使用机器学习来确定2年内在患有鼻多的慢性鼻炎的患者中定义mepolizumab治疗反应
María Sandra Domínguez-Sosa1,2, María Soledad Cabrera-Ramírez1, Miriam Del Carmen Marrero-Ramos1
1Hospital Universitario de Gran Canaria Dr Negrin, Las Palmas de Gran Canaria, Spain.
Frontiers in allergy
|March 2, 2026
概括
机器学习准确地预测了在具有鼻息肉的慢性鼻炎 (CRSwNP) 中的梅波利祖马布反应. 高基线血中乙氨基和中性粒细胞计数识别"超级响应者",优化治疗.
科学领域:
- 免疫学 免疫学 免疫学
- 数据科学数据科学数据科学
- 耳鼻喉科 耳鼻喉科 耳鼻喉科
背景情况:
- 带鼻息肉的慢性鼻炎 (CRSwNP) 是一种复杂的上呼吸道炎症疾病.
- 梅波利祖马布是一种有效的生物疗法,但预测个体患者的反应仍然具有挑战性.
- 确定最佳反应的生物标志物对于个性化治疗策略至关重要.
研究的目的:
- 利用机器学习算法识别临床生物标志物,预测CRSwNP患者对梅波利祖马布的反应.
- 为了比较不同机器学习模型的预测准确度,以识别治疗响应者.
- 调查基线血液学参数和并发症在预测长期反应中的作用.
主要方法:
- 这是一项追溯观察性研究,对84名CRSwNP患者进行了治疗,他们接受了mepolizumab.
- 评估四种机器学习算法:决策树,物流回归,K-最近邻居和极端梯度提升 (XGBoost).
- 使用K-Fold交叉验证与超参数优化来确保模型的稳定性并防止过拟合.
主要成果:
- 在6,12和24个月后 (p < 0.001) 观察到SNOT-22,VAS得分,ACT和NPS的显著改善.
- 44.1%的患者在24个月后被归类为"超级响应者".
- 在预测超级反应方面,XGBoost表现出最高的准确性 (ROC-AUC 0.766),识别出高基线血中氨基酸细胞计数 (BEC) 和血中中性细胞计数 (BNC) 作为显著预测因素.
结论:
- 机器学习模型,特别是XGBoost,可以有效地预测严重CRSwNP中的美波利祖马布超级反应.
- 关键预测因素包括高基线BEC,高基线BNC和与阿司匹林加剧呼吸道疾病 (AERD) 并发症的过敏性鼻炎.
- 这些发现可以指导临床决策和个性化CRSwNP治疗,以改善患者的治疗结果.
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