Sleep as a driver of pre- and postnatal brain development.

In 1966, Howard Roffwarg proposed the ontogenic sleep hypothesis, relating neural plasticity and development to rapid eye movement (REM) sleep, a hypothesis that current fetal and neonatal sleep research is still exploring. Recently, technological advances have enabled researchers to automatically quantify neonatal sleep architecture, which has caused a resurgence of research in this field as attempts are made to further elucidate the important role of sleep in pre- and postnatal brain development. This article will review our current understanding of the role of sleep as a driver of brain development and identify possible areas for future research. IMPACT: The evidence to date suggests that Roffwarg's ontogenesis hypothesis of sleep and brain development is correct. A better understanding of the relationship between sleep and the development of functional connectivity is needed. Reliable, non-invasive tools to assess sleep in the NICU and at home need to be tested in a real-world environment and the best way to promote healthy sleep needs to be understood before clinical trials promoting and optimizing sleep quality in neonates could be undertaken.

Synthetic magnetic resonance-based relaxometry and brain volume: cutoff values for predicting neurocognitive outcomes in very preterm infants.

BACKGROUND: Early neurorehabilitation can enhance neurocognitive outcomes in very preterm infants (<32 weeks), and conventional magnetic resonance imaging (MRI) is commonly used to assess neonatal brain injury; however, the predictive value for neurodevelopmental delay is limited. Timely predictive quantitative biomarkers are needed to improve early identification and management of infants at risk of neurodevelopmental delay. OBJECTIVE: To evaluate the potential of quantitative synthetic MRI measurements at term-equivalent age as predictive biomarkers of neurodevelopmental impairment and establish practical cutoff values to guide clinical decision-making. MATERIALS AND METHODS: This retrospective study included 93 very preterm infants who underwent synthetic MRI at term-equivalent age between January 2017 and September 2020. Clinical outcomes were assessed using the Bayley-III scale of infant development (mean age 2.1 years). The predictive value for impaired development was analyzed using receiver operating characteristic curves for synthetic MRI-based volumetry and T1 and T2 relaxation measurements. RESULTS: The T1 relaxation time in the posterior limb of the internal capsule was a potent predictor of severe (sensitivity, 92%; specificity, 80%; area under the curve (AUC), 0.91) and mild or severe (AUC, 0.75) developmental impairment. T2 relaxation time in the posterior limb of the internal capsule was a significant predictor of severe impairment (AUC, 0.76), whereas the brain parenchymal volume was a significant predictor of severe (AUC, 0.72) and mild or severe impairment (AUC, 0.71) outperforming the reported qualitative MRI scores (AUC, 0.66). CONCLUSION: The proposed cutoff values for T1 relaxation time in the posterior limb of the internal capsule and for total brain volume measurements, derived from synthetic MRI, show promise as predictors of both mild and severe neurodevelopmental impairment in very preterm infants.

Association between sleep stages and brain microstructure in preterm infants: Insights from DTI analysis.

STUDY OBJECTIVES: The aim of this study was to investigate the relationship between sleep stages and neural microstructure - measured using diffusion tensor imaging - of the posterior limb of the internal capsule and corticospinal tract in preterm infants. METHODS: A retrospective cohort of 50 preterm infants born between 24 + 4 and 29 + 3 weeks gestational age was included in the study. Sleep stages were continuously measured for 5-7 consecutive days between 29 + 0 and 31 + 6 weeks postmenstrual age using an in-house-developed, and recently published, automated sleep staging algorithm based on routinely measured heart rate and respiratory rate. Additionally, a diffusion tensor imaging scan was conducted at term equivalent age as part of standard care. Region of interest analysis of the posterior limb of the internal capsule was performed, and tractography was used to analyze the corticospinal tract. The association between sleep and white matter microstructure of the posterior limb of the internal capsule and corticospinal tract was examined using a multiple linear regression model, adjusted for potential confounders. RESULTS: The results of the analyses revealed an interaction effect between sleep stage and days of invasive ventilation on the fractional anisotropy of the left and right posterior limb of the internal capsule (ß = 0.04, FDR-adjusted p = 0.001 and ß = 0.04, FDR-adjusted p = 0.02, respectively). Furthermore, an interaction effect between sleep stage and days of invasive ventilation was observed for the radial diffusivity of the mean of the left and right PLIC (ß = -4.1e-05, FDR-adjusted p = 0.04). CONCLUSIONS: Previous research has shown that, in very preterm infants, invasive ventilation has a negative effect on white matter tract maturation throughout the brain. A positive association between active sleep and white matter microstructure of the posterior limb of the internal capsule, may indicate a protective role of sleep in this vulnerable population.

