Hemodynamic-impact-based prioritization of ventricular tachycardia alarms

Ventricular tachycardia (V-tach) is a very serious condition that occurs when the ventricles are driven at high rates. The abnormal excitation pathways make ventricular contraction less synchronous resulting in less effective filling and emptying of the left ventricles. However, almost half of the V-tach alarms declared through processing of patterns observed in electrocardiography are not clinically actionable. The focus of this study is to provide guidance on determining whether a technically-correct V-tach alarm is clinically-actionable by determining its “hemodynamic impact”. A supervisory learning approach based on conditional inference trees to determine the hemodynamic impact of a V-tach alarm based on extracted features is described. According to preliminary results on a subset of Multiparameter intelligent monitoring in intensive care II (MIMIC-II) database, true positive rate of more than 90% can be achieved.