Background and Objectives: The saphenous vein is one of the most common used grafts (SVG) for surgical revascularization. The mechanism of the SVGs occlusion is still unknown. Surgical preparation techniques have an important role in the early and late graft occlusion. Our study analyzed the influence of the three different surgical techniques on the histological and immunohistochemical characteristics of the vein grafts. Methods: Between June 2019 and December 2020, 83 patients who underwent surgical revascularization were prospectively randomly assigned to one of the three groups, according to saphenous vein graft harvesting (conventional (CVH), no-touch (NT) and endoscopic (EVH)) technique. The vein graft samples were sent on the histological (hematoxylin-eosin staining) and immunohistochemical (CD31, Factor VIII, Caveolin and eNOS) examinations. Results: The CVH, NT, and EVH groups included 27 patients (mean age 67.66 ± 5.6), 31 patients (mean age 66.5 ± 7.4) and 25 patients (mean age 66 ± 5.5), respectively. Hematoxylin-eosin staining revealed a lower grade of microstructural vein damage in the NT group (2, IQR 1-2) in comparison with CVH and EVH (3, IQR 2-4), (4, IQR 2-4) respectively (p < 0.001). Immunohistochemical examination revealed a high grade of staining in the NT group compared to the CVH and EVH group (CD 31 antibody p = 0.02, FVIII, p < 0.001, Caveolin, p = 0.001, and eNOS, p = 0.003). Conclusion: The best preservation of the structural vein integrity was in the NT group, while the lowest rate of leg wound complication was in the EVH group. These facts increase the interest in developing and implementing the endoscopic no-touch technique.

Background and Objectives: Despite significant advances in surgical techniques and perioperative care, major adverse cardiovascular events (MACE) remain a leading cause of postoperative morbidity and mortality in patients undergoing coronary artery bypass grafting and/or aortic valve replacement. Accurate preoperative risk stratification is essential yet often limited by models that overlook atrial mechanics and underutilized biomarkers. Materials and Methods: This study aimed to develop an interpretable machine learning model for predicting perioperative MACE by integrating clinical, biochemical, and echocardiographic features, with a particular focus on novel physiological markers. A retrospective cohort of 131 patients was analyzed. An Extreme Gradient Boosting (XGBoost) classifier was trained on a comprehensive feature set, and SHapley Additive exPlanations (SHAPs) were used to quantify each variable’s contribution to model predictions. Results: In a stratified 80:20 train–test split, the model initially achieved an AUC of 1.00. Acknowledging the potential for overfitting in small datasets, additional validation was performed using 10 independent random splits and 5-fold cross-validation. These analyses yielded an average AUC of 0.846 ± 0.092 and an F1-score of 0.807 ± 0.096, supporting the model’s stability and generalizability. The most influential predictors included total atrial conduction time, mitral and tricuspid annular orifice areas, and high-density lipoprotein (HDL) cholesterol. These variables, spanning electrophysiological, structural, and metabolic domains, significantly enhanced discriminative performance, even in patients with preserved left ventricular function. The model’s transparency provides clinically intuitive insights into individual risk profiles, emphasizing the significance of non-traditional parameters in perioperative assessments. Conclusions: This study demonstrates the feasibility and potential clinical value of combining advanced echocardiographic, biochemical, and machine learning tools for individualized cardiovascular risk prediction. While promising, these findings require prospective validation in larger, multicenter cohorts before being integrated into routine clinical decision-making.

Postoperative atrial fibrillation (POAF) is a common complication after cardiac surgery, increasing morbidity and healthcare costs. This study aimed to identify echocardiographic predictors of POAF to improve risk stratification. A total of 131 patients undergoing cardiac surgery were analyzed and divided into two groups based on POAF occurrence. Echocardiographic analysis showed that patients with POAF had larger left and right atrial dimensions and impaired atrial function. Prolonged total atrial conduction time (TACT), reduced atrial emptying volumes, and contractile function were more common in the POAF group. Univariable analysis identified LAEF (χ² = 71.8, p < 0.001), LAKE (χ² = 70.1, p < 0.001), RATEF (χ² = 65.7, p < 0.001), and RAAEF (χ² = 66.8, p < 0.001) as significant predictors of POAF, each with an area under the curve (AUC) greater than 0.89. In multivariable analysis, LAKE (OR = 0.27, p < 0.001), hypertension (OR = 11.87, p = 0.035), left ventricular ejection fraction (OR = 1.08, p = 0.020), and peripheral vascular disease (OR = 40.28, p = 0.002) were independent predictors. The final model showed a significant discriminatory ability (AUC = 0.94). LAKE and clinical factors remained independent predictors after adjustment.

Objective: The aim of this study was to establish the efficacy of three different strengths of compression systems in the prevention of venous leg ulcer (VLU) recurrences during a 10-year follow up period. Methods: An open, prospective, randomized, single-center study included 477 patients (240 men, 237 women; mean age 59 years). Patients were randomized into three groups: Group A) 149 patients (allocated to wear elastic stocking 18–25 mmHg). Group B) 167 patients (wearing compression device exerting 25–35 mmHg), and Group C) 161 patients (treated with multilayer compression system exerting 35–50 mmHg). Results: Overall, 65% (234/360) of patients had recurrent VLU within 10 years. Recurrence occurred in 120 (96%) of 125 in group A, in 89 (66.9%) of 133 patients in group B and in 25 (24.5%) of 102 patients in group C ( p < 0.05) Conclusion: Compression systems with the higher compression class provide lower recurrence rate.

Background and Objectives: Postoperative delirium (POD) is a frequent and severe complication following cardiac surgery, particularly in high-risk patients undergoing coronary artery bypass grafting (CABG) and aortic valve replacement (AVR). Despite extensive research, predicting POD remains challenging due to the multifactorial and often non-linear nature of its risk factors. This study aimed to improve POD prediction using an interpretable machine learning approach and to explore the combined effects of clinical, biochemical, and perioperative variables. Materials and Methods: This study included 131 patients who underwent CABG or AVR. POD occurrence was assessed using standard diagnostic criteria. Clinical, biochemical, and perioperative variables were collected, including patient age, sedation type, and mechanical ventilation status. Machine learning analysis was performed using an XGBoost classifier, with model interpretation achieved through SHapley Additive exPlanations (SHAP). Univariate logistic regression was applied to identify significant predictors, while SHAP analysis revealed variable interactions. Results: POD occurred in 34.3% of patients (n = 45). Patients who developed POD were significantly older (67.7 ± 6.5 vs. 64.5 ± 8.7 years, p = 0.020). Sedation with mechanical ventilation and the type of sedative used were strongly associated with POD (both p < 0.001). Sedation during mechanical ventilation showed the strongest association (OR = 2520.0; 95% CI: 80.9–78,506.7; p < 0.00001). XGBoost classifier achieved excellent performance (AUC = 0.998, accuracy = 97.6%, F1 score = 0.976). SHAP analysis identified sedation, mechanical ventilation, and their interactions with fibrinogen, troponin I, leukocyte parameters, and lung infection as key predictors. Conclusions: This study demonstrates that an interpretable machine learning approach can enhance POD prediction, providing insights into the combined impact of multiple clinical, biochemical, and perioperative factors. Integration of such models into perioperative workflows may enable early identification of high-risk patients and support individualized preventive strategies.