追踪医院内COVID-19结果:一个多州模型探索 (TRACE)
Hamed Mohammadi1, Hamid Reza Marateb1,2, Mohammadreza Momenzadeh3
1Biomedical Engineering Department, Engineering Faculty, University of Isfahan, Isfahan 81746-73441, Iran.
Life (Basel, Switzerland)
|September 28, 2024
概括
这项研究开发了一种多州模型来分析COVID-19患者住院时间,揭示出院,ICU转移和死亡的平均持续时间. 该模型有助于预测患者流量和重症监护室 (ICU) 需求.
科学领域:
- 流行病学 流行病学
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- COVID-19大流行严重影响了医院资源分配和患者管理.
- 准确预测住院时间 (LOS) 对于有效的医疗保健规划至关重要.
- 现有的模型可能无法充分捕捉COVID-19患者通过不同的护理状态的动态过渡.
研究的目的:
- 开发和应用一种新的多州模型,用于估计和预测COVID-19患者住院时间 (LOS).
- 在没有外部软件包的情况下分析影响患者轨迹和资源利用的因素.
- 为管理患者流量和预测重症监护室 (ICU) 需求提供见解.
主要方法:
- 一个多州模型被开发并应用于来自2285名住院COVID-19患者的数据.
- 计算了状态之间的过渡概率和风险率 (入院,ICU转移,出院,死亡).
- 分析了包括年龄,性别,并发症和COVID-19浪潮在内的关键因素.
主要成果:
- 住院患者的平均LOS:11.90天 (出院),2.84天 (ICU转移),34.21天 (死亡).
- 在ICU患者的平均LOS:24.08天 (初始ICU停留),124.30天 (出院),35.44天 (死亡).
- 该模型确定了影响患者发展轨迹的重要因素,并提供了分组特定的ICU负载预测.
结论:
- 开发的多州模型为COVID-19患者流动和医疗保健资源利用提供了关键的见解.
- 结果突出了不同的住院时间和患者的发展轨迹,有助于为未来的健康危机做好准备.
- 未来的工作包括将该模型集成到健康信息系统 (HIS) 中,并通过递归方法提高其效率.
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