Prediction of optimal cardiac resynchronization by vectors extracted from electrograms in dyssynchronous canine hearts

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Cardiac resynchronization therapy (CRT) has been shown to improve morbidity and mortality in patients with heart failure and left bundle branch block (LBBB).1 However, the response to CRT is heterogeneous, leaving a considerable amount of patients as nonresponders.2 One of the factors contributing to the nonresponse is a poor optimization of the CRT‐device settings.3
The CRT‐device settings that can be programmed are the time intervals between electrical stimulation of the right atrium and the ventricles (AV interval) and between the right (RV) and left ventricle (LV; VV interval). These settings determine LV filling characteristics and the degree of ventricular resynchronization. Currently, the techniques for in‐hospital optimization of the CRT‐device use echocardiographic ventricular filling parameters or LV systolic function variables. However, these methods are time consuming, have high measurement variability, and/or are invasive.4 While such a single optimization of the CRT‐device settings is probably valuable, regular optimization, preferably in an automated fashion, seems more desirable. Therefore, new algorithms were developed using electrogram (EGM)7 or electrocardiogram (ECG)9 signals. However, all these algorithms are based on parameters measured during intrinsic activation and estimate optimal stimulation based on average data from a group of patients. Therefore, in the current study we investigated the possibility to individually and automatically optimize the CRT‐device settings continuously using signals acquired during the paced situation.
An earlier study from our group in canine hearts with LBBB showed the importance of electrical resynchronization for hemodynamic response to CRT.10 Van Deursen et al.11 previously showed that a three‐dimensional (3‐D) vectorcardiogram (VCG) or two frontal plane vectors extracted from the ECG, reflect electrical interventricular dyssynchrony and are reliable and reproducible tools for CRT‐device optimization. It was also shown that the optimal AV and VV interval was predicted by the smallest QRS area. In the present study, we explored the possibility to derive vectors from the electrograms (EGMs) obtained from the pacing electrodes (EGM‐based vector; EGMV) and to use the EGMV‐based QRS area for CRT optimization in LBBB dog hearts with or without myocardial infarction (MI). This would allow automatic, individual, and virtually continuous AV and VV interval optimization.
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