AUTHOR=Zhao Xiaoxiao , Liu Chen , Zhou Peng , Sheng Zhaoxue , Li Jiannan , Zhou Jinying , Chen Runzhen , Wang Ying , Chen Yi , Song Li , Zhao Hanjun , Yan Hongbing 

TITLE=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

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=7

YEAR=2020

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2020.603621

DOI=10.3389/fcvm.2020.603621

ISSN=2297-055X

ABSTRACT=<p><bold>Background:</bold> The population with myocardial infarction (MI) undergoing primary percutaneous coronary intervention (PPCI) is growing, but validated models to guide their clinical management are lacking. This study aimed to develop and validate prognostic models to predict major adverse cardiovascular events (MACEs) in patients with MI undergoing PPCI.</p><p><bold>Methods and Results:</bold> Models were developed in 4,151 patients with MI who underwent PPCI in Fuwai Hospital between January 2010 and June 2017, with a median follow-up of 698 days during which 544 MACEs occurred. The predictors included in the models were age, a history of diabetes mellitus, atrial fibrillation, chronic kidney disease, coronary artery bypass grafting, the Killip classification, ejection fraction at admission, the high-sensitivity C-reactive protein (hs-CRP) level, the estimated glomerular filtration rate, the d-dimer level, multivessel lesions, and the culprit vessel. The models had good calibration and discrimination in the derivation and internal validation with C-indexes of 0.74 and 0.60, respectively, for predicting MACEs. The new prediction model and Thrombolysis in Myocardial Infarction (TIMI) risk score model were compared using the receiver operating characteristic curve. The areas under the curve of the new prediction model and TIMI risk score model were 0.806 and 0.782, respectively (difference between areas = 0.024 &lt; 0.05; <italic>z</italic> statistic, 1.718).</p><p><bold>Conclusion:</bold> The new prediction model could be used in clinical practice to support risk stratification as recommended in clinical guidelines.</p>