Akkaya Emre
Department of Cardiology, Bossan Hospital, Gaziantep 27580, Turkey.
J Clin Med. 2024 Nov 21;13(23):7003. doi: 10.3390/jcm13237003.
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. 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). The RAR, TGI, and PIV were significantly higher in the CAD group ( < 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, < 0.01), while elevated TGI was also linked to an increased risk of CAD (OR = 1.5, 95% CI: 1.3-1.8, < 0.01). Similarly, a high PIV was strongly associated with CAD risk (OR = 2.0, 95% CI: 1.7-2.4, < 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% ( < 0.01). 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.
本研究旨在探讨红细胞分布宽度与白蛋白比值(RAR)、甘油三酯-葡萄糖指数(TGI)和全免疫炎症值(PIV)对冠状动脉疾病(CAD)患者预后预测的影响,并评估这些生物标志物在临床决策中的潜在应用。这项回顾性研究纳入了2020年至2024年期间诊断和治疗的患者。研究人群包括被诊断为CAD的个体(n = 450)以及无CAD的对照组(n = 150)。CAD组的RAR、TGI和PIV显著更高(均P < 0.01)。此外,发现高RAR是CAD的一个危险因素(OR = 1.4,95%CI:1.2 - 1.7,P < 0.01),而TGI升高也与CAD风险增加有关(OR = 1.5,95%CI:1.3 - 1.8,P < 0.01)。同样,高PIV与CAD风险密切相关(OR = 2.0,95%CI:1.7 - 2.4,P < 0.01)。RAR、TGI和PIV的联合分析得出AUC值为0.78(0.75 - 0.81),表明这些生物标志物共同对CAD具有较高的诊断准确性,敏感性为74%,特异性为77%(P < 0.01)。总之,我们的研究不仅强调了传统危险因素在CAD中的重要性,还突出了可提高预测准确性的新生物标志物。RAR、TGI和PIV等生物标志物的联合使用在诊断CAD方面具有更高的准确性。因此,我们的研究提出了一种创新方法,有可能加强CAD风险的预测和管理。