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Background: Preterm infants commonly present with oral feeding problems. The role of maturation of esophageal bolus transport mechanisms herein remains unclear. Objectives: To characterize esophageal motility and function of esophagogastric junction (EGJ) during deglutitive swallowing in healthy preterm infants and to describe maturational changes. Methods: Four consecutive high-resolution manometry studies with impedance studies were performed weekly to investigate esophageal motility and EGJ function. Esophageal pressure topography and pressure-impedance metrics were derived. Mixed models with repeated measures were used for statistical analysis. Results: We analyzed 137 nutritive swallows from 36 motility studies in 10 preterm infants. The mean gestational age was 30.17 ± 0.94 weeks; the mean postmenstrual age at time point 1 and 4 was 34.42 ± 0.86 and 37.45 ± 1.16 weeks, respectively. Esophageal peristaltic wave patterns in response to nutritive swallows were observed in all patients. At later time points, esophageal body peristalsis became more rapid, evidenced by a faster distal contractile velocity and shorter distal latency (p = 0.002 and p < 0.0001, respectively). In addition, 4-s integrated relaxation pressures increased and distal contractile integral decreased at later time points (p = 0.003 and p = 0.021, respectively). Bolus clearance also improved at later age (p = 0.008). Conclusions: Preterm infants demonstrate peristaltic esophageal motility following nutritive swallows. However, alterations in esophageal bolus transport in relation to peristalsis are demonstrated. Peristaltic progression becomes more rapid, while deglutitive relaxation pressures increase with increasing age. These maturational changes may suggest further development of the enteric

BACKGROUND: The altered neurodevelopment of children operated on during the neonatal period might be due to peri-operative changes in the homeostasis of brain perfusion. Monitoring of vital signs is a standard of care, but it does not usually include monitoring of the bra

Background: Esophageal atresia is a major congenital foregut anomaly. Affected patients often suffer from respiratory and gastro-intestinal morbidity. The objective of this study is to identify possible neonatal predictive factors contributing to a long-term complicated clinical course in patients after repair of esophageal atresia. Methods: A total of 93 patients born between 1993 and 2013, with esophageal atresia and surviving the neonatal period were included in this retrospective study. A complicated clinical course was defined as the occurrence of 1 of these complications: severe gastroesophageal reflux, esophageal stricture requiring dilatations, need for tube feeding for >100 days, severe tracheomalacia, severe chronic respiratory disease and death. We used linear models with a binomial distribution to determine risk factors for gastro-intestinal or respiratory complicated evolution and a backward stepwise elimination procedure to reduce models until only significant variables remained in the model. Multinomial logistic regression was used to assess risk factors for different evolutions of complication. Model parameter estimates were used to calculate odds ratios for significant risk factors. Results: Fifty-seven patients (61%) had a complicated clinical course in the first year of life and 47 (51%) had a complicated evolution during years 1e6. In the first year, prematurity was a significant factor for complicated gastro-intestinal (OR 2.84) and respiratory evolution (OR 2.93). After 1 year, gastro-intestinal morbidity in childhood was associated with VACTERL association (OR 12.2) and a complicated first year (OR 36.1). Respiratory morbidity was associated with congenital heart disease (OR 12.9) and a complicated first year (OR 86.9). Multinomial logistic regression showed that prematu

Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published studies describing features and seizure classifiers, the features were hand-engineered, which may not be optimal. The main goal of the present paper is using deep convolutional neural networks (CNNs) and random forest to automatically optimize feature selection and classification. The input of the proposed classifier is raw multi-channel EEG and the output is the class label: seizure/nonseizure. By training this network, the required features are optimized, while fitting a nonlinear classifier on the features. After training the network with EEG recordings of 26 neonates, five end layers performing the classification were replaced with a random forest classifier in order to improve the performance. This resulted in a false alarm rate of 0.9 per hour and seizure detection rate of 77% using a test set of EEG recordings of 22 neonates that also included dubious seizu

Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published studies describing features and seizure classifiers, the features were hand-engineered, which may not be optimal. The main goal of the present paper is using deep convolutional neural networks (CNNs) and random forest to automatically optimize feature selection and classification. The input of the proposed classifier is raw multi-channel EEG and the output is the class label: seizure/nonseizure. By training this network, the required features are optimized, while fitting a nonlinear classifier on the features. After training the network with EEG recordings of 26 neonates, five end layers performing the classification were replaced with a random forest classifier in order to improve the performance. This resulted in a false alarm rate of 0.9 per hour and seizure detection rate of 77% using a test set of EEG recordings of 22 neonates that also included dubious seizures. The newly proposed CNN classifier outperformed three data-driven feature-based approaches and performed similar to a previously developed heuristic method.

A quantitative and objective assessment of background electroencephalograph (EEG) in sick neonates remains an everyday clinical challenge. We studied whether long range temporal correlations quantified by detrended fluctuation analysis (DFA) could be used in the neonatal EEG to distinguish different grades of abnormality in the background EEG activity. Long-term EEG records of 34 neonates were collected after perinatal asphyxia, and their background was scored in 1 h epochs (8 h in each neonate) as mild, moderate or severe. We applied DFA on 15 min long, non-overlapping EEG epochs (n = 1088) filtered from 3 to 8 Hz. Our for

Clinical pharmacology intends to predict drug-specific effects and side effects based on pharmacokinetics (i.e. absorption, distribution, metabolism and elimination) and pharmacodynamics (i.e. dose/effect relationship). Developmental pharmacology focuses on the maturational aspects of these phenomena during perinatal life and later stages of infancy. In general, phenotypic variation in drug metabolism is based on constitutional, environmental and genetic factors but in early neonatal life, it mainly reflects ontogeny. Although the major site of drug metabolism is the liver, the gastrointestinal tract, blood cells and other organs like kidneys or lungs might also be involved in drug metabolism. Important alterations in hepatic drug metabolism occur in early neonatal life. These alterations are of relevance when age-dependent aspects of pharmacokinetics, -dynamics or toxicology are considered. Age dependent maturation of various phase I and II processe

Objectives: To document pain expression characteristics in former preterm infants in the first year of life in whom systematic evaluation and treatment of pain was provided. Methods: Data on the Modified Behavioral Pain Scale (MBPS), Visual Analogue Scale (VAS, crying time and heart rate were collected during immunization. The effect of clinical characteristics {postconception and gestational age [PCA], surgery, ventilation, fentanyl, length of stay (LOS)} on bio-behavioral parameters was evaluated. Results: 121 procedures were recorded in 49 infants. GA was 28 (25-32) weeks. PCA was 60 (34-90) weeks. Median MBPS was 8 (2-10), median crying time 44 (0-112) s. Median pre-procedural heart rate was 144 (103-201) and increased to 184 (147-220) afterwards, resulting in a relative increase of 29 (5-99)%. Median VAS score was 7 (1-10).