Assessment of an ECG-Based System for Localizing Ventricular Arrhythmias in Patients With Structural Heart Disease.
Shijie ZhouAmir AbdelWahabJohn L SappEric SungKonstantinos N AronisJames W WarrenPaul J MacInnisRushil ShahB Milan HoráčekRonald D BergerHarikrishna TandriNatalia A TrayanovaJonathan ChrispinPublished in: Journal of the American Heart Association (2021)
Background We have previously developed an intraprocedural automatic arrhythmia-origin localization (AAOL) system to identify idiopathic ventricular arrhythmia origins in real time using a 3-lead ECG. The objective was to assess the localization accuracy of ventricular tachycardia (VT) exit and premature ventricular contraction (PVC) origin sites in patients with structural heart disease using the AAOL system. Methods and Results In retrospective and prospective case series studies, a total of 42 patients who underwent VT/PVC ablation in the setting of structural heart disease were recruited at 2 different centers. The AAOL system combines 120-ms QRS integrals of 3 leads (III, V2, V6) with pace mapping to predict VT exit/PVC origin site and projects that site onto the patient-specific electroanatomic mapping surface. VT exit/PVC origin sites were clinically identified by activation mapping and/or pace mapping. The localization error of the VT exit/PVC origin site was assessed by the distance between the clinically identified site and the estimated site. In the retrospective study of 19 patients with structural heart disease, the AAOL system achieved a mean localization accuracy of 6.5±2.6 mm for 25 induced VTs. In the prospective study with 23 patients, mean localization accuracy was 5.9±2.6 mm for 26 VT exit and PVC origin sites. There was no difference in mean localization error in epicardial sites compared with endocardial sites using the AAOL system (6.0 versus 5.8 mm, P=0.895). Conclusions The AAOL system achieved accurate localization of VT exit/PVC origin sites in patients with structural heart disease; its performance is superior to current systems, and thus, it promises to have potential clinical utility.
Keyphrases
- end stage renal disease
- high resolution
- pulmonary hypertension
- heart failure
- chronic kidney disease
- ejection fraction
- left ventricular
- newly diagnosed
- deep learning
- multiple sclerosis
- peritoneal dialysis
- heart rate variability
- mass spectrometry
- climate change
- risk assessment
- machine learning
- oxidative stress
- patient reported outcomes
- high glucose
- drug induced
- smooth muscle