Predictive value of residual inflammation risk-based nomogram model for major adverse cardiovascular events in acute myocardial infarction patients after percutaneous coronary intervention

Abstract

Objective To develop a nomogram prediction model based on residual inflammation risk (RIR) and evaluate its value in the prediction for the risk of major adverse cardiovascular events (MACE) in patients with acute myocardial infarction (AMI) after percutaneous coronary intervention (PCI). Methods Clinical data of 297 AMI patients who underwent PCI in our department from June 2017 to March 2019 were collected and retrospectively analyzed. According to their levels of preoperative hypersensitive-CRP (hs-CRP) and low-density lipoprotein cholesterol (LDL-C), they were divided into RIR group (n=28) and non RIR group (n=269). They also were assigned into the MACE group (n=102) and non-MACE group (n=195) according to whether MACEs occurred during hospitalization. Least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate logistic regression analysis were used to evaluate the correlation between the risk of RIR and the occurrence of MACE, and other independent risk predictors of MACE were screened out. The nomogram model based on the indicators of RIR was constructed and verified by the R language software. Results The incidences of pump failure, cardiogenic shock, malignant arrhythmia and death, and overall MACE were significantly higher in the RIR group than the non-RIR group (P < 0.05). The Results of LASSO regression and multivariate regression analyses showed that left ventricular ejection fraction (LVEF) and hemoglobin concentration were negatively correlated with the risk of MACE in hospital (P < 0.05), while residual inflammation risk, hemoglobinA1c (Hb1Ac), leukocyte count and N-terminal pro-B type natriuretic peptide (NT-proBNP) level were positively correlated with the risk of MACE (P < 0.05). The ROC curve indicated that RIR was not good in prediction of MACE (AUC=0.592, 95%CI: 0.551~0.634). Based on the above 6 indicators, the nomogram model was constructed. The Harrel's C-index of the nomogram was 0.872 (95%CI: 0.827~0.917), the AUC of the nomogram after re-sampling 1 000 times was 0.866 (95% CI: 0.818~0.907), and the Hosmer lemeshow deviation test showed that the prediction probability of nomogram was consistent with the actual frequency (Chi-square=8.420, P=0.393). The clinical decision curve showed that when the threshold probability of MACE occurrence is between 0.08 and 0.88, and the nomogram model could obtain the highest net benefit, which indicated that the model had good clinical applicability. Conclusion The RIR-based nomogram model and other 5 factors have good prediction efficiency and clinical applicability in the prediction of the risk of MACE in AMI patients.

Keywords

• acute myocardial infarction
• adverse cardiovascular events
• residual inflammation
• risk prediction
• nomogram