Prediction Factors of 6-Month Poor Prognosis in Acute Myocardial Infarction Patients

Jianhua Yao1, Yuan Xie2, Yang Liu2

  • 1Department of Cardiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China.

Insights

Predicting poor prognosis after acute myocardial infarction (AMI) is crucial. Higher Killip class, elevated myoglobin or creatinine, and female gender independently predict mortality and readmission within six months.

Area of Science:

  • Cardiology
  • Internal Medicine
  • Biomarkers

Background:

  • Acute myocardial infarction (AMI) is a leading global cause of death.
  • Patients with AMI face risks of recurrent cardiovascular events, rehospitalization, and mortality.
  • Identifying predictors of poor prognosis post-AMI is essential for patient management.

Purpose of the Study:

  • To identify factors predicting poor prognosis (mortality and/or readmission) within 6 months after AMI.
  • To evaluate the combined predictive value of clinical and biomarker data for AMI prognosis.

Main Methods:

  • 206 first-time AMI patients were enrolled, with data collected on demographics, medical history, clinical status, and labs within 24 hours of admission.
  • Patients were followed for 6 months post-discharge to assess composite endpoints of death and/or readmission.
  • Statistical analysis identified independent predictors and assessed the predictive capacity of combined markers.

Main Results:

  • Serum myoglobin ≥651 ng/mL, serum creatinine ≥96 μM, Killip classification 2-4, and female gender were independent predictors of 6-month mortality/readmission.
  • Combining Killip class 2-4 with myoglobin (AUC=0.784) or creatinine (AUC=0.805) significantly enhanced prediction accuracy.
  • These factors identified a high-risk group among AMI patients.

Conclusions:

  • Patients with AMI and higher Killip classifications require close monitoring.
  • A multibiomarker approach, incorporating Killip class with myoglobin or creatinine, offers effective 6-month prognosis prediction for AMI patients.

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