Applicability of body surface potential map in computerized optimization of biventricular pacing

Autocorrelation maps for optimal setting in cardiac resynchronization therapy

BACKGROUND AND OBJECTIVE

Patients with chronic heart failure are treated with implanted devices artificially stimulating the ventricular myocardium to support the ventricular activation propagation dynamics. The criterion for stimulation/pacing timing is a shortening of the QRS duration in the ECG signal. The study suggests additional ECG parameters that could be helpful in cardiac resynchronization therapy (CRT) device pacing settings.

METHODS

This issue was approached by computing and evaluating autocorrelation maps derived from body surface potential maps during the QRS complex. The autocorrelation maps were calculated from the body surface potential maps of seventeen patients, fourteen of whom were diagnosed with the left bundle branch block (LBBB) and three with the right bundle branch block (RBBB). Eleven of the LBBB patients were responders, and all three RBBB patients and three LBBB patient were non-responders. The body surface potential maps were measured during their spontaneous heart rhythm and optimal CRT setting. The patients' autocorrelation maps were compared with the autocorrelation maps of a control group of 33 healthy persons using two-sample Kolmogorov-Smirnov and Wilcoxon rank-sum statistical tests.

RESULTS

The autocorrelation maps from spontaneous rhythm were significantly different (p < 0.00008) in healthy and LBBB groups, which was shown on 19 parameters extracted from the autocorrelation maps by both the statistical tests of equality. In the optimal CRT setting in the LBBB responders, four of the studied parameters (Shannon entropy of the histogram of the autocorrelation map's values, and mean, standard deviation, and geometrical mean of the autocorrelation map's positive values) were not significantly different from the parameters of the healthy subjects (p > 0.19).

CONCLUSIONS

Selected parameters of autocorrelation maps can be used as additional parameters for optimal CRT pacing settings, leading to patients' positive responses to the treatment.

Computational Modeling for Cardiac Resynchronization Therapy

Cardiac resynchronization therapy (CRT) is an effective treatment for heart failure (HF) patients with an electrical substrate pathology causing ventricular dyssynchrony. However 40-50% of patients do not respond to treatment. Cardiac modeling of the electrophysiology, electromechanics, and hemodynamics of the heart has been used to study mechanisms behind HF pathology and CRT response. Recently, multi-scale dyssynchronous HF models have been used to study optimal device settings and optimal lead locations, investigate the underlying cardiac pathophysiology, as well as investigate emerging technologies proposed to treat cardiac dyssynchrony. However the breadth of patient and experimental data required to create and parameterize these models and the computational resources required currently limits the use of these models to small patient numbers. In the future, once these technical challenges are overcome, biophysically based models of the heart have the potential to become a clinical tool to aid in the diagnosis and treatment of HF.

A framework for personalization of computational models of the human atria

A framework for step-by-step personalization of a computational model of human atria is presented. Beginning with anatomical modeling based on CT or MRI data, next fiber structure is superimposed using a rule-based method. If available, late-enhancement-MRI images can be considered in order to mark fibrotic tissue. A first estimate of individual electrophysiology is gained from BSPM data solving the inverse problem of ECG. A final adjustment of electrophysiology is realized using intracardiac measurements. The framework is applied using several patient data. First clinical application will be computer assisted planning of RF-ablation for treatment of atrial flutter and atrial fibrillation.