Multivariable prediction model to estimate the probability of restenosis at proximal edge after 2nd-generation drug-eluting-stent implantation: development and internal validation using a quantitative coronary angiography from the post-marketing surveillance studies of everolimus-eluting stent in Japan

Edge restenosis has still been reported after second-generation drug-eluting stent (DES) implantation. It was more likely attributable to post-procedural angiographic results than to the patient’s background. The aim of this study was to develop and internally validate a prediction model for restenosis in proximal edge after 2nd-generation DES stent implantation using angiographic data. Data were obtained from several post-marketing surveillance (PMS) studies of the cobalt–chromium everolimus-eluting stent (CoCr-EES) and platinum–chromium everolimus-eluting stent (PtCr-EES), second-generation DES, in Japan. Angiographic analysis was conducted at baseline and after 8 or 12 months. We focused on the proximal edge of angiographic analysis. The main outcome was restenosis defined as ≥ 50% diameter stenosis at follow-up. The predictive performance of the prediction model based on multivariable logistic regression was assessed in terms of discrimination and calibration, which were internally validated by the bootstrap method. We also performed decision curve analysis to assess threshold of predicted probability of restenosis at which additional intervention was considered. Among 2053 lesions in 1860 patients, restenosis rates in proximal edge was 2.8%. The final model was constructed with % post-procedural diameter stenosis (DS) and post-procedural reference diameter (RD) as strong predictors for edge restenosis. Discrimination and calibration were satisfactory with optimism-corrected C-statistics 0.75. Predicted probability between 0.03 and 0.24 was preferable threshold for restenosis treatments. Our prediction model can be used to obtain valid prediction for restenosis in proximal edge, assisting to know complete stent coverage of lesion.