外部验证和风险预测模型的应用为呼吸机相关的肺炎在ICU患者机械呼吸:一个前性队列研究
Jiaying Li1, Guifang Li2, Ziqing Liu3
1School of Nursing, Ningxia Medical University, Yinchuan, Ningxia 750004, China; Department of Critical Care Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong 510120, China.
International journal of medical informatics
|April 8, 2025
概括
这项研究验证了一种与呼吸机相关的肺炎 (VAP) 预测模型,发现它可靠用于临床使用. 该模型在预测机械通风 (MV) 患者的VAP风险方面表现稳定,具有普遍性.
科学领域:
- 关键护理医学 关键护理医学
- 传染病流行病学 传染病流行病学
- 医疗信息学 医疗信息学
背景情况:
- 呼吸机相关性肺炎 (VAP) 是一个重大的临床挑战,需要早期识别和预防策略.
- 可靠的预测模型对于需要机械通风 (MV) 的患者有效的临床决策至关重要.
研究的目的:
- 通过使用不同时间段和医院的数据,对先前开发的VAP预测模型进行外部验证.
- 开发和评估一个基于Web的计算器,用于临床应用VAP预测模型.
主要方法:
- 在中国西北地区的两个三级ICU中从MV患者收集未来的数据,以进行时间和地理验证.
- 使用接收器操作特征曲线 (AUC) 下面的面积,Hosmer-Lemeshow测试,校准曲线和决策曲线分析 (DCA) 评估模型性能.
- 开发一个基于网络的计算器,并使用混矩阵和诊断测试评估其性能.
主要成果:
- 时间和地理验证队列显示出良好的区分 (AUC 0.814和0.800) 和校准.
- 基于网络的计算器在临床实践中实现了高精度 (0.911),灵敏度 (0.900) 和特异性 (0.912).
- 决策曲线分析表明了跨各种值概率的临床效用.
结论:
- 该VAP预测模型证明了稳定可靠的性能和通用性.
- 经过验证的模型及其基于网络的计算器具有重要的临床应用和促进VAP预防的价值.
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