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.

Background: Solutions targeting early recognition of congestion in heart failure (HF) patients have the potential to prevent readmissions and can thus significantly reduce the burden on HF care. The gold standard measure of congestion is invasively measured pulmonary capillary wedge pressure (PCWP). However, the invasive nature and accessibility of this measurement limits its clinical use. Non-invasive approximation of the PCWP using biosensing wearables could be a promising replacement for HF monitoring.
Purpose: The primary aim of this retrospective study was to create a model that estimates the PCWP based on non-invasive measurements of vital signs using both traditional statistics and machine learning (ML) techniques.

Methods: The study cohort comprised right-sided heart catheterisations between 23/6/2017 and 19/8/2022 performed in the Erasmus MC, Rotterdam, The Netherlands. The following models were used: linear regression or classification, k-nearest neighbours, random forest, gradient boosting, and multilayer perceptron. The outcome measure for the regression models was the continuous PCWP as measured during the catheterisation. The two outcome classes for the classification models were low (<12 mmHg) and high (≥12 mmHg) PCWP. Non-invasive mean arterial blood pressure (MAP), saturation, heart rate, weight and temperature measured at most 72 hours before or after the catheterisation were collected as the features for the models, as well as the age and gender of the patient. Additionally, ECG-signals acquired during the catheterisation were used to calculate the heart rate variability (HRV). The data was split into a validation (20%) and training (80%) data set. The models were built based on the training set and then applied on the validation set to determine the coefficients of determination (R2) for the regression models and the area under the curve (AUC) for the classification models.

Results: A total of 853 catheterisation patients were included of which 31.3% had HF as primary diagnosis and 48.7% had a PCWP of 12 mmHg or higher. The average age of the cohort was 58.9 ± 13.7 years and 52.1% were male. The HRV had the highest correlation with the PCWP with a correlation of 0.16. All the regression models resulted in low R2 values of up to 0.11 and the classification models in AUC values of up to 0.59.

Conclusion: In the current study, PCWP could not be approximated with non-invasive measurements using traditional statistics and ML techniques. These findings support the notion that traditional measures for monitoring HF are poorly correlated with hemodynamic parameters. Perhaps repeated measurements over time, e.g. trends, or continuously measured signals such as photoplethysmography (PPG) could overcome the shortcomings of a single vital signs measurement for congestion evaluation. Therefore, future prospective research is needed to evaluate the potential of wearable devices measuring trends based on PPG signals in predicting hemodynamics.