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

Sponsor

Xinqiao Hospital of Chongqing (Other)

Overall Status

Unknown status

CT.gov ID

NCT04347369

Collaborator

(none)

1,000

Enrollment

Location

11.5

Anticipated Duration (Months)

87.2

Patients Per Site Per Month

Study Details

Study Description

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.

Condition or Disease	Intervention/Treatment	Phase
COVID-19 Disease	Other: other

Study Design

Study Type:

Observational

Anticipated Enrollment :

1000 participants

Observational Model:

Case-Only

Time Perspective:

Retrospective

Official Title:

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

Actual Study Start Date :

Jan 17, 2020

Anticipated Primary Completion Date :

Aug 30, 2020

Anticipated Study Completion Date :

Dec 31, 2020

Arms and Interventions

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

Arm

Intervention/Treatment

Observed group

The patients who were detected COVID-19 disease by RT-PCR and CT imaging.

Other: other

clinical diagnosis

Outcome Measures

Primary Outcome Measures

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.

Eligibility Criteria

Criteria

Ages Eligible for Study:

18 Years to 80 Years

Sexes Eligible for Study:

All

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

Contacts and Locations

Locations

	Site	City	State	Country	Postal Code
1	Xinqiao Hospital of Chongqing	Chongqing		China	400000

Sponsors and Collaborators

Xinqiao Hospital of Chongqing

Investigators

None specified.

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.

Responsible Party:

Jianguo Sun, Deputy Director,Head of Oncology department, Principal Investigator, Clinical Professor, Xinqiao Hospital of Chongqing

ClinicalTrials.gov Identifier:

NCT04347369

Other Study ID Numbers:

XQonc-015

First Posted:

Apr 15, 2020

Last Update Posted:

Jul 14, 2020

Last Verified:

Jul 1, 2020

Studies a U.S. FDA-regulated Drug Product:

Studies a U.S. FDA-regulated Device Product:

Additional relevant MeSH terms:

COVID-19

Study Results

No Results Posted as of Jul 14, 2020