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

July 20, 2022 updated by: University of Milano Bicocca

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

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.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Detailed Description

BACKGROUND:

In February, the first case of SARS-CoV2 positive patient was recorded in Lombardy (Italy), a virus capable of causing a severe form of acute respiratory failure called Coronavirus Disease 2019 (COVID-19).

Qualitative assessments of lung morphology have been identified to describe macroscopic characteristics of this infection upon admission and during the hospitalization of patients.

At the moment, there are no studies that have exhaustively described the parenchymal lung damage induced by SARS-CoV2 by quantitative analysis.

The hypothesis of this study is that specific morphological and quantitative alterations of the lung parenchyma assessed by means of CT scan in patients suffering from severe respiratory insufficiency induced by SARS-CoV2 may have an impact on the severity of the degree of alteration of the respiratory exchanges (oxygenation and clearance of the CO2) and have an impact on patient outcome.

The presence of characteristic lung morphological patterns assessed by CT scan could allow the recognition of specific patient clusters who can benefit from intensive treatment differently, making a significant contribution to stratifying the severity of patients and their risk of mortality.

This is an exploratory clinical descriptive study of lung CT images in a completely new patient population who are nucleic acid amplification test confirmed SARS-CoV2 positive.

SAMPLE SIZE (n. patients):

The study will collect all patients with the inclusion criteria; a total of 500 patients are expected to be collected.

About 80 patients will be enrolled for each local experimental center.

The following patient data will be analyzed:

  • blood gas analytical data assigned to the CT scan, checks performed upon entering the hospital, at the time of performing the CT scan, admission to intensive care and 7 days after entry
  • patient characteristics such as age, gender and body mass index (BMI)
  • comorbidity
  • presence of organ dysfunction with the Sequential Organ Failure Assessment (SOFA)
  • laboratory data relating to hospital admission and symptoms prior to hospitalization.
  • ventilator and hemodynamic parameters upon entering the hospital, at the time of carrying out the CT scan, upon admission to intensive care and 7 days after entry.

The machine learning approach of lung CT scan analysis will aim at evaluating:

  1. Quantitative and qualitative lung alterations;
  2. The stratification of such morphological characteristics in specific morphological lung clusters identified by the means of artificial intelligence using deep learning algorithms.

ETHICAL ASPECTS:

The lung CT scan images will be collected and anonymized. Images will be subsequently sent by University of Milano-Bicocca Institutional google drive account to the University of Pennsylvania, Department of Anesthesiology and Critical Care and the Department of Radiology in a deidentified format for advanced quantitative analysis taking advantage of artificial intelligence using deep learning algorithms.

The data will be collected in a pseudo-anonymous way through paper Case Report Form (CRF) and analyzed by the scientific coordinator of the project.

Given the retrospective nature of the study and in the presence of technical difficult in obtaining an informed consent of patients in this period of pandemic emergency, informed consent will be waived.

STATISTICAL ANALYSIS:

Continuous data will be expressed as mean ± standard deviation or median and interquartile range, according to data distribution that will be evaluated by the Shapiro-Wilk test. Categorical variables will be expressed as proportions (frequency).

The deep learning segmentation algorithm will segment the lung parenchyma from the entire CT lung. Lung volume, lung weight and opacity intensity distribution analysis will be applied. Second, clustering analysis to stratify the patients will be performed. Both an intensity and a spatial clustering algorithm will be tested. Third, a model will be trained to predict the injury progression using the images and all other patient data. Statistical significance will be considered in the presence of a p<0.05 (two-tailed).

Study Type

Observational

Enrollment (Actual)

44

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Italy
      • Bergamo, Italy
        • Ospedale Papa Giovanni XXIII
      • Bergamo, Italy
        • Policlinico San Marco-San Donato group
      • Ferrara, Italy
        • Azienda Ospedaliero-Universitaria di Ferrara
      • Lecco, Italy
        • ASST di Lecco Ospedale Alessandro Manzoni
      • Melzo, Italy
        • ASST Melegnano-Martesana, Ospedale Santa Maria delle Stelle
      • Monza, Italy
        • ASST Monza
      • Rimini, Italy
        • AUSL Romagna-Ospedale Infermi di Rimini
  • San Marino
      • San Marino, San Marino
        • Istituto per la Sicurezza Sociale-Ospedale della Repubblica di San Marino

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

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.

Description

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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Observational Models: Cohort
  • Time Perspectives: Retrospective

Cohorts and Interventions

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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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.
Until patient discharge from the hospital (approximately 6 months)
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.
Until patient discharge from the hospital (approximately 6 months)

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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.
Until patient discharge from the hospital (approximately 6 months)
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.
Until patient discharge from the hospital (approximately 6 months)
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.
Until patient discharge from the hospital (approximately 6 months)
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.
Until patient discharge from the hospital (approximately 6 months)
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.
Until patient discharge from the hospital (approximately 6 months)
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.
Until patient discharge from the hospital (approximately 6 months)

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

University of Milano Bicocca

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Helpful Links

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

May 7, 2020

Primary Completion (Actual)

June 15, 2021

Study Completion (Actual)

March 31, 2022

Study Registration Dates

First Submitted

May 18, 2020

First Submitted That Met QC Criteria

May 19, 2020

First Posted (Actual)

May 20, 2020

Study Record Updates

Last Update Posted (Actual)

July 21, 2022

Last Update Submitted That Met QC Criteria

July 20, 2022

Last Verified

July 1, 2022

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • TAC-COVID19

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

No

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

Clinical Trials on covid19

Clinical Trials on Lung CT scan analysis in COVID-19 patients

Search Similar Trials

Sponsors and Collaborators

Medical Conditions

Drug Interventions

CROs by country

CROs in Belgium

Conditions

Rare Diseases

Drug Interventions

Dietary Supplements

Sponsor/Collaborators

Locations

3
Subscribe