ACS covers a range of conditions that include patients with recent changes in clinical symptoms or signs, with or without changes on the electrocardiogram (ECG), and with or without a sharp increase in cardiac troponin (cTn) concentrations. ACS is associated with a wide range of clinical manifestations, including both asymptomatic patients and patients with persistent symptoms of chest pain or discomfort, as well as patients with cardiac arrest, electrical or hemodynamic instability, and cardiogenic shock [2]. Due to significant successes in the organization of medical care for patients with ST-segment elevation ACS (ST ACS), the introduction of percutaneous coronary interventions (PCI) into widespread practice, over the past few years, it has been possible to reduce in-hospital mortality from this pathology [3]. However, the mortality rate of patients with ACS, especially with cardiogenic shock, is still high [4, 5]. Moreover, most of the deaths occur in the early stages of the onset of ACS, i.e., in the first 24 hours of the patient's hospitalization [3]. For this reason, when ST-elevation ACS or non-ST-elevation ACS (ST-elevation) develops, the physician needs a "tool" to predict the risk of death, in order to make quick decisions and optimize patient management. To date, such a "tool" for assessing the risk of an adverse outcome in patients is scales based on multivariate analysis, the strength and significance of which are confirmed by ROC analysis [4]. Currently, there are many scales and methods for assessing the risk of death (GRACE, TIMI, PURSUIT, EuroSCORE II, RECORD), however, they mainly take into account well-known "classical" risk factors [8, 9]. However, when analyzing the research data, it should be noted that the search for universal predictors for assessing the risk of in-hospital mortality continues, combining a number of criteria: ease of use, taking into account the impact of comorbidity, as well as the results of laboratory and instrumental research methods [5]. That is why the establishment of a set of prognostic factors can help optimize risk stratification and accurately assess the probability of death at the hospital stage.

ACS, predictors, lethality, comorbidity.

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