Predictors of the Occurrence of Post Coronavirus Disease Syndrome Among COVID-19 Patients in Indonesia
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Covid19
- Sponsor
- Hasanuddin University
- Enrollment
- 6051
- Locations
- 2
- Primary Endpoint
- Presence of post COVID 19 Symptoms two weeks
- Status
- Completed
- Last Updated
- 2 years ago
Overview
Brief Summary
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:
- There is an association between duration of COVID episode, repeated COVID episode, and the presence of persistent COVID 19 Symptoms
- Vaccinated individual who was infected with Severe Acute Respiratory Syndrome (SARS) Coronavirus 2 (COV2) will have less persistent COVID 19 symptoms
- Individuals with comorbidities are prone to persistent COVID 19 Symptoms
- Appropriate medications (including early administration of antiviral therapy) lead to a lower probability of persistent COVID 19 Symptoms
Detailed Description
Target Population: As explained in the study population section Recruitment 1. Snowball technique from the COVID 19 survivor groups 2. Online questionnaire is provided to obtain the data Data Source: 1. Medical Resume 2. Laboratory Information possessed by individuals 3. Telemedicine observation possessed by individuals Predictors: 1. Demographic factors (age at diagnosis and current age at data collection, sex at birth, occupation, education, province of domicile, and possession of health insurance during COVID 19 infection) 2. General health status (Body Mass Index, presence of chronic disease and comorbidities, smoking, alcohol drinking, moderate physical activity) 3. History of COVID 19 vaccination (date, type of vaccine, booster dose, side effect, and medication following the vaccination) 4. COVID 19 episode (date of diagnosis, method of diagnosis confirmation, history of suspected SARS COV2 reinfection, Cycle-Threshold (CT) value, the symptoms and duration of the symptoms, medication, oxygen supplementation, hospitalization, or receiving plasma convalescent therapy) List of persistent COVID 19 symptoms in this study (and not limited to) 1. Neurological and Psychiatric symptoms * Anxiety * Depression * Sleep disturbances * PTSD * Cognitive impairment 2. Ear Nose Throat symptoms * Persistent anosmia * Persistent ageusia * Tinnitus and other hearing disorders 3. Respiratory Symptoms * Chronic cough * Shortness of breath 4. Cardiovascular symptoms * Peripheral artery disease * New onset of arrhythmia * Carditis (either pericarditis or myocarditis) 5. Hematological symptoms • Thromboembolic event 6. Renal Disorder • Reduced filtration function 7. Musculoskeletal disorder * Chronic fatigue * Joint pain * Muscular pain 8. Dermatology disorder * Rash * Hair loss 9. Gastrointestinal disorder * Chronic Diarrhea * Irritable Bowel Syndrome Study Size 1. The one-sample proportion formula 2. Type I error value as 5%. 3. The prevalence of COVID 19 in Indonesia is 1% 4. Absolute value of margin of error set as 0.5% 5. the total sample needed is 1152 participants. Proposed Statistical Analysis 1. Data cleaning was conducted 2. No imputation to missing data 3. Descriptive statistics and normality tests 4. Logistic regression to analyze the associated factors of each outcome followed by estimating the adjusted odds ratio. 5. The time-to-event analysis for post COVID symptoms was conducted in a certain subgroup of the variables using the cox regression model. 6. Neural Network model and deployment into a web-based application
Investigators
Bumi Herman
Assistant Lecturer
Hasanuddin University
Eligibility Criteria
Inclusion Criteria
- Not provided
Exclusion Criteria
- Not provided
Outcomes
Primary Outcomes
Presence of post COVID 19 Symptoms two weeks
Time Frame: within two weeks after declared cured
Any COVID-related symptoms persist after declared cured. Defined as a binary response, yes or no
Presence of post COVID 19 Symptoms eight weeks
Time Frame: within eight weeks after declared cured
Any COVID-related symptoms persist after declared cured. Defined as a binary response, yes or no
Presence of post COVID 19 Symptoms twelve weeks
Time Frame: within twelve weeks after declared cured
Any COVID-related symptoms persist after declared cured. Defined as a binary response, yes or no
Presence of post COVID 19 Symptoms six months
Time Frame: within six months after declared cured
Any COVID-related symptoms persist after declared cured. Defined as a binary response, yes or no
Presence of post COVID 19 Symptoms four weeks
Time Frame: within four weeks after declared cured
Any COVID-related symptoms persist after declared cured. Defined as a binary response, yes or no
Secondary Outcomes
- Presence of post COVID 19 Symptoms among vaccinated individual diagnosed with COVID 19(within six months after declared cured)