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Background and Objective Persistent symptoms after COVID 19 episodes (or referred to as Long COVID) can appear at a certain period and affect the quality of life of the patients, as well as introduce other comorbidities. It is important to address the associated factors of persistent symptoms after the COVID 19 episode. By identifying these factors, a screening method could be deployed to detect individuals that are prone to persistent COVID 19 symptoms.
Method:
This cohort study recruit COVID 19 patients at all stages in Indonesia (including people who underwent home isolation). Patient-based clinical information is collected from the patient including the demographic information, general health status, COVID 19 vaccination, and COVID 19 treatment. The outcome is the occurrence of persistent COVID 19-related symptoms after being declared as cured. A logistic regression model and Cox Regression are applied to the model to find the associated factors. Machine learning and Deep Learning model will be constructed and deployed into a web-based application for a further screening program.
Hypothesis:
Full description
Target Population:
As explained in the study population section
Recruitment
Data Source:
Predictors:
List of persistent COVID 19 symptoms in this study (and not limited to)
Neurological and Psychiatric symptoms
Ear Nose Throat symptoms
Respiratory Symptoms
Cardiovascular symptoms
Hematological symptoms
• Thromboembolic event
Renal Disorder
• Reduced filtration function
Musculoskeletal disorder
Dermatology disorder
Gastrointestinal disorder
Study Size
Proposed Statistical Analysis
Enrollment
Sex
Ages
Volunteers
Inclusion and exclusion criteria
Inclusion criteria
Exclusion Criteria
6,051 participants in 2 patient groups
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Central trial contact
Pramon Viwattanakulvanid, Ph.D; Bumi Herman, M.D.Ph.D
Data sourced from clinicaltrials.gov
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