Personalized prediction algorithm for hemodynamically significant coronary artery stenosis in COVID-19 survivors with chronic coronary syndrome

Aim . To optimize the risk assessment of hemodynamically significant coronary artery (CA) stenosis in COVID-19 survivors with chronic coronary syndrome (CCS) based on a comprehensive analysis of anamnestic, clinical and paraclinical parameters. Material and methods. The study included 431 patients with CCS and positive SARS-CoV-2 serological tests for >12 weeks prior to enrollment (203 with mild and 228 with moderate COVID-19). The examination included clinical and laboratory parameters (including apolipoprotein A1 and B, lipoprotein(a)), systemic inflammation biomarkers, echocardiography, coronary angiography, and residual risk assessment. Multivariate logistic regression analysis was performed to identify independent predictors of hemodynamically significant coronary artery stenosis. Statistical processing was performed using RStudio 2025.05.0 (R 4.4.2), with p16 years (OR 1,85 [0,98; 3,6], p=0,064) and age >56 years (OR 1,65 [0,88; 3,09], p=0,116). Based on the resulting multifactorial model, an online risk prediction calculator for hemodynamically significant CA stenosis in COVID-19 survivors with CCS. Conclusion . Previous COVID-19, especially with moderate course, is associated with a more unfavorable clinical and angiographic phenotype of CCS. The developed risk prediction algorithm for hemodynamically significant CA stenosis can serve as a practical tool for early risk stratification and optimization of patient management in the post-COVID period.