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Background: Platelets play an important role in the development of cardiovascular disease (CVD). Mean platelet volume (MPV) is considered as biological marker of platelets activity and function. The aim of the present study was to evaluate MPV values and its possible correlation with arterial stiffness and subclinical myocardial damage, in normal glucose tolerance patients (NGT), in newly diagnosed type 2 diabetic (T2DM) patients and in individuals with pre-diabetes. Methods: We enrolled 400 newly diagnosed hypertensive patients. All patients underwent an Oral Glucose Tolerance test (OGTT). Arterial stiffness (AS) was evaluated with the measurement of carotid-femoral pulse wave velocity (PWV), augmentation pressure (AP) and augmentation index (AI). Echocardiographic recordings were performed using an E-95 Pro ultrasound system. Results: Among groups there was an increase in fasting plasma glucose (FPG) (p < 0.0001), fasting plasma insulin (FPI) (p < 0.0001), high sensitivity c reactive protein (hs-CRP) levels (p < 0.0001) and a decrease in renal function as demonstrated by e-GFR values (p < 0.0001). From the NGT group to the T2DM group there was a rise in MPV value (p < 0.0001). Moreover, in the evaluation of arterial stiffness and subclinical myocardial damage, MPV showed a positive correlation with these parameters. Conclusions: In the present study we highlighted that MPV is significantly increased, not only in newly diagnosed T2DM patients, but also in early stage of diabetes, indicating that subjects with pre-diabetes present increased platelets reactivity. Moreover, our results suggest that MPV is associated with increased arterial stiffness and subclinical myocardial damage, indicating MPV as new marker of CV risk. [ABSTRACT FROM AUTHOR]

Chronic heart failure (CHF) is a clinical syndrome characterised by symptoms and signs due to structural and/or functional abnormalities of the heart. CHF confers risk for cardiovascular deterioration events which cause recurrent hospitalisations and high mortality rates. The early prediction of these events is very important to limit serious consequences, improve the quality of care, and reduce its burden. CHF is a progressive condition in which patients may remain asymptomatic before the onset of symptoms, as observed in heart failure with a preserved ejection fraction. The early detection of underlying causes is critical for treatment optimisation and prognosis improvement. To develop models to predict cardiovascular deterioration events in patients with chronic heart failure, a real dataset was constructed and a knowledge discovery task was implemented in this study. The dataset is imbalanced, as it is common in real-world applications. It thus posed a challenge because imbalanced datasets tend to be overwhelmed by the abundance of majority-class instances during the learning process. To address the issue, a pipeline was developed specifically to handle imbalanced data. Different predictive models were developed and compared. To enhance sensitivity and other performance metrics, we employed multiple approaches, including data resampling, cost-sensitive methods, and a hybrid method that combines both techniques. These methods were utilised to assess the predictive capabilities of the models and their effectiveness in handling imbalanced data. By using these metrics, we aimed to identify the most effective strategies for achieving improved model performance in real scenarios with imbalanced datasets. The best model for predicting cardiovascular events achieved mean a sensitivity 65%, a mean specificity 55%, and a mean area under the curve of 0.71. The results show that cost-sensitive models combined with over/under sampling approaches are effective for the meaningful prediction of cardiovascular events in CHF patients. [ABSTRACT FROM AUTHOR]