Developmental anatomy of the thalamus, perinatal lesions, and neurological development.

The thalamic nuclei develop before a viable preterm age. GABAergic neuronal migration is especially active in the third trimester. Thalamic axons meet cortical axons during subplate activation and create the definitive cortical plate in the second and third trimesters. Default higher-order cortical driver connections to the thalamus are then replaced by the maturing sensory networks, in a process that is driven by first-order thalamic neurons. Surface electroencephalographic activity, generated first in the subplate and later in the cortical plate, gradually show oscillations based on the interaction of the cortex with thalamus, which is controlled by the thalamic reticular nucleus. In viable newborn infants, in addition to sensorimotor networks, the thalamus already contributes to visual, auditory, and pain processing, and to arousal and sleep. Isolated thalamic lesions may present as clinical seizures. In addition to asphyxia and stroke, infection and network injury are also common. Cranial ultrasound can be used to classify neonatal thalamic injuries based on functional parcelling of the mature thalamus. We provide ample illustration and a detailed description of the impact of neonatal focal thalamic injury on neurological development, and discuss the potential for neuroprotection based on thalamocortical plasticity.

Morbidity and trends in length of hospitalisation of very and extremely preterm infants born between 2008 and 2021 in the Netherlands: a cohort study.

OBJECTIVES: This study investigated changes in the length of stay (LoS) at a level III/IV neonatal intensive care unit (NICU) and level II neonatology departments until discharge home for very preterm infants and identified factors influencing these trends. DESIGN: Retrospective cohort study based on data recorded in the Netherlands Perinatal Registry between 2008 and 2021. SETTING: A single level III/IV NICU and multiple level II neonatology departments in the Netherlands. PARTICIPANTS: NICU-admitted infants (n=2646) with a gestational age (GA) <32 weeks. MAIN OUTCOME MEASURES: LoS at the NICU and overall LoS until discharge home. RESULTS: The results showed an increase of 5.1 days (95% CI 2.2 to 8, p<0.001) in overall LoS in period 3 after accounting for confounding variables. This increase was primarily driven by extended LoS at level II hospitals, while LoS at the NICU remained stable. The study also indicated a strong association between severe complications of preterm birth and LoS. Treatment of infants with a lower GA and more (severe) complications (such as severe retinopathy of prematurity) during the more recent periods may have increased LoS. CONCLUSION: The findings of this study highlight the increasing overall LoS for very preterm infants. LoS of very preterm infants is presumably influenced by the occurrence of complications of preterm birth, which are more frequent in infants at a lower gestational age.

Noncontact respiration monitoring techniques in young children: A scoping review.

Pediatric sleep-related breathing disorders, or sleep-disordered breathing (SDB), cover a range of conditions, including obstructive sleep apnea, central sleep apnea, sleep-related hypoventilation disorders, and sleep-related hypoxemia disorder. Pediatric SDB is often underdiagnosed, potentially due to difficulties associated with performing the gold standard polysomnography in children. This scoping review aims to: (1) provide an overview of the studies reporting on safe, noncontact monitoring of respiration in young children, (2) describe the accuracy of these techniques, and (3) highlight their respective advantages and limitations. PubMed and EMBASE were searched for studies researching techniques in children <12 years old. Both quantitative data and the quality of the studies were analyzed. The evaluation of study quality was conducted using the QUADAS-2 tool. A total of 19 studies were included. Techniques could be grouped into bed-based methods, microwave radar, video, infrared (IR) cameras, and garment-embedded sensors. Most studies either measured respiratory rate (RR) or detected apneas; n = 2 aimed to do both. At present, bed-based approaches are at the forefront of research in noncontact RR monitoring in children, boasting the most sophisticated algorithms in this field. Yet, despite extensive studies, there remains no consensus on a definitive method that outperforms the rest. The accuracies reported by these studies tend to cluster within a similar range, indicating that no single technique has emerged as markedly superior. Notably, all identified methods demonstrate capability in detecting body movements and RR, with reported safety for use in children across the board. Further research into contactless alternatives should focus on cost-effectiveness, ease-of-use, and widespread availability.

