Statistical Perspective on Coronary Angiography Findings: Examining the Influence of Hereditary Characteristics, Behaviour, and Self-Control Factors among Study Participants

Abstract

Introduction: According to the World Health Organisation (WHO), the American College of Cardiology (ACC), and the American Heart Association (AHA), the issued prediction charts can be used to make an absolute prediction of a study variable’s Cardiovascular Disease (CVD) risk. This study aimed to examine the combined effects of gender, Family History (FH) of CVD, tobacco use, alcohol consumption, smoking, physical activity levels, and other health conditions on the presence of CVD confirmed by angiography. Predicting CVD risk is crucial; particularly because young individuals aged 25 to 40 are affected by these diseases and from the foundation of any nation. Moreover, CVDs significantly contribute to human mortality compared to other ailments. This approach sought to identify the risk factors for CVD. Aim: To investigate the impact and the association between hereditary characteristics, behaviour, and self-control factors on coronary angiography findings among the study participants from a statistical viewpoint. Materials and Methods: An observational cross-sectional study related to angiography report of 274 study participants visiting the Department of cardiology at Krishna Vishwa Vidyapeeth (Deemed to be University), Karad, Maharashtra with complaints of CVD was conducted from January 2023 to May 2023. Statistical Analysis was performed with the help of Statistical Package for Social Sciences (SPSS) version 28.0, InStat and Microsoft Excel. Chi-square test was applied to study association of demographic study parameters with CVD. Logistic regression was carried out to develop the regression model. Results: Angiography significant CVD was associated with father’s history of CVD (40, 71.42%), alcohol consumption (68, 70.8%), tobacco chewing (115, 69.7%), no exercise (89,79.46%), diabetes (83,75.45%), and diabetic with medication (14,93.33%) showed significant associations with CVD. Logistic regression analysis identified these variables as the best predictors of CVD. Genetics, lifestyle choices, and co-morbidities all contribute to the risk of Coronary Artery Disease (CAD). Conclusion: The study effectively identifies and quantifies the relationships between hereditary characteristics, behavioural factors, and self-control measures with coronary angiography findings. Exercise and diabetic status are significant predictors of the outcome, while tobacco-chewing leans towards significance and other studied variables are at their reference levels. This predictive model will help clinicians, patients, and their families in mentally preparing for confirmation of the disease’s presence or absence.

Keywords

• cardiovascular disease
• invasive diagnosis
• logistic regression