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老年急性护理风险评分:一种基于机器学习的实用工具,用于选高风险老年住院患者

Sunghwan Ji¹, Geon Young Jang², Ji Yeon Baek²

¹Department of Information Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea; Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea.

Journal of the American Medical Directors Association

|August 25, 2025

概括

一个新的机器学习风险评分,即老年急性护理 (ACE) 风险评分,使用现有数据有效预测住院老年患者的不良结果. 这种工具有助于早期识别和针对高风险个体的干预.

科学领域:

老年医学
医疗保健中的人工智能
临床风险预测

背景情况:

住院的老年患者有很高的副作用风险.
现有的风险评估工具可能无法完全捕捉这种风险.
早期识别高危患者对于及时干预至关重要.

研究的目的:

制定和验证老年急性护理 (ACE) 风险评分.
将临床脆弱度表 (CFS) 与临床和实验室数据结合起来.
预测老年患者的不良结果.

主要方法:

使用机器学习框架 (AutoScore) 的回顾性队列研究.
包括21,757名65岁以上的住院患者.
开发了一个5个变量的节模型:CFS,白蛋白,C-反应蛋白,血红蛋白,和入院前的药物.

主要成果:

与单独使用CFS (AUC 0. 798) 和年龄 (AUC 0. 630) 相比,ACE风险评分显示出更高的预测性能 (AUC 0. 837).
较高的ACE分数与安全事件,再入院,更长的住院时间和快速反应小组激活的风险增加相关.
该评分有效预测了住院妄,压力,跌倒和死亡的复合结果.

结论:

ACE风险评分是一个实用,可解释和可扩展的工具,用于早期识别高风险的老年住院患者.
使用在入院第一天可用的数据来支持及时,有针对性的老年干预.
促进在医院环境中更广泛地实施基于风险的老年人护理策略.

关键词:

脆弱性机器学习患者安全

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