Development and validation of a nomogram prediction model for factors influencing 131I-refractory Graves’ hyperthyroidism

Objective To examine the factors influencing 131 I-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 131 I treatment at our hospital from January 2021 to January 2022 were randomly 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 131 I treatment. They were randomly divided into a training group (n=190) and an internal validation group (n=82) in a 7:3 ratio. Multiple factors that might affect the efficacy of 131 I 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. Following LASSO regression analysis, six predictive variables associated with 131 I-refractory hyperthyroidism were identified: the duration of hyperthyroidism, nighttime sleep quality, the presence of Graves' ophthalmopathy (GO), the effective half-life of thyroid 131 I, thyroid uptake 99m Tc value, and thyroid mass. The area under the ROC curve (AUC) for the risk of 131 I 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 when the threshold probability for equal patients ranged from 0.04 to 0.86 in the training group and from 0.09 to 0.87 in the validation group, using the nomogram prediction model to predict the risk of refractory hyperthyroidism after 131 I treatment was more beneficial. Conclusion This study found that the duration of GD hyperthyroidism, nighttime sleep quality, GO, effective half-life of thyroid 131 I, thyroid uptake 99m Tc value, and thyroid mass are independent influencing factors of 131 I refractoriness. A risk prediction model including these six factors was established. This model provides guidance for the diagnosis and treatment decisions of 131 I refractory GD hyperthyroidism, offers a quantitative tool for clinical assessment of 131 I efficacy, and aids in personalized treatment decisions, reducing the burden of ineffective or inefficient treatments.

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