Multifactorial prediction of adverse outcome of acute coronary syndrome combined with post-COVID syndrome

Aim – to build a multivariate model for predicting adverse outcomes of acute coronary syndrome with and without ST segment elevation in patients with post-COVID syndrome.

Material and methods. The study included 118 patients (61 men and 57 women) with acute coronary syndrome and post-COVID syndrome. All patients underwent medical history review, clinical examination, laboratory tests, coronary angiography, echocardiography, electrocardiography, and molecular genetic marker testing. The influence of each factor on the probability of developing a combined endpoint, including the total number of cardiovascular complications and fatal outcomes, was assessed using logistic regression analysis. The statistical significance of the model was determined by the χ² test. The sensitivity and specificity of the model were assessed using ROC analysis.

Results. The constructed multivariate regression model showed that the development of an unfavorable outcome in patients with acute coronary syndrome in combination with PCS is associated with the presence of chronic heart failure, elevated soluble fms-like tyrosine kinase-1, hypokinesis zones on echocardiography, carrier status of the TT/AA genotype of the genetic marker rs2285666 of the ACE2 gene (χ² = 38.416, p <0.001). The sensitivity of the model is 93.5%, and the specificity is 21.8%, the accuracy is 76.6%, the area under the curve (AUC) = 0.8.

Conclusion. A multivariate regression model was constructed and tested to predict, with high accuracy, the development of an unfavorable outcome of acute coronary syndrome in combination with post-COVID syndrome.