Acute myocardial infarction (AMI) remains a major cause of mortality and morbidity, and cardiogenic shock (CS) a major cause of hospital mortality after AMI. Especially for ST elevation myocardial infarction (STEMI) patients, fast intervention is essential.
Few proteins have proven clinically applicable for AMI. Most proposed biomarkers are based on a priori hypothesis-driven studies of single proteins, not enabling identification of novel candidates. For clinical use, the ability to predict AMI is important; however, studies of proteins in prediction models are surprisingly scarce.
Consequently, we applied proteome data for identifying proteins associated with definitive STEMI, CS, and all-cause mortality after admission, and examined the ability of the proteins to predict these outcomes.
Methods and Results:
Proteome-wide data of 497 patients with suspected STEMI were investigated; 381 patients were diagnosed with STEMI, 35 with CS, and 51 died during the first year. Data analysis was conducted by logistic and Cox regression modeling for association analysis, and by multivariable LASSO regression models for prediction modeling.
Association studies identified 4 and 29 proteins associated with definitive STEMI or mortality, respectively. Prediction models for CS and mortality (holding two and five proteins, respectively) improved the prediction ability as compared with protein-free prediction models; AUC of 0.92 and 0.89, respectively.
The association analyses propose individual proteins as putative protein biomarkers for definitive STEMI and survival after suspected STEMI, while the prediction models put forward sets of proteins with putative predicting ability of CS and survival. These proteins may be verified as biomarkers of potential clinical relevance.