Computer simulation study on the effect of electrode–tissue contact force on thermal lesion size in cardiac radiofrequency ablation

Abstract

Purpose

In cardiac radiofrequency (RF) ablation, RF energy is often used to create a series of transmural lesions for blocking accessory conduction pathways. Electrode–tissue contact force (CF) is one of the key determinants of lesion formation during RF ablation. Low electrode–tissue CF is associated with ineffective RF lesion formation, whereas excessive CF may increase the risk of steam pop and perforation. By using finite element analysis, we studied lesion size and features at different values of electrode–tissue CF in cardiac RF ablation.

Materials and methods

A computer-model-coupled electrode–tissue CF field, RF electric field, and thermal field were developed to study temperature distribution and lesion dimensions in cardiac tissue subjected to CF of 2, 5, 10, 20, 30, and 40 g with identical RF voltage and duration.

Results

Increasing CF was associated with an increase in lesion depth, width, and cross-section area. The lesion cross-section area exhibited a linear increase, and the lesion width was significantly greater than lesion depth under the identical ablation condition. The relationship between CF value and lesion size is a power function: Lesion Size = a × CF^b (Lesion Depth = 3.17 × CF^0.14 and Lesion Width = 5.17 × CF^0.14).

Conclusions

This study confirmed that CF is a major determinant of RF lesion size and that electrode–tissue CF affects the amount of power dissipated in tissue. At a constant RF voltage and application time, RF lesion size increases as CF increases.

Keywords:

• Contact force
• lesion size
• cardiac radiofrequency ablation
• finite element method
• predicted lesions

Disclosure statement

The authors alone are responsible for the content and writing of the paper.

Additional information

Funding

This work received financial support from National Natural Science Foundation of China, grant no. 61801123 and 61171009, the China Postdoctoral Science Foundation, grant no. 2019M651367, Shanghai Municipal Commission of economy and information technology, grant no. GYQJ-2018-2-05, and the Shanghai Municipal Science and Technology Major Project, grant no. 2017SHZDZX01 and 16441907900.