Cargando…

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

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Baek, Han-Sang, Lee, Jaejun, Jeong, Chai-Ho, Lee, Jeongmin, Ha, Jeonghoon, Jo, Kwanhoon, Kim, Min-Hee, Cho, Jae Hyoung, Kang, Moo Il, Lim, Dong-Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9012332/
https://www.ncbi.nlm.nih.gov/pubmed/35441120
http://dx.doi.org/10.1210/jendso/bvac023
_version_ 1784687772473229312
author Baek, Han-Sang
Lee, Jaejun
Jeong, Chai-Ho
Lee, Jeongmin
Ha, Jeonghoon
Jo, Kwanhoon
Kim, Min-Hee
Cho, Jae Hyoung
Kang, Moo Il
Lim, Dong-Jun
author_facet Baek, Han-Sang
Lee, Jaejun
Jeong, Chai-Ho
Lee, Jeongmin
Ha, Jeonghoon
Jo, Kwanhoon
Kim, Min-Hee
Cho, Jae Hyoung
Kang, Moo Il
Lim, Dong-Jun
author_sort Baek, Han-Sang
collection PubMed
description 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 (P = 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 (P = 0.041) but did not in the male group (P = 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.
format Online
Article
Text
id pubmed-9012332
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-90123322022-04-18 The Prediction Model Using Thyroid-stimulating Immunoglobulin Bioassay For Relapse of Graves’ Disease Baek, Han-Sang Lee, Jaejun Jeong, Chai-Ho Lee, Jeongmin Ha, Jeonghoon Jo, Kwanhoon Kim, Min-Hee Cho, Jae Hyoung Kang, Moo Il Lim, Dong-Jun J Endocr Soc Clinical Research Article 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 (P = 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 (P = 0.041) but did not in the male group (P = 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. Oxford University Press 2022-02-16 /pmc/articles/PMC9012332/ /pubmed/35441120 http://dx.doi.org/10.1210/jendso/bvac023 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of the Endocrine Society. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Clinical Research Article
Baek, Han-Sang
Lee, Jaejun
Jeong, Chai-Ho
Lee, Jeongmin
Ha, Jeonghoon
Jo, Kwanhoon
Kim, Min-Hee
Cho, Jae Hyoung
Kang, Moo Il
Lim, Dong-Jun
The Prediction Model Using Thyroid-stimulating Immunoglobulin Bioassay For Relapse of Graves’ Disease
title The Prediction Model Using Thyroid-stimulating Immunoglobulin Bioassay For Relapse of Graves’ Disease
title_full The Prediction Model Using Thyroid-stimulating Immunoglobulin Bioassay For Relapse of Graves’ Disease
title_fullStr The Prediction Model Using Thyroid-stimulating Immunoglobulin Bioassay For Relapse of Graves’ Disease
title_full_unstemmed The Prediction Model Using Thyroid-stimulating Immunoglobulin Bioassay For Relapse of Graves’ Disease
title_short The Prediction Model Using Thyroid-stimulating Immunoglobulin Bioassay For Relapse of Graves’ Disease
title_sort prediction model using thyroid-stimulating immunoglobulin bioassay for relapse of graves’ disease
topic Clinical Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9012332/
https://www.ncbi.nlm.nih.gov/pubmed/35441120
http://dx.doi.org/10.1210/jendso/bvac023
work_keys_str_mv AT baekhansang thepredictionmodelusingthyroidstimulatingimmunoglobulinbioassayforrelapseofgravesdisease
AT leejaejun thepredictionmodelusingthyroidstimulatingimmunoglobulinbioassayforrelapseofgravesdisease
AT jeongchaiho thepredictionmodelusingthyroidstimulatingimmunoglobulinbioassayforrelapseofgravesdisease
AT leejeongmin thepredictionmodelusingthyroidstimulatingimmunoglobulinbioassayforrelapseofgravesdisease
AT hajeonghoon thepredictionmodelusingthyroidstimulatingimmunoglobulinbioassayforrelapseofgravesdisease
AT jokwanhoon thepredictionmodelusingthyroidstimulatingimmunoglobulinbioassayforrelapseofgravesdisease
AT kimminhee thepredictionmodelusingthyroidstimulatingimmunoglobulinbioassayforrelapseofgravesdisease
AT chojaehyoung thepredictionmodelusingthyroidstimulatingimmunoglobulinbioassayforrelapseofgravesdisease
AT kangmooil thepredictionmodelusingthyroidstimulatingimmunoglobulinbioassayforrelapseofgravesdisease
AT limdongjun thepredictionmodelusingthyroidstimulatingimmunoglobulinbioassayforrelapseofgravesdisease
AT baekhansang predictionmodelusingthyroidstimulatingimmunoglobulinbioassayforrelapseofgravesdisease
AT leejaejun predictionmodelusingthyroidstimulatingimmunoglobulinbioassayforrelapseofgravesdisease
AT jeongchaiho predictionmodelusingthyroidstimulatingimmunoglobulinbioassayforrelapseofgravesdisease
AT leejeongmin predictionmodelusingthyroidstimulatingimmunoglobulinbioassayforrelapseofgravesdisease
AT hajeonghoon predictionmodelusingthyroidstimulatingimmunoglobulinbioassayforrelapseofgravesdisease
AT jokwanhoon predictionmodelusingthyroidstimulatingimmunoglobulinbioassayforrelapseofgravesdisease
AT kimminhee predictionmodelusingthyroidstimulatingimmunoglobulinbioassayforrelapseofgravesdisease
AT chojaehyoung predictionmodelusingthyroidstimulatingimmunoglobulinbioassayforrelapseofgravesdisease
AT kangmooil predictionmodelusingthyroidstimulatingimmunoglobulinbioassayforrelapseofgravesdisease
AT limdongjun predictionmodelusingthyroidstimulatingimmunoglobulinbioassayforrelapseofgravesdisease