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Lung CT Scan Analysis of SARS-CoV2 Induced Lung Injury (TAC-COVID19)

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT04395482
Recruitment Status : Completed
First Posted : May 20, 2020
Last Update Posted : July 21, 2022
Sponsor:
Information provided by (Responsible Party):
University of Milano Bicocca

Study Description
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Brief Summary:
This is a multicenter observational retrospective cohort study that aims to study the morphological characteristics of the lung parenchyma of SARS-CoV2 positive patients identifiable in patterns through artificial intelligence techniques and their impact on patient outcome.

Condition or disease Intervention/treatment
covid19 Other: Lung CT scan analysis in COVID-19 patients

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Study Design
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Study Type : Observational
Actual Enrollment : 44 participants
Observational Model: Cohort
Time Perspective: Retrospective
Official Title: Lung CT Scan Analysis of SARS-CoV2 Induced Lung Injury by Machine Learning: a Multicenter Retrospective Cohort Study.
Actual Study Start Date : May 7, 2020
Actual Primary Completion Date : June 15, 2021
Actual Study Completion Date : March 31, 2022

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Groups and Cohorts
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Group/Cohort Intervention/treatment
covid-19 pneumonia related patients
The study aims to collect the highest number possible of lung CT scan images performed in patients with COVID-19, in order to obtain a large sample size that will allow us to characterize the extent of lung injury, the presence of specific patterns of lung alteration, and their potential association with the outcome of patients - in view of assisting the medical staff in better understanding the grade of the severity impairment in these patients which might be potentially candidates to more intensive therapeutic strategies.
 Other: Lung CT scan analysis in COVID-19 patients
This research project will evaluate the morphological characteristics of the lung by CT scan analysis in COVID-19 patients which will be identified as specific patterns using artificial intelligence technology and their impact on outcome.


Outcome Measures
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Primary Outcome Measures :
  1. A qualitative analysis of parenchymal lung damage induced by COVID-19 [ Time Frame: Until patient discharge from the hospital (approximately 6 months) ]
    Describe the parenchymal lung damage induced by COVID-19 through a qualitative analysis with chest CT through artificial intelligence techniques.

  2. A quantitative analysis of parenchymal lung damage induced by COVID-19 [ Time Frame: Until patient discharge from the hospital (approximately 6 months) ]
    Describe the parenchymal lung damage induced by COVID-19 through a quantitative analysis with chest CT through artificial intelligence techniques.


Secondary Outcome Measures :
  1. The potential impact of parenchymal morphological CT scans in patients with severe moderate respiratory failure. [ Time Frame: Until patient discharge from the hospital (approximately 6 months) ]
    The potential impact of parenchymal morphological CT scans in patients with severe moderate respiratory failure assessed as intensive care mortality.

  2. The potential impact of parenchymal morphological CT scans in patients with severe moderate respiratory failure. [ Time Frame: Until patient discharge from the hospital (approximately 6 months) ]
    The potential impact of parenchymal morphological CT scans in patients with severe moderate respiratory failure assessed as hospital mortality.

  3. The potential impact of parenchymal morphological CT scans in patients with severe moderate respiratory failure. [ Time Frame: Until patient discharge from the hospital (approximately 6 months) ]
    The potential impact of parenchymal morphological CT scans in patients with severe moderate respiratory failure assessed as days free from mechanical ventilation.

  4. Automated segmentation of lung scans of patients with COVID-19 and ARDS. [ Time Frame: Until patient discharge from the hospital (approximately 6 months) ]
    The hypothesis is that the uso of deep neural network models for lung segmentation in Acute Respiratory Distress Syndrome (ARDS) in animal models and Chronic Obstructive Pulmonary Disease (COPD) in patients that could be applied to self-segment the lungs of COVID-19 patients through a learning transfer mechanism with artificial intelligence.

  5. Knowledge of chest CT features in COVID-19 patients and their detail through the use of machine learning and other quantitative techniques. [ Time Frame: Until patient discharge from the hospital (approximately 6 months) ]
    Expand the knowledge of chest CT features in COVID-19 patients and their detail through the use of machine learning and other quantitative techniques comparing CT patterns of COVID-19 patients to those of patients with ARDS.

  6. The ability within which the analysis of artificial intelligence that uses deep learning models can be used to predict clinical outcomes [ Time Frame: Until patient discharge from the hospital (approximately 6 months) ]
    Determine the capacity within which the artificial intelligence analysis that uses deep learning models can be used to predict clinical outcomes from the analysis of the characteristics of the chest CT obtained within 7 days of hospital admission; combining quantitative CT data with clinical data.


Eligibility Criteria
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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
The goal is to collect as many lung CT scan images as possible in patients with COVID-19; according to the preliminary evaluation estimate, a total of 500 patients are expected to be collected.
Criteria

Inclusion Criteria (COVID-19 cohort):

  • Patients 18 years old or above;
  • Positive confirmation with nucleic acid amplification test or serology of SARS-CoV2 by naso-pharyngeal swab, bronchoaspirate sample or bronchoalveolar lavage;
  • Lung CT scan performed within 7 days of hospital admission;

Inclusion criteria (ARDS cohort):

  • Patients above 18 years old or above;
  • Patients admitted to the hospital with a diagnosis of ARDS according to the Berlin criteria;
  • Lung CT scan performed within 7 days of ARDS diagnosis;

Exclusion criteria (ARDS cohort):

● Positive confirmation with nucleic acid amplification test or serology of SARS-CoV2 by naso-pharyngeal swab, bronchoaspirate sample or bronchoalveolar lavage

Contacts and Locations
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Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04395482


Locations
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Italy
Ospedale Papa Giovanni XXIII
Bergamo, Italy
Policlinico San Marco-San Donato group
Bergamo, Italy
Azienda Ospedaliero-Universitaria di Ferrara
Ferrara, Italy
ASST di Lecco Ospedale Alessandro Manzoni
Lecco, Italy
ASST Melegnano-Martesana, Ospedale Santa Maria delle Stelle
Melzo, Italy
ASST Monza
Monza, Italy
AUSL Romagna-Ospedale Infermi di Rimini
Rimini, Italy
San Marino
Istituto per la Sicurezza Sociale-Ospedale della Repubblica di San Marino
San Marino, San Marino
Sponsors and Collaborators
University of Milano Bicocca
More Information
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Additional Information:

Publications:

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Responsible Party: University of Milano Bicocca
ClinicalTrials.gov Identifier: NCT04395482    
Other Study ID Numbers: TAC-COVID19
First Posted: May 20, 2020    Key Record Dates
Last Update Posted: July 21, 2022
Last Verified: July 2022
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by University of Milano Bicocca:
Lung injury
sars-covid-2
coronavirus infection
Additional relevant MeSH terms:
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Lung Injury
Lung Diseases
Respiratory Tract Diseases
Thoracic Injuries
Wounds and Injuries