COVID-19 Clinical Status Associated With Outcome Severity: An Unsupervised Machine Learning Approach

NCT05119465 | Completed

• 1 other identifier: 19400_21052021
• Study Type: observational
• Target: 268
• Locations: 1 country
• Sites: 1

Brief 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 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.

Conditions

COVID-19

Keywords

COVID-19; Hospitalization; Machine Learning; Artificial Intelligence

Outcome Measures

Primary Outcomes (1)

• 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

  1 year

Study Arms (1)

Group

Hospitalized Patients with Corona virus disease

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

patients that came into emergency department and diagnosed with COVID-19 infection

You may qualify if:

• patients that came into emergency department and diagnosed with COVID-19 infection

You may not qualify if:

• none

Sponsors & Collaborators

• Aristotle University Of Thessaloniki: lead

Study Sites (1)

University General Hospital of Thessaloniki AHEPA

Thessaloniki, 54621, Greece

Location

MeSH Terms

Conditions

COVID-19

Condition Hierarchy (Ancestors)

Pneumonia, Viral; Pneumonia; Respiratory Tract Infections; Infections; Virus Diseases; Coronavirus Infections; Coronaviridae Infections; Nidovirales Infections; RNA Virus Infections; Lung Diseases; Respiratory Tract Diseases

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Professor of Internal Medicine-Diabetology

Study Record Dates

• First Submitted: November 12, 2021
• First Posted: November 15, 2021
• Study Start: November 1, 2019
• Primary Completion: June 30, 2021
• Study Completion: June 30, 2021
• Last Updated: May 16, 2023

Record last verified: 2023-05

Data Sharing

• IPD Sharing: Will not share

Locations

GR (1)