Characterising the motion and cardiorespiratory interaction of preterm infants can improve the classification of their sleep state.

AIM: This study aimed to classify quiet sleep, active sleep and wake states in preterm infants by analysing cardiorespiratory signals obtained from routine patient monitors. METHODS: We studied eight preterm infants, with an average postmenstrual age of 32.3 ± 2.4 weeks, in a neonatal intensive care unit in the Netherlands. Electrocardiography and chest impedance respiratory signals were recorded. After filtering and R-peak detection, cardiorespiratory features and motion and cardiorespiratory interaction features were extracted, based on previous research. An extremely randomised trees algorithm was used for classification and performance was evaluated using leave-one-patient-out cross-validation and Cohen's kappa coefficient. RESULTS: A sleep expert annotated 4731 30-second epochs (39.4 h) and active sleep, quiet sleep and wake accounted for 73.3%, 12.6% and 14.1% respectively. Using all features, and the extremely randomised trees algorithm, the binary discrimination between active and quiet sleep was better than between other states. Incorporating motion and cardiorespiratory interaction features improved the classification of all sleep states (kappa 0.38 ± 0.09) than analyses without these features (kappa 0.31 ± 0.11). CONCLUSION: Cardiorespiratory interactions contributed to detecting quiet sleep and motion features contributed to detecting wake states. This combination improved the automated classifications of sleep states.

Effect of Systemic Hydrocortisone on Brain Abnormalities and Regional Brain Volumes in Ventilator-dependent Infants Born Preterm: Substudy of the SToP-BPD Study.

OBJECTIVE: To evaluate whether a high cumulative dose of systemic hydrocortisone affects brain development compared with placebo when initiated between 7 and 14 days after birth in ventilated infants born preterm. STUDY DESIGN: A double-blind, placebo-controlled, randomized trial was conducted in 16 neonatal intensive care units among infants born at <30 weeks of gestation or with a birth weight of <1250 g who were ventilator-dependent in the second week after birth. Three centers performed MRI at term-equivalent age. Brain injury was assessed on MRI using the Kidokoro scoring system and compared between the 2 treatment groups. Both total and regional brain volumes were calculated using an automatic segmentation method and compared using multivariable regression analysis adjusted for baseline variables. RESULTS: From the 3 centers, 78 infants participated in the study and 59 had acceptable MRI scans (hydrocortisone group, n = 31; placebo group, n = 28). Analyses of the median global brain abnormality score of the Kidokoro score showed no difference between the hydrocortisone and placebo groups (median, 7; IQR, 5-9 vs median, 8, IQR, 4-10, respectively; P = .92). In 39 infants, brain tissue volumes were measured, showing no differences in the adjusted mean total brain tissue volumes, at 352 ± 32 mL in the hydrocortisone group and 364 ± 51 mL in the placebo group (P = .80). CONCLUSIONS: Systemic hydrocortisone started in the second week after birth in ventilator-dependent infants born very preterm was not found to be associated with significant differences in brain development compared with placebo treatment. TRIAL REGISTRATION: The SToP-BPD study was registered with the Netherlands Trial Register (NTR2768; registered on 17 February 2011; https://www.trialregister.nl/trial/2640) and the European Union Clinical Trials Register (EudraCT, 2010-023777-19; registered on 2 November 2010; https://www.clinicaltrialsregister.eu/ctr-search/trial/2010-023777-19/NL).

A comparison of ultrafast and conventional spectral Doppler ultrasound to measure cerebral blood flow velocity during inguinal hernia repair in infants.

