Background: Patients suffering from critical congenital heart disease (cCHD) require cardiac intervention within the first year of life. During the postoperative period, patients are at risk of haemodynamic instability resulting in insufficient organ perfusion and subsequent organ failure. To prevent this, patients are placed in the paediatric intensive care unit (PICU) where various vital parameters can be monitored. However, interpreting these continuous data streams can be challenging. Machine learning offers the potential to support clinical decision-making in this setting, but challenges remain, particularly in labelling haemodynamic instability and accounting for varying physiology of this patient demographic. The goal of this study was to improve the retrospective labelling of haemodynamic instability and evaluate the affect of age-stratified subpopulation on model performance.

Methods: This study used a retrospective dataset of continuously measured parameters (heart rate, respiratory rate, mean arterial pressure, central venous pressure, oxygen saturation, and perfusion index) collected from post-operative cCHD patients admitted to the PICU of Erasmus MC Sophia Children's Hospital, the Netherlands, between January 2016 and April 2025. A new scoring system was developed to quantify the haemodynamic support received by patients and to identify intervention times at which support was increased. These interventions were used to label haemodynamic instability in the a period dT prior to intervention. The resulting labelling was applied to train a random forest algorithm to predict haemodynamic instability, and the model was subsequently retrained on age-based subpopulations.

Results: A total of 425 patients were included for this study. The new labelling method resulted in 5.7% of the data being labelled as haemodynamically unstable. The random forest using the new labelling achieved an average (SD) area under precision-recall curve (AUCPR) of 0.233 (0.041) on the test set during cross-validation and final test AUCPR of 0.203. The largest age subpopulations were 0--30 days and 90--180 days. The class prior of instability was 9.2% in the 0--30 days subpopulation and the prediction model achieved an AUCPR of 0.244 (0.064). In the 90--180 day subpopulation the class prior was 4.9% and an AUCPR of 0.221 (0.105) was achieved.

Conclusion: This study proposed a new method of retrospectively labelling haemodynamic instability with the goal of training a predictive model to predict these instabilities. A random forest model trained using the new labelling showed limited improvement, with an AUCPR of 0.204 and an AUCROC of 0.762. Age-based subpopulation analysis indicated potential for reduced data variation, though larger cohorts are needed for better generalisation. Further refinements in the retrospective labelling of haemodynamic instability are required for an effective prediction model to be developed.

Introduction: In critically ill paediatric patients, sleep is essential for recovery and development, yet sleep disturbances are common in the paediatric intensive care unit (PICU), which highlight the need to integrate sleep monitoring into clinical practice. While automated sleep stage classification using machine learning (ML) on single-channel electroencephalography (EEG) data has shown promise in mostly healthy adult populations, its application to critically ill children is challenged by age-specific sleep architecture, medication effects, pathological conditions, and artefacts. This study evaluates whether deep learning (DL) feature extraction and dynamic models can improve sleep staging performance in this population and explores the use of an unsupervised ML model to gain deeper insight into the complex sleep structures in this population.

Methods: This study utilised EEG recordings from three datasets—healthy adults, non-critically ill children, and critically ill children—to train and evaluate supervised and unsupervised sleep stage classification models. As supervised models, a convolutional neural network (CNN) was used for feature extraction, followed by dynamic models including a long short-term memory (LSTM) network and a hidden Markov model (HMM) to account for temporal dependencies in sleep. Additionally, an unsupervised HMM was applied to explore underlying structures in the sleep EEG data without predefined labels.

Results: Supervised models achieved good performance in healthy adults and non-critically ill children, with maximum accuracies of 90.2\% and 77.4\%, respectively, for three-state classification. The added value of dynamic models over the CNN alone varied per dataset and model type and was not consistent. In critically ill children, classification performance was low, with a maximum accuracy of 61.4\%, and notably low macro-F1 and Cohen’s kappa scores (45.9\% and 26.5\%, respectively). The unsupervised HMM revealed that identifying distinct and stable clusters over time was challenging in all datasets. For critically ill children, the model often failed to identify multiple distinct clusters within individual patients, and substantial variability in cluster assignments was observed across patients.

Discussion: This study demonstrates that DL-based feature extraction and dynamic modelling using single-channel EEG can achieve strong sleep staging performance in healthy adults and non-critically ill children, highlighting the potential for (semi-)automated scoring tools in more stable populations. In contrast, performance in critically ill children was notably lower, likely due to factors such as high variability in sleep architecture, signal artefacts, limited data quality, and the uncertain reliability of manually assigned labels. These results suggest that conventional sleep stages do not generalise well to this population, and a purely data-driven, unsupervised approach does not offer a viable alternative. Overall, the findings emphasise the need for a larger dataset of critically ill children, further evaluation of relevant patient and data characteristics, the inclusion of alternative signals such as electrocardiography, and greater focus on model interpretability.

