使用接触级数据对抗微生物药物使用风险调整进行比较:可行性和变量选择
Rebekah W Moehring1, Michael E Yarrington1, Elizabeth Dodds Ashley1
1Duke University, Department of Medicine, Division of Infectious Diseases, Duke Center for Antimicrobial Stewardship and Infection Prevention, Durham, NC, USA.
概括
对医院抗菌药物使用 (AU) 的外部比较可以为管理策略提供信息. 经验级数据和机器学习模型在风险调整方面被证明是可行的和有意义的,不可知论方法的表现与专家评判的方法相比.
科学领域:
- 医疗保健分析 医疗保健分析
- 医疗信息学 医疗信息学
- 抗微生物药物管理委员会
背景情况:
- 医院抗菌药物使用 (AU) 的外部比较需要使用遭遇特征进行风险调整,以告知抗菌药物管理计划战略.
- 遭遇级建模的障碍包括数据收集的可行性和风险调整的最佳变量选择.
研究的目的:
- 测量在多系统医院协作中共享验证的,接触级别的AU数据方面的成就.
- 使用回顾性分析对AU风险调整模型的变量选择策略进行比较.
主要方法:
- 利用来自50家美国医院 (2020-2021) 的电子健康记录数据进行模型培训和测试.
- 我们比较了四种输入变量策略:与诊断相关的组,Elixhauser并发症,不可知论的临床分类软件精制 (CCSR) 和裁决的CCSR.
- 采用梯度增强的机器树型模型来估计抗菌治疗日 (DOT),以平均绝对误差 (MAE) 测量准确度.
主要成果:
- 76家医院中的50家医院成功共享了经过验证的数据集.
- 具有更多CCSR输入的建模策略产生了最低的MAE.
- 无神论和判断策略显示高度相关的估计和类似的有影响力的变量.
结论:
- 专家判定是资源密集的,与不可知论方法相比,它没有产生优异的结果.
- 使用广泛的遭遇级数据和机器学习进行风险调整是可行的,对于未来的医院AU评估是有价值的.
相关概念视频
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