A Novel Nomogram to Predict Severity of COVID-19

February 22, 2024 updated by: Jianguo Sun, Xinqiao Hospital of Chongqing
Investigators use clinical data from a large sample of COVID-19 disease patients to screen out biomarkers associated with disease severity. Then, a novel nomogram model will be established to predict covid-19 disease severity, which could provide important assistance and supplement for clinical work. In the case of extremely shortage of front-line medical resources, patients with potential severe diseases will be timely treated with the help of the novel nomogram model.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Actual)

1000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

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

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

N/A

Sampling Method

Probability Sample

Study Population

COVID-19 disease patients

Description

Inclusion Criteria:

  • COVID-19 disease patients confirmed by virus nucleic acid RT-PCR and CT

Exclusion Criteria:

  • unconfirmed suspected cases
  • Patients during pregnancy and lactation
  • incomplete clinical data
  • investigators considered patients ineligible for the trial
  • Child patients

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

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

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
the consistency of predicted severe rate and observed severe rate of COVID-19 patients
Time Frame: up to 3 months
We aim to use the clinical data of COVID-19 patients to construct a nomogram model to predict the severe rate of each patient, then the the consistency of predicted severe rate and observed severe rate will be evaluated by calibration plot.
up to 3 months
Duration of severe illness
Time Frame: up to 3 months
the duration of severe illness of each patient will evaluated
up to 3 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Xinqiao Hospital of Chongqing

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 17, 2020

Primary Completion (Actual)

August 30, 2020

Study Completion (Actual)

December 31, 2021

Study Registration Dates

First Submitted

April 21, 2020

First Submitted That Met QC Criteria

April 27, 2020

First Posted (Actual)

April 28, 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

Other Study ID Numbers

  • XQonc-016

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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