Wavelet-based tachycardia discrimination in ICDs: Impact of posture and electrogram configuration.
BACKGROUND
Inappropriate therapy delivery is an important concern in the management of patients with implantable cardioverter defibrillators (ICDs). Recently, a morphology-based algorithm (wavelet feature) has been introduced for differentiation of ventricular and supraventricular tachycardia. In this study, we evaluated the performance of the wavelet algorithm using various electrogram (EGM) configurations during different body positions.


METHODS
Patients with a single-chamber Medtronic model 7230 ICD (Minneapolis, MN, USA) and a double-coil lead were included. EGM templates were collected during baseline rhythm in supine position for different EGM sources (right ventricular [RV] coil-can, RV coil-superior vena cava [SVC] coil, tip-ring, SVC coil-can). For each EGM configuration, morphologic similarity (match percentage) of EGMs obtained during different body positions (supine, left and right lateral, sitting, standing, walking) were compared with the templates.


RESULTS
Twenty-eight patients (24 males; age 58 +/- 17 years) were studied. A total of 9,775 intracardiac EGMs were analyzed. Median match percentage (interquartile range) was 88% (85-94), 88% (82-94), 82% (76-88), and 73 (58-85) for the RV coil-can, RV coil-SVC coil, tip-ring, and SVC coil-can configurations, respectively. Correct classification rates, as defined by match percentage of 70% or higher, were significantly higher with the RV coil-can, RV coil-SVC coil, and tip-ring EGM configurations, as compared to the SVC coil-can configuration (95, 91, and 91 vs 58% > or =70% match percent, P < 0.001).


CONCLUSIONS
Wavelet-based morphology scores in ICDs may change with various body positions. These variations are relatively minor using the nominal configuration (RV coil-can), as well as by using RV coil-SVC coil and tip-ring. However, morphology scores can vary considerably when SVC coil-can is used; therefore, this configuration should be avoided while using the wavelet algorithm.
Wolber
Thomas
T
Cardiovascular Center, Cardiology, University Hospital Zurich, Zurich, Switzerland. thomas.wolber@usz.ch
Binggeli
Christian
C
Holzmeister
Johannes
J
Brunckhorst
Corinna
C
Strobel
Ulla
U
Boes
Claudia
C
Moser
Ray
R
Becker
Daniel
D
Duru
Firat
F
D000465:Algorithms; D016477:Artifacts; D017147:Defibrillators, Implantable; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D016002:Discriminant Analysis; D004554:Electric Countershock / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011187:Posture; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013610:Tachycardia / Q000175:diagnosis* / Q000517:prevention & control; D013813:Therapy, Computer-Assisted / Q000379:methods*