Correlation Between the Atherogenic Index of Plasma and Major Adverse Long-Term Prognosis in Patients With Coronary Artery Disease and 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. Therefore, 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 1816 patients with angiographically confirmed CAD and stage 2-5 CKD treated at a tertiary center between January 2019 and June 2023. The AIP was calculated as log10 (TG/HDL-C). The primary endpoint was major adverse cardiac and cerebrovascular events (MACCEs), including cardiac death, non-fatal myocardial infarction (MI), non-fatal stroke, and ischemia-driven revascularization. Patients were stratified into high- and low-AIP groups according to the optimal receiver operating characteristic (ROC) curve-derived cutoff value (0.148). The association between the AIP and incidence of MACCEs was primarily evaluated in the overall cohort using multivariable Cox regression. To minimize the influence of confounding and improve comparability between groups, Kaplan-Meier analysis and model performance metrics, including the C-statistic, net reclassification improvement (NRI), and integrated discrimination improvement (IDI), were also assessed.

RESULTS

MACCEs occurred in 379 patients (20.9%). High AIP independently predicted increased risk of MACCEs (adjusted hazard ratio (HR): 1.95; 95% confidence interval (CI): 1.45-2.63; p < 0.001). Specifically, the high-AIP group showed a significantly elevated risk 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). Incorporation of AIP values into the established Global Registry of Acute Coronary Events (GRACE) risk model improved predictive accuracy (C-statistic: 0.624 to 0.679; NRI = 0.139; p = 0.01; IDI = 0.037; p < 0.001) with an optimal AIP cutoff of 0.148 for predicting-MACCEs.

CONCLUSIONS

AIP is an independent predictor of adverse long-term outcomes in patients with CAD and stage 2-5 CKD. Integration of the AIP into existing risk models significantly enhances risk stratification and facilitates the identification of high-risk individuals for personalized management.