Background: The subendocardial viability ratio (SEVR), also known as the Buckberg index, is a parameter of arterial stiffness with indirect prognostic value in assessing long-term cardiovascular risk. Materials and Methods: We conducted a prospective cohort study on 70 patients with uncomplicated hypertension admitted to a county medical reference hospital. We analyzed demographics, laboratory data, arterial stiffness parameters and cardiovascular risk scores (SCORE and Framingham risk scores) and aimed to identify paraclinical parameters associated with increased cardiovascular risk. Results: Of the arterial stiffness parameters, SEVR correlates statistically significantly with age, central and peripheral systolic blood pressure, as well as with heart rate. SEVR seems to have prognostic value among hypertensive patients by increasing the risk of major cardiovascular events assessed by SCORE and Framingham risk scores. SEVR correlates statistically significantly with serum fibrinogen (p = 0.02) and hemoglobin (p = 0.046). Between pulse wave velocity and lipid parameters (p = 0.021 for low-density lipoprotein cholesterol and p = 0.030 for triglycerides) a statistically significant relationship was found for the study group. The augmentation index of the aorta also correlated with serum LDL-cholesterol (p = 0.032) and the hemoglobin levels (p = 0.040) of hypertensive patients. Conclusions: Age, abdominal circumference and Framingham score are independent predictors for SEVR in our study group, further highlighting the need for early therapeutic measures to control risk factors in this category of patients.

Sustained physical activity induces morphological and functional changes in the cardiovascular system. While mostly physiological, they can also become a trigger for major adverse cardiovascular events, the most severe of which are sudden cardiac arrest and sudden cardiac death. Therefore, any novel method which can help more accurately estimate the cardiovascular risk should be considered for further studying and future implementation in the standard protocols. The study of biomarkers is gaining more and more ground as they have already established their utility in diagnosing ischemic cardiac disease or in evaluating cardiac dysfunction in patients with heart failure. Nowadays, they are being implemented in the screening of apparently healthy individuals for the assessment of the cardiovascular risk. The aim of this paper is to gather published data regarding the measurements of cardiac biomarkers in athletes, i.e., troponins, myoglobin, CK-MB, NT-proBNP, and D-Dimers, and their potential use in the field of sports cardiology.

Background and Objectives: Cardiac rehabilitation (CR) plays an essential role in peripheral artery disease (PAD), leading to improved functional status, increased quality of life, and reduced arterial stiffness. We aimed to assess factors associated with clinical improvement 6 months after enrolment in a rehabilitation program at an academic medical center in north-eastern Europe. Materials and Methods: We conducted a prospective cohort study on 97 patients with PAD admitted to a single tertiary referral center. At the 6-months follow-up, 75 patients (77.3%) showed improved clinical status. We analyzed demographics and clinical and paraclinical parameters in order to explore factors associated with a favorable outcome. Results: Hypertension (p = 0.002), diabetes mellitus (p = 0.002), dyslipidemia (p = 0.045), and obesity (p = 0.564) were associated with no clinical improvement. Smoking cessation (p < 0.001), changing sedentary lifestyle (p = 0.032), and improvement of lipid and carbohydrate profile as well as functional status parameters and ambulatory arterial stiffness index (p = 0.008) were factors associated with clinical improvement at the 6-months follow-up. Conclusions: PAD patients require an integrative, multidisciplinary management to maintain functional status and increase quality of life. Improving carbohydrate and lipid profile, adopting a healthy lifestyle, quitting smoking and increasing exercise capacity are predictors for clinical improvement 6 months after enrolment in a CR program.

Cardiac rehabilitation (CR) is an integral part of the management of various cardiovascular disease such as coronary artery disease (CAD), peripheral artery disease (PAD), or chronic heart failure (CHF), with proven morbidity and mortality benefits. This article aims to review and summarize the scientific literature related to cardiac rehabilitation programs for patients with PAD and how they were adapted during the COVID-19 pandemic. The implementation of CR programs has been problematic since the COVID-19 pandemic due to social distancing and work-related restrictions. One of the main challenges for physicians and health systems alike has been the management of PAD patients. COVID-19 predisposes to coagulation disorders that can lead to severe thrombotic events. Home-based walking exercises are more accessible and easier to accept than supervised exercise programs. Cycling or other forms of exercise are more entertaining or challenging alternatives to exercise therapy. Besides treadmill exercises, upper- and lower-extremity ergometry also has great functional benefits, especially regarding walking endurance. Supervised exercise therapy has a positive impact on both functional capacity and also on the quality of life of such patients. The most effective manner to acquire this seems to be by combining revascularization therapy and supervised exercise. Rehabilitation programs proved to be a mandatory part of the integrative approach in these cases, increasing quality of life, and decreasing stress levels, depression, and anxiety.

The management of patients with peripheral artery disease (PAD) is integrative and multidisciplinary, in which cardiac rehabilitation (CR) plays a prognostic role in terms of functional status, quality of life, and long-term impact on morbidity and mortality. We conducted a prospective cohort study on 97 patients with PAD admitted to a single tertiary referral center. Based on a prognostic index developed to stratify long-term mortality risk in PAD patients, we divided the cohort into two groups: low and low-intermediate risk group (45 cases) and high-intermediate and high risk group (52 cases). We analyzed demographics, clinical parameters, and paraclinical parameters in the two groups, as well as factors associated with cardiological reassessment prior to the established deadline of 6 months. Obesity (p = 0.048), renal dysfunction (p < 0.001), dyslipidemia (p < 0.001), tobacco use (p = 0.048), and diabetes mellitus (p < 0.001) are comorbidities with long-term prognostic value. Low-density lipoprotein cholesterol (p = 0.002), triglycerides (p = 0.032), fasting glucose (p = 0.011), peak oxygen uptake (p = 0.005), pain-free walking distance (p = 0.011), maximum walking time (p < 0.001), and maximum walking distance (p = 0.002) influence the outcome of PAD patients by being factors associated with clinical improvement at the 6-month follow-up. PAD patients benefit from enrollment in CR programs, improvement of clinical signs, lipid and carbohydrate profile, and weight loss and maintenance of blood pressure profile within normal limits, as well as increased exercise capacity being therapeutic targets.

Background/Objectives: Glioblastoma (GB) remains the most prevalent primary malignant brain tumor in adults, characterized by its aggressive nature and poor prognosis. The present study endeavored to contribute to the development of advanced computational tools for neuro-oncology by integrating artificial intelligence (AI)-based segmentation and multi-model machine learning (ML) approaches. Methods: A retrospective analysis was conducted on patients with GB. AI-driven algorithms were utilized to perform volumetric segmentation of GB. These quantitative metrics were subsequently integrated into a multi-model ML framework to analyze correlations with patient survival and evaluate the predictive accuracy of the resulting models. Results: A total of 79 patients were ultimately included in the study after meeting all eligibility criteria. The results showed that larger GB tumors were associated with shorter post-treatment survival. Necrotic patterns within GB tumors impacted patient survival rates and response to therapy. Quantitative volumetric analysis of tumor enhancement, shape features, and morphological metrics were associated with patient outcomes. The Neural Network remained the top ML model performer overall for discrimination, but the Random Forest model also showed strong practical performance. Conclusions: As a summary, our study contributes to the development of advanced computational tools for neuro-oncology by integrating AI-based segmentation and multi-model ML approaches, and the results highlight the importance of imaging biomarkers in understanding GB prognosis.