Coronary sinus and great cardiac vein electroanatomic mapping predicts the activation delay of the coronary sinus branches.

BACKGROUND: Implantation of left ventricular (LV) lead in segments with delayed electrical activation may improve response to cardiac resynchronization therapy (CRT). The search for the latest LV electrical delay (LVED) site can be time-consuming. OBJECTIVE: To assess if electrical mapping of coronary sinus (CS) and magna cardiac vein can help to identify the latest activated CS branch. METHODS: We retrospectively evaluated 48 consecutive patients who underwent electroanatomic mapping system-guided (EAMS)-CRT device implantation with ≥2 mapped CS branches. The activation mapping of the CS and relative branches were performed using an insulated guide wire. LVED was defined as the interval between the beginning of the QRS complex on the surface electrocardiogram and the local electrogram and expressed in milliseconds (ms). RESULTS: Thirty-two (67%) patients showed left bundle branch block (LBBB) and 16 (33%) non-LBBB electrocardiographic patterns. A total of 116 CS branches (mean, 2.4/patient; range, 2-5) were mapped. In the left oblique view, most patients (N = 39, 81%) showed the latest CS-LVED in lateral segments while nine (19%) showed the latest CS-LVED in anterior or posterior segments. Specifically, 94% of patients with LBBB showed the latest CS-LVED in lateral segments while CS activation among non-LBBB patients was heterogeneous. In all patients, the CS branch that demonstrated the highest LVED originated from the latest activated segment of the CS. CONCLUSION: Electrical mapping of CS allows identifying the latest activated branches. This finding may contribute to simplify CRT device implantation compared to activation mapping of all the branches.

Computational generation of the Purkinje network driven by clinical measurements: the case of pathological propagations.

To properly describe the electrical activity of the left ventricle, it is necessary to model the Purkinje fibers, responsible for the fast and coordinate ventricular activation, and their interaction with the muscular propagation. The aim of this work is to propose a methodology for the generation of a patient-specific Purkinje network driven by clinical measurements of the activation times related to pathological propagations. In this case, one needs to consider a strongly coupled problem between the network and the muscle, where the feedback from the latter to the former cannot be neglected as in a normal propagation. We apply the proposed strategy to data acquired on three subjects, one of them suffering from muscular conduction problems owing to a scar and the other two with a muscular pre-excitation syndrome (Wolff-Parkinson-White). To assess the accuracy of the proposed method, we compare the results obtained by using the patient-specific Purkinje network generated by our strategy with the ones obtained by using a non-patient-specific network. The results show that the mean absolute errors in the activation time is reduced for all the cases, highlighting the importance of including a patient-specific Purkinje network in computational models.

Patient-specific generation of the Purkinje network driven by clinical measurements of a normal propagation.

The propagation of the electrical signal in the Purkinje network is the starting point for the activation of the ventricular muscular cells leading to the contraction of the ventricle. In the computational models, describing the electrical activity of the ventricle is therefore important to account for the Purkinje fibers. Until now, the inclusion of such fibers has been obtained either by using surrogates such as space-dependent conduction properties or by generating a network based on an a priori anatomical knowledge. The aim of this work was to propose a new method for the generation of the Purkinje network using clinical measures of the activation times on the endocardium related to a normal electrical propagation, allowing to generate a patient-specific network. The measures were acquired by means of the EnSite NavX system. This system allows to measure for each point of the ventricular endocardium the time at which the activation front, that spreads through the ventricle, has reached the subjacent muscle. We compared the accuracy of the proposed method with the one of other strategies proposed so far in the literature for three subjects with a normal electrical propagation. The results showed that with our method we were able to reduce the absolute errors, intended as the difference between the measured and the computed data, by a factor in the range 9-25 %, with respect to the best of the other strategies. This highlighted the reliability of the proposed method and the importance of including a patient-specific Purkinje network in computational models.