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
Approximately 9 in 1000 children are born with congenital heart disease (CHD), of whom a quarter are classified as critical CHD (CCHD) and require an intervention within their first year. Monitoring these patients in the Paediatric Intensive Care Unit (PICU) is crucial, yet with increasing amounts of data, detecting subtle changes that are important for the disease progression interpretation of all vital signs becomes difficult, even for skilled physicians. Machine learning (ML) offers potential solutions, however, challenges such as inter-patient variability and the absence of clear definitions for haemodynamic instability persist. This study aims to develop a ML algorithm for early prediction of haemodynamic instability in CCHD patients with high frequency vital-signs, addressing these challenges through objective labelling methods and stratification approaches.

Methods
Two approaches, on population and patient level, were developed with nested cross-validation (CV). Due to a high inter-patient variability, the patient specific approach was added. A first iteration of objectively labelling haemodynamic instability was proposed, based on medical interventions such as medication administration and fluid therapy. Since it is difficult to retrospectively determine for how long patients were unstable, multiple values for instability duration (dT ) were added to the analysis.
To capture the temporal dependency of time-series data, lag-analysis was performed, adding the relation between the vital signs and their previous values to the model development. Lag-analysis included a sliding window that moved over the data. The width of sliding window (W ) was optimised during the model development. Additionally, a horizon (r ) was implemented, so the data within the sliding window were predicting future timestamps.

Results
This retrospective study included a total of 224 admissions in the analysis. Two random forest classifiers were trained using a nested CV structure to detect haemodynamic instability in CCHD patients. For both approaches the same temporal settings (W : 50 minutes, r : 45 minutes, dT: 120 minutes) were used. This study has shown that the between-patient approach had notable differences between the mean train (85%, AUCPR) and test performance (40%, AUCPR). The in-patient approach, while using 20% and 10% of the test data for training, still yielded a test performance of 96% (AUCPR) and 90% (AUCPR), respectively.

Discussion and conclusion
Generally speaking, the experiments suggest that the first iterations of the models were not robust and generalised poorly. It is most likely caused by a large inter-patient variability and a simple labelling system that is still depending on subjectivity.
This study has shown that the proposed prediction model, which combines high frequency vital signs, labels, and temporal settings (W, r, dT ), requires additional refinement before it can be considered clinically feasible to implement this model as a reliable bedside tool for predicting haemodynamic instability.

Background: Postoperative junctional ectopic tachycardia (JET) is an arrhythmia associated with increased morbidity and mortality rates in children with congenital heart disease. Developing an automated detection algorithm could aid in early identification and timely treatment of JET.

Methods: A retrospective study was conducted using monitor electrocardiogram (ECG) data of pediatric patients who experienced JET during their admission to the pediatric intensive care unit. A manual decision tree was developed that aimed to differentiate between JET and sinus rhythm based on distinctive characteristics. These features were derived using signal analysis on both two-dimensional vectorcardiograms and ECG data. For the latter, ECG metrics were detected in a fictive lead that was created in the direction with the highest amplitudes. Metrics were identified within adaptive intervals that were dependent on ECG morphology rather than relying on fixed time intervals.

Results: A classification performance was achieved with a sensitivity of 96.3%, specificity of 71.4%, positive predictive value (PPV) of 86.7% and an accuracy of 87.8%. R peaks, Q peaks, S peaks, T peaks and P waves were detected with an accuracy of respectively 99.9%, 95.7%, 89.7%, 98.1% and 54.8%. The computational time of the classification of 41 minutes of data was 4 minutes and 48 seconds.

Conclusion: A manual decision tree algorithm for JET detection was developed, using signal analysis for feature extraction based on JET characteristics. This method with a low computational time and a high sensitivity and PPV holds potential for clinical application as a bedside tool. Implementing this proposed algorithm would allow for treatment in an earlier phase, thereby potentially reducing JET associated morbidity and mortality rates.

