Identifying Key Inflammatory Markers of Mortality in Acute Ischemic Stroke Using Logistic Regression Analysis

Aim: This study evaluates the prognostic significance of inflammatory markers in predicting in-hospital mortality among acute ischemic stroke (AIS) patients and constructs a logistic regression model to identify the most relevant predictors. Materials and Methods: A retrospective analysis was conducted on 259 AIS patients. Laboratory findings were analyzed to compare survivors and non-survivors. To address potential multicollinearity, a correlation matrix and variance inflation factor analysis were applied to identify the most relevant inflammatory markers for logistic regression. Results: Non-survivors exhibited significantly higher levels of C-reactive protein (CRP), CRP/albumin ratio (CAR), white blood cell count, neutrophil count, neutrophil/lymphocyte ratio, and platelet/lymphocyte ratio, while lymphocyte and albumin levels were lower. Univariable analysis identified albumin and CAR as key predictors, with albumin significantly associated with increased mortality risk [odds ratio (OR): 1.16, 95% confidence interval (CI): 1.081-1.253, p<0.001], whereas CAR was associated with reduced mortality (OR: 0.729, 95% CI: 0.589-0.902, p=0.004). However, in the final multivariable model, only albumin remained statistically significant (OR: 1.137, 95% CI: 1.043-1.240, p=0.03), suggesting its independent prognostic value. Conclusion: Careful selection of variables for inclusion in the logistic regression model is crucial, as not all variables may exert a significant influence on survival or mortality outcomes. Albumin emerged as an independent predictor of in-hospital mortality in AIS patients, while other inflammatory markers lost significance in multivariable logistic regression analysis. This underscores the potential role of albumin in early risk stratification and prognosis.