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The International Journal of Cardiovascular Imaging

, Volume 32, Issue 6, pp 871–883

First Online: 22 February 2016Received: 22 December 2015Accepted: 08 February 2016


The degree of stent-scaffold embedment could be a surrogate parameter of the vessel wall-stent-scaffold interaction and could have biological implications in the vascular response. We have developed a new specific software for the quantitative evaluation of embedment of struts by optical coherence tomography OCT. In the present study, we described the algorithm of the embedment analysis and its reproducibility. The degree of embedment was evaluated as the ratio of the embedded part versus the whole strut height and subdivided into quartiles. The agreement and the inter- and intra-observer reproducibility were evaluated using the kappa and the interclass correlation coefficient ICC. A total of 4 pullbacks of OCT images in 4 randomly selected coronary lesions with 3.0 × 18 mm devices 2 lesions with Absorb BVS and 2 lesions with XIENCE both from Abbott Vascular, Santa Clara, CA, USA from Absorb Japan trial were evaluated by two investigators with QCU-CMS software version 4.69 Leiden University Medical Center, Leiden, The Netherlands. Finally, 1481 polymeric struts in 174 cross-sections and 1415 metallic struts in 161 cross-sections were analyzed. Inter- and intra-observer reproducibility of quantitative measurements of embedment ratio and categorical assessment of embedment in Absorb BVS and XIENCE had excellent agreement with ICC ranging from 0.958 to 0.999 and kappa ranging from 0.850 to 0.980. The newly developed embedment software showed excellent reproducibility. Computer-assisted embedment analysis could be a feasible tool to assess the strut penetration into the vessel wall that could be a surrogate of acute injury caused by implantation of devices.

KeywordsStrut embedment Polymeric scaffold Metallic stent Reproducibility  Download fulltext PDF

Autor: Yohei Sotomi - Hiroki Tateishi - Pannipa Suwannasom - Jouke Dijkstra - Jeroen Eggermont - Shengnan Liu - Erhan Tenekecioglu

Fuente: https://link.springer.com/

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