Why the study?
Does a device-based automatic algorithm accurately detect effective left-ventricular pacing compared to surface ECG classification in CRT-defibrillator patients?
Does a device-based automatic algorithm accurately detect effective left-ventricular pacing compared to surface ECG classification in CRT-defibrillator patients?
A novel device-based algorithm can accurately detect effective left-ventricular pacing beat-by-beat, potentially facilitating optimization of CRT response.
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Device algorithm for LV pacing detection may aid CRT optimization; leaves open prospective outcome validation.
AIMS: Cardiac resynchronization therapy (CRT) devices report percentage pacing as a diagnostic but cannot determine the effectiveness of each paced beat in capturing left-ventricular (LV) myocardium. Reasons for ineffective LV pacing include improper timing (i.e. pseudofusion) or inadequate pacing output. Device-based determination of effective LV pacing may facilitate optimization of CRT response. METHODS AND RESULTS: Effective capture at the LV cathode results in a negative deflection (QS or QS-r morphology) on a unipolar electrogram (EGM). Morphological features of LV cathode-RV coil EGMs were analysed to develop a device-based automatic algorithm, which classified each paced beat as effective or ineffective LV pacing. The algorithm was validated using acute data from 28 CRT-defibrillator patients. Effective LV pacing and pseudofusion was simulated by pacing at various AV delays. Loss of LV capture was simulated by RV-only pacing. The algorithm always classified LV or biventricular (BV) pacing with AV delays ≤60% of patient's intrinsic AV delay as effective pacing. As AV delays increased, the percentage of beats classified as effective LV pacing decreased. Algorithm results were compared against a classification truth based on correlation coefficients between paced QRS complexes and intrinsic rhythm QRS templates from three surface ECG leads. An average correlation >0.9 defined a classification truth of ineffective pacing. Compared against the classification truth, the algorithm correctly classified 98.2% (3240/3300) effective LV pacing beats, 75.8% (561/740) of pseudofusion beats, and 100% (540/540) of beats with loss of LV capture. CONCLUSION: A device-based algorithm for beat-by-beat monitoring of effective LV pacing is feasible.
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Ghosh et al. (2015) studied this question.
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