在一般实践中使用机器学习对levothyroxine进行模型告知精确剂量:开发,验证和临床模拟试验
Jules M Janssen Daalen1, Djoeke Doesburg2, Liesbeth Hunik3
1Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.
使用机器学习的模型知情精确剂量 (MIPD) 改善了初级保健中莱沃西的剂量. 这种人工智能工具减少了剂量错误,并增加了最佳起始剂量,提高了患者的安全性和治疗效率.
科学领域:
- 药理学 药理学是指药理学的学科.
- 人工智能的人工智能
- 临床医学 临床医学
背景情况:
- 利沃甲氧是一种被广泛处方的药物,由于个体的变化和狭窄的治疗窗口,剂量具有挑战性.
- 目前的初级保健中剂量实践缺乏先进的决策支持,导致潜在的低剂量或过量剂量.
- 人工智能开发人员和临床医生之间存在差距,阻碍了医疗保健算法的采用.
研究的目的:
- 开发,验证和临床模拟第一个基于模型的精确剂量 (MIPD) 应用程序,用于初级保健中使用levothyroxine.
- 与传统剂量方法相比,评估MIPD的安全性,可行性和临床影响.
- 为了提高全科医生最初选择levothyroxine剂量的准确性.
主要方法:
- 在国家初级保健数据库 (n=19,004) 上训练并验证了一种多类随机森林模型,以预测最佳的莱沃西剂量类.
- 确定的主要预测特征包括TSH,FT4,体重和年龄.
- 一项临床模拟研究涉及51名全科医生,他们为20个病例开处方Levothyroxine,有或没有MIPD支持.
主要成果:
- MIPD模型实现了0.71的加权AUC来预测剂量类,即使在亚临床甲状腺功能低下症中也显示出有效性.
- MIPD显著降低了过量剂量率 (30.5%至23.9%) 和大小 (中位数为50至37.5μg).
- 使用MIPD增加了最佳起始剂量的处方 (18.3%至30.2%),全科医生更频繁地考虑实验室结果.
结论:
- 开发的MIPD应用程序是第一种在初级保健中用于levothyroxine的应用程序,证明了临床相关性和安全性.
- MIPD有效地帮助全科医生选择更安全和更优的Levothyroxine起始剂量.
- 该研究强调了人工智能驱动的决策支持的潜力,以提高精准医学和改善患者在常规临床实践中的结果.
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