A Retrospective Study of Neural Network Model to Dynamically Quantificate the Severity in COVID-19 Disease
- Conditions
- COVID-19 Disease
- Interventions
- Other: other
- Registration Number
- NCT04347369
- Lead Sponsor
- Xinqiao Hospital of Chongqing
- Brief Summary
The research aim to collect large samples of COVID-19 disease patients with clinical symptoms, laboratory and imaging examination data. Screening the biological indicators which are related to the occurrence of severe diseases. Then, investigators using artificial intelligence (AI) technology deep learning method to find a prediction model that can dynamically quantify COVID-19 disease severity.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 1000
- Patients of COVID-19 disease confirmed by virus nucleic acid RT-PCR and CT
- unconfirmed suspected cases
- Patients during pregnancy and lactation
- incomplete clinical data
- inestigators considered patients ineligible for the trial
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Arm && Interventions
Group Intervention Description Observed group other The patients who were detected COVID-19 disease by RT-PCR and CT imaging.
- Primary Outcome Measures
Name Time Method Calibration up to 3 months The calibration curves analysis is used to show error between the predicted clinical phenotype with prediction model and actual clinical phenotype.
Net benefit up to 3 months Decision curve analysis was used to determine whether the models could be considered useful tools for clinical decisionmaking by comparing the net benefits at any threshold.
discrimination up to 3 months The performance of our prediction model is evaluated with the receiver operating characteristic (ROC) curves, areas under the curves (AUCs) and concordance index (c-index).
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (1)
Xinqiao Hospital of Chongqing
🇨🇳Chongqing, China