本研究旨在开发和验证一个预测模型，用于评估免疫检查点抑制剂 (ICIs) 治疗后甲状腺毒性的风险。对 586 名被诊断患有恶性肿瘤并接受程序性细胞死亡的患者进行了回顾性分析1（ PD-1)/程序性死亡配体 1 (PD-L1) 抑制剂。患者按 7:3 的比例随机分为训练组和验证组。对训练集进行逻辑回归分析，以确定甲状腺功能障碍的危险因素，并根据这些结果开发列线图。使用验证集上的 K 折交叉验证进行内部验证。列线图的性能根据辨别和校准进行评估。此外，还利用决策曲线分析（DCA）来证明模型的决策效率。我们的临床预测模型由甲状腺免疫相关不良事件（irAE）的四个独立预测因子组成，即基线促甲状腺素（TSH，OR=1.427，95） %CI:1.163-1.876)、基线甲状腺球蛋白抗体 (TgAb, OR=1.105, 95%CI:1.035-1.180)、基线甲状腺过氧化物酶抗体 (TPOAb, OR=1.172, 95%CI:1.110-1.237) 和基线血小板计数（PLT，OR=1.004，95%CI：1.000-1.007）。开发的列线图在训练组和内部验证组中实现了出色的区分度，曲线下面积 (AUC) 分别为 0.863 (95% CI: 0.817-0.909) 和 0.885 (95% CI: 0.827-0.944)。校准曲线表现出良好的拟合度，决策曲线显示出良好的临床效益。所提出的列线图可作为预测甲状腺 irAE 风险的有效且直观的工具，有助于临床医生根据患者特定信息做出个体化决策。版权所有 © 2024 AACE 。由爱思唯尔公司出版。保留所有权利。

This study was designed to develop and validate a predictive model for assessing the risk of thyroid toxicity following treatment with immune checkpoint inhibitors (ICIs).A retrospective analysis was conducted on a cohort of 586 patients diagnosed with malignant tumors who received programmed cell death 1 (PD-1)/programmed death-ligand 1 (PD-L1) inhibitors. The patients were randomly divided into training and validation cohorts in a 7:3 ratio. Logistic regression analyses were performed on the training set to identify risk factors of thyroid dysfunction, and a nomogram was developed based on these findings. Internal validation was performed using K-fold cross-validation on the validation set. The performance of the nomogram was assessed in terms of discrimination and calibration. Additionally, decision curve analysis (DCA) was utilized to demonstrate the decision efficiency of the model.Our clinical prediction model consisted of four independent predictors of thyroid immune-related adverse events (irAEs), namely baseline thyrotropin (TSH, OR=1.427, 95%CI:1.163-1.876), baseline thyroglobulin antibody (TgAb, OR=1.105, 95%CI:1.035-1.180), baseline thyroid peroxidase antibody (TPOAb, OR=1.172, 95%CI:1.110-1.237), and baseline platelet count (PLT, OR=1.004, 95%CI:1.000-1.007). The developed nomogram achieved excellent discrimination with an area under the curve (AUC) of 0.863 (95%CI: 0.817-0.909) and 0.885 (95%CI: 0.827-0.944) in the training and internal validation cohorts respectively. Calibration curves exhibited a good fit, and the decision curve indicated favorable clinical benefits.The proposed nomogram serves as an effective and intuitive tool for predicting the risk of thyroid irAEs, facilitating clinicians making individualized decisions based on patient-specific information.Copyright © 2024 AACE. Published by Elsevier Inc. All rights reserved.