在尿路感染中,算法抗生素决策使用处方医生的治疗效益预测
Alex Howard1,2, Peter L Green3,4,5, Yinzheng Zhong3,4
1Department of Clinical Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, William Henry Duncan Building, University of Liverpool, Liverpool, UK. alexander.howard@liverpool.ac.uk.
NPJ digital medicine
|January 26, 2026
概括
这项研究开发了一种抗生素决策算法,该算法将临床医生的判断与数据相结合,以预测治疗结果. 该算法改善了抗生素处方,与人类处方相比,选择了更合适的口服抗生素.
科学领域:
- 传染性疾病 传染性疾病
- 临床信息学 临床信息学
- 药理学 药理学是指药理学的学科.
背景情况:
- 抗生素耐药性是一个日益增长的威胁,需要改进处方实践.
- 目前用于预测抗生素治疗结果的方法缺乏临床医生的定量投入.
- 优化抗生素选择对于有效的治疗和抗菌药物管理至关重要.
研究的目的:
- 开发和评估一个算法,通过整合临床数据和临床医生的判断来预测抗生素治疗结果.
- 将算法的抗生素处方决定与模拟环境中人类临床医生的决定进行比较.
主要方法:
- 开发了使用93,906名患者的抗生素处方和尿液培养数据的临床预测模型.
- 权重模型预测与49名临床医生的治疗决定在排名练习中.
- 使用紧急部门数据模拟算法性能.
主要成果:
- 算法选择了更多的世界卫生组织 (WHO) 获取类别抗生素 (75.6%对11.9%) 和口服抗生素 (69%对22.6%),而不是人类处方药.
- 与人类处方者相比,该算法处方的静脉注射抗生素较少 (31.2%对65.8%).
- 在所有比较中都实现了统计显著性 (p < 0.001).
结论:
- 开发的算法有效地将数据驱动的预测与人类专业知识相结合,用于抗生素决策.
- 这种方法有可能提高抗生素处方的适当性,并打击耐药性.
- 将临床医生的判断整合到预测模型中可以提高抗生素选择的准确性.
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