Estimation of Major Adverse Cardiovascular Events in Patients With Myocardial Infarction Undergoing Primary Percutaneous Coronary Intervention: A Risk Prediction Score Model From a Derivation and Validation Study - Zhao, Xiaoxiao; Liu, Chen; Zhou, Peng; Sheng, Zhaoxue; Li, Jiannan; Zhou, Jinying; Chen, Runzhen; Wang, Ying; Chen, Yi; Song, Li; Zhao, Hanjun; Yan, Hongbing (Frontiers in Cardiovascular Medicine, 2020)
Background The population with myocardial infarction (MI) undergoing primary percutaneous coronary intervention (PPCI) is growing but validated models to guide their clinical management are lacking. We developed and validated prognostic models to predict major adverse cardiovascular events (MACE) in patients with MI undergoing PPCI. Methods and Results Models were developed in utter 4151 MI patients who has underwent PPCI from the Fuwai hospital between January 2010 and June 2017, with media follow-up of 698 days during which 544 MACE events were observed. The predictors in the models were age, the history of diabetes mellitus, atrial fibrillation, chronic kidney disease, coronary artery bypass grafting, Killip classification, ejection fraction measurement of the admission, high sensitive C reaction protein concentration (hs-CRP), estimated glomerular filtration rate (eGFR), D-dimer level and triple-vessel lesions. The models had good calibration and discrimination in derivation and internal validation with C-indexes which were 0.73 (P<0.0001, SD=0.037) and 0.60 (p=0.0009, SD=0.051) for predicting MACE. We have made a comparison between new predicting model and Grace risk score by receiver operating characteristic curve. The area under curve (AUC) of new model is 0.821 and the AUC of grace score is 0.794(95% CI 0.012-0.043). Difference between areas = 0.0007; z statistic, 3.395. Conclusion The predicted model could be used in clinical practice to support risk stratiﬁcation as recommended in clinical guidelines.