| Autor: |
Lian Duan, Han-Yu Zhang, Min Lv, Han Zhang, Yao Chen, Ting Wang, Yan Li, Yan Wu, Junfeng Li, Kefeng Li |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Endocrine Connections, Vol 11, Iss 5, Pp 1-10 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2049-3614 |
| DOI: |
10.1530/EC-22-0119 |
| Popis: |
Background and objective: Radioiodine therapy (RAI) is one of the most common treatment solutions for Graves’ disease (GD). However, many pat ients 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 h ypothyroidism. Methods: Four hundred and seventy-one GD patients who underwent RAI bet ween January 2016 and June 2019 were retrospectively recruited and r andomly split into the training set (310 patients) and the validation set (161 pat ients). 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 multipl e ML algorithms were conducted to identify the features associated with the occurren ce of hypothyroidism 6 months after RAI. Results: An integrated multivariate model containing patients’ age, thy roid mass, 24-h radioactive iodine uptake, serum concentrations of aspartate am inotransferase, 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 th e 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 w as 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 tre ated differently before the therapy. Further study is needed to validate the developed prognostic model at independent sites. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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