The Naples score is a new prognostic score developed according to inflammatory and nutritional status and frequently evaluated in cancer patients. The present study aimed to evaluate using the Naples prognostic score (NPS) to predict the development of decreased left ventricular ejection fraction (LVEF) after acute ST-segment elevation myocardial infarction (STEMI). The study has a multicenter and retrospective design and included 2280 patients with STEMI who underwent primary percutaneous coronary intervention (pPCI) between 2017 and 2022. All participants were divided into 2 groups according to their NPS. The relationship between these 2 groups and LVEF was evaluated. The low-Naples risk group (Group-1) included 799 patients, and the high-Naples risk group (Group-2) had 1481 patients. Hospital mortality, shock, and no-reflow rates were found to be higher in Group 2 compared with Group 1 ( P < .001, P = .032, P = .004). The NPS was significantly inversely associated with discharge LVEF (B coefficient: −1.51, 95% CI-2.26; −.76, P = .001). NPS, a simple and easily calculated risk score, may help identify high-risk STEMI patients. To the best of our knowledge, the present study is the first to demonstrate the relationship between low LVEF and NPS in patients with STEMI.

Heart failure (HF) remains a significant health problem despite advances in diagnosis and treatment options. Malnutrition and increased inflammation predict poor disease prognosis. The parameters of the Naples prognostic score (NPS) include albumin, total cholesterol, neutrophil-lymphocyte ratio (NLR), and lymphocyte-monocyte ratio (LMR). We aimed to assess the potential of NPS as a predictor of long-term mortality in patients with HF. A total of 1728 patients with HF who applied to our center between 2018 and 2022 were included in this study. The NPS was computed and the patients were divided into three groups according to their NPS values as follows: NPS = 0 (Group 1), NPS = 1-2 (Group 2), and NPS = 3-4 (Group 3). We also evaluated the association between NPS value and HF mortality. The patients were followed for a mean follow-up duration of 30 months. The mortality rate was 8.3% (145 patients). We carried out Model-1 and -2 Cox regression analyses to identify long-term mortality determinants. Model-2 was constructed by adding NPS to Model-1. NPS was significantly associated with HF mortality (Hazard Ratio: 2.194, 95% Confidence Interval: 1.176-4.091, p = 0.014). According to the Kaplan-Meier plot and log-rank analyses, there was a statistically significant difference in the long-term mortality of patients with HF and their NPS values for the entire cohort. Based on our findings, NPS showed promise as an independent predictor of long-term mortality in individuals with HF.

Background Coronary artery disease (CAD) is one of the leading causes of death worldwide. CRP/albumin ratio is a sensitive indicator of inflammatory status. It has been shown that this parameter may be associated with poor short-term outcomes in CAD. In this study, we investigated the relationship between long-term mortality and the CRP/albumin ratio in patients with acute coronary syndromes (ACS). Material and methods This study was conducted on patients who applied to our hospital between January 2015 and December 2019 and were diagnosed with ACS. A total of 1689 patients were included in the study. Patients were divided into two groups according to mortality status, and long-term mortality predictors were investigated. Results The average follow-up period was 38.9±10.3 months. The mean age of the entire study group was 56.6±12.2 years, and 1440 (80.5%) of the patients were male. Comorbid diseases and blood parameters were significant between the two groups. In the regression analysis, creatine, hemoglobin, white blood cell count (WBC), neutrophil-lymphocyte ratio (NLR), and CRP albumin ratio (CAR) were found to be independent predictors. In the ROC analysis, it was observed that CAR had the best predictive value. Discussion An increased CAR level is an independent predictor of long-term mortality in ACS patients. It can be used in both short-term and long-term risk stratification for ACS patients.

Objective: The prognosis of cardiovascular diseases (CVDs) is directly associated with systolic function based on the measurement of ejection fraction (EF), and many studies have indicated that the left ventricular global strain (LVGS) provides better predictivity than the EF measurement in the diagnosis, prognosis, survival, and CVD staging. However, these studies did not investigate the correlation between the EF measurement and the LVGS parameters, or which parameters are better correlated with LVGS, but we analyzed the association between three EF measurement methods and LVGS. Materials and Methods: This study included 62 patients that applied to the clinic between October 2015 and March 2016. An echocardiography examination of these patients was performed. The exclusion criteria were atrial fibrillation and suboptimal image quality. Results: Sixty-two patients (the average age 61.0±12.6 years; 56% male and 44% female) were enrolled in the study. A statistically significant association was found between the visual EF and Simpson EF measurements and the LVGS parameters (p<0.001). While the visual EF was moderately correlated with the LVGS parameters (r=0.44), there was a good correlation between the Simpson EF and the LVGS parameters (r=0.710). Conclusion: In this study, we demonstrate that the Simpson’s rule LVEF correlates better with LVGS than the Teicholtz method or visual EF and that it has a better area under the curve value for determining an abnormal LVGS. Therefore, we recommend the use of the Simpson EF for the EF measurement that has a better correlation with the LVGS values in the patients whose ventricle functions should be evaluated. Cite this article as: Bayram E, Gulcu O, Aksu U, Aksakal E, Birdal O, Kalkan K. Evaluating the Association Between the Three Different Ejection Fraction Measurement Techniques and Left Ventricle Global Strain. Eurasian J Med 2018; 50(3): 173-177.

ST-segment elevation myocardial infarction (STEMI) represents the most harmful clinical manifestation of coronary artery disease. Risk assessment plays a beneficial role in determining both the treatment approach and the appropriate time for discharge. Hierarchical agglomerative clustering (HAC), a machine learning algorithm, is an innovative approach employed for the categorization of patients with comparable clinical and laboratory features. The aim of the present study was to investigate the role of HAC in categorizing STEMI patients and to compare the results of these patients. A total of 3205 patients who were diagnosed with STEMI at the university hospital emergency clinic between 2015 and 2023 were included in the study. The patients were divided into 2 different phenotypic disease clusters using the HAC method, and their outcomes were compared. In the present study, a total of 3205 STEMI patients were included; 2731 patients were in cluster 1, and 474 patients were in cluster 2. Mortality was observed in 147 (5.4%) patients in cluster 1 and 108 (23%) patients in cluster 2 (chi-square P value < 0.01). Survival analysis revealed that patients in cluster 2 had a significantly greater risk of death than patients in cluster 1 did (log-rank P < 0.001). After adjustment for age and sex in the Cox proportional hazards model, cluster 2 exhibited a notably greater risk of death than did cluster 1 (HR = 3.51, 95% CI = 2.71–4.54; P < 0.001). Our study showed that the HAC method may be a potential tool for predicting one-month mortality in STEMI patients.