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开发和验证在免疫治疗期间患者甲状腺功能障碍的预测模型
Qian Wang1, Tingting Wu1, Ru Zhao2
1Department of Endocrinology, Endocrine and Metabolic Disease Medical Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China; Branch of National Clinical Research Centre for Metabolic Diseases, Nanjing, China.
这项研究开发了一个名图,用于预测免疫检查点抑制剂对甲状腺的毒性. 该模型准确地识别了有风险的患者,帮助针对甲状腺免疫相关不良事件的个性化治疗决策.
科学领域:
- 在瘤学瘤学.
- 内分泌学 在内分泌学.
- 免疫学 免疫学 免疫学
背景情况:
- 免疫检查点抑制剂 (ICI),如PD-1/PD-L1抑制剂,可以引起与免疫相关的不良事件 (irAEs).
- 甲状腺功能障碍是与ICI治疗相关的显著的irAE.
- 需要预测模型来识别患有ICI诱导甲状腺毒性风险的患者.
研究的目的:
- 开发和验证一种预测模型,用于评估ICI治疗患者甲状腺毒性风险.
- 为了确定甲状腺irAEs的关键预测因素.
- 为临床风险评估创建一个直观的工具.
主要方法:
- 对586名接受PD-1/PD-L1抑制剂治疗的患者进行了回顾性分析.
- 在培训队列 (70%) 上使用后勤回归的预测模型的开发.
- 内部验证使用K折交叉验证对验证队列 (30%),评估歧视和校准.
主要成果:
- 开发了一种纳米图,其中包含了基线甲状腺激素 (TSH),甲状腺蛋白抗体 (TgAb),甲状腺过氧化酶抗体 (TPOAb) 和血小板计数.
- 该模型显示出优异的歧视,AUC为0.863 (培训) 和0.885 (验证).
- 校准曲线显示很适合,决策曲线分析证实了临床效用.
结论:
- 开发的诺米图是预测接受ICI的患者甲状腺irrAEs的有效工具.
- 这个工具帮助临床医生做出个性化治疗决策.
- 早期识别有风险的患者可能会减轻甲状腺毒性.
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