Enhanced predictive performance of the GRACE risk score by incorporating lipoprotein(a) for major adverse cardiac events in acute myocardial infarction patients undergoing PCI.

Background: As scientific research advances, the landscape of detection indicators and methodologies evolves continuously. Our current study aimed to identify some novel perioperative indicators that can enhance the predictive accuracy of the Global Registry of Acute Coronary Events (GRACE) score for the in-hospital major adverse cardiovascular events (MACEs) in patients with acute myocardial infarction. Methods: A total of 647 adult patients with AMI admitted to the emergency department were consecutively enrolled in the retrospective research starting from June 2016 to September 2019. The endpoint was in-hospital MACE. Stepwise regression analysis and multivariate logistic regression were performed to select the indicators for the union model established by nomogram. Bootstrap with 1000 replicates was chosen as the internal validation of the union model. The area under the receiver operating curve (AUC) and calibration plot were used to evaluate the discrimination and calibration. Decision curve analysis (DCA) was performed to evaluate the clinical sufficiency of the nomogram. Akaike's information criterion (AIC) and Bayesian Information Criterion (BIC) were used to evaluate the goodness of fit. Results: Lipoprotein(a) combined with serum uric acid, fasting blood glucose, and hemoglobin could improve the GRACE risk score. The AUC of the union model was 0.86, which indicated a better discriminative ability than the GRACE risk score alone (AUC, 0.81; Conclusion: Lipoprotein(a) has shown promise in augmenting the predictive capability of the GRACE risk score, however, it may be beneficial to integrate it with other commonly used indicators.