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医院住院时间的建模:对抗风险分析的线性和组合模型的比较
Juan Carlos Espinosa-Moreno1,2, Fernando García-García1, Naia Mas-Bilbao3
1Basque Center for Applied Mathematics (BCAM), Bilbao, Basque Country, Spain.
PloS one
|August 26, 2025
概括
这项研究使用竞争性风险分析模拟住院时间 (LoS). 通过先进的生命体征数据处理,比标准的早期预警得分显著提高了预测准确性.
科学领域:
- 医疗信息学
- 生物统计学
- 医疗保健管理
背景情况:
- 住院时间 (LoS) 是一个关键的医疗效率指标,通常与医院获得的并发症有关.
- 准确的LOS预测对于资源分配和患者结果管理至关重要.
研究的目的:
- 探索时间到事件分析,特别是竞争性风险 (CR) 模型,用于预测住院患者的LoS.
- 用转换的纵向生命体征来比较CR模型的性能与已建立的临床分数 (NEWS,MEWS).
主要方法:
- 使用竞争风险模型 (因果特异性Cox,分分布危险,随机生存森林) 来分析LoS.
- 将稀疏的纵向生命体征数据转化为横截面统计数据用于建模.
- 包括患者的人口统计 (性别,年龄) 和生命体征作为预测因素.
- 在2-15天的时间内使用综合障碍得分和C指数等指标评估模型性能.
主要成果:
- 最好的模型是使用格雷的测试分割的随机生存森林.
- 该模型的综合障碍得分为0.386,C指数高于99%,障碍得分低于0.006.
- 使用生命体征统计的拟议方法超过了国家预警分数 (NEWS) 和修改预警分数 (MEWS).
结论:
- 竞争的风险模型,特别是具有转换生命体标志数据的随机生存森林,在预测住院时间方面提供了卓越的准确性.
- 这种以数据为导向的方法为医疗管理提供了比传统的早期预警分数更精确的工具.
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