Establishment and validation of a clinical prediction model for in-stent restenosis after intracranial and extracranial stent implantation.

This study aims to analyze the risk factors for in-stent restenosis in patients who have undergone successful cerebral artery stent implantation and to develop a nomogram-based predictive model. We utilized data retrospectively collected from 488 patients at Hebei Provincial People's Hospital between April 2019 and March 2024. After applying the inclusion criteria, 390 patients were further analyzed and divided into a training group (n = 274) and a validation group (n = 116). In the training group, we used univariate and multivariate logistic regression to identify independent risk factors for stroke recurrence and then created a nomogram. The nomogram's discrimination and calibration were assessed by examining various metrics, including the concordance index (C-index), the area under the Receiver Operating Characteristic (ROC) curve (AUC), and calibration plots. Decision curve analysis (DCA) was employed to evaluate the clinical utility of the nomogram by quantifying the net benefit for patients at different probability thresholds. The nomogram for predicting in-stent restenosis in patients undergoing cerebral artery stenting included seven variables: triglyceride-glucose index (TyG), presence of Diabetes Mellitus, postoperative dual antiplatelet therapy, body mass index (BMI), and preoperative MRS score. The C-index (0.807 for the training cohort and 0.804 for the validation cohort) indicated satisfactory discriminative ability of the nomogram. Furthermore, DCA indicated a clinical net benefit from the nomogram. The predictive model constructed includes six predictive factors: TyG, presence of Diabetes Mellitus, postoperative dual antiplatelet therapy, BMI, and preoperative MRS score. The model demonstrates good predictive ability and can be utilized to predict ischemic stroke recurrence in patients with symptomatic ICAS after successful stent placement.