临床决策支持在急诊室的败血性休克:一个集群随机试验
Halden F Scott1,2, Carter J Sevick3, Kathryn L Colborn3,4
1Section of Pediatric Emergency Medicine, Department of Pediatrics, University of Colorado School of Medicine, Aurora, Colorado.
Pediatrics
|June 9, 2025
概括
使用机器学习的临床决策支持 (CDS) 并没有改善儿童早期感染性休克治疗. 虽然该系统是可行的,并被医疗服务提供者接受,但它并没有减少向低血压性败血症休克的进展.
科学领域:
- 儿科急救医学 儿科急救医学
- 临床信息学 临床信息学
- 医疗保健中的人工智能
背景情况:
- 儿童感染性休克诊断延迟导致可预防的死亡率.
- 有限的证据表明,在儿科败血症中有效的早期识别策略存在.
- 基于机器学习的临床决策支持 (CDS) 假设可以改善早期识别和治疗.
研究的目的:
- 评估基于机器学习的CDS在增加及时接受败血症休克治疗的儿童比例方面的有效性.
- 评估CDS对关键临床结果的影响,包括服用抗生素的时间和低血压败血症休克的发生.
- 确定在儿科急诊室实施预测性CDS的可行性和提供者的接受度.
主要方法:
- 在4个儿科急诊室进行了一项前性的,阶梯形的,集群随机试验.
- 根据电子健康记录数据,CDS利用机器学习模型识别患有败血症的高风险儿科患者.
- 通过在怀疑败血症后1小时内接受抗生素和液体玻尿酸治疗的患者比例来衡量有效性.
主要成果:
- 在干预组和对照组 (39.0%与38.9%) 之间,在初级结局 (抗生素和1小时内注射) 中没有发现显著差异.
- CDS没有显著影响次要结果,包括低血压性败血症休克的发生率或抗生素及时性.
- 提供商发现CDS有价值,不引人注目的,表明良好的采用和维护试验后.
结论:
- 对儿科败血症实施预测性CDS是可行的,并且可以接受医疗保健提供者.
- 尽管被接受,但研究中的CDS并没有改善疑似败血症的早期治疗率,也没有防止进展为低血压休克.
- 可能需要进一步的研究来完善预测模型或实施策略,以改善儿科败血症休克结果.
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