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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

Additional Value of 3-Month Cranial Magnetic Resonance Imaging in Infants with Neonatal Encephalopathy following Perinatal Asphyxia.

OBJECTIVE: To assess the evolution of neonatal brain injury noted on magnetic resonance imaging (MRI), develop a score to assess brain injury on 3-month MRI, and determine the association of 3-month MRI with neurodevelopmental outcome in neonatal encephalopathy (NE) following perinatal asphyxia. METHODS: This was a retrospective, single-center study including 63 infants with perinatal asphyxia and NE (n = 28 cooled) with cranial MRI <2 weeks and 2-4 months after birth. Both scans were assessed using biometrics, a validated injury score for neonatal MRI, and a new score for 3-month MRI, with a white matter (WM), deep gray matter (DGM), and cerebellum subscore. The evolution of brain lesions was assessed, and both scans were related to 18- to 24-month composite outcome. Adverse outcome included cerebral palsy, neurodevelopmental delay, hearing/visual impairment, and epilepsy. RESULTS: Neonatal DGM injury generally evolved into DGM atrophy and focal signal abnormalities, and WM/watershed injury evolved into WM and/or cortical atrophy. Although the neonatal total and DGM scores were associated with composite adverse outcomes, the 3-month DGM score (OR 1.5, 95% CI 1.2-2.0) and WM score (OR 1.1, 95% CI 1.0-1.3) also were associated with composite adverse outcomes (occurring in n = 23). The 3-month multivariable model (including the DGM and WM subscores) had higher positive (0.88 vs 0.83) but lower negative predictive value (0.83 vs 0.84) than neonatal MRI. Inter-rater agreement for the total, WM, and DGM 3-month score was 0.93, 0.86, and 0.59. CONCLUSIONS: In particular, DGM abnormalities on 3-month MRI, preceded by DGM abnormalities on the neonatal MRI, were associated with 18- to 24-month outcome, indicating the utility of 3-month MRI for treatment evaluation in neuroprotective trials. However, the clinical usefulness of 3-month MRI seems limited compared with neonatal MRI.

Self-reported quantity and quality of sleep in children and adolescents with a chronic condition compared to healthy controls.

To assess self-reported quantity and quality of sleep in Dutch children with a chronic condition compared to healthy controls and to the recommended hours of sleep for youth. Sleep quantity and quality were analyzed in children with a chronic condition (cystic fibrosis, chronic kidney disease, congenital heart disease, (auto-)immune disease, and medically unexplained symptoms (MUS); n = 291; 15 ± 3.1 years, 63% female. A subset of 171 children with a chronic condition were matched to healthy controls using Propensity Score matching, based on age and sex, ratio 1:4. Self-reported sleep quantity and quality were assessed with established questionnaires. Children with MUS were analyzed separately to distinguish between chronic conditions with and without an identified pathophysiological cause. Generally, children with a chronic condition met the recommended amount of sleep, however 22% reported poor sleep quality. No significant differences in sleep quantity and quality were found between the diagnosis groups. Children with a chronic condition and with MUS slept significantly more than healthy controls at ages 13, 15, and 16. Both at primary and secondary school, poor sleep quality was least frequent reported in children with a chronic condition and most often reported in children with MUS.  Conclusion: Overall, children with chronic conditions, including MUS, met the recommended hours of sleep for youth, and slept more than healthy controls. However, it is important to obtain a better understanding of why a substantial subset of children with chronic conditions, mostly children with MUS, still perceived their sleep quality as poor. What is Known: • According to the Consensus statement of the American Academy of Sleep medicine, typically developing children (6 to 12 years) should sleep 9 to 12 h per night, and adolescents (13 to 18 years) should sleep 8 to 10 h per night. • Literature on the optimal quantity and quality of sleep in children with a chronic condition is very limited. What is New: Our findings are important and provide novel insights: • In general, children with a chronic condition sleep according to the recommended hours of sleep. • A substantial subset of children with chronic conditions, perceived their sleep quality as poor. Although this was reported mostly by children with medically unexplained symptoms (MUS), the found poor sleep quality was independent of specific diagnosis.

Susceptibility weighted imaging can be a sensitive sequence to detect brain damage in neonates with kernicterus: a case report.

