Radial wall strain: a novel angiographic measure of plaque composition and vulnerability

Huihong Hong; Chunming Li; Juan Luis Gutiérrez-Chico; Zhiqing Wang; Jiayue Huang; Miao Chu; Takashi Kubo; Lianglong Chen; William Wijns; Shengxian Tu

doi:10.4244/EIJ-D-22-00537

Radial wall strain: a novel angiographic measure of plaque composition and vulnerability

EuroIntervention. 2022 Sep 8;18(12):1001-1010. doi: 10.4244/EIJ-D-22-00537. Online ahead of print.

Authors

Huihong Hong^{1

2}, Chunming Li², Juan Luis Gutiérrez-Chico³, Zhiqing Wang¹, Jiayue Huang⁴, Miao Chu², Takashi Kubo⁵, Lianglong Chen¹, William Wijns⁴, Shengxian Tu^{1

2}

Affiliations

¹ Department of Cardiology, Fujian Medical University Union Hospital, Fuzhou, China.
² Biomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
³ Department of Cardiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
⁴ The Lambe Institute for Translational Medicine, The Smart Sensors Laboratory and Curam, National University of Ireland Galway, Galway, Ireland.
⁵ Department of Cardiovascular Medicine, Wakayama Medical University, Wakayama, Japan.

Abstract

Background: The lipid-to-cap ratio (LCR) and thin-cap fibroatheroma (TCFA) derived from optical coherence tomography (OCT) are indicative of plaque vulnerability.

Aims: We aimed to explore the association of a novel method to estimate radial wall strain (RWS) from angiography with plaque composition and features of vulnerability assessed by OCT.

Methods: Anonymised data from patients with intermediate stenosis who underwent coronary angiography (CAG) and OCT were analysed in a core laboratory. Angiography-derived RWS_max was computed as the maximum deformation of lumen diameter throughout the cardiac cycle, expressed as a percentage of the largest lumen diameter. The LCR and TCFA were automatically determined on OCT images by a recently validated algorithm based on artificial intelligence.

Results: OCT and CAG images from 114 patients (124 vessels) were analysed. The average time for the analysis of RWS_max was 57 (39-82) seconds. The RWS_max in the interrogated plaques was 12% (10-15%) and correlated positively with the LCR (r=0.584; p<0.001) and lipidic plaque burden (r=0.411; p<0.001), and negatively with fibrous cap thickness (r= -0.439; p<0.001). An RWS_max >12% was an angiographic predictor for an LCR >0.33 (area under the curve [AUC]=0.86, 95% confidence interval [CI]: 0.78-0.91; p<0.001) and TCFA (AUC=0.72, 95% CI: 0.63-0.80; p<0.001). Lesions with RWS_max >12% had a higher prevalence of TCFA (22.0% versus 1.5%; p<0.001), thinner fibrous cap thickness (71 μm versus 101 μm; p<0.001), larger lipidic plaque burden (23.3% versus 15.4%; p<0.001), and higher maximum LCR (0.41 versus 0.18; p<0.001) compared to lesions with RWS_max ≤12%.

Conclusions: Angiography-derived RWS was significantly correlated with plaque composition and known OCT features of plaque vulnerability in patients with intermediate coronary stenosis.