CoronavirusNCT04337502
Clinical and Radiomic Model of COVID-19
An observational study of Coronavirus and Machine Learning, run by Maastricht University at 1 site in China.
Trial summary
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). Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Coronavirus, Machine Learning
- What is being tested?
- Machine learning model
- How many people
- 300 participants
- Where
- 1 siteChina
- Study dates
- 23 Dec 2019to 3 Mar 2020
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: COVID-19 patients
Who may be able to join
- 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.
Who cannot join
- patients with severe illness when admitted;
- time interval > 2 days between the admission and examinations;
- absent data or delayed results
Trial locations and contacts
1 site in 1 country.
About this study
Official title: 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).
Read the 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).
- Conditions
- Coronavirus, Machine Learning
- Being tested
- Machine learning model
- Medical subject headings
- Coronavirus Infections
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Machine learning modelDiagnostic test
Machine learning, such as logistic regression, random forest, and deep learning
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Diagnostic Test: Machine learning model | |
| 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. Receives: Diagnostic Test: Machine learning model |
What is being measured
The primary outcome is the main question the study is designed to answer.
Predictive performance
AUC, accuracy, sensitivity, and specificity
Measured: Janunary 1, 2020, to February 13, 2020
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Apr 2020
- Study start23 Dec 2019
- Primary completion20 Jan 2020
- Study completion3 Mar 2020
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for CoronavirusStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 300 participants (actual)
Sponsor and investigators
- Lead sponsor
- Maastricht University
- Sponsor type
- University, hospital or other
- Collaborators
- Wuhan Central Hospital
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04337502
- Sponsor's protocol ID
- UM_2020_GY_COVID-19
- Status last verified
- Mar 2020
- First submitted
- 6 Apr 2020
- Last updated
- 7 Apr 2020
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: No share plan
Study at a glance
From the registry record, last updated by the sponsor on 7 Apr 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Clinical and Radiological Model to Predict the Prognosis for COVID-19 Patients
- ClinicalTrials.gov ID
- NCT04337502
- Acronym / sponsor ID
- UM_2020_GY_COVID-19
- Conditions
- Coronavirus, Machine Learning
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 300 participants (actual)
- Lead sponsor
- Maastricht UniversityUniversity, hospital or other
- Collaborators
- Wuhan Central Hospital
- Study start
- 23 Dec 2019 (actual)
- Primary completion
- 20 Jan 2020 (actual)
- Study completion
- 3 Mar 2020 (actual)
- First posted
- 7 Apr 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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).
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 design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| severe group | – | Diagnostic Test: Machine learning modelThe 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. |
| non-severe group | – | Diagnostic Test: Machine learning modelThe non-severe group was designated when the patients did not occur in the mentioned severe criteria until discharged from the hospital. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- COVID-19 patients
Key inclusion criteria (1 listed)
- 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.
Key exclusion criteria (3 listed)
- patients with severe illness when admitted;
- time interval > 2 days between the admission and examinations;
- absent data or delayed results
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Machine learning modelMachine learning, such as logistic regression, random forest, and deep learning | Diagnostic test | non-severe group, severe group |
Primary outcome
Predictive performance
Time frame: Janunary 1, 2020, to February 13, 2020
AUC, accuracy, sensitivity, and specificity
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 7 Apr 2020.
- Individual participant dataSharing planned: No. No share plan
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Coronavirus and Machine Learning, run by Maastricht University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 7 Apr 2020. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Predictive performance (time frame: Janunary 1, 2020, to February 13, 2020). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 7 Apr 2020.