Analysis of the efficacy of a model for auxiliary diagnosis of aortic aneurysms based on clinical pathological features.

Abstract

This study evaluated a multi-parameter serological model for the auxiliary diagnosis of aortic aneurysm (AA). Clinical data and serological indicators were collected from 1397 AA patients and 2000 healthy controls recruited from the Second Affiliated Hospital of Harbin Medical University (June 2021-June 2025). A comprehensive diagnostic model was developed based on serum biomarkers. Significant differences were observed between AA patients and healthy controls in blood pressure, body mass index (BMI), inflammatory markers ( e.g., white blood cell count, and neutrophil count), hepatic and renal function indicators ( e.g., urea, total bilirubin, and alkaline phosphatase), and nutritional parameters ( e.g., albumin, total protein, and hemoglobin) ( P < 0.05). Multivariate analysis identified BMI, systolic blood pressure, urea, and neutrophil count as independent risk factors (OR > 1), while lymphocyte count, albumin, and serum potassium were protective factors (OR < 1). Twenty-three key variables were further selected by least absolute shrinkage and selection operator (LASSO) regression. The constructed comprehensive RiskScore showed a highly significant difference between the AA group and the control group (-16.27 ± 4.69 vs. -21.36 ± 1.46, P < 0.001), with the model demonstrating excellent discriminatory performance (concordance index [C-index] > 0.9). The results indicate that a multi-parameter serological diagnostic model distinguishes between AA patients and healthy controls, demonstrating strong discriminatory ability and potential uitility for auxiliary diagnosis of AA.