Xu Qianjie, Li Xiaosheng, Yuan Yuliang, Hu Zuhai, Zhang Wei, Wang Ying, Shen Ai, Lei Haike
Chongqing Cancer Multi-omics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital, Chongqing, China.
Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, China.
Biosci Trends. 2025 May 9;19(2):202-210. doi: 10.5582/bst.2024.01351. Epub 2025 Feb 1.
Immune checkpoint inhibitors (ICIs) have been widely used in various types of cancer, but they have also led to a significant number of adverse events, including ICI-induced immune-mediated hepatitis (IMH). This study aimed to explore the risk factors for IMH in patients treated with ICIs and to develop and validate a new nomogram model to predict the risk of IMH. Detailed information was collected between January 1, 2020, and December 31, 2023. Univariate logistic regression analysis was used to assess the impact of each clinical variable on the occurrence of IMH, followed by stepwise multivariate logistic regression analysis to determine independent risk factors for IMH. A nomogram model was constructed based on the results of the multivariate analysis. The performance of the nomogram model was evaluated via the area under the receiver operating characteristic curve (AUC), calibration curves, decision curve analysis (DCA), and clinical impact curve (CIC) analysis. A total of 216 (8.82%) patients developed IMH. According to stepwise multivariate logistic analysis, hepatic metastasis, the TNM stage, the WBC count, LYM, ALT, TBIL, ALB, GLB, and ADA were identified as risk factors for IMH. The AUC for the nomogram model was 0.817 in the training set and 0.737 in the validation set. The calibration curves, DCA results, and CIC results indicated that the nomogram model had good predictive accuracy and clinical utility. The nomogram model is intuitive and straightforward, making it highly suitable for rapid assessment of the risk of IMH in patients receiving ICI therapy in clinical practice. Implementing this model enables early adoption of preventive and therapeutic strategies, ultimately reducing the likelihood of immune-related adverse events (IRAEs), and especially IMH.
免疫检查点抑制剂（ICIs）已广泛应用于各类癌症，但它们也导致了大量不良事件，包括ICI诱导的免疫介导性肝炎（IMH）。本研究旨在探讨接受ICIs治疗的患者发生IMH的危险因素，并开发和验证一种新的列线图模型以预测IMH风险。在2020年1月1日至2023年12月31日期间收集详细信息。采用单因素逻辑回归分析评估各临床变量对IMH发生的影响，随后进行逐步多因素逻辑回归分析以确定IMH的独立危险因素。基于多因素分析结果构建列线图模型。通过受试者操作特征曲线（AUC）下面积、校准曲线、决策曲线分析（DCA）和临床影响曲线（CIC）分析评估列线图模型的性能。共有216例（8.82%）患者发生IMH。根据逐步多因素逻辑分析，肝转移、TNM分期、白细胞计数、淋巴细胞、谷丙转氨酶、总胆红素、白蛋白、球蛋白和腺苷脱氨酶被确定为IMH的危险因素。列线图模型在训练集中的AUC为0.817，在验证集中为0.737。校准曲线、DCA结果和CIC结果表明列线图模型具有良好的预测准确性和临床实用性。列线图模型直观明了，非常适合在临床实践中快速评估接受ICI治疗患者的IMH风险。应用该模型能够尽早采取预防和治疗策略，最终降低免疫相关不良事件（IRAEs）尤其是IMH的发生可能性。
