Prediction Factors of 6-Month Poor Prognosis in Acute Myocardial Infarction Patients - Yao, Jianhua; Xie, Yuan; Liu, Yang; Tang, Yu; Xu, Jiahong (Frontiers in Cardiovascular Medicine, 2020)
Background: Acute myocardial infarction (AMI) is among the leading causes of death worldwide. Patients with AMI may have the risk of developing recurrent cardiovascular events leading to rehospitalization or even death. The present study aimed to investigate the prediction factors of poor prognosis (mortality and/or readmission) after AMI during a 6-month follow-up. Method: A total of 206 consecutive patients hospitalized for the first visit with AMI were enrolled. Data collection included demographic characteristics, medical history, clinical information, laboratory results, and oral medications within 24 hours of admission. At 1, 3, and 6 months after discharge, AMI patients were followed up to assess the occurrence of composite endpoint events including in-hospital and out-of-hospital death and/or readmission due to recurrent MI or exacerbated symptoms of heart failure following MI. Results: After 6-month follow-up, a total of 197 AMI patients were available and divided in 2 groups according to good prognosis (n=144) and poor prognosis (n=53). Our data identified serum myoglobin ≥ 651 ng/mL, serum creatinine ≥ 96 µM, Killip classification 2-4, and female gender as independent predictors of 6-month mortality and/or readmission after AMI. Moreover, we demonstrated that Killip classification 2-4 combined with either myoglobin (AUCKillip class 2-4+Myoglobin=0.784, Sensitivity: 69.8%, Specificity: 79.9%) or creatinine (AUCKillip class 2-4+creatinine=0.805, Sensitivity: 75.5%, Specificity: 77.1%) could further enhance the predictive capacity of poor 6-month prognosis among AMI patients. Conclusions: In conclusion, AMI patients ranked in the higher Killip class need to be evaluated and monitored with attention. Multi-biomarker approach using Killip classification 2-4 and myoglobin or creatinine may be an effective way for 6-month prognosis prediction in AMI patients.