Nomogram construction and evaluation for predicting non-remission after a single radioactive iodine therapy for Graves’ hyperthyroidism: a retrospective cohort study

Background Radioactive iodine (RAI) therapy is a widely used treatment for Graves’ Hyperthyroidism (GH). However, various factors can impact the non-remission rate of GH after single RAI therapy. This study aimed to develop an online dynamic nomogram to assist physicians in providing personalized therapy for GH. Methods Data from 454 GH patients who received RAI therapy were retrospectively reviewed and included in the present study. The univariate and multivariate analysis were conducted to investigate and identify independent influencing factors. The nomogram was developed based on the training cohort to explore non-remission rates. Finally, the reliability and accuracy of the constructed nomogram model were verified in the validation cohort via the calibration, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). Results 24-hours radioactive iodine uptake (RAIU 24h ), effective half-life (Teff), total iodine dose (TID) and iodine dose per gram of thyroid tissue (IDPG) were independent predictors. The nomogram had a high C-index 0.922 (95% CI: 0.892–0.953), for predicting non-remission. The calibration curves demonstrated excellent consistency between the predicted and the actual probability of non-remission. ROC analysis showed that the AUC of the nomogram model and the four independent factors in the training cohort were 0.922, 0.673, 0.760, 0.761, and 0.786, respectively. The optimal cutoff value for the total nomogram scores was determined to be 155. A total score of ≥155 indicates a higher likelihood of non-remission after a single RAI therapy for GH, whereas a score below 155 suggests a greater likelihood of remission. Additionally, the DCA curve indicated that this nomogram had good clinical utility in predicting non-remission. Conclusion An online nomogram was constructed with good predictive performance, which can be used as a practical approach to predict and assist physicians in making personalized therapy decisions for GH patients.