METHODS: This is a multicenter observational cohort study in spontaneously breathing patients with COVID-19 respiratory failure exposed to early lung CT within 7 days of admission. We explored lung CT images using deep learning approaches to quantitative and qualitative analyses; latent class analysis (LCA) by using clinical, laboratory and lung CT variables; regional differences between subphenotypes following 3D spatial trajectories.
RESULTS: Complete datasets were available in 559 patients. LCA identified two subphenotypes (subphenotype 1 and 2). As compared with subphenotype 2 (n = 403), subphenotype 1 patients (n = 156) were older, had higher inflammatory biomarkers, and were more hypoxemic. Lungs in subphenotype 1 had a higher density gravitational gradient with a greater proportion of consolidated lungs as compared with subphenotype 2. In contrast, subphenotype 2 had a higher density submantellar-hilar gradient with a greater proportion of ground glass opacities as compared with subphenotype 1. Subphenotype 1 showed higher prevalence of comorbidities associated with endothelial dysfunction and higher 90-day mortality than subphenotype 2, even after adjustment for clinically meaningful variables.
CONCLUSIONS: Integrating lung-CT data in a LCA allowed us to identify two subphenotypes of COVID-19, with different clinical trajectories. These exploratory findings suggest a role of automated imaging characterization guided by machine learning in subphenotyping patients with respiratory failure.
BACKGROUND: ClinicalTrials.gov Identifier: NCT04395482. Registration date: 19/05/2020.
方法:这是一项多中心观察性队列研究,在入院7天内暴露于早期肺部CT的COVID-19呼吸衰竭自主呼吸患者中进行。我们使用深度学习方法对肺部CT图像进行定量和定性分析;通过使用临床,实验室和肺部CT变量;3D空间轨迹后,亚表型之间的区域差异。
结果:559例患者获得了完整的数据集。LCA鉴定了两种亚表型(亚表型1和2)。与亚表型2(n=403)相比,亚表型1患者(n=156)年龄较大,有更高的炎症生物标志物,和更多的低氧血症。与亚表型2相比,亚表型1中的肺具有更高的密度重力梯度,合并肺的比例更高。相比之下,与亚表型1相比,亚表型2具有更高的密度下骨-肺门梯度,毛玻璃混浊的比例更高。亚表型1显示与内皮功能障碍相关的合并症的患病率和90天死亡率高于亚表型2,即使在调整了有临床意义的变量后也是如此。
结论:在LCA中整合肺CT数据使我们能够识别COVID-19的两种亚型,具有不同的临床轨迹。这些探索性发现表明,机器学习指导的自动成像表征在呼吸衰竭患者的亚表型中的作用。
背景:ClinicalTrials.gov标识符:NCT04395482。注册日期:2020-05-19。