AIM: To analyze the influencing factors of cognitive dysfunction in patients with open angle glaucoma, and construct a risk prediction decision tree model. METHODS:Retrospective study. The clinical data of patients with open angle glaucoma treated in the hospital from February 2022 to November 2024 were collected, and patients were divided into a cognitive dysfunction group and a cognitive function normal group according to the evaluation results of the Montreal Cognitive Assessment Scale(MoCA)at the time of admission, and the clinical data of the two groups were compared. The influencing factors of cognitive dysfunction in patients with open angle glaucoma were analyzed through Logistic regression model, and the decision tree model was analyzed and constructed based on the clinical data of the two groups through the decision tree CHAID algorithm, and the predictive performance of two models was compared using receiver operating characteristic(ROC)curves. RESULTS:The total of 179 patients with open angle glaucoma were included in this study, and were divided into a cognitive dysfunction group of 107 cases(59 males and 48 females, with 66 cases aged ≥60 y)and a cognitive function normal group of 72 cases(34 males and 38 females, with 28 cases aged ≥60 y)according to the MoCA assessment results at the time of patient admission.The incidence of cognitive dysfunction was 59.8%(107/179). The proportion of age ≥60y, education level of junior high school or below, hypertension, sleep disorders, and middle/late stage diseases in the cognitive dysfunction group were higher than those in the cognitive function normal group(all P<0.05). Logistic regression analysis showed that age ≥60 y, education level of junior high school or below, hypertension, sleep disorders, and middle/late stage disease were all risk factors for cognitive dysfunction in patients with open angle glaucoma(all P<0.05). The risk prediction decision tree model constructed using the decision tree CHAID algorithm included 4 layers and 11 nodes, and it outputed 5 risk variables: disease severity, age, sleep disorders, education level, and hypertension. Among them,the disease severity was the first layer risk variable. The ROC curve showed that the area under curve for predicting cognitive dysfunction in patients with open angle glaucoma using the risk prediction decision tree model and logistic regression model was 0.849 and 0.842 respectively, and there was no statistically significant difference in the predictive value between the two models(P>0.05). CONCLUSION: The disease severity, age, sleep disorders, education level, and hypertension are influencing factors for cognitive dysfunction in patients with open angle glaucoma, and the risk prediction decision tree model analyzed and constructed by the decision tree CHAID algorithm has good predictive value for cognitive dysfunction.

Humans ; Dermatomyositis/pathology* ; Interferon-Induced Helicase, IFIH1/genetics* ; Lung Diseases, Interstitial ; Autoantibodies ; Retrospective Studies