Objective:To construct and validate a predictive model for the occurrence of malignant cerebral edema(MCE)in the elderly with acute large hemispheric infarction(LHI)of the anterior cerebral artery.Methods:Clinical, laboratory and imaging data of 301 elderly patients with acute LHI of the anterior cerebral artery admitted to the Department of Neurology of the Third Affiliated Hospital of Soochow University between January 2018 and April 2023 were retrospectively analyzed.Patients were divided into a modeling group(211 cases)and a validation group(90 cases)by the simple random sampling method with a ratio of 7∶3.According to the occurrence of MCE, univariate and multivariate Logistic regression analyses were performed with data from the modeling group to screen for independent predictors of the development of MCE.Nomograms were created and internally validated using R software.Additionally, external validation was performed with data from the validation group, and the performance of the model was assessed by receiver operating characteristic(ROC)curves, calibration plots, and clinical decision curve analysis(DCA), respectively.Results:The MCE incidence and baseline data between the modeling and validation groups were not statistically significantly different and were actually comparable.Multivariate Logistic analysis in the modeling group showed that a history of atrial fibrillation( OR=3.459, 95% CI: 1.202-9.955, P=0.021), Acute Physiology and Chronic Health Evaluation Ⅱ(APACHE Ⅱ)score( OR=1.202, 95% CI: 1.052-1.373, P=0.007), National Institutes of Health Stroke Scale(NIHSS)score( OR=1.163, 95% CI: 1.039-1.3013, P=0.008), Alberta Stroke Program Early CT Score(ASPECTS)( OR=0.782, 95% CI: 0.639-0.958, P=0.018), and collateral score(CS)( OR=0.414, 95% CI: 0.221-0.777, P=0.006)were independent predictors of the occurrence of MCE in the elderly patients with LHI.Based on the nomogram model constructed using the independent predictors, the ROC value for the risk of developing MCE was 0.912(95% CI: 0.867-0.957)in the modeling group and 0.957(95% CI: 0.902-0.997)in the validation group.The predicted probabilities from the nomograms in the modeling and validation groups were close to the actual probabilities, indicating good calibration.The DCA curves in the validation group showed that the predictive model had good clinical utility. Conclusions:The nomogram model established in this study exhibits good discrimination and calibration for the prediction of MCE, and has some predictive value.