The prognostic value of inflammatory prognostic index in patients with chronic total occlusion undergoing successful PCI: a single-center prospective cohort study
摘要
Accumulating evidence suggests that inflammatory and nutritional status play key roles in the prognosis of cardiovascular diseases (CVD). The Inflammatory Prognostic Index (IPI), integrating both inflammatory and nutritional metrics, has been linked to multiple subtypes of coronary artery disease (CAD). However, the association between IPI and long-term prognosis in patients with chronic total occlusion (CTO) after successful PCI remains unclear. Therefore, we aimed to investigate the prognostic role of IPI in patients with CTO who underwent successful PCI.
MethodsA total of 525 patients with CTO who underwent successful PCI were enrolled in this study. Participants were stratified into a MACE group and a control group, as well as three subgroups (T1, T2, T3) by IPI tertiles. Baseline characteristics were compared, and Cox regression analysis were performed. The receiver operating characteristic curve (ROC) was used to determine the predictive value of IPI for major adverse cardiovascular events (MACE).
ResultsA total of 76 patients with 82 MACE were recorded during the follow-up. Patients with MACE exhibited significantly higher C-reactive protein (CRP) and IPI levels (p < 0.05). Patients in T3 (high IPI group) had higher white blood cell count, neutrophil count, CRP, creatinine, and IPI, while they had lower lymphocyte count, albumin (ALB), and left ventricular ejection fraction (LVEF) (p < 0.05). In addition, patients in the highest IPI tertile exhibited a higher risk of target vessel revascularization (TVR) and composite MACE (p < 0.05). Multivariate Cox regression analysis demonstrated that IPI was an independent predictor of MACE in patients with CTO who underwent successful PCI (p < 0.05). Moreover, patients in the highest IPI tertile had a 2.484-fold higher risk of MACE (p < 0.05).
ConclusionIPI may serve as a supplementary prognostic tool due to its simplicity and low cost. However, given its modest predictive performance, it should be combined with existing risk models to improve predictive value and facilitate clinical decision-making.