Investigating the Influence Factors of Quality of Life in Thyroid Eye Disease: Insights from Machine Learning Approaches.

Abstract

Aims: Thyroid eye disease (TED) is an autoimmune orbital disorder that diminishes the quality of life (QOL) for affected individuals. The Graves' ophthalmopathy (GO)-QOL questionnaire effectively assesses TED's impact on patients. This study aims to investigate factors influencing visual functioning (QOL-VF) and physical appearance (QOL-AP) scores in Chinese TED patients using innovative data analysis methods.

Methods: This cross-sectional study included 211 TED patients whose initial visit to our clinic was from July 2022 to March 2023. Patients with previous ophthalmic surgery or concurrent severe diseases were excluded. GO-QOL questionnaires, detailed medical histories, and clinical examinations were collected. The distribution of GO-QOL scores was analyzed, and linear regression and machine learning algorithms were utilized.

Results: The median QOL-VF and QOL-AP scores were 64.29 and 62.5, respectively. Multivariate linear regression analysis revealed age (p=0.013), ocular motility pain (p=0.012), vertical strabismus (p<0.001), and diplopia scores as significant predictors for QOL-VF. For QOL-AP, gender (p=0.013) and clinical activity (p=0.086) were significant. The XGBoost model demonstrated superior performance, with an R2 of 0.872 and an Root Mean Square Error (RMSE) of 11.083. SHapley Additive exPlanations (SHAP) analysis highlighted the importance of vertical strabismus, diplopia score, and age in influencing QOL-VF, and age, clinical activity, and sex in QOL-AP.

Conclusion: TED significantly impacts patient QOL. The study highlights the efficacy of XGBoost and SHAP analyses in identifying key factors influencing QOL in TED patients. Identifying impactful interventions and considering specific demographic characteristics are essential to improving the QOL of patients with TED.