Non-invasive Assessment of Cerebral Blood Flow and Oxygen Metabolism in Neonates during Hypothermic Cardiopulmonary Bypass: Feasibility and Clinical Implications

The neonatal brain is extremely vulnerable to injury during periods of hypoxia and/or ischemia. Risk of brain injury is increased during neonatal cardiac surgery, where pre-existing hemodynamic instability and metabolic abnormalities are combined with long periods of low cerebral blood flow and/or circulatory arrest. Our understanding of events associated with cerebral hypoxia-ischemia during cardiopulmonary bypass (CPB) remains limited, largely due to inadequate tools to quantify cerebral oxygen delivery and consumption non-invasively and in real-time. This pilot study aims to evaluate cerebral blood flow (CBF) and oxygen metabolism (CMRO2) intraoperatively in neonates by combining two novel non-invasive optical techniques: frequency-domain near-infrared spectroscopy (FD-NIRS) and diffuse correlation spectroscopy (DCS). CBF and CMRO2 were quantified before, during and after deep hypothermic cardiopulmonary bypass (CPB) in nine neonates. Our results show significantly decreased CBF and CMRO2 during hypothermic CPB. More interestingly, a change of coupling between both variables is observed during deep hypothermic CPB in all subjects. Our results are consistent with previous studies using invasive techniques, supporting the concept of FD-NIRS/DCS as a promising technology to monitor cerebral physiology in neonates providing the potential for individual optimization of surgical management

A New Look at Quantifying Tobacco Exposure during Pregnancy Using Fuzzy Clustering

Background—Prenatal tobacco exposure is a risk factor for the development of externalizing behaviors and is associated with several adverse health outcomes. Because pregnancy smoking is a complex behavior with both daily fluctuations and changes over the course of pregnancy, quantifying tobacco exposure is a significant challenge. To better measure the degree of tobacco exposure, costly biological specimens and repeated self-report measures of smoking typically are collected throughout pregnancy. With such designs, there are multiple, and substantially correlated, indices that can be integrated via new statistical methods to identify patterns of prenatal exposure. Method—A multiple-imputation-based fuzzy clustering technique was designed to characterize topography of prenatal exposure. This method leveraged all repeatedly measured maternal smoking variables in our sample data, including (a) cigarette brand; (b) Fagerstrom nicotine dependence item scores; (c) self-reported smoking; and (d) cotinine level in maternal urine and infant meconium samples. Identified exposure groups then were confirmed using a suite of clustering validation indices based on multiple imputed datasets. The classifications were validated against irritable reactivity in the first month of life and birth weight of 361 neonates (Male_n = 185; Female_n = 176; Gestational Age_Mean = 39 weeks). Results—This proposed approach identified three exposure groups, non-exposed, lightertobacco- exposed, and heavier-tobacco-exposed based on high-dimensional attributes. Unlike cutoff score derived groups, these groupings reflect complex smoking behavior and individual variation of nicotine metabolism across pregnancy. The identified groups predicted differences in birth weight and in the pattern of change in neonatal irritable reactivity, as well as resulted in increased predictive power. Multiple-imputation based fuzzy clustering appears to be a useful method to categorize patterns of exposure and their impact on outcomes

Prenatal Tobacco Exposure: Developmental Outcomes in the Neonatal Period

Smoking during pregnancy is a persistent public health problem that has been linked to later adverse outcomes. The neonatal period— the first month of life—carries substantial developmental change in regulatory skills and is the period when tobacco metabolites are cleared physiologically. Studies to date mostly have used cross-sectional designs that limit characterizing potential impacts of prenatal tobacco exposure on the development of key self-regulatory processes and cannot disentangle short-term withdrawal effects from residual exposure-related impacts. In this study, pregnant participants (N = 304) were recruited prospectively during pregnancy, and smoking was measured at multiple time points, with both self-report and biochemical measures. Neonatal attention, irritable reactivity, and stress dysregulation were examined longitudinally at three time points during the first month of life, and physical growth indices were measured at birth. Tobacco-exposed infants showed significantly poorer attention skills after birth, and the magnitude of the difference between exposed and nonexposed groups attenuated across the neonatal period. In contrast, exposure- related differences in irritable reactivity largely were not evident across the 1st month of life, differing marginally at 4 weeks of age only. Third-trimester smoking was associated with pervasive, deleterious, dose–response impacts on physical growth measured at birth, whereas nearly all smoking indicators throughout pregnancy predicted level and growth rates of early attention. The observed neonatal pattern is consistent with the neurobiology of tobacco on the developing nervous system and fits with developmental vulnerabilities observed later in lif

DRD2 Genotype and Prenatal Exposure to Tobacco Interact to Influence Infant Attention and Reactivity

The present study examined the effects of dopamine receptor D2 genotype and PTE status on early infant neurobehavior

Neurodevelopmental outcomes after cardiac surgery in infancy

BACKGROUND Neurodevelopmental disability is the most common complication for survivors of surgery for congenital heart disease (CHD). METHODS We analyzed individual participant data from studies of children evaluated with the Bayley Scales of Infant Development, second edition, after cardiac surgery between 1996 and 2009. The primary outcome was Psychomotor Development Index (PDI), and the secondary outcome was Mental Development Index (MDI). RESULTS Among 1770 subjects from 22 institutions, assessed at age 14.5 ± 3.7 months, PDIs and MDIs (77.6 ± 18.8 and 88.2 ± 16.7, respectively) were lower than normative means (each P < .001). Later calendar year of birth was associated with an increased proportion of high-risk infants (complexity of CHD and prevalence of genetic/extracardiac anomalies). After adjustment for center and type of CHD, later year of birth was not significantly associated with better PDI or MDI. Risk factors for lower PDI were lower birth weight, white race, and presence of a genetic/extracardiac anomaly (all P ≤ .01). After adjustment for these factors, PDIs improved over time (0.39 points/year, 95% confidence interval 0.01 to 0.78; P = .045). Risk factors for lower MDI were lower birth weight, male gender, less maternal education, and presence of a genetic/extracardiac anomaly (all P < .001). After adjustment for these factors, MDIs improved over time (0.38 points/year, 95% confidence interval 0.05 to 0.71; P = .02). CONCLUSIONS Early neurodevelopmental outcomes for survivors of cardiac surgery in infancy have improved modestly over time, but only after adjustment for innate patient risk factors. As more high-risk CHD infants undergo cardiac surgery and survive, a growing population will require significant societal resources

Pattern Recognition of Longitudinal Trial Data with Nonignorable Missingness: An Empirical Case Study

Methods for identifying meaningful growth patterns of longitudinal trial data with both nonignorable intermittent and drop-out missingness are rare. In this study, a combined approach with statistical and data mining techniques is utilized to address the nonignorable missing data issue in growth pattern recognition. First, a parallel mixture model is proposed to model the nonignorable missing information from a real-world patient-oriented study and concurrently to estimate the growth trajectories of participants. Then, based on individual growth parameter estimates and their auxiliary feature attributes, a fuzzy clustering method is incorporated to identify the growth patterns. This case study demonstrates that the combined multi-step approach can achieve both statistical generality and computational efficiency for growth pattern recognition in longitudinal studies with nonignorable missing data