Ghosh S, Stadler RW, Mittal S. Automated detection of effective left-ventricular pacing: going beyond percentage pacing counters.
Europace 2015;
17:1555-62. [PMID:
25862307 PMCID:
PMC4617370 DOI:
10.1093/europace/euv062]
[Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2015] [Accepted: 02/23/2015] [Indexed: 12/12/2022] Open
Abstract
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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