Abstract: Objective: To analyze factors influencing psychological distress in patients with intracranial aneurysms (IA) after interventional embolization,and construct a nomogram prediction model. Methods: Using convenience sampling method,126 IA patients undergoing interventional embolization were selected as the study subjects.Univariate analysis and multivariate logistic regression analysis were utilized to identify factors influencing psychological distress in IA patients after interventional embolization,and a nomogram prediction model was constructed.The ROC curve was used to evaluate the efficacy of the prediction model. Results: The psychological distress score of IA patients after interventional embolization was (5.49±1.76) points,and 87 patients (69.05%) had significant psychological distress.Univariate analysis revealed that there were statistically significant differences in age,combined chronic diseases,educational level,average monthly household income,and activity of daily living (ADL) score between patients with significant psychological distress and those without significant psychological distress (P<0.05 to P<0.01).Multivariate logistic regression analysis showed that the presence of combined chronic diseases and an increase in ADL score were risk factors for psychological distress in IA patients after interventional embolization (P<0.01 and P<0.05);age,educational level,and average monthly household income were protective factors for psychological distress (P<0.05).Based on these influencing factors,a visual nomogram risk prediction model was constructed.The area under the ROC curve of the model was 0.823 (95%CI:0.747-0.898,P<0.01),with the sensitivity of 66.7% and specificity of 84.6%.Internal validation of the model showed that C-index was 0.842,and the calibration curve fitted well with the ideal curve. Conclusions: Age,combined chronic diseases,educational level,average monthly household income,and ADL score are influencing factors of psychological distress in IA patients after interventional embolization.The risk prediction model constructed based on these influencing factors exhibits good efficacy and can help identify high-risk patients as early as possible.