Association Between SYNTAX Score and Leukoglycemic Index In Patients Wiht Acute Coronary Syndrome

This observational study aims to evaluate the association between the SYNTAX score-a validated angiographic tool used to assess the complexity of coronary artery disease-and the leukoglycemic index (LGI), a composite biomarker calculated from white blood cell count and blood glucose level. The study population consists of patients diagnosed with acute coronary syndrome (ACS) who were admitted to the hospital and underwent coronary angiography as part of their clinical evaluation. At the time of initial hospital admission, routine laboratory parameters were obtained, including complete blood count and fasting glucose levels. These values were used to calculate the LGI. Patients were stratified into two groups based on their SYNTAX scores: Group I (SYNTAX score ≤22) and Group II (SYNTAX score \>22). Demographic characteristics, family history of cardiovascular disease, and chronic comorbidities (such as diabetes mellitus, hypertension, and hyperlipidemia) were extracted from patient medical records and clinical anamnesis. The primary objective of the study is to determine whether LGI correlates with the angiographic severity of coronary artery disease as measured by the SYNTAX score. Secondary objectives include identifying independent predictors of coronary complexity and evaluating the potential role of LGI as a non-invasive biomarker in risk stratification. Statistical analyses will be performed using SPSS software. Normality of data distribution will be assessed using the Shapiro-Wilk test. Depending on distribution characteristics, appropriate parametric (e.g., Student's t-test) or non-parametric (e.g., Mann-Whitney U test) methods will be applied for group comparisons. Pearson correlation analysis will be used to assess the relationship between LGI and SYNTAX score. Multivariate linear and logistic regression models will be constructed to identify independent predictors of high SYNTAX scores, adjusting for potential confounders. This study is expected to contribute to the understanding of LGI as a simple, cost-effective, and accessible biomarker for predicting coronary artery disease complexity in ACS patients