自动警报对死亡率的异质影响
Benjamin D Wissel1,2, Zana Percy3, Tanner J Zachem2
1Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, United States.
Journal of the American Medical Informatics Association : JAMIA
|December 25, 2025
概括
针对急性损伤 (AKI) 的电子警报对患者死亡率有不同的影响. 对预测受益的患者进行个性化的警报传递可以改善结果并减少死亡.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 医疗信息学 医疗信息学
- 临床决策支持 临床决策支持
背景情况:
- 电子警报旨在通过标记急性损伤 (AKI) 等潜在健康问题来改善患者的治疗结果.
- 这些警报的有效性在不同患者群体之间可能会有很大差异.
- 了解这种异质性对于优化警报系统至关重要.
研究的目的:
- 调查急性损伤 (AKI) 电子警报对治疗效果的多样性.
- 确定从AKI警报中受益或可能受到损害的患者子组.
- 探索警报触发的提供者行动如何影响患者死亡率.
主要方法:
- 来自三项随机对照试验的个人患者数据的二次分析.
- 利用机器学习来预测14天全因死亡率的个性化警报效应.
- 进行了内部和外部验证,包括对个别患者数据的元分析.
主要成果:
- 预计受益于警报的患者与预计受到伤害的患者相比,死亡风险较低.
- 机器学习在患有高血压和预测风险较低的患者中发现了警报降低的死亡率.
- 在非城市和非教学医院发出警报时,观察到死亡率增加,提供者反应各异.
结论:
- 在AKI警报对患者死亡率的影响方面存在显著的异质性.
- 根据预测的好处量身定制警报可能会减少伤害并改善临床结果.
- 个性化警报有潜力降低全因死亡率,因此有必要进行未来的试验.
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