Objective To construct a nomogram model for predicting lower extremity deep vein throm-bosis(DVT)during hospitalization in the patients undergoing mechanical thrombectomy due to acute ische-mic stroke.Methods A total of 901 patients with acute anterior circulation large vessel occlusion undergoing mechanical thrombectomy in the hospital from January 1,2017 to January 1,2024 were selected as the study subjects and divided into the lower extremity DVT group(n=112)and non-DVT group(n=789)according to whether DVT occurred after surgery.The observation indicators included the clinically relevant data,perio-perative related indicators and related laboratory indicators.The multivariate logistic regression was used to analyze the relevant influencing factors,and then the nomogram model was established.The receiver operating characteristic(ROC)curve and area under the curve(AUC)were used to analyze the predictive efficiency of the model.The clinical benefit of the predictive model was assessed by the clinical decision curve analysis(DCA)curve.Results There were statistically significant differences in the age,NIHSS score at admission,history of diabetes mellitus,history of smoking and history of DVT between the two groups(P<0.05).There were statistically significant differences in the time from onset to femoral artery puncture,time from onset to admission,time from femoral artery puncture to revascularization and postoperative complicating pul-monary infection between the two groups(P<0.05).There were statistically significant differences in D-di-mer,venous blood glucose and PLT at admission between the two groups(P<0.05).The multivariate logis-tic regression analysis results showed that the NIHSS score at admission,diabetes history,age,D-dimer,time from onset to femoral artery puncture and postoperative complicating pulmonary infection were the independ-ent influencing factors for lower extremity DVT during hospitalization in the patients with acute ischemic stroke treated with mechanical thrombectomy(P<0.05).The ROC curve and Bootstrap method verification results all showed that the nomogram predictive ability was strong.The DCA curve showed that when the threshold value was 0.12-0.96,the clinical benefit and applicability of the model were the best.Conclusion The constructed nomogram model can better predict the clinical outcome of the patients,and has a wide range of clinical applicability.