COVID-19NCT05119465
COVID-19 Clinical Status Associated With Outcome Severity: An Unsupervised Machine Learning Approach
An observational study of COVID-19, run by Aristotle University Of Thessaloniki at 1 site in Greece.
Trial summary
Since the beginning of the COVID-19 pandemic, 195 million people have been infected and 4.2 million have died from the disease or its side-effects. Physicians, healthcare scientists and medical staff continuously try to deal with overloaded hospital admissions, while in parallel, they try to identify meaningful… Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- COVID-19
- How many people
- 268 participants
- Where
- 1 siteGreece
- Study dates
- 1 Nov 2019to 30 Jun 2021
- 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: patients that came into emergency department and diagnosed with COVID-19 infection
Who may be able to join
- patients that came into emergency department and diagnosed with COVID-19 infection
Who cannot join
- none
Trial locations and contacts
1 site in 1 country.
Greece1 site
University General Hospital of Thessaloniki AHEPA
Thessaloniki, 54621
About this study
Official title: Does Corona Virus Disease (COVID)-19 Clinical Status Associates With Outcome Severity?An Unsupervised Machine Learning Approach for Knowledge Extraction
Since the beginning of the COVID-19 pandemic, 195 million people have been infected and 4.2 million have died from the disease or its side-effects. Physicians, healthcare scientists and medical staff continuously try to deal with overloaded hospital admissions, while in parallel, they try to identify meaningful correlations between the severity of infected patients with their symptoms, comorbidities and biomarkers. Artificial Intelligence (AI) and Machine Learning (ML) have been used recently in many areas related to COVID-19 healthcare. The main goal is to manage effectively the wide variety of issues related to COVID-19 and its consequences. The existing applications of ML to COVID-19 healthcare are based on supervised classification which require a labeled training dataset, serving as reference point for learning, as well as predefined classes. However, the existing knowledge about COVID-19 and its consequences is still not solid and the points of common agreement among different scientific communities are still unclear.
Therefore, this study aimed to follow an unsupervised clustering approach, where prior knowledge is not required (tabula rasa).
Read more
More specifically, 268 hospitalized patients at the First Propaedeutic Department of Internal Medicine of AHEPA University Hospital of Thessaloniki were assessed in terms of 40 clinical variables (numerical and categorical), leading to a high-dimensionality dataset. Dimensionality reduction was performed by applying Principal Component Analysis (PCA) on the numerical part of the dataset and Multiple Correspondence Analysis (MCA) on the categorical part of the dataset. Then, the Bayesian Information Criterion(BIC) was applied to Gaussian Mixture Models (GMM) in order to identify the optimal number of clusters, under which, the best grouping of patients occurs.
The proposed methodology identified 4 clusters of patients with similar clinical characteristics. The analysis revealed a cluster of asymptomatic patients that resulted in death at a rate of 23.8%.
This striking result forces us to reconsider the relationship between the severity of COVID-19 clinical symptoms and patient's mortality.
Read the detailed description
An algorithmic pipeline based on unsupervised machine learning algorithms, which aims to operate in tandem with physicians and provide additional knowledge for the proper categorization of COVID-19 infected patients based on their severity, is proposed in this study. Data from patients hospitalized in our clinic are collected and stored in separate Microsoft Excel files (.xlsx), which are loaded into memory. A script is concatenating them all into a single dataframe where they are checked for NaN (Not a Number) values. Because of the nature of the data, patients with missing information are discarded entirely from the dataset, since information inference would be a biased practice for the particular application. Next, we apply data normalization by scaling all numerical variables between the (0,1) range, so that the range of all numerical variables is the same, and any bias towards certain variables is avoided .A thorough and detailed data collection process was designed in order to collect information for the patients, without disturbing the clinical treatment, or upsetting them in the process.
- Conditions
- COVID-19
- Keywords
- COVID-19, Hospitalization, Machine Learning, Artificial Intelligence
- Medical subject headings
- COVID-19
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Group | Hospitalized Patients with Corona virus disease |
What is being measured
The primary outcome is the main question the study is designed to answer.
Cluster of patients depending on severity of infection
Algorithm produced with artificial intelligence and machine learning approach to classify patients according their status of COVID-19 infection
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted15 Nov 2021
- Study start1 Nov 2019
- Primary completion30 Jun 2021
- Study completion30 Jun 2021
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for COVID-19Studies of the same condition, open and under way.
- More trials run by Aristotle University Of ThessalonikiThe sponsor's other registered studies.
