Evidence map›Paper›PMID 39103945›Full record

Observational studyCritical care (London, England)2024

Phenotyping COVID-19 respiratory failure in spontaneously breathing patients with AI on lung CT-scan.

Emanuele Rezoagli, Yi Xin, Davide Signori, Wenli Sun, Sarah Gerard, Kevin L Delucchi, Aurora Magliocca, Giovanni Vitale, Matteo Giacomini, Linda Mussoni and 13 more

Registry-linked trialAbstract readMulticenter StudyObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in Critical care (London, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04395482 (Lung CT Scan Analysis of SARS-CoV2 Induced Lung Injury by Machine Learning), which is not on this map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT04395482 completednot on this map

Lung CT Scan Analysis of SARS-CoV2 Induced Lung Injury by Machine Learning: a Multicenter Retrospective Cohort Study.

TypeobservationalSponsorUniversity of Milano BicoccaRan2020 to 2022Enrolled44Conditionscovid19ArmsLung CT scan analysis in COVID-19 patients
3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

23 authors.

Emanuele RezoagliSchool of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy. emanuele.rezoagli@unimib.it.
Yi XinDepartment of Anesthesiology, Critical Care, and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, USA.
Davide SignoriSchool of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy.
Wenli SunDepartment of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, USA.
Sarah GerardRoy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA, USA.
Kevin L DelucchiDepartment of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, CA, USA.
Aurora MaglioccaDepartment of Anesthesia and Intensive Care Medicine, Policlinico San Marco, Gruppo Ospedaliero San Donato, Bergamo, Italy.
Giovanni VitaleDepartment of Anesthesia and Intensive Care Medicine, Policlinico San Marco, Gruppo Ospedaliero San Donato, Bergamo, Italy.
Matteo GiacominiDepartment of Anesthesia and Intensive Care Medicine, Policlinico San Marco, Gruppo Ospedaliero San Donato, Bergamo, Italy.
Linda MussoniIstituto per la Sicurezza Sociale, San Marino, San Marino.
Jonathan MontomoliDepartment of Anesthesia and Intensive Care, Infermi Hospital, AUSL Romagna, Rimini, Italy.
Matteo SubertDepartment of Anesthesia and Intensive Care Medicine, Melzo-Gorgonzola Hospital, Azienda Socio-Sanitaria Territoriale Melegnano e della Martesana, Melegnano, Milan, Italy.
Alessandra PontiDepartment of Anesthesiology and Intensive Care, ASST Lecco, Lecco, Italy.
Savino SpadaroAnesthesia and Intensive Care, Azienda Ospedaliero-Universitaria of Ferrara, Ferrara, Italy.
Giancarla PoliDepartment of Anaesthesia and Critical Care Medicine, Papa Giovanni XXIII Hospital, Bergamo, Italy.
Francesco CasolaDepartment of Physics, Harvard University, 17 Oxford St., Cambridge, MA, 02138, USA.
Jacob HerrmannRoy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA, USA.
Giuseppe FotiSchool of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy.
Carolyn S CalfeeDepartment of Medicine, Cardiovascular Research Institute, University of California, San Francisco, CA, USA.
John LaffeySchool of Medicine, National University of Ireland Galway, Galway, Ireland.
Giacomo BellaniUniversity of Trento, Centre for Medical Sciences-CISMed, Trento, Italy.
Maurizio CeredaDepartment of Anesthesiology, Critical Care, and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, USA.
CT-COVID19 Multicenter Study Group

Funding

Precision Medicine in the Acute Respiratory Distress SyndromeR35HL140026 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI CALFEE, CAROLYN · 2018 to 2024
$6.8M
An Integrated Approach to Predict and Improve the Outcomes of Lung InjuryR01HL137389 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI CEREDA, MAURIZIO FRANCO · 2018 to 2021
$3.0M
Imaging the Pulmonary Circulation to Aid Personalized Management of Acute Respiratory Distress SyndromeR01HL171199 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI Maurizio Franco Cereda · 2024 to 2026
$2.4M
NHLBI NIH HHS R01 HL137389NHLBI NIH HHS R01 HL171199NHLBI NIH HHS R35 HL140026NIH HHS R01-HL137389NIH HHS R35 HL140026Università degli Studi di Milano-Bicocca Starting grant 2020
6 · The paper itself

Abstract

backgroundAutomated 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.

methodsThis 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.

resultsComplete 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.

conclusionsIntegrating 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 registrationClinicalTrials.gov Identifier: NCT04395482. Registration date: 19/05/2020.

Indexed as

COVID-19LungRespiratory InsufficiencyAdultAgedCohort StudiesFemaleHumansMaleMiddle AgedPhenotypeTomography, X-Ray ComputedArtificial intelligenceComputed tomographyCOVID-19Latent class analysisRespiratory failureSubphenotypes

Identifiers

PMID39103945
PMCPMC11301830

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.