Key result
Downloaded wavelet-transform algorithms in ICDs show 100% sensitivity for discriminating VT from SVT.
Why the study?
Prior SVT-VT discrimination algorithms based on electrogram morphology have not undergone real-time evaluation in ambulatory patients before permanent incorporation into ICDs, limiting assessment of their performance under varying conditions.
Does a downloaded wavelet-transform morphology algorithm accurately discriminate ventricular tachycardia from supraventricular tachycardia in patients with implanted ICDs?
Population
23 patients with implanted ICDs
Comparison
Wavelet-transform morphology algorithm vs baseline electrogram morphology
Design
Observational study with downloaded software in implanted ICDs
Follow-up
Median 6 months
Authors
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May enhance SVT discrimination in ICDs; leaves open prospective validation before practice change.
Observational (n=23)
Does a downloaded wavelet-transform morphology algorithm accurately discriminate ventricular tachycardia from supraventricular tachycardia in patients with implanted ICDs?
A downloadable wavelet morphology algorithm demonstrated 100% sensitivity for VT detection and 78% specificity for SVT rejection in ambulatory patients with ICDs.
Swerdlow et al. (2002) conducted an observational in Implantable cardioverter defibrillator (ICD) recipients (n=23). Downloaded wavelet-transform morphology algorithm was evaluated on Sensitivity for detection of ventricular tachycardia (VT) using a 70% match-percent threshold. A downloaded wavelet-transform morphology algorithm in implanted ICDs demonstrated 100% sensitivity for detecting ventricular tachycardia and 78% specificity for rejecting supraventricular tachycardia.
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