Journal article
Phenotyping COVID-19 respiratory failure in spontaneously breathing patients with AI on lung CT-scan
Critical care (London, England), Vol.28(1), 263
08/05/2024
DOI: 10.1186/s13054-024-05046-3
PMCID: PMC11301830
PMID: 39103945
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.
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 - Massachusetts General HospitalDavide Signori - University of Milano-BicoccaWenli Sun - University of PennsylvaniaSarah Gerard - University of IowaKevin L. Delucchi - University of California, San FranciscoAurora Magliocca - IRCCS Policlinico San DonatoGiovanni Vitale - IRCCS Policlinico San DonatoMatteo Giacomini - IRCCS Policlinico San DonatoLinda Mussoni - Istituto per la Sicurezza Sociale, San Marino, San MarinoJonathan Montomoli - Ospedale Infermi di RiminiMatteo Subert - ASST Melegnano e della MartesanaAlessandra Ponti - Alessandro Manzoni HospitalSavino Spadaro - Arcispedale Sant'AnnaGiancarla 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 - Ospedale Santa ChiaraMaurizio Cereda - Massachusetts General HospitalCT-COVID19 Multicenter Study Group
- Resource Type
- Journal article
- Publication Details
- Critical care (London, England), Vol.28(1), 263
- Publisher
- BioMed Central
- DOI
- 10.1186/s13054-024-05046-3
- PMID
- 39103945
- PMCID
- PMC11301830
- ISSN
- 1364-8535
- eISSN
- 1466-609X
- Grant note
CT-COVID19 multicenter study group collaborators:Bergamo: Ferdinando Luca Lorini, Pietro Bonaffini, Matteo Cazzaniga; Ferrara: Irene Ottaviani; Lecco: Mario Tavola, Asia Borgo; Melzo: Livio Ferraris; Monza: Filippo Serra, Stefano Gatti, Davide Ippolito; Repubblica di San Marino: Beatrice Tamagnini, Marino Gatti, Massimo Arlotti; Rimini: Emiliano Gamberini, Enrico Cavagna; Zingonia: Giuseppe Galbiati, Davide De Ponti.DAS:The datasets generated and/or analysed during the current study are not publicly available due to local regulations but are available from the corresponding author on reasonable request.
- Language
- English
- Date published
- 08/05/2024
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering
- Record Identifier
- 9984696856302771
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