Predictors of the Occurrence of Post Coronavirus Disease Syndrome Among COVID-19 Patients in Indonesia View Homepage


Ontology type: schema:MedicalStudy     


Clinical Trial Info

YEARS

2021-2023

ABSTRACT

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 SymptomsVaccinated individual who was infected with Severe Acute Respiratory Syndrome (SARS) Coronavirus 2 (COV2) will have less persistent COVID 19 symptomsIndividuals with comorbidities are prone to persistent COVID 19 SymptomsAppropriate 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 sectionRecruitmentSnowball technique from the COVID 19 survivor groupsOnline questionnaire is provided to obtain the dataData Source:Medical ResumeLaboratory Information possessed by individualsTelemedicine observation possessed by individualsPredictors: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)General health status (Body Mass Index, presence of chronic disease and comorbidities, smoking, alcohol drinking, moderate physical activity)History of COVID 19 vaccination (date, type of vaccine, booster dose, side effect, and medication following the vaccination)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)Neurological and Psychiatric symptomsAnxietyDepressionSleep disturbancesPTSDCognitive impairmentEar Nose Throat symptomsPersistent anosmiaPersistent ageusiaTinnitus and other hearing disordersRespiratory SymptomsChronic coughShortness of breathCardiovascular symptomsPeripheral artery diseaseNew onset of arrhythmiaCarditis (either pericarditis or myocarditis)Hematological symptoms• Thromboembolic eventRenal Disorder• Reduced filtration functionMusculoskeletal disorderChronic fatigueJoint painMuscular painDermatology disorderRashHair lossGastrointestinal disorderChronic DiarrheaIrritable Bowel SyndromeStudy SizeThe one-sample proportion formulaType I error value as 5%.The prevalence of COVID 19 in Indonesia is 1%Absolute value of margin of error set as 0.5%the total sample needed is 1152 participants.Proposed Statistical AnalysisData cleaning was conductedNo imputation to missing dataDescriptive statistics and normality testsLogistic regression to analyze the associated factors of each outcome followed by estimating the adjusted odds ratio.The time-to-event analysis for post COVID symptoms was conducted in a certain subgroup of the variables using the cox regression model.Neural Network model and deployment into a web-based application More... »

URL

https://clinicaltrials.gov/show/NCT05060562

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