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

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

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.

Study Overview

• Status: Completed
• Conditions: COVID-19 Disease
• Intervention / Treatment: Other: other

• Study Type: Observational
• Enrollment (Actual): 1000

Contacts and Locations

Study Locations

• China
  • Chongqing, China, 400000
    • Xinqiao Hospital of Chongqing

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 80 years (Adult, Older Adult)
• Accepts Healthy Volunteers: N/A

• Sampling Method: Probability Sample

Study Population

Patients of COVID-19 disease

Description

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 Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Observed group; The patients who were detected COVID-19 disease by RT-PCR and CT imaging.	Other: other; clinical diagnosis

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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
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
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

Collaborators and Investigators

• Sponsor: Xinqiao Hospital of Chongqing

Study record dates

Study Major Dates

• Study Start (Actual): January 17, 2020
• Primary Completion (Actual): August 30, 2020
• Study Completion (Actual): December 31, 2020

Study Registration Dates

• First Submitted: March 18, 2020
• First Submitted That Met QC Criteria: April 12, 2020
• First Posted (Actual): April 15, 2020

Study Record Updates

• Last Update Posted (Actual): February 23, 2024
• Last Update Submitted That Met QC Criteria: February 22, 2024
• Last Verified: February 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Coronavirus Infections; Coronaviridae Infections; Nidovirales Infections; RNA Virus Infections; Virus Diseases; Infections; Respiratory Tract Infections; Respiratory Tract Diseases; Pneumonia, Viral; Pneumonia; Lung Diseases; COVID-19
• Other Study ID Numbers: XQonc-015

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No