Automated detection of effective left-ventricular pacing: going beyond percentage pacing counters

• Published in: Europace (London, England) England), 2015-10, Vol.17 (10), p.1555-1562
• Main Authors: Ghosh, Subham, Stadler, Robert W, Mittal, Suneet
• Format: Article
• Language: English
• Subjects: Aged; Algorithms; Cardiac Resynchronization Therapy; Cardiac Resynchronization Therapy Devices - adverse effects; Clinical Research; Female; Heart Failure - therapy; Heart Ventricles - physiopathology; Humans; Male; Middle Aged
• ISSN: 1099-5129; 1532-2092
• DOI: 10.1093/europace/euv062

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. 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. A device-based algorithm for beat-by-beat monitoring of effective LV pacing is feasible.