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.

The large environmental impact (EI) of healthcare is of growing concern, especially given the increasing negative health effects of environmental deterioration.1,2 Paediatric intensive care units (PICU) are major contributors to this EI, partly due to the use of consumables and electricity.3–5 Environmental hotspots in clinical pathways (CP) must be identified to guide practical and effective interventions to lower the EI.5,6 Life cycle assessment (LCA) is currently the golden standard for EI assessment. However, LCA is time-consuming and highly complex. Thus, execution of LCAs on a large scale to analyse full CPs is not feasible in healthcare settings. Furthermore, the required data are often not available. Financial costs may be used as a proxy (spend-based LCA), but their representativeness is questionable.6,7 Furthermore, the CPs of patients in the PICU differ highly and cannot be represented by a single standard CP. Therefore, this research aimed to develop a process-based framework for environmental hotspot identification that requires less time and expertise compared to LCA and accounts for differences between patients, using a case study of six PICU post-cardiac surgery patients. Modules were designed as building blocks to represent medical events in the CP with the flexibility to deal with interpatient variation. Associated consumable and electricity use were allocated to each module based on medical protocols and discussions with clinical staff. From this iterative process, a set of allocation rules was established. The material composition of each product was analysed and recorded in a database. Per module, the median frequency of occurrence was calculated in the patient cohort data. The carbon emissions (kg CO2-equivalent) associated with each module per occurrence were calculated based on impact factors from an open-source database.8 The total of each module was defined as the EI per module occurrence (in kg material and in kg CO2-equivalent) multiplied by the median module frequency. This research was a first step towards an accurate and flexible approach to environmental hotspot identification within CPs related to consumable weight and bedside electricity consumption. The modules and the data analysis algorithm can be reused and expanded for other CPs, saving time and effort in future analyses. The allocation rules ensure standardised allocation of consumables and electricity across the current modules and when new modules are added. Challenges in this approach lie in the availability of product information from manufacturers and reliable, open-source impact factors, especially for pharmaceuticals.

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.