Influential Factors And Predictive Model Of Futile Recanalization After Endovascular Therapy In Acute Ischemic Stroke

BackgroundEndovascular therapy has significantly improved the treatment of acute ischemic stroke.Although the recanalization rate is as high as 90%,about 50% of patients still have a poor functional outcome at 90 days,that is,futile recanalization.Some patients have complications such as malignant cerebral edema in the acute phase,and the mortality rate is extremely high.Therefore,early identification of patients at high risk of futile recanalization is of great significance.At present,there are few models used to predict the risk of futile recanalization after EVT,and the predictors reflecting the postoperative condition of the patients are generally lacking in the models.ObjectiveThis study aims to explore the independent influencing factors of futile recanalization after EVT,construct a clinically applicable risk prediction model for futile recanalization based on the independent influencing factors,and evaluate its predictive ability for early identification of high-risk patients for futile recanalization,formulate personalized perioperative treatment plans,and improve stroke outcomes.MethodsIn this retrospective study,355 patients with cerebral infarction due to anterior circulation large vessel occlusion who underwent successful recanalization by EVT in the Department of Neurointervention,Dalian Central Hospital from January 2019 to June2022 were enrolled.The clinical and imaging data of the patients were collected.According to the definition of futile recanalization,the patients were divided into futile recanalization group(n=203)and non-futile recanalization group(n=152),and the clinical data of the two groups were compared.IBM SPSS 25.0 statistical software was used for data analysis,all tests were performed by two-tailed test,and P<0.05 was statistically significant.Binary Logistic regression analysis was used to screen the independent influencing factors related to futile recanalization.The predictive ability of independent influencing factors was estimated by the area under the receiver operating characteristic curve.Spearman correlation coefficient was used to analyze the correlation between independent influencing factors and futile recanalization.Random number table was used for sampling,the enrolled patients were divided into a training set(n=237)and a validation set(n=118)at a ratio of 2:1.R language was used to construct a prediction model and draw a nomogram.The calibration degree of the model was evaluated by the calibration curve,and the discrimination degree of the model was evaluated by the area under the ROC curve of the training set and the validation set.ResultsBinary Logistic regression multivariate analysis showed that age(OR=1.049,95%CI:1.003-1.096,P=0.035),diabetes mellitus(OR=3.106,95%CI: 1.327-7.270,P=0.009),malignant cerebral edema(OR=4.870,95%CI: 1.358-17.460,P=0.015),24h-NIHSS score(OR=1.166,95%CI: 1.088-1.250,P < 0.001),postoperative NLR(OR=1.099,95%CI: 1.006-1.199,P=0.036)and postoperative D-dimer(OR=1.140,95%CI: 1.003-1.296,P=0.045)were independent influencing factors for futile recanalization.ROC curve showed that the area under the ROC curve of age was 0.676(95%CI:0.620-0.731,P < 0.001),the maximum cut-off value was 73,the specificity was 0.842,and the sensitivity was 0.468.The area under the ROC curve of diabetes mellitus was0.584(95%CI: 0.525-0.644,P=0.006),the specificity was 0.849,and the sensitivity was0.320.The area under the ROC curve of malignant cerebral edema was 0.692(95%CI:0.638-0.746,P < 0.001),the specificity was 0.961,and the sensitivity was 0.424.The area under the ROC curve of 24h-NIHSS score was 0.814(95%CI: 0.770-0.858,P < 0.001),the maximum cut-off value was 18,the specificity was 0.862,and the sensitivity was0.602.The area under the ROC curve of postoperative NLR was 0.722(95%CI: 0.667-0.776,P < 0.001),the maximum cut-off value was 7.03,the specificity was 0.690,and the sensitivity was 0.683.The area under the ROC curve of postoperative D-dimer was0.626(95%CI: 0.566-0.689,P < 0.001),the maximum cut-off value was 2.14,the specificity was 0.648,and the sensitivity was 0.566.When the above variables were combined,the area under the ROC curve was 0.882(95%CI: 0.844-0.920,P < 0.001),the specificity was 0.861,and the sensitivity was 0.769.Spearman correlation analysis showed that: Age(r=0.301,P < 0.001),diabetes mellitus(r=0.194,P < 0.001),malignant cerebral edema(r=0.434,P < 0.001),24 hNIHSS score(r=0.540,P < 0.001),postoperative NLR(r=0.378,P < 0.001),and postoperative D-dimer(r=0.216,P < 0.001)were both positively correlated with futile recanalization,and they were both independent risk factors for futile recanalization.The nomogram constructed by R language showed that the patients with advanced age,diabetes mellitus,malignant cerebral edema,high 24h-NIHSS score,high postoperative NLR and high postoperative D-dimer had a high probability of futile recanalization.A total score of the predictors variable between 52 and 118 corresponds to a futile recanalization probability of 0.1 to 0.9.The calibration curves of the training set and the validation set basically coincided with the diagonal line,indicating that the calibration of the model was favorable.The area under the ROC curve of the training set and the validation set were 0.870(95%CI:0.820-0.920,P<0.001)and 0.911(95%CI:0.854-0.968,P<0.001),indicating that the model had good discrimination.ConclusionThis study found that age,diabetes mellitus,malignant cerebral edema,24h-NIHSS score,postoperative NLR and postoperative D-dimer were independent risk factors for futile recanalization of endovascular therapy in patients with acute ischemic stroke.The model constructed in this study has favorable calibration and discrimination,which can be used to predict the risk of futile recanalization after endovascular therapy in patients with acute ischemic stroke.