Purpose: Despite all medical efforts and discoveries, heart failure (HF) remains one of the most important and common public health problems, with high mortality and hospitalization rates, due to decompensation of HF. In the present study, we aimed to identify a predictive factor through which we can evaluate the risk of readmission and mortality in the first year, given the initial admission of a patient with decompensated heart failure. Patients and Methods: The parameter we have investigated is the mean platelet volume (MPV). Studies have shown that there is a significant correlation between the value of MPV and the risk of cardiovascular disease (CVD) and cardiovascular (CV) death. In this study, we enrolled 130 patients hospitalized for decompensated chronic HF (NYHA class IV HF or acute pulmonary edema) and analyzed whether there is a relationship between the value of the MPV at admission and 6-month rehospitalization, and 1-year mortality, respectively. Results: The statistical analysis revealed significantly different values (p = 0.041) for MPV at admission between the group of patients without decompensated chronic HF compared to the group of patients with decompensated chronic HF (8.74 fl vs 9.08 fl). Also, the results of our study revealed that patients with decompensated chronic heart failure who were readmitted at 6 months and died at 1 year, respectively, had a higher MPV at admission (> 9 fl), compared to those without the above-mentioned events, with a statistical significance. Conclusion: A higher MPV at admission can be considered in our study as an independent predictor for rehospitalization and 1-year mortality of patients with decompensated chronic HF. Graphical Abstract:

Background: Arterial stiffness is a crucial factor in the pathogenesis of cardiovascular disease, often associated with aging. However, the impact of smoking on arterial stiffness is frequently underestimated. This study aims to investigate the intricate relationship between smoking and arterial stiffness to advance our understanding of and therapeutic approaches to cardiovascular health. Methods: A prospective analysis was conducted from January to July 2024, focusing on arterial stiffness parameters in a cohort of students from the Carol Davila University of Medicine and Pharmacy. Participants were categorized as smokers or non-smokers based on self-reported smoking status. The study endpoints included correlations between high pulse wave velocity, elevated peripheral and central systolic blood pressure, increased peripheral and central pulse pressure, and smoking status. These markers were assessed using an arteriograph device measuring the time difference between the initial forward pulse wave and the reflected pulse wave in the brachial artery to indirectly estimate the PWV using oscillometric pulsations. Results: Our investigation, involving 102 young individuals aged 20 to 26 (69 females, 33 males), revealed that smokers exhibited significantly higher average values of arterial stiffness indicators compared to non-smokers. Current smokers had higher mean systolic blood pressure (130.65 vs. 123.05 mmHg), higher mean peripheral pulse pressure (53.19 vs. 45.64 mmHg), higher mean central pulse pressure (33.66 vs. 29.69 mmHg), and higher mean pulse wave velocity (5.27 vs. 5.03 m/s). Conclusions: The utilization of arterial stiffness markers as predictive tools offers opportunities for personalized treatment strategies, potentially enhancing cardiovascular health outcomes.

(1) Background: Despite advancements in medical research and discoveries, heart failure (HF) still represents a significant and prevalent public health challenge. It is characterized by persistently high mortality and morbidity rates, along with increased rates of readmissions, particularly among the elderly population. (2) Methods: This study was conducted retrospectively on 260 patients with stable or decompensated chronic HF. The parameter of interest in the study population was the mean platelet volume (MPV), and the main objective of the research was to identify a possible relationship between MPV and several variables—biological (NT-proBNP, presepsin, red cell distribution width (RDW)), electrocardiographic (atrial fibrillation (AFib) rhythm, sinus rhythm (SR)), and echocardiographic (left ventricle ejection fraction (LVEF), left atrial (LA) diameter, left ventricle (LV) diameter, pulmonary hypertension (PH)). (3) Results: By applying logistic and linear regression models, we assessed whether there is a correlation between MPV and biological, electrocardiographic, and echocardiographic variables in patients with HF. The results revealed linear relationships between MPV and NT pro-BNP values and between MPV and RDW values, and an increased probability for the patients to have an AFib rhythm, reduced LVEF, dilated LA, dilated LV, and PH as their MPV value increases. The results were deemed statistically relevant based on a p-value below 0.05. (4) Conclusions: Through regression model analyses, our research revealed that certain negative variables in HF patients such as increased levels of NT-proBNP, increased levels of RDW, AFib rhythm, reduced LVEF, dilated LA, dilated LV, and PH, could be predicted based on MPV values.

(1) Background: Heart failure (HF) remains one of the most frequent public health problems, with high mortality and hospitalization rates. Identifying an indicator with a prognostic role in patients with HF could represent a salutary solution in the future, as it could identify patients with a negative prognosis. Thus, our study focused on mean platelet volume (MPV) as a possible prognostic factor in patients with HF. (2) Methods: The research was a type of correlational study, which was conducted in a retrospective manner on 260 patients with chronic HF. The statistical analysis assessed whether there was a plausible relationship between the MPV value and several target variables based on a regression model analysis. (3) Results: Using logistic and linear regression models, we estimated the impact of the MPV value on certain events and the probabilities of these events depending on MPV value. The results obtained using regression model analysis were considered statistically significant (α = 0.05) based on a p-value below 0.05. The results showed that patients with class IV NYHA HF or acute pulmonary edema had higher MPV than class II NYHA HF, and that patients with higher MPV had a greater probability of having certain events, represented by 3-month hospitalization, 6-month rehospitalization, 1-year death, and prolonged hospitalization. (4) Conclusions: Using statistical modeling based on regression model analysis, our study showed that certain negative events in HF patients could be predicted based on the MPV value.

Background: Beneath the surface of the acute ST-elevation myocardial infarction (STEMI) iceberg lies a hidden peril, obscured by the well-known cardiovascular risk factors that tip the iceberg. Before delving into the potential time bomb these risk factors represent, it is crucial to recognize the obscured danger lurking under the surface. What secrets does the STEMI iceberg hold? To unveil these mysteries, a closer look at the pathophysiology of STEMI is imperative. Inflammation, the catalyst of the STEMI cascade, sets off a chain reaction within the cardiovascular system. Surprisingly, the intricate interplay between red blood cells (RBC) and lymphocytes remains largely unexplored in previous research. Materials and methods: The study encompassed 163 patients diagnosed with STEMI. Utilizing linear and logistic regression, the lymphocyte-to-red blood cell ratio (LRR) was scrutinized as a potential predictive biomarker. Results: There was a statistically significant correlation between LRR and the prognosis of STEMI patients. Building upon this discovery, an innovative scoring system was proposed that integrates LRR as a crucial parameter. Conclusions: Uncovering novel predictive markers for both immediate and delayed complications in STEMI is paramount. These markers have the potential to revolutionize treatment strategies by tailoring them to individual risk profiles, ultimately enhancing patient outcomes.