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 and aims
The timing of extubation is a difficult decision for the medical team on the PICU. With negative impact on patient outcome when extubating too late or too early. The aim of this study was to create machine learning models for extubation failure prediction after surgery in patients with congenital heart disease. The goal was to assess the influence of time variant features on the performance.

Methods
Data from post cardiac surgery patients admitted to the PICU of the University Medical Centre Utrecht, The Netherlands, between 2009 and 2018 was collected. Ventilator and monitor parameters were extracted in 12-hour segments. Different representations of time-variant features were calculated (per hour/ per 12-hour segment), these representations were tested against machine learning trained on only time-invariant features (age, weight diagnosis). Machine learning algorithms tested were: long short-term memory network (LSTM), logistic regression and random forest model. Models were evaluated by comparing the areas under the receiver operator curves

Results
With only time invariant features a performance of 75% [95%CI 81%-90%] using logistic regression. Adding the time-variant features to a LSTM model a performance was reached 77% [95%CI 80%-90%]. Important features from the logistic regression models were age, weight, heart rate and respiratory rate.

Conclusions
Based on the overall results we concluded that the chosen representations of time variant features did not significantly improve the performance of the models. To improve performance and implementation of machine learning models in the future, transparent and externally validated models need to be developed.