Analysis of risk factors for re-occlusion after mechanical thrombectomy in patients with cerebral infarction and construction of a predictive nomogram model
Yuan Lei
Liu Yang
Wang Ming
Tao Luling
Abstract：Objective To investigate the risk factors for re-occlusion after mechanical thrombectomy in patients with cerebral infarction and to construct a Nomogram predictive model.Methods A total of 257 patients with cerebral infarction treated with mechanical thrombectomy were selected and divided into a modeling set(180 cases)and a validation set(77 cases).After a 1-year follow-up,patients in the modeling set were grouped based on the occurrence of re-occlusion,with 50 cases in the re-occlusion group and 130 cases in the non-re-occlusion group.The incidence of re-occlusion after mechanical thrombectomy was approximately 27.78%.Baseline data of patients with cerebral infarction in the modeling set were analyzed.Univariate and logistic regression analyses were employed to screen for risk factors for re-occlusion after mechanical thrombectomy in patients with cerebral infarction.A nomogram predictive model was constructed using R software,and a goodness-of-fit test was performed.Results Statistically significant differences were observed between the two groups of patients with cerebral infarction in terms of the infarction site,time from onset to recanalization,presence of comorbid diabetes,D-dimer levels,platelet count,and white blood cell count(all P<0.05).The infarction site,time from onset to recanalization,comorbid diabetes,D-dimer levels,and white blood cell count were identified as risk factors for re-occlusion after mechanical thrombectomy in patients with cerebral infarction(all P<0.05).The nomogram model demonstrated excellent performance in predicting the risk of re-occlusion after mechanical thrombectomy in patients with cerebral infarction,with a C-index of 0.991(95%CI:0.982-1.000).The calibration curve indicated high consistency between observed and predicted data.The decision curve showed that the model effectively provided standardized net benefits when the risk threshold was set at>0.14.Additionally,the validation set demonstrated good predictive accuracy of the model.Conclusions The infarction site,time from onset to recanalization,comorbid diabetes,D-dimer levels,and white blood cell count all influence the occurrence of re-occlusion after mechanical thrombectomy in patients with cerebral infarction.The nomogram model constructed based on these risk factors exhibits good predictive performance.
Keywords：cerebral infarctionmechanical thrombectomyre-occlusionrisk factorspredictive modeling
Publication Date：2025-07-20
Online Publishing Date：2025-08-21(First online date of this platform, not the publication date of the document)
Pages：6( 402-407 )