AUTHOR=Zhou Yangbin , Zhou Yitao , Yang Huijie , Wang Xiaoyan , Zhang Xiping , Huang Ganying 

TITLE=Predictive role of a combined model for futile recanalization in acute ischemic stroke: a retrospective cohort study

JOURNAL=Frontiers in Neurology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1566842

DOI=10.3389/fneur.2025.1566842

ISSN=1664-2295

ABSTRACT=ObjectiveThere is a lack of data regarding patients with acute ischemic stroke caused by large vessel occlusions (LVOs) undergoing mechanical thrombectomy (MT) and their predictors of futile recanalization (FR). We sought to investigate the predictors of FR in patients with AIS-LVO undergoing mechanical thrombectomy.MethodA retrospective analysis was conducted on 229 acute AIS patients who received MT, after eliminating the 31 patients not meet the requirements. The patients were categorized into the FR group and the useful recanalization (UR) group. Multivariate logistic regression analysis was used to explore the factors that influence FR after mechanical thrombectomy. ROC curve was used to plot the ability to predict FR after MT, and then the combined model was constructed and evaluate the predictive ability of this model to FR.Results198 patients who achieved successful recanalization were included in the analysis, of whom 124 experienced UR and 74 experienced FR. Patients with FR had higher Baseline NIHSS; they were more frequently on hypertension history and had longer door-to-puncture time (DPT) and door-to-recanalization time (DRT). Multivariable regression analysis showed that the hypertension history, Admission NIHSS, Admission DBP, Admission blood glucose, ischemic core, and DPT were associated with an increased probability of FR. The combined model was better than the models alone in predicting the risk of FR.ConclusionAdmission blood pressure, admission NIHSS scores, admission DBP, ischemic core and DPT are independent risk factors for FR after MT in patients with AIS, and the combined model established by them has high predictive efficacy for FR risk after MT.