BACKGROUND: Ultrafast cerebral Doppler ultrasound enables simultaneous quantification and visualization of cerebral blood flow velocity. The aim of this study is to compare the use of conventional and ultrafast spectral Doppler during anesthesia and their potential to show the effect of anesthesiologic procedures on cerebral blood flow velocities, in relation to blood pressure and cerebral oxygenation in infants undergoing inguinal hernia repair. METHODS: A single-center prospective observational cohort study in infants up to six months of age. We evaluated conventional and ultrafast spectral Doppler cerebral ultrasound measurements in terms of number of successful measurements during the induction of anesthesia, after sevoflurane induction, administration of caudal analgesia, a fluid bolus and emergence of anesthesia. Cerebral blood flow velocity was quantified in pial arteries using conventional spectral Doppler and in the cerebral cortex using ultrafast Doppler by peak systolic velocity, end diastolic velocity and resistivity index. RESULTS: Twenty infants were included with useable conventional spectral Doppler images in 72/100 measurements and ultrafast Doppler images in 51/100 measurements. Intraoperatively, the success rates were 53/60 (88.3%) and 41/60 (68.3%), respectively. Cerebral blood flow velocity increased after emergence for both conventional (end diastolic velocity, from 2.01 to 2.75 cm/s, p < 0.001) and ultrafast spectral Doppler (end diastolic velocity, from 0.59 to 0.94 cm/s), whereas cerebral oxygenation showed a reverse pattern with a decrease after the emergence of the infant (85% to 68%, p < 0.001). CONCLUSION: It is possible to quantify cortical blood flow velocity during general anesthesia using conventional and ultrafast spectral Doppler cerebral ultrasound. Cerebral blood flow velocity and blood pressure decreased, while regional cerebral oxygenation increased during general anesthesia. Ultrafast spectral Doppler ultrasound offers novel insights into perfusion within the cerebral cortex, unattainable through conventional spectral ultrasound. Yet, ultrafast Doppler is curtailed by a lower success rate and a more rigorous learning curve compared to conventional method.

Automatic Max-Likelihood Envelope Detection Algorithm for Quantitative High-Frame-Rate Ultrasound for Neonatal Brain Monitoring.

OBJECTIVE: Post-operative brain injury in neonates may result from disturbed cerebral perfusion, but accurate peri-operative monitoring is lacking. High-frame-rate (HFR) cerebral ultrasound could visualize and quantify flow in all detectable vessels using spectral Doppler; however, automated quantification in small vessels is challenging because of low signal amplitude. We have developed an automatic envelope detection algorithm for HFR pulsed wave spectral Doppler signals, enabling neonatal brain quantitative parameter maps during and after surgery. METHODS: HFR ultrasound data from high-risk neonatal surgeries were recorded with a custom HFR mode (frame rate = 1000 Hz) on a Zonare ZS3 system. A pulsed wave Doppler spectrogram was calculated for each pixel containing blood flow in the image, and spectral peak velocity was tracked using a max-likelihood estimation algorithm of signal and noise regions in the spectrogram, where the most likely cross-over point marks the blood flow velocity. The resulting peak systolic velocity (PSV), end-diastolic velocity (EDV) and resistivity index (RI) were compared with other detection schemes, manual tracking and RIs from regular pulsed wave Doppler measurements in 10 neonates. RESULTS: Envelope detection was successful in both high- and low-quality arterial and venous flow spectrograms. Our technique had the lowest root mean square error for EDV, PSV and RI (0.46 cm/s, 0.53 cm/s and 0.15, respectively) when compared with manual tracking. There was good agreement between the clinical pulsed wave Doppler RI and HFR measurement with a mean difference of 0.07. CONCLUSION: The max-likelihood algorithm is a promising approach to accurate, automated cerebral blood flow monitoring with HFR imaging in neonates.

Machine Learning-Derived Active Sleep as an Early Predictor of White Matter Development in Preterm Infants.

