Investigating factors influencing quality of life in thyroid eye disease: insight from machine learning approaches
Authors' 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.
Study context
- Population
- Chinese thyroid eye disease (TED) patients at first clinic visit, July 2022 to March 2023; previous ophthalmic surgery or concurrent severe diseases excluded
- Participants
- 211 participants
- Outcome
- Factors influencing GO-QOL visual functioning (QOL-VF) and physical appearance (QOL-AP) scores
Methodological detailslinear regression and machine learning algorithms
- Linear regression and machine learning algorithms
Main finding
As reported in the abstract
- Median QOL-VF score
- 64.29
- Median QOL-AP score
- 62.5
- Multivariate linear regression, predictor of QOL-VF: age
- P = 0.013
- Multivariate linear regression, predictor of QOL-VF: ocular motility pain
- P = 0.012
- Multivariate linear regression, predictor of QOL-VF: vertical strabismus
- P < 0.001
- Multivariate linear regression, QOL-AP: gender
- P = 0.013
- Multivariate linear regression, QOL-AP: clinical activity
- P = 0.086
- XGBoost model showed superior performance: R2
- 0.872
- XGBoost model: root mean square error
- 11.083
Observational data: associations, not effects.
Original publication
Investigating factors influencing quality of life in thyroid eye disease: insight from machine learning approaches. Eur Thyroid J. Published 16 December 2024. doi:10.
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