Introduction: In patients with acute coronary syndrome, old age is recognized as one of the risk factors for the development of adverse outcomes: recurrent acute myocardial infarction, relapse of stroke, repeated hospitalization in hospitals, death. According to the GRACE registry, older patients are less likely to receive interventional treatment than younger patients. This additionally determines the importance of choosing treatment with an assessment of outcomes after ACS. The optimal assessment of this syndrome is possible on the basis of screening for cardiovascular risk, followed by the determination of management tactics and the determination of both short-term and long-term treatment prospects with an assessment of the rehabilitation reserve. Purpose: to develop mathematical models for predicting adverse outcomes after acute coronary syndrome in patients with conservative treatment tactics. Materials and Methods: 302 patients with ACS were examined. The main criterion for inclusion in the main group was a confirmed diagnosis of ST-ACS, NSTE-ACS and unstable angina. By gender and age: 166 men (55%) and 136 women (45%). According to the applied tactics in patients with ACS: conservative therapy in 137 patients (45.4%) and invasive tactics (PCI and CABG) in 165 patients (54.6%). Mathematical models for predicting adverse outcomes were built using logistic regression equations and ROC analysis. Results: Prognostic mathematical models based on regression analysis have been developed. With the help of ROC-analysis, “cut-off thresholds” were calculated: 0.289 is the p-value, above which the risk of an unfavorable outcome of stroke within 12 months increases for patients. At this cut-off threshold, the sensitivity of the resulting model based on biochemical parameters was 76.9%, the specificity was 83.9% (AUC 0.867 (0.794-0.941); 0.123 is the p value, above which the risk of an adverse outcome of MI in patients increases. within 12 months.The sensitivity of the resulting model was 76.9%, the specificity was 77.0% (AUC 0.835 (0.760-0.910). 0.292 is the p value, above which the risk of an unfavorable outcome of readmission within 12 months increases for patients. At this cut-off threshold, the sensitivity of the resulting model based on biochemical parameters was 70.0%, the specificity was 70.9% (AUC 0.781 (0.701-0.861). Conclusions. The developed mathematical models of logistic regression for predicting the development of adverse events within 12 months after ACS in patients undergoing conservative treatment will reduce the risk of adverse outcomes against the background of the impossibility of surgical treatment.