This study looks at people who come to the hospital with acute coronary syndrome, a serious heart condition. When these patients first arrive, doctors take routine blood tests. The investigators use these test results to calculate a value called the leukoglycemic index (LGI), which combines white blood cell count and blood sugar level. The investigators also use a heart imaging score called the SYNTAX score to see how complex their coronary artery disease is.

Patients are divided into two groups based on their SYNTAX scores: one group with lower scores (22 or less), and one with higher scores (above 22). The investigators compare their health data to see if LGI is linked to the severity of their heart disease. The investigators also check if LGI can help predict how complex the disease is. Information like age, family history, and chronic illnesses is collected from patient records and medical interviews.

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

Inclusion Criteria:

• Adult patients aged 18 years and older
• Patients who underwent coronary angiography due to acute coronary syndrome

Exclusion Criteria:

• Patients with diabetes mellitus (Diagnosis based on current use of oral antidiabetic or insulin therapy and/or fasting glucose level ≥126 mg/dL and HbA1c ≥6.5%)
• Patients with atrial fibrillation, history of pacemaker implantation, asthma, pulmonary hypertension, pregnancy, anemia, malignancy, rheumatoid arthritis, or peripheral artery disease
• Obese individuals (defined as body mass index [BMI] >30 kg/m²)
• Patients whose medical records could not be accessed

Patients with acute coronary syndrome

Adverse events were not monitored/assessed during the study

All-Cause Mortality, Serious, and Other (Not Including Serious) Adverse Events were not monitored/assessed during the study.

There was no limitation about the study