Prognostic model for acute myocardial infarction in young patients with COVID-19

   Introduction. COVID-19 is associated with a high risk of cardiovascular complications, including acute myocardial infarction (AMI), even in young patients who may exhibit minimal traditional risk factors. Timely identification of individuals at high risk for AMI remains a clinical challenge, necessitating the development of novel prognostic models based on pathophysiologically relevant biomarkers.

   Aim. To develop a prognostic model for AMI risk in young patients with confirmed COVID-19 based on the expression levels of TNF-related apoptosis-inducing ligand (TRAIL) and transforming growth factor beta 1 (TGFβ1).

   Materials and methods. The study included 52 patients with moderate COVID-19, divided into two groups: the main group (n = 28) with AMI developed during hospitalization, and the comparison group (n = 24) without AMI. Peripheral blood samples were collected within the first 72 hours of admission. TRAIL (using APC-conjugated monoclonal antibodies) and TGFβ1 (using PE-conjugated monoclonal antibodies) expression on peripheral blood monocytes was assessed by flow cytometry on a BD FACS Canto II (USA).

   Results. In the main group, TRAIL expression (58.7% [52.1; 64.3]) and TGFβ1 expression (17.8 % [15.2; 21.4]) were significantly higher than in the comparison group (14.2 % [10.8; 18.6] and 4.5 % [3.1; 6.2], respectively; p < 0.001). Using discriminant analysis, a mathematical prognostic index (PI) for AMI was developed: PI = –13.197 + 0.355 × TRAIL + 0.276 × TGFβ1. A cutoff value of 12.90 was established: PI ≥ 12.90 indicates high risk of AMI; PI < 12.90 indicates low risk of AMI.

   Conclusion. The proposed prognostic model based on TRAIL and TGFβ1 expression levels on peripheral blood monocytes is a highly informative tool for assessing AMI risk in young patients with COVID-19. Implementation of this algorithm enables early initiation of preventive and therapeutic interventions aimed at reducing fatal cardiovascular complications and improving clinical outcomes.

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