Risk Prediction of Major Adverse Cardiovascular Events After Percutaneous Coronary Intervention Using Inflammatory...

This study aimed to build and validate a risk prediction model for 1-year major adverse cardiovascular events (MACE) in patients with acute coronary syndrome (ACS) undergoing percutaneous coronary intervention (PCI), utilizing novel inflammatory biomarkers. This single-center retrospective cohort study enrolled 1,337 patients with ACS who underwent PCI between January 2021 and December 2023. Six novel inflammatory indexes (NLR, MHR, NHR, SII, SIRI, AISI) were derived from pre-PCI blood tests. After a 7:3 random split into training (n = 936) and validation (n = 401) cohorts, LASSO regression and multivariable Cox proportional hazards models identified independent predictors, and a combined biomarker-based model was constructed. Age, diabetes, Killip Class ≥ II, reduced LVEF, multivessel disease, no-reflow phenomenon, NHR, and SIRI were identified as independent predictors. The combined model achieved an AUC of 0.81 (95% CI: 0.78-0.84), which remained stable after optimism correction via bootstrapping. This performance was substantially higher than that of any single biomarker (maximum AUC: 0.71) and demonstrated significant improvements in NRI and IDI (all P < 0.001). Risk stratification demonstrated a clear gradient in MACE incidence: 6.3% (low-risk), 15.1% (intermediate-risk), and 25.3% (high-risk), P. < 0.0001, with consistent predictive performance across all evaluated clinical subgroups. The novel inflammatory biomarker-based model substantially improves risk prediction over clinical variables alone, providing a valuable framework for risk stratification and identifying patients at high residual inflammatory risk who may require closer clinical surveillance.