- 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
- Other
- Time perspective
- Retrospective
- Enrolment
- 268 participants (actual)
Sponsor and investigators
- Lead sponsor
- Aristotle University Of Thessaloniki
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Prof. Triantafyllos Didangelos, Aristotle University Of Thessaloniki
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05119465
- Sponsor's protocol ID
- 19400_21052021
- Status last verified
- May 2023
- First submitted
- 12 Nov 2021
- Last updated
- 16 May 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 16 May 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Does Corona Virus Disease (COVID)-19 Clinical Status Associates With Outcome Severity?An Unsupervised Machine Learning Approach for Knowledge Extraction
- ClinicalTrials.gov ID
- NCT05119465
- Acronym / sponsor ID
- 19400_21052021
- Conditions
- COVID-19
- Keywords
- COVID-19, Hospitalization, Machine Learning, Artificial Intelligence
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 268 participants (actual)
- Lead sponsor
- Aristotle University Of ThessalonikiUniversity, hospital or other
- Study start
- 1 Nov 2019 (actual)
- Primary completion
- 30 Jun 2021 (actual)
- Study completion
- 30 Jun 2021 (actual)
- First posted
- 15 Nov 2021 (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.
Since the beginning of the COVID-19 pandemic, 195 million people have been infected and 4.2 million have died from the disease or its side-effects. Physicians, healthcare scientists and medical staff continuously try to deal with overloaded hospital admissions, while in parallel, they try to identify meaningful correlations between the severity of infected patients with their symptoms, comorbidities and biomarkers. Artificial Intelligence (AI) and Machine Learning (ML) have been used recently in many areas related to COVID-19 healthcare. The main goal is to manage effectively the wide variety of issues related to COVID-19 and its consequences. The existing applications of ML to COVID-19 healthcare are based on supervised classification which require a labeled training dataset, serving as reference point for learning, as well as predefined classes. However, the existing knowledge about COVID-19 and its consequences is still not solid and the points of common agreement among different scientific communities are still unclear.
Therefore, this study aimed to follow an unsupervised clustering approach, where prior knowledge is not required (tabula rasa).
More specifically, 268 hospitalized patients at the First Propaedeutic Department of Internal Medicine of AHEPA University Hospital of Thessaloniki were assessed in terms of 40 clinical variables (numerical and categorical), leading to a high-dimensionality dataset. Dimensionality reduction was performed by applying Principal Component Analysis (PCA) on the numerical part of the dataset and Multiple Correspondence Analysis (MCA) on the categorical part of the dataset. Then, the Bayesian Information Criterion(BIC) was applied to Gaussian Mixture Models (GMM) in order to identify the optimal number of clusters, under which, the best grouping of patients occurs.
The proposed methodology identified 4 clusters of patients with similar clinical characteristics. The analysis revealed a cluster of asymptomatic patients that resulted in death at a rate of 23.8%.
This striking result forces us to reconsider the relationship between the severity of COVID-19 clinical symptoms and patient's mortality.
Detailed description
An algorithmic pipeline based on unsupervised machine learning algorithms, which aims to operate in tandem with physicians and provide additional knowledge for the proper categorization of COVID-19 infected patients based on their severity, is proposed in this study. Data from patients hospitalized in our clinic are collected and stored in separate Microsoft Excel files (.xlsx), which are loaded into memory. A script is concatenating them all into a single dataframe where they are checked for NaN (Not a Number) values. Because of the nature of the data, patients with missing information are discarded entirely from the dataset, since information inference would be a biased practice for the particular application. Next, we apply data normalization by scaling all numerical variables between the (0,1) range, so that the range of all numerical variables is the same, and any bias towards certain variables is avoided .A thorough and detailed data collection process was designed in order to collect information for the patients, without disturbing the clinical treatment, or upsetting them in the process.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Group | – | –Hospitalized Patients with Corona virus disease |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- patients that came into emergency department and diagnosed with COVID-19 infection
Key inclusion criteria (1 listed)
- patients that came into emergency department and diagnosed with COVID-19 infection
Key exclusion criteria (1 listed)
- none
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Cluster of patients depending on severity of infection
Time frame: 1 year
Algorithm produced with artificial intelligence and machine learning approach to classify patients according their status of COVID-19 infection
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 16 May 2023.
- Individual participant dataSharing planned: No
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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 COVID-19, run by Aristotle University Of Thessaloniki at 1 site in Greece.
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 1 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 16 May 2023. 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 Greece. 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?
Cluster of patients depending on severity of infection (time frame: 1 year). 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 16 May 2023.