A Risk Prediction Model for Normal-Tension Glaucoma Progression Integrating Genetic Markers and Corneal Biomechanics.

Abstract

Objective: Normal-tension glaucoma (NTG) is characterized by progressive optic nerve damage despite intraocular pressure remaining consistently within the normal range. Predicting disease progression in patients with confirmed NTG remains challenging. This study aimed to develop and validate an interpretable machine learning model integrating genetic risk scores and corneal biomechanical parameters to predict progression risk in patients with NTG, identify independent predictors, and quantify the contribution of individual features to support precision clinical management.

Methods: A total of 342 patients with NTG were consecutively enrolled at a tertiary hospital and randomly allocated to training set (n = 238) and validation set (n = 104) at a ratio of 7:3. Baseline characteristics and six core indicators were collected. Candidate predictors were selected through univariate analysis and least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation and the λ-1se criterion. Independent predictors were subsequently identified using multivariable logistic regression. Three machine learning models-random forest (RF), support vector machine (SVM), and logistic regression (LR)-were developed. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) analysis was performed to interpret feature contributions.

Results: Univariate analysis revealed significant differences in all six indicators between the progression and non-progression groups (p < .05). Multivariable logistic regression further confirmed that all six indicators were independently associated with NTG progression (p < .05). The RF model demonstrated the best predictive performance, with an AUC of 0.760 (95% confidence interval (CI) : 0.678-0.842) in the training set and 0.747 (95% CI: 0.623-0.871) in the validation set. It outperformed both the SVM model (training AUC = 0.704; validation AUC = 0.694) and the LR model (training AUC = 0.742; validation AUC = 0.729). SHAP analysis ranked the features, in descending order of contribution, as mean retinal nerve fibre layer (RNFL) thickness, first applanation velocity, visual field mean deviation, relative tear GNAI1 level, polygenic risk score for NTG, and relative tear PRDX4 level. The calibration curves showed good agreement between predicted and observed probabilities, while DCA demonstrated a high clinical net benefit across a broad range of threshold probabilities.

Conclusion: A model integrating corneal biomechanical parameters, genetic risk scores, and tear molecular biomarkers was developed to predict the risk of NTG progression and demonstrated potential clinical utility. This model may provide a quantitative reference for risk stratification and personalised management in patients with NTG.