Cerebral autoregulation, brain injury, and the transitioning premature infant

Improvements in clinical management of the preterm infant have reduced the rates of the two most common forms of brain injury, such as severe intraventricular hemorrhage and white matter injury, both of which are contributory factors in the development of cerebral palsy. Nonetheless, they remain a persistent challenge and are associated with a significant increase in the risk of adverse neurodevelopment outcomes. Repeated episodes of ischemia–reperfusion represent a common pathway for both forms of injury, arising from discordance between systemic blood flow and the innate regulation of cerebral blood flow in the germinal matrix and periventricular white matter. Nevertheless, establishing firm hemodynamic boundaries, as a part of neuroprotective strategy, has challenged researchers. Existing measures either demonstrate inconsistent relationships with injury, as in the case of mean arterial blood pressure, or are not feasible for long-term monitoring, such as cardiac output estimated by echocardiography. These challenges have led some researchers to focus on the mechanisms that control blood flow to the brain, known as cerebrovascular autoregulation. Historically, the function of the cerebrovascular autoregulatory system has been difficult to quantify; however, the evolution of bedside monitoring devices, particularly near-infrared spectroscopy, has enabled new insights into these mechanisms and how impairment of blood flow regulation may contribute to catastrophic injury. In this review, we first seek to examine how technological advancement has changed the assessment of cerebrovascular autoregulation in premature infants. Next, we explore how clinical factors, including hypotension, vasoactive medications, acute and chronic hypoxia, and ventilation, alter the hemodynamic state of the preterm infant. Additionally, we examine how developmentally linked or acquired dysfunction in cerebral autoregulation contributes to preterm brain injury. In conclusion, we address exciting new approaches to the measurement of autoregulation and discuss the feasibility of translation to the bedside

Racial disparities in calculated risk for bronchopulmonary dysplasia: A dataset

Bronchopulmonary dysplasia (BPD) is a severe pulmonary complication of prematurity and is associated with significant morbidity or death. Early use of systemic corticosteroids may alter the trajectory of the disease and improve outcomes. A BPD Outcomes estimator, developed by the NICHD using a large population dataset, can be used to calculate individual risk. Risk above a certain threshold may indicate that the benefits of corticosteroids outweigh the risks. Empiric analysis of this calculator by systematic entry of synthetic patient information reveals a marked racial disparity; black infants have lower risk of moderate/severe BPD due to a higher risk of death despite equivalent severity of illness. Interpretation and analysis of this finding can be found in The challenge of risk stratification of preterm infants in the setting of competing and disparate healthcare outcomes [1]. In this report, we provide the underlying data used in this analysis. Calculator output for 108 example patients, systematically varied by sex, birthweight, race, type of ventilator, and fraction of inspired oxygen (Fi

Predicting mortality risk for preterm infants using deep learning models with time-series vital sign data

Mortality remains an exceptional burden of extremely preterm birth. Current clinical mortality prediction scores are calculated using a few static variable measurements, such as gestational age, birth weight, temperature, and blood pressure at admission. While these models do provide some insight, numerical and time-series vital sign data are also available for preterm babies admitted to the NICU and may provide greater insight into outcomes. Computational models that predict the mortality risk of preterm birth in the NICU by integrating vital sign data and static clinical variables in real time may be clinically helpful and potentially superior to static prediction models. However, there is a lack of established computational models for this specific task. In this study, we developed a novel deep learning model, DeepPBSMonitor (Deep Preterm Birth Survival Risk Monitor), to predict the mortality risk of preterm infants during initial NICU hospitalization. The proposed deep learning model can effectively integrate time-series vital sign data and fixed variables while resolving the influence of noise and imbalanced data. The proposed model was evaluated and compared with other approaches using data from 285 infants. Results showed that the DeepPBSMonitor model outperforms other approaches, with an accuracy, recall, and AUC score of 0.888, 0.780, and 0.897, respectively. In conclusion, the proposed model has demonstrated efficacy in predicting the real-time mortality risk of preterm infants in initial NICU hospitalization

Neighborhood deprivation and association with neonatal intensive care unit mortality and morbidity for extremely premature infants

