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.

Background and objectives: Junctional Ectopic Tachycardia (JET) is a tachyarrhythmia most commonly observed in infants and children in the postoperative setting. An automatic detection algorithm could be valuable for early identification and timely treatment of JET. However, the detection is challenging as the initial changes on the electrocardiogram (ECG) are often subtle and monitor data commonly contains substantial noise and artefacts. The objective of this study was to investigate which features contribute to accurate JET detection and to develop an automated detection model.

Methods: A retrospective study was conducted using monitor ECG data of paediatric patients admitted to the Paediatric Intensive Care Unit. The training set consisted of 17 patients, and the test set of 8 patients. ECG metrics were detected, in order to segment the signal and to derive several features. The two-dimensional vectorcardiogram (VCG) was computed for calculating features representing the beat-to-beat variability of the signal. Automatic feature selection methods were applied to identify which features most effectively differentiate JET from sinus rhythm (SR), based on balanced accuracy. Logistic regression (LR) and random forest (RF) models were finally created and performance was validated.

Results: The LR and RF models achieved balanced accuracy scores of 0,989 and 0,988, respectively, on the training dataset. The selected features included the number of P waves and the variance of the PQ interval. For the RF model, the standard deviation (SD) of the RR interval was also selected. VCG-features did not prove effective in distinguishing JET from SR. A secondary validation on the test set yielded lower scores of 0,899 and 0,892. An analysis of misclassifications revealed that they were all attributed to errors in peak detection, which occurred in cases of deviating ECG morphologies or the presence of artefacts and noise.

Conclusions: This study demonstrates that P wave-related features are most effective for distinguishing JET from SR, with simple machine learning models based on these features showing promising results for automated JET detection. Peak detection is currently the most important limiting factor for the robustness and generalisability of this method. Interpatient variability and the low quality of monitor ECG data remain important challenges. Expanding the dataset, improving the data quality and implementing signal quality assessment (SQA) methods are recommended to improve the robustness of the models.

Background: Neurovascular coupling (NVC) is the physiological mechanism linking neural activity to cerebral blood flow (CBF). When local neural activity increases, NVC ensures a corresponding rise in CBF, which often exceeds the metabolic demand. NVC monitoring could improve neuroprognostication and evaluation of treatment interventions for critically ill children in the pediatric intensive care unit (PICU). This may be particularly relevant for patients following cardiac arrest (CA), where outcome prediction after return of spontaneous circulation (ROSC) remains challenging, but is essential for guiding treatment and informing families.

Objectives: This proof-of-concept study aimed to develop a bedside method for quantifying NVC using simultaneous electroencephalography (EEG) and near-infrared spectroscopy (NIRS) recordings, and to identify biomarkers that best describe NVC.

Methods: Simultaneous EEG and NIRS recordings were obtained from pediatric post-ROSC patients admitted to the PICU at Sophia Children's Hospital between 2017 and 2023. Wavelet coherence analysis was performed between NIRS-derived cerebral tissue oxygen saturation (SctO2) and six EEG features reflecting amplitude, power and continuity aspects of the EEG.

Results: Nine patients were included: five who survived to PICU discharge and four who did not. Survivors exhibited continuous EEG background patterns and showed predominant in-phase coherence between amplitude or power EEG features and SctO2 within the very low-frequency range. Non-survivors, who all exhibited isoelectric EEG patterns, predominantly showed an opposite phase relationship compared to survivors. Significant in-phase coherence between aEEG bandwidth and SctO2 within the 0.2–0.5 and 1–3 mHz frequency ranges yielded the highest percentage of significant in-phase coherence among all EEG features and best distinguished survivors from nonsurvivors. Within this frequency band, survivors showed a median of 6.0% (Q1–Q3: 5.8%–7.2%) significant in-phase coherence, while non-survivors showed a median of 0.8% (Q1–Q3: 0.6–1.0%).

Discussion: Significant in-phase coherence within the 0.2-0.5 and 1-3 mHz frequency ranges between aEEG bandwidth - SctO2 may serve as a biomarker for intact NVC, indicating synchronized increases in cerebral activity and CBF in survivors with a continuous EEG pattern. Validation in larger and more heterogeneous cohorts is required before this biomarker can be used clinically and its prognostic value must be further studied. A key limitation is the need for long, artifact-free recordings, which may limit feasibility in PICU settings.

Background: Continuous monitoring in paediatric intensive care units (PICUs) generates frequent clinical alarms, 87-97% of which are nonactionable, contributing to alarm fatigue and patient safety risks. At the Erasmus MC Sophia Children’s Hospital PICU, improved alarm management is needed. Machine learning offers a promising strategy to distinguish actionable from nonactionable alarms.

Objectives: The primary objective was to develop a machine learning algorithm to classify actionable alarms from multimodal vital signs. Secondary objectives were to characterise the PICU alarm burden, capture stakeholder perspectives and evaluate model feasibility for clinical application.

Methods: Retrospective Dräger monitoring data and alarms from 2,582 PICU patients (Nov 2021 – Oct 2024) were analysed. A machine learning algorithm was developed using ART M, HR, RESP and SpO2 data from 26 patients, with alarms annotated using clinical interventions, small signal deviations and temporal associations with other parameters. Logistic regression, decision tree, random forest and XGBoost were evaluated with nested cross-validation using pre-alarm features. Performance was assessed by sensitivity, specificity, balanced accuracy, with AUROC and F1-score as complementary metrics. Semi-structured interviews with four nurses and one psychologist explored alarm experiences, impacts and reduction strategies.

Results: The best model, a decision tree, showed limited performance (sensitivity 0.36-0.49, specificity 0.51-0.70, balanced accuracy ≈0.50). No discriminative features were identified, and substantial overlap and outliers limited classification. Most alarms involved SpO2 desaturation, with unit variation and temporal patterns linked to ward activity. Interviews highlighted overstimulation and desensitisation, but also that nonactionable alarms can serve as early warnings, with interpretation requiring clinical context.

Discussion: This study provides an evaluation of machine learning-based alarm classification in the PICU and integrates clinician perspectives to guide future interventions. The machine learning model was not suitable for clinical use because of retrospective labelling, the small annotated dataset and the absence of clinical context. Future research should focus on prospective annotation, larger and more diverse datasets and complementary strategies. Alarm dashboards and daily reviews are recommended to reduce alarm burden and mitigate alarm fatigue while maintaining patient safety.