se of AI in Cardiovascular Risk Prediction.
Not Applicable
Not yet recruiting
- Conditions
- Health Condition 1: E116- Type 2 diabetes mellitus with other specified complications
- Registration Number
- CTRI/2023/07/054781
- Lead Sponsor
- A
- Brief Summary
Not available
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- Not Yet Recruiting
- Sex
- Not specified
- Target Recruitment
- 0
Inclusion Criteria
T2DM
Diabetes Duration minimum 5 year.
Exclusion Criteria
T1DM
Genetic Diabetes
Gestational Diabetes
Terminal Illness (Cancer)
Study & Design
- Study Type
- Observational
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method To use ML methods, as well as mediation & moderation analysis, to predict the incidence & prevalence of cardiovascular events, using nominal and scale variables; fatal or nonfatal MI, fatal and nonfatal stroke, congestive heart failure (CHF), blood pressure, body composition (anthropometry, waist circumference and DXA), exercise capacity and lipid control, using multiple variables of interest from the F-COHORT data.Timepoint: 12 MONTHS
- Secondary Outcome Measures
Name Time Method To evaluate the importance of the different co-factors & scaled covariates in the training & testing samples. <br/ ><br>â?¢ To identify the different variables of importance concerning the development of (1) primary cardiovascular endpoint- fatal or nonfatal myocardial infarction, <br/ ><br>(2) fatal or nonfatal stroke, <br/ ><br>(3) occurrence of microvascular complications-retinopathy, nephropathy. <br/ ><br>â?¢ To devise and operationalize a computerized algorithmic model to predict blood pressure, lipid control, and exercise capacity. <br/ ><br>â?¢ To perform a mediation analysis to assess the direct and indirect effects of different variables in predicting cardiovascular events (MACE), in persons with T2DM.Timepoint: 12 MONTHS