framework to stabilize and evaluate these infants, but it is primarily based upon clinical assessment only. However, clinical assessment in sick neonates is limited in identifying underlying pathophysiology. The Crashing Neonate Protocol (CNP), utilizing point-of-care ultrasound (POCUS), is specifically designed for use in neonatal emergencies. It can be applied both in term and pre-term neonates in the neonatal intensive care unit (NICU). The proposed protocol involves a stepwise systematic assessment with basic ultrasound views which can be easily learnt and reproduced with focused structured training on the use of portable ultrasonography (similar to the FAST and BLUE protocols in adult clinical practice). We conducted a literature review of the evidence-based use of POCUS in neonatal practice. We then applied stepwise voting process with a modified DELPHI strategy (electronic voting) utilizing an international expert group to prioritize recommendations. We also conducted an international survey among a group of neonatologists practicing POCUS. The lead expert authors identified a specific list of recommendations to be included in the proposed CNP. This protocol involves pre-defined steps focused on identifying the underlying etiology of clinical instability and assessing the response to intervention.Conclusion: To conclude, the newly proposed POCUS-based CNP should be used as an adjunct to the current recommendations for neonatal resuscitation and not replace them, especially in infants unresponsive to standard resuscitation steps, or where the underlying cause of deterioration remains unclear. What is known? • Point-of-care ultrasound (POCUS) is helpful in evaluation of the underlying pathophysiologic mechanisms in sick infants. What is new? • The Crashing Neonate Protocol (CNP) is proposed as an adjunct to the current recommendations for neonatal resuscitation, with pre-defined steps focused on gaining information regarding the underlying pathophysiology in unexplained “crashing” neonates. • The proposed CNP can help in targeting specific and early therapy based upon the underlying pathophysiology, and it allows assessment of the response to intervention(s) in a timely fashion.

Objective: The aim of this study was to evaluate the neurodevelopmental outcome at 18–24 months in surviving preterm infants with grades I–IV intraventricular hemorrhages (IVHs) compared to those with no IVH. Study Design: We included preterm survivors <29 weeks’ GA admitted to the Canadian Neonatal Network’s NICUs from April 2009 to September 2011 with follow-up data at 18–24 months in a retrospective cohort study. The neonates were grouped based on the severity of the IVH detected on a cranial ultrasound scan and recorded in the database: no IVH; subependymal hemorrhage or IVH without ventricular dilation (grades I–II); IVH with ventricular dilation (grade III); and persistent parenchymal echogenicity/lucency (grade IV). The primary outcomes of neurodevelopmental impairment (NDI), significant neurodevelopmental impairment (sNDI), and the effect modification by other short-term neonatal morbidities were assessed. Using multivariable regression analysis, the adjusted ORs (AOR) and 95% of the CIs were calculated. Results: 2327 infants were included. The odds of NDI were higher in infants with grades III and IV IVHs (AOR 2.58, 95% CI 1.56, 4.28 and AOR 2.61, 95% CI 1.80, 3.80, respectively) compared to those without IVH. Infants with an IVH grade ≤II had similar outcomes for NDI (AOR 1.08, 95% CI 0.86, 1.35) compared to those without an IVH, but the odds of sNDI were higher (AOR 1.58, 95% CI 1.16, 2.17). Conclusions: There were increased odds of sNDI in infants with grades I–II IVHs, and an increased risk of adverse NDI in infants with grades ≥III IVHs is corroborated with the current literature.

Despite significant improvement in perinatal care and research, hypoxic ischemic encephalopathy (HIE) remains a global healthcare challenge. From both published research and reports of QI initiatives, we have identified a number of distinct opportunities that can serve as targets of quality improvement (QI) initiatives focused on reducing HIE. Specifically, (i) implementation of perinatal interventions to anticipate and timely manage high-risk deliveries; (ii) enhancement of team training and communication; (iii) optimization of early HIE diagnosis and management in referring centers and during transport; (iv) standardization of the approach when managing neonates with HIE during therapeutic hypothermia; (v) and establishment of protocols for family integration and follow-up, have been identified as important in successful QI initiatives. We also provide a framework and examples of tools that can be used to support QI work and discuss some of the perceived challenges and future opportunities for QI targeting HIE.

Purpose: Is machine learning (ML) superior to the traditionally used logistic regression (LR) in prediction of neurodevelopmental outcomes in preterm infants? Objectives: To develop and internally validate a ML model to predict neurodevelopmental impairment (NDI) in very preterm infants (<31 weeks) at 36 months corrected age, using clinical predictors. Methods: A retrospective cohort of very preterm infants (23^0–30⁶ weeks) born between January 2004 and December 2016 in Nova Scotia, Canada. Survivors with neurodevelopmental assessment at 36 months corrected age were included. The study sample was randomly split (80:20) into a development and testing datasets. We compared four methods: LR, elastic net (EN), random forest ensemble (RF) and gradient boosting (XGB), in relation to discrimination (AUC), calibration, and diagnostic properties. Results: Of 811 eligible infants, 663 were included (mean gestational age 28 weeks, mean birth weight 1137 g and 52% male). Of those, 195 (29%) developed NDI and 468 (71%) did not. On internal validation using the testing dataset, all four models provided good discrimination of NDI with comparable AUC. RF was superior to the other three methods with a higher AUC (0.79 vs. 0.74, 0.74, and 0.73 for XGB, EN and LR, respectively), but all models have overlapped CIs. Conclusions: In this population-based cohort of very preterm infants, RF was superior to conventional LR in prediction of NDI at 3 years corrected age. Accurate prediction of preterm infants at risk of NDI enables early referrals for intervention programs and resources allocation toward those who are most likely to benefit.