Abstract
Objectives: This study aimed to investigate the impact of the RDW-albumin ratio (RAR), Triglyceride-glucose index (TGI), and pan-immune-inflammation value (PIV) on predicting prognosis in patients with coronary artery disease (CAD) and to assess the potential use of these biomarkers in clinical decision-making. Materials and Methods: This retrospective study involved patients diagnosed and treated from 2020 to 2024. The study population included individuals diagnosed with CAD (n = 450) as well as a control group without CAD (n = 150). Results: The RAR, TGI, and PIV were significantly higher in the CAD group (p < 0.01 for all). Furthermore, a high RAR was found to be a risk factor for CAD (OR = 1.4, 95% CI: 1.2-1.7, p < 0.01), while elevated TGI was also linked to an increased risk of CAD (OR = 1.5, 95% CI: 1.3-1.8, p < 0.01). Similarly, a high PIV was strongly associated with CAD risk (OR = 2.0, 95% CI: 1.7-2.4, p < 0.01). The combined analysis of RAR, TGI, and PIV yielded an AUC value of 0.78 (0.75-0.81), indicating that these biomarkers collectively provide high diagnostic accuracy for CAD, with a sensitivity of 74% and specificity of 77% (p < 0.01). Conclusions: In conclusion, our study not only emphasizes the significance of traditional risk factors in CAD, but also highlights new biomarkers that could improve predictive accuracy. The combined use of biomarkers such as the RAR, TGI, and PIV offers greater accuracy in diagnosing CAD. Thus, our research presents an innovative approach with the potential to enhance the prediction and management of CAD risk.