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

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...

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Autores principales:	Woorim Kim, Young-Ah Cho, Dong-Chul Kim, A-Ra Jo, Kyung-Hyun Min, Kyung-Eun Lee
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	immune checkpoint inhibitors risk factors hyperthyroidism hypothyroidism machine learning Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/e347eae95b294156992d3457b0c3abe9
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spelling	oai:doaj.org-article:e347eae95b294156992d3457b0c3abe92021-11-11T15:33:35ZFactors Associated with Thyroid-Related Adverse Events in Patients Receiving PD-1 or PD-L1 Inhibitors Using Machine Learning Models10.3390/cancers132154652072-6694https://doaj.org/article/e347eae95b294156992d3457b0c3abe92021-10-01T00:00:00Zhttps://www.mdpi.com/2072-6694/13/21/5465https://doaj.org/toc/2072-6694Targets 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, <i>p</i> = 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, <i>p</i> = 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.Woorim KimYoung-Ah ChoDong-Chul KimA-Ra JoKyung-Hyun MinKyung-Eun LeeMDPI AGarticleimmune checkpoint inhibitorsrisk factorshyperthyroidismhypothyroidismmachine learningNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENCancers, Vol 13, Iss 5465, p 5465 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	immune checkpoint inhibitors risk factors hyperthyroidism hypothyroidism machine learning Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	immune checkpoint inhibitors risk factors hyperthyroidism hypothyroidism machine learning Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Woorim Kim Young-Ah Cho Dong-Chul Kim A-Ra Jo Kyung-Hyun Min Kyung-Eun Lee Factors Associated with Thyroid-Related Adverse Events in Patients Receiving PD-1 or PD-L1 Inhibitors Using Machine Learning Models
description	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, <i>p</i> = 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, <i>p</i> = 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.
format	article
author	Woorim Kim Young-Ah Cho Dong-Chul Kim A-Ra Jo Kyung-Hyun Min Kyung-Eun Lee
author_facet	Woorim Kim Young-Ah Cho Dong-Chul Kim A-Ra Jo Kyung-Hyun Min Kyung-Eun Lee
author_sort	Woorim Kim
title	Factors Associated with Thyroid-Related Adverse Events in Patients Receiving PD-1 or PD-L1 Inhibitors Using Machine Learning Models
title_short	Factors Associated with Thyroid-Related Adverse Events in Patients Receiving PD-1 or PD-L1 Inhibitors Using Machine Learning Models
title_full	Factors Associated with Thyroid-Related Adverse Events in Patients Receiving PD-1 or PD-L1 Inhibitors Using Machine Learning Models
title_fullStr	Factors Associated with Thyroid-Related Adverse Events in Patients Receiving PD-1 or PD-L1 Inhibitors Using Machine Learning Models
title_full_unstemmed	Factors Associated with Thyroid-Related Adverse Events in Patients Receiving PD-1 or PD-L1 Inhibitors Using Machine Learning Models
title_sort	factors associated with thyroid-related adverse events in patients receiving pd-1 or pd-l1 inhibitors using machine learning models
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/e347eae95b294156992d3457b0c3abe9
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Factors Associated with Thyroid-Related Adverse Events in Patients Receiving PD-1 or PD-L1 Inhibitors Using Machine Learning Models

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