The aim of the work: to develop a predictive model for fatal outcomes of COVID-19 in hospitalized patients with arterial hypertension. Material and methods. A retrospective analysis was conducted using 200 medical records of patients with a confirmed diagnosis of COVID-19 and concomitant arterial hypertension who received inpatient treatment at the Yakut Republican Clinical Hospital (Yakutsk) between 2020 and 2022. Based on outcomes, patients were divided into two groups: Group 1 included 150 (75%) patients discharged with improvement, and Group 2 included 50 (25%) patients with a fatal outcome. To analyze the association of risk factors with adverse outcomes of COVID-19, binary logistic regression was performed using StatTech software v.4.8.11. To compare the two groups, the Student’s t-test or the Mann-Whitney U-test were used, depending on the distribution. Categorical data was expressed in absolute values and percentages. The effect was assessed using the odds ratio with a 95% confidence interval. The predictive model was built using logistic regression, the Nigelkirk coefficient R2 and ROC curves. The cut-off value was determined by the maximum of the Juden index. Results. Multivariate analysis of 200 parameters from patients with COVID-19 and hypertension identified four predictors associated with fatal outcome: age (p < 0,011), SpO2 (p = 0,001), BNP levels (p = 0,003), and C-reactive protein levels (p = 0,020). The developed model was evaluated using the area under the ROC curve (AUC = 79,8%) and demonstrated high sensitivity (72,0%) and specificity (85,3%). Conclusion. Predictors of increased risk for adverse outcomes of coronavirus infection in patients with arterial hypertension included age, BNP and C-reactive protein concentrations, and oxygen saturation (SpO2) at admission. A predictive model for assessing the risk of adverse outcomes of COVID-19 in hospitalized patients with hypertension was developed, demonstrating high specificity and significant practical value.