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
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: 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.