Cardiovascular diseases (CVDs) pose a significant and escalating burden globally, and this burden is particularly pronounced among populations experiencing rapid health transitions, such as in India. The results of the Global Burden of Disease study state an age-standardized CVD death rate of 272 per 100000 population in India which is much higher than the of global average of 235. The current trend appears that there is an early onset of CVD in India which is a decade earlier than the Western population with rapid progression and high mortality rate.
Addressing the escalating burden of CVDs in India requires a comprehensive approach that includes public health interventions targeting modifiable risk factors, improving access to healthcare and preventive services, addressing socio-economic disparities, promoting healthy lifestyles, and conducting research to better understand the genetic and environmental factors contributing to CVDs in this population. Especially, research that unearths early onset of the disease (markers that are causal) that can enable early intervention, but also provide vital clues to pharmaceutical industry for discovering new drug(s) that is(are) more effective.
The primary objective of the study is to identify the causal factors or predictors contributing to the early onset of cardiovascular disease (CVD). This involves understanding the prevalence of CVD across different segments of society. Additionally, the study aims to compile a repository of data (clinical, blood biochemistry, genomics) and imaging details to construct an artificial intelligence (AI) platform. This platform would be designed to predict an individual’s risk of developing CVD and support mass screening efforts.
1. Data Collection: Gathering data from various sources including medical records, demographic information, lifestyle factors, and genetic predispositions related to cardiovascular health.
2. Data Analysis: Analyzing the collected data to identify patterns, correlations, and potential causal factors associated with the early onset of CVD.
3. Model Development: Using the collected data to develop predictive models leveraging AI and machine learning techniques. These models would be trained to predict an individual’s risk of developing CVD based on their specific characteristics and medical history.
4. Validation: Validating the predictive models using separate datasets to ensure their accuracy and reliability.
5. Implementation: Building an AI platform based on the validated models, which can be used for mass screening purposes to assess an individual’s risk of developing CVD.
6. Public Health Impact: Assessing the potential public health impact of the AI platform in terms of early detection of CVD, preventive interventions, and resource allocation for healthcare services.
Overall, this study aims to advance our understanding of the early onset of cardiovascular disease, develop predictive tools for risk assessment, and contribute to the improvement of public health strategies for preventing and managing CVD in Indian population. Data has already been collected on 5000 participants and initial analysis is underway.