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.

Introduction: Sleep deprivation is commonly encountered in critically ill children admitted to the pediatric intensive care unit (PICU) and is associated with poor clinical outcome. Automated electroencephalography (EEG)-based depth of sleep monitoring enables real-time continuous study of sleep in PICU patients without the need for visual assessment of the EEG signals, the gold standard. This study aims to evaluate the classification performance of various index measures and machine learning models for sleep monitoring in critically ill children.   Method: Two EEG-index-based approaches, calculated as the ratio gamma/delta and of gamma/(theta+delta) spectral powers, as well as three machine learning models - decision tree (DT), support vector machine (SVM) and extreme gradient boosting (XGBoost) - were trained and evaluated. The classification into three as well as four sleep states was evaluated. Polysomnography (PSG) recordings of 120 non-critically ill patients were used for model optimization, training and internal validation. As a proof-of-concept, the models were tested on the PSG data of 10 PICU patients.   Results: Whereas the machine learning models outperformed the index-measures in both three- as well as four-state classification in PSG recordings of non-critically ill children, the opposite was true for the PICU PSG data. Best results for PSG data of non-critically ill patients were obtained with the XGBoost model, with a 5-fold cross-validation accuracy of 0.79 (± 0.01) for three-state classification. Performances for PICU PSG data were remarkably worse for all models. The best results for PICU data were obtained with the index-based approach (accuracy = 0.60) and the gamma/delta and gamma/(theta+delta) performed equally. The individual assessment of model performances per PICU patient revealed large variation between them.   Conclusion: A simple index measure is a promising method to monitor sleep in PICU patients. Machine learning models developed in non-critically ill patients cannot easily be applied to PICU patients in whom the sleep EEG is frequently deviant. Future efforts should focus on further tuning, training and validating the classification models with more PICU data.