IISES International Academic Conference, Rome

LOGICAL-COMBINATORIAL METHODS FOR CARDIOVASCULAR RISK FACTOR ANALYSIS AND ASSESSMENT

SERGO TSIRAMUA, ELZA NIKOLEISHVILI, ELISABED ASABASHVILI, GEORGE TSIRAMUA

Abstract:

Cardiovascular diseases (CVD) remain the leading cause of mortality and morbidity worldwide, posing significant challenges to healthcare systems and society at large. Despite advancements in prevention and treatment, the prevalence of CVD continues to rise, driven by non-modifiable risk factors such as age, gender, genetic predisposition, and ethnicity (World Health Organization, 2021). Accurate and comprehensive assessment of CVD risk factors is vital for effective prevention and intervention strategies. Conventional tools for assessing individual CVD risk factors, such as the Framingham Risk Score (FRS), Atherosclerotic Cardiovascular Disease Risk Calculator (ASCVD), QRISK Calculator, and SCORE (Systematic Coronary Risk Evaluation), provide percentage estimates of a 10-year CVD risk based on specific populations and datasets. However, from a Public Health perspective, there is a need for innovative approaches that quantitatively assess, analyze, and predict overall CVD risks on a Global, Regional, or National scale. This paper proposes the use of logical-combinatorial methods to quantitatively evaluate non-modifiable CVD risk factors, including age, gender, genetic predisposition, and ethnicity, based on global statistical data. The analysis involves estimating the probabilities of the simultaneous presence of two, three, or four non-modifiable risk factors. The study presents key findings from the quantitative assessment of various risk factors combinations and draws actionable conclusions for public health practice. This method provides a scalable framework for global risk assessment and targeted public health intervention planning. Integrating logical-combinatorial approaches into Public Health research is critical for developing more effective strategies to mitigate the burden of CVD and improve population health outcomes globally. Acknowledgments: The work was supported by the Shota Rustaveli National Science Foundation of Georgia. Grant #SC-24-753

Keywords: Cardiovascular diseases (CVD), CVD risk factors, non-modifiable CVD risk factors, modifiable CVD risk factors, logical-combinatorial analysis