White matter dysmaturation is commonly seen in preterm infants admitted to the neonatal intensive care unit (NICU). Animal research has shown that active sleep is essential for early brain plasticity. This study aimed to determine the potential of active sleep as an early predictor for subsequent white matter development in preterm infants. Using heart and respiratory rates routinely monitored in the NICU, we developed a machine learning-based automated sleep stage classifier in a cohort of 25 preterm infants (12 females). The automated classifier was subsequently applied to a study cohort of 58 preterm infants (31 females) to extract active sleep percentage over 5-7 consecutive days during 29-32 weeks of postmenstrual age. Each of the 58 infants underwent high-quality T2-weighted magnetic resonance brain imaging at term-equivalent age, which was used to measure the total white matter volume. The association between active sleep percentage and white matter volume was examined using a multiple linear regression model adjusted for potential confounders. Using the automated classifier with a superior sleep classification performance [mean area under the receiver operating characteristic curve (AUROC) = 0.87, 95% CI 0.83-0.92], we found that a higher active sleep percentage during the preterm period was significantly associated with an increased white matter volume at term-equivalent age [ß = 0.31, 95% CI 0.09-0.53, false discovery rate (FDR)-adjusted p-value = 0.021]. Our results extend the positive association between active sleep and early brain development found in animal research to human preterm infants and emphasize the potential benefit of sleep preservation in the NICU setting.

Early qualitative and quantitative amplitude-integrated electroencephalogram and raw electroencephalogram for predicting long-term neurodevelopmental outcomes in extremely preterm infants in the Netherlands: a 10-year cohort study.

BACKGROUND: Extremely preterm infants (<28 weeks of gestation) are at great risk of long-term neurodevelopmental impairments. Early amplitude-integrated electroencephalogram (aEEG) accompanied by raw EEG traces (aEEG-EEG) has potential for predicting subsequent outcomes in preterm infants. We aimed to determine whether and which qualitative and quantitative aEEG-EEG features obtained within the first postnatal days predict neurodevelopmental outcomes in extremely preterm infants. METHODS: This study retrospectively analysed a cohort of extremely preterm infants (born before 28 weeks and 0 days of gestation) who underwent continuous two-channel aEEG-EEG monitoring during their first 3 postnatal days at Wilhelmina Children's Hospital, Utrecht, the Netherlands, between June 1, 2008, and Sept 30, 2018. Only infants who did not have genetic or metabolic diseases or major congenital malformations were eligible for inclusion. Features were extracted from preprocessed aEEG-EEG signals, comprising qualitative parameters grouped in three types (background pattern, sleep-wake cycling, and seizure activity) and quantitative metrics grouped in four categories (spectral content, amplitude, connectivity, and discontinuity). Machine learning-based regression and classification models were used to evaluate the predictive value of the extracted aEEG-EEG features for 13 outcomes, including cognitive, motor, and behavioural problem outcomes, at 2-3 years and 5-7 years. Potential confounders (gestational age at birth, maternal education, illness severity, morphine cumulative dose, the presence of severe brain injury, and the administration of antiseizure, sedative, or anaesthetic medications) were controlled for in all prediction analyses. FINDINGS: 369 infants were included and an extensive set of 339 aEEG-EEG features was extracted, comprising nine qualitative parameters and 330 quantitative metrics. The machine learning-based regression models showed significant but relatively weak predictive performance (ranging from r=0·13 to r=0·23) for nine of 13 outcomes. However, the machine learning-based classifiers exhibited acceptable performance in identifying infants with intellectual impairments from those with optimal outcomes at age 5-7 years, achieving balanced accuracies of 0·77 (95% CI 0·62-0·90; p=0·0020) for full-scale intelligence quotient score and 0·81 (0·65-0·96; p=0·0010) for verbal intelligence quotient score. Both classifiers maintained identical performance when solely using quantitative features, achieving balanced accuracies of 0·77 (95% CI 0·63-0·91; p=0·0030) for full-scale intelligence quotient score and 0·81 (0·65-0·96; p=0·0010) for verbal intelligence quotient score. INTERPRETATION: These findings highlight the potential benefits of using early postnatal aEEG-EEG features to automatically recognise extremely preterm infants with poor outcomes, facilitating the development of an interpretable prognostic tool that aids in decision making and therapy planning. FUNDING: European Commission Horizon 2020.

Combining Cardiorespiratory Signals and Video-Based Actigraphy for Classifying Preterm Infant Sleep States.

