Correlation Between the Arteriosclerosis Index of Plasma and Major Adverse Long-Term Prognosis in Patients with Coronary Artery Disease Combined with Stage 2–5 Chronic Kidney Disease

Background The prognostic utility of the atherogenic index of plasma (AIP) in patients with coronary artery disease (CAD) and chronic kidney disease (CKD) remains unclear. This study aimed to evaluate the association between AIP and long-term outcomes in patients with CAD and Stage 2–5 CKD. Methods This retrospective study included 1,816 patients with angiographically confirmed CAD and renal insufficiency (Stage 2–5 CKD) from a tertiary center between January 2019 and June 2023. AIP was defined as the logarithmic ratio of plasma triglycerides to HDL-C. The primary endpoint was major adverse cardiac and cerebrovascular events (MACCE), including cardiac death, non-fatal myocardial infarction (MI), non-fatal stroke, and ischemia-driven revascularization. Patients were stratified into high and low AIP groups based on the optimal ROC cut-off (0.148). Propensity score matching (PSM) yielded 589 well-balanced pairs. Multivariable Cox regression, Kaplan–Meier survival analysis, and model performance metrics (C-statistic, NRI, IDI) were used to assess prognostic value. Results MACCE occurred in 379 patients (20.9%). High AIP independently predicted increased MACCE risk (adjusted HR: 1.95; 95% CI: 1.45–2.63; P < 0.001). Specifically, the high-AIP group showed significantly elevated risks of cardiac death (HR: 2.07; 95% CI: 1.13–3.80; P = 0.019), non-fatal MI (HR: 1.97; 95% CI: 1.02–3.78; P = 0.043), and ischemia-driven revascularization (HR: 2.97; 95% CI: 1.76–5.02; P < 0.001). Integration of AIP improved the predictive accuracy of the GRACE risk model (C-statistic: 0.624 to 0.679; NRI = 0.139, P = 0.01; IDI = 0.037, P < 0.001). Optimal AIP cutoff for predicting MACCE was 0.148. Conclusions AIP is an independent predictor of adverse outcomes in patients with CAD and Stage 2–5 CKD. Its integration into existing risk models significantly enhances risk stratification and facilitates identification of high-risk individuals for personalized management.