BACKGROUND Non-ST segment elevation myocardial infarction (NSTEMI) poses significant challenges in clinical management due to its diverse outcomes. Understanding the prognostic role of hematological parameters and derived ratios in NSTEMI patients could aid in risk stratification and improve patient care. AIM To evaluate the predictive value of hemogram-derived ratios for major adverse cardiovascular events (MACE) in NSTEMI patients, potentially improving clinical outcomes. METHODS A prospective, observational cohort study was conducted in 2021 at the Internal Medicine Clinic of the University Hospital in Tuzla, Bosnia and Herzegovina. The study included 170 patients with NSTEMI, who were divided into a group with MACE and a control group without MACE. Furthermore, the MACE group was subdivided into lethal and non-lethal groups for prognostic analysis. Alongside hematological parameters, an additional 13 hematological-derived ratios (HDRs) were monitored, and their prognostic role was investigated. RESULTS Hematological parameters did not significantly differ between non-ST segment elevation myocardial infarction (NSTEMI) patients with MACE and a control group at T1 and T2. However, significant disparities emerged in HDRs among NSTEMI patients with lethal and non-lethal outcomes post-MACE. Notably, neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) were elevated in lethal outcomes. Furthermore, C-reactive protein-to-lymphocyte ratio (CRP/Ly) at T1 (> 4.737) demonstrated predictive value [odds ratio (OR): 3.690, P = 0.024]. Both NLR at T1 (> 4.076) and T2 (> 4.667) emerged as significant predictors, with NLR at T2 exhibiting the highest diagnostic performance, as indicated by an area under the curve of 0.811 (95%CI: 0.727-0.859) and OR of 4.915 (95%CI: 1.917-12.602, P = 0.001), emphasizing its important role as a prognostic marker. CONCLUSION This study highlights the significant prognostic value of hemogram-derived indexes in predicting MACE among NSTEMI patients. During follow-up, NLR, PLR, and CRP/Ly offer important insights into the inflammatory processes underlying cardiovascular events.

Introduction: Insulin resistance (IR) is a complex pathophysiological condition multifactorial etiology characterized by diminished responsiveness of insulin target tissues. Today, various diagnostic approaches involving different laboratory parameters are available, but simple and non-invasive indices based on mathematical models are increasingly used in practice. This study aims to assess the effectiveness of various clinical surrogate indices in predicting IR across a population with varying body weights. Methods: The matched case-control study was conducted between January 2021 and December 2022. Secondary data extracted from the medical records of 129 subjects was analyzed, including demographic characteristics (age and gender), anthropometric measures (height and weight), and biochemical laboratory test results. y further divided into two subgroups based on body mass index (BMI): overweight (BMI between 25 and 29.9 kg/m2) and obese (BMI of 30 kg/m2 or higher). Using laboratory data values for six widely used clinical surrogate markers were calculated: Homeostatic model assessment for IR (HOMA-IR), quantitative insulin sensitivity check index (QUICKI), Mcauley index (MCAi), metabolic score for IR (METS-IR), Triglyceride to Glucose Index (TyG), and TyG to BMI (TyG-BMI). Results: Significant differences in HOMA-IR levels were observed between the groups (p < 0.001). A similar pattern was found for the TyG-BMI, with notable differences (p < 0.001). The obese participants had the highest mean levels for METS-IR and the TyG index while the control group had the highest mean values for the QUICKI and MCAi indices (p < 0.001). According to the analysis, three indices showed statistical significance in predicting IR independent of BMI (p < 0.05). Sensitivity and specificity were higher in the obese group (0.704 and 0.891) than in the overweight group (0.631 and 0.721). Conclusion: Given that IR is a multifactorial disease, using derived indices based on a combination of biochemical parameters and anthropometric indicators can significantly aid in predicting and mitigating numerous complications.