The classification of sleep state in preterm infants, particularly in distinguishing between active sleep (AS) and quiet sleep (QS), has been investigated using cardiorespiratory information such as electrocardiography (ECG) and respiratory signals. However, accurately differentiating between AS and wake remains challenging; therefore, there is a pressing need to include additional information to further enhance the classification performance. To address the challenge, this study explores the effectiveness of incorporating video-based actigraphy analysis alongside cardiorespiratory signals for classifying the sleep states of preterm infants. The study enrolled eight preterm infants, and a total of 91 features were extracted from ECG, respiratory signals, and video-based actigraphy. By employing an extremely randomized trees (ET) algorithm and leave-one-subject-out cross-validation, a kappa score of 0.33 was achieved for the classification of AS, QS, and wake using cardiorespiratory features only. The kappa score significantly improved to 0.39 when incorporating eight video-based actigraphy features. Furthermore, the classification performance of AS and wake also improved, showing a kappa score increase of 0.21. These suggest that combining video-based actigraphy with cardiorespiratory signals can potentially enhance the performance of sleep-state classification in preterm infants. In addition, we highlighted the distinct strengths and limitations of video-based actigraphy and cardiorespiratory data in classifying specific sleep states.

Ultra-Wideband Radar for Simultaneous and Unobtrusive Monitoring of Respiratory and Heart Rates in Early Childhood: A Deep Transfer Learning Approach.

Unobtrusive monitoring of children's heart rate (HR) and respiratory rate (RR) can be valuable for promoting the early detection of potential health issues, improving communication with healthcare providers and reducing unnecessary hospital visits. A promising solution for wireless vital sign monitoring is radar technology. This paper presents a novel approach for the simultaneous estimation of children's RR and HR utilizing ultra-wideband (UWB) radar using a deep transfer learning algorithm in a cohort of 55 children. The HR and RR are calculated by processing radar signals via spectrogram from time epochs of 10 s (25 sample length of hamming window with 90% overlap) and then transforming the resultant representation into 2-dimensional images. These images were fed into a pre-trained Visual Geometry Group-16 (VGG-16) model (trained on ImageNet dataset), with weights of five added layers fine-tuned using the proposed data. The prediction on the test data achieved a mean absolute error (MAE) of 7.3 beats per minute (BPM < 6.5% of average HR) and 2.63 breaths per minute (BPM < 7% of average RR). We also achieved a significant Pearson's correlation of 77% and 81% between true and extracted for HR and RR, respectively. HR and RR samples are extracted every 10 s.

Characterizing light-dark cycles in the Neonatal Intensive Care Unit: a retrospective observational study.

Objectives: To characterize bedside 24-h patterns in light exposure in the Neonatal Intensive Care Unit (NICU) and to explore the environmental and individual patient characteristics that influence these patterns in this clinical setting. Methods: We conducted a retrospective cohort study that included 79 very preterm infants who stayed in an incubator with a built-in light sensor. Bedside light exposure was measured continuously (one value per minute). Based on these data, various metrics (including relative amplitude, intradaily variability, and interdaily stability) were calculated to characterize the 24-h patterns of light exposure. Next, we determined the association between these metrics and various environmental and individual patient characteristics. Results: A 24-h light-dark cycle was apparent in the NICU with significant differences in light exposure between the three nurse shifts (p < 0.001), with the highest values in the morning and the lowest values at night. Light exposure was generally low, with illuminances rarely surpassing 75 lux, and highly variable between patients and across days within a single patient. Furthermore, the season of birth and phototherapy had a significant effect on 24-h light-dark cycles, whereas no effect of bed location and illness severity were observed. Conclusion: Even without an official lighting regime set, a 24-h light-dark cycle was observed in the NICU. Various rhythmicity metrics can be used to characterize 24-h light-dark cycles in a clinical setting and to study the relationship between light patterns and health outcomes.

Evaluation of Sleep Practices and Knowledge in Neonatal Healthcare.