BACKGROUND: Kernicterus in the acute phase is difficult to diagnose. It depends on a high signal on T1 at the globus pallidum and subthalamic nucleus level. Unfortunately, these areas also show a relatively high signal on T1 in neonates as an expression of early myelination. Therefore, a less myelin-dependent sequence, like SWI, may be more sensitive to detecting damage in the globus pallidum area. CASE PRESENTATION: A term baby developed jaundice on day three following an uncomplicated pregnancy and delivery. Total bilirubin peaked at 542 µmol/L on day four. Phototherapy was started, and an exchange transfusion was performed. ABR showed absent responses on day 10. MRI on day eight demonstrated abnormal high signal globus pallidus on T1w, isointense on T2w, without diffusion restriction, and high signal on SWI at globus pallidal and subthalamus level and phase image at globus pallidal level. These findings were consistent with the challenging diagnosis of kernicterus. On follow-up, the infant presented with sensorineural hearing loss and had a work-up for cochlear implant surgery. At 3 months of age, the follow-up MR shows normalization of the T1 and SWI signals and a high signal on T2. CONCLUSIONS: SWI seems more sensitive to injury than the T1w and lacks the disadvantage of the T1w sequence, where early myelin confers a high signal.

A Scoping Review of Cerebral Doppler Arterial Waveforms in Infants.

Cerebral Doppler ultrasound has been an important tool in pediatric diagnostics and prognostics for decades. Although the Doppler spectrum can provide detailed information on cerebral perfusion, the measured spectrum is often reduced to simple numerical parameters. To help pediatric clinicians recognize the visual characteristics of disease-associated Doppler spectra and identify possible areas for future research, a scoping review of primary studies on cerebral Doppler arterial waveforms in infants was performed. A systematic search in three online bibliographic databases yielded 4898 unique records. Among these, 179 studies included cerebral Doppler spectra for at least five infants below 1 y of age. The studies describe variations in the cerebral waveforms related to physiological changes (43%), pathology (62%) and medical interventions (40%). Characteristics were typically reported as resistance index (64%), peak systolic velocity (43%) or end-diastolic velocity (39%). Most studies focused on the anterior (59%) and middle (42%) cerebral arteries. Our review highlights the need for a more standardized terminology to describe cerebral velocity waveforms and for precise definitions of Doppler parameters. We provide a list of reporting variables that may facilitate unambiguous reports. Future studies may gain from combining multiple Doppler parameters to use more of the information encoded in the Doppler spectrum, investigating the full spectrum itself and using the possibilities for long-term monitoring with Doppler ultrasound.

Sleep and 24-hour rhythm characteristics in preschool children born very preterm and full term.

STUDY OBJECTIVES: Sleep impacts the quality of life and is associated with cardiometabolic and neurocognitive outcomes. Little is known about the sleep of preterm-born children at preschool age. We, therefore, studied sleep and 24-hour rhythms of preschool children born very preterm compared with full-term children. METHODS: This was a prospective cohort study comparing sleep quality and quantity of children born very preterm (gestational age [GA] < 30 weeks) with full-term children at the (corrected) age of 3 years, using (1) 2 parent-reported questionnaires (Brief Infant Sleep Questionnaire and The Munich Chronotype Questionnaire) and (2) at least 3 days of triaxial wrist actigraphy combined with sleep diary. We performed regression analyses with adjustment for sex (corrected), age, and birth weight standard deviation (SD) score. RESULTS: Ninety-seven very-preterm-born (median GA 27+5; interquartile range 26 + 3;29 + 0 weeks) and 92 full-term children (GA 39 + 3; 38 + 4;40 + 4 weeks) were included. Sleep problems and other reported sleep parameters were not different between groups. As measured with actigraphy, sleep and 24-hour rhythm were similar between groups, except for very-preterm born children waking up 21 minutes (4;38) minutes later than full-term children (adjusted P = .001). CONCLUSIONS: Based on parent reports and actigraphy, very-preterm-born children sleep quite similar to full-term controls at the corrected age of 3 years. Reported sleep problems were not different between groups. Actigraphy data suggest that preterm-born children may wake up later than children born full term. Further studies are needed to explore how sleep relates to cardiometabolic and neurodevelopmental outcomes after preterm birth and whether early interventions are useful to optimize 24-hour rhythm and sleep. CITATION: Bijlsma A, Beunders VAA, Dorrepaal DJ, et al. Sleep and 24-hour rhythm characteristics in preschool children born very preterm and full term. J Clin Sleep Med. 2023;19(4):685-693.