Introduction: Critically ill children admitted to the Paediatric Intensive Care Unit (PICU) have a high risk of disruption of their normal sleep rhythm, which is associated with disturbances in physiology and negative effects on psychological and cognitive functioning. There is a need for real-time, automatic sleep monitoring to minimise disruptions in sleep patterns. The main objective of this thesis was to develop a machine learning model that can classify sleep based on vital signs in critically ill children. In addition, methods were investigated to optimise the decision criteria in multi-class problems.
Methods: Three machine learning algorithms, logistic regression, random forest and extreme gradient boosting (XGBoost), were developed based on both the combination of features extracted from electrocardiogram (ECG) signal and pulse transit time (PTT) and on ECG features alone. To gain insight into the number of sleep stages that could be distinguished, the models were developed for two-class, three-class, four-class and five-class staging. The models were developed, trained and evaluated on a diagnostic dataset (n = 90) containing polysomnography (PSG) measurements of non-critically ill children. During model development, the decision criteria for the different classes were jointly optimised. To evaluate whether the models were generalisable to the PICU population, external validation was performed on a set of 8 of PICU patients.
Results: For each number of sleep stages, the three models performed similarly. However, there was an increase in performance with a decrease in the number of sleep stages with balanced accuracies varying between 0.70 and 0.72 in two-class staging and between 0.41 and 0.42 in five-class staging. External validation on the PICU dataset showed a markedly worse performance for all three models with balanced accuracies varying between 0.56 and 0.62 in two-class staging and between 0.22 and 0.23 in five-class staging.
Conclusion: Machine learning models for sleep classification in children based on vital signs have been developed and show promising results. Nonetheless, the developed models are not generalisable to the PICU population. Further research is recommended with a focus on improving the models such that they can be applied in the PICU. Combining information extracted from vital signs with EEG signal and developing the models directly on PICU data should be considered to improve the performance and thereby contribute to personalised care and minimising sleep disturbances.

Introduction: Congenital diaphragmatic hernia (CDH) is a rare developmental defect of the diaphragm characterised by herniation of abdominal organs into the thoracic cavity during prenatal development. This herniation is usually accompanied by pulmonary hypertension (PH) and cardiac dysfunction (CD). Although several parameters are known to predict the clinical outcomes in CDH, most of these parameters do not monitor the degree of PH and CD and cannot be continuously measured. This retrospective observational trial aimed to monitor the degree of PH and CD in CDH using the oxygen saturation index (OSI), peripheral oxygen saturation (SpO2), heart rate (HR), heart rate variability (HRV), arterial blood pressure (ABP) and derivatives of these parameters.

Methods: the study population consisted of neonates admitted to the paediatric intensive care unit between 2019 and 2022 for treatment of CDH. The degree of PH and CD was determined for each cardiac ultrasound (CUS) performed. A 15-minute window of vital parameters, mechanical ventilator, and electrocardiogram data before each CUS was extracted to calculate the predictors. After preprocessing the data and meeting the statistical assumptions, both univariable and multivariable logistic mixed effects models were fitted and validated.

Results: in total, 136 CUS of 57 patients were included in the study. Of the univariable linear mixed-effects models, the median values of HR, pulse pressure (PP), preductal SpO2, dSpO2, OSI and the interquartile range (IQR) of HR were statistically significant predictors of PH. For the prediction of CD, this was the case for the power of HRV in the very low frequency band (HRV-VLF) and for the median values of HR, mean arterial pressure (MAP) and OSI. The multivariable model for the prediction of PH contained the median values of of dSpO2, HR, PP and OSI and the standard deviation of the normal-to-normal beat intervals (SDNN). The multivariable model for the prediction of CD included the median of dSpO2, HRV-VLF, SDNN and the IQR of the systolic arterial pressure (SAP) as predictors.

Conclusions: the most promising predictors are the median values of preductal SpO2, dSpO2 and OSI for the prediction of PH. For the prediction of CD, HRV-VLF and the median values of HR, OSI and dSpO2 were the most promising predictors. Despite limited predictive performance of the regression models, this study contributes to the improvement of monitoring of patients with CDH, which can lead to more timely interventions and eventually improved outcomes within this patient population.

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.