Abstract：
OBJECTIVE: This study was designed to develop and validate a predictive model for assessing the risk of thyroid toxicity following treatment with immune checkpoint inhibitors.
METHODS: 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 was utilized to demonstrate the decision efficiency of the model.
RESULTS: Our clinical prediction model consisted of 4 independent predictors of thyroid immune-related adverse events, 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 (platelet, OR = 1.004, 95%CI:1.000-1.007). The developed nomogram achieved excellent discrimination with an area under the curve 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.
CONCLUSIONS: The proposed nomogram serves as an effective and intuitive tool for predicting the risk of thyroid immune-related adverse events, facilitating clinicians making individualized decisions based on patient-specific information.
METHODS: 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 was utilized to demonstrate the decision efficiency of the model.
RESULTS: Our clinical prediction model consisted of 4 independent predictors of thyroid immune-related adverse events, 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 (platelet, OR = 1.004, 95%CI:1.000-1.007). The developed nomogram achieved excellent discrimination with an area under the curve 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.
CONCLUSIONS: The proposed nomogram serves as an effective and intuitive tool for predicting the risk of thyroid immune-related adverse events, facilitating clinicians making individualized decisions based on patient-specific information.
摘要：
目的：本研究旨在开发和验证用于评估免疫检查点抑制剂（ICIs）治疗后甲状腺毒性风险的预测模型。
方法：对接受程序性细胞死亡1（PD-1）/程序性死亡-配体1（PD-L1）抑制剂的586例被诊断为恶性肿瘤的患者进行回顾性分析。患者以7:3的比例随机分为训练和验证队列。对训练集进行Logistic回归分析，以确定甲状腺功能异常的危险因素。并根据这些发现制定了列线图。在验证集上使用K折交叉验证进行内部验证。根据辨别和校准来评估列线图的性能。此外,利用决策曲线分析(DCA)验证了模型的决策效率。
结果：我们的临床预测模型包括甲状腺免疫相关不良事件（irAE）的四个独立预测因子，即基线促甲状腺激素（TSH，OR=1.427，95CI：1.163-1.876），基线甲状腺球蛋白抗体（TgAb，OR=1.105，95CI：1.035-1.180），基线甲状腺过氧化物酶抗体（TPOAb，OR=1.172，95CI：1.110-1.237），和基线血小板计数（PLT，OR=1.004，95CI：1.000-1.007）。在训练和内部验证队列中，开发的列线图分别具有0.863（95CI：0.817-0.909）和0.885（95CI：0.827-0.944）的曲线下面积（AUC）。校准曲线表现出良好的拟合，和决策曲线表明良好的临床效益。
结论：拟议的列线图可作为预测甲状腺铁不良事件风险的有效且直观的工具，促进临床医生根据患者特定信息做出个性化决策。
方法：对接受程序性细胞死亡1（PD-1）/程序性死亡-配体1（PD-L1）抑制剂的586例被诊断为恶性肿瘤的患者进行回顾性分析。患者以7:3的比例随机分为训练和验证队列。对训练集进行Logistic回归分析，以确定甲状腺功能异常的危险因素。并根据这些发现制定了列线图。在验证集上使用K折交叉验证进行内部验证。根据辨别和校准来评估列线图的性能。此外,利用决策曲线分析(DCA)验证了模型的决策效率。
结果：我们的临床预测模型包括甲状腺免疫相关不良事件（irAE）的四个独立预测因子，即基线促甲状腺激素（TSH，OR=1.427，95CI：1.163-1.876），基线甲状腺球蛋白抗体（TgAb，OR=1.105，95CI：1.035-1.180），基线甲状腺过氧化物酶抗体（TPOAb，OR=1.172，95CI：1.110-1.237），和基线血小板计数（PLT，OR=1.004，95CI：1.000-1.007）。在训练和内部验证队列中，开发的列线图分别具有0.863（95CI：0.817-0.909）和0.885（95CI：0.827-0.944）的曲线下面积（AUC）。校准曲线表现出良好的拟合，和决策曲线表明良好的临床效益。
结论：拟议的列线图可作为预测甲状腺铁不良事件风险的有效且直观的工具，促进临床医生根据患者特定信息做出个性化决策。
