Machine learning identifies baseline clinical features that predict early hypothyroidism in patients with Graves’ disease after radioiodine therapy

Abstract

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 early prediction of post-RAI hypothyroidism. Methods: 471 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 at 6 months after RAI. Results: An integrated multivariate model containing patients’ age, thyroid mass, 24 h radioactive iodine uptake, serum concentrations of aspartate aminotransferase (AST), thyrotropin-receptor antibodies (TRAb), thyroid microsomal antibodies (TMA), and blood neutrophil count demonstrated an area under the ROC (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.

Early prediction of hypothyroidism based on feature selection and explainable artificial intelligence

“…The use of ML models is demonstrating promise in detect hypothyroidism early [Duan et al 2022, Hu et al 2022]. This allows quickly and accurately check medical information for patterns and important details [Cavalcante et al 2023].…”