Identification of independent risk factors for restenosis following bare-metal stent implantation: Role of bare-metal stents in the era of drug-eluting stents

Abstract

In the era of drug-eluting stents (DESs), the ability of clinicians to predict which patients have a low risk of coronary restenosis following bare-metal stent (BMS) implantion is likely to be of beneft. The study population consisted of 2,711 patients who underwent BMS implantation in 3,770 lesions between 1995 and 2004. With clinical and 6 month follow-up angiographic data, we retrospectively sought to identify the independent risk predictors of restenosis, applied a previously proposed prediction model and assessed the characteristics of patients with a low likelihood of coronary restenosis within 6 months of BMS implantation. A 6-month follow-up coronary angiography was performed in 65.0% of the patients who had undergone the BMS implantation and the rate of restenosis was 26.6%. Using multivariate analysis, diabetes [odds ratio (OR), 1.294; 95% confidence interval (CI), 1.094-1.483; P=0.005], current smoking (OR, 1.294; 95% CI, 1.094-1.483; P=0.002), a reference vessel diameter of <3.25 mm (OR, 1.238; 95% CI, 1.021-1.501; P<0.001), a lesion length of >30 mm (OR, 1.645; 95% CI, 1.336-2.026; P<0.001), ostial lesion (OR, 1.858; 95% CI, 1.437-2.402; P<0.001), post-stenting minimal luminal diameter (OR, 0.576; 95% CI, 0.484-0.685; P<0.001) and bifurcation lesion (OR, 1.353; 95% CI, 1.070-1.711; P=0.012) were identified as significant independent predictors of restenosis. However, the accuracy of the prediction obtained with the current model, which used the clinical and angiographic variables correlated with the risk of restenosis, was poor. Various clinical and angiographic independent risk variables were revealed to be correlated with the risk of restenosis following BMS implantation in the present large dataset. Certain groups of patients with a relatively low risk of restenosis may be considered for BMS implantation as an alternative to DESs. However, the prediction models used at present are incomplete and further studies are required.