This study aims to integrate single photon emission computed tomography/computed tomography (SPECT/CT) parameters with other biomarkers to develop a predictive model for assessing the risk of non-response to tocilizumab (TCZ) therapy in patients with thyroid eye disease (TED). Univariate Cox regression, least absolute shrinkage and selection operator (LASSO) method, and multivariate Cox regression were used to identify predictors of non-response to TCZ and construct a nomogram. The model’s performance was validated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). In this retrospective study, 195 patients with moderate-to-severe TED who received subcutaneous TCZ were included. The nomogram included four predictors: intraocular pressure (IOP), thyroglobulin (TG) level, maximum standardized uptake value (SUVmax) of extraocular muscles (EOMs), and SUVmax of the lacrimal glands. In the training set, the model achieved 1-, 2-, and 3-month area under the ROC curve (AUC) values of 0.787 (95% CI: 0.681–0.893), 0.747 (95% CI: 0.641–0.853), and 0.834 (95% CI: 0.734–0.934), respectively. In the validation set, the corresponding AUC values were 0.761 (95% CI: 0.567–0.955), 0.799 (95% CI: 0.663–0.934), and 0.833 (95% CI: 0.655-1). Calibration curves demonstrated good agreement between predicted and observed probabilities, while DCA confirmed that both the nomogram and individual predictors provided varying degrees of clinical net benefit. This study successfully integrated SPECT/CT parameters with other biomarkers to develop a nomogram for predicting non-response to TCZ in moderate-to-severe TED. The model, incorporating four predictive factors, may help personalize treatment strategies and improve clinical decision-making for TED patients.
Mei et al. (Wed,) studied this question.