Investigating factors influencing quality of life in thyroid eye disease: insight from machine learning approaches.

Abstract

Aims: Thyroid eye disease (TED) is an autoimmune orbital disorder that diminishes the quality of life (QOL) in affected individuals. Graves' ophthalmopathy (GO)-QOL questionnaire effectively assesses TED's effect on patients. This study aims to investigate the 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 a root mean square error 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 affects patient QOL. The study highlights the efficacy of XGBoost and SHAP analyses in identifying key factors influencing the QOL in TED patients. Identifying effective interventions and considering specific demographic characteristics are essential to improving the QOL of patients with TED.