预测肺栓塞死亡率:一种机器学习方法,在COVID-19患者中具有外部验证.
Diana Alexandra Mîțu1,2, Alexandru Cristian Cindrea1,3, Alexandra Maria Borita2
1Doctoral School, Faculty of General Medicine, "Victor Babes" University of Medicine and Pharmacy, 300041 Timisoara, Romania.
Medicina (Kaunas, Lithuania)
|February 27, 2026
概括
机器学习模型在非COVID患者中改善了肺栓塞 (PE) 风险预测,但在COVID-19患者中表现下降. 需要对COVID-19特定模型进行进一步的研究,以提高紧急部门的准确性.
科学领域:
- 心脏病学 心脏病学
- 肺部病理学 肺部病理学
- 传染性疾病 传染性疾病
- 数据科学数据科学数据科学
背景情况:
- 肺栓塞 (PE) 是SARS-CoV-2感染的常见并发症,导致显著的早期死亡率.
- 在急诊室 (ED) 中精确地分类PE的风险是具有挑战性的,特别是在同时出现的COVID-19的情况下.
- 在COVID-19患者中,现有的PE预后工具的性能尚不清楚.
研究的目的:
- 评估机器学习 (ML) 模型与肺栓塞严重性指数 (PESI) 相比的有效性,以预测急性肺栓塞患者的住院死亡率.
- 评估这些模型在患有和没有COVID-19的患者中的性能.
主要方法:
- 追溯性,单中心研究538急性PE患者通过ED.
- 使用单变量分析和机器学习模型 (XGBoost,随机森林,SVM) 来评估死亡风险.
- 对COVID-19患者进行了外部验证.
主要成果:
- 在非COVIDPE患者中,机器学习模型 (XGBoost,RF) 与PESI相比显示出优异的歧视 (AUC0.864,0.834与0.725).
- 在COVID-19患者中,ML模型和PESI的性能显著下降 (XGBoost AUC 0.635,RF 0.614,PESI 0.584).
- 败血症,PESI V类,高中性粒细胞计数,血小板计数和NT-proBNP在单变量分析中与死亡率有关.
结论:
- 使用常规可用的ED变量的机器学习模型改善了与PESI相比,非COVIDPE的住院死亡率预测.
- 目前的ML模型的通用性在COVID-19患者中是有限的,需要对COVID进行特定的改进.
- 需要进行未来的多中心验证,以确认这些发现,并在COVID-19的背景下开发更准确的PE预后工具.
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