Abstract
OBJECTIVE: To identify risk factors for prognosis in diabetic foot patients undergoing digital subtraction angiography (DSA) intervention and analyze their correlation with ulcer severity. METHODS: This retrospective study analyzed 135 diabetic foot patients who underwent DSA-guided intervention between August 2023 and January 2025. Patients were classified good and poor prognosis groups based on 6-month outcomes. We compared demographic data and clinical laboratory indexes between groups. Statistically significant variables were analyzed using Logistic regression to identify independent risk factors. The receiver operating characteristic (ROC) curves and Pearson correlation analysis were employed to assess the diagnostic value of these factors and correlation with ulcer severity. RESULTS: The stratified diabetic foot ulcer risk score (SINBAD) was significantly higher in patients with poor prognosis (7.15±2.76) compared to those with good prognosis (3.24±1.81); Serum levels of procalcitonin (PCT), galactoagglutinin-3 protein (Gal-3), noncoding RNA molecule with circular structure (Hsa_circ_0057362), interleukin-6 (IL-6), and serum C-reactive protein (CRP) was significantly elevated in the poor prognosis group (P < 0.05). Pearson correlation analysis revealed positive corrections between these biomarkers and ulcer severity (r=0.283, 0.240,0.434, 0.370, 0.443, respectively; all P < 0.05); Logistic regression analysis identified PCT, Gal-3, Hsa_circ_0057362, IL-6, and CRP as independent influencing factors for poor prognosis in diabetic foot. Furthermore, ROC curve analysis demonstrated that each of these indicators possessed a certain degree of predictive value for poor prognosis following diabetic foot surgery. CONCLUSION: A plethora of risk factors, including PCT, Gal-3, Hsa_circ_0057362, IL-6 and CRP, influence poor prognosis in diabetic foot patients undergoing DSA-guided intervention. These biomarkers demonstrate significant correlations with ulcer severity and hold substantial clinical utility in the predicting postoperative outcomes. Early identification of patients at risk for poor prognosis enables the implementation of targeted interventions, thereby effectively improving patient outcomes.