ORIGINAL RESEARCH article
Front. Endocrinol.
Sec. Thyroid Endocrinology
Volume 16 - 2025 | doi: 10.3389/fendo.2025.1628226
Development and validation of a nomogram prediction model for factors influencing 131 I-refractory Graves' hyperthyroidism
Provisionally accepted
- People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
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Objective: To examine the factors influencing 131I-refractory Graves' disease (GD) hyperthyroidism in patients, develop a nomogram prediction model, and conduct its validation. Methods: A total of 272 hyperthyroidism patients who received initial 131I treatment were selected. Patients were divided into refractory hyperthyroidism group (92 cases) and non-refractory hyperthyroidism group (180 cases) based on whether they were cured after one course of 131I treatment. They were randomly divided into a training group (n=190) and a internal validation group (n=82) in a 7:3 ratio. Multiple factors that might affect the efficacy of 131I treatment were collected, including 16 variables such as clinical characteristics, laboratory, and imaging examinations. LASSO regression was used for optimization and selection, and a multivariate logistic regression model was constructed to create a nomogram prediction model. The model's discrimination, calibration, and clinical validity were evaluated using the receiver operating characteristic (ROC) curve, Hosmer-Lemeshow calibration curve, and decision curve analysis (DCA). Results: There were no statistically significant differences (P>0.05) in the comparison of the 16 variables between the training and validation groups. After LASSO regression analysis, six predictive variables related to 131I refractory hyperthyroidism were selected. The results of the multivariate logistic regression analysis showed that the duration of hyperthyroidism, nighttime sleep quality, Graves' ophthalmopathy (GO), effective half-life of thyroid 131I (RAIU1/2), thyroid uptake/salivary gland 99mTc ratio (thyroid uptake 99mTc value), and thyroid mass were risk factors for poor efficacy of 131I treatment (P<0.05). The area under the ROC curve (AUC) for the risk of 131I refractory hyperthyroidism in the training group was 0.943 (95% CI: 0.909-0.977), and the AUC for the validation group was 0.926 (95% CI: 0.870-0.983). The Hosmer-Lemeshow calibration curve showed good fit (training group P=0.876; validation group P=0.202). DCA demonstrated that using the nomogram prediction model to predict the risk of refractory hyperthyroidism after 131I treatment was more beneficial. Conclusion: This study found that the duration of GD hyperthyroidism, nighttime sleep quality, GO, effective half-life of thyroid 131I, thyroid uptake 99mTc value, and thyroid mass are independent influencing factors of 131I refractoriness. A risk prediction model including these six factors was established.
Keywords: Graves' hyperthyroidism, nomogram, Prediction model, LASSO regression, Radioiodine therapy 1.Introduction
Received: 14 May 2025; Accepted: 21 Jul 2025.
Copyright: © 2025 Liao, Wei, Chen, Meng, Mo, Sun, Song, Lu and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Lu Lu, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
Wentan Huang, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
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