使用基于DRG的住院数据预测30天的再入院:来自第三级医院的大型现实世界后勤回归模型
Wei Shao1, Lixin Shu2, Xufang Wang2
1Department of Pharmacy, Liaoning Institute of Basic Medical Sciences, Shenyang, China.
Frontiers in public health
|March 13, 2026
概括
一个新的模型使用诊断相关组 (DRG) 数据预测30天的再入院. 较长的住院时间增加了再接收风险,而DRG类别是关键预测因素,有助于改善医疗保健的质量.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
背景情况:
- 早期非计划性再接收是基于诊断相关群体 (DRG) 的支付系统中的关键质量指标.
- 有限的大规模数据存在于中国正在进行DRG改革的三级医院30天再入院的全医院预测器.
- 这项研究解决了使用真实世界数据的强大预测模型的需求.
研究的目的:
- 开发和评估基于DRG的后勤回归模型,用于预测30天的非计划性医院再入院.
- 为了确定30天内在中国上海一家三级医院内重新入院的关键预测因素.
- 为DRG支付环境提供风险分层和质量改善工具.
主要方法:
- 一项回顾性研究,利用来自大量第三级医院的行政住院数据 (2023年1月至2024年12月).
- 对62,085例住院患者的分析,不包括那些缺少DRG变量的人.
- 预测因素包括年龄,停留时间,总成本,放电年和主要的DRG类别;使用AUC,Brier分数和校准图表评估模型性能.
主要成果:
- 30天的再接收率为13.0%.
- 较长的逗留时间显著与再接收风险增加有关 (或每天1.016).
- 主要DRG类别显示出与再接收的强烈关联;该模型实现了中等至良好的歧视 (AUC=0.743) 和可接受的校准.
结论:
- 基于DRG的后勤回归模型有效地预测30天的再入院,性能良好.
- 临床病例组合 (DRG类别) 和患者复杂性 (停留时间) 是早期再接收的关键决定因素.
- 该模型可以支持医疗保健质量监测,风险分层以及关于DRG支付改革的政策讨论.
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