Abstract 17016: T Wave Variability and T Wave Morphology Predict Ventricular Tachyarrhythmias in Arvc Patients
Introduction: Predicting episodes of ventricular tachyarrhythmia (VT) and especially fast VT events in arrhythmogenic right ventricular cardiomyopathy (ARVC) patients could be useful for identification of patients for early ICD implantations. This study investigates parameters acquired from Holter Recordings and their value in predicting VT and fast VT events in patients with ARVC.
Methods: We studied 63 probands diagnosed with ARVC according to the 2010 Task Force criteria. Three orthogonal (X,Y, and Z) lead ECG from Digital Holter were acquired at enrollment. The following parameters were calculated HRV time -domain from 24-hours, HRV frequency-domain from supine 5-minute recordings; QT variability from 20-minute supine; TWA from 20-minute supine and QT/RR slope. Best-subsets proportional-hazards regression analysis involving variables were used to identify the best set of continue parameters from Holters recordings predicting the VT events. Separate models were developed and tested for each of the endpoints.
Results: The risk for VT was 40 % and for fast VT 17% at 30 years of age (Figure 1). The best Holter recording parameters as predictors for VT or fast VT and were used in the model with adjustment for age, female and negative T wave in II, II and Avf. Percent of premature ventricular beats (PVC) (HR: 1.1 CI: 1.003 -1.21; p<0.05), T wave morphology expressed as R tangent (HR: 2.42 CI: 1.15-5.079; p<0.05) and T wave variability expressed as SD of mean T wave variability (HR: 3.2 CI: 1.49- 6.87; p<0.01) were significant predictors of VT events in patients with ARVC. For fast VT events percent of PVCs sustain a borderline predictor (HR: 1.25 CI: 0.99-1.57; p=0.05).
Conclusions: Electrophysiological parameters acquired from Holter Recordings related to T wave morphology and T wave variability along with percent of PVCs are good predictors for VT in patients with ARVC and could be used in clinical practice to identify patients for early ICD intervention.
Author Disclosures: B. Szepietowska: None. X. Xia: None. B. Polonsky: None. J. Couderc: None. W. Zareba: None.
- © 2016 by American Heart Association, Inc.