CT Biomarkers Identification by Artificial Intelligence for COVID-19 Prognosis (COVID 19-IA)

Identification of Thoracic CT Scan Biomarkers by Deep Learning for Evaluating the Prognosis of Patients With COVID-19 Disease

The study hypothesis is that low-dose computed tomography (LDCT) coupled with artificial intelligence by deep learning would generate imaging biomarkers linked to the patient's short- and medium-term prognosis.

The purpose of this study is to rapidly make available an early decision-making tool (from the first hospital consultation of the patient with symptoms related to SARS-CoV-2) based on the integration of several biomarkers (clinical, biological, imaging by thoracic scanner) allowing both personalized medicine and better anticipation of the patient's evolution in terms of care organization.

Study Overview

• Status: Active, not recruiting
• Conditions: Covid-19
• Intervention / Treatment: Diagnostic test: Imaging by thoracic scanner

• Study Type: Observational
• Enrollment (Anticipated): 1000

Contacts and Locations

Study Locations

• France
  • Marseille, France
    • CHU La Timone
  • Montpellier, France
    • CHU Montpellier
  • Nîmes, France
    • CHU de Nimes
  • Poitiers, France
    • CHU Poitiers
  • Strasbourg, France
    • CHU Strasbourg
• Martinique
  • Fort-de-France, Martinique
    • CHU Martinique

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

Patients hospitalized for Covid-19 confirmed by RT-PCR and undergoing CT scan

Description

Inclusion Criteria:

• Patients positive for SARS-CoV-2 according to RT-PCR test between 1st March and 31st May 2020
• Patients undergoing low dose CT scan to establish Covid-19 lung damage
• Available for at least 8 days follow-up

Exclusion Criteria:

• Patients opposing the retrospective use of their data

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Patients positive for SARS-CoV-2	Diagnostic test: Imaging by thoracic scanner; Low-dose computed tomography

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Vital status	Dead/alive	Day 8
Patient requiring more than 3 liters of oxygen to maintain a saturation >95% (intensive care unit or resuscitation department)	Yes/no	Day 8
Percentage of lung affected on CT	% ground glass and condensation calculated by deep learning	Day 0
Percentage of lung affected by ground glass opacity on scan	% calculated by deep learning	Day 0
Percentage of lung affected by condensation on scan	% calculated by deep learning	Day 0

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Vital status	Dead/alive	Day 16
Vital status	Dead/alive	Day 30
Length of hospitalization	Days	Maximum 30 days
rehospitalization	Yes/no	Day 30
Duration of intubation	Days	Day 30
Percentage of lung affected on CT	% ground glass and condensation calculated by deep learning	Day 16
Percentage of lung affected by ground glass opacity on scan	% calculated by deep learning	Day 16
Percentage of lung affected by condensation on scan	% calculated by deep learning	Day 16
Software operating time	Speed of image loading and image processing depending of brand of scanner	End of study (August 2020)
C-reactive protein levels	mg/L	Admission Day 0
lactate dehydrogenase	U/L	Admission Day 0
lymphocytemia	g/L	Admission Day 0
D Dimers level	µg/L	Admission Day 0
Time until onset of symptoms	Days	Admission Day 0
Time between RT-PCR positive results and first scan	Hours	Admission Day 0
Age	Years	Admission Day 0
BMI> 30	Yes/no:	Admission Day 0
Medical history of cardiovascular disease	Yes/no: hypertension, coronary artery disease, congestive heart failure, cardiac arrhythmia	Admission Day 0
Diabetes	Yes/no	Admission Day 0
Medical history of respiratory disease	Yes/no: Chronic obstructive pulmonary disease, chronic respiratory failure	Admission Day 0
Medical history of immunosuppressed condition	Yes/no: steroid use, pre-existing immunological condition, current chemotherapy for cancer	Admission Day 0
Current or previous history of smoking	Yes/no:	Admission Day 0
Calculate a prognostic score from clinical, biological and CT parameters	Deep learning algorithm	Day 8
Calculate a prognostic score from clinical and biological parameters only	Deep learning algorithm	Day 8
Compare receiver operating curves of prognostic scores with and without CT parameters		Day 8

Collaborators and Investigators

• Sponsor: Centre Hospitalier Universitaire de Nīmes
• Investigators: Principal Investigator: Julien Frandon, Chu Nimes

Study record dates

Study Major Dates

• Study Start (Actual): March 1, 2020
• Primary Completion (Anticipated): September 1, 2021
• Study Completion (Anticipated): September 1, 2021

Study Registration Dates

• First Submitted: June 4, 2020
• First Submitted That Met QC Criteria: June 4, 2020
• First Posted (Actual): June 5, 2020

Study Record Updates

• Last Update Posted (Actual): August 9, 2021
• Last Update Submitted That Met QC Criteria: August 6, 2021
• Last Verified: August 1, 2021

More Information

Terms related to this study

• Keywords: biomarkers; artificial intelligence; low dose CT scan
• Additional Relevant MeSH Terms: Coronavirus Infections; Coronaviridae Infections; Nidovirales Infections; RNA Virus Infections; Virus Diseases; Infections; Respiratory Tract Infections; Respiratory Tract Diseases; Pneumonia, Viral; Pneumonia; Lung Diseases; COVID-19
• Other Study ID Numbers: NIMAO/2020/COVID 19-IA/JF-01

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No