Name (Synonyms) | Correlation | |
---|---|---|
D003141 | Communicable Diseases NIH | 0.10 |
D007239 | Infection NIH | 0.07 |
D045169 | Severe Acute Respiratory Syndrome NIH | 0.06 |
D018352 | Coronavirus Infections NIH | 0.05 |
Name (Synonyms) | Correlation |
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There is one clinical trial.
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.
Description: Dead/alive
Measure: Vital status Time: Day 8Description: Yes/no
Measure: Patient requiring more than 3 liters of oxygen to maintain a saturation >95% (intensive care unit or resuscitation department) Time: Day 8Description: % ground glass and condensation calculated by deep learning
Measure: Percentage of lung affected on CT Time: Day 0Description: % calculated by deep learning
Measure: Percentage of lung affected by ground glass opacity on scan Time: Day 0Description: % calculated by deep learning
Measure: Percentage of lung affected by condensation on scan Time: Day 0Description: Dead/alive
Measure: Vital status Time: Day 16Description: Dead/alive
Measure: Vital status Time: Day 30Description: Days
Measure: Length of hospitalization Time: Maximum 30 daysDescription: Yes/no
Measure: rehospitalization Time: Day 30Description: Days
Measure: Duration of intubation Time: Day 30Description: % ground glass and condensation calculated by deep learning
Measure: Percentage of lung affected on CT Time: Day 16Description: % calculated by deep learning
Measure: Percentage of lung affected by ground glass opacity on scan Time: Day 16Description: % calculated by deep learning
Measure: Percentage of lung affected by condensation on scan Time: Day 16Description: Speed of image loading and image processing depending of brand of scanner
Measure: Software operating time Time: End of study (August 2020)Description: mg/L
Measure: C-reactive protein levels Time: Admission Day 0Description: U/L
Measure: lactate dehydrogenase Time: Admission Day 0Description: g/L
Measure: lymphocytemia Time: Admission Day 0Description: µg/L
Measure: D Dimers level Time: Admission Day 0Description: Days
Measure: Time until onset of symptoms Time: Admission Day 0Description: Hours
Measure: Time between RT-PCR positive results and first scan Time: Admission Day 0Description: Years
Measure: Age Time: Admission Day 0Description: Yes/no:
Measure: BMI> 30 Time: Admission Day 0Description: Yes/no: hypertension, coronary artery disease, congestive heart failure, cardiac arrhythmia
Measure: Medical history of cardiovascular disease Time: Admission Day 0Description: Yes/no
Measure: Diabetes Time: Admission Day 0Description: Yes/no: Chronic obstructive pulmonary disease, chronic respiratory failure
Measure: Medical history of respiratory disease Time: Admission Day 0Description: Yes/no: steroid use, pre-existing immunological condition, current chemotherapy for cancer
Measure: Medical history of immunosuppressed condition Time: Admission Day 0Description: Yes/no:
Measure: Current or previous history of smoking Time: Admission Day 0Description: Deep learning algorithm
Measure: Calculate a prognostic score from clinical, biological and CT parameters Time: Day 8Description: Deep learning algorithm
Measure: Calculate a prognostic score from clinical and biological parameters only Time: Day 8