Automated analysis of lung computed tomography (CT) scans may help characterize subphenotypes of acute respiratory illness. We integrated lung CT features measured via deep learning with clinical and laboratory data in spontaneously breathing subjects to enhance the identification of COVID-19 subphenotypes. 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. 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. 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. Trial registration: ClinicalTrials.gov Identifier: NCT04395482. Registration date: 19/05/2020.

中文翻译：

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通过肺部 CT 扫描对患有 AI 的自主呼吸患者进行 COVID-19 呼吸衰竭表型分析


肺部计算机断层扫描 (CT) 扫描的自动分析可能有助于表征急性呼吸道疾病的亚表型。我们将通过深度学习测量的肺部 CT 特征与自主呼吸受试者的临床和实验室数据相结合，以增强对 COVID-19 亚表型的识别。这是一项多中心观察性队列研究，对象是患有自主呼吸的 COVID-19 呼吸衰竭患者，入院 7 天内接受早期肺部 CT 检查。我们使用深度学习方法进行定量和定性分析，探索肺部 CT 图像；使用临床、实验室和肺部 CT 变量进行潜在类别分析 (LCA)；遵循 3D 空间轨迹的亚表型之间的区域差异。 559 名患者的完整数据集可用。 LCA 鉴定出两种亚表型（亚表型 1 和 2）。与亚表型 2 (n = 403) 相比，亚表型 1 患者 (n = 156) 年龄更大，炎症生物标志物更高，并且低氧血症更严重。与亚表型 2 相比，亚表型 1 的肺具有更高的密度重力梯度，实变肺的比例更大。相反，与亚表型 1 相比，亚表型 2 的窦下-肺门密度梯度更高，磨玻璃样混浊的比例更大。即使在对有临床意义的变量进行调整后，亚表型 1 也比亚表型 2 表现出更高的与内皮功能障碍相关的合并症发生率和更高的 90 天死亡率。将肺部 CT 数据整合到 LCA 中，使我们能够识别具有不同临床轨迹的两种 COVID-19 亚表型。这些探索性发现表明，机器学习指导下的自动成像表征在呼吸衰竭患者亚表型分析中的作用。 试验注册：ClinicalTrials.gov 标识符：NCT04395482。注册日期：2020年5月19日。