Correlation Analysis Between Low-Density Lipoprotein and Clinical Prognosis in Aneurysmal Subarachnoid Hemorrhage: A...

Low-density lipoprotein (LDL) serves as a critical biomarker for cardiovascular and cerebrovascular diseases, potentially influencing disease progression and prognosis. However, the relationship between LDL and the clinical prognostic value of aneurysmal subarachnoid hemorrhage (aSAH) remains underexplored.

This retrospective observational study enrolled 204 patients diagnosed with aneurysmal aSAH. Data collected from 2015 to 2022 encompassed demographic characteristics, preoperative and postoperative LDL levels, aneurysm details, and Glasgow Coma Scale (GCS) scores. Patients were stratified into 2 groups based on their clinical outcomes. Multivariate logistic regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression models were used to identify key predictors. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were utilized to evaluate model performance.

Significant differences in LDL levels were observed between the good and adverse prognosis groups. Elevated LDL levels, particularly preoperative, postoperative, and mean LDL levels, were associated with adverse clinical outcomes. Multivariate regression analysis revealed that increased LDL levels contributed to poorer postoperative clinical prognoses. Integrating LDL levels with clinical characteristics enhanced prediction accuracy, achieving an area under the curve (AUC) of 0.836.

LDL level represents a crucial predictor of postoperative clinical prognosis in patients with aSAH, especially preoperative and mean LDL levels. A model combining LDL data with clinical characteristics can effectively guide clinical decision-making, facilitate the identification of high-risk patients, and optimize intervention strategies.