Prediction of recurrent myocardial infarction in working-age patients

Aim. To determine independent predictors of recurrent myocardial infarction (MI) and to create a model for predicting recurrent coronary events in working-age patients.

Material and methods. The study included 424 patients (median age 50 (43,5; 55,0) years). In 2017, all patients underwent treatment at the Perm Regional Vascular Center due to the first MI. We retrospectively analyzed the patient data with regard to medical history, comorbidities, diagnostic results, and treatment. After 2 years, information on recurrent MI was collected by analyzing data from electronic medical records of patients. Depending on the outcome, all patients were divided into two groups: with (n=78) and without (n=346) recurrent MI. Using the SPSS Statistics v.20, v.23 software package, we compared the central demographic, clinical, diagnostic parameters in the groups. Univariate and multivariate regression analyzes were performed to determine independent predictors of recurrent MI.

Results. Multivariate regression established the following independent predictors of recurrent MI: left ventricular ejection fraction <50% (odds ratio (OR) 5,5, 95% confidence interval (CI) 1,56-19,34, p=0,008), anemia (OR=2,95, 95% CI 1,089- 9,765, p=0,046), multivessel coronary artery disease (OR 2,24, 95% CI 1,285- 3,909, p=0,004). Logistic regression model was created that allows predicting the 2-year risk of recurrent MI after the initial hospitalization with a sensitivity of 73,7% and a specificity of 79,6%.

Conclusion. The results of this study suggest that risk stratification for recurrent MI in working-age patients may need to take into account decreased left ventricular ejection fraction, hemoglobin level, and multivessel coronary artery disease.

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