Abstract:AIM:To construct a risk prediction model for postoperative dry eye in glaucoma patients using the XGBoost algorithm.
METHODS:A retrospective analysis was performed on glaucoma patients who received surgical treatment at the hospital from July 2022 to June 2025. All patients were divided into a dry eye group and a non-dry eye group according to the occurrence of postoperative dry eye disease. Clinical data of the patients were collected, and univariate and multivariate logistic regression analyses were employed to screen out the risk factors for postoperative dry eye. The patients were randomly allocated into a training set and a validation set at a ratio of 7:3. An XGboost risk prediction model was built with the risk factors as feature variables, and the SHapley Additive exPlanations(SHAP)bar plot and beeswarm plot were used for visual interpretation of the model. The predictive efficacy of the model was evaluated via receiver operating characteristic(ROC)curve analysis.
RESULTS:The study included 300 glaucoma patients(300 eyes). The non-dry eye group comprised 204 patients(204 eyes, 104 males and 100 females), and the dry eye group comprised 96 patients(96 eyes, 55 males and 41 females). The incidence rate of postoperative dry eye was 32.0%. Univariate analysis revealed statistically significant differences between the two groups in terms of age, comorbid diabetes, meibum viscosity grade, tear film breakup time, meibomian gland dysfunction, and operative time(all P<0.05). Multivariate logistic regression analysis showed that all the above factors were risk factors for postoperative dry eye(all P<0.01). The XGBoost model showed that these risk factors were ranked in descending order of predictive importance as: tear film breakup time, comorbid diabetes, operative time, age, meibomian gland dysfunction, and meibum viscosity grade. ROC curve analysis demonstrated that the area under the curve(AUC)of the XGboost model was 0.84(95%CI: 0.78-0.90)for the training set and 0.83(95%CI: 0.74-0.92)for the validation set, with both values showing statistical significance(both P<0.05).
CONCLUSION:The XGboost algorithm-based risk prediction model for postoperative dry eye in glaucoma patients exhibits favorable predictive performance. It can be clinically applied to identify patients at high risk of developing postoperative dry eye, thereby facilitating targeted interventions for preventive purposes.