IMPORTANCE: Socioeconomic status affects pregnancy and neurodevelopment, but its association with hospital outcomes among premature infants is unknown. The Area Deprivation Index (ADI) is a validated measure of neighborhood disadvantage that uses US Census Bureau data on income, educational level, employment, and housing quality. OBJECTIVE: To determine whether ADI is associated with neonatal intensive care unit (NICU) mortality and morbidity in extremely premature infants. DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study was performed at 4 level IV NICUs in the US Northeast, Mid-Atlantic, Midwest, and South regions. Non-Hispanic White and Black infants with gestational age of less than 29 weeks and born between January 1, 2012, and December 31, 2020, were included in the analysis. Addresses were converted to census blocks, identified by Federal Information Processing Series codes, to link residences to national ADI percentiles. EXPOSURES: ADI, race, birth weight, sex, and outborn status. MAIN OUTCOMES AND MEASURES: In the primary outcome, the association between ADI and NICU mortality was analyzed using bayesian logistic regression adjusted for race, birth weight, outborn status, and sex. Risk factors were considered significant if the 95% credible intervals excluded zero. In the secondary outcome, the association between ADI and NICU morbidities, including late-onset sepsis, necrotizing enterocolitis (NEC), and severe intraventricular hemorrhage (IVH), were also analyzed. RESULTS: A total of 2765 infants with a mean (SD) gestational age of 25.6 (1.7) weeks and mean (SD) birth weight of 805 (241) g were included in the analysis. Of these, 1391 (50.3%) were boys, 1325 (47.9%) reported Black maternal race, 498 (18.0%) died before NICU discharge, 692 (25.0%) developed sepsis or NEC, and 353 (12.8%) had severe IVH. In univariate analysis, higher median ADI was found among Black compared with White infants (77 [IQR, 45-93] vs 57 [IQR, 32-77]; P \u3c .001), those who died before NICU discharge vs survived (71 [IQR, 45-89] vs 64 [IQR, 36-86]), those with late-onset sepsis or NEC vs those without (68 [IQR, 41-88] vs 64 [IQR, 35-86]), and those with severe IVH vs those without (69 [IQR, 44-90] vs 64 [IQR, 36-86]). In a multivariable bayesian logistic regression model, lower birth weight, higher ADI, and male sex were risk factors for mortality (95% credible intervals excluded zero), while Black race and outborn status were not. The ADI was also identified as a risk factor for sepsis or NEC and severe IVH. CONCLUSIONS AND RELEVANCE: The findings of this cohort study of extremely preterm infants admitted to 4 NICUs in different US geographic regions suggest that ADI was a risk factor for mortality and morbidity after adjusting for multiple covariates

A Web-Based Calculator for the Prediction of Severe Neurodevelopmental Impairment in Preterm Infants Using Clinical and Imaging Characteristics

Although the most common forms of brain injury in preterm infants have been associated with adverse neurodevelopmental outcomes, existing MRI scoring systems lack specificity, do not incorporate clinical factors, and are technically challenging to perform. The objective of this study was to develop a web-based, clinically-focused prediction system which differentiates severe neurodevelopmental outcomes from normal-moderate outcomes at two years. Infants were retrospectively identified as those who were born ≤30 weeks gestation and who had MRI imaging at term-equivalent age and neurodevelopmental testing at 18⁻24 months. Each MRI was scored on injury in three domains (intraventricular hemorrhage, white matter injury, and cerebellar hemorrhage) and clinical factors that were strongly predictive of an outcome were investigated. A binary logistic regression model was then generated from the composite of clinical and imaging components. A total of 154 infants were included (mean gestational age = 26.1 ± 1.8 weeks, birth weight = 889.1 ± 226.2 g). The final model (imaging score + ventilator days + delivery mode + antenatal steroids + retinopathy of prematurity requiring surgery) had strong discriminatory power for severe disability (AUC = 0.850), with a PPV (positive predictive value) of 76% and an NPV (negative predictive value) of 90%. Available as a web-based tool, it can be useful for prognostication and targeting early intervention services to infants who may benefit the most from such services

Neuro-Behçets in a Child

We describe a case of neuro-Behçet disease diagnosed in a 12-year-old girl. This patient presented with recurrent oral ulcers, incontinence, spastic gait, blurry vision, and asymmetrical lower extremity hypertonia. Extensive testing revealed punctate lesions through the central nervous system, vitritis, papillitis, and uveitis. A thorough infectious and neoplastic workup was negative. She was treated with pulse steroids and azathioprine with gradual improvement in her gait and ophthalmologic findings. Although rare, primary neuro-Behçet should be considered in pediatric patients with neurologic abnormalities and recurrent aphthous ulcers without other explanation