AUTHOR=Li Chen-Yan , Wu Hai-Bo , Duan Ya-Wei , Gao Peng , Li Hong-Xiao , Wang Xue-Chao , Wang Yun-Can , Wang Yan-Qing , Bai Shi-Ru , Jia Yuan , Du Rong-Pin 

TITLE=A nomogram model based on HALP score and sST2 for predicting 1-year MACE risk after PCI in acute myocardial infarction patients

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1641855

DOI=10.3389/fcvm.2025.1641855

ISSN=2297-055X

ABSTRACT=ObjectiveTo develop a nomogram model integrating the HALP score (a composite score of hemoglobin, albumin, lymphocytes, and platelets) and sST2 for predicting the risk of major adverse cardiovascular events (MACE) within 1 year after percutaneous coronary intervention (PCI) in patients with acute myocardial infarction (AMI).MethodsThis retrospective analysis included 236 AMI patients undergoing emergency PCI (2019–2024), categorized into MACE (n = 102) and non-MACE (n = 134) groups. Independent predictors were identified through multivariate logistic regression analysis, and a nomogram model was constructed. Model performance was validated using receiver operating characteristic (ROC) curves and the Bootstrap method (N = 1,000).ResultsMultivariate analysis revealed that Killip class IV (OR = 3.758, P = 0.009), high sST2 levels (OR = 1.008, P = 0.009), high LDL-C (OR = 1.533, P = 0.041), high LVEDD (OR = 1.106, P = 0.009), and low HALP score (OR = 0.958, P = 0.023) were independent predictors of MACE. The combined model exhibited significantly better predictive performance than single indicators (AUC = 0.833, 95% CI: 0.781–0.886), with a sensitivity of 87.3% and specificity of 68.7%. The nomogram demonstrated good calibration after Bootstrap validation (Hosmer-Lemeshow test P = 0.157).ConclusionThe nomogram model developed in this study, which integrates the HALP score (reflecting inflammatory-nutritional status) and sST2 (a marker of myocardial fibrosis) along with clinical indicators, can effectively predict the risk of MACE after PCI and provides a visual tool for individualized risk stratification.