Background. Angiotensin-converting enzyme inhibitors (ACE inhibitors), in particular enalapril, have retained a leading role in the treatment of arterial hypertension (AH) over the past decades, but at the same time have a side effect in the form of cough, which is one of the main reasons for a significant decrease in the quality of life of patients and subsequent drug withdrawal. An important clinical task is to create an algorithm for predicting the risk of cough development for personalizing enalapril therapy.
Objective. Creation of an algorithm for predicting the risk of cough development while taking enalapril for personalizing ACE inhibitor therapy in hypertensive patients.
Methods. To create an algorithm for predicting the risk of cough development while taking enalapril, multivariate modeling was performed using the logistic regression method.
Results. During the multivariate logistic regression analysis, genetic factors were selected in the form of rs2306283 SLCO1B1, rs495828 ABO, rs8176746 ABO gene polymorphisms, as well as data on the presence of allergic diseases and manifestations in close relatives, which together formed a model for predicting the risk of cough development while taking enalapril in hypertensive patients.
Conclusion. The presented model demonstrates high prognostic significance, has sufficient information content (R2=0.31) and allows to predict the development of cough while taking enalapril with an accuracy of 76.5%.

Keywords: angiotensin-converting enzyme inhibitors, cough, enalapril, arterial hypertension