Factors affecting in-stent restenosis after angioplasty with the Enterprise stent for intracranial atherosclerotic diseases.

This study investigated factors affecting the safety and in-stent restenosis after intracranial stent angioplasty using the Enterprise stent for symptomatic intracranial atherosclerotic stenosis. Between January 2017 and March 2019, patients with intracranial atherosclerotic stenosis treated with Enterprise stent angioplasty were enrolled, including 400 patients in the modeling group and 89 patients in the validation group. The clinical factors affecting in-stent restenosis after Enterprise stent angioplasty in the modeling group were analyzed, and a logistic regression model of these factors was established and validated in the validation group. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) were analyzed. In the modeling group with 400 patients, there were 410 lesions, including 360 stenotic lesions and 50 occluded lesions, with 176 (42.9%) lesions in the anterior circulation and 234 (57.1%) in the posterior circulation. Successful stenting was performed in 398 patients (99.5%). Stenosis was significantly (P < 0.05) improved after stenting compared with before stenting (27.7% ± 2.9% vs. 77.9% ± 8.0%). Periprocedural complications included ischemic stroke (3.25%), hemorrhagic stroke (0.75%), and death (0.50%), with a total periprocedural complication rate of 4.0%. The first follow-up angiography was performed in 348 (87.0%) patients with 359 lesions 3.5-14 months (mean 5.7 months) after stenting. In-stent restenosis occurred in 62 (17.3%) lesions, while the other 295 (82.7%) had no restenosis. Lesion location, calcification degree, balloon expansion pressure, residual stenosis, intraprocedural dissection, and cerebral blood flow TICI grade were significant (P < 0.05) risk factors for in-stent restenosis. The in-stent restenosis prediction model was established as follows: P = 1/[1 + e]. In the validation group, the AUC in the ROC curve analysis was 0.902 (95% CI: 0.836-0.969), and when the cutoff value was 0.50, the sensitivity and specificity of this model were shown to be 76.92% and 80.26%, respectively, in predicting in-stent restenosis at angiographic follow-up, with a total coincidence rate of 79.78%. In conclusion, in-stent restenosis after intracranial Enterprise stenting is affected by stenosis location, calcification, balloon inflation pressure, intraprocedural arterial dissection, residual stenosis, and cerebral flow grade, and establishment of a logistic model with these factors can effectively predict in-stent restenosis.