Integrating ocular and clinical features to enhance intravenous glucocorticoid response prediction in thyroid eye disease: a machine learning approach.

Abstract

Purposes: Predicting intravenous glucocorticoid (IVGC) efficacy in thyroid eye disease (TED) is vital for personalized treatment and minimizing side effects. Current methods haven't fully utilized ocular features. This study aims to integrate ocular features into predictive model to assess their impact on improving IVGC efficacy prediction.

Methods: This retrospective study recruited 130 TED patients who received 4.5 g of IVGC treatment and collected their clinical features. After Least Absolute Shrinkage and Selection Operator (LASSO) regression for feature selection, two key features, lid aperture and CAS, were identified and incorporated into a predictive model. Subsequently, five ocular features were added, resulting in a model using both clinical and ocular features. Six machine learning classifiers were tested on both models, and the performances of two models were compared. The best-performing predictive model was analyzed using SHapley Additive exPlanations (SHAP) to interpret the model.

Results: In the LASSO regression, CAS and lid aperture were selected as key features for predicting IVGC efficacy. In the model using only clinical features, the best-performing classifier was Logistic Regression, with an AUC of 0.701. However, when ocular features were incorporated, the XGBoost classifier outperformed all others, with the AUC improving to 0.821. SHAP analysis further indicated that conjunctival edema was the most important feature for prediction.

Conclusions: This study identified features associated with the prediction of IVGC efficacy and demonstrated that incorporating ocular features into clinical parameters improves the ability to predict treatment outcomes. Additionally, SHAP analysis highlighted the importance of ocular features in predicting treatment efficacy, providing a basis for further mechanistic exploration.