Assistent Professor Baylor College of Medicine Houston, Texas, United States
Abstract: Objectives Assess the degree of instability in the electrocardiogram (ECG) waveform in patients with single-ventricle physiology before a cardiac arrest and compare them with similar patients who did not experience a cardiac arrest. Method
Design: Retrospective control study. Setting: Nine-hundred seventy-three bed pediatric hospital with a 54-bed cardiovascular ICU. Patients: Patients with single-ventricle physiology who underwent Norwood, Blalock- Taussig shunt, pulmonary artery band, and aortic arch repair from 2013 to 2018. Interventions: None. Materials and Main Results Electronic medical records were obtained for all included patients. For each subject, 6 hours of ECG data were analyzed. In the arrest group, the end of the sixth hour coincides with the cardiac arrest. In the control group, the 6-hour windows were randomly selected. Finally, we used a Markov chain framework to classify the arrest and control groups based on the measured ECG instability of the time-series data. The study dataset consists of 38 cardiac arrest events and 67 control events. Our Markov model was able to classify the arrest and control groups based on the ECG instability with a ROC AUC of 82% at the hour preceding the cardiac arrests. Conclusion We designed a method using the Markov chain framework to measure the level of instability in the beat-to-beat ECG morphology. Furthermore, we were able to show that the Markov model performed well to distinguish patients in the arrest group compared to the control group.
