机器学习方法用于评估接受免疫检查点抑制剂治疗的患者上腺功能衰竭的危险因素
Woorim Kim1, Young Ah Cho2,3, Kyung Hyun Min4
1College of Pharmacy, Kangwon National University, Chuncheon 24341, Republic of Korea.
Pharmaceuticals (Basel, Switzerland)
|August 26, 2023
概括
这项研究开发了一种风险评分系统,以预测上腺功能不充分,这是免疫检查点抑制剂 (ICI) 的罕见副作用. 该系统可以识别高风险患者,帮助在使用PD-1/PD-L1药物治疗期间做出临床决策.
科学领域:
- 在瘤学瘤学.
- 免疫学 免疫学 免疫学
- 药理学 药理学是指药理学的学科.
背景情况:
- 上腺功能衰竭是一种罕见但严重的与免疫检查点抑制剂 (ICI) 相关的免疫相关的不良事件.
- 预测和管理这种并发症对于癌症免疫治疗期间的患者安全至关重要.
研究的目的:
- 在接受抗编程细胞死亡1 (PD-1) 或抗编程细胞死亡1 (PD-L1) 剂的患者中开发上腺功能不全的风险评分系统.
- 利用机器学习方法来预测上腺功能不足的风险.
主要方法:
- 分析了209名接受ICI治疗的患者队列.
- 采用了单变量,多变量分析和机器学习算法 (逻辑回归,随机森林,SVM,弹性网).
- 风险因素和预测模型使用调整赔率比率 (AOR) 和接收器运行特征曲线 (AUROC) 下的面积等指标进行评估.
主要成果:
- 质子抑制剂 (PPI) 和α抑制剂是显著的危险因素 (AOR分别为4.5和6.0).
- 物流回归和弹性净模型显示出良好的预测性能 (AUROC 0.75和0.76).
- 创建了一个风险评分系统,结合了女性性别,65岁以上,PPI,α阻断剂和抗精神病药物等因素.
结论:
- 开发的风险评分系统可以帮助评估和管理接受PD-1/PD-L1抑制剂治疗的患者的上腺功能衰竭风险.
- 这种工具可以支持临床决策,以改善癌症免疫疗法患者的治疗结果.
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