| Objective: To construct a Nomogram model for predicting the risk of cognitive impairment in patients with acute cerebral infarction (ACI). Methods: The clinical data of 279 patients with ACI admitted to our hospital from September 2020 to March 2023 were retrospectively analyzed. They were divided into the modeling group (n=186) and the verification group (n=93) according to the 2:1 ratio, and they were divided into the cognitive dysfunction group and normal cognitive function group according to the Montreal Cognitive Assessment score. Multivariate Logistic regression analysis was used to analyze the risk factors of cognitive dysfunction in ACI patients, and a risk prediction Nomogram was constructed based on this. The Bootstrap method was used to verify the Nomogram model, and the calibration curve was drawn to evaluate the calibration of the Nomogram model. The receiver operating characteristic (ROC) curve was drawn to analyze the predictive efficacy of the Nomogram model, and the decision curve was drawn to verify the clinical net benefit rate of the model. Results: Hypertension, diabetes, large atherosclerotic stroke, multiple cerebral infarction, leukoaraiosis, National Institutes of Health Stroke Scale (NIHSS) score > 10 at admission, and high level of homocysteine (Hcy) and high level ofC-reactive protein (CRP) at admission were risk factors for cognitive dysfunction in ACI patients (P<0.05). The Nomogram model constructed based on the above influencing factors was verified by Bootstrap method, and the consistency indexes of the modeling group and the verification group were 0.833 and 0.821, respectively, and the calibration curve and ideal curve fitting were both good. The results of ROC curve showed that the area under the curve of the Nomogram of the modeling group and the validation group for predicting cognitive dysfunction in ACI patients was 0.898 and 0.852, respectively. The decision curve showed that there were a high net benefit value when the risk threshold probability of the modeling group was between 1% and 74%, and the risk threshold probability of the validation group was between 1% and 80%. Conclusion: Hypertension, diabetes, large atherosclerosis stroke, multiple cerebral infarction, leukoaraiosis, NIHSS score>10 at admission, high level of Hcy and CRP at admission are all factors that affect the cognitive dysfunction of ACI patients. The Nomogram prediction model constructed on this basis has good calibration, prediction efficiency and clinical application effect. |