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Machine learning identifies baseline clinical features that predict early hypothyroidism in patients with Graves’ disease after radioiodine therapy
BACKGROUND AND OBJECTIVE: Radioiodine therapy (RAI) is one of the most common treatment solutions for Graves’ disease (GD). However, many patients will develop hypothyroidism as early as 6 months after RAI. This study aimed to implement machine learning (ML) algorithms for the early prediction of po...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Bioscientifica Ltd
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9175589/ https://www.ncbi.nlm.nih.gov/pubmed/35521803 http://dx.doi.org/10.1530/EC-22-0119 |
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author | Duan, Lian Zhang, Han-Yu Lv, Min Zhang, Han Chen, Yao Wang, Ting Li, Yan Wu, Yan Li, Junfeng Li, Kefeng |
author_facet | Duan, Lian Zhang, Han-Yu Lv, Min Zhang, Han Chen, Yao Wang, Ting Li, Yan Wu, Yan Li, Junfeng Li, Kefeng |
author_sort | Duan, Lian |
collection | PubMed |
description | BACKGROUND AND OBJECTIVE: Radioiodine therapy (RAI) is one of the most common treatment solutions for Graves’ disease (GD). However, many patients will develop hypothyroidism as early as 6 months after RAI. This study aimed to implement machine learning (ML) algorithms for the early prediction of post-RAI hypothyroidism. METHODS: Four hundred and seventy-one GD patients who underwent RAI between January 2016 and June 2019 were retrospectively recruited and randomly split into the training set (310 patients) and the validation set (161 patients). These patients were followed for 6 months after RAI. A set of 138 clinical and lab test features from the electronic medical record (EMR) were extracted, and multiple ML algorithms were conducted to identify the features associated with the occurrence of hypothyroidism 6 months after RAI. RESULTS: An integrated multivariate model containing patients’ age, thyroid mass, 24-h radioactive iodine uptake, serum concentrations of aspartate aminotransferase, thyrotropin-receptor antibodies, thyroid microsomal antibodies, and blood neutrophil count demonstrated an area under the receiver operating curve (AUROC) of 0.72 (95% CI: 0.61–0.85), an F1 score of 0.74, and an MCC score of 0.63 in the training set. The model also performed well in the validation set with an AUROC of 0.74 (95% CI: 0.65–0.83), an F1 score of 0.74, and a MCC of 0.63. A user-friendly nomogram was then established to facilitate the clinical utility. CONCLUSION: The developed multivariate model based on EMR data could be a valuable tool for predicting post-RAI hypothyroidism, allowing them to be treated differently before the therapy. Further study is needed to validate the developed prognostic model at independent sites. |
format | Online Article Text |
id | pubmed-9175589 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Bioscientifica Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-91755892022-06-14 Machine learning identifies baseline clinical features that predict early hypothyroidism in patients with Graves’ disease after radioiodine therapy Duan, Lian Zhang, Han-Yu Lv, Min Zhang, Han Chen, Yao Wang, Ting Li, Yan Wu, Yan Li, Junfeng Li, Kefeng Endocr Connect Research BACKGROUND AND OBJECTIVE: Radioiodine therapy (RAI) is one of the most common treatment solutions for Graves’ disease (GD). However, many patients will develop hypothyroidism as early as 6 months after RAI. This study aimed to implement machine learning (ML) algorithms for the early prediction of post-RAI hypothyroidism. METHODS: Four hundred and seventy-one GD patients who underwent RAI between January 2016 and June 2019 were retrospectively recruited and randomly split into the training set (310 patients) and the validation set (161 patients). These patients were followed for 6 months after RAI. A set of 138 clinical and lab test features from the electronic medical record (EMR) were extracted, and multiple ML algorithms were conducted to identify the features associated with the occurrence of hypothyroidism 6 months after RAI. RESULTS: An integrated multivariate model containing patients’ age, thyroid mass, 24-h radioactive iodine uptake, serum concentrations of aspartate aminotransferase, thyrotropin-receptor antibodies, thyroid microsomal antibodies, and blood neutrophil count demonstrated an area under the receiver operating curve (AUROC) of 0.72 (95% CI: 0.61–0.85), an F1 score of 0.74, and an MCC score of 0.63 in the training set. The model also performed well in the validation set with an AUROC of 0.74 (95% CI: 0.65–0.83), an F1 score of 0.74, and a MCC of 0.63. A user-friendly nomogram was then established to facilitate the clinical utility. CONCLUSION: The developed multivariate model based on EMR data could be a valuable tool for predicting post-RAI hypothyroidism, allowing them to be treated differently before the therapy. Further study is needed to validate the developed prognostic model at independent sites. Bioscientifica Ltd 2022-04-22 /pmc/articles/PMC9175589/ /pubmed/35521803 http://dx.doi.org/10.1530/EC-22-0119 Text en © The authors https://creativecommons.org/licenses/by-nc/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. (https://creativecommons.org/licenses/by-nc/4.0/) |
spellingShingle | Research Duan, Lian Zhang, Han-Yu Lv, Min Zhang, Han Chen, Yao Wang, Ting Li, Yan Wu, Yan Li, Junfeng Li, Kefeng Machine learning identifies baseline clinical features that predict early hypothyroidism in patients with Graves’ disease after radioiodine therapy |
title | Machine learning identifies baseline clinical features that predict early hypothyroidism in patients with Graves’ disease after radioiodine therapy |
title_full | Machine learning identifies baseline clinical features that predict early hypothyroidism in patients with Graves’ disease after radioiodine therapy |
title_fullStr | Machine learning identifies baseline clinical features that predict early hypothyroidism in patients with Graves’ disease after radioiodine therapy |
title_full_unstemmed | Machine learning identifies baseline clinical features that predict early hypothyroidism in patients with Graves’ disease after radioiodine therapy |
title_short | Machine learning identifies baseline clinical features that predict early hypothyroidism in patients with Graves’ disease after radioiodine therapy |
title_sort | machine learning identifies baseline clinical features that predict early hypothyroidism in patients with graves’ disease after radioiodine therapy |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9175589/ https://www.ncbi.nlm.nih.gov/pubmed/35521803 http://dx.doi.org/10.1530/EC-22-0119 |
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