BACKGROUND: Struts have been considered as covered when tissue overlying the struts is >0 μm by optical coherence tomography (OCT). However, there is no confirmatory study to validate this definition by histology which is the gold standard. The aim of the present study was to assess the appropriate cutoff value of neointimal thickness of stent strut coverage by OCT with histology confirmation. METHODS: We performed ex vivo OCT imaging of human coronary arteries with stents at autopsy. A total of 46 stents in 39 vessels from 25 patients were examined in this study, and a total of 165 cross-sectional images were co-registered with histology to determine the optimal cutoff value for strut coverage by OCT which was defined as luminal endothelial cells with 2 abluminal layers of smooth muscles cells and matrix. Considering the resolution of OCT is 10 to 20 μm, the cutoff values were assessed at ≥20, ≥40, and ≥60 μm. RESULTS: A total of 2235 struts were reviewed by histology, 1216 were considered as well-matched struts which were analyzed in this study. By histology, 160 struts were identified as uncovered, while 1056 struts were covered. The OCT assessment without consideration of neointimal thickness yielded a poor specificity of 37.5% and sensitivity 100%. Of 3 cutoff values, the cutoff value of ≥40 μm yielded the best sensitivity (99.3%), specificity (91.0%), positive predictive value (98.6%), and negative predictive value (95.6%) as compared with ≥20 and ≥60 μm. CONCLUSIONS: Neointimal thickness ≥40 μm by OCT yielded the most accurate cutoff value to identify stent strut coverage validated by histology.
BACKGROUND: Struts have been considered as covered when tissue overlying the struts is >0 μm by optical coherence tomography (OCT). However, there is no confirmatory study to validate this definition by histology which is the gold standard. The aim of the present study was to assess the appropriate cutoff value of neointimal thickness of stent strut coverage by OCT with histology confirmation. METHODS: We performed ex vivo OCT imaging of human coronary arteries with stents at autopsy. A total of 46 stents in 39 vessels from 25 patients were examined in this study, and a total of 165 cross-sectional images were co-registered with histology to determine the optimal cutoff value for strut coverage by OCT which was defined as luminal endothelial cells with 2 abluminal layers of smooth muscles cells and matrix. Considering the resolution of OCT is 10 to 20 μm, the cutoff values were assessed at ≥20, ≥40, and ≥60 μm. RESULTS: A total of 2235 struts were reviewed by histology, 1216 were considered as well-matched struts which were analyzed in this study. By histology, 160 struts were identified as uncovered, while 1056 struts were covered. The OCT assessment without consideration of neointimal thickness yielded a poor specificity of 37.5% and sensitivity 100%. Of 3 cutoff values, the cutoff value of ≥40 μm yielded the best sensitivity (99.3%), specificity (91.0%), positive predictive value (98.6%), and negative predictive value (95.6%) as compared with ≥20 and ≥60 μm. CONCLUSIONS: Neointimal thickness ≥40 μm by OCT yielded the most accurate cutoff value to identify stent strut coverage validated by histology.
Authors: Makoto Araki; Seung-Jung Park; Harold L Dauerman; Shiro Uemura; Jung-Sun Kim; Carlo Di Mario; Thomas W Johnson; Giulio Guagliumi; Adnan Kastrati; Michael Joner; Niels Ramsing Holm; Fernando Alfonso; William Wijns; Tom Adriaenssens; Holger Nef; Gilles Rioufol; Nicolas Amabile; Geraud Souteyrand; Nicolas Meneveau; Edouard Gerbaud; Maksymilian P Opolski; Nieves Gonzalo; Guillermo J Tearney; Brett Bouma; Aaron D Aguirre; Gary S Mintz; Gregg W Stone; Christos V Bourantas; Lorenz Räber; Sebastiano Gili; Kyoichi Mizuno; Shigeki Kimura; Toshiro Shinke; Myeong-Ki Hong; Yangsoo Jang; Jin Man Cho; Bryan P Yan; Italo Porto; Giampaolo Niccoli; Rocco A Montone; Vikas Thondapu; Michail I Papafaklis; Lampros K Michalis; Harmony Reynolds; Jacqueline Saw; Peter Libby; Giora Weisz; Mario Iannaccone; Tommaso Gori; Konstantinos Toutouzas; Taishi Yonetsu; Yoshiyasu Minami; Masamichi Takano; O Christopher Raffel; Osamu Kurihara; Tsunenari Soeda; Tomoyo Sugiyama; Hyung Oh Kim; Tetsumin Lee; Takumi Higuma; Akihiro Nakajima; Erika Yamamoto; Krzysztof L Bryniarski; Luca Di Vito; Rocco Vergallo; Francesco Fracassi; Michele Russo; Lena M Seegers; Iris McNulty; Sangjoon Park; Marc Feldman; Javier Escaned; Francesco Prati; Eloisa Arbustini; Fausto J Pinto; Ron Waksman; Hector M Garcia-Garcia; Akiko Maehara; Ziad Ali; Aloke V Finn; Renu Virmani; Annapoorna S Kini; Joost Daemen; Teruyoshi Kume; Kiyoshi Hibi; Atsushi Tanaka; Takashi Akasaka; Takashi Kubo; Satoshi Yasuda; Kevin Croce; Juan F Granada; Amir Lerman; Abhiram Prasad; Evelyn Regar; Yoshihiko Saito; Mullasari Ajit Sankardas; Vijayakumar Subban; Neil J Weissman; Yundai Chen; Bo Yu; Stephen J Nicholls; Peter Barlis; Nick E J West; Armin Arbab-Zadeh; Jong Chul Ye; Jouke Dijkstra; Hang Lee; Jagat Narula; Filippo Crea; Sunao Nakamura; Tsunekazu Kakuta; James Fujimoto; Valentin Fuster; Ik-Kyung Jang Journal: Nat Rev Cardiol Date: 2022-04-21 Impact factor: 49.421