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.

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.