Predictive Value of a Nomogram Model Constructed on the Basis of Residual Cholesterol in Predicting Major Post-Interventional Adverse Cardiovascular Events in Patients with Acute Myocardial Infarction.

Abstract

The aim of this study was to construct a residual cholesterol (RC)-based nomogram prediction model and assess its value in predicting the risk of major adverse cardiovascular events (MACE) after emergency percutaneous coronary intervention (PCI) in patients with acute myocardial infarction (AMI).Retrospective analysis of patients from Fuyang People's Hospital who underwent emergency PCI for AMI at our hospital between January 2022 and December 2023 was performed, and univariate logistic regression was used to screen the risk factors for the first occurrence of MACE in the patients, while multivariable logistic regression analysis was used to construct a prediction model. Internal validation was performed using 1,000 bootstrap resampling. The predictive effect of the nomogram model was evaluated using the receiver operating characteristic curve (ROC), Hosmer-Lemeshow deviance test, and decision curve analysis (DCA).Logistic regression analysis showed that residual cholesterol, greater than 90 minutes from symptom onset to first medical contact (SO-to-FMC > 90 minutes), number of involved coronary vessels, Killip scale II-IV, and hemoglobin concentration were factors influencing the occurrence of MACE after PCI in these AMI patients (P < 0.05). The area under the curve (ROC-AUC) of the nomogram model for predicting the risk of developing postoperative MACE was 0.780 (0.721-0.839); the result of the Hosmer-Lemeshow test of deviance, χ² = 4.758 (P = 0.783), suggests that the model shows a moderately discriminatory and calibrated decision analysis curve; DCA shows a net clinical benefit with the nomogram model.RC is a promising biomarker for identifying AMI patients at high risk of postoperative MACE, and multivariate models based on RC can be used as quick and easy tools to identify these patients.