PDF(Pubmed)
Abstract：
BACKGROUND: 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.
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.
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.
摘要：
背景：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亚型的识别。
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。
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。
