用机器学习方法优化住院患者的初始万科米辛剂量,以提高治疗结果:算法开发和验证研究
Heonyi Lee1, Yi-Jun Kim2,3, Jin-Hong Kim4
1Interdisciplinary Program in Bioinformatics, College of Natural Sciences, Seoul National University, Seoul, Republic of Korea.
Journal of medical Internet research
|March 31, 2025
概括
一个新的机器学习算法,OPTIVAN,通过预测治疗范围来优化初始万科米辛剂量,减少调整的需要. 这种个性化的方法提高了万科米辛治疗的有效性.
科学领域:
- 药理动力学和药理动力学
- 机器学习在医学中的应用
- 抗生素剂量优化 抗生素剂量优化
背景情况:
- 标准的万科米辛剂量往往导致不理想的结果,由于患者间的变化.
- 治疗药物监测 (TDM) 的调整是反应性的,强调了需要预测性,个性化的初始剂量.
研究的目的:
- 开发和评估基于机器学习 (ML) 的算法,用于预测最佳初始万科米辛剂量.
- 为了确保万科米辛的初始剂量处于治疗范围内,该范围由曲线下的24小时区域定义为最小抑制度.
主要方法:
- 用于培训和测试 (415名接受静脉注射万科米辛的患者) 的回顾性队列 (7:3比率).
- 四个ML模型 (梯度提升,随机森林,SVM,XGB) 被评估,以开发OPTIVAN算法.
- 使用外部队列 (n=268) 验证了OPTIVAN算法,并集成到基于Web的临床支持工具中.
主要成果:
- 纳入OPTIVAN的支持矢量机 (SVM) 模型显示出最佳的预测性能,AUROC为0.832 (培训) 和0.720 (验证).
- SVM模型的关键共变量包括年龄,BMI,葡萄糖,BUN,eGFR,血红素和剂量/体重.
- 在各种患者亚组中观察到一致的表现,包括不同的功能,性别和BMI.
结论:
- OPTIVAN算法在个性化的初始万科米辛剂量方面取得了重大进展,克服了当前TDM的局限性.
- 这种基于ML的方法可以减少随后的剂量调整,并提高万科米辛治疗的疗效.
- 一个用户友好的,基于网络的应用程序的OPTIVAN算法可用于实际的临床使用.
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