COVID-19 Disease
Conditions
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.
Interventions
OTHERother
clinical diagnosis
Sponsors
Xinqiao Hospital of Chongqing
Study design
Observational model
CASE_ONLY
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 80 Years
Inclusion criteria
* Patients of COVID-19 disease confirmed by virus nucleic acid RT-PCR and CT
Exclusion criteria
* unconfirmed suspected cases * Patients during pregnancy and lactation * incomplete clinical data * inestigators considered patients ineligible for the trial
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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). |
| 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. |
Countries
China
Outcome results
None listed