Key result
A new ventricular automatic capture algorithm for ICDs demonstrated 99.5% sensitivity and 99.0% specificity for detecting fusion beats compared to visual surface ECG classification.
Why the study?
Does a new automatic capture verification algorithm accurately detect fusion beats compared to surface ECG in patients undergoing ICD implant or replacement?
Observational (n=27)
Does a new automatic capture verification algorithm accurately detect fusion beats compared to surface ECG in patients undergoing ICD implant or replacement?
A new automatic capture verification algorithm demonstrated high sensitivity (99.5%) and specificity (99.0%) for detecting fusion beats in ICD patients.
Answers are grounded in this paper and cite their sources.
Supports automated fusion detection in ICD implants; leaves open prospective outcome validation before routine adoption.
This study evaluated a newly developed automatic capture verification scheme for implantable cardioverter defibrillators (ICDs) regarding discrimination of capture, fusion, and noncapture beats, with an emphasis on fusion detection. The algorithm uses evoked response detection based on a sensing vector from right ventricular shocking coil to Can. Patients undergoing ICD implant or replacement were enrolled in this study. An external system was used for pacing and data acquisition. To provoke ventricular fusion beats, VVI patients were paced close to the rate of their intrinsic rhythm and DDD patients were paced close at their intrinsic PR interval. Surface ECG and wideband filtered intracardiac electrograms were recorded for off-line analysis. Each paced beat was independently classified visually by surface ECG and by the automatic detection algorithm. The algorithm performance was then evaluated by comparing the classification results. Twenty-seven patients (22 males, 5 females; 63.8 +/- 12.5 years) were analyzed. Device and lead demographics were: 18 DDD/9 VVI; 16 dedicated bipolar, 11 integrated bipolar leads; 18 acute, 9 chronic (3.7 +/- 2.0 years) leads. In total, 2064 beats were analyzed, including 1,477 fusion beats and 587 capture beats. Fusion detection sensitivity and specificity were 99.5% and 99.0%, respectively. Seven true-fusion beats (0.5%) were classified as capture and 6 capture beats (1.0%) were identified as fusions. Capture or fusion beats were never detected as non-capture beats. It is concluded that the algorithm was effective in detecting fusion beats. It could potentially be used in ICD applications that need accurate fusion detection.
No takes yet. Share an insight, caveat, or question.
Boriani et al. (2005) conducted an observational in Patients undergoing ICD implant or replacement (n=27). Automatic capture verification algorithm vs. Visual classification by surface ECG was evaluated on Fusion detection sensitivity and specificity (Sensitivity 99.5%, Specificity 99.0%). A new ventricular automatic capture algorithm for ICDs demonstrated 99.5% sensitivity and 99.0% specificity for detecting fusion beats compared to visual surface ECG classification.
Synapse has enriched 10 related papers. Consider them for comparative context: