Implementing a Hospital-Based Safe Sleep Program for Newborns and Infants.

BACKGROUND: An unsafe sleep environment remains the leading contributor to unexpected infant death. PURPOSE: To determine the effectiveness of a quality improvement initiative developed to create a hospital-based safe sleep environment for all newborns and infants. METHODS: A multidisciplinary team from the well-baby nursery (WBN) and neonatal intensive care unit (NICU) of a 149-bed academic, quaternary care, regional referral center developed and implemented safe sleep environments within the hospital for all prior to discharge. To monitor compliance, the following were tracked monthly: documentation of parent education, caregiver surveys, and hospital crib check audits. On the inpatient general pediatric units, only hospital crib check audits were tracked. Investigators used Plan-Do-Study-Act (PDSA) cycles to evaluate the impact of the initiative from October 2015 through February 2018. RESULTS: Safe sleep education was documented for all randomly checked records (n = 440). A survey (n = 348) revealed that almost all caregivers (95.4%) reported receiving information on safe infant sleep. Initial compliance with all criteria in WBN (n = 281), NICU (n = 285), and general pediatric inpatient units (n = 121) was 0%, 0%, and 8.3%, respectively. At 29 months, WBN and NICU compliance with all criteria was 90% and 100%, respectively. At 7 months, general pediatric inpatient units\u27 compliance with all criteria was 20%. IMPLICATIONS FOR PRACTICE: WBN, NICU and general pediatric inpatient unit collaboration with content experts led to unit-specific strategies that improved safe sleep practices. IMPLICATIONS FOR RESEARCH: Future studies on the impact of such an initiative at other hospitals are needed

Predicting 2-year neurodevelopmental outcomes in extremely preterm infants using graphical network and machine learning approachesResearch in context

Summary: 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 Stroke U01NS077953 and U01NS077955

Neuroprotection in a rabbit model of intraventricular haemorrhage by cyclooxygenase-2, prostanoid receptor-1 or tumour necrosis factor-alpha inhibition

Intraventricular haemorrhage is a major complication of prematurity that results in neurological dysfunctions, including cerebral palsy and cognitive deficits. No therapeutic options are currently available to limit the catastrophic brain damage initiated by the development of intraventricular haemorrhage. As intraventricular haemorrhage leads to an inflammatory response, we asked whether cyclooxygenase-2, its derivative prostaglandin E2, prostanoid receptors and pro-inflammatory cytokines were elevated in intraventricular haemorrhage; whether their suppression would confer neuroprotection; and determined how cyclooxygenase-2 and cytokines were mechanistically-linked. To this end, we used our rabbit model of intraventricular haemorrhage where premature pups, delivered by Caesarian section, were treated with intraperitoneal glycerol at 2 h of age to induce haemorrhage. Intraventricular haemorrhage was diagnosed by head ultrasound at 6 h of age. The pups with intraventricular haemorrhage were treated with inhibitors of cyclooxygenase-2, prostanoid receptor-1 or tumour necrosis factor-α; and cell-infiltration, cell-death and gliosis were compared between treated-pups and vehicle-treated controls during the first 3 days of life. Neurobehavioural performance, myelination and gliosis were assessed in pups treated with cyclooxygenase-2 inhibitor compared to controls at Day 14. We found that both protein and messenger RNA expression of cyclooxygenase-2, prostaglandin E2, prostanoid receptor-1, tumour necrosis factor-α and interleukin-1β were consistently higher in the forebrain of pups with intraventricular haemorrhage relative to pups without intraventricular haemorrhage. However, cyclooxygenase-1 and prostanoid receptor 2–4 levels were comparable in pups with and without intraventricular haemorrhage. Cyclooxygenase-2, prostanoid receptor-1 or tumour necrosis factor-α inhibition reduced inflammatory cell infiltration, apoptosis, neuronal degeneration and gliosis around the ventricles of pups with intraventricular haemorrhage. Importantly, cyclooxygenase-2 inhibition alleviated neurological impairment, improved myelination and reduced gliosis at 2 weeks of age. Cyclooxygenase-2 or prostanoid receptor-1 inhibition reduced tumour necrosis factor-α level, but not interleukin-1β. Conversely, tumour necrosis factor-α antagonism did not affect cyclooxygenase-2 expression. Hence, prostanoid receptor-1 and tumour necrosis factor-α are downstream to cyclooxygenase-2 in the inflammatory cascade induced by intraventricular haemorrhage, and cyclooxygenase-2-inhibition or suppression of downstream molecules—prostanoid receptor-1 or tumour necrosis factor-α—might be a viable neuroprotective strategy for minimizing brain damage in premature infants with intraventricular haemorrhage