Study objectives: Conventional sleep scoring is based on the scoring criteria of the American Association of Sleep Medicine (AASM) but may not be suited to describe sleep in critically ill children admitted to the Pediatric Intensive Care Unit (PICU). In this study, an anomaly detection model using Gaussian Models trained on sleep stages in data from non-critically ill children is developed to assess if polysomnography(PSG)-derived electroencephalography (EEG) data from critically ill children can be categorized into sleep stages based on these AASM scoring criteria.
Methods: A retrospective study at Erasmus MC Sophia Children’s Hospital, using PSG recordings obtained in non-critically ill children between 2017 and 2021 and in critically ill children between 2020 and 2022.
Gaussian Models were individually trained for each sleep stage using data from non-critically ill children. Anomaly detection was carried out by computing the Mahalanobis Distances and assigning datapoints to specific sleep stages or categorizing them as anomalous. Errors were quantified by calculating the ratio of anomalous epochs to the total number of epochs. The trained Gaussian Models were applied to distinct sleep stages in the data from non-critically ill children. Subsequently, the models were applied to data from critically ill children to determine the categorization of their epochs. This was also analyzed over time and involved comparisons related to medication, mechanical ventilation, and the severity of illness assessed by the PELOD-2 score.
Results: In non-critically ill children the models obtained validation errors aligning with the margin error of the training set. The models could not fully differentiate the distinct sleep stages. In critically ill children, the majority of epochs were classified into multiple sleep stages. High error rates were evident for sleep stages N1, R, and N. Some patients exhibited elevated error rates specifically for sleep stage N1. REM sleep was reduced, consistent with findings from previous studies. In contrast, N3 sleep did not show a reduction. When compared to the sleep stage labels assigned by neurophysiologists, the model classified epochs into multiple sleep stages, while neurophysiologists frequently used the label N. A higher PELOD-2 score did not consistently correlate with an increased occurrence of anomalous classifications in the epochs of these patients to those with lower PELOD-2 scores.
Discussion: Overlap of sleep stages was observed in non-critically ill children. Epochs from critically ill children were classified into multiple sleep stages without clear associations in time or severity of illness. Building upon the established anomaly detection framework is recommended by employing more advanced anomaly detection methods using an informative feature selection. This study marks an initial step, indicating that applying the AASM.

Introduction Traumatic brain injury (TBI) is a leading cause of childhood morbidity, disability and mortality worldwide. In the treatment of TBI, neuromonitoring is essential to prevent secondary neurological damage. However, the use of neuromonitoring and specifically therapeutic targets is currently limited evidence-based.
Objective The primary objective is to implement suitable neuromonitoring parameters into a novel bedside dashboard for paediatric TBI patients. In addition, basic insights into neuromonitoring parameters between different outcome groups are provided.
Methods Intracranial pressure (ICP), cumulative ICP (ICP_cum), cerebral perfusion pressure (CPP), pressure reactivity index (PRx), wavelet transform PRx, optimal CPP (CPP_opt), partial pressure of arterial oxygen (P_aO₂), partial pressure of arterial carbon dioxide (P_aCO₂), end tidal CO2 (etCO₂), temperature and sodium levels were implemented into a dashboard. The parameters (except wPRx) were retrospectively analysed in TBI patients admitted to the paediatric intensive care unit (PICU) of the Erasmus MC- Sophia. Patients were divided into a good (GO) or bad (BO) outcome group. Per outcome group, the mean and 95 % confidence interval or the median and interquartile range of parameters were calculated.
Results A total of 35 patients (24 GO, 11 BO) were included. Significant differences between GO and BO were observed in median PRx (-0.06 [-0.13 – 0.01] vs. 0.17 [-0.05 – 0.39]), median percentage outside of the CPP_opt (curve) range (15 % [4 – 26] vs. 30 % [26 – 34]), ICP_cum distributions (12.0 mmHg [8.0 – 16.0] vs. 12.0 mmHg [8.5 – 15.5]) and mean sodium levels (146.4 mmol/L (144.5 – 148.3) vs. 151.2 mmol/L (148.6 – 153.8)).
Conclusion In a retrospective analysis, PRx, CPP_opt (curve) and ICP_cum showed potential to improve prognostication for paediatric TBI and to determine therapeutic targets. Based on our results, we recommend implementing MAP, ICP, ICP_cum, CPP alongside CPP_opt (curve), and sodium levels in a future neuromonitoring dashboard.