使用机器学习预测患有类风湿性关节炎的患者早期停止服用阿达利穆马布:一种基于专业药房的方法
Angie H Yoon1,2, Peter Gedeck2, Marlette Oelofsen3
1Healthdyne Specialty Pharmacy, Lakeland, FL.
Journal of managed care & specialty pharmacy
|February 27, 2026
概括
专业药房现在可以预测类风湿性关节炎 (RA) 患者可能会提前停止阿达利木马布治疗. 机器学习模型识别高风险个体,使药剂师能够及时干预,以改善结果并减少浪费.
科学领域:
- 药理学和药房实践中的药理学和药房实践
- 医疗保健中的机器学习
- 类风湿病学 类风湿病学
背景情况:
- 类风湿性关节炎 (RA) 患者通常会在6个月内停止服用阿达利穆马布,因为他们认为它缺乏疗效.
- 鉴定患有早期停止治疗高风险的患者是当前工具的挑战.
- 专业药房可以进行干预,但需要预测能力.
研究的目的:
- 开发一种机器学习 (ML) 模型,用于预测RA患者早期停止服用阿达利穆马布的情况.
- 通过识别高风险个体,使药剂师能够进行有针对性的干预.
- 改善治疗坚持和RA管理患者的结果.
主要方法:
- 在专业药房 (2020-2023) 开始服用阿达利木马布的RA患者的回顾性分析.
- 利用分发和临床管理数据的38个特征,选择了19个预测因素.
- 经过训练和评估的ML分类模型,包括弹性网,使用AUC-ROC和F1分数.
主要成果:
- 37.7%的RA患者被确定为早期停用阿达利穆马布的高风险患者.
- 关键预测因素包括性别,年龄,疼痛评分,关节胀和RA持续时间.
- 弹性网模型实现了高性能 (AUC-ROC=0.886,F1得分=0.741).
结论:
- 常规收集的专业药房数据可用于预测RA早期停用阿达利穆马布的模型.
- 弹性网模型在识别有风险的患者方面表现出高的区分能力.
- 这种预测能力可以支持药剂师主导的干预,减少浪费并改善患者的治疗结果.
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