Dataset
Phenotyping COVID-19 respiratory failure in spontaneously breathing patients with AI on lung CT-scan
01/01/2024
DOI: 10.6084/m9.figshare.c.7388898
Abstract
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. Trial registration: ClinicalTrials.gov Identifier: NCT04395482. Registration date: 19/05/2020.
Details
- Title: Subtitle
- Phenotyping COVID-19 respiratory failure in spontaneously breathing patients with AI on lung CT-scan
- Creators
- Emanuele Rezoagli - University of Milano-BicoccaYi Xin - Harvard UniversityDavide Signori - University of Milano-BicoccaWenli Sun - University of PennsylvaniaSarah Gerard - University of IowaKevin L. Delucchi - University of California, San FranciscoAurora MaglioccaGiovanni VitaleMatteo GiacominiLinda MussoniJonathan Montomoli - Ospedale Infermi di RiminiMatteo Subert - ASST Melegnano e della MartesanaAlessandra PontiSavino Spadaro - University of FerraraGiancarla Poli - Ospedale Papa Giovanni XXIIIFrancesco Casola - Harvard UniversityJacob Herrmann - University of IowaGiuseppe Foti - University of Milano-BicoccaCarolyn S. Calfee - University of California, San FranciscoJohn Laffey - Ollscoil na Gaillimhe – University of GalwayGiacomo Bellani - University of TrentoMaurizio Cereda - Harvard University
- Resource Type
- Dataset
- DOI
- 10.6084/m9.figshare.c.7388898
- Language
- English
- Date published
- 01/01/2024
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering
- Record Identifier
- 9985242770802771
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