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NCT04347369 Clinical Trial

A Retrospective Study of Neural Network Model to Dynamically Quantificate the Severity in COVID-19 Disease

COVID-19 Disease Clinical Trial

Official title:
a Retrospective Study of Neural Network Model to Dynamically Quantificate the Severity in COVID-19 Disease

Clinical Trial Details — Status: Completed

Administrative data
NCT number NCT04347369
Other study ID # XQonc-015
Secondary ID
Status Completed
Phase
First received
Last updated
Start date January 17, 2020
Est. completion date December 31, 2020

Study information
Verified date February 2024
Source Xinqiao Hospital of Chongqing
Contact n/a
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial 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.

Recruitment information / eligibility
Status Completed
Enrollment 1000
Est. completion date December 31, 2020
Est. primary completion date August 30, 2020
Accepts healthy volunteers
Gender All
Age group 18 Years to 80 Years
Eligibility 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

Study Design

Related Conditions & MeSH terms

Intervention

Other:
• other
clinical diagnosis

Locations
Country Name City State
China Xinqiao Hospital of Chongqing Chongqing

Sponsors (1)
Lead Sponsor Collaborator
Xinqiao Hospital of Chongqing

Country where clinical trial is conducted

China, 

Outcome
Type Measure Description Time frame Safety issue
Primary discrimination 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). up to 3 months
Primary Calibration The calibration curves analysis is used to show error between the predicted clinical phenotype with prediction model and actual clinical phenotype. up to 3 months
Primary Net benefit 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. up to 3 months
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