BACKGROUND: Developmental care is designed to optimize early brain maturation by integrating procedures that support a healing environment. Protecting preterm sleep is important in developmental care. However, it is unclear to what extent healthcare professionals are aware of the importance of sleep and how sleep is currently implemented in the day-to-day care in the neonatal intensive care unit (NICU). PURPOSE: Identifying the current state of knowledge among healthcare professionals regarding neonatal sleep and how this is transferred to practice. METHODS: A survey was distributed among Dutch healthcare professionals. Three categories of data were sought, including (1) demographics of respondents; (2) questions relating to sleep practices; and (3) objective knowledge questions relating to sleep physiology and importance of sleep. Data were analyzed using Spearman's rho test and Cramer's V test. Furthermore, frequency tables and qualitative analyses were employed. RESULTS: The survey was completed by 427 participants from 34 hospitals in 25 Dutch cities. While healthcare professionals reported sleep to be especially important for neonates admitted in the NICU, low scores were achieved in the area of knowledge of sleep physiology. Most healthcare professionals (91.8%) adapted the timing of elective care procedures to sleep. However, sleep assessments were not based on scientific knowledge. Therefore, the difference between active sleep and wakefulness may often be wrongly assessed. Finally, sleep is rarely discussed between colleagues (27.4% regularly/always) and during rounds (7.5%-14.3% often/always). IMPLICATIONS: Knowledge about sleep physiology should be increased through education among neonatal healthcare professionals. Furthermore, sleep should be considered more often during rounds and handovers.

Development and clinical impact assessment of a machine-learning model for early prediction of late-onset sepsis.

BACKGROUND AND AIM: Preterm infants are prone to neonatal infections such as late-onset sepsis (LOS). The consequences of LOS can be severe and potentially life-threatening. Unfortunately, LOS often presents with unspecific symptoms, and early screening laboratory tests have limited diagnostic value and are often late. This study aimed to build a predictive algorithm to aid doctors in the early detection of LOS in very preterm infants. METHODS: In a retrospective cohort study, all consecutively admitted preterm infants (GA ≤ 32 weeks) from 2008 until 2019 were included. They were classified as LOS or control according to blood culture results, currently the gold standard. To generate features, routine and continuously measured oxygen saturation and heart rate data with a minute-by-minute sampling rate were extracted from electronic medical records. Care was taken not to include variables indicative of existing LOS suspicion. The timing of a positive blood culture served as a proxy for LOS-onset. An equivalent timestamp was generated in gestational-age-matched control patients without a positive blood culture. Three machine learning (ML) techniques (generalized additive models, logistic regression, and XGBoost) were used to build a classification algorithm. To simulate the performance of the algorithm in clinical practice, a simulation using multiple alarm thresholds was performed on hourly predictions for the total hospitalization period. RESULTS: 292 infants with LOS were matched to 1497 controls. The median gestational age before matching was 28.1 and 30.3 weeks, respectively. Evaluation of the overall discriminative power of the LR algorithm yielded an AUC of 0.73 (p < 0.05) at the moment of clinical suspicion (t = 0). In the longitudinal simulation, our algorithm detects LOS in at least 47% of the patients before clinical suspicion without exceeding the alarm fatigue threshold of 3 alarms per day. Furthermore, medical experts evaluated the algorithm as clinically relevant regarding the feature contributions in the model explanations. CONCLUSIONS: An ML algorithm was trained for the early detection of LOS. Performance was evaluated on both prediction horizons and in a clinical impact simulation. To the best of our knowledge, our assessment of clinical impact with a retrospective simulation on longitudinal data is the most extensive in the literature on LOS prediction to date. The clinically relevant algorithm, based on routinely collected data, can potentially accelerate clinical decisions in the early detection of LOS, even with limited inputs.

Respiratory Rate Extraction from Neonatal Near-Infrared Spectroscopy Signals.

Background: Near-infrared spectroscopy (NIRS) relative concentration signals contain 'noise' from physiological processes such as respiration and heart rate. Simultaneous assessment of NIRS and respiratory rate (RR) using a single sensor would facilitate a perfectly time-synced assessment of (cerebral) physiology. Our aim was to extract respiratory rate from cerebral NIRS intensity signals in neonates admitted to a neonatal intensive care unit (NICU). Methods: A novel algorithm, NRR (NIRS RR), is developed for extracting RR from NIRS signals recorded from critically ill neonates. In total, 19 measurements were recorded from ten neonates admitted to the NICU with a gestational age and birth weight of 38 ± 5 weeks and 3092 ± 990 g, respectively. We synchronously recorded NIRS and reference RR signals sampled at 100 Hz and 0.5 Hz, respectively. The performance of the NRR algorithm is assessed in terms of the agreement and linear correlation between the reference and extracted RRs, and it is compared statistically with that of two existing methods. Results: The NRR algorithm showed a mean error of 1.1 breaths per minute (BPM), a root mean square error of 3.8 BPM, and Bland-Altman limits of agreement of 6.7 BPM averaged over all measurements. In addition, a linear correlation of 84.5% (p < 0.01) was achieved between the reference and extracted RRs. The statistical analyses confirmed the significant (p < 0.05) outperformance of the NRR algorithm with respect to the existing methods. Conclusions: We showed the possibility of extracting RR from neonatal NIRS in an intensive care environment, which showed high correspondence with the reference RR recorded. Adding the NRR algorithm to a NIRS system provides the opportunity to record synchronously different physiological sources of information about cerebral perfusion and respiration by a single monitoring system. This allows for a concurrent integrated analysis of the impact of breathing (including apnea) on cerebral hemodynamics.

Brain injury and long-term outcome after neonatal surgery for non-cardiac congenital anomalies.

BACKGROUND: There is growing evidence that neonatal surgery for non-cardiac congenital anomalies (NCCAs) in the neonatal period adversely affects long-term neurodevelopmental outcome. However, less is known about acquired brain injury after surgery for NCCA and abnormal brain maturation leading to these impairments. METHODS: A systematic search was performed in PubMed, Embase, and The Cochrane Library on May 6, 2022 on brain injury and maturation abnormalities seen on magnetic resonance imaging (MRI) and its associations with neurodevelopment in neonates undergoing NCCA surgery the first month postpartum. Rayyan was used for article screening and ROBINS-I for risk of bias assessment. Data on the studies, infants, surgery, MRI, and outcome were extracted. RESULTS: Three eligible studies were included, reporting 197 infants. Brain injury was found in n = 120 (50%) patients after NCCA surgery. Sixty (30%) were diagnosed with white matter injury. Cortical folding was delayed in the majority of cases. Brain injury and delayed brain maturation was associated with a decrease in neurodevelopmental outcome at 2 years of age. CONCLUSIONS: Surgery for NCCA was associated with high risk of brain injury and delay in maturation leading to delay in neurocognitive and motor development. However, more research is recommended for strong conclusions in this group of patients. IMPACT: Brain injury was found in 50% of neonates who underwent NCCA surgery. NCCA surgery is associated with a delay in cortical folding. There is an important research gap regarding perioperative brain injury and NCCA surgery.

The influence of nutrition on white matter development in preterm infants: a scoping review.

White matter (WM) injury is the most common type of brain injury in preterm infants and is associated with impaired neurodevelopmental outcome (NDO). Currently, there are no treatments for WM injury, but optimal nutrition during early preterm life may support WM development. The main aim of this scoping review was to assess the influence of early postnatal nutrition on WM development in preterm infants. Searches were performed in PubMed, EMBASE, and COCHRANE on September 2022. Inclusion criteria were assessment of preterm infants, nutritional intake before 1 month corrected age, and WM outcome. Methods were congruent with the PRISMA-ScR checklist. Thirty-two articles were included. Negative associations were found between longer parenteral feeding duration and WM development, although likely confounded by illness. Positive associations between macronutrient, energy, and human milk intake and WM development were common, especially when fed enterally. Results on fatty acid and glutamine supplementation remained inconclusive. Significant associations were most often detected at the microstructural level using diffusion magnetic resonance imaging. Optimizing postnatal nutrition can positively influence WM development and subsequent NDO in preterm infants, but more controlled intervention studies using quantitative neuroimaging are needed. IMPACT: White matter brain injury is common in preterm infants and associated with impaired neurodevelopmental outcome. Optimizing postnatal nutrition can positively influence white matter development and subsequent neurodevelopmental outcome in preterm infants. More studies are needed, using quantitative neuroimaging techniques and interventional designs controlling for confounders, to define optimal nutritional intakes in preterm infants.