Clinical and Radiomic Model of COVID-19

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

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).

Study Overview

• Status: Completed
• Conditions: Coronavirus; Machine Learning
• Intervention / Treatment: Diagnostic test: Machine learning model

Detailed Description

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).

• Study Type: Observational
• Enrollment (Actual): 300

Contacts and Locations

Study Locations

• China
  • Hubei
    • Wuhan, Hubei, China
      • The Central Hospital of Wuhan

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

COVID-19 patients

Description

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:

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

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
severe group; The severe group was designated when the patients had one of the following criteria during hospitalization issued by the Chinese National Health Committee (Version 3-5). 1) Respiratory distress with respiratory frequency ≥ 30/min; 2) Pulse Oximeter Oxygen Saturation ≤ 93% at rest; 3) Oxygenation index (artery partial pressure of oxygen/inspired oxygen fraction, PaO2/FiO2) ≤ 300 mmHg; 4) One of the conditions as following: a) respiratory failure occurs and requires mechanical ventilation; b) Shock occurs; c) ICU admission is required for combined organ failure.	Diagnostic test: Machine learning model; Machine learning, such as logistic regression, random forest, and deep learning
non-severe group; The non-severe group was designated when the patients did not occur in the mentioned severe criteria until discharged from the hospital.	Diagnostic test: Machine learning model; Machine learning, such as logistic regression, random forest, and deep learning

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Predictive performance	AUC, accuracy, sensitivity, and specificity	Janunary 1, 2020, to February 13, 2020

Collaborators and Investigators

• Sponsor: Maastricht University
• Collaborators: Wuhan Central Hospital

Study record dates

Study Major Dates

• Study Start (Actual): December 23, 2019
• Primary Completion (Actual): January 20, 2020
• Study Completion (Actual): March 3, 2020

Study Registration Dates

• First Submitted: April 6, 2020
• First Submitted That Met QC Criteria: April 6, 2020
• First Posted (Actual): April 7, 2020

Study Record Updates

• Last Update Posted (Actual): April 7, 2020
• Last Update Submitted That Met QC Criteria: April 6, 2020
• Last Verified: March 1, 2020

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Coronaviridae Infections; Nidovirales Infections; RNA Virus Infections; Virus Diseases; Infections; Coronavirus Infections
• Other Study ID Numbers: UM_2020_GY_COVID-19

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: No share plan

Drug and device information, study documents

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