CLINICAL STRATEGIES IN THE CARE OF PATIENTS WITH ACUTE CORONARY SYNDROMEASSISTANT

Abstract

Acute coronary syndrome (ACS) encompasses a spectrum of clinical conditions characterized by recent changes in symptoms or clinical signs, which may occur with or without electrocardiographic (ECG) alterations and with or without a marked rise in cardiac troponin (cTn) levels. The clinical presentation of ACS is highly heterogeneous, ranging from asymptomatic cases to patients experiencing persistent chest pain or discomfort, as well as those presenting with cardiac arrest, electrical or hemodynamic instability, or cardiogenic shock [2]. Significant advances in the organization of medical care for patients with ST-segment elevation ACS (STE-ACS), along with the widespread implementation of percutaneous coronary interventions (PCI), have led to a reduction in in-hospital mortality in recent years [3]. Nevertheless, overall mortality among patients with ACS—particularly those complicated by cardiogenic shock—remains unacceptably high [4, 5]. Importantly, the majority of fatal outcomes occur during the earliest phase of ACS, predominantly within the first 24 hours after hospital admission [3]. Consequently, in cases of both ST-elevation and non–ST-elevation ACS, clinicians require reliable tools for early prediction of mortality risk to support prompt decision-making and optimize patient management strategies. At present, risk assessment is primarily based on multivariable prognostic scales, the predictive value and validity of which have been confirmed by ROC analysis [4]. Numerous risk stratification systems are currently available, including GRACE, TIMI, PURSUIT, EuroSCORE II, and RECORD; however, these models predominantly rely on traditional, well-established risk factors [8, 9]. Analysis of contemporary research data indicates that the search for universal predictors of in-hospital mortality continues, with an emphasis on combining simplicity of application, consideration of comorbid conditions, and integration of laboratory and instrumental findings [5]. Therefore, identifying a comprehensive set of prognostic factors may improve risk stratification and enable more accurate estimation of mortality risk during the hospital phase of ACS.