Factors Associated with Thyroid-Related Adverse Events in Patients Receiving PD-1 or PD-L1 Inhibitors Using Machine Learning Models

Simple Summary: Although immune checkpoint inhibitors have a potential role in thyroid-related complications, no study has investigated factors associated with such adverse events. This study aims to explore the factors associated with thyroid-related adverse events in patients with anti-PD-1/PD-L1 agents by training predictive models utilizing various machine learning approaches. The results of this study could be used to develop individually tailored intervention strategies to prevent immune checkpoint inhibitor-induced thyroid-related outcomes.Targets of immune checkpoint inhibitors (ICIs) regulate immune homeostasis and prevent autoimmunity by downregulating immune responses and by inhibiting T cell activation. Although ICIs are widely used in immunotherapy because of their good clinical efficacy, they can also induce autoimmune-related adverse events. Thyroid-related adverse events are frequently associated with anti-programmed cell death 1 (PD-1) or anti-programmed cell death-ligand 1 (PD-L1) agents. The present study aims to investigate the factors associated with thyroid dysfunction in patients receiving PD-1 or PD-L1 inhibitors and to develop various machine learning approaches to predict complications. A total of 187 patients were enrolled in this study. Logistic regression analysis was conducted to investigate the association between such factors and adverse events. Various machine learning methods were used to predict thyroid-related complications. After adjusting for covariates, we found that smoking history and hypertension increase the risk of thyroid dysfunction by approximately 3.7 and 4.1 times, respectively (95% confidence intervals (CIs) 1.338-10.496 and 1.478-11.332, p = 0.012 and 0.007). In contrast, patients taking opioids showed an approximately 4.0-fold lower risk of thyroid-related complications than those not taking them (95% CI 1.464-11.111, p = 0.007). Among the machine learning models, random forest showed the best prediction, with an area under the receiver operating characteristic of 0.770 (95% CI 0.648-0.883) and an area under the precision-recall of 0.510 (95%CI 0.357-0.666). Hence, this study utilized various machine learning models for prediction and showed that factors such as smoking history, hypertension, and opioids are associated with thyroid-related adverse events in cancer patients receiving PD-1/PD-L1 inhibitors.

Automated summary

This study aims to investigate factors associated with thyroid dysfunction in cancer patients receiving PD-1/PD-L1 inhibitors and develop machine learning approaches to predict complications. The results showed smoking history, hypertension, and opioids are associated with thyroid-related adverse events. Machine learning models, particularly random forest, demonstrated good predictive performance for such events.