CT Biomarkers Identification by Artificial Intelligence for COVID-19 Prognosis

Covid-19 Clinical Trial

— COVID 19-IA  

Official title:

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

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT04418245
• Other study ID #: NIMAO/2020/COVID 19-IA/JF-01
• Status: Active, not recruiting
• Start date: March 1, 2020
• Est. completion date: September 2021

Study information

• Verified date: August 2021
• Source: Centre Hospitalier Universitaire de Nimes
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 1000
• Est. completion date: September 2021
• Est. primary completion date: September 2021
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A and older

Eligibility

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

Related Conditions & MeSH terms

• Covid-19

Intervention

Diagnostic Test:
• Imaging by thoracic scanner
Low-dose computed tomography

Locations

Country	Name	City	State
France	CHU la Timone	Marseille
France	CHU Montpellier	Montpellier
France	CHU de Nimes	Nîmes
France	CHU Poitiers	Poitiers
France	CHU Strasbourg	Strasbourg
Martinique	CHU Martinique	Fort-de-France

Sponsors (1)

Lead Sponsor	Collaborator
Centre Hospitalier Universitaire de Nimes

Countries where clinical trial is conducted

France,  Martinique, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Vital status	Dead/alive	Day 8
Primary	Patient requiring more than 3 liters of oxygen to maintain a saturation >95% (intensive care unit or resuscitation department)	Yes/no	Day 8
Primary	Percentage of lung affected on CT	% ground glass and condensation calculated by deep learning	Day 0
Primary	Percentage of lung affected by ground glass opacity on scan	% calculated by deep learning	Day 0
Primary	Percentage of lung affected by condensation on scan	% calculated by deep learning	Day 0
Secondary	Vital status	Dead/alive	Day 16
Secondary	Vital status	Dead/alive	Day 30
Secondary	Length of hospitalization	Days	Maximum 30 days
Secondary	rehospitalization	Yes/no	Day 30
Secondary	Duration of intubation	Days	Day 30
Secondary	Percentage of lung affected on CT	% ground glass and condensation calculated by deep learning	Day 16
Secondary	Percentage of lung affected by ground glass opacity on scan	% calculated by deep learning	Day 16
Secondary	Percentage of lung affected by condensation on scan	% calculated by deep learning	Day 16
Secondary	Software operating time	Speed of image loading and image processing depending of brand of scanner	End of study (August 2020)
Secondary	C-reactive protein levels	mg/L	Admission Day 0
Secondary	lactate dehydrogenase	U/L	Admission Day 0
Secondary	lymphocytemia	g/L	Admission Day 0
Secondary	D Dimers level	µg/L	Admission Day 0
Secondary	Time until onset of symptoms	Days	Admission Day 0
Secondary	Time between RT-PCR positive results and first scan	Hours	Admission Day 0
Secondary	Age	Years	Admission Day 0
Secondary	BMI> 30	Yes/no:	Admission Day 0
Secondary	Medical history of cardiovascular disease	Yes/no: hypertension, coronary artery disease, congestive heart failure, cardiac arrhythmia	Admission Day 0
Secondary	Diabetes	Yes/no	Admission Day 0
Secondary	Medical history of respiratory disease	Yes/no: Chronic obstructive pulmonary disease, chronic respiratory failure	Admission Day 0
Secondary	Medical history of immunosuppressed condition	Yes/no: steroid use, pre-existing immunological condition, current chemotherapy for cancer	Admission Day 0
Secondary	Current or previous history of smoking	Yes/no:	Admission Day 0
Secondary	Calculate a prognostic score from clinical, biological and CT parameters	Deep learning algorithm	Day 8
Secondary	Calculate a prognostic score from clinical and biological parameters only	Deep learning algorithm	Day 8
Secondary	Compare receiver operating curves of prognostic scores with and without CT parameters		Day 8