Clinical trial NCT04395482: Lung CT Scan Analysis of SARS-CoV2 Induced Lung Injury
Official title: Lung CT Scan Analysis of SARS-CoV2 Induced Lung Injury by Machine Learning: a Multicenter Retrospective Cohort Study.
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: covid19
Keywords: Lung injury
sars-covid-2
coronavirus infection
Lead sponsor: University of Milano Bicocca
Sponsor class: OTHER
First posted: 2020-05-20
Enrollment: 44
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
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.

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.

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).

Eligibility 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

Primary outcomes:
measure: A qualitative analysis of parenchymal lung damage induced by COVID-19
timeFrame: Until patient discharge from the hospital (approximately 6 months)
measure: A quantitative analysis of parenchymal lung damage induced by COVID-19
timeFrame: Until patient discharge from the hospital (approximately 6 months)
Secondary outcomes:
measure: The potential impact of parenchymal morphological CT scans in patients with severe moderate respiratory failure.
timeFrame: Until patient discharge from the hospital (approximately 6 months)
measure: The potential impact of parenchymal morphological CT scans in patients with severe moderate respiratory failure.
timeFrame: Until patient discharge from the hospital (approximately 6 months)
measure: The potential impact of parenchymal morphological CT scans in patients with severe moderate respiratory failure.
timeFrame: Until patient discharge from the hospital (approximately 6 months)
measure: Automated segmentation of lung scans of patients with COVID-19 and ARDS.
timeFrame: Until patient discharge from the hospital (approximately 6 months)
measure: Knowledge of chest CT features in COVID-19 patients and their detail through the use of machine learning and other quantitative techniques.
timeFrame: Until patient discharge from the hospital (approximately 6 months)
measure: The ability within which the analysis of artificial intelligence that uses deep learning models can be used to predict clinical outcomes
timeFrame: Until patient discharge from the hospital (approximately 6 months)
Other outcomes:
Locations:
city: Bergamo
country: Italy
facility: Ospedale Papa Giovanni XXIII
city: Bergamo
country: Italy
facility: Policlinico San Marco-San Donato group
city: Ferrara
country: Italy
facility: Azienda Ospedaliero-Universitaria di Ferrara
city: Lecco
country: Italy
facility: ASST di Lecco Ospedale Alessandro Manzoni
city: Melzo
country: Italy
facility: ASST Melegnano-Martesana, Ospedale Santa Maria delle Stelle
city: Monza
country: Italy
facility: ASST Monza
city: Rimini
country: Italy
facility: AUSL Romagna-Ospedale Infermi di Rimini
city: San Marino
country: San Marino
facility: Istituto per la Sicurezza Sociale-Ospedale della Repubblica di San Marino
Study officials:
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