无监督机器学习分析,以确定ICU药物使用模式,以预测液体过载
Kelli Keats1, Shiyuan Deng2, Xianyan Chen2
1Augusta University Medical Center, Department of Pharmacy, Augusta, GA.
静脉注射药物会导致ICU中的液体过载. 机器学习确定了一个与流体过载密切相关的特定药物集群,改进了预测模型.
科学领域:
- 关键护理医学 关键护理医学
- 药理学 药理学是指药理学的学科.
- 数据科学数据科学数据科学
背景情况:
- 静脉注射 (IV) 药物是重症监护室 (ICU) 液体过载 (FO) 的重要原因之一.
- 静脉注射药物管理模式与FO发生之间的确切关系仍然不完全理解.
- 了解这些关联对于优化患者管理和预防不良结果至关重要.
研究的目的:
- 调查静脉注射药物管理模式与ICU患者的FO发生之间的关联.
- 通过使用先进的分析技术,识别与FO发展相关的特定IV药物集群.
主要方法:
- 成年ICU患者的回顾性队列研究,详细的液体平衡数据.
- 流体过载 (FO) 被定义为体液平衡≥7%的体重在ICU入院后72小时内.
- 主要成分分析 (PCA) 和受限制的博尔兹曼机器 (RBM) 用于集群IV药物管理数据,然后对FO和非FO组进行统计比较.
主要成果:
- 在927名注册患者中,13.7%的患者出现了流体过载 (FO).
- 确定了十个不同的静脉注射药物集群;集群7显示了与FO最强的关联 (平均药物:FO中的25.6与非FO中的10.9,p<0.0001).
- 集群7药物,包括连续输液,抗生素和镇静剂/止痛药,当添加到现有模型中时,改善了FO预测 (AUROC 0.65,p=0.0004).
结论:
- 机器学习有效地确定了一组特定的静脉注射药物,这些药物与ICU患者的FO发展密切相关.
- 将这种药物集群集成到预测模型中可以提高FO风险评估的准确性.
- 这种方法具有实时临床应用的潜力,以促进FO的早期检测并改善患者的治疗结果.
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