Skip to main content
OpenTrials
Completed

NCT Number: NCT04337502

Clinical and Radiomic Model of COVID-19

To develop and validate a machine-learning model based on clinical, laboratory, and radiological characteristics alone or combination of COVID-19 patients to facilitate risk Assessment before and after symptoms and triage (home, hospitalization inward or ICU).

Completed

Looking for future studies?

Notify Me

Key information

Sex eligibility

All sexes

Study type

Observational

Primary location

The central hospital of Wuhan

Wuhan, Hubei, China

About this study

In December 2019, a novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; earlier named as 2019-nCoV), emerged in Wuhan, China. The diseases caused by SARS-CoV-2 is COVID-19. As of March 8, 2020, more than 100 000 COVID-19 patients have been reported globally (more than 80 000 cases in China, more than 20 000 in other countries), and 3 600 patients (3 100 in China, 500 outside of China) have died. The outbreak of COVID-19 constitutes a Public Health Emergency of International Concern.

Among COVID-19 patients, around 80% are mild (non-severe) illness patients, who usually heal within two weeks. However, another 20% of patients may aggravate into a severe or critical illness which results in a longer hospital stay, and the mortality rate for such patients is 13.4%. Therefore, inchoate identification of the high-risk severe patients is extremely important for patient management and medical resource allocation. General quarantine and symptomatic treatment can be used for most non-severe patients, while a higher level of care and green channel to the intensive care unit (ICU) are helpful for severe patients. Previous studies have summarized the clinical and radiological characteristics of severe COVID-19 patients, while which factors are important predictors is still unclear.

Machine learning is a branch of artificial intelligence that enables us to learn knowledge and potential laws from the given data and to build a model for solving problems as human needs. In recent years, machine learning has been developed as a novel tool to analyze large amounts of data from medical records or images. Previous modeling studies focused on forecasting the potential international spread of COVID-19.

Therefore, our purpose is to develop and validate a machine-learning model based on clinical, laboratory, and radiological characteristics alone or combination of COVID-19 patients in the early stage without severe illness from multiple centers for the prediction of severe (or critical) illness in the following hospitalization to facilitate risk Assessment before and after symptoms and triage (home, hospitalization inward or ICU).

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • confirmed COVID-19 patients by high-throughput sequencing or real-time reverse-transcriptase polymerase-chain-reaction (RT-PCR) assay for nasal and pharyngeal swab specimens.

Exclusion criteria

  • patients with severe illness when admitted;
  • time interval > 2 days between the admission and examinations;
  • absent data or delayed results

Treatment and study plan

Machine learning model

Diagnostic Test

Machine learning, such as logistic regression, random forest, and deep learning

Primary outcomes

  1. Predictive performance

    Time frame: Janunary 1, 2020, to February 13, 2020

    AUC, accuracy, sensitivity, and specificity

Sponsors and collaborators

Lead sponsor

Maastricht University

Other

Collaborators

  • Wuhan Central Hospital

Registry information

Official study title

A Clinical and Radiological Model to Predict the Prognosis for COVID-19 Patients

Important dates

Study start
2019
Primary completion
2020
Study completion
2020
First posted
Apr 7, 2020
Registry last updated
Apr 7, 2020

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.