Prediction models of intravenous glucocorticoids therapy response in thyroid eye disease
Authors' conclusion
This meta-analysis comprehensively reviews the predictive models for IVGC therapy response in TED. It underscores that integrating clinical characteristics with laboratory or imaging indicators and employing advanced techniques like multivariate regression or radiomics analysis significantly enhance the efficacy of prediction. Our research findings offer valuable insights that can guide future studies on prediction models for IVGC therapy in TED.
Study context
- Population
- Patients with thyroid eye disease (TED) receiving intravenous glucocorticoid (IVGC) therapy; studies from PubMed, Embase and Web of Science12 studies · 47 models
- Outcome
- Performance (AUC, sensitivity, specificity) of prediction models of IVGC therapy response
Methodological detailsmeta-regression, subgroup analysis, sensitivity analysis
- Meta-regression
- Subgroup analysis
- Sensitivity analysis
Pooled result
As reported in the abstract
Pooled prediction model performance
Pooled AUC
0.81
Combined sensitivity
0.75
Combined specificity
0.79
Further pooled results
Sources of heterogeneity and AUC by marker and modeling type
- Marker and modeling types may be possible causes of heterogeneity
- P < 0.001
- Imaging metrics alone, AUC
- 0.81
- Clinical characteristics combined with other markers, AUC
- 0.87
- Multivariate regression, AUC
- 0.84
- Radiomics analysis, AUC
- 0.91
- Multiple meta-regression and subgroup analysis
Original publication
Prediction models of intravenous glucocorticoids therapy response in thyroid eye disease. Eur Thyroid J. Published 26 August 2024. doi:10.
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