Predictive value of graft patency and major adverse cardiac and cerebrovascular events (MACCEs) in coronary artery bypass grafting (CABG) based on Fourier transform (FFT)

Yanxiong Jia; Hongyi Xu; Pixiong Su; Jie Gao; Song Gu; Yan Liu; Xiangguang An; Jun Yan; Xitao Zhang

doi:10.21037/jtd-21-178

Predictive value of graft patency and major adverse cardiac and cerebrovascular events (MACCEs) in coronary artery bypass grafting (CABG) based on Fourier transform (FFT)

J Thorac Dis. 2021 May;13(5):2705-2715. doi: 10.21037/jtd-21-178.

Authors

Yanxiong Jia^{1

2}, Hongyi Xu³, Pixiong Su², Jie Gao², Song Gu², Yan Liu², Xiangguang An², Jun Yan², Xitao Zhang²

Affiliations

¹ Capital Medical University, Beijing, China.
² Department of Cardiovascular Surgery, Beijing Chao-yang Hospital, Capital Medical University, Beijing, China.
³ Beijing Institute of Electronic System Engineering, Beijing, China.

Abstract

Background: Transit time flow measurement (TTFM) is widely used in coronary artery bypass grafting (CABG); however, its predictive value is unclear. We aimed to identify new factors to evaluate graft quality using fast Fourier transform (FFT).

Methods: Intraoperative and postoperative 2-year follow-up data of 114 patients undergoing CABG from January 2017 to December 2018 were collected. The TTFM waveform was transformed by FFT. Mean graft flow (MGF), pulse index, the amplitude of the main wave in FFT (H₀), the amplitude of the first harmonic (H₁), H₀/H₁, and the frequency of the first harmonic (P) were analyzed as predictors using logistic regression and receiver operating characteristic (ROC) curves.

Results: The overall graft patency rate was 80.3%, and the incidence of major adverse cardiac and cerebrovascular events (MACCEs) was 14.9%. The results demonstrate that compared with the graft failure group, MGF, H₀, and H₁ were higher, but H₁ and P were lower in the patent group. With univariate and multivariate logistic regression analyses, the decrease in H₀ and H₁ and the increase in P were independent risk factors for graft failure, while the decrease in MGF and the increase in H₀/H₁ were only statistically significant with a univariate analysis. In the cardiovascular events group, the increase in P was an independent risk factor. With a ROC curve analysis, MGF, H₀, H₁, H₀/H₁, and P predicted graft failure, while only P predicted cardiovascular events. None of the indicators showed predictive value for MACCEs.

Conclusions: TTFM waveforms after FFT can be used to evaluate graft quality and cardiovascular events, but have no predictive value for MACCEs.

Keywords: Coronary artery bypass; fast Fourier transform (FFT); graft patency rate; major adverse cardiac and cerebrovascular events (MACCEs); waveform analysis.