Association of residual lipoprotein-cholesterol and atherogenic index of plasma with in-hospital major adverse cardiovascular and cerebrovascular events after percutaneous coronary intervention in patients with acute ST-segment elevation myocardial infarction

In recent years, non-traditional lipid indices have emerged as significant predictors for cardiovascular events following emergency percutaneous coronary intervention (PCI) for ST-segment elevation myocardial infarction (STEMI). However, the relationship of residual lipoprotein-cholesterol (RLP-C) and atherogenic index of plasma (AIP) with in-hospital outcomes, especially their predictive value for major adverse cardiovascular and cerebrovascular events (MACCEs) after PCI in STEMI patients, remains underexplored and warrants further investigation. This retrospective cohort study included 526 STEMI patients who underwent emergency PCI between January 2023 and August 2024. We collected baseline demographic, clinical, and laboratory data. RLP-C and AIP were calculated from lipid profiles obtained before PCI. Independent predictors of in-hospital MACCEs were identified using multivariate logistic regression, and model discrimination was evaluated using receiver operating characteristic (ROC) curve analysis. Among 526 STEMI patients receiving PCI, 92 (17.49%) developed in-hospital MACCEs. Multivariate analysis identified RLP-C (OR = 3.97, 95%CI: 1.71–9.21; P = 0.001) and AIP (OR = 2.42, 95%CI: 1.01–5.76; P = 0.047) as independent predictors of MACCEs after adjusting for conventional risk factors. The integrated model with hsTnT, ApoB, RLP-C, and AIP demonstrated superior predictive accuracy (AUC 0.744). Dose-response analysis revealed a borderline nonlinear relationship between AIP and MACCEs risk (P for nonlinearity = 0.050), while RLP-C demonstrated a linear dose-response relationship with MACCEs risk (P for nonlinearity = 0.522). RLP-C and AIP are independent predictors of in-hospital MACCEs following PCI in STEMI patients. Combined assessment of these indices improves risk stratification and may facilitate early targeted interventions to improve outcomes.