Browsing by Author "Yip, Courtney Rachel"

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    Evaluating the prognostic value of risk factors contributing to cardiovascular disease and established risk score estimations
    (University of the Western Cape, 2024) Yip, Courtney Rachel
    Evaluating the prognostic value of risk factors contributing to cardiovascular disease and established risk score estimations.Evaluating the Prognostic Value of Risk Factors Contributing to Cardiovascular Disease and Established Risk Score Estimations CR Yip M. Clin Pharm Mini-Thesis, School of Pharmacy, University of the Western Cape Introduction: Cardiovascular disease (CVD) remains a leading cause of morbidity and mortality worldwide, with South Africa experiencing epidemiological transition. Although predictive models, such as the Framingham Risk Score (FRS), are widely used to predict CVD risks, these models often lack accuracy for diverse populations, including those in South Africa. Therefore, there is an urgent need to integrate additional risk factors that reflect the unique demographic and lifestyle characteristics of South Africa. This sub-study analysis aimed to improve the predictive accuracy and specificity of the FRS by incorporating a broad range of biometric, biochemical, and lifestyle variables. This study examined the extent to which these factors could enhance existing risk prediction models and offer more tailored recommendations for cardiovascular risk assessment in South Africa. Methods: Using convenience sampling, a correlational design was used to explore the relationship between health parameters and CVD risk among staff members at the University of the Western Cape (UWC). Data collection involved comprehensive questionnaires on demographic, lifestyle, and dietary factors, as well as biomarker measurements obtained through point-of-care (POCT) testing devices. The FRS and Prospective Cardiovascular Munster Study Risk Score (PROCAM) were calculated using the cleaned and validated data in Microsoft Excel. Analyses, including correlation matrices and multiple linear regression, were performed using Python. The model’s robustness was ensured by testing key assumptions, including linearity, independence, constant variance, and normality of residuals through residual plots and statistical tests.