基于国家紧急部门拥挤研究 (NEDOCS) 评分的紧急部门拥挤情况的多变量预测模型的开发和验证
Diego Hernán Giunta1,2,3,4, Diego Sanchez Thomas5, Lucrecia Bustamante6
1Internal Medicine Research Unit, Hospital Italiano de Buenos Aires, CABA, Argentina.
Internal and emergency medicine
|December 31, 2024
概括
使用可访问的数据,可以准确预测急诊室拥挤情况 (EDOC). 开发的模型预测EDOC,使得有效的资源管理和改善患者护理.
科学领域:
- 紧急医疗 紧急医疗
- 医疗保健服务研究 医疗服务研究
- 预测分析是一种预测分析.
背景情况:
- 有效的紧急部门 (ED) 管理依赖于预测潜在的拥挤情况.
- 开发对ED过度拥挤的准确预测模型对于资源分配至关重要.
研究的目的:
- 使用可访问的数据,开发和验证持续关键ED过度拥挤 (EDOC) 的预测模型.
- 确定用于预测EDOC的发病,持续时间和发生的关键变量.
主要方法:
- 从2016年6月到2018年5月的ED数据的回顾性队列研究.
- 估计每小时的全国紧急情况部门拥挤研究 (NEDOCS) 评分.
- 开发三种物流回归模型,以预测持续关键EDOC的开始,结束或发生,并通过内部和外部验证.
主要成果:
- 主要模型,包括NEDOCS,天气和患者流动变量,在预测EDOC发生时显示出高差异性 (AUC 0.997).
- 内部验证显示出优异的校准,在外部验证上可以接受的校准.
- 预测EDOC开始和结束的模型还纳入了相关的临床和操作因素.
结论:
- 可以使用易于获得的变量开发持续关键EDOC的验证预测模型.
- 这些模型在ED过度拥挤期间为有效的资源管理提供了宝贵的工具.
- 这些发现支持主动策略,以减轻ED过度拥挤对患者护理的影响.
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