AUTHOR=Fang Caoyang , Chen Zhenfei , Zhang Jinig , Jin Xiaoqin , Yang Mengsi 

TITLE=Construction and evaluation of nomogram model for individualized prediction of risk of major adverse cardiovascular events during hospitalization after percutaneous coronary intervention in patients with acute ST-segment elevation myocardial infarction

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2022.1050785

DOI=10.3389/fcvm.2022.1050785

ISSN=2297-055X

ABSTRACT=Background:Emergency percutaneous coronary intervention (PCI) in patients with acute ST-segment elevation myocardial infarction (STEMI) helps to reduce the occurrence of major adverse cardiovascular events (MACE) such as death, cardiogenic shock, and malignant arrhythmia, but in-hospital MACEs may still occur after emergency PCI, and their mortality is significantly increased once they occur. This study aimed to investigate the risk factors associated with MACE during hospitalization after PCI in STEMI patients, construct a nomogram prediction model and evaluate its effectiveness. Results:The results of LASSO regression showed that systolic blood pressure, diastolic blood pressure, Killip grade II-IV, urea nitrogen and left ventricular ejection fraction (LVEF) were important predictors with non-zero coefficients, and multivariate logistic regression analysis was performed to analyze that Killip grade II-IV, urea and LVEF were independent factors for in-hospital MACE after PCI in STEMI patients; a nomogram model for predicting the risk of in-hospital MACE after PCI in STEMI patients was constructed with the above independent predictors, with a C-index of 0.773 (95% CI: 0.721-0.824) having a good predictive power; the results of H-L goodness of fit test showed χ2 = 0.44, P = 0.51, the model calibration curve was close to the ideal model, and the internal validation C-index was 0.769; clinical decision analysis also showed that the nomogram model had a good clinical efficacy, especially when the threshold probability was 0.15-0.98, the nomogram model could bring clinical net benefits to patients. The nomogram model predicted a greater AUC (0.773) than the TIMI score (0.696) for in-hospital MACE after PCI in STEMI patients. Conclusion:Urea, Killip class II-IV, and LVEF are independent factors for in-hospital MACE after PCI in STEMI patients, and nomogram models constructed based on the above factors have high predictive efficacy and feasibility.