预测模型,风险因子得分和与呼吸机相关的肺炎:一个两阶段病例控制研究
Hua Meng1, Yuxin Shi1, Kaming Xue2
1Department of Nosocomial Infection Management, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
一个新的风险因素评分系统有效地预测了重症监护病房的呼吸机相关肺炎 (VAP) 风险. 该工具识别了关键预测因素,有助于制定有针对性的VAP预防策略,以获得更好的患者结果.
科学领域:
- 关键护理医学 关键护理医学
- 传染性疾病 传染性疾病
- 生物统计学 生物统计学
背景情况:
- 呼吸机相关肺炎 (VAP) 是机械呼吸患者在医院获得的重大感染.
- 预测和预防VAP的有效工具目前有限.
研究的目的:
- 开发和验证一个加权风险评分系统 (RFS) 来预测VAP.
- 评估开发的RFS的诊断性能.
主要方法:
- 进行了一项两阶段的VAP病例控制研究.
- 预测模型 (LASSO,RF,XGBoost) 用于识别重要的VAP预测因素.
- 在独立的队列中计算和验证了加权的RFS.
主要成果:
- 对VAP的关键预测指标包括MV前停留时间,MV持续时间,手术,气管切割,MDRO感染,CRP,PaO2和APACHE II分数.
- RFS显示了与VAP风险的显著线性关联 (OR=2.699).
- 在发现 (AUC=0.857) 和验证 (AUC=0.879) 两个阶段,RFS显示出对VAP的优异歧视.
结论:
- 多种因素导致VAP风险.
- 拟议的风险因子评分系统显示了作为预防VAP的有价值的临床工具的潜力.
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