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The Prediction Model Using Thyroid-stimulating Immunoglobulin Bioassay For Relapse of Graves' Disease. | LitMetric

The Prediction Model Using Thyroid-stimulating Immunoglobulin Bioassay For Relapse of Graves' Disease.

J Endocr Soc

Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.

Published: May 2022


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Article Abstract

Objective: Thyroid-stimulating immunoglobulin (TSI) bioassay has a better ability to predict the relapse rate of Graves' disease (GD) than the thyroid-stimulating hormone (TSH)-binding inhibitory immunoglobulin method in terms of measuring the TSH receptor antibody. However, the optimal TSI bioassay cutoff for predicting relapse after antithyroid drug (ATD) withdrawal is not well evaluated.

Methods: This retrospective study enrolled GD patients who had been treated with ATD and obtained their TSI bioassay <140% from January 2010 to December 2019 in a referral hospital.

Results: Among 219 study subjects, 86 patients (39.3%) experienced relapse. The TSI bioassay value of 66.5% significantly predicted the relapse of GD ( = 0.049). The group with a TSI bioassay value > 66.5% were expected to show a 23.8% relapse rate at 2 from ATD withdrawal, and the group with a TSI < 66.5% had a 12.7% relapse rate based on Kaplan-Meier curves analysis. The TSI bioassay showed a good ability to predict relapse GD in the female group ( = 0.041) but did not in the male group ( = 0.573). The risk scoring based on the nomogram with risk factors for GD relapse, which was constructed to overcome the limitation, increased the predictive ability of GD relapse by 11.5% compared to the use of the TSI bioassay alone.

Conclusions: The cutoff value of the TSI bioassay to predict GD relapse should be lower than that for diagnosing GD. However, as the single use of the TSI bioassay has limitations, a nomogram with multiple risk factors including TSI bioassay could be helpful to predict GD relapse.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9012332PMC
http://dx.doi.org/10.1210/jendso/bvac